From ca479b0ff2a4417bc1960398e59336cfa4aa4be7 Mon Sep 17 00:00:00 2001
From:  <Cissou@DESKTOP-4131M6E.localdomain>
Date: Tue, 19 Mar 2019 10:23:27 +0100
Subject: [PATCH 001/496] encoding.py & export.py

---
 nautilus_nlp/utils/encoding.py | 50 ++++++++++++++++++++++++++++++++++
 nautilus_nlp/utils/export.py   |  9 ++++++
 2 files changed, 59 insertions(+)
 create mode 100644 nautilus_nlp/utils/encoding.py
 create mode 100644 nautilus_nlp/utils/export.py

diff --git a/nautilus_nlp/utils/encoding.py b/nautilus_nlp/utils/encoding.py
new file mode 100644
index 0000000..22cfa10
--- /dev/null
+++ b/nautilus_nlp/utils/encoding.py
@@ -0,0 +1,50 @@
+import pandas as pd
+import chardet
+import glob
+import json
+
+#Import an encoded csv or txt file as a pandas DataFrame
+def import_from_file_as_pd(file, sep=',', header = None, verbose=True):
+    encods = ['utf-8', 'ISO-8859-1', 'latin1', 'cp1252']
+    try: 
+        for encod in encods:
+            try:
+                data = pd.read_csv(file, sep = sep, encoding = encod, header=header)
+                break
+            except:
+                continue
+        if verbose:
+            print('Success:',encod)
+    except:
+        rawdata = open(file, 'rb').read()
+        result = chardet.detect(rawdata)['encoding']
+        print(result)
+        data = pd.read_csv(file, encoding = result, header=header)
+    return data
+
+
+#Import encoded csv or txt files from a folder as a pandas DataFrame
+def import_from_folder_as_pd(path, sep = ',', verbose=False):
+    all_files = glob.glob(path + "/*.csv")
+    li = []
+    
+    for file in all_files:
+        data = import_from_file_as_pd(file, sep = sep, verbose=verbose)
+        li.append(data)
+    
+    frame = pd.concat(li, axis=0)
+    return frame
+
+#Import a json file as a pandas DataFrame
+def import_json_to_pd(file):
+    with open(file) as json_file:  
+        data = json.load(json_file)
+        json.dumps(data)
+        df = pd.DataFrame(data)
+    return df
+
+#Import a json file as a dictionary
+def json_to_dict(file):
+    with open(file) as json_file:  
+        data = json.load(json_file)
+    return data 
\ No newline at end of file
diff --git a/nautilus_nlp/utils/export.py b/nautilus_nlp/utils/export.py
new file mode 100644
index 0000000..d6b4ad5
--- /dev/null
+++ b/nautilus_nlp/utils/export.py
@@ -0,0 +1,9 @@
+import pandas as pd
+
+#Save a pandas DataFrame as an encoded csv file
+def df_to_csv (df, path, sep =',', encoding='utf-8'):
+    df.to_csv(path, sep = sep, encoding = encoding)
+
+#Save a pandas DataFrame as a json file    
+def df_to_json (df, path, orient):
+    df.to_json(path, orient = orient)
\ No newline at end of file

From df284d87f500d8f9146d3d8c86e9540b3b912d46 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 16:54:54 +0100
Subject: [PATCH 002/496] Change readme for installing the requirements

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 6edc2f7..751ed6d 100644
--- a/README.md
+++ b/README.md
@@ -18,6 +18,10 @@ To install this library you should first clone the repository:
 
 `git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp`
 
+**If you don't use the docker container, we strongly advise you to do these steps in a virtual environnement**
+
+First you need to install the required files:
+`pip install -r requirements.txt`
 then you can install it via pip:
 
 `pip install -e .`

From cf98cd29ec84a1c96eeefa3f5c3595f37a78a1ac Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 16:55:41 +0100
Subject: [PATCH 003/496] Change docker build script to build library directly

---
 notebooks/Language_identification.ipynb | 614 ++++++++++++++++++++++++
 1 file changed, 614 insertions(+)
 create mode 100644 notebooks/Language_identification.ipynb

diff --git a/notebooks/Language_identification.ipynb b/notebooks/Language_identification.ipynb
new file mode 100644
index 0000000..0bd6927
--- /dev/null
+++ b/notebooks/Language_identification.ipynb
@@ -0,0 +1,614 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-49-5bdfc092f0f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
+     ]
+    }
+   ],
+   "source": [
+    "import csv  \n",
+    "import re\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Cleaning:\n",
+    "Pour télécharger les datas pour ce notebook, vous pouvez utilisez le [script associé](/nautilus_nlp/data/external/get_language_dataset.sh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../data/external/wili/x_test.txt',encoding='utf-8') as fp:\n",
+    "    test=fp.readlines()\n",
+    "    test=[doc.strip('\\n') for doc in test]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le Dataset Wili est un dataset d'identification de langue basé sur Wikipedia. Des exemples du tests set suivent: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Ne l fin de l seclo XIX l Japon era inda çconhecido i sótico pa l mundo oucidental. Cula antroduçon de la stética japonesa, particularmente na Sposiçon Ounibersal de 1900, an Paris, l Oucidente adquiriu un apetite ansaciable pul Japon i Heiarn se tornou mundialmente coincido pula perfundidade, ouriginalidade i sinceridade de ls sous cuntos. An sous radadeiros anhos, alguns críticos, cumo George Orwell, acusórun Heiarn de trasferir sou nacionalismo i fazer l Japon parecer mais sótico, mas, cumo l'home qu'oufereciu al Oucidente alguns de sous purmeiros lampeijos de l Japon pré-andustrial i de l Período Meiji, sou trabalho inda ye balioso até hoije.\""
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "test[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Schiedam is gelegen tussen Rotterdam en Vlaardingen, oorspronkelijk aan de Schie en later ook aan de Nieuwe Maas. Per 30 april 2017 had de gemeente 77.833 inwoners (bron: CBS). De stad is vooral bekend om haar jenever, de historische binnenstad met grachten, en de hoogste windmolens ter wereld.'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ГIурусаз батальонал, гьоркьор гIарадабиги лъун, ункъбокIон (каре) гьабун чIезарун руго. ТIаде гIарададул сачмаги гуллаги байдал, АхIмадханил бо тIурун буго. ГIумаханас цойгидал боязе тIаде кIанцIизе буюрухъ кьун буго. Гьезулги жо ккун гьечIо. Цинги живго ГIумахан кIанцIун вуго тушманасде тIаде («угъузилал рачун, дайтилал рачун, маххулъан бер баккун хунз цадахъ рачун»). Нахъе къалел ругел магIарулазда гьес гьарулеб букIун буго: «Нужеца яхI бахъе, гIолохъаби! Нилъеда данде гьал чIоларо», – ян.'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ರಾಜ್ಯಶಾಸ್ತ್ರದ ಪಿತಾಮಹೆ ಅರಿಸ್ಟಾಟಲ್. ರಾಜ್ಯಶಾಸ್ತ್ರದ ವೈಜ್ನಾನಿಕವಾದ್ ಅದ್ಯಾಯನ ಮಲ್ದಿನಾರ್ ಅರಿಸ್ಟಾಟಲ್. ಮೇರ್ 158 ರಾಷ್ಟ್ರದ ಸಂವಿಧಾನಲೆನ್ ಅರ್ಥ ಮಲ್ತೊನ್ದ್ the politics ಕೃತಿ ರಚನೆ ಮಲ್ದೆರ್. ಮೇರ್ ಕ್ರಿ.ಪೂ 387 ಗ್ರೀಕ್ ಸ್ಟಾಗಿರಡ್ ಜನಿಸಿಯೆರ್. ಮೆರೆನ ಅಮ್ಮೆರ್ ಮೆಸೆದೊನಿಯ ಅರಸೆರ್ನ ರಾಜವೈದ್ಯರ್ ಅದುದ್ ಇತ್ತೆರ್. ಎಲ್ಳಡೆ ಮೆರ್ ತತ್ವಶಾಸ್ತ್ರ,ಅರ್ಥಶಾಸ್ತ್ರ,ಸಂಗೀತ,ವಿಜ್ನಾನ,ಪೌರನೀತಿ,ಗಣಿತ ನೆಟ್ಟ್ ಪರಂಗತೆರ್ ಅದುದ್ ಇತ್ತೆರ್. ವಿದ್ಯಾಭ್ಯಾಸ ಮುಗಿ ಬೊಕ್ಕ ಪ್ಲೇಟೋ ಗ್ರೀಕ್ ತತ್ವಜ್ನಾನಿನೊಟ್ ಸೆರೊಂಡೆರ್. ಪ್ಲೇಟೋನ ಅಕಾಡೆಮಿಡ್ ಮಸ್ತ್ ವರ್ಷ ಬೇಲೆ ಮಲ್ತೆರ್. ಅಕಾಡೆಮಿಡ್ ನಿರ್ದೆಶೆಕೆರ್ನ ಹುದ್ದೆ ತಿಕ್ಕಂದೆ ಬೆಜರ್ ಅದುದ್ ಲೈಸಿಯಮ್ ಪನ್ಪಿನ ವಿಶ್ವವಿದ್ಯಾನಿಲಯ ಸ್ಥಾಪನೆ ಮಲ್ತೆರ್. ಬಿಕ್ಕ ಒಂತೆ ಸಮಯ ಅಲೆಗ್ಸಾಂಡರ್ ಚಕ್ರವರ್ತಿಗ್ ಗುರುವಾದ್ ಬೇಲೆ ಮಲ್ತೆರ್. ಮೇರ್ ಬರೆತಿನ ಪುಸ್ತಕ the politics,history of animals,metaphysics ಇತ್ಯಾದಿ.'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Тухайн үед дан ганц 20кг-ын Орос баллон хэрэглэдэг байсан уламжлалыг халж хэрэглэгчдийг сонголт ихтэй болгож 5,10,14,20 кг хэмжээтэй, баллонуудыг захиалан үйлдвэрлүүлж, өөрийн брэнд болгон хэрэглээнд нэвтрүүлж, хийн түлшээр ажилладаг тулга, төрөл бүрийн халаагуурууд, тоног төхөөрөмжийн ашиглалт, хийн түлшний ашиг тусыг хэвлэл мэдээллийн хэрэгслэлээр тасралтгүй сурталчилсаны үр дүнд 2002 оны 5 сар гэхэд хийн түлшний хэрэглээ сард 30 тн, 9 сард 50 тн хүрч өссөн юм.'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../data/external/wili/y_test.txt',encoding='utf-8') as fp:\n",
+    "    y=fp.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('../data/external/wili/labels.csv',encoding='utf-8') as fp:\n",
+    "    labels=fp.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Label;English;Wiki Code;ISO 369-3;German;Language family;Writing system;Remarks;Synonyms\\n',\n",
+       " 'ace;Achinese;ace;ace;Achinesisch;Austronesian;;;\\n',\n",
+       " 'afr;Afrikaans;af;afr;Afrikaans;Indo-European;;;\\n',\n",
+       " 'als;Alemannic German;als;gsw;Alemannisch;Indo-European;;(ursprünglich nur\\xa0Elsässisch);\\n',\n",
+       " 'amh;Amharic;am;amh;Amharisch;Afro-Asiatic;;;\\n',\n",
+       " 'ang;Old English ;ang;ang;Altenglisch;Indo-European;;(ca. 450-1100);Angelsächsisch\\n',\n",
+       " 'ara;Arabic;ar;ara;Arabisch;Afro-Asiatic;;;\\n',\n",
+       " 'arg;Aragonese;an;arg;Aragonesisch;Indo-European;;;\\n',\n",
+       " 'arz;Egyptian Arabic;arz;arz;Ägyptisch-Arabisch;Afro-Asiatic;;;\\n',\n",
+       " 'asm;Assamese;as;asm;Assamesisch;Indo-European;;;\\n',\n",
+       " 'ast;Asturian;ast;ast;Asturisch;Indo-European;;;\\n',\n",
+       " 'ava;Avar;av;ava;Awarisch;Northeast Caucasian;;;\\n',\n",
+       " 'aym;Aymara;ay;aym;Aymara;Aymaran;;;\\n',\n",
+       " 'azb;South Azerbaijani;azb;azb;Südaserbaidschanisch;Turkic;Arabic;;\\n',\n",
+       " 'aze;Azerbaijani;az;aze;Aserbaidschanisch;Turkic;Latin;;\\n',\n",
+       " 'bak;Bashkir;ba;bak;Baschkirisch;Turkic;;;\\n',\n",
+       " 'bar;Bavarian;bar;bar;Bairisch;Indo-European;;;\\n',\n",
+       " 'bcl;Central Bikol;bcl;bcl;Bikolano;Austronesian;;;\\n',\n",
+       " 'be-tarask;Belarusian (Taraschkewiza);be-tarask;;Weißrussisch (Taraschkewiza);Indo-European;;;\\n',\n",
+       " 'bel;Belarusian;be;bel;Weißrussisch;Indo-European;;(normativ);\\n',\n",
+       " 'ben;Bengali;bn;ben;Bengalisch;Indo-European;;;\\n',\n",
+       " 'bho;Bhojpuri;bh;bho;Bhojpuri;Indo-European;;;\\n',\n",
+       " 'bjn;Banjar;bjn;bjn;Banjaresisch;Austronesian;;;\\n',\n",
+       " 'bod;Tibetan;bo;bod;Tibetisch;Sino-Tibetan;;;\\n',\n",
+       " 'bos;Bosnian;bs;bos;Bosnisch;Indo-European;;;\\n',\n",
+       " 'bpy;Bishnupriya;bpy;bpy;Bishnupriya Manipuri;Indo-European;;;\\n',\n",
+       " 'bre;Breton;br;bre;Bretonisch;Indo-European;;;\\n',\n",
+       " 'bul;Bulgarian;bg;bul;Bulgarisch;Indo-European;;;\\n',\n",
+       " 'bxr;Buryat;bxr;bxr;Burjatisch;Mongolic;Cyrillic:::Mongolian script:::Vagindra script:::Latin;;Buriat\\n',\n",
+       " 'cat;Catalan;ca;cat;Katalanisch;Indo-European;Latin;;\\n',\n",
+       " 'cbk;Chavacano;cbk-zam;cbk;Chabacano;Indo-European;;;\\n',\n",
+       " 'cdo;Min Dong;cdo;cdo;Min Dong;Sino-Tibetan;;;\\n',\n",
+       " 'ceb;Cebuano;ceb;ceb;Cebuano;Austronesian;Latin;;\\n',\n",
+       " 'ces;Czech;cs;ces;Tschechisch;Indo-European;Latin;;\\n',\n",
+       " 'che;Chechen;ce;che;Tschetschenisch;Northeast Caucasian;;;\\n',\n",
+       " 'chr;Cherokee;chr;chr;Cherokee;Iroquoian;;;\\n',\n",
+       " 'chv;Chuvash;cv;chv;Tschuwaschisch;Turkic;;;\\n',\n",
+       " 'ckb;Central Kurdish;ckb;ckb;Sorani;Indo-European;;;\\n',\n",
+       " 'cor;Cornish;kw;cor;Kornisch;Indo-European;;;\\n',\n",
+       " 'cos;Corsican;co;cos;Korsisch;Indo-European;;;\\n',\n",
+       " 'crh;Crimean Tatar;crh;crh;Krimtatarisch;Turkic;;;\\n',\n",
+       " 'csb;Kashubian;csb;csb;Kaschubisch;Indo-European;;;\\n',\n",
+       " 'cym;Welsh;cy;cym;Walisisch;Indo-European;;;\\n',\n",
+       " 'dan;Danish;da;dan;Dänisch;Indo-European;;;\\n',\n",
+       " 'deu;German;de;deu;Deutsch;Indo-European;Latin;;\\n',\n",
+       " 'diq;Dimli;diq;diq;Süd-Zazaisch;Indo-European;;;\\n',\n",
+       " 'div;Dhivehi;dv;div;Dhivehi;Indo-European;;;\\n',\n",
+       " 'dsb;Lower Sorbian;dsb;dsb;Niedersorbisch;Indo-European;;;\\n',\n",
+       " 'dty;Doteli;dty;dty;Doteli;Indo-European;;;\\n',\n",
+       " 'egl;Emilian;eml;egl;Emilianisch;Indo-European;;;\\n',\n",
+       " 'ell;Modern Greek;el;ell;Griechisch;Indo-European;;(1453-);\\n',\n",
+       " 'eng;English;en;eng;Englisch;Indo-European;Latin;;\\n',\n",
+       " 'epo;Esperanto;eo;epo;Esperanto;Constructed;;;\\n',\n",
+       " 'est;Estonian;et;est;Estnisch;Uralic;;;\\n',\n",
+       " 'eus;Basque;eu;eus;Baskisch;Language isolate;;;\\n',\n",
+       " 'ext;Extremaduran;ext;ext;Extremadurisch;Indo-European;;;\\n',\n",
+       " 'fao;Faroese;fo;fao;Färöisch;Indo-European;;;\\n',\n",
+       " 'fas;Persian;fa;fas;Persisch;Indo-European;;;\\n',\n",
+       " 'fin;Finnish;fi;fin;Finnisch;Uralic;Latin;;\\n',\n",
+       " 'fra;French;fr;fra;Französisch;Indo-European;Latin;;\\n',\n",
+       " 'frp;Arpitan;frp;frp;Frankoprovenzalisch;Indo-European;;;\\n',\n",
+       " 'fry;Western Frisian;fy;fry;Westfriesisch;Indo-European;;;\\n',\n",
+       " 'fur;Friulian;fur;fur;Furlanisch;Indo-European;;;\\n',\n",
+       " 'gag;Gagauz;gag;gag;Gagausisch;Turkic;;;\\n',\n",
+       " 'gla;Scottish Gaelic;gd;gla;Schottisch-Gälisch;Indo-European;;;\\n',\n",
+       " 'gle;Irish;ga;gle;Irisch;Indo-European;;;\\n',\n",
+       " 'glg;Galician;gl;glg;Galicisch;Indo-European;;;\\n',\n",
+       " 'glk;Gilaki;glk;glk;Gilaki;Indo-European;;;\\n',\n",
+       " 'glv;Manx;gv;glv;Manx;Indo-European;;;\\n',\n",
+       " 'grn;Guarani;gn;grn;Guaraní;Tupi-Guarani;;;\\n',\n",
+       " 'guj;Gujarati;gu;guj;Gujarati;Indo-European;;;\\n',\n",
+       " 'hak;Hakka Chinese;hak;hak;Hakka;Sino-Tibetan;;;\\n',\n",
+       " 'hat;Haitian Creole;ht;hat;Haitianisch;Indo-European;;;\\n',\n",
+       " 'hau;Hausa;ha;hau;Hausa;Afro-Asiatic;Latin;;Chadic\\n',\n",
+       " 'hbs;Serbo-Croatian;sh;hbs;Serbokroatisch;Indo-European;;;\\n',\n",
+       " 'heb;Hebrew;he;heb;Hebräisch;Afro-Asiatic;;;\\n',\n",
+       " 'hif;Fiji Hindi;hif;hif;Fidschi-Hindi;Indo-European;;;\\n',\n",
+       " 'hin;Hindi;hi;hin;Hindi;Indo-European;;;\\n',\n",
+       " 'hrv;Croatian;hr;hrv;Kroatisch;Indo-European;;;\\n',\n",
+       " 'hsb;Upper Sorbian;hsb;hsb;Obersorbisch;Indo-European;;;\\n',\n",
+       " 'hun;Hungarian;hu;hun;Ungarisch;Uralic;;;\\n',\n",
+       " 'hye;Armenian;hy;hye;Armenisch;Indo-European;;;\\n',\n",
+       " 'ibo;Igbo;ig;ibo;Igbo;Niger-Congo;Latin;;\\n',\n",
+       " 'ido;Ido;io;ido;Ido;Constructed;;;\\n',\n",
+       " 'ile;Interlingue;ie;ile;Interlingue;Constructed;;;\\n',\n",
+       " 'ilo;Iloko;ilo;ilo;Ilokano;Austronesian;;;\\n',\n",
+       " 'ina;Interlingua;ia;ina;Interlingua;Constructed;;;\\n',\n",
+       " 'ind;Indonesian;id;ind;Indonesisch;Austronesian;Latin;;\\n',\n",
+       " 'isl;Icelandic;is;isl;Isländisch;Indo-European;;;\\n',\n",
+       " 'ita;Italian;it;ita;Italienisch;Indo-European;Latin;;\\n',\n",
+       " 'jam;Jamaican Patois;jam;jam;Jamaikanisch-kreolisch;Indo-European;;;\\n',\n",
+       " 'jav;Javanese;jv;jav;Javanisch;Austronesian;;;\\n',\n",
+       " 'jbo;Lojban;jbo;jbo;Lojban;Constructed;Latin;;\\n',\n",
+       " 'jpn;Japanese;ja;jpn;Japanisch;Japonic;;;\\n',\n",
+       " 'kaa;Karakalpak;kaa;kaa;Karakalpakisch;Turkic;;;\\n',\n",
+       " 'kab;Kabyle;kab;kab;Kabylisch;Afro-Asiatic;;;\\n',\n",
+       " 'kan;Kannada;kn;kan;Kannada;Dravidian;;;\\n',\n",
+       " 'kat;Georgian;ka;kat;Georgisch;South Caucasian;;;\\n',\n",
+       " 'kaz;Kazakh;kk;kaz;Kasachisch;Turkic;;;\\n',\n",
+       " 'kbd;Kabardian;kbd;kbd;Kabardinisch;Northeast Caucasian;Cyrillic:::Latin:::Arabic;;Kabardino-Cherkess:::East Circassian\\n',\n",
+       " 'khm;Central Khmer;km;khm;Khmer;Austronesian;;;\\n',\n",
+       " 'kin;Kinyarwanda;rw;kin;Kinyarwanda;Niger-Congo;Latin;;Fumbira\\n',\n",
+       " 'kir;Kirghiz;ky;kir;Kirgisisch;Turkic;;;\\n',\n",
+       " 'koi;Komi-Permyak;koi;koi;Komi-Permjakisch;Uralic;;;\\n',\n",
+       " 'kok;Konkani;gom;kok;Konkani;Indo-European;;;\\n',\n",
+       " 'kom;Komi;kv;kom;Komi;Uralic;;;\\n',\n",
+       " 'kor;Korean;ko;kor;Koreanisch;Koreanic;;;\\n',\n",
+       " 'krc;Karachay-Balkar;krc;krc;Karatschai-balkarisch;Turkic;;;\\n',\n",
+       " 'ksh;Ripuarisch;ksh;ksh;Kölsch;Indo-European;;;\\n',\n",
+       " 'kur;Kurdish;ku;kur;Kurdisch;Indo-European;Latin;;\\n',\n",
+       " 'lad;Ladino;lad;lad;Judenspanisch;Indo-European;;;\\n',\n",
+       " 'lao;Lao;lo;lao;Laotisch;Tai-Kadai;;;\\n',\n",
+       " 'lat;Latin;la;lat;Latein;Indo-European;;;\\n',\n",
+       " 'lav;Latvian;lv;lav;Lettisch;Indo-European;;;\\n',\n",
+       " 'lez;Lezghian;lez;lez;Lesgisch;Northeast Caucasian;;;\\n',\n",
+       " 'lij;Ligurian;lij;lij;Ligurisch;Indo-European;;(Romanisch);\\n',\n",
+       " 'lim;Limburgan;li;lim;Limburgisch;Indo-European;;;\\n',\n",
+       " 'lin;Lingala;ln;lin;Lingála;Niger-Congo;;;\\n',\n",
+       " 'lit;Lithuanian;lt;lit;Litauisch;Indo-European;;;\\n',\n",
+       " 'lmo;Lombard;lmo;lmo;Lombardisch;Indo-European;;;\\n',\n",
+       " 'lrc;Northern Luri;lrc;lrc;nördliches Luri;Indo-European;;;\\n',\n",
+       " 'ltg;Latgalian;ltg;ltg;Lettgallisch;Indo-European;;;\\n',\n",
+       " 'ltz;Luxembourgish;lb;ltz;Luxemburgisch;Indo-European;;;\\n',\n",
+       " 'lug;Luganda;lg;lug;Luganda;Niger-Congo;Latin;;Ganda\\n',\n",
+       " 'lzh;Literary Chinese;zh-classical;lzh;klassisches Chinesisch;Sino-Tibetan;;;\\n',\n",
+       " 'mai;Maithili;mai;mai;Maithili;Indo-European;;;\\n',\n",
+       " 'mal;Malayalam;ml;mal;Malayalam;Dravidian;;;\\n',\n",
+       " 'map-bms;Banyumasan;map-bms;map-bms;Banyumasan;Austronesian;;Javanese;\\n',\n",
+       " 'mar;Marathi;mr;mar;Marathi;Indo-European;;;\\n',\n",
+       " 'mdf;Moksha;mdf;mdf;Mokschanisch;Uralic;Cyrillic;;\\n',\n",
+       " 'mhr;Eastern Mari;mhr;mhr;Ostmari;Uralic;;;\\n',\n",
+       " 'min;Minangkabau;min;min;Minangkabauisch;Austronesian;;;\\n',\n",
+       " 'mkd;Macedonian;mk;mkd;Mazedonisch;Indo-European;;;\\n',\n",
+       " 'mlg;Malagasy;mg;mlg;Malagasy;Austronesian;;;\\n',\n",
+       " 'mlt;Maltese;mt;mlt;Maltesisch;Afro-Asiatic;;;\\n',\n",
+       " 'mon;Mongolian;mn;mon;Mongolisch;Mongolic;;;\\n',\n",
+       " 'mri;Maori;mi;mri;Maori;Austronesian;;;\\n',\n",
+       " 'mrj;Western Mari;mrj;mrj;Westmari;Uralic;;;\\n',\n",
+       " 'msa;Malay;ms;msa;Malaiisch;Austronesian;;;\\n',\n",
+       " 'mwl;Mirandese;mwl;mwl;Mirandés;Indo-European;;;\\n',\n",
+       " 'mya;Burmese;my;mya;Birmanisch;Sino-Tibetan;;;\\n',\n",
+       " 'myv;Erzya;myv;myv;Ersja-Mordwinisch;Uralic;;;\\n',\n",
+       " 'mzn;Mazanderani;mzn;mzn;Masanderanisch;Indo-European;;;\\n',\n",
+       " 'nan;Min Nan Chinese;zh-min-nan;nan;Min Nan;Sino-Tibetan;;;\\n',\n",
+       " 'nap;Neapolitan;nap;nap;Neapolitanisch;Indo-European;;;\\n',\n",
+       " 'nav;Navajo;nv;nav;Navajo;Dené-Yeniseian;;;\\n',\n",
+       " 'nci;Classical Nahuatl;nah;nci;Nahuatl;Uto-Aztecan;;;\\n',\n",
+       " 'nds;Low German;nds;nds;Niedersächsisch/Ostniederdeutsch;Indo-European;;;\\n',\n",
+       " 'nds-nl;West Low German;nds-nl;nds;Nedersaksisch;Indo-European;;;\\n',\n",
+       " 'nep;Nepali (macrolanguage);ne;nep;Nepali;Indo-European;;;\\n',\n",
+       " 'new;Newari;new;new;Newari;Sino-Tibetan;;;\\n',\n",
+       " 'nld;Dutch;nl;nld;Niederländisch;Indo-European;Latin;;\\n',\n",
+       " 'nno;Norwegian Nynorsk;nn;nno;Nynorsk;Indo-European;;;\\n',\n",
+       " 'nob;Bokmål;no;nob;Bokmål;Indo-European;Latin;Norwegian;\\n',\n",
+       " 'nrm;Narom;nrm;nrm;Normannisch;Austronesian;;;\\n',\n",
+       " 'nso;Northern Sotho;nso;nso;Nord-Sotho;Niger-Congo;;;\\n',\n",
+       " 'oci;Occitan;oc;oci;Okzitanisch;Indo-European;;(post 1500);\\n',\n",
+       " 'olo;Livvi-Karelian;olo;olo;Olonetzisch;Uralic;;;\\n',\n",
+       " 'ori;Oriya;or;ori;Oriya;Indo-European;;;\\n',\n",
+       " \"orm;Oromo;om;orm;Oromo;Afro-Asiatic;Latin:::Ge'ez ;;\\n\",\n",
+       " 'oss;Ossetian;os;oss;Ossetisch;Indo-European;;;\\n',\n",
+       " 'pag;Pangasinan;pag;pag;Pangasinensisch;Austronesian;;;\\n',\n",
+       " 'pam;Pampanga;pam;pam;Kapampangan;Austronesian;;;\\n',\n",
+       " 'pan;Panjabi;pa;pan;Panjabi\\xa0in\\xa0Gurmukhi-Schrift;Indo-European;;;\\n',\n",
+       " 'pap;Papiamento;pap;pap;Papiamentu;Indo-European;;;\\n',\n",
+       " 'pcd;Picard;pcd;pcd;Picardisch;Indo-European;;;\\n',\n",
+       " 'pdc;Pennsylvania German;pdc;pdc;Pennsylvaniadeutsch;Indo-European;;;Deitsch:::Pennsylvania Deitsch:::Pennsilfaanisch Deitsch:::Pennsylvania Dutch\\n',\n",
+       " 'pfl;Palatine German;pfl;pfl;Pfälzisch;Indo-European;;;\\n',\n",
+       " 'pnb;Western Panjabi;pnb;pnb;Panjabi;Indo-European;Arabic;;\\n',\n",
+       " 'pol;Polish;pl;pol;Polnisch;Indo-European;Latin;;\\n',\n",
+       " 'por;Portuguese;pt;por;Portugiesisch;Indo-European;Latin;;\\n',\n",
+       " 'pus;Pushto;ps;pus;Paschtunisch;Indo-European;;;\\n',\n",
+       " 'que;Quechua;qu;que;Quechua;Quechuan;;;\\n',\n",
+       " 'roa-tara;Tarantino dialect;roa-tara;;Tarandíne;Indo-European;;;\\n',\n",
+       " 'roh;Romansh;rm;roh;Bündnerromanisch;Indo-European;;;\\n',\n",
+       " 'ron;Romanian;ro;ron;Rumänisch;Indo-European;;;\\n',\n",
+       " 'rue;Rusyn;rue;rue;Karpato-Russinisch;Indo-European;;;\\n',\n",
+       " 'rup;Aromanian;roa-rup;rup;Aromunisch;Indo-European;Latin;Macedo-Romanian::Vlach;\\n',\n",
+       " 'rus;Russian;ru;rus;Russisch;Indo-European;Cyrillic;;\\n',\n",
+       " 'sah;Yakut;sah;sah;Jakutisch;Turkic;;;\\n',\n",
+       " 'san;Sanskrit;sa;san;Sanskrit;Indo-European;;;\\n',\n",
+       " 'scn;Sicilian;scn;scn;Sizilianisch;Indo-European;;;\\n',\n",
+       " 'sco;Scots;sco;sco;Scots;Indo-European;;;\\n',\n",
+       " 'sgs;Samogitian;bat-smg;sgs;Schemaitisch;Indo-European;;;\\n',\n",
+       " 'sin;Sinhala;si;sin;Singhalesisch;Indo-European;;;\\n',\n",
+       " 'slk;Slovak;sk;slk;Slowakisch;Indo-European;;;\\n',\n",
+       " 'slv;Slovene;sl;slv;Slowenisch;Indo-European;;;\\n',\n",
+       " 'sme;Northern Sami;se;sme;Nordsamisch;Uralic;;;\\n',\n",
+       " 'sna;Shona;sn;sna;Shona;Niger-Congo;;;\\n',\n",
+       " 'snd;Sindhi;sd;snd;Sindhi;Indo-European;;;\\n',\n",
+       " 'som;Somali;so;som;Somali;Afro-Asiatic;;;\\n',\n",
+       " 'spa;Spanish;es;spa;Spanisch;Indo-European;Latin;;\\n',\n",
+       " 'sqi;Albanian;sq;sqi;Albanisch;Indo-European;;;\\n',\n",
+       " 'srd;Sardinian;sc;srd;Sardisch;Indo-European;;;\\n',\n",
+       " 'srn;Sranan;srn;srn;Sranantongo;Indo-European;;;Sranan Tongo:::Sranantongo:::Surinaams:::Surinamese:::Surinamese Creole:::Taki Taki\\n',\n",
+       " 'srp;Serbian;sr;srp;Serbisch;Indo-European;;;\\n',\n",
+       " 'stq;Saterfriesisch;stq;stq;Saterfriesisch;Indo-European;;;\\n',\n",
+       " 'sun;Sundanese;su;sun;Sundanesisch;Austronesian;;;\\n',\n",
+       " 'swa;Swahili (macrolanguage);sw;swa;Swahili;Niger-Congo;;;\\n',\n",
+       " 'swe;Swedish;sv;swe;Schwedisch;Indo-European;Latin;;\\n',\n",
+       " 'szl;Silesian;szl;szl;Schlesisch;Indo-European;;(polnischer Dialekt);\\n',\n",
+       " 'tam;Tamil;ta;tam;Tamil;Dravidian;;;\\n',\n",
+       " 'tat;Tatar;tt;tat;Tatarisch;Turkic;;;\\n',\n",
+       " 'tcy;Tulu;tcy;tcy;Tulu;Dravidian;Kannada:::Tigalari;;\\n',\n",
+       " 'tel;Telugu;te;tel;Telugu;Dravidian;;;\\n',\n",
+       " 'tet;Tetum;tet;tet;Tetum;Austronesian;;;\\n',\n",
+       " 'tgk;Tajik;tg;tgk;Tadschikisch;Indo-European;;;\\n',\n",
+       " 'tgl;Tagalog;tl;tgl;Tagalog;Austronesian;;;\\n',\n",
+       " 'tha;Thai;th;tha;Thailändisch;Tai-Kadai;;;\\n',\n",
+       " 'ton;Tongan;to;ton;Tongaisch;Austronesian;Latin;;\\n',\n",
+       " 'tsn;Tswana;tn;tsn;Setswana;Niger-Congo;Latin;;Setswana\\n',\n",
+       " 'tuk;Turkmen;tk;tuk;Turkmenisch;Turkic;;;\\n',\n",
+       " 'tur;Turkish;tr;tur;Türkisch;Turkic;;;\\n',\n",
+       " 'tyv;Tuvan;tyv;tyv;Tuwinisch;Turkic;Cyrillic;;\\n',\n",
+       " 'udm;Udmurt;udm;udm;Udmurtisch;Uralic;;;\\n',\n",
+       " 'uig;Uighur;ug;uig;Uigurisch;Turkic;;;\\n',\n",
+       " 'ukr;Ukrainian;uk;ukr;Ukrainisch;Indo-European;Cyrillic;;\\n',\n",
+       " 'urd;Urdu;ur;urd;Urdu;Indo-European;;;\\n',\n",
+       " 'uzb;Uzbek;uz;uzb;Usbekisch;Turkic;;;\\n',\n",
+       " 'vec;Venetian;vec;vec;Venetisch;Indo-European;;;\\n',\n",
+       " 'vep;Veps;vep;vep;Wepsisch;Uralic;;;\\n',\n",
+       " 'vie;Vietnamese;vi;vie;Vietnamesisch;Austronesian;Latin;;\\n',\n",
+       " 'vls;Vlaams;vls;vls;Westflämisch;Indo-European;;;\\n',\n",
+       " 'vol;Volapük;vo;vol;Volapük;Constructed;;;\\n',\n",
+       " 'vro;Võro;fiu-vro;vro;Võro;Uralic;;;\\n',\n",
+       " 'war;Waray;war;war;Wáray-Wáray;Austronesian;Latin;;\\n',\n",
+       " 'wln;Walloon;wa;wln;Wallonisch;Indo-European;;;\\n',\n",
+       " 'wol;Wolof;wo;wol;Wolof;Niger-Congo;Latin:::Arabic;;\\n',\n",
+       " 'wuu;Wu Chinese;wuu;wuu;Wu;Sino-Tibetan;;;\\n',\n",
+       " 'xho;Xhosa;xh;xho;isiXhosa;Niger-Congo;Latin;;isiXhosa\\n',\n",
+       " 'xmf;Mingrelian;xmf;xmf;Mingrelisch;Kartvelian;;;\\n',\n",
+       " 'yid;Yiddish;yi;yid;Jiddisch;Indo-European;;;\\n',\n",
+       " 'yor;Yoruba;yo;yor;Yoruba;Niger-Congo;;;\\n',\n",
+       " 'zea;Zeeuws;zea;zea;Seeländisch;Indo-European;;;\\n',\n",
+       " 'zh-yue;Cantonese;zh-yue;;Kantonesisch;Sino-Tibetan;;;\\n',\n",
+       " 'zho;Standard Chinese;zh;zho;Chinesisch;Sino-Tibetan;;;\\n']"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Language identification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models import Fasttext_classifier "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ici on va load le modèle pré-entrainer par Fasttext, disponible dans le folder data.\n",
+    "On peut utiliser soit le modèle quantizé (900ko) soit le classique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = Fasttext_classifier.Fasttext_clf('../nautilus_nlp/data/lang_identification.ftz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N\t100000\n",
+      "P@1\t0.759\n",
+      "R@1\t0.759\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_results(*model.test(valid_data))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(('__label__kn',), array([0.99790311]))"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(test[3])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il faut donc 13 secondes pour entrainer un modèle sur 1.5 Millions de tweets, avec une précision de 0.759. Not bad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save_model('/home/nautilus_nlp/models/sentiments.bin')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-r--r-- 1 root root 232M Mar 14 12:51 /home/nautilus_nlp/models/sentiments.bin\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh '/home/nautilus_nlp/models/sentiments.bin'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ce modèle fait environ 230Mo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maintenant si on quantize ce modèle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N\t100000\n",
+      "P@1\t0.759\n",
+      "R@1\t0.759\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.quantize(input=train_data, qnorm=True, retrain=True, cutoff=100000)\n",
+    "print_results(*model.test(valid_data))\n",
+    "model.save_model('/home/nautilus_nlp/models/sentiments.ftz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-rw-r--r-- 1 root root 6.8M Mar 14 12:53 /home/nautilus_nlp/models/sentiments.ftz\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh '/home/nautilus_nlp/models/sentiments.ftz'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 81c6b326afb9e88f21cb462fd0ce7d08c107a4dc Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 16:56:02 +0100
Subject: [PATCH 004/496] Change docker build script to build library directly

---
 docker/Dockerfile      | 3 ++-
 docker/build_docker.sh | 4 ++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/docker/Dockerfile b/docker/Dockerfile
index db45c75..18b674a 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -6,4 +6,5 @@ RUN git clone https://github.com/facebookresearch/fastText.git && cd fastText &&
 RUN pip install --upgrade pip && pip install pandas schedule nltk pendulum bounter spacy==2.1 jupyterlab confluent-kafka xxhash scikit-learn
 RUN python -m spacy download fr &&  python -m spacy download en && python -m spacy download de && python -m spacy download it && python -m spacy download xx
 RUN python -m nltk.downloader stopwords 
-#RUN git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp && pip install -e . 
+COPY nautilus_nlp /nautilus_nlp
+RUN cd nautilus_nlp && pip install -r requirements.txt && pip install -e . 
diff --git a/docker/build_docker.sh b/docker/build_docker.sh
index 122cd45..bfd80cc 100644
--- a/docker/build_docker.sh
+++ b/docker/build_docker.sh
@@ -1,3 +1,3 @@
 #!/bin/bash
-
-docker build -t nautilus_nlp .
\ No newline at end of file
+cp docker/Dockerfile .
+docker build -t nautilus_nlp:latest .

From 139c98b5c1cb618ef1c2bb7edad7289616858c5b Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 17:37:07 +0100
Subject: [PATCH 005/496] Add pytest as requireemnts

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 1172a43..1831703 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,6 +9,7 @@ awscli
 flake8
 python-dotenv>=0.5.1
 pillow
+pytest
 
 #library requirements
 spacy==2.1

From d8e9228e368b7f3243d37764f67731a8118770aa Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 17:52:10 +0100
Subject: [PATCH 006/496] First test for preprocessing

---
 tests/test_preprocessor.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 tests/test_preprocessor.py

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
new file mode 100644
index 0000000..17ba489
--- /dev/null
+++ b/tests/test_preprocessor.py
@@ -0,0 +1,23 @@
+import pytest
+import numpy as np
+from nautilus_nlp.utils.preprocess import (remove_multiple_spaces_and_strip_text, remove_accents)
+
+
+@pytest.mark.parametrize("input_str, expected_str", [
+    ("hello   world", "hello world"),
+    ("\n   hello world    ", "hello world"),
+    ("----- hello\tworld *****", "hello world"),
+    ("hello-world", "hello-world"),
+    ("hello - world", "hello world")
+])
+def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
+    result = remove_multiple_spaces_and_strip_text(input_str)
+    np.testing.assert_string_equal(result, expected_str)
+
+
+def test_remove_accents():
+    input_str = "éèëêàù"
+    expected_str = "eeeeau"
+
+    result = remove_accents(input_str)
+    np.testing.assert_string_equal(result, expected_str)

From 5649198ea3e464ccd9b4d5333fa7e44f3bce0d0c Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 17:52:37 +0100
Subject: [PATCH 007/496] Correct Dockerfile

---
 docker/Dockerfile | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docker/Dockerfile b/docker/Dockerfile
index 18b674a..5dca4a0 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -6,5 +6,7 @@ RUN git clone https://github.com/facebookresearch/fastText.git && cd fastText &&
 RUN pip install --upgrade pip && pip install pandas schedule nltk pendulum bounter spacy==2.1 jupyterlab confluent-kafka xxhash scikit-learn
 RUN python -m spacy download fr &&  python -m spacy download en && python -m spacy download de && python -m spacy download it && python -m spacy download xx
 RUN python -m nltk.downloader stopwords 
-COPY nautilus_nlp /nautilus_nlp
-RUN cd nautilus_nlp && pip install -r requirements.txt && pip install -e . 
+RUN ls
+COPY . /nautilus_nlp
+WORKdIR /nautilus_nlp
+RUN  pip install -r requirements.txt && pip install -e . 

From 0a1815931cc99fd0e8c761b93ad360bbdf1016e5 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 17:53:14 +0100
Subject: [PATCH 008/496] Spacy POS tagger

---
 nautilus_nlp/models/Spacy_model.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/models/Spacy_model.py b/nautilus_nlp/models/Spacy_model.py
index 11bf7e7..c7eca79 100644
--- a/nautilus_nlp/models/Spacy_model.py
+++ b/nautilus_nlp/models/Spacy_model.py
@@ -1,7 +1,7 @@
 import spacy
 
-
 class spacy_model:
+
     def __init__(self, lang: str):
         try:
             self.model = spacy.load(lang)
@@ -55,3 +55,7 @@ def get_lemma_from_document(spacydoc: spacy.tokens.doc.Doc) -> list:
     def get_lemma_from_str(self, text: str):
         spacydoc = self.model(text)
         return [token.lemma_ for token in spacydoc]
+
+    def get_pos_from_str(self, text: str):
+        spacydoc = self.model(text)
+        return [token.pos_ for token in spacydoc]
\ No newline at end of file

From b21f147e4e73480ea5967a490bc60c56038063fe Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 17:53:53 +0100
Subject: [PATCH 009/496] Cleaning func to remove multiples spaces due to
 cleaning

---
 nautilus_nlp/utils/preprocess.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index 0edfdbf..13f7898 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -15,6 +15,24 @@
 from . import constants
 
 
+
+
+def remove_multiple_spaces_and_strip_text(text):
+    """Remove multiple spaces, strip text, and remove '-', '*' characters.
+    Parameters
+    ----------
+    text : str,
+        Header content.
+    Returns
+    -------
+    str
+    """
+    regex_remove_multiple_spaces_list= ["\\t", "[\\s\\-\\*]{2,}"]
+    for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
+        text = re.sub(regex_remove_multiple_spaces, ' ', text)
+        text = text.strip()
+    return text
+
 def remove_tokens_with_nonletters(tokens):
     '''
     Inputs a list of tokens, outputs a list of tokens without tokens that
@@ -224,6 +242,11 @@ def remove_accents(text, method="unicode") -> str:
         msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
         raise ValueError(msg)
 
+def remove_emoji(word):
+
+    RE_EMOJI = re.compile('[\U00010000-\U0010ffff]', flags=re.UNICODE)
+    word = RE_EMOJI.sub(r'', word)
+    return word
 
 def preprocess_text(
     text,

From a87b42fbf5e2f48ef88c9070a2acd2a3cc890e77 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 22 Mar 2019 17:54:21 +0100
Subject: [PATCH 010/496] stopwords package in requirements

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 1831703..c3f187c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,4 +25,4 @@ stopwords
 textblob
 textblob_fr
 vaderSentiment
-
+stop_words
\ No newline at end of file

From a4b79fbf17e06c0fcdec3e9b9d1b6ded29c79a46 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 27 Mar 2019 10:37:19 +0100
Subject: [PATCH 011/496] Update readme for feature requests

---
 README.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/README.md b/README.md
index 751ed6d..8b4abab 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,13 @@ This library can help you with:
     4. Help you discover topics and cluster your data
 
 
+# Feature Request
+
+As an Artefact user, you might be working on a NLP use case, and wish to use Nautilus.
+
+ However, if you think Nautilus is lacking features that can be useful not only to your use case but also others, feel free to to fill up an issue with the label "Feature-request".
+
+ We will try to put it in the roadmap and implement it as soon as possible.
 
 # Installation
 
@@ -21,7 +28,9 @@ To install this library you should first clone the repository:
 **If you don't use the docker container, we strongly advise you to do these steps in a virtual environnement**
 
 First you need to install the required files:
+
 `pip install -r requirements.txt`
+
 then you can install it via pip:
 
 `pip install -e .`

From cf763e32787d9b88df6f70795f6bd300bbd799ef Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 29 Mar 2019 18:28:19 +0100
Subject: [PATCH 012/496] emoji2word+ emoji2sent

---
 nautilus_nlp/utils/emoji.py | 1967 +++++++++++++++++++++++++++++++++++
 1 file changed, 1967 insertions(+)
 create mode 100644 nautilus_nlp/utils/emoji.py

diff --git a/nautilus_nlp/utils/emoji.py b/nautilus_nlp/utils/emoji.py
new file mode 100644
index 0000000..d4d58d6
--- /dev/null
+++ b/nautilus_nlp/utils/emoji.py
@@ -0,0 +1,1967 @@
+"""
+This dataset is based on:
+    Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut and Mozetič, Igor, 2015,
+    Emoji Sentiment Ranking 1.0, Slovenian language resource repository CLARIN.SI,
+    http://hdl.handle.net/11356/1048.
+"""
+
+def rebuilt_emoji_dictionaries(filename):
+    emoji2unicode_name, emoji2sentiment = {}, {}
+    with open(filename) as csvin:
+        for emoji in csv.DictReader(csvin):
+            for key, value in emoji.items():
+                if key in ('Occurrences', 'Positive', 'Neutral', 'Negative'):
+                    emoji[key] = int(value)
+                elif key in ('Position',):
+                    emoji[key] = float(value)
+
+            emoji['Sentiment'] = (emoji['Positive'] - emoji['Negative']) / \
+                                max(100, (emoji['Positive'] + emoji['Neutral'] + emoji['Negative']))
+            emoji2unicode_name[emoji['Emoji']] = emoji['Unicode name']
+            emoji2sentiment[emoji['Emoji']] = emoji['Sentiment']
+
+    return emoji2unicode_name, emoji2sentiment
+
+emoji2unicode_name = {
+    '😂': 'FACE WITH TEARS OF JOY',
+    '❤': 'HEAVY BLACK HEART',
+    '♥': 'BLACK HEART SUIT',
+    '😍': 'SMILING FACE WITH HEART-SHAPED EYES',
+    '😭': 'LOUDLY CRYING FACE',
+    '😘': 'FACE THROWING A KISS',
+    '😊': 'SMILING FACE WITH SMILING EYES',
+    '👌': 'OK HAND SIGN',
+    '💕': 'TWO HEARTS',
+    '👏': 'CLAPPING HANDS SIGN',
+    '😁': 'GRINNING FACE WITH SMILING EYES',
+    '☺': 'WHITE SMILING FACE',
+    '♡': 'WHITE HEART SUIT',
+    '👍': 'THUMBS UP SIGN',
+    '😩': 'WEARY FACE',
+    '🙏': 'PERSON WITH FOLDED HANDS',
+    '✌': 'VICTORY HAND',
+    '😏': 'SMIRKING FACE',
+    '😉': 'WINKING FACE',
+    '🙌': 'PERSON RAISING BOTH HANDS IN CELEBRATION',
+    '🙈': 'SEE-NO-EVIL MONKEY',
+    '💪': 'FLEXED BICEPS',
+    '😄': 'SMILING FACE WITH OPEN MOUTH AND SMILING EYES',
+    '😒': 'UNAMUSED FACE',
+    '💃': 'DANCER',
+    '💖': 'SPARKLING HEART',
+    '😃': 'SMILING FACE WITH OPEN MOUTH',
+    '😔': 'PENSIVE FACE',
+    '😱': 'FACE SCREAMING IN FEAR',
+    '🎉': 'PARTY POPPER',
+    '😜': 'FACE WITH STUCK-OUT TONGUE AND WINKING EYE',
+    '☯': 'YIN YANG',
+    '🌸': 'CHERRY BLOSSOM',
+    '💜': 'PURPLE HEART',
+    '💙': 'BLUE HEART',
+    '✨': 'SPARKLES',
+    '😳': 'FLUSHED FACE',
+    '💗': 'GROWING HEART',
+    '★': 'BLACK STAR',
+    '█': 'FULL BLOCK',
+    '☀': 'BLACK SUN WITH RAYS',
+    '😡': 'POUTING FACE',
+    '😎': 'SMILING FACE WITH SUNGLASSES',
+    '😢': 'CRYING FACE',
+    '💋': 'KISS MARK',
+    '😋': 'FACE SAVOURING DELICIOUS FOOD',
+    '🙊': 'SPEAK-NO-EVIL MONKEY',
+    '😴': 'SLEEPING FACE',
+    '🎶': 'MULTIPLE MUSICAL NOTES',
+    '💞': 'REVOLVING HEARTS',
+    '😌': 'RELIEVED FACE',
+    '🔥': 'FIRE',
+    '💯': 'HUNDRED POINTS SYMBOL',
+    '🔫': 'PISTOL',
+    '💛': 'YELLOW HEART',
+    '💁': 'INFORMATION DESK PERSON',
+    '💚': 'GREEN HEART',
+    '♫': 'BEAMED EIGHTH NOTES',
+    '😞': 'DISAPPOINTED FACE',
+    '😆': 'SMILING FACE WITH OPEN MOUTH AND TIGHTLY-CLOSED EYES',
+    '😝': 'FACE WITH STUCK-OUT TONGUE AND TIGHTLY-CLOSED EYES',
+    '😪': 'SLEEPY FACE',
+    '�': 'REPLACEMENT CHARACTER',
+    '😫': 'TIRED FACE',
+    '😅': 'SMILING FACE WITH OPEN MOUTH AND COLD SWEAT',
+    '👊': 'FISTED HAND SIGN',
+    '💀': 'SKULL',
+    '😀': 'GRINNING FACE',
+    '😚': 'KISSING FACE WITH CLOSED EYES',
+    '😻': 'SMILING CAT FACE WITH HEART-SHAPED EYES',
+    '©': 'COPYRIGHT SIGN',
+    '👀': 'EYES',
+    '💘': 'HEART WITH ARROW',
+    '🐓': 'ROOSTER',
+    '☕': 'HOT BEVERAGE',
+    '👋': 'WAVING HAND SIGN',
+    '✋': 'RAISED HAND',
+    '🎊': 'CONFETTI BALL',
+    '🍕': 'SLICE OF PIZZA',
+    '❄': 'SNOWFLAKE',
+    '😥': 'DISAPPOINTED BUT RELIEVED FACE',
+    '😕': 'CONFUSED FACE',
+    '💥': 'COLLISION SYMBOL',
+    '💔': 'BROKEN HEART',
+    '😤': 'FACE WITH LOOK OF TRIUMPH',
+    '😈': 'SMILING FACE WITH HORNS',
+    '►': 'BLACK RIGHT-POINTING POINTER',
+    '✈': 'AIRPLANE',
+    '🔝': 'TOP WITH UPWARDS ARROW ABOVE',
+    '😰': 'FACE WITH OPEN MOUTH AND COLD SWEAT',
+    '⚽': 'SOCCER BALL',
+    '😑': 'EXPRESSIONLESS FACE',
+    '👑': 'CROWN',
+    '😹': 'CAT FACE WITH TEARS OF JOY',
+    '👉': 'WHITE RIGHT POINTING BACKHAND INDEX',
+    '🍃': 'LEAF FLUTTERING IN WIND',
+    '🎁': 'WRAPPED PRESENT',
+    '😠': 'ANGRY FACE',
+    '🐧': 'PENGUIN',
+    '☆': 'WHITE STAR',
+    '🍀': 'FOUR LEAF CLOVER',
+    '🎈': 'BALLOON',
+    '🎅': 'FATHER CHRISTMAS',
+    '😓': 'FACE WITH COLD SWEAT',
+    '😣': 'PERSEVERING FACE',
+    '😐': 'NEUTRAL FACE',
+    '✊': 'RAISED FIST',
+    '😨': 'FEARFUL FACE',
+    '😖': 'CONFOUNDED FACE',
+    '💤': 'SLEEPING SYMBOL',
+    '💓': 'BEATING HEART',
+    '👎': 'THUMBS DOWN SIGN',
+    '💦': 'SPLASHING SWEAT SYMBOL',
+    '✔': 'HEAVY CHECK MARK',
+    '😷': 'FACE WITH MEDICAL MASK',
+    '⚡': 'HIGH VOLTAGE SIGN',
+    '🙋': 'HAPPY PERSON RAISING ONE HAND',
+    '🎄': 'CHRISTMAS TREE',
+    '💩': 'PILE OF POO',
+    '🎵': 'MUSICAL NOTE',
+    '➡': 'BLACK RIGHTWARDS ARROW',
+    '😛': 'FACE WITH STUCK-OUT TONGUE',
+    '😬': 'GRIMACING FACE',
+    '👯': 'WOMAN WITH BUNNY EARS',
+    '💎': 'GEM STONE',
+    '🌿': 'HERB',
+    '🎂': 'BIRTHDAY CAKE',
+    '🌟': 'GLOWING STAR',
+    '🔮': 'CRYSTAL BALL',
+    '❗': 'HEAVY EXCLAMATION MARK SYMBOL',
+    '👫': 'MAN AND WOMAN HOLDING HANDS',
+    '🏆': 'TROPHY',
+    '✖': 'HEAVY MULTIPLICATION X',
+    '☝': 'WHITE UP POINTING INDEX',
+    '😙': 'KISSING FACE WITH SMILING EYES',
+    '⛄': 'SNOWMAN WITHOUT SNOW',
+    '👅': 'TONGUE',
+    '♪': 'EIGHTH NOTE',
+    '🍂': 'FALLEN LEAF',
+    '💏': 'KISS',
+    '🔪': 'HOCHO',
+    '🌴': 'PALM TREE',
+    '👈': 'WHITE LEFT POINTING BACKHAND INDEX',
+    '🌹': 'ROSE',
+    '🙆': 'FACE WITH OK GESTURE',
+    '➜': 'HEAVY ROUND-TIPPED RIGHTWARDS ARROW',
+    '👻': 'GHOST',
+    '💰': 'MONEY BAG',
+    '🍻': 'CLINKING BEER MUGS',
+    '🙅': 'FACE WITH NO GOOD GESTURE',
+    '🌞': 'SUN WITH FACE',
+    '🍁': 'MAPLE LEAF',
+    '⭐': 'WHITE MEDIUM STAR',
+    '▪': 'BLACK SMALL SQUARE',
+    '🎀': 'RIBBON',
+    '━': 'BOX DRAWINGS HEAVY HORIZONTAL',
+    '☷': 'TRIGRAM FOR EARTH',
+    '🐷': 'PIG FACE',
+    '🙉': 'HEAR-NO-EVIL MONKEY',
+    '🌺': 'HIBISCUS',
+    '💅': 'NAIL POLISH',
+    '🐶': 'DOG FACE',
+    '🌚': 'NEW MOON WITH FACE',
+    '👽': 'EXTRATERRESTRIAL ALIEN',
+    '🎤': 'MICROPHONE',
+    '👭': 'TWO WOMEN HOLDING HANDS',
+    '🎧': 'HEADPHONE',
+    '👆': 'WHITE UP POINTING BACKHAND INDEX',
+    '🍸': 'COCKTAIL GLASS',
+    '🍷': 'WINE GLASS',
+    '®': 'REGISTERED SIGN',
+    '🍉': 'WATERMELON',
+    '😇': 'SMILING FACE WITH HALO',
+    '☑': 'BALLOT BOX WITH CHECK',
+    '🏃': 'RUNNER',
+    '😿': 'CRYING CAT FACE',
+    '│': 'BOX DRAWINGS LIGHT VERTICAL',
+    '💣': 'BOMB',
+    '🍺': 'BEER MUG',
+    '▶': 'BLACK RIGHT-POINTING TRIANGLE',
+    '😲': 'ASTONISHED FACE',
+    '🎸': 'GUITAR',
+    '🍹': 'TROPICAL DRINK',
+    '💫': 'DIZZY SYMBOL',
+    '📚': 'BOOKS',
+    '😶': 'FACE WITHOUT MOUTH',
+    '🌷': 'TULIP',
+    '💝': 'HEART WITH RIBBON',
+    '💨': 'DASH SYMBOL',
+    '🏈': 'AMERICAN FOOTBALL',
+    '💍': 'RING',
+    '☔': 'UMBRELLA WITH RAIN DROPS',
+    '👸': 'PRINCESS',
+    '🇪': 'REGIONAL INDICATOR SYMBOL LETTER E',
+    '░': 'LIGHT SHADE',
+    '🍩': 'DOUGHNUT',
+    '👾': 'ALIEN MONSTER',
+    '☁': 'CLOUD',
+    '🌻': 'SUNFLOWER',
+    '😵': 'DIZZY FACE',
+    '📒': 'LEDGER',
+    '↿': 'UPWARDS HARPOON WITH BARB LEFTWARDS',
+    '🐯': 'TIGER FACE',
+    '👼': 'BABY ANGEL',
+    '🍔': 'HAMBURGER',
+    '😸': 'GRINNING CAT FACE WITH SMILING EYES',
+    '👶': 'BABY',
+    '↾': 'UPWARDS HARPOON WITH BARB RIGHTWARDS',
+    '💐': 'BOUQUET',
+    '🌊': 'WATER WAVE',
+    '🍦': 'SOFT ICE CREAM',
+    '🍓': 'STRAWBERRY',
+    '👇': 'WHITE DOWN POINTING BACKHAND INDEX',
+    '💆': 'FACE MASSAGE',
+    '🍴': 'FORK AND KNIFE',
+    '😧': 'ANGUISHED FACE',
+    '🇸': 'REGIONAL INDICATOR SYMBOL LETTER S',
+    '😮': 'FACE WITH OPEN MOUTH',
+    '▓': 'DARK SHADE',
+    '🚫': 'NO ENTRY SIGN',
+    '😽': 'KISSING CAT FACE WITH CLOSED EYES',
+    '🌈': 'RAINBOW',
+    '🙀': 'WEARY CAT FACE',
+    '⚠': 'WARNING SIGN',
+    '🎮': 'VIDEO GAME',
+    '╯': 'BOX DRAWINGS LIGHT ARC UP AND LEFT',
+    '🍆': 'AUBERGINE',
+    '🍰': 'SHORTCAKE',
+    '✓': 'CHECK MARK',
+    '👐': 'OPEN HANDS SIGN',
+    '🙇': 'PERSON BOWING DEEPLY',
+    '🍟': 'FRENCH FRIES',
+    '🍌': 'BANANA',
+    '💑': 'COUPLE WITH HEART',
+    '👬': 'TWO MEN HOLDING HANDS',
+    '🐣': 'HATCHING CHICK',
+    '🎃': 'JACK-O-LANTERN',
+    '▬': 'BLACK RECTANGLE',
+    '': 'OBJECT REPLACEMENT CHARACTER',
+    '😟': 'WORRIED FACE',
+    '🐾': 'PAW PRINTS',
+    '🎓': 'GRADUATION CAP',
+    '🏊': 'SWIMMER',
+    '🍫': 'CHOCOLATE BAR',
+    '📷': 'CAMERA',
+    '👄': 'MOUTH',
+    '🌼': 'BLOSSOM',
+    '🚶': 'PEDESTRIAN',
+    '🐱': 'CAT FACE',
+    '║': 'BOX DRAWINGS DOUBLE VERTICAL',
+    '🐸': 'FROG FACE',
+    '🇺': 'REGIONAL INDICATOR SYMBOL LETTER U',
+    '👿': 'IMP',
+    '🚬': 'SMOKING SYMBOL',
+    '✿': 'BLACK FLORETTE',
+    '📖': 'OPEN BOOK',
+    '🐒': 'MONKEY',
+    '🌍': 'EARTH GLOBE EUROPE-AFRICA',
+    '┊': 'BOX DRAWINGS LIGHT QUADRUPLE DASH VERTICAL',
+    '🐥': 'FRONT-FACING BABY CHICK',
+    '🌀': 'CYCLONE',
+    '🐼': 'PANDA FACE',
+    '🎥': 'MOVIE CAMERA',
+    '💄': 'LIPSTICK',
+    '💸': 'MONEY WITH WINGS',
+    '⛔': 'NO ENTRY',
+    '●': 'BLACK CIRCLE',
+    '🏀': 'BASKETBALL AND HOOP',
+    '💉': 'SYRINGE',
+    '💟': 'HEART DECORATION',
+    '🚗': 'AUTOMOBILE',
+    '😯': 'HUSHED FACE',
+    '📝': 'MEMO',
+    '═': 'BOX DRAWINGS DOUBLE HORIZONTAL',
+    '♦': 'BLACK DIAMOND SUIT',
+    '💭': 'THOUGHT BALLOON',
+    '🌙': 'CRESCENT MOON',
+    '🐟': 'FISH',
+    '👣': 'FOOTPRINTS',
+    '☞': 'WHITE RIGHT POINTING INDEX',
+    '✂': 'BLACK SCISSORS',
+    '🗿': 'MOYAI',
+    '🍝': 'SPAGHETTI',
+    '👪': 'FAMILY',
+    '🍭': 'LOLLIPOP',
+    '🌃': 'NIGHT WITH STARS',
+    '❌': 'CROSS MARK',
+    '🐰': 'RABBIT FACE',
+    '💊': 'PILL',
+    '🚨': 'POLICE CARS REVOLVING LIGHT',
+    '😦': 'FROWNING FACE WITH OPEN MOUTH',
+    '🍪': 'COOKIE',
+    '🍣': 'SUSHI',
+    '╭': 'BOX DRAWINGS LIGHT ARC DOWN AND RIGHT',
+    '✧': 'WHITE FOUR POINTED STAR',
+    '🎆': 'FIREWORKS',
+    '╮': 'BOX DRAWINGS LIGHT ARC DOWN AND LEFT',
+    '🎎': 'JAPANESE DOLLS',
+    '🇩': 'REGIONAL INDICATOR SYMBOL LETTER D',
+    '✅': 'WHITE HEAVY CHECK MARK',
+    '👹': 'JAPANESE OGRE',
+    '📱': 'MOBILE PHONE',
+    '🙍': 'PERSON FROWNING',
+    '🍑': 'PEACH',
+    '🎼': 'MUSICAL SCORE',
+    '🔊': 'SPEAKER WITH THREE SOUND WAVES',
+    '🌌': 'MILKY WAY',
+    '🍎': 'RED APPLE',
+    '🐻': 'BEAR FACE',
+    '─': 'BOX DRAWINGS LIGHT HORIZONTAL',
+    '╰': 'BOX DRAWINGS LIGHT ARC UP AND RIGHT',
+    '💇': 'HAIRCUT',
+    '♬': 'BEAMED SIXTEENTH NOTES',
+    '♚': 'BLACK CHESS KING',
+    '🔴': 'LARGE RED CIRCLE',
+    '🍱': 'BENTO BOX',
+    '🍊': 'TANGERINE',
+    '🍒': 'CHERRIES',
+    '🐭': 'MOUSE FACE',
+    '👟': 'ATHLETIC SHOE',
+    '🌎': 'EARTH GLOBE AMERICAS',
+    '🍍': 'PINEAPPLE',
+    '🐮': 'COW FACE',
+    '📲': 'MOBILE PHONE WITH RIGHTWARDS ARROW AT LEFT',
+    '☼': 'WHITE SUN WITH RAYS',
+    '🌅': 'SUNRISE',
+    '🇷': 'REGIONAL INDICATOR SYMBOL LETTER R',
+    '👠': 'HIGH-HEELED SHOE',
+    '🌽': 'EAR OF MAIZE',
+    '💧': 'DROPLET',
+    '❓': 'BLACK QUESTION MARK ORNAMENT',
+    '🍬': 'CANDY',
+    '😺': 'SMILING CAT FACE WITH OPEN MOUTH',
+    '🐴': 'HORSE FACE',
+    '🚀': 'ROCKET',
+    '¦': 'BROKEN BAR',
+    '💢': 'ANGER SYMBOL',
+    '🎬': 'CLAPPER BOARD',
+    '🍧': 'SHAVED ICE',
+    '🍜': 'STEAMING BOWL',
+    '🐏': 'RAM',
+    '🐘': 'ELEPHANT',
+    '👧': 'GIRL',
+    '⠀': 'BRAILLE PATTERN BLANK',
+    '🏄': 'SURFER',
+    '➤': 'BLACK RIGHTWARDS ARROWHEAD',
+    '⬆': 'UPWARDS BLACK ARROW',
+    '🍋': 'LEMON',
+    '🆗': 'SQUARED OK',
+    '⚪': 'MEDIUM WHITE CIRCLE',
+    '📺': 'TELEVISION',
+    '🍅': 'TOMATO',
+    '⛅': 'SUN BEHIND CLOUD',
+    '🐢': 'TURTLE',
+    '👙': 'BIKINI',
+    '🏡': 'HOUSE WITH GARDEN',
+    '🌾': 'EAR OF RICE',
+    '◉': 'FISHEYE',
+    '✏': 'PENCIL',
+    '🐬': 'DOLPHIN',
+    '🍤': 'FRIED SHRIMP',
+    '🇹': 'REGIONAL INDICATOR SYMBOL LETTER T',
+    '♣': 'BLACK CLUB SUIT',
+    '🐝': 'HONEYBEE',
+    '🌝': 'FULL MOON WITH FACE',
+    '🇮': 'REGIONAL INDICATOR SYMBOL LETTER I',
+    '🔋': 'BATTERY',
+    '🐍': 'SNAKE',
+    '♔': 'WHITE CHESS KING',
+    '🍳': 'COOKING',
+    '🔵': 'LARGE BLUE CIRCLE',
+    '😾': 'POUTING CAT FACE',
+    '🌕': 'FULL MOON SYMBOL',
+    '🐨': 'KOALA',
+    '🔐': 'CLOSED LOCK WITH KEY',
+    '💿': 'OPTICAL DISC',
+    '❁': 'EIGHT PETALLED OUTLINED BLACK FLORETTE',
+    '🌳': 'DECIDUOUS TREE',
+    '👰': 'BRIDE WITH VEIL',
+    '❀': 'WHITE FLORETTE',
+    '⚓': 'ANCHOR',
+    '🚴': 'BICYCLIST',
+    '▀': 'UPPER HALF BLOCK',
+    '👗': 'DRESS',
+    '➕': 'HEAVY PLUS SIGN',
+    '💬': 'SPEECH BALLOON',
+    '▒': 'MEDIUM SHADE',
+    '🔜': 'SOON WITH RIGHTWARDS ARROW ABOVE',
+    '🍨': 'ICE CREAM',
+    '💲': 'HEAVY DOLLAR SIGN',
+    '⛽': 'FUEL PUMP',
+    '🍙': 'RICE BALL',
+    '🍗': 'POULTRY LEG',
+    '🍲': 'POT OF FOOD',
+    '🍥': 'FISH CAKE WITH SWIRL DESIGN',
+    '▸': 'BLACK RIGHT-POINTING SMALL TRIANGLE',
+    '♛': 'BLACK CHESS QUEEN',
+    '😼': 'CAT FACE WITH WRY SMILE',
+    '🐙': 'OCTOPUS',
+    '👨': 'MAN',
+    '🍚': 'COOKED RICE',
+    '🍖': 'MEAT ON BONE',
+    '♨': 'HOT SPRINGS',
+    '🎹': 'MUSICAL KEYBOARD',
+    '♕': 'WHITE CHESS QUEEN',
+    '▃': 'LOWER THREE EIGHTHS BLOCK',
+    '🚘': 'ONCOMING AUTOMOBILE',
+    '🍏': 'GREEN APPLE',
+    '👩': 'WOMAN',
+    '👦': 'BOY',
+    '🇬': 'REGIONAL INDICATOR SYMBOL LETTER G',
+    '🇧': 'REGIONAL INDICATOR SYMBOL LETTER B',
+    '☠': 'SKULL AND CROSSBONES',
+    '🐠': 'TROPICAL FISH',
+    '🚹': 'MENS SYMBOL',
+    '💵': 'BANKNOTE WITH DOLLAR SIGN',
+    '✰': 'SHADOWED WHITE STAR',
+    '╠': 'BOX DRAWINGS DOUBLE VERTICAL AND RIGHT',
+    '👛': 'PURSE',
+    '🚙': 'RECREATIONAL VEHICLE',
+    '🌱': 'SEEDLING',
+    '💻': 'PERSONAL COMPUTER',
+    '🌏': 'EARTH GLOBE ASIA-AUSTRALIA',
+    '▄': 'LOWER HALF BLOCK',
+    '👓': 'EYEGLASSES',
+    '◄': 'BLACK LEFT-POINTING POINTER',
+    '⚾': 'BASEBALL',
+    '🌲': 'EVERGREEN TREE',
+    '👴': 'OLDER MAN',
+    '🏠': 'HOUSE BUILDING',
+    '🍇': 'GRAPES',
+    '🍘': 'RICE CRACKER',
+    '🍛': 'CURRY AND RICE',
+    '🐇': 'RABBIT',
+    '🔞': 'NO ONE UNDER EIGHTEEN SYMBOL',
+    '👵': 'OLDER WOMAN',
+    '◀': 'BLACK LEFT-POINTING TRIANGLE',
+    '🔙': 'BACK WITH LEFTWARDS ARROW ABOVE',
+    '🌵': 'CACTUS',
+    '🐽': 'PIG NOSE',
+    '🍮': 'CUSTARD',
+    '🎇': 'FIREWORK SPARKLER',
+    '🐎': 'HORSE',
+    '➔': 'HEAVY WIDE-HEADED RIGHTWARDS ARROW',
+    '💶': 'BANKNOTE WITH EURO SIGN',
+    '🐤': 'BABY CHICK',
+    '╩': 'BOX DRAWINGS DOUBLE UP AND HORIZONTAL',
+    '🛀': 'BATH',
+    '🌑': 'NEW MOON SYMBOL',
+    '🚲': 'BICYCLE',
+    '🐑': 'SHEEP',
+    '🏁': 'CHEQUERED FLAG',
+    '🍞': 'BREAD',
+    '🎾': 'TENNIS RACQUET AND BALL',
+    '╚': 'BOX DRAWINGS DOUBLE UP AND RIGHT',
+    '🈹': 'SQUARED CJK UNIFIED IDEOGRAPH-5272',
+    '🐳': 'SPOUTING WHALE',
+    '👮': 'POLICE OFFICER',
+    '☹': 'WHITE FROWNING FACE',
+    '🐵': 'MONKEY FACE',
+    '✪': 'CIRCLED WHITE STAR',
+    '◕': 'CIRCLE WITH ALL BUT UPPER LEFT QUADRANT BLACK',
+    '🗼': 'TOKYO TOWER',
+    '▐': 'RIGHT HALF BLOCK',
+    '♠': 'BLACK SPADE SUIT',
+    '┳': 'BOX DRAWINGS HEAVY DOWN AND HORIZONTAL',
+    '👺': 'JAPANESE GOBLIN',
+    '🐚': 'SPIRAL SHELL',
+    '👂': 'EAR',
+    '🗽': 'STATUE OF LIBERTY',
+    '🍵': 'TEACUP WITHOUT HANDLE',
+    '🆒': 'SQUARED COOL',
+    '🍯': 'HONEY POT',
+    '🐺': 'WOLF FACE',
+    '⇨': 'RIGHTWARDS WHITE ARROW',
+    '➨': 'HEAVY CONCAVE-POINTED BLACK RIGHTWARDS ARROW',
+    '🌓': 'FIRST QUARTER MOON SYMBOL',
+    '🔒': 'LOCK',
+    '╬': 'BOX DRAWINGS DOUBLE VERTICAL AND HORIZONTAL',
+    '👳': 'MAN WITH TURBAN',
+    '🌂': 'CLOSED UMBRELLA',
+    '🚌': 'BUS',
+    '♩': 'QUARTER NOTE',
+    '🍡': 'DANGO',
+    '❥': 'ROTATED HEAVY BLACK HEART BULLET',
+    '🎡': 'FERRIS WHEEL',
+    '💌': 'LOVE LETTER',
+    '🐩': 'POODLE',
+    '🌜': 'LAST QUARTER MOON WITH FACE',
+    '⌚': 'WATCH',
+    '🚿': 'SHOWER',
+    '🐖': 'PIG',
+    '🔆': 'HIGH BRIGHTNESS SYMBOL',
+    '🌛': 'FIRST QUARTER MOON WITH FACE',
+    '💂': 'GUARDSMAN',
+    '🐔': 'CHICKEN',
+    '🙎': 'PERSON WITH POUTING FACE',
+    '🏩': 'LOVE HOTEL',
+    '🇫': 'REGIONAL INDICATOR SYMBOL LETTER F',
+    '🔨': 'HAMMER',
+    '📢': 'PUBLIC ADDRESS LOUDSPEAKER',
+    '🐦': 'BIRD',
+    '🐲': 'DRAGON FACE',
+    '♻': 'BLACK UNIVERSAL RECYCLING SYMBOL',
+    '🌘': 'WANING CRESCENT MOON SYMBOL',
+    '🍐': 'PEAR',
+    '🌔': 'WAXING GIBBOUS MOON SYMBOL',
+    '╥': 'BOX DRAWINGS DOWN DOUBLE AND HORIZONTAL SINGLE',
+    '❊': 'EIGHT TEARDROP-SPOKED PROPELLER ASTERISK',
+    '👖': 'JEANS',
+    '🚺': 'WOMENS SYMBOL',
+    '😗': 'KISSING FACE',
+    '🎭': 'PERFORMING ARTS',
+    '🐄': 'COW',
+    '◟': 'LOWER LEFT QUADRANT CIRCULAR ARC',
+    '🍢': 'ODEN',
+    '🎨': 'ARTIST PALETTE',
+    '⬇': 'DOWNWARDS BLACK ARROW',
+    '🚼': 'BABY SYMBOL',
+    '⛲': 'FOUNTAIN',
+    '▁': 'LOWER ONE EIGHTH BLOCK',
+    '🇴': 'REGIONAL INDICATOR SYMBOL LETTER O',
+    '🌗': 'LAST QUARTER MOON SYMBOL',
+    '🌖': 'WANING GIBBOUS MOON SYMBOL',
+    '🔅': 'LOW BRIGHTNESS SYMBOL',
+    '👜': 'HANDBAG',
+    '🐌': 'SNAIL',
+    '💼': 'BRIEFCASE',
+    '🚕': 'TAXI',
+    '🐹': 'HAMSTER FACE',
+    '🌠': 'SHOOTING STAR',
+    '🐈': 'CAT',
+    '⇧': 'UPWARDS WHITE ARROW',
+    '☎': 'BLACK TELEPHONE',
+    '🌁': 'FOGGY',
+    '⚫': 'MEDIUM BLACK CIRCLE',
+    '♧': 'WHITE CLUB SUIT',
+    '🏰': 'EUROPEAN CASTLE',
+    '🚵': 'MOUNTAIN BICYCLIST',
+    '🎢': 'ROLLER COASTER',
+    '🎷': 'SAXOPHONE',
+    '🎐': 'WIND CHIME',
+    '┈': 'BOX DRAWINGS LIGHT QUADRUPLE DASH HORIZONTAL',
+    '╗': 'BOX DRAWINGS DOUBLE DOWN AND LEFT',
+    '╱': 'BOX DRAWINGS LIGHT DIAGONAL UPPER RIGHT TO LOWER LEFT',
+    '🌇': 'SUNSET OVER BUILDINGS',
+    '⏰': 'ALARM CLOCK',
+    '⇩': 'DOWNWARDS WHITE ARROW',
+    '🚂': 'STEAM LOCOMOTIVE',
+    '◠': 'UPPER HALF CIRCLE',
+    '🎿': 'SKI AND SKI BOOT',
+    '✦': 'BLACK FOUR POINTED STAR',
+    '🆔': 'SQUARED ID',
+    '⛪': 'CHURCH',
+    '🌒': 'WAXING CRESCENT MOON SYMBOL',
+    '🐪': 'DROMEDARY CAMEL',
+    '╔': 'BOX DRAWINGS DOUBLE DOWN AND RIGHT',
+    '╝': 'BOX DRAWINGS DOUBLE UP AND LEFT',
+    '👔': 'NECKTIE',
+    '🔱': 'TRIDENT EMBLEM',
+    '🆓': 'SQUARED FREE',
+    '🐋': 'WHALE',
+    '▽': 'WHITE DOWN-POINTING TRIANGLE',
+    '▂': 'LOWER ONE QUARTER BLOCK',
+    '🐛': 'BUG',
+    '👕': 'T-SHIRT',
+    '🚋': 'TRAM CAR',
+    '💳': 'CREDIT CARD',
+    '🌆': 'CITYSCAPE AT DUSK',
+    '🏧': 'AUTOMATED TELLER MACHINE',
+    '💡': 'ELECTRIC LIGHT BULB',
+    '🔹': 'SMALL BLUE DIAMOND',
+    '⬅': 'LEFTWARDS BLACK ARROW',
+    '🍠': 'ROASTED SWEET POTATO',
+    '🐫': 'BACTRIAN CAMEL',
+    '🏪': 'CONVENIENCE STORE',
+    '۩': 'ARABIC PLACE OF SAJDAH',
+    '🇱': 'REGIONAL INDICATOR SYMBOL LETTER L',
+    '📹': 'VIDEO CAMERA',
+    '👞': 'MANS SHOE',
+    '🚑': 'AMBULANCE',
+    '🆘': 'SQUARED SOS',
+    '👚': 'WOMANS CLOTHES',
+    '🚍': 'ONCOMING BUS',
+    '□': 'WHITE SQUARE',
+    '🐂': 'OX',
+    '🚣': 'ROWBOAT',
+    '✳': 'EIGHT SPOKED ASTERISK',
+    '🏉': 'RUGBY FOOTBALL',
+    '🗻': 'MOUNT FUJI',
+    '🐀': 'RAT',
+    '╦': 'BOX DRAWINGS DOUBLE DOWN AND HORIZONTAL',
+    '⛺': 'TENT',
+    '🐕': 'DOG',
+    '🏂': 'SNOWBOARDER',
+    '👡': 'WOMANS SANDAL',
+    '📻': 'RADIO',
+    '✒': 'BLACK NIB',
+    '🌰': 'CHESTNUT',
+    '🏢': 'OFFICE BUILDING',
+    '🎒': 'SCHOOL SATCHEL',
+    '⌒': 'ARC',
+    '🏫': 'SCHOOL',
+    '📴': 'MOBILE PHONE OFF',
+    '🚢': 'SHIP',
+    '🚚': 'DELIVERY TRUCK',
+    '🐉': 'DRAGON',
+    '❒': 'UPPER RIGHT SHADOWED WHITE SQUARE',
+    '🐊': 'CROCODILE',
+    '🔔': 'BELL',
+    '◢': 'BLACK LOWER RIGHT TRIANGLE',
+    '🏥': 'HOSPITAL',
+    '❔': 'WHITE QUESTION MARK ORNAMENT',
+    '🚖': 'ONCOMING TAXI',
+    '🃏': 'PLAYING CARD BLACK JOKER',
+    '▼': 'BLACK DOWN-POINTING TRIANGLE',
+    '▌': 'LEFT HALF BLOCK',
+    '☛': 'BLACK RIGHT POINTING INDEX',
+    '✩': 'STRESS OUTLINED WHITE STAR',
+    '💒': 'WEDDING',
+    '🚤': 'SPEEDBOAT',
+    '🐐': 'GOAT',
+    '■': 'BLACK SQUARE',
+    '🔚': 'END WITH LEFTWARDS ARROW ABOVE',
+    '🎻': 'VIOLIN',
+    '🔷': 'LARGE BLUE DIAMOND',
+    '🚦': 'VERTICAL TRAFFIC LIGHT',
+    '🔓': 'OPEN LOCK',
+    '🎽': 'RUNNING SHIRT WITH SASH',
+    '📅': 'CALENDAR',
+    '🎺': 'TRUMPET',
+    '✯': 'PINWHEEL STAR',
+    '🍈': 'MELON',
+    '✉': 'ENVELOPE',
+    '╣': 'BOX DRAWINGS DOUBLE VERTICAL AND LEFT',
+    '◤': 'BLACK UPPER LEFT TRIANGLE',
+    '○': 'WHITE CIRCLE',
+    '🍼': 'BABY BOTTLE',
+    '📀': 'DVD',
+    '🚛': 'ARTICULATED LORRY',
+    '📓': 'NOTEBOOK',
+    '☉': 'SUN',
+    '💴': 'BANKNOTE WITH YEN SIGN',
+    '┼': 'BOX DRAWINGS LIGHT VERTICAL AND HORIZONTAL',
+    '🐃': 'WATER BUFFALO',
+    '➰': 'CURLY LOOP',
+    '🔌': 'ELECTRIC PLUG',
+    '🍄': 'MUSHROOM',
+    '📕': 'CLOSED BOOK',
+    '📣': 'CHEERING MEGAPHONE',
+    '🚓': 'POLICE CAR',
+    '🐗': 'BOAR',
+    '↪': 'RIGHTWARDS ARROW WITH HOOK',
+    '⛳': 'FLAG IN HOLE',
+    '┻': 'BOX DRAWINGS HEAVY UP AND HORIZONTAL',
+    '┛': 'BOX DRAWINGS HEAVY UP AND LEFT',
+    '┃': 'BOX DRAWINGS HEAVY VERTICAL',
+    '👱': 'PERSON WITH BLOND HAIR',
+    '⏳': 'HOURGLASS WITH FLOWING SAND',
+    '💺': 'SEAT',
+    '🏇': 'HORSE RACING',
+    '☻': 'BLACK SMILING FACE',
+    '📞': 'TELEPHONE RECEIVER',
+    'Ⓐ': 'CIRCLED LATIN CAPITAL LETTER A',
+    '🌉': 'BRIDGE AT NIGHT',
+    '🚩': 'TRIANGULAR FLAG ON POST',
+    '✎': 'LOWER RIGHT PENCIL',
+    '📃': 'PAGE WITH CURL',
+    '🏨': 'HOTEL',
+    '📌': 'PUSHPIN',
+    '♎': 'LIBRA',
+    '💷': 'BANKNOTE WITH POUND SIGN',
+    '🚄': 'HIGH-SPEED TRAIN',
+    '▲': 'BLACK UP-POINTING TRIANGLE',
+    '⛵': 'SAILBOAT',
+    '🔸': 'SMALL ORANGE DIAMOND',
+    '⌛': 'HOURGLASS',
+    '🚜': 'TRACTOR',
+    '🐆': 'LEOPARD',
+    '👒': 'WOMANS HAT',
+    '❕': 'WHITE EXCLAMATION MARK ORNAMENT',
+    '🔛': 'ON WITH EXCLAMATION MARK WITH LEFT RIGHT ARROW ABOVE',
+    '♢': 'WHITE DIAMOND SUIT',
+    '🇲': 'REGIONAL INDICATOR SYMBOL LETTER M',
+    '❅': 'TIGHT TRIFOLIATE SNOWFLAKE',
+    '👝': 'POUCH',
+    '✞': 'SHADOWED WHITE LATIN CROSS',
+    '◡': 'LOWER HALF CIRCLE',
+    '🎋': 'TANABATA TREE',
+    '👥': 'BUSTS IN SILHOUETTE',
+    '📵': 'NO MOBILE PHONES',
+    '🐡': 'BLOWFISH',
+    '◆': 'BLACK DIAMOND',
+    '🏯': 'JAPANESE CASTLE',
+    '☂': 'UMBRELLA',
+    '🔭': 'TELESCOPE',
+    '🎪': 'CIRCUS TENT',
+    '🐜': 'ANT',
+    '♌': 'LEO',
+    '☐': 'BALLOT BOX',
+    '👷': 'CONSTRUCTION WORKER',
+    '↳': 'DOWNWARDS ARROW WITH TIP RIGHTWARDS',
+    '🔈': 'SPEAKER',
+    '📄': 'PAGE FACING UP',
+    '📍': 'ROUND PUSHPIN',
+    '🚐': 'MINIBUS',
+    '🚔': 'ONCOMING POLICE CAR',
+    '🌋': 'VOLCANO',
+    '📡': 'SATELLITE ANTENNA',
+    '⏩': 'BLACK RIGHT-POINTING DOUBLE TRIANGLE',
+    '🚳': 'NO BICYCLES',
+    '✘': 'HEAVY BALLOT X',
+    '۞': 'ARABIC START OF RUB EL HIZB',
+    '☾': 'LAST QUARTER MOON',
+    '🅰': 'NEGATIVE SQUARED LATIN CAPITAL LETTER A',
+    '📥': 'INBOX TRAY',
+    '🇼': 'REGIONAL INDICATOR SYMBOL LETTER W',
+    '┓': 'BOX DRAWINGS HEAVY DOWN AND LEFT',
+    '┣': 'BOX DRAWINGS HEAVY VERTICAL AND RIGHT',
+    'Ⓛ': 'CIRCLED LATIN CAPITAL LETTER L',
+    'Ⓔ': 'CIRCLED LATIN CAPITAL LETTER E',
+    '🔦': 'ELECTRIC TORCH',
+    '👤': 'BUST IN SILHOUETTE',
+    '🚁': 'HELICOPTER',
+    '🎠': 'CAROUSEL HORSE',
+    '🐁': 'MOUSE',
+    '📗': 'GREEN BOOK',
+    '┐': 'BOX DRAWINGS LIGHT DOWN AND LEFT',
+    '☮': 'PEACE SYMBOL',
+    '♂': 'MALE SIGN',
+    '◞': 'LOWER RIGHT QUADRANT CIRCULAR ARC',
+    '📯': 'POSTAL HORN',
+    '🔩': 'NUT AND BOLT',
+    '👢': 'WOMANS BOOTS',
+    '◂': 'BLACK LEFT-POINTING SMALL TRIANGLE',
+    '📰': 'NEWSPAPER',
+    '📶': 'ANTENNA WITH BARS',
+    '🚥': 'HORIZONTAL TRAFFIC LIGHT',
+    '🌄': 'SUNRISE OVER MOUNTAINS',
+    '🗾': 'SILHOUETTE OF JAPAN',
+    '🔶': 'LARGE ORANGE DIAMOND',
+    '🏤': 'EUROPEAN POST OFFICE',
+    '🎩': 'TOP HAT',
+    'Ⓜ': 'CIRCLED LATIN CAPITAL LETTER M',
+    '🔧': 'WRENCH',
+    '🐅': 'TIGER',
+    '♮': 'MUSIC NATURAL SIGN',
+    '🅾': 'NEGATIVE SQUARED LATIN CAPITAL LETTER O',
+    '🔄': 'ANTICLOCKWISE DOWNWARDS AND UPWARDS OPEN CIRCLE ARROWS',
+    '☄': 'COMET',
+    '☨': 'CROSS OF LORRAINE',
+    '📦': 'PACKAGE',
+    '🚊': 'TRAM',
+    '🔲': 'BLACK SQUARE BUTTON',
+    '🔁': 'CLOCKWISE RIGHTWARDS AND LEFTWARDS OPEN CIRCLE ARROWS',
+    '△': 'WHITE UP-POINTING TRIANGLE',
+    '📆': 'TEAR-OFF CALENDAR',
+    '❛': 'HEAVY SINGLE TURNED COMMA QUOTATION MARK ORNAMENT',
+    '📉': 'CHART WITH DOWNWARDS TREND',
+    '▵': 'WHITE UP-POINTING SMALL TRIANGLE',
+    '🔎': 'RIGHT-POINTING MAGNIFYING GLASS',
+    '☜': 'WHITE LEFT POINTING INDEX',
+    '🇯': 'REGIONAL INDICATOR SYMBOL LETTER J',
+    '🇵': 'REGIONAL INDICATOR SYMBOL LETTER P',
+    '📘': 'BLUE BOOK',
+    '✡': 'STAR OF DAVID',
+    'ⓔ': 'CIRCLED LATIN SMALL LETTER E',
+    '🔑': 'KEY',
+    '🔃': 'CLOCKWISE DOWNWARDS AND UPWARDS OPEN CIRCLE ARROWS',
+    '👃': 'NOSE',
+    '⭕': 'HEAVY LARGE CIRCLE',
+    '🔘': 'RADIO BUTTON',
+    'ⓒ': 'CIRCLED LATIN SMALL LETTER C',
+    '🚭': 'NO SMOKING SYMBOL',
+    '🚉': 'STATION',
+    '🚪': 'DOOR',
+    '➳': 'WHITE-FEATHERED RIGHTWARDS ARROW',
+    '🚃': 'RAILWAY CAR',
+    '┯': 'BOX DRAWINGS DOWN LIGHT AND HORIZONTAL HEAVY',
+    '🏬': 'DEPARTMENT STORE',
+    '☽': 'FIRST QUARTER MOON',
+    '🆙': 'SQUARED UP WITH EXCLAMATION MARK',
+    '🆖': 'SQUARED NG',
+    '☪': 'STAR AND CRESCENT',
+    '┗': 'BOX DRAWINGS HEAVY UP AND RIGHT',
+    '🚮': 'PUT LITTER IN ITS PLACE SYMBOL',
+    '┫': 'BOX DRAWINGS HEAVY VERTICAL AND LEFT',
+    'Ⓞ': 'CIRCLED LATIN CAPITAL LETTER O',
+    '❇': 'SPARKLE',
+    '✴': 'EIGHT POINTED BLACK STAR',
+    '┌': 'BOX DRAWINGS LIGHT DOWN AND RIGHT',
+    '☊': 'ASCENDING NODE',
+    '🔕': 'BELL WITH CANCELLATION STROKE',
+    '⬛': 'BLACK LARGE SQUARE',
+    '❝': 'HEAVY DOUBLE TURNED COMMA QUOTATION MARK ORNAMENT',
+    '❞': 'HEAVY DOUBLE COMMA QUOTATION MARK ORNAMENT',
+    '🚞': 'MOUNTAIN RAILWAY',
+    '🍶': 'SAKE BOTTLE AND CUP',
+    '🌐': 'GLOBE WITH MERIDIANS',
+    '♀': 'FEMALE SIGN',
+    '🚅': 'HIGH-SPEED TRAIN WITH BULLET NOSE',
+    '🚒': 'FIRE ENGINE',
+    '➣': 'THREE-D BOTTOM-LIGHTED RIGHTWARDS ARROWHEAD',
+    '♋': 'CANCER',
+    '♍': 'VIRGO',
+    '🕝': 'CLOCK FACE TWO-THIRTY',
+    'ⓐ': 'CIRCLED LATIN SMALL LETTER A',
+    '✗': 'BALLOT X',
+    '📙': 'ORANGE BOOK',
+    'Ⓢ': 'CIRCLED LATIN CAPITAL LETTER S',
+    '📋': 'CLIPBOARD',
+    '⇢': 'RIGHTWARDS DASHED ARROW',
+    '🎱': 'BILLIARDS',
+    '🐞': 'LADY BEETLE',
+    '🔺': 'UP-POINTING RED TRIANGLE',
+    'ⓡ': 'CIRCLED LATIN SMALL LETTER R',
+    '🎍': 'PINE DECORATION',
+    '♤': 'WHITE SPADE SUIT',
+    '🎲': 'GAME DIE',
+    '🎯': 'DIRECT HIT',
+    '〠': 'POSTAL MARK FACE',
+    '🔉': 'SPEAKER WITH ONE SOUND WAVE',
+    '↩': 'LEFTWARDS ARROW WITH HOOK',
+    '🚾': 'WATER CLOSET',
+    '🎣': 'FISHING POLE AND FISH',
+    '🔣': 'INPUT SYMBOL FOR SYMBOLS',
+    '❎': 'NEGATIVE SQUARED CROSS MARK',
+    '➥': 'HEAVY BLACK CURVED DOWNWARDS AND RIGHTWARDS ARROW',
+    '🅱': 'NEGATIVE SQUARED LATIN CAPITAL LETTER B',
+    '🎌': 'CROSSED FLAGS',
+    '◣': 'BLACK LOWER LEFT TRIANGLE',
+    '⏬': 'BLACK DOWN-POINTING DOUBLE TRIANGLE',
+    '♭': 'MUSIC FLAT SIGN',
+    '💠': 'DIAMOND SHAPE WITH A DOT INSIDE',
+    'ⓞ': 'CIRCLED LATIN SMALL LETTER O',
+    '🔳': 'WHITE SQUARE BUTTON',
+    '🏭': 'FACTORY',
+    '🔰': 'JAPANESE SYMBOL FOR BEGINNER',
+    '🎳': 'BOWLING',
+    '☚': 'BLACK LEFT POINTING INDEX',
+    '➽': 'HEAVY WEDGE-TAILED RIGHTWARDS ARROW',
+    '➫': 'BACK-TILTED SHADOWED WHITE RIGHTWARDS ARROW',
+    '➖': 'HEAVY MINUS SIGN',
+    '🏮': 'IZAKAYA LANTERN',
+    '📛': 'NAME BADGE',
+    '꒰': 'YI RADICAL SHY',
+    '꒱': 'YI RADICAL VEP',
+    '◝': 'UPPER RIGHT QUADRANT CIRCULAR ARC',
+    '📑': 'BOOKMARK TABS',
+    '🎦': 'CINEMA',
+    'ⓧ': 'CIRCLED LATIN SMALL LETTER X',
+    '🇨': 'REGIONAL INDICATOR SYMBOL LETTER C',
+    '🇳': 'REGIONAL INDICATOR SYMBOL LETTER N',
+    '🔟': 'KEYCAP TEN',
+    '〓': 'GETA MARK',
+    'ⓜ': 'CIRCLED LATIN SMALL LETTER M',
+    '➠': 'HEAVY DASHED TRIANGLE-HEADED RIGHTWARDS ARROW',
+    '🚆': 'TRAIN',
+    '🚠': 'MOUNTAIN CABLEWAY',
+    '℅': 'CARE OF',
+    '☃': 'SNOWMAN',
+    '🚽': 'TOILET',
+    '📐': 'TRIANGULAR RULER',
+    'ⓝ': 'CIRCLED LATIN SMALL LETTER N',
+    '✮': 'HEAVY OUTLINED BLACK STAR',
+    '⇦': 'LEFTWARDS WHITE ARROW',
+    '👲': 'MAN WITH GUA PI MAO',
+    '🚡': 'AERIAL TRAMWAY',
+    '🎑': 'MOON VIEWING CEREMONY',
+    '🔬': 'MICROSCOPE',
+    '➗': 'HEAVY DIVISION SIGN',
+    '📈': 'CHART WITH UPWARDS TREND',
+    '⌘': 'PLACE OF INTEREST SIGN',
+    '⏪': 'BLACK LEFT-POINTING DOUBLE TRIANGLE',
+    '╹': 'BOX DRAWINGS HEAVY UP',
+    '◎': 'BULLSEYE',
+    '🔼': 'UP-POINTING SMALL RED TRIANGLE',
+    '꒦': 'YI RADICAL GGUO',
+    '📎': 'PAPERCLIP',
+    '⑅': 'OCR BOW TIE',
+    '⍝': 'APL FUNCTIONAL SYMBOL UP SHOE JOT',
+    '📁': 'FILE FOLDER',
+    '✭': 'OUTLINED BLACK STAR',
+    '➲': 'CIRCLED HEAVY WHITE RIGHTWARDS ARROW',
+    '♓': 'PISCES',
+    '┏': 'BOX DRAWINGS HEAVY DOWN AND RIGHT',
+    '☇': 'LIGHTNING',
+    '♺': 'RECYCLING SYMBOL FOR GENERIC MATERIALS',
+    '♞': 'BLACK CHESS KNIGHT',
+    '࿎': 'TIBETAN SIGN RDEL NAG RDEL DKAR',
+    '📠': 'FAX MACHINE',
+    '👘': 'KIMONO',
+    '↙': 'SOUTH WEST ARROW',
+    'Ⓕ': 'CIRCLED LATIN CAPITAL LETTER F',
+    'Ⓦ': 'CIRCLED LATIN CAPITAL LETTER W',
+    'Ⓟ': 'CIRCLED LATIN CAPITAL LETTER P',
+    '🕑': 'CLOCK FACE TWO OCLOCK',
+    '💽': 'MINIDISC',
+    '🕛': 'CLOCK FACE TWELVE OCLOCK',
+    '🎫': 'TICKET',
+    '♈': 'ARIES',
+    '📟': 'PAGER',
+    '℃': 'DEGREE CELSIUS',
+    '↬': 'RIGHTWARDS ARROW WITH LOOP',
+    '🕒': 'CLOCK FACE THREE OCLOCK',
+    '🇰': 'REGIONAL INDICATOR SYMBOL LETTER K',
+    '↱': 'UPWARDS ARROW WITH TIP RIGHTWARDS',
+    '✍': 'WRITING HAND',
+    '⇐': 'LEFTWARDS DOUBLE ARROW',
+    '🏦': 'BANK',
+    '🔻': 'DOWN-POINTING RED TRIANGLE',
+    'ⓟ': 'CIRCLED LATIN SMALL LETTER P',
+    'ⓕ': 'CIRCLED LATIN SMALL LETTER F',
+    'ⓘ': 'CIRCLED LATIN SMALL LETTER I',
+    '♿': 'WHEELCHAIR SYMBOL',
+    '⇗': 'NORTH EAST DOUBLE ARROW',
+    '⇘': 'SOUTH EAST DOUBLE ARROW',
+    'ⓨ': 'CIRCLED LATIN SMALL LETTER Y',
+    'ⓙ': 'CIRCLED LATIN SMALL LETTER J',
+    '▫': 'WHITE SMALL SQUARE',
+    '🔇': 'SPEAKER WITH CANCELLATION STROKE',
+    '⌃': 'UP ARROWHEAD',
+    '🔖': 'BOOKMARK',
+    '📜': 'SCROLL',
+    '♏': 'SCORPIUS',
+    '🚝': 'MONORAIL',
+    '☢': 'RADIOACTIVE SIGN',
+    '🎏': 'CARP STREAMER',
+    '┘': 'BOX DRAWINGS LIGHT UP AND LEFT',
+    '✝': 'LATIN CROSS',
+    '❖': 'BLACK DIAMOND MINUS WHITE X',
+    '⍣': 'APL FUNCTIONAL SYMBOL STAR DIAERESIS',
+    '📮': 'POSTBOX',
+    '🕕': 'CLOCK FACE SIX OCLOCK',
+    '🇭': 'REGIONAL INDICATOR SYMBOL LETTER H',
+    '◜': 'UPPER LEFT QUADRANT CIRCULAR ARC',
+    '🔯': 'SIX POINTED STAR WITH MIDDLE DOT',
+    '➸': 'HEAVY BLACK-FEATHERED RIGHTWARDS ARROW',
+    '꒵': 'YI RADICAL JJY',
+    '🕥': 'CLOCK FACE TEN-THIRTY',
+    '♙': 'WHITE CHESS PAWN',
+    '▿': 'WHITE DOWN-POINTING SMALL TRIANGLE',
+    '⚃': 'DIE FACE-4',
+    '✽': 'HEAVY TEARDROP-SPOKED ASTERISK',
+    '📼': 'VIDEOCASSETTE',
+    '🕐': 'CLOCK FACE ONE OCLOCK',
+    '🀄': 'MAHJONG TILE RED DRAGON',
+    '✾': 'SIX PETALLED BLACK AND WHITE FLORETTE',
+    '✬': 'BLACK CENTRE WHITE STAR',
+    '🆑': 'SQUARED CL',
+    '✫': 'OPEN CENTRE BLACK STAR',
+    '🕔': 'CLOCK FACE FIVE OCLOCK',
+    '❣': 'HEAVY HEART EXCLAMATION MARK ORNAMENT',
+    '➱': 'NOTCHED UPPER RIGHT-SHADOWED WHITE RIGHTWARDS ARROW',
+    '🆕': 'SQUARED NEW',
+    '➢': 'THREE-D TOP-LIGHTED RIGHTWARDS ARROWHEAD',
+    '↕': 'UP DOWN ARROW',
+    '📫': 'CLOSED MAILBOX WITH RAISED FLAG',
+    '🉐': 'CIRCLED IDEOGRAPH ADVANTAGE',
+    '♊': 'GEMINI',
+    '🈂': 'SQUARED KATAKANA SA',
+    '🎰': 'SLOT MACHINE',
+    '҂': 'CYRILLIC THOUSANDS SIGN',
+    '╤': 'BOX DRAWINGS DOWN SINGLE AND HORIZONTAL DOUBLE',
+    '➛': 'DRAFTING POINT RIGHTWARDS ARROW',
+    '♝': 'BLACK CHESS BISHOP',
+    '❋': 'HEAVY EIGHT TEARDROP-SPOKED PROPELLER ASTERISK',
+    '✆': 'TELEPHONE LOCATION SIGN',
+    '📔': 'NOTEBOOK WITH DECORATIVE COVER'
+}
+
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+    '📐': 0.0,
+    'ⓝ': 0.02,
+    '✮': 0.0,
+    '⇦': 0.02,
+    '👲': 0.01,
+    '🚡': -0.01,
+    '🎑': 0.0,
+    '🔬': 0.02,
+    '➗': -0.01,
+    '📈': 0.01,
+    '⌘': 0.0,
+    '⏪': 0.01,
+    '╹': 0.0,
+    '◎': 0.02,
+    '🔼': 0.0,
+    '꒦': -0.02,
+    '📎': 0.02,
+    '⑅': 0.02,
+    '⍝': 0.0,
+    '📁': 0.0,
+    '✭': 0.02,
+    '➲': 0.0,
+    '♓': 0.01,
+    '┏': 0.02,
+    '☇': 0.02,
+    '♺': 0.0,
+    '♞': 0.0,
+    '࿎': -0.02,
+    '📠': 0.0,
+    '👘': 0.02,
+    '↙': 0.02,
+    'Ⓕ': 0.01,
+    'Ⓦ': 0.01,
+    'Ⓟ': 0.01,
+    '🕑': 0.01,
+    '💽': 0.0,
+    '🕛': 0.01,
+    '🎫': 0.0,
+    '♈': -0.01,
+    '📟': 0.0,
+    '℃': 0.0,
+    '↬': 0.01,
+    '🕒': 0.0,
+    '🇰': 0.0,
+    '↱': 0.0,
+    '✍': 0.01,
+    '⇐': 0.0,
+    '🏦': 0.01,
+    '🔻': 0.01,
+    'ⓟ': 0.01,
+    'ⓕ': 0.01,
+    'ⓘ': 0.01,
+    '♿': 0.01,
+    '⇗': 0.01,
+    '⇘': 0.01,
+    'ⓨ': 0.01,
+    'ⓙ': 0.01,
+    '▫': 0.01,
+    '🔇': 0.01,
+    '⌃': -0.01,
+    '🔖': 0.01,
+    '📜': 0.01,
+    '♏': 0.0,
+    '🚝': 0.01,
+    '☢': 0.0,
+    '🎏': 0.0,
+    '┘': -0.01,
+    '✝': -0.01,
+    '❖': 0.0,
+    '⍣': -0.01,
+    '📮': -0.01,
+    '🕕': -0.01,
+    '🇭': 0.0,
+    '◜': 0.0,
+    '🔯': 0.01,
+    '➸': 0.01,
+    '꒵': 0.01,
+    '🕥': -0.01,
+    '♙': 0.0,
+    '▿': 0.0,
+    '⚃': 0.0,
+    '✽': 0.01,
+    '📼': 0.01,
+    '🕐': -0.01,
+    '🀄': 0.01,
+    '✾': 0.0,
+    '✬': 0.01,
+    '🆑': 0.0,
+    '✫': 0.01,
+    '🕔': -0.01,
+    '❣': 0.01,
+    '➱': 0.0,
+    '🆕': 0.0,
+    '➢': 0.0,
+    '↕': 0.0,
+    '📫': 0.01,
+    '🉐': 0.01,
+    '♊': 0.0,
+    '🈂': -0.01,
+    '🎰': -0.01,
+    '҂': -0.01,
+    '╤': -0.01,
+    '➛': 0.0,
+    '♝': 0.0,
+    '❋': 0.0,
+    '✆': 0.0,
+    '📔': 0.01
+}
\ No newline at end of file

From d004b7866f284d10549eab5e29574675d73c4f92 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Tue, 2 Apr 2019 12:18:27 +0200
Subject: [PATCH 013/496]  Change Tokenizer code for spacy to use just the
 tokenizer static rules

---
 nautilus_nlp/utils/tokenizer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index c9340bd..01cd4a4 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -5,14 +5,14 @@
 nltk.download('punkt')
 
 try:
-    french_spacy = spacy.load('fr')
+    french_spacy = spacy.lang.fr.French()
 except OSError:
     raise OSError("""You must install French langage to use SpaCy. 
                     python -m spacy download fr
                     See https://spacy.io/usage/ for details
                 """)
 try:
-    english_spacy = spacy.load('en')
+    english_spacy = spacy.lang.en.English()
 except OSError:
     raise OSError("""You must install english langage to use SpaCy. 
                     python -m spacy download en

From 552e7b44e32fd6c484d1e7b8c7513c535ba2d585 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 3 Apr 2019 15:10:01 +0200
Subject: [PATCH 014/496] add file loader and notebook

---
 nautilus_nlp/utils/file_loader.py             | 143 ++++---
 ... a list of files and detect encoding.ipynb | 401 ++++++++++++++++++
 2 files changed, 482 insertions(+), 62 deletions(-)
 create mode 100644 notebooks/Open a file, a list of files and detect encoding.ipynb

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index f0c8f6e..7baa483 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -1,25 +1,87 @@
-import os
+import io
+import chardet
+import glob
+import re
+from os.path import isfile, isdir
 
-#################
-## Text loaders
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+
+def open_textfile(filepath, encoding='utf-8'):
+    with io.open(filepath, 'r', encoding=encoding) as f:
+        string = f.read()
+    return string
 
-def load_text_file(file_path):
+
+def detect_encoding(file_path_or_string, n_lines=100):
+    '''
+    Predict a file's encoding using chardet
+    '''
+    if isfile(file_path_or_string):
+        with open(file_path_or_string, 'rb') as f:                      # Open the file as binary data
+            rawdata = b''.join([f.readline() for _ in range(n_lines)])  # Join binary lines for specified number of lines
+    elif type(file_path_or_string) is bytes:
+        rawdata = file_path_or_string
+    return chardet.detect(rawdata)
+
+
+def text_loader(filepath, encoding=None, detectencoding=True):
+    '''
+    Args:
+        detect_encoding[bool]= If file is not encoded into UTF-8, try to detect encoding using the chardet library.
+    '''
+    if encoding is not None:
+        return open_textfile(filepath, encoding=encoding)
+    else:
+        try:
+            return open_textfile(filepath, encoding='utf-8')
+        except UnicodeDecodeError:
+            logging.warning('Encoding is not UTF-8.')
+            if detectencoding is True:
+                logging.warning('Trying to detect encoding for {}'.format(filepath))
+                detected_encoding = detect_encoding(filepath)
+                logging.info('detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(encod=detected_encoding['encoding'],
+                                                                                                        conf_rate=detected_encoding['confidence']))
+                return open_textfile(filepath, encoding=detected_encoding['encoding'])
+
+
+def list_files(filepath:str):
+    '''
+    inputs a filepath. 
+    Outputs a list of filepath. 
+    Supports regex
     '''
-    load a file as string
-    Inputs a file path, returns a string'''
-    with open(file_path, errors="ignore") as handle:
-        text = handle.read()
-    return text
+    if isdir(filepath) and len(re.findall(r"[\w.]$",filepath)):
+        filepath=filepath+'/*'
+    if filepath.endswith('/'):
+        filepath=filepath+'*'
+    return[file for file in glob.glob(filepath) if isfile(file)]            
 
 
-def load_texts_as_string(filenames):
-    """ Input is a list of path, output is a dict """
-    from collections import defaultdict
-    loaded_text = defaultdict(str)  # each value is a string, the text
-    for filename in filenames:
-        with open(filename, errors="ignore") as handle:
-            loaded_text[filename] = handle.read()
-    return loaded_text
+def documents_loader(filepath, encoding=None):
+    '''
+    Input a filepath, a filepath with wildcard (eg. *.txt), 
+    or a list of filepaths.
+    Output a string, or a dict of strings.
+    '''
+    
+    if type(filepath) is str:
+        documents = list_files(filepath)
+        nb_of_documents = len(documents)
+    elif type(filepath) is list:
+        nb_of_documents = len(filepath)
+        documents = filepath
+    else:
+        raise IOError('Please enter a valid filepath or a valid list of filepath')
+    
+    if nb_of_documents == 1:
+        return text_loader(documents[0],encoding=encoding)
+    elif nb_of_documents > 1:
+        return { document : text_loader(documents[0], encoding=encoding) for document in documents}
+    else:
+        raise IOError('No files detected in {}'.format(filepath))        
 
 
 #################
@@ -38,55 +100,12 @@ def decode_columns(df, columns_to_encode):
     apply json.loads on columns
     '''
     for col in columns_to_encode:
-        df[col] = df[col].apply(json.loads)                
-
-
-#################
-## Functions to list a folder and get filepaths
-
-def get_filenames(folder):
-    from os import listdir
-    from os.path import isfile, join
-
-    if not folder.endswith("/"):
-        folder = folder + "/"
-    # this will return only the filenames, not folders inside the path
-    return [folder + f for f in listdir(folder)
-        if isfile(join(folder, f)) and f != ".DS_Store"]    
-
-
-def get_filepath(folder):
-    """
-    Return the path of all the files of a directory
-    """
-    import os
-    if not folder.endswith("/"):
-        folder = folder + "/"
-    res = []
-    for root, dirs, files in os.walk(folder, topdown=False):
-        for name in files:
-            res.append(os.path.join(root, name))
-    return res        
+        df[col] = df[col].apply(json.loads)
+  
 
 #################
 ## Encoding functions
 
-def predict_encoding(file_path_or_string, file=True, n_lines=20):
-    '''
-    Predict a file's encoding using chardet
-    '''
-    import chardet
-
-    if file is True:
-        # Open the file as binary data
-        with open(file_path_or_string, 'rb') as f:
-            # Join binary lines for specified number of lines
-            rawdata = b''.join([f.readline() for _ in range(n_lines)])
-    else:
-        rawdata = file_path_or_string
-
-    return chardet.detect(rawdata)['encoding']
-
 
 def convert_encoding(file_path, input_encoding, output_encoding):
     '''
diff --git a/notebooks/Open a file, a list of files and detect encoding.ipynb b/notebooks/Open a file, a list of files and detect encoding.ipynb
new file mode 100644
index 0000000..8c26c57
--- /dev/null
+++ b/notebooks/Open a file, a list of files and detect encoding.ipynb	
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Open Text File with encoding handling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Example of a latin1 file\n",
+    "s = \"J'aime les frites bien grasse étalon châpeau!\"\n",
+    "encoded_s = s.encode('latin-1')\n",
+    "with open('somefile.txt', 'wb') as f:\n",
+    "    f.write(encoded_s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## text loader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't know what the encoding is, you can use the `text_loader()`. Il will try to open with UTF-8, and if it fails it will apply `detect_encoding()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import text_loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"J'aime les frites bien grasse étalon châpeau!\""
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text_loader('somefile.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## open text file when you know the encoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you know the encoding. (you can also use `open_textfile()`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"J'aime les frites bien grasse étalon châpeau!\""
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text_loader('somefile.txt',encoding='latin-1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## detect encoding "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import detect_encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detect_encoding('somefile.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List files in a folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import list_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./somefile.txt',\n",
+       " './Open a file, a list of files and detect encoding.ipynb',\n",
+       " './Visualization tools.ipynb',\n",
+       " './Language_identification.ipynb',\n",
+       " './someadditionalfile.txt',\n",
+       " './somefile',\n",
+       " './Common Text Processing operations.ipynb',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './Spacy_model.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('.') # list files from current folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./Open a file, a list of files and detect encoding.ipynb',\n",
+       " './Visualization tools.ipynb',\n",
+       " './Language_identification.ipynb',\n",
+       " './Common Text Processing operations.ipynb',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './Spacy_model.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('./*.ipynb') # List files matching specific pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/hugo/Documents/NAUTILUS/nautilus-nlp/LICENSE',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/requirements.txt',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/Makefile',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/README.md',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/setup.py',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/tox.ini',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/test_environment.py']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only files will be printed, not folders\n",
+    "list_files('/Users/hugo/Documents/NAUTILUS/nautilus-nlp/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open several text files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Let's add another document \n",
+    "s = \"Un deuxième exemple de texte en utf-8 cette fois!\"\n",
+    "encoded_s = s.encode('utf-8')\n",
+    "with open('someadditionalfile.txt', 'wb') as f:\n",
+    "    f.write(encoded_s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import documents_loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n",
+      "WARNING:root:Encoding is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents_loader('*.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n",
+      "WARNING:root:Encoding is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents_loader(['somefile.txt','someadditionalfile.txt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Un deuxième exemple de texte en utf-8 cette fois!'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents_loader('someadditionalfile.txt',encoding='utf-8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 61cb9ccc409806aac13bbf9df34a83479703cb2e Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 4 Apr 2019 12:41:58 +0200
Subject: [PATCH 015/496] requirements

---
 requirements.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c3f187c..869399b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,4 +25,5 @@ stopwords
 textblob
 textblob_fr
 vaderSentiment
-stop_words
\ No newline at end of file
+stop_words
+cld2_cffi~=0.1

From 3cf84db6cf50ede8c89328e780bf1cbcb6c5fe76 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 4 Apr 2019 12:42:27 +0200
Subject: [PATCH 016/496] Abstraction of doc

---
 nautilus_nlp/doc.py | 394 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 394 insertions(+)
 create mode 100644 nautilus_nlp/doc.py

diff --git a/nautilus_nlp/doc.py b/nautilus_nlp/doc.py
new file mode 100644
index 0000000..e88743c
--- /dev/null
+++ b/nautilus_nlp/doc.py
@@ -0,0 +1,394 @@
+import functools
+import pkg_resources
+import nautilus_nlp
+lang_path = pkg_resources.resource_filename('nautilus_nlp.data', 'lang_identification.ftz')
+
+
+class NautilusMissingModelException(Exception):
+    """Raised when the requested model is missing"""
+
+    pass
+
+
+class Doc:
+    """
+    Create a doc instance of text, obtain cleaned, readable text and
+    metadata from this doc.
+
+    Properties:
+    raw: incoming, unedited text
+    language: 2-letter code for the language of the text
+    is_detected_language: is the language detected or specified beforehand
+    is_reliable_language: is the language specified or was it reliably detected
+    _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
+    """
+
+    def __init__(self, raw, language=None, spacy_nlps=None,langdetect = None):
+        self.raw = raw
+        self._spacy_nlps = spacy_nlps or dict()
+        self._language = language
+        self._is_reliable_language = True if language else None
+        self._language_detector=langdetect
+        self._text_stats = {}
+
+    @property
+    def _spacy_doc(self):
+        """
+        Loads the default spacy doc or creates one if necessary
+
+        >>> doc = Doc('Test sentence for testing text')
+        >>> type(doc._spacy_doc)
+        <class 'spacy.tokens.doc.Doc'>
+        """
+        lang = self.language if self.is_reliable_language else self.hint_language
+
+        return self._load_spacy_doc(lang)
+
+    def _load_spacy_doc(self, lang, model_name=None):
+        """
+        Loads a spacy doc or creates one if necessary
+        """
+        # Load default spacy model if necessary, if not loaded already
+        if lang not in self._spacy_nlps or (
+            model_name is None and model_name not in self._spacy_nlps[lang]
+        ):
+            if lang not in self._spacy_nlps:
+                self._spacy_nlps[lang] = {}
+            self._spacy_nlps[lang][None] = self._get_default_nlp(lang)
+        if model_name not in self._spacy_nlps[lang] and model_name is not None:
+            raise NautilusMissingModelException(
+                f"Custom model {model_name} " f"is missing."
+            )
+        nlp = self._spacy_nlps[lang][model_name]
+        doc = nlp(self.clean_text())
+        return doc
+
+    @staticmethod
+    @functools.lru_cache()
+    def _get_default_nlp(lang):
+        """
+        Loads the spacy default language module for the Doc's language
+        """
+        try:
+            return spacy.load(
+                "{}_core_{}_sm".format(lang, "web" if lang == "en" else "news")
+            )
+        except IOError:
+            raise NautilusMissingModelException(
+                f'Default model for language "{lang}" is not available.'
+            )
+
+    @property
+    def entities(self):
+        """
+        A list of the named entities with sensible defaults.
+
+        >>> doc = Doc('Sentence for testing Google text')
+        >>> doc.entities
+        [('Google', 'ORG')]
+        """
+        return self.find_entities()
+
+    @functools.lru_cache()
+    def find_entities(self, model_name=None):
+        """
+        Extract a list of the named entities in text, with the possibility of using a custom model.
+        >>> doc = Doc('Sentence for testing Google text')
+        >>> doc.find_entities()
+        [('Google', 'ORG')]
+        """
+        lang = self.language if self.is_reliable_language else self.hint_language
+        return list(
+            {
+                (ent.text, ent.label_)
+                for ent in self._load_spacy_doc(lang, model_name).ents
+            }
+        )
+
+    @property
+    def n_sentences(self):
+        """
+        Extract the number of sentences from text
+
+        >>> doc = Doc('Test sentence for testing text. And another sentence for testing!')
+        >>> doc.n_sentences
+        2
+        """
+        return len(list(self._spacy_doc.sents))
+
+    @property
+    def sentences(self):
+        """
+        Extract the text and character offset (begin) of sentences from text
+
+        >>> doc = Doc('Test sentence for testing text. And another one with, some, punctuation! And stuff.')
+        >>> doc.sentences
+        [('Test sentence for testing text.', 0), ('And another one with, some, punctuation!', 32), ('And stuff.', 73)]
+        """
+
+        return [(span.text, span.start_char) for span in self._spacy_doc.sents]
+
+    @property
+    def n_words(self):
+        """
+        Extract the number of words from text
+
+        >>> doc = Doc('Test sentence for testing text')
+        >>> doc.n_words
+        5
+        """
+        return len(self.n_words)
+
+    @property
+    def words(self):
+        """
+        Extract the text and character offset (begin) of words from text
+
+        >>> doc = Doc('Test sentence for testing text.')
+        >>> doc.words
+        [('Test', 0), ('sentence', 5), ('for', 14), ('testing', 18), ('text', 26), ('.', 30)]
+        """
+
+        return [(token.text, token.idx) for token in self._spacy_doc]
+
+    @property
+    def word_counts(self):
+        """
+        Extract words with their counts
+
+        >>> doc = Doc('Test sentence for testing vectorisation of a sentence.')
+        >>> doc.word_counts
+        {'Test': 1, 'sentence': 2, 'for': 1, 'testing': 1, 'vectorisation': 1, 'of': 1, 'a': 1, '.': 1}
+        """
+
+        return dict(Counter(word for word, _ in self.words))
+
+    @property
+    def complexity(self):
+        """
+        Determine the complexity of text using the Flesch
+        reading ease test ranging from 0.0 - 100.0 with 0.0
+        being the most difficult to read.
+
+        >>> doc = Doc('Test sentence for testing text')
+        >>> doc.complexity
+        83.32000000000004
+        """
+        if not self._text_stats:
+            self._text_stats = textacy.TextStats(self._spacy_doc)
+        if self._text_stats.n_syllables == 0:
+            return 100
+        return self._text_stats.flesch_reading_ease
+
+    @property
+    def sentiment(self):
+        """
+        Returns polarity score (-1 to 1) and a subjectivity score (0 to 1)
+
+        Currently only English, Dutch, French and Italian supported
+
+        >>> doc = Doc('Dit is een leuke zin.')
+        >>> doc.sentiment
+        (0.6, 0.9666666666666667)
+        """
+
+        if self.language == "en":
+            from pattern.text.en import sentiment as sentiment_en
+
+            return sentiment_en(self.clean)
+        elif self.language == "nl":
+            from pattern.text.nl import sentiment as sentiment_nl
+
+            return sentiment_nl(self.clean)
+        elif self.language == "fr":
+            from pattern.text.fr import sentiment as sentiment_fr
+
+            return sentiment_fr(self.clean)
+        elif self.language == "it":
+            from pattern.text.it import sentiment as sentiment_it
+
+            return sentiment_it(self.clean)
+
+        raise NautilusMissingModelException(f"No sentiment model for {self.language}")
+
+    @functools.lru_cache()
+    def extract_keyterms(self, ranker="textrank", n_terms=10, **kwargs):
+        """
+        Extract and rank key terms in the document by proxying to
+        `textacy.keyterms`. Returns a list of (term, score) tuples. Depending
+        on the ranking algorithm used, terms can consist of multiple words.
+
+        Available rankers are TextRank (textrank), SingleRank (singlerank) and
+        SGRank ('sgrank').
+
+        >>> doc = Doc('Amsterdam is the awesome capital of the Netherlands.')
+        >>> doc.extract_keyterms(n_terms=3)
+        [('awesome', 0.32456160227748454), ('capital', 0.32456160227748454), ('Amsterdam', 0.17543839772251532)]
+        >>> doc.extract_keyterms(ranker='sgrank')
+        [('awesome capital', 0.5638711013322963), ('Netherlands', 0.22636566128805719), ('Amsterdam', 0.20976323737964653)]
+        >>> doc.extract_keyterms(ranker='sgrank', ngrams=(1))
+        [('Netherlands', 0.4020557546031188), ('capital', 0.29395103364295216), ('awesome', 0.18105611227666252), ('Amsterdam', 0.12293709947726655)]
+        """
+        if self.nwords < 1:
+            return []
+        rankers = ["textrank", "sgrank", "singlerank"]
+        if ranker not in rankers:
+            raise ValueError(
+                f'ranker "{ranker}" not available; use one ' f"of {rankers}"
+            )
+        ranking_fn = getattr(textacy.keyterms, ranker)
+        return ranking_fn(self._spacy_doc, n_keyterms=n_terms, **kwargs)
+
+    @property
+    def keyterms(self):
+        """
+        Return textranked keyterms for the document.
+
+        >>> doc = Doc('Amsterdam is the awesome capital of the Netherlands.')
+        >>> doc.extract_keyterms(n_terms=3)
+        [('awesome', 0.32456160227748454), ('capital', 0.32456160227748454), ('Amsterdam', 0.17543839772251532)]
+        """
+        return self.extract_keyterms()
+
+    @property
+    def minhash(self):
+        """
+        A cheap way to compute a hash for finding similarity of docs
+        Source: https://ekzhu.github.io/datasketch/minhash.html
+        >>> doc = Doc('Sentence for computing the minhash')
+        >>> doc.minhash[:5]
+        [407326892, 814360600, 1099082245, 1176349439, 1735256]
+        """
+        return self.find_minhash()
+
+    @functools.lru_cache()
+    def find_minhash(self, num_perm=128):
+        words = self.words
+        doc_hash = MinHash(num_perm=num_perm)
+        for word, _ in words:
+            doc_hash.update(word.encode("utf8"))
+        return list(doc_hash.digest())
+
+    def similarity(self, other_doc, metric="jaccard", hash_method="minhash"):
+        """
+        Computes similarity for two documents.
+        Only minhash Jaccard similarity is implemented.
+        >>> doc1 = Doc('Sentence for computing the minhash')
+        >>> doc2 = Doc('Sentence for computing the similarity')
+        >>> doc1.similarity(doc2)
+        0.7265625
+        """
+        if hash_method == "minhash" and metric == "jaccard":
+            hash1 = MinHash(hashvalues=self.minhash)
+            hash2 = MinHash(hashvalues=other_doc.minhash)
+            return hash1.jaccard(hash2)
+        else:
+            raise NotImplementedError(
+                f"Metric/hash method combination {metric}"
+                f"/{hash_method} is not implemented as similarity metric"
+            )
+
+    @property
+    def word_vectors(self):
+        """
+        Returns word embeddings for the words in the document.
+        """
+        return self.generate_word_vectors()
+
+    @functools.lru_cache()
+    def generate_word_vectors(self, model_name=None):
+        """
+        Returns word embeddings for the words in the document.
+        The default spacy models don't have "true" word vectors
+        but only context-sensitive tensors that are within the document.
+
+        Returns:
+        A dictionary mapping words from the document to a dict with the
+        corresponding values of the following variables:
+
+        has vector: Does the token have a vector representation?
+        vector norm: The L2 norm of the token's vector (the square root of the
+                    sum of the values squared)
+        OOV: Out-of-vocabulary (This variable always gets the value True since
+                                there are no vectors included in the model)
+        vector: The vector representation of the word
+
+        >>> doc = Doc('Test sentence')
+        >>> doc.word_vectors['Test']['is_oov']
+        True
+        >>> len(doc.word_vectors['Test']['vector'])
+        96
+        >>> doc.word_vectors['Test']['vector_norm'] == doc.word_vectors['sentence']['vector_norm']
+        False
+        """
+        lang = self.language if self.is_reliable_language else self.hint_language
+        return {
+            token.text: {
+                "has_vector": token.has_vector,
+                "vector_norm": token.vector_norm,
+                "is_oov": token.is_oov,
+                "vector": token.vector.tolist(),
+            }
+            for token in self._load_spacy_doc(lang, model_name)
+        }
+
+    @property
+    def doc_vector(self):
+        """
+        Returns document embeddings based on the words in the document.
+
+        >>> import numpy
+        >>> numpy.array_equiv(Doc('a b').doc_vector, Doc('a b').doc_vector)
+        True
+        >>> numpy.array_equiv(Doc('a b').doc_vector, Doc('a a b').doc_vector)
+        False
+        """
+        return self.aggregate_word_vectors()
+
+    @functools.lru_cache()
+    def aggregate_word_vectors(
+        self, model_name=None, aggregation="mean", normalize=False, exclude_oov=False
+    ):
+        """
+        Returns document embeddings based on the words in the document.
+
+        >>> import numpy
+        >>> doc1 = Doc('a b')
+        >>> doc2 = Doc('a a b')
+        >>> numpy.array_equiv(doc1.aggregate_word_vectors(), doc1.aggregate_word_vectors())
+        True
+        >>> numpy.array_equiv(doc1.aggregate_word_vectors(), doc2.aggregate_word_vectors())
+        False
+        >>> numpy.array_equiv(doc1.aggregate_word_vectors(aggregation='mean'), doc2.aggregate_word_vectors(aggregation='sum'))
+        False
+        >>> numpy.array_equiv(doc1.aggregate_word_vectors(aggregation='mean'), doc2.aggregate_word_vectors(aggregation='var'))
+        False
+        >>> numpy.array_equiv(doc1.aggregate_word_vectors(aggregation='sum'), doc2.aggregate_word_vectors(aggregation='var'))
+        False
+        >>> doc = Doc('sentence with an out of vector word lsseofn')
+        >>> len(doc.aggregate_word_vectors())
+        96
+        >>> numpy.array_equiv(doc.aggregate_word_vectors(exclude_oov=False), doc.aggregate_word_vectors(exclude_oov=True))
+        False
+        """
+        lang = self.language if self.is_reliable_language else self.hint_language
+        tokens = [
+            token
+            for token in self._load_spacy_doc(lang, model_name)
+            if not exclude_oov or not token.is_oov
+        ]
+        vectors = [
+            token.vector / token.vector_norm if normalize else token.vector
+            for token in tokens
+        ]
+
+        if aggregation == "mean":
+            return numpy.mean(vectors, axis=0).tolist()
+        elif aggregation == "sum":
+            return numpy.sum(vectors, axis=0).tolist()
+        elif aggregation == "var":
+            return numpy.var(vectors, axis=0).tolist()
+        else:
+            raise NotImplementedError(
+                f"Aggregation method {aggregation} is not implemented."
+            )

From 7384336cc31b838dca81e0f1ff8dd872c589a9c8 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 4 Apr 2019 12:42:59 +0200
Subject: [PATCH 017/496] wiki/Entities_extraction.md

---
 wiki/README.md              | 22 ++++++++++++++++++++++
 wiki/Speech_to_text.md      |  6 ++++++
 wiki/Text_classification.md |  7 +++++++
 wiki/Text_generation.md     |  1 +
 wiki/Text_processing.md     | 37 +++++++++++++++++++++++++++++++++++++
 wiki/Topic_Modeling.md      |  7 +++++++
 6 files changed, 80 insertions(+)
 create mode 100644 wiki/README.md
 create mode 100644 wiki/Speech_to_text.md
 create mode 100644 wiki/Text_classification.md
 create mode 100644 wiki/Text_generation.md
 create mode 100644 wiki/Text_processing.md
 create mode 100644 wiki/Topic_Modeling.md

diff --git a/wiki/README.md b/wiki/README.md
new file mode 100644
index 0000000..b98f83e
--- /dev/null
+++ b/wiki/README.md
@@ -0,0 +1,22 @@
+# Nautilus NLP SUMMARY
+
+This Mardown is used as the summary for the following parts:
+
+## **Text Based NLP**
+
+### [*Topic Modeling*](Topic_Modeling.md)
+
+### [*Text Classification*](Text_classification.md)
+
+### [*Entities Extraction*](Entities_extraction.md)
+
+### [*Text Processing*](Text_processing.md)
+
+### [*Text Generation*](Text_generation.md)
+
+
+## **Audio-Based NLP**
+
+### [*Speech to Text*](Speech_to_text.md)
+
+### [*Voice Classification*](Voice_Classification.md)
\ No newline at end of file
diff --git a/wiki/Speech_to_text.md b/wiki/Speech_to_text.md
new file mode 100644
index 0000000..0f6eb92
--- /dev/null
+++ b/wiki/Speech_to_text.md
@@ -0,0 +1,6 @@
+# Speech to Text
+
+
+## Corpus
+
+## State of the art
diff --git a/wiki/Text_classification.md b/wiki/Text_classification.md
new file mode 100644
index 0000000..b0d8c19
--- /dev/null
+++ b/wiki/Text_classification.md
@@ -0,0 +1,7 @@
+# Text Classification (Applied to Sentiment Analysis)
+
+## Machine-learning based
+
+## Word-vector based
+
+## Deep-learning based
\ No newline at end of file
diff --git a/wiki/Text_generation.md b/wiki/Text_generation.md
new file mode 100644
index 0000000..1ae1192
--- /dev/null
+++ b/wiki/Text_generation.md
@@ -0,0 +1 @@
+# Text Generation
\ No newline at end of file
diff --git a/wiki/Text_processing.md b/wiki/Text_processing.md
new file mode 100644
index 0000000..a9c7740
--- /dev/null
+++ b/wiki/Text_processing.md
@@ -0,0 +1,37 @@
+# Text Processing
+
+## Introduction & Best practice
+
+## Encoding / Decoding
+
+In our python programming lives,  pretty much everyone working with text data encountered one day the terrible:
+
+    `UnicodeDecodeError: 'machine' codec can't decode character 'somethingsomething' in position x: ordinal not in range(z)`
+And you probably spend the next hour trying to figure out to get out of this mess.
+
+Usually, most of programming language automatically infer the encoding of the text. However, Python does not, and this can lead to a ton of problem.
+
+Encoding is the table of representation between the binary code understood by the computer and the letters as we see them. Therefore, everytime you see text on a screen, it is encoded in a way.
+The problem is, pretty much every country in the world had his own way of encoding and these encodings still persist: The encoding module of python can understand around 100 different encoding.
+
+So, how do we get out of this mess?
+
+
+### **The Absolute Rule:**
+Use 'UTF-8' as default encoding for your processes.
+
+### If you are still in Python2.7
+*And you want to use it until its end of life(December 2019)*
+
+
+
+### If you are in Python 3
+
+
+## Stemmatization / Lemmatization
+
+## Vector Representation
+
+### Bag of Word & TF-IDF
+
+### Word Embeddings
diff --git a/wiki/Topic_Modeling.md b/wiki/Topic_Modeling.md
new file mode 100644
index 0000000..9a04ef4
--- /dev/null
+++ b/wiki/Topic_Modeling.md
@@ -0,0 +1,7 @@
+# Topic Modelling
+
+## LDA
+
+## LSA 
+
+## Topic clustering/propagation?
\ No newline at end of file

From 4fecab65652af18aa98fb891f756628064a777cc Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 4 Apr 2019 12:43:27 +0200
Subject: [PATCH 018/496] Add wiki markdown

---
 wiki/Entities_extraction.md | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 wiki/Entities_extraction.md

diff --git a/wiki/Entities_extraction.md b/wiki/Entities_extraction.md
new file mode 100644
index 0000000..7fbbf2c
--- /dev/null
+++ b/wiki/Entities_extraction.md
@@ -0,0 +1,5 @@
+# Entities Extraction
+
+## Part of Speech Tagging
+
+## Entity detections
\ No newline at end of file

From a09ac5731ac4f2f4b514212c6ac60b8a841ab698 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 4 Apr 2019 14:13:14 +0200
Subject: [PATCH 019/496] Fix Spacy import for lang model tokenizer

---
 nautilus_nlp/utils/tokenizer.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 01cd4a4..94eb7aa 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -1,18 +1,20 @@
 import nltk
 from sacremoses import MosesTokenizer, MosesDetokenizer
 import spacy
-
-nltk.download('punkt')
+from spacy.lang.fr import French
+from spacy.lang.en import English
+import spacy.lang as spacylang
+#nltk.download('punkt')
 
 try:
-    french_spacy = spacy.lang.fr.French()
+    french_spacy = spacylang.fr.French()
 except OSError:
     raise OSError("""You must install French langage to use SpaCy. 
                     python -m spacy download fr
                     See https://spacy.io/usage/ for details
                 """)
 try:
-    english_spacy = spacy.lang.en.English()
+    english_spacy = spacylang.en.English()
 except OSError:
     raise OSError("""You must install english langage to use SpaCy. 
                     python -m spacy download en

From b80892b7793d56dd319e306d8d52fc2ac3a5ba88 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 4 Apr 2019 14:19:03 +0200
Subject: [PATCH 020/496] Update readme for Python2 notice for spacy tokenizer
 issue #52

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 8b4abab..32e323a 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,8 @@ As an Artefact user, you might be working on a NLP use case, and wish to use Nau
 
 # Installation
 
+Beware, this package has been tested on Python **3.6** & **3.7**, and will probably not be working under python **2.7** as **Python2.7** EOL is scheduled for December 2019. 
+
 To install this library you should first clone the repository:
 
 `git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp`

From bda6df5c67a0d0f4c98515151b492873eb2424be Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 10:56:33 +0200
Subject: [PATCH 021/496] Add language identification notebook

---
 notebooks/Language_identification.ipynb | 534 +++---------------------
 1 file changed, 61 insertions(+), 473 deletions(-)

diff --git a/notebooks/Language_identification.ipynb b/notebooks/Language_identification.ipynb
index 0bd6927..68049ee 100644
--- a/notebooks/Language_identification.ipynb
+++ b/notebooks/Language_identification.ipynb
@@ -1,28 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'pandas'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-49-5bdfc092f0f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
-     ]
-    }
-   ],
-   "source": [
-    "import csv  \n",
-    "import re\n",
-    "import pandas as pd"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,11 +10,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open('../data/external/wili/x_test.txt',encoding='utf-8') as fp:\n",
+    "with open('../data/external/wili/x_test.txt') as fp:\n",
     "    test=fp.readlines()\n",
     "    test=[doc.strip('\\n') for doc in test]"
    ]
@@ -46,540 +23,151 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Le Dataset Wili est un dataset d'identification de langue basé sur Wikipedia. Des exemples du tests set suivent: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Ne l fin de l seclo XIX l Japon era inda çconhecido i sótico pa l mundo oucidental. Cula antroduçon de la stética japonesa, particularmente na Sposiçon Ounibersal de 1900, an Paris, l Oucidente adquiriu un apetite ansaciable pul Japon i Heiarn se tornou mundialmente coincido pula perfundidade, ouriginalidade i sinceridade de ls sous cuntos. An sous radadeiros anhos, alguns críticos, cumo George Orwell, acusórun Heiarn de trasferir sou nacionalismo i fazer l Japon parecer mais sótico, mas, cumo l'home qu'oufereciu al Oucidente alguns de sous purmeiros lampeijos de l Japon pré-andustrial i de l Período Meiji, sou trabalho inda ye balioso até hoije.\""
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "test[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Schiedam is gelegen tussen Rotterdam en Vlaardingen, oorspronkelijk aan de Schie en later ook aan de Nieuwe Maas. Per 30 april 2017 had de gemeente 77.833 inwoners (bron: CBS). De stad is vooral bekend om haar jenever, de historische binnenstad met grachten, en de hoogste windmolens ter wereld.'"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ГIурусаз батальонал, гьоркьор гIарадабиги лъун, ункъбокIон (каре) гьабун чIезарун руго. ТIаде гIарададул сачмаги гуллаги байдал, АхIмадханил бо тIурун буго. ГIумаханас цойгидал боязе тIаде кIанцIизе буюрухъ кьун буго. Гьезулги жо ккун гьечIо. Цинги живго ГIумахан кIанцIун вуго тушманасде тIаде («угъузилал рачун, дайтилал рачун, маххулъан бер баккун хунз цадахъ рачун»). Нахъе къалел ругел магIарулазда гьес гьарулеб букIун буго: «Нужеца яхI бахъе, гIолохъаби! Нилъеда данде гьал чIоларо», – ян.'"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test[2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ರಾಜ್ಯಶಾಸ್ತ್ರದ ಪಿತಾಮಹೆ ಅರಿಸ್ಟಾಟಲ್. ರಾಜ್ಯಶಾಸ್ತ್ರದ ವೈಜ್ನಾನಿಕವಾದ್ ಅದ್ಯಾಯನ ಮಲ್ದಿನಾರ್ ಅರಿಸ್ಟಾಟಲ್. ಮೇರ್ 158 ರಾಷ್ಟ್ರದ ಸಂವಿಧಾನಲೆನ್ ಅರ್ಥ ಮಲ್ತೊನ್ದ್ the politics ಕೃತಿ ರಚನೆ ಮಲ್ದೆರ್. ಮೇರ್ ಕ್ರಿ.ಪೂ 387 ಗ್ರೀಕ್ ಸ್ಟಾಗಿರಡ್ ಜನಿಸಿಯೆರ್. ಮೆರೆನ ಅಮ್ಮೆರ್ ಮೆಸೆದೊನಿಯ ಅರಸೆರ್ನ ರಾಜವೈದ್ಯರ್ ಅದುದ್ ಇತ್ತೆರ್. ಎಲ್ಳಡೆ ಮೆರ್ ತತ್ವಶಾಸ್ತ್ರ,ಅರ್ಥಶಾಸ್ತ್ರ,ಸಂಗೀತ,ವಿಜ್ನಾನ,ಪೌರನೀತಿ,ಗಣಿತ ನೆಟ್ಟ್ ಪರಂಗತೆರ್ ಅದುದ್ ಇತ್ತೆರ್. ವಿದ್ಯಾಭ್ಯಾಸ ಮುಗಿ ಬೊಕ್ಕ ಪ್ಲೇಟೋ ಗ್ರೀಕ್ ತತ್ವಜ್ನಾನಿನೊಟ್ ಸೆರೊಂಡೆರ್. ಪ್ಲೇಟೋನ ಅಕಾಡೆಮಿಡ್ ಮಸ್ತ್ ವರ್ಷ ಬೇಲೆ ಮಲ್ತೆರ್. ಅಕಾಡೆಮಿಡ್ ನಿರ್ದೆಶೆಕೆರ್ನ ಹುದ್ದೆ ತಿಕ್ಕಂದೆ ಬೆಜರ್ ಅದುದ್ ಲೈಸಿಯಮ್ ಪನ್ಪಿನ ವಿಶ್ವವಿದ್ಯಾನಿಲಯ ಸ್ಥಾಪನೆ ಮಲ್ತೆರ್. ಬಿಕ್ಕ ಒಂತೆ ಸಮಯ ಅಲೆಗ್ಸಾಂಡರ್ ಚಕ್ರವರ್ತಿಗ್ ಗುರುವಾದ್ ಬೇಲೆ ಮಲ್ತೆರ್. ಮೇರ್ ಬರೆತಿನ ಪುಸ್ತಕ the politics,history of animals,metaphysics ಇತ್ಯಾದಿ.'"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test[3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Тухайн үед дан ганц 20кг-ын Орос баллон хэрэглэдэг байсан уламжлалыг халж хэрэглэгчдийг сонголт ихтэй болгож 5,10,14,20 кг хэмжээтэй, баллонуудыг захиалан үйлдвэрлүүлж, өөрийн брэнд болгон хэрэглээнд нэвтрүүлж, хийн түлшээр ажилладаг тулга, төрөл бүрийн халаагуурууд, тоног төхөөрөмжийн ашиглалт, хийн түлшний ашиг тусыг хэвлэл мэдээллийн хэрэгслэлээр тасралтгүй сурталчилсаны үр дүнд 2002 оны 5 сар гэхэд хийн түлшний хэрэглээ сард 30 тн, 9 сард 50 тн хүрч өссөн юм.'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test[5]"
+    "Le Dataset Wili est un dataset d'identification de langue basé sur Wikipedia.\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../data/external/wili/y_test.txt',encoding='utf-8') as fp:\n",
-    "    y=fp.readlines()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../data/external/wili/labels.csv',encoding='utf-8') as fp:\n",
-    "    labels=fp.readlines()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Label;English;Wiki Code;ISO 369-3;German;Language family;Writing system;Remarks;Synonyms\\n',\n",
-       " 'ace;Achinese;ace;ace;Achinesisch;Austronesian;;;\\n',\n",
-       " 'afr;Afrikaans;af;afr;Afrikaans;Indo-European;;;\\n',\n",
-       " 'als;Alemannic German;als;gsw;Alemannisch;Indo-European;;(ursprünglich nur\\xa0Elsässisch);\\n',\n",
-       " 'amh;Amharic;am;amh;Amharisch;Afro-Asiatic;;;\\n',\n",
-       " 'ang;Old English ;ang;ang;Altenglisch;Indo-European;;(ca. 450-1100);Angelsächsisch\\n',\n",
-       " 'ara;Arabic;ar;ara;Arabisch;Afro-Asiatic;;;\\n',\n",
-       " 'arg;Aragonese;an;arg;Aragonesisch;Indo-European;;;\\n',\n",
-       " 'arz;Egyptian Arabic;arz;arz;Ägyptisch-Arabisch;Afro-Asiatic;;;\\n',\n",
-       " 'asm;Assamese;as;asm;Assamesisch;Indo-European;;;\\n',\n",
-       " 'ast;Asturian;ast;ast;Asturisch;Indo-European;;;\\n',\n",
-       " 'ava;Avar;av;ava;Awarisch;Northeast Caucasian;;;\\n',\n",
-       " 'aym;Aymara;ay;aym;Aymara;Aymaran;;;\\n',\n",
-       " 'azb;South Azerbaijani;azb;azb;Südaserbaidschanisch;Turkic;Arabic;;\\n',\n",
-       " 'aze;Azerbaijani;az;aze;Aserbaidschanisch;Turkic;Latin;;\\n',\n",
-       " 'bak;Bashkir;ba;bak;Baschkirisch;Turkic;;;\\n',\n",
-       " 'bar;Bavarian;bar;bar;Bairisch;Indo-European;;;\\n',\n",
-       " 'bcl;Central Bikol;bcl;bcl;Bikolano;Austronesian;;;\\n',\n",
-       " 'be-tarask;Belarusian (Taraschkewiza);be-tarask;;Weißrussisch (Taraschkewiza);Indo-European;;;\\n',\n",
-       " 'bel;Belarusian;be;bel;Weißrussisch;Indo-European;;(normativ);\\n',\n",
-       " 'ben;Bengali;bn;ben;Bengalisch;Indo-European;;;\\n',\n",
-       " 'bho;Bhojpuri;bh;bho;Bhojpuri;Indo-European;;;\\n',\n",
-       " 'bjn;Banjar;bjn;bjn;Banjaresisch;Austronesian;;;\\n',\n",
-       " 'bod;Tibetan;bo;bod;Tibetisch;Sino-Tibetan;;;\\n',\n",
-       " 'bos;Bosnian;bs;bos;Bosnisch;Indo-European;;;\\n',\n",
-       " 'bpy;Bishnupriya;bpy;bpy;Bishnupriya Manipuri;Indo-European;;;\\n',\n",
-       " 'bre;Breton;br;bre;Bretonisch;Indo-European;;;\\n',\n",
-       " 'bul;Bulgarian;bg;bul;Bulgarisch;Indo-European;;;\\n',\n",
-       " 'bxr;Buryat;bxr;bxr;Burjatisch;Mongolic;Cyrillic:::Mongolian script:::Vagindra script:::Latin;;Buriat\\n',\n",
-       " 'cat;Catalan;ca;cat;Katalanisch;Indo-European;Latin;;\\n',\n",
-       " 'cbk;Chavacano;cbk-zam;cbk;Chabacano;Indo-European;;;\\n',\n",
-       " 'cdo;Min Dong;cdo;cdo;Min Dong;Sino-Tibetan;;;\\n',\n",
-       " 'ceb;Cebuano;ceb;ceb;Cebuano;Austronesian;Latin;;\\n',\n",
-       " 'ces;Czech;cs;ces;Tschechisch;Indo-European;Latin;;\\n',\n",
-       " 'che;Chechen;ce;che;Tschetschenisch;Northeast Caucasian;;;\\n',\n",
-       " 'chr;Cherokee;chr;chr;Cherokee;Iroquoian;;;\\n',\n",
-       " 'chv;Chuvash;cv;chv;Tschuwaschisch;Turkic;;;\\n',\n",
-       " 'ckb;Central Kurdish;ckb;ckb;Sorani;Indo-European;;;\\n',\n",
-       " 'cor;Cornish;kw;cor;Kornisch;Indo-European;;;\\n',\n",
-       " 'cos;Corsican;co;cos;Korsisch;Indo-European;;;\\n',\n",
-       " 'crh;Crimean Tatar;crh;crh;Krimtatarisch;Turkic;;;\\n',\n",
-       " 'csb;Kashubian;csb;csb;Kaschubisch;Indo-European;;;\\n',\n",
-       " 'cym;Welsh;cy;cym;Walisisch;Indo-European;;;\\n',\n",
-       " 'dan;Danish;da;dan;Dänisch;Indo-European;;;\\n',\n",
-       " 'deu;German;de;deu;Deutsch;Indo-European;Latin;;\\n',\n",
-       " 'diq;Dimli;diq;diq;Süd-Zazaisch;Indo-European;;;\\n',\n",
-       " 'div;Dhivehi;dv;div;Dhivehi;Indo-European;;;\\n',\n",
-       " 'dsb;Lower Sorbian;dsb;dsb;Niedersorbisch;Indo-European;;;\\n',\n",
-       " 'dty;Doteli;dty;dty;Doteli;Indo-European;;;\\n',\n",
-       " 'egl;Emilian;eml;egl;Emilianisch;Indo-European;;;\\n',\n",
-       " 'ell;Modern Greek;el;ell;Griechisch;Indo-European;;(1453-);\\n',\n",
-       " 'eng;English;en;eng;Englisch;Indo-European;Latin;;\\n',\n",
-       " 'epo;Esperanto;eo;epo;Esperanto;Constructed;;;\\n',\n",
-       " 'est;Estonian;et;est;Estnisch;Uralic;;;\\n',\n",
-       " 'eus;Basque;eu;eus;Baskisch;Language isolate;;;\\n',\n",
-       " 'ext;Extremaduran;ext;ext;Extremadurisch;Indo-European;;;\\n',\n",
-       " 'fao;Faroese;fo;fao;Färöisch;Indo-European;;;\\n',\n",
-       " 'fas;Persian;fa;fas;Persisch;Indo-European;;;\\n',\n",
-       " 'fin;Finnish;fi;fin;Finnisch;Uralic;Latin;;\\n',\n",
-       " 'fra;French;fr;fra;Französisch;Indo-European;Latin;;\\n',\n",
-       " 'frp;Arpitan;frp;frp;Frankoprovenzalisch;Indo-European;;;\\n',\n",
-       " 'fry;Western Frisian;fy;fry;Westfriesisch;Indo-European;;;\\n',\n",
-       " 'fur;Friulian;fur;fur;Furlanisch;Indo-European;;;\\n',\n",
-       " 'gag;Gagauz;gag;gag;Gagausisch;Turkic;;;\\n',\n",
-       " 'gla;Scottish Gaelic;gd;gla;Schottisch-Gälisch;Indo-European;;;\\n',\n",
-       " 'gle;Irish;ga;gle;Irisch;Indo-European;;;\\n',\n",
-       " 'glg;Galician;gl;glg;Galicisch;Indo-European;;;\\n',\n",
-       " 'glk;Gilaki;glk;glk;Gilaki;Indo-European;;;\\n',\n",
-       " 'glv;Manx;gv;glv;Manx;Indo-European;;;\\n',\n",
-       " 'grn;Guarani;gn;grn;Guaraní;Tupi-Guarani;;;\\n',\n",
-       " 'guj;Gujarati;gu;guj;Gujarati;Indo-European;;;\\n',\n",
-       " 'hak;Hakka Chinese;hak;hak;Hakka;Sino-Tibetan;;;\\n',\n",
-       " 'hat;Haitian Creole;ht;hat;Haitianisch;Indo-European;;;\\n',\n",
-       " 'hau;Hausa;ha;hau;Hausa;Afro-Asiatic;Latin;;Chadic\\n',\n",
-       " 'hbs;Serbo-Croatian;sh;hbs;Serbokroatisch;Indo-European;;;\\n',\n",
-       " 'heb;Hebrew;he;heb;Hebräisch;Afro-Asiatic;;;\\n',\n",
-       " 'hif;Fiji Hindi;hif;hif;Fidschi-Hindi;Indo-European;;;\\n',\n",
-       " 'hin;Hindi;hi;hin;Hindi;Indo-European;;;\\n',\n",
-       " 'hrv;Croatian;hr;hrv;Kroatisch;Indo-European;;;\\n',\n",
-       " 'hsb;Upper Sorbian;hsb;hsb;Obersorbisch;Indo-European;;;\\n',\n",
-       " 'hun;Hungarian;hu;hun;Ungarisch;Uralic;;;\\n',\n",
-       " 'hye;Armenian;hy;hye;Armenisch;Indo-European;;;\\n',\n",
-       " 'ibo;Igbo;ig;ibo;Igbo;Niger-Congo;Latin;;\\n',\n",
-       " 'ido;Ido;io;ido;Ido;Constructed;;;\\n',\n",
-       " 'ile;Interlingue;ie;ile;Interlingue;Constructed;;;\\n',\n",
-       " 'ilo;Iloko;ilo;ilo;Ilokano;Austronesian;;;\\n',\n",
-       " 'ina;Interlingua;ia;ina;Interlingua;Constructed;;;\\n',\n",
-       " 'ind;Indonesian;id;ind;Indonesisch;Austronesian;Latin;;\\n',\n",
-       " 'isl;Icelandic;is;isl;Isländisch;Indo-European;;;\\n',\n",
-       " 'ita;Italian;it;ita;Italienisch;Indo-European;Latin;;\\n',\n",
-       " 'jam;Jamaican Patois;jam;jam;Jamaikanisch-kreolisch;Indo-European;;;\\n',\n",
-       " 'jav;Javanese;jv;jav;Javanisch;Austronesian;;;\\n',\n",
-       " 'jbo;Lojban;jbo;jbo;Lojban;Constructed;Latin;;\\n',\n",
-       " 'jpn;Japanese;ja;jpn;Japanisch;Japonic;;;\\n',\n",
-       " 'kaa;Karakalpak;kaa;kaa;Karakalpakisch;Turkic;;;\\n',\n",
-       " 'kab;Kabyle;kab;kab;Kabylisch;Afro-Asiatic;;;\\n',\n",
-       " 'kan;Kannada;kn;kan;Kannada;Dravidian;;;\\n',\n",
-       " 'kat;Georgian;ka;kat;Georgisch;South Caucasian;;;\\n',\n",
-       " 'kaz;Kazakh;kk;kaz;Kasachisch;Turkic;;;\\n',\n",
-       " 'kbd;Kabardian;kbd;kbd;Kabardinisch;Northeast Caucasian;Cyrillic:::Latin:::Arabic;;Kabardino-Cherkess:::East Circassian\\n',\n",
-       " 'khm;Central Khmer;km;khm;Khmer;Austronesian;;;\\n',\n",
-       " 'kin;Kinyarwanda;rw;kin;Kinyarwanda;Niger-Congo;Latin;;Fumbira\\n',\n",
-       " 'kir;Kirghiz;ky;kir;Kirgisisch;Turkic;;;\\n',\n",
-       " 'koi;Komi-Permyak;koi;koi;Komi-Permjakisch;Uralic;;;\\n',\n",
-       " 'kok;Konkani;gom;kok;Konkani;Indo-European;;;\\n',\n",
-       " 'kom;Komi;kv;kom;Komi;Uralic;;;\\n',\n",
-       " 'kor;Korean;ko;kor;Koreanisch;Koreanic;;;\\n',\n",
-       " 'krc;Karachay-Balkar;krc;krc;Karatschai-balkarisch;Turkic;;;\\n',\n",
-       " 'ksh;Ripuarisch;ksh;ksh;Kölsch;Indo-European;;;\\n',\n",
-       " 'kur;Kurdish;ku;kur;Kurdisch;Indo-European;Latin;;\\n',\n",
-       " 'lad;Ladino;lad;lad;Judenspanisch;Indo-European;;;\\n',\n",
-       " 'lao;Lao;lo;lao;Laotisch;Tai-Kadai;;;\\n',\n",
-       " 'lat;Latin;la;lat;Latein;Indo-European;;;\\n',\n",
-       " 'lav;Latvian;lv;lav;Lettisch;Indo-European;;;\\n',\n",
-       " 'lez;Lezghian;lez;lez;Lesgisch;Northeast Caucasian;;;\\n',\n",
-       " 'lij;Ligurian;lij;lij;Ligurisch;Indo-European;;(Romanisch);\\n',\n",
-       " 'lim;Limburgan;li;lim;Limburgisch;Indo-European;;;\\n',\n",
-       " 'lin;Lingala;ln;lin;Lingála;Niger-Congo;;;\\n',\n",
-       " 'lit;Lithuanian;lt;lit;Litauisch;Indo-European;;;\\n',\n",
-       " 'lmo;Lombard;lmo;lmo;Lombardisch;Indo-European;;;\\n',\n",
-       " 'lrc;Northern Luri;lrc;lrc;nördliches Luri;Indo-European;;;\\n',\n",
-       " 'ltg;Latgalian;ltg;ltg;Lettgallisch;Indo-European;;;\\n',\n",
-       " 'ltz;Luxembourgish;lb;ltz;Luxemburgisch;Indo-European;;;\\n',\n",
-       " 'lug;Luganda;lg;lug;Luganda;Niger-Congo;Latin;;Ganda\\n',\n",
-       " 'lzh;Literary Chinese;zh-classical;lzh;klassisches Chinesisch;Sino-Tibetan;;;\\n',\n",
-       " 'mai;Maithili;mai;mai;Maithili;Indo-European;;;\\n',\n",
-       " 'mal;Malayalam;ml;mal;Malayalam;Dravidian;;;\\n',\n",
-       " 'map-bms;Banyumasan;map-bms;map-bms;Banyumasan;Austronesian;;Javanese;\\n',\n",
-       " 'mar;Marathi;mr;mar;Marathi;Indo-European;;;\\n',\n",
-       " 'mdf;Moksha;mdf;mdf;Mokschanisch;Uralic;Cyrillic;;\\n',\n",
-       " 'mhr;Eastern Mari;mhr;mhr;Ostmari;Uralic;;;\\n',\n",
-       " 'min;Minangkabau;min;min;Minangkabauisch;Austronesian;;;\\n',\n",
-       " 'mkd;Macedonian;mk;mkd;Mazedonisch;Indo-European;;;\\n',\n",
-       " 'mlg;Malagasy;mg;mlg;Malagasy;Austronesian;;;\\n',\n",
-       " 'mlt;Maltese;mt;mlt;Maltesisch;Afro-Asiatic;;;\\n',\n",
-       " 'mon;Mongolian;mn;mon;Mongolisch;Mongolic;;;\\n',\n",
-       " 'mri;Maori;mi;mri;Maori;Austronesian;;;\\n',\n",
-       " 'mrj;Western Mari;mrj;mrj;Westmari;Uralic;;;\\n',\n",
-       " 'msa;Malay;ms;msa;Malaiisch;Austronesian;;;\\n',\n",
-       " 'mwl;Mirandese;mwl;mwl;Mirandés;Indo-European;;;\\n',\n",
-       " 'mya;Burmese;my;mya;Birmanisch;Sino-Tibetan;;;\\n',\n",
-       " 'myv;Erzya;myv;myv;Ersja-Mordwinisch;Uralic;;;\\n',\n",
-       " 'mzn;Mazanderani;mzn;mzn;Masanderanisch;Indo-European;;;\\n',\n",
-       " 'nan;Min Nan Chinese;zh-min-nan;nan;Min Nan;Sino-Tibetan;;;\\n',\n",
-       " 'nap;Neapolitan;nap;nap;Neapolitanisch;Indo-European;;;\\n',\n",
-       " 'nav;Navajo;nv;nav;Navajo;Dené-Yeniseian;;;\\n',\n",
-       " 'nci;Classical Nahuatl;nah;nci;Nahuatl;Uto-Aztecan;;;\\n',\n",
-       " 'nds;Low German;nds;nds;Niedersächsisch/Ostniederdeutsch;Indo-European;;;\\n',\n",
-       " 'nds-nl;West Low German;nds-nl;nds;Nedersaksisch;Indo-European;;;\\n',\n",
-       " 'nep;Nepali (macrolanguage);ne;nep;Nepali;Indo-European;;;\\n',\n",
-       " 'new;Newari;new;new;Newari;Sino-Tibetan;;;\\n',\n",
-       " 'nld;Dutch;nl;nld;Niederländisch;Indo-European;Latin;;\\n',\n",
-       " 'nno;Norwegian Nynorsk;nn;nno;Nynorsk;Indo-European;;;\\n',\n",
-       " 'nob;Bokmål;no;nob;Bokmål;Indo-European;Latin;Norwegian;\\n',\n",
-       " 'nrm;Narom;nrm;nrm;Normannisch;Austronesian;;;\\n',\n",
-       " 'nso;Northern Sotho;nso;nso;Nord-Sotho;Niger-Congo;;;\\n',\n",
-       " 'oci;Occitan;oc;oci;Okzitanisch;Indo-European;;(post 1500);\\n',\n",
-       " 'olo;Livvi-Karelian;olo;olo;Olonetzisch;Uralic;;;\\n',\n",
-       " 'ori;Oriya;or;ori;Oriya;Indo-European;;;\\n',\n",
-       " \"orm;Oromo;om;orm;Oromo;Afro-Asiatic;Latin:::Ge'ez ;;\\n\",\n",
-       " 'oss;Ossetian;os;oss;Ossetisch;Indo-European;;;\\n',\n",
-       " 'pag;Pangasinan;pag;pag;Pangasinensisch;Austronesian;;;\\n',\n",
-       " 'pam;Pampanga;pam;pam;Kapampangan;Austronesian;;;\\n',\n",
-       " 'pan;Panjabi;pa;pan;Panjabi\\xa0in\\xa0Gurmukhi-Schrift;Indo-European;;;\\n',\n",
-       " 'pap;Papiamento;pap;pap;Papiamentu;Indo-European;;;\\n',\n",
-       " 'pcd;Picard;pcd;pcd;Picardisch;Indo-European;;;\\n',\n",
-       " 'pdc;Pennsylvania German;pdc;pdc;Pennsylvaniadeutsch;Indo-European;;;Deitsch:::Pennsylvania Deitsch:::Pennsilfaanisch Deitsch:::Pennsylvania Dutch\\n',\n",
-       " 'pfl;Palatine German;pfl;pfl;Pfälzisch;Indo-European;;;\\n',\n",
-       " 'pnb;Western Panjabi;pnb;pnb;Panjabi;Indo-European;Arabic;;\\n',\n",
-       " 'pol;Polish;pl;pol;Polnisch;Indo-European;Latin;;\\n',\n",
-       " 'por;Portuguese;pt;por;Portugiesisch;Indo-European;Latin;;\\n',\n",
-       " 'pus;Pushto;ps;pus;Paschtunisch;Indo-European;;;\\n',\n",
-       " 'que;Quechua;qu;que;Quechua;Quechuan;;;\\n',\n",
-       " 'roa-tara;Tarantino dialect;roa-tara;;Tarandíne;Indo-European;;;\\n',\n",
-       " 'roh;Romansh;rm;roh;Bündnerromanisch;Indo-European;;;\\n',\n",
-       " 'ron;Romanian;ro;ron;Rumänisch;Indo-European;;;\\n',\n",
-       " 'rue;Rusyn;rue;rue;Karpato-Russinisch;Indo-European;;;\\n',\n",
-       " 'rup;Aromanian;roa-rup;rup;Aromunisch;Indo-European;Latin;Macedo-Romanian::Vlach;\\n',\n",
-       " 'rus;Russian;ru;rus;Russisch;Indo-European;Cyrillic;;\\n',\n",
-       " 'sah;Yakut;sah;sah;Jakutisch;Turkic;;;\\n',\n",
-       " 'san;Sanskrit;sa;san;Sanskrit;Indo-European;;;\\n',\n",
-       " 'scn;Sicilian;scn;scn;Sizilianisch;Indo-European;;;\\n',\n",
-       " 'sco;Scots;sco;sco;Scots;Indo-European;;;\\n',\n",
-       " 'sgs;Samogitian;bat-smg;sgs;Schemaitisch;Indo-European;;;\\n',\n",
-       " 'sin;Sinhala;si;sin;Singhalesisch;Indo-European;;;\\n',\n",
-       " 'slk;Slovak;sk;slk;Slowakisch;Indo-European;;;\\n',\n",
-       " 'slv;Slovene;sl;slv;Slowenisch;Indo-European;;;\\n',\n",
-       " 'sme;Northern Sami;se;sme;Nordsamisch;Uralic;;;\\n',\n",
-       " 'sna;Shona;sn;sna;Shona;Niger-Congo;;;\\n',\n",
-       " 'snd;Sindhi;sd;snd;Sindhi;Indo-European;;;\\n',\n",
-       " 'som;Somali;so;som;Somali;Afro-Asiatic;;;\\n',\n",
-       " 'spa;Spanish;es;spa;Spanisch;Indo-European;Latin;;\\n',\n",
-       " 'sqi;Albanian;sq;sqi;Albanisch;Indo-European;;;\\n',\n",
-       " 'srd;Sardinian;sc;srd;Sardisch;Indo-European;;;\\n',\n",
-       " 'srn;Sranan;srn;srn;Sranantongo;Indo-European;;;Sranan Tongo:::Sranantongo:::Surinaams:::Surinamese:::Surinamese Creole:::Taki Taki\\n',\n",
-       " 'srp;Serbian;sr;srp;Serbisch;Indo-European;;;\\n',\n",
-       " 'stq;Saterfriesisch;stq;stq;Saterfriesisch;Indo-European;;;\\n',\n",
-       " 'sun;Sundanese;su;sun;Sundanesisch;Austronesian;;;\\n',\n",
-       " 'swa;Swahili (macrolanguage);sw;swa;Swahili;Niger-Congo;;;\\n',\n",
-       " 'swe;Swedish;sv;swe;Schwedisch;Indo-European;Latin;;\\n',\n",
-       " 'szl;Silesian;szl;szl;Schlesisch;Indo-European;;(polnischer Dialekt);\\n',\n",
-       " 'tam;Tamil;ta;tam;Tamil;Dravidian;;;\\n',\n",
-       " 'tat;Tatar;tt;tat;Tatarisch;Turkic;;;\\n',\n",
-       " 'tcy;Tulu;tcy;tcy;Tulu;Dravidian;Kannada:::Tigalari;;\\n',\n",
-       " 'tel;Telugu;te;tel;Telugu;Dravidian;;;\\n',\n",
-       " 'tet;Tetum;tet;tet;Tetum;Austronesian;;;\\n',\n",
-       " 'tgk;Tajik;tg;tgk;Tadschikisch;Indo-European;;;\\n',\n",
-       " 'tgl;Tagalog;tl;tgl;Tagalog;Austronesian;;;\\n',\n",
-       " 'tha;Thai;th;tha;Thailändisch;Tai-Kadai;;;\\n',\n",
-       " 'ton;Tongan;to;ton;Tongaisch;Austronesian;Latin;;\\n',\n",
-       " 'tsn;Tswana;tn;tsn;Setswana;Niger-Congo;Latin;;Setswana\\n',\n",
-       " 'tuk;Turkmen;tk;tuk;Turkmenisch;Turkic;;;\\n',\n",
-       " 'tur;Turkish;tr;tur;Türkisch;Turkic;;;\\n',\n",
-       " 'tyv;Tuvan;tyv;tyv;Tuwinisch;Turkic;Cyrillic;;\\n',\n",
-       " 'udm;Udmurt;udm;udm;Udmurtisch;Uralic;;;\\n',\n",
-       " 'uig;Uighur;ug;uig;Uigurisch;Turkic;;;\\n',\n",
-       " 'ukr;Ukrainian;uk;ukr;Ukrainisch;Indo-European;Cyrillic;;\\n',\n",
-       " 'urd;Urdu;ur;urd;Urdu;Indo-European;;;\\n',\n",
-       " 'uzb;Uzbek;uz;uzb;Usbekisch;Turkic;;;\\n',\n",
-       " 'vec;Venetian;vec;vec;Venetisch;Indo-European;;;\\n',\n",
-       " 'vep;Veps;vep;vep;Wepsisch;Uralic;;;\\n',\n",
-       " 'vie;Vietnamese;vi;vie;Vietnamesisch;Austronesian;Latin;;\\n',\n",
-       " 'vls;Vlaams;vls;vls;Westflämisch;Indo-European;;;\\n',\n",
-       " 'vol;Volapük;vo;vol;Volapük;Constructed;;;\\n',\n",
-       " 'vro;Võro;fiu-vro;vro;Võro;Uralic;;;\\n',\n",
-       " 'war;Waray;war;war;Wáray-Wáray;Austronesian;Latin;;\\n',\n",
-       " 'wln;Walloon;wa;wln;Wallonisch;Indo-European;;;\\n',\n",
-       " 'wol;Wolof;wo;wol;Wolof;Niger-Congo;Latin:::Arabic;;\\n',\n",
-       " 'wuu;Wu Chinese;wuu;wuu;Wu;Sino-Tibetan;;;\\n',\n",
-       " 'xho;Xhosa;xh;xho;isiXhosa;Niger-Congo;Latin;;isiXhosa\\n',\n",
-       " 'xmf;Mingrelian;xmf;xmf;Mingrelisch;Kartvelian;;;\\n',\n",
-       " 'yid;Yiddish;yi;yid;Jiddisch;Indo-European;;;\\n',\n",
-       " 'yor;Yoruba;yo;yor;Yoruba;Niger-Congo;;;\\n',\n",
-       " 'zea;Zeeuws;zea;zea;Seeländisch;Indo-European;;;\\n',\n",
-       " 'zh-yue;Cantonese;zh-yue;;Kantonesisch;Sino-Tibetan;;;\\n',\n",
-       " 'zho;Standard Chinese;zh;zho;Chinesisch;Sino-Tibetan;;;\\n']"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "labels"
+    "# Language identification"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Language identification"
+    "Two types of detector can be implemented: THe Compact language detector (CLD2) and the Fasttext language identification one"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nautilus_nlp.models import Fasttext_classifier "
+    "from nautilus_nlp.models import langdetector\n",
+    "detector= langdetector.LangDetector(typemodel='fasttext')\n",
+    "detector2= langdetector.LangDetector(typemodel='cld2')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ici on va load le modèle pré-entrainer par Fasttext, disponible dans le folder data.\n",
-    "On peut utiliser soit le modèle quantizé (900ko) soit le classique"
+    "The language identification part is simply done by calling the detect language method of the detector"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = Fasttext_classifier.Fasttext_clf('../nautilus_nlp/data/lang_identification.ftz')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N\t100000\n",
-      "P@1\t0.759\n",
-      "R@1\t0.759\n"
+      "Ne l fin de l seclo XIX l Japon era inda çconhecido i sótico pa l mundo oucidental. Cula antroduçon de la stética japonesa, particularmente na Sposiçon Ounibersal de 1900, an Paris, l Oucidente adquiriu un apetite ansaciable pul Japon i Heiarn se tornou mundialmente coincido pula perfundidade, ouriginalidade i sinceridade de ls sous cuntos. An sous radadeiros anhos, alguns críticos, cumo George Orwell, acusórun Heiarn de trasferir sou nacionalismo i fazer l Japon parecer mais sótico, mas, cumo l'home qu'oufereciu al Oucidente alguns de sous purmeiros lampeijos de l Japon pré-andustrial i de l Período Meiji, sou trabalho inda ye balioso até hoije.\n",
+      "('pt', 0.41855213046073914)\n",
+      "('pt', 0.54)\n",
+      "\n",
+      "\n",
+      "Schiedam is gelegen tussen Rotterdam en Vlaardingen, oorspronkelijk aan de Schie en later ook aan de Nieuwe Maas. Per 30 april 2017 had de gemeente 77.833 inwoners (bron: CBS). De stad is vooral bekend om haar jenever, de historische binnenstad met grachten, en de hoogste windmolens ter wereld.\n",
+      "('nl', 0.9266586899757385)\n",
+      "('nl', 0.99)\n",
+      "\n",
+      "\n",
+      "ГIурусаз батальонал, гьоркьор гIарадабиги лъун, ункъбокIон (каре) гьабун чIезарун руго. ТIаде гIарададул сачмаги гуллаги байдал, АхIмадханил бо тIурун буго. ГIумаханас цойгидал боязе тIаде кIанцIизе буюрухъ кьун буго. Гьезулги жо ккун гьечIо. Цинги живго ГIумахан кIанцIун вуго тушманасде тIаде («угъузилал рачун, дайтилал рачун, маххулъан бер баккун хунз цадахъ рачун»). Нахъе къалел ругел магIарулазда гьес гьарулеб букIун буго: «Нужеца яхI бахъе, гIолохъаби! Нилъеда данде гьал чIоларо», – ян.\n",
+      "('ru', 0.38813528418540955)\n",
+      "('uz', 0.99)\n",
+      "\n",
+      "\n",
+      "ರಾಜ್ಯಶಾಸ್ತ್ರದ ಪಿತಾಮಹೆ ಅರಿಸ್ಟಾಟಲ್. ರಾಜ್ಯಶಾಸ್ತ್ರದ ವೈಜ್ನಾನಿಕವಾದ್ ಅದ್ಯಾಯನ ಮಲ್ದಿನಾರ್ ಅರಿಸ್ಟಾಟಲ್. ಮೇರ್ 158 ರಾಷ್ಟ್ರದ ಸಂವಿಧಾನಲೆನ್ ಅರ್ಥ ಮಲ್ತೊನ್ದ್ the politics ಕೃತಿ ರಚನೆ ಮಲ್ದೆರ್. ಮೇರ್ ಕ್ರಿ.ಪೂ 387 ಗ್ರೀಕ್ ಸ್ಟಾಗಿರಡ್ ಜನಿಸಿಯೆರ್. ಮೆರೆನ ಅಮ್ಮೆರ್ ಮೆಸೆದೊನಿಯ ಅರಸೆರ್ನ ರಾಜವೈದ್ಯರ್ ಅದುದ್ ಇತ್ತೆರ್. ಎಲ್ಳಡೆ ಮೆರ್ ತತ್ವಶಾಸ್ತ್ರ,ಅರ್ಥಶಾಸ್ತ್ರ,ಸಂಗೀತ,ವಿಜ್ನಾನ,ಪೌರನೀತಿ,ಗಣಿತ ನೆಟ್ಟ್ ಪರಂಗತೆರ್ ಅದುದ್ ಇತ್ತೆರ್. ವಿದ್ಯಾಭ್ಯಾಸ ಮುಗಿ ಬೊಕ್ಕ ಪ್ಲೇಟೋ ಗ್ರೀಕ್ ತತ್ವಜ್ನಾನಿನೊಟ್ ಸೆರೊಂಡೆರ್. ಪ್ಲೇಟೋನ ಅಕಾಡೆಮಿಡ್ ಮಸ್ತ್ ವರ್ಷ ಬೇಲೆ ಮಲ್ತೆರ್. ಅಕಾಡೆಮಿಡ್ ನಿರ್ದೆಶೆಕೆರ್ನ ಹುದ್ದೆ ತಿಕ್ಕಂದೆ ಬೆಜರ್ ಅದುದ್ ಲೈಸಿಯಮ್ ಪನ್ಪಿನ ವಿಶ್ವವಿದ್ಯಾನಿಲಯ ಸ್ಥಾಪನೆ ಮಲ್ತೆರ್. ಬಿಕ್ಕ ಒಂತೆ ಸಮಯ ಅಲೆಗ್ಸಾಂಡರ್ ಚಕ್ರವರ್ತಿಗ್ ಗುರುವಾದ್ ಬೇಲೆ ಮಲ್ತೆರ್. ಮೇರ್ ಬರೆತಿನ ಪುಸ್ತಕ the politics,history of animals,metaphysics ಇತ್ಯಾದಿ.\n",
+      "('kn', 0.9979031085968018)\n",
+      "('kn', 0.96)\n",
+      "\n",
+      "\n",
+      "Halukum adalah kelenjar tiroid nang menonjol di gulu lalakian. Awan bibinian penonjolan nangini ada ai jua, tagal kada pati rancak. Lamun penampilan halukum dirasa pina talalu ganal, hal ini kawa diperbaiki awan operasi pelastik.\n",
+      "('id', 0.4194313585758209)\n",
+      "('ms', 0.99)\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "print_results(*model.test(valid_data))\n"
+    "for i in range(0,5):\n",
+    "    print(test[i])\n",
+    "    print(detector.detect_language(test[i]))\n",
+    "    print(detector2.detect_language(test[i]))\n",
+    "    print('\\n')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 23,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(('__label__kn',), array([0.99790311]))"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "model.predict(test[3])\n"
+    "We can see that the results are pretty similar, the value of the confidence is changing though"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Il faut donc 13 secondes pour entrainer un modèle sur 1.5 Millions de tweets, avec une précision de 0.759. Not bad"
+    "# Performance"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "model.save_model('/home/nautilus_nlp/models/sentiments.bin')"
+    "## Inference time:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-r--r-- 1 root root 232M Mar 14 12:51 /home/nautilus_nlp/models/sentiments.bin\n"
+      "83.8 ms ± 836 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
-    "!ls -lh '/home/nautilus_nlp/models/sentiments.bin'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ce modèle fait environ 230Mo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Maintenant si on quantize ce modèle"
+    "%%timeit\n",
+    "for i in range(0,1000):\n",
+    "    detector.detect_language(test[i])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N\t100000\n",
-      "P@1\t0.759\n",
-      "R@1\t0.759\n"
+      "32.2 ms ± 1.59 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
-    "model.quantize(input=train_data, qnorm=True, retrain=True, cutoff=100000)\n",
-    "print_results(*model.test(valid_data))\n",
-    "model.save_model('/home/nautilus_nlp/models/sentiments.ftz')"
+    "%%timeit\n",
+    "for i in range(0,1000):\n",
+    "    detector2.detect_language(test[i])"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 13,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-rw-r--r-- 1 root root 6.8M Mar 14 12:53 /home/nautilus_nlp/models/sentiments.ftz\n"
-     ]
-    }
-   ],
    "source": [
-    "!ls -lh '/home/nautilus_nlp/models/sentiments.ftz'"
+    "## Accuracy\n",
+    "To do"
    ]
   },
   {
@@ -606,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,

From b3a57a88bd7949d4a6e7a74633a4be83e6a126cb Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 11:57:04 +0200
Subject: [PATCH 022/496] Put the document abstraction into Nautilus. This
 Abstraction also solves #51

---
 nautilus_nlp/doc.py | 263 +++++++++++++++-----------------------------
 1 file changed, 88 insertions(+), 175 deletions(-)

diff --git a/nautilus_nlp/doc.py b/nautilus_nlp/doc.py
index e88743c..9a23062 100644
--- a/nautilus_nlp/doc.py
+++ b/nautilus_nlp/doc.py
@@ -1,8 +1,16 @@
 import functools
 import pkg_resources
 import nautilus_nlp
-lang_path = pkg_resources.resource_filename('nautilus_nlp.data', 'lang_identification.ftz')
-
+import spacy
+import textacy
+from collections import Counter
+import re
+import unicodedata
+import spacy
+import spacy.matcher
+import textacy
+import textacy.keyterms
+import textacy.text_utils
 
 class NautilusMissingModelException(Exception):
     """Raised when the requested model is missing"""
@@ -23,14 +31,47 @@ class Doc:
     _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
     """
 
-    def __init__(self, raw, language=None, spacy_nlps=None,langdetect = None):
+    def __init__(
+        self,
+        raw,
+        language=None,
+        spacy_nlps=None,
+        langdetect=None,
+        sentiment_detect=None,
+    ):
         self.raw = raw
         self._spacy_nlps = spacy_nlps or dict()
         self._language = language
-        self._is_reliable_language = True if language else None
-        self._language_detector=langdetect
+        self._is_reliable_language = 1 if language else None
+        self._language_detector = langdetect
+        self._sentiment_detector = sentiment_detect
         self._text_stats = {}
 
+    @property
+    def language(self):
+        """
+        Provided or detected language of a text
+        >>> from nautilus_nlp.doc import Doc
+        >>> Doc('Test sentence for testing text').language
+        'en'
+        >>> Doc('Test sentence for testing text', language='en').language
+        'en'
+        >>> Doc('Test', hint_language='nl').language
+        'nl'
+        """
+
+        if not self._language:
+            if self._language_detector:
+                self._is_reliable_language, self._language = self._language_detector.detect_language(
+                    self.raw
+                )
+            else:
+                raise NautilusMissingModelException(
+                    "You must either provide a language for the document or an instance of LangDetector"
+                )
+
+        return self._language
+
     @property
     def _spacy_doc(self):
         """
@@ -40,7 +81,7 @@ def _spacy_doc(self):
         >>> type(doc._spacy_doc)
         <class 'spacy.tokens.doc.Doc'>
         """
-        lang = self.language if self.is_reliable_language else self.hint_language
+        lang = self.language
 
         return self._load_spacy_doc(lang)
 
@@ -75,9 +116,43 @@ def _get_default_nlp(lang):
             )
         except IOError:
             raise NautilusMissingModelException(
-                f'Default model for language "{lang}" is not available.'
+                f'Default model for language "{lang}" is not available. You should try to run python -m spacy download {lang}_core_news_sm'
+            
             )
 
+    @property
+    def clean(self):
+        """
+        Cleaned text with sensible defaults.
+        >>> doc = Doc('“Please clean this piece… of text</b>„')
+        >>> doc.clean
+        '"Please clean this piece... of text"'
+        
+        Right now this is done here by a simple regex. Next step is to use the preprocessing functions
+        """
+
+        return self.clean_text()
+
+    @functools.lru_cache()
+    def clean_text(self, clean_dots=True, clean_quotes=True, clean_whitespace=True):
+        """
+        Clean text and normalise punctuation.
+        >>> doc = Doc('“Please clean this piece… of text„')
+        >>> doc.clean_text(False, False, False, False) == doc.raw
+        True
+        """
+        text = self.raw
+
+        if clean_dots:
+            text = re.sub(r"…", "...", text)
+        if clean_quotes:
+            text = re.sub(r"[`‘’‛⸂⸃⸌⸍⸜⸝]", "'", text)
+            text = re.sub(r"[„“]|(\'\')|(,,)", '"', text)
+        if clean_whitespace:
+            text = re.sub(r"\s+", " ", text).strip()
+
+        return text
+
     @property
     def entities(self):
         """
@@ -97,11 +172,11 @@ def find_entities(self, model_name=None):
         >>> doc.find_entities()
         [('Google', 'ORG')]
         """
-        lang = self.language if self.is_reliable_language else self.hint_language
+
         return list(
             {
                 (ent.text, ent.label_)
-                for ent in self._load_spacy_doc(lang, model_name).ents
+                for ent in self._load_spacy_doc(self.language, model_name).ents
             }
         )
 
@@ -137,7 +212,7 @@ def n_words(self):
         >>> doc.n_words
         5
         """
-        return len(self.n_words)
+        return len(self.words)
 
     @property
     def words(self):
@@ -185,30 +260,11 @@ def sentiment(self):
         """
         Returns polarity score (-1 to 1) and a subjectivity score (0 to 1)
 
-        Currently only English, Dutch, French and Italian supported
-
-        >>> doc = Doc('Dit is een leuke zin.')
+        >>> doc = Doc('C'est trop cool !.')
         >>> doc.sentiment
-        (0.6, 0.9666666666666667)
+        (0.8, 0.9666666666666667)
         """
 
-        if self.language == "en":
-            from pattern.text.en import sentiment as sentiment_en
-
-            return sentiment_en(self.clean)
-        elif self.language == "nl":
-            from pattern.text.nl import sentiment as sentiment_nl
-
-            return sentiment_nl(self.clean)
-        elif self.language == "fr":
-            from pattern.text.fr import sentiment as sentiment_fr
-
-            return sentiment_fr(self.clean)
-        elif self.language == "it":
-            from pattern.text.it import sentiment as sentiment_it
-
-            return sentiment_it(self.clean)
-
         raise NautilusMissingModelException(f"No sentiment model for {self.language}")
 
     @functools.lru_cache()
@@ -229,7 +285,7 @@ def extract_keyterms(self, ranker="textrank", n_terms=10, **kwargs):
         >>> doc.extract_keyterms(ranker='sgrank', ngrams=(1))
         [('Netherlands', 0.4020557546031188), ('capital', 0.29395103364295216), ('awesome', 0.18105611227666252), ('Amsterdam', 0.12293709947726655)]
         """
-        if self.nwords < 1:
+        if self.n_words < 1:
             return []
         rankers = ["textrank", "sgrank", "singlerank"]
         if ranker not in rankers:
@@ -249,146 +305,3 @@ def keyterms(self):
         [('awesome', 0.32456160227748454), ('capital', 0.32456160227748454), ('Amsterdam', 0.17543839772251532)]
         """
         return self.extract_keyterms()
-
-    @property
-    def minhash(self):
-        """
-        A cheap way to compute a hash for finding similarity of docs
-        Source: https://ekzhu.github.io/datasketch/minhash.html
-        >>> doc = Doc('Sentence for computing the minhash')
-        >>> doc.minhash[:5]
-        [407326892, 814360600, 1099082245, 1176349439, 1735256]
-        """
-        return self.find_minhash()
-
-    @functools.lru_cache()
-    def find_minhash(self, num_perm=128):
-        words = self.words
-        doc_hash = MinHash(num_perm=num_perm)
-        for word, _ in words:
-            doc_hash.update(word.encode("utf8"))
-        return list(doc_hash.digest())
-
-    def similarity(self, other_doc, metric="jaccard", hash_method="minhash"):
-        """
-        Computes similarity for two documents.
-        Only minhash Jaccard similarity is implemented.
-        >>> doc1 = Doc('Sentence for computing the minhash')
-        >>> doc2 = Doc('Sentence for computing the similarity')
-        >>> doc1.similarity(doc2)
-        0.7265625
-        """
-        if hash_method == "minhash" and metric == "jaccard":
-            hash1 = MinHash(hashvalues=self.minhash)
-            hash2 = MinHash(hashvalues=other_doc.minhash)
-            return hash1.jaccard(hash2)
-        else:
-            raise NotImplementedError(
-                f"Metric/hash method combination {metric}"
-                f"/{hash_method} is not implemented as similarity metric"
-            )
-
-    @property
-    def word_vectors(self):
-        """
-        Returns word embeddings for the words in the document.
-        """
-        return self.generate_word_vectors()
-
-    @functools.lru_cache()
-    def generate_word_vectors(self, model_name=None):
-        """
-        Returns word embeddings for the words in the document.
-        The default spacy models don't have "true" word vectors
-        but only context-sensitive tensors that are within the document.
-
-        Returns:
-        A dictionary mapping words from the document to a dict with the
-        corresponding values of the following variables:
-
-        has vector: Does the token have a vector representation?
-        vector norm: The L2 norm of the token's vector (the square root of the
-                    sum of the values squared)
-        OOV: Out-of-vocabulary (This variable always gets the value True since
-                                there are no vectors included in the model)
-        vector: The vector representation of the word
-
-        >>> doc = Doc('Test sentence')
-        >>> doc.word_vectors['Test']['is_oov']
-        True
-        >>> len(doc.word_vectors['Test']['vector'])
-        96
-        >>> doc.word_vectors['Test']['vector_norm'] == doc.word_vectors['sentence']['vector_norm']
-        False
-        """
-        lang = self.language if self.is_reliable_language else self.hint_language
-        return {
-            token.text: {
-                "has_vector": token.has_vector,
-                "vector_norm": token.vector_norm,
-                "is_oov": token.is_oov,
-                "vector": token.vector.tolist(),
-            }
-            for token in self._load_spacy_doc(lang, model_name)
-        }
-
-    @property
-    def doc_vector(self):
-        """
-        Returns document embeddings based on the words in the document.
-
-        >>> import numpy
-        >>> numpy.array_equiv(Doc('a b').doc_vector, Doc('a b').doc_vector)
-        True
-        >>> numpy.array_equiv(Doc('a b').doc_vector, Doc('a a b').doc_vector)
-        False
-        """
-        return self.aggregate_word_vectors()
-
-    @functools.lru_cache()
-    def aggregate_word_vectors(
-        self, model_name=None, aggregation="mean", normalize=False, exclude_oov=False
-    ):
-        """
-        Returns document embeddings based on the words in the document.
-
-        >>> import numpy
-        >>> doc1 = Doc('a b')
-        >>> doc2 = Doc('a a b')
-        >>> numpy.array_equiv(doc1.aggregate_word_vectors(), doc1.aggregate_word_vectors())
-        True
-        >>> numpy.array_equiv(doc1.aggregate_word_vectors(), doc2.aggregate_word_vectors())
-        False
-        >>> numpy.array_equiv(doc1.aggregate_word_vectors(aggregation='mean'), doc2.aggregate_word_vectors(aggregation='sum'))
-        False
-        >>> numpy.array_equiv(doc1.aggregate_word_vectors(aggregation='mean'), doc2.aggregate_word_vectors(aggregation='var'))
-        False
-        >>> numpy.array_equiv(doc1.aggregate_word_vectors(aggregation='sum'), doc2.aggregate_word_vectors(aggregation='var'))
-        False
-        >>> doc = Doc('sentence with an out of vector word lsseofn')
-        >>> len(doc.aggregate_word_vectors())
-        96
-        >>> numpy.array_equiv(doc.aggregate_word_vectors(exclude_oov=False), doc.aggregate_word_vectors(exclude_oov=True))
-        False
-        """
-        lang = self.language if self.is_reliable_language else self.hint_language
-        tokens = [
-            token
-            for token in self._load_spacy_doc(lang, model_name)
-            if not exclude_oov or not token.is_oov
-        ]
-        vectors = [
-            token.vector / token.vector_norm if normalize else token.vector
-            for token in tokens
-        ]
-
-        if aggregation == "mean":
-            return numpy.mean(vectors, axis=0).tolist()
-        elif aggregation == "sum":
-            return numpy.sum(vectors, axis=0).tolist()
-        elif aggregation == "var":
-            return numpy.var(vectors, axis=0).tolist()
-        else:
-            raise NotImplementedError(
-                f"Aggregation method {aggregation} is not implemented."
-            )

From 6f6d3086454f8aca29f6493cf98d5fa458c5551b Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 12:01:32 +0200
Subject: [PATCH 023/496] Add Language detector module, using either fasttext
 or cdl2. This solves #29

---
 nautilus_nlp/models/Language_detector.py | 37 ++++++++++++++++++++++++
 notebooks/Language_identification.ipynb  | 20 ++++++-------
 2 files changed, 47 insertions(+), 10 deletions(-)
 create mode 100644 nautilus_nlp/models/Language_detector.py

diff --git a/nautilus_nlp/models/Language_detector.py b/nautilus_nlp/models/Language_detector.py
new file mode 100644
index 0000000..b168473
--- /dev/null
+++ b/nautilus_nlp/models/Language_detector.py
@@ -0,0 +1,37 @@
+from nautilus_nlp.models.Fasttext_classifier import Fasttext_clf as langdetect
+import cld2
+import pkg_resources
+lang_path = pkg_resources.resource_filename('nautilus_nlp.data', 'lang_identification.ftz')
+class LangDetector():
+    """ This class is to instantiante a language detector.
+
+    """
+    def __init__(self,typemodel,path=lang_path,):
+        self.typemodel=typemodel
+        self.path = None if path is None else lang_path 
+        self.model=langdetect(self.path) if typemodel=='fasttext' else None
+    
+    def detect_language(self, text_to_detect=None):
+        """
+        Detected the language of a text
+
+        Args:
+        hint_language: language you expect your text to be
+
+        Returns:
+        is_reliable: is the top language is much better than 2nd best language?
+        language: 2-letter code for the language of the text
+        """
+        if self.typemodel!='fasttext':
+            _, _, best_guesses = cld2.detect(text_to_detect,
+                                                    bestEffort=True)
+
+            if len(best_guesses) == 0 or len(best_guesses[0]) != 4 or best_guesses[0][1] == 'un':
+                return 'un',0
+
+            return  best_guesses[0][1],(best_guesses[0][2]/100)
+        else:
+            best_guesses=self.model.predict(text_to_detect)
+            return best_guesses[0][0].replace('__label__',''),best_guesses[1][0]
+
+
diff --git a/notebooks/Language_identification.ipynb b/notebooks/Language_identification.ipynb
index 68049ee..4f00ebf 100644
--- a/notebooks/Language_identification.ipynb
+++ b/notebooks/Language_identification.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,13 +42,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nautilus_nlp.models import langdetector\n",
-    "detector= langdetector.LangDetector(typemodel='fasttext')\n",
-    "detector2= langdetector.LangDetector(typemodel='cld2')"
+    "from nautilus_nlp.models.Language_detector import LangDetector\n",
+    "detector= LangDetector(typemodel='fasttext')\n",
+    "detector2= LangDetector(typemodel='cld2')"
    ]
   },
   {
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -126,14 +126,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "83.8 ms ± 836 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "87.5 ms ± 3.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -145,14 +145,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "32.2 ms ± 1.59 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "32 ms ± 1.19 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],

From bcf5d1235f0f62781d6c6b03e01dc383d1416ef9 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 13:59:32 +0200
Subject: [PATCH 024/496] Add tests for Document abstractions

---
 tests/test_doc.py | 144 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 144 insertions(+)
 create mode 100644 tests/test_doc.py

diff --git a/tests/test_doc.py b/tests/test_doc.py
new file mode 100644
index 0000000..9d7787f
--- /dev/null
+++ b/tests/test_doc.py
@@ -0,0 +1,144 @@
+"""
+Testing for textpipe doc.py
+"""
+import pytest
+import random
+import spacy
+from nautilus_nlp.models.Language_detector import LangDetector
+from nautilus_nlp.doc import Doc, NautilusMissingModelException
+
+TEXT_1 = """
+Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock. They incorporated Google as a privately held company on September 4, 1998. An initial public offering (IPO) took place on August 19, 2004, and Google moved to its headquarters in Mountain View, California, nicknamed the Googleplex. In August 2015, Google announced plans to reorganize its various interests as a conglomerate called Alphabet Inc. Google is Alphabet's leading subsidiary and will continue to be the umbrella company for Alphabet's Internet interests. Sundar Pichai was appointed CEO of Google, replacing Larry Page who became the CEO of Alphabet.
+"""
+
+TEXT_2 = """Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données.
+            Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer,
+            mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres.
+            L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique  
+"""
+
+TEXT_3 = ""
+
+TEXT_4 = """this is a paragraph
+this is a paragraph
+"""
+
+TEXT_5 = """Mark Zuckerberg is sinds de oprichting van Facebook de directeur van het bedrijf."""
+
+TEXT_6 = """
+မြန်မာဘာသာစကားသည် တိဘက်-ဗမာနွယ် ဘာသာစကားများ အုပ်စုတွင် ပါဝင်သည်။
+တိဘက်-ဗမာနွယ် ဘာသာစကားများ အုပ်စုသည် တရုတ်-တိဗက်နွယ် ဘာသာစကားများ
+မိသားစု ထဲတွင် ပါသည်။ မြန်မာဘာသာသည် တက်ကျသံရှိသော
+၊နိမ့်မြင့်အမှတ်အသားရှိ ဖြစ်သော၊ ဧကဝဏ္ဏစကားလုံး အလွန်များသော ဘာသာစကား
+ဖြစ်သည်။ ကတ္တား-ကံ-တြိယာ စကားလုံးအစီအစဉ်ဖြင့် ရေးသော သရုပ်ခွဲဘာသာစကား
+လည်းဖြစ်သည်။ မြန်မာအက္ခရာများသည် ဗြာဟ္မီအက္ခရာ သို့မဟုတ် ဗြာဟ္မီအက္ခရာမှ
+ဆက်ခံထားသောမွန်အက္ခရာတို့မှ ဆင်းသက်လာသည်။
+"""
+
+TEXT_7 = """\nHi <<First Name>>\nthis is filler text \xa325 more filler.\nadditilnal 
+filler.\nyet more\xa0still more\xa0filler.\n\xa0\nmore\nfiller.\x03\n\t\t\t\t\t\t    
+almost there \n\\n\nthe end\n"""
+
+ents_model = spacy.blank("nl")
+custom_spacy_nlps = {"nl": {"ents": ents_model}}
+detector = LangDetector(typemodel="fasttext")
+
+DOC_1 = Doc(TEXT_1, language="en")
+DOC_2 = Doc(TEXT_2, language="fr")
+DOC_4 = Doc(TEXT_4, "en")
+DOC_5 = Doc(TEXT_5, language="nl", spacy_nlps=custom_spacy_nlps)
+DOC_6 = Doc(TEXT_6, langdetect=detector)
+DOC_7 = Doc(TEXT_7, "en")
+
+
+def test_load_custom_model():
+    """
+    The custom spacy language modules should be correctly loaded into the doc.
+    """
+    model_mapping = {"nl": "ents"}
+    lang = DOC_5.language
+    assert lang == "nl"
+    assert sorted(DOC_5.find_entities()) == sorted(
+        [("Mark Zuckerberg", "PER"), ("Facebook", "PER")]
+    )
+    assert DOC_5.find_entities(model_mapping[lang]) == []
+
+
+def test_nwords_nsents():
+    assert DOC_1.n_words == 145
+    assert DOC_2.n_words == 83
+    assert DOC_1.n_sentences == 7
+    assert DOC_2.n_sentences == 3
+
+
+def test_entities():
+    assert sorted(DOC_1.entities) == sorted(
+        [
+            ("1998", "DATE"),
+            ("56 percent", "PERCENT"),
+            ("Alphabet", "GPE"),
+            ("Alphabet", "ORG"),
+            ("Alphabet Inc. Google", "ORG"),
+            ("August 19, 2004", "DATE"),
+            ("August 2015", "DATE"),
+            ("California", "GPE"),
+            ("Google", "ORG"),
+            ("Googleplex", "ORG"),
+            ("IPO", "ORG"),
+            ("Larry Page", "PERSON"),
+            ("Mountain View", "GPE"),
+            ("Ph.D.", "PERSON"),
+            ("September 4, 1998", "DATE"),
+            ("Sergey Brin", "PERSON"),
+            ("Stanford University", "ORG"),
+            ("Sundar Pichai", "PERSON"),
+            ("about 14 percent", "PERCENT"),
+        ]
+    )
+    assert sorted(DOC_2.entities) == sorted(
+        [
+            ("Exalead", "ORG"),
+            ("Google", "ORG"),
+            ("HITS", "MISC"),
+            ("PageRank", "MISC"),
+            ("Yahoo!", "ORG"),
+        ]
+    )
+
+
+def test_complexity():
+    assert DOC_1.complexity == 48.06961538461539
+    assert DOC_2.complexity == 78.634_035_087_719_3
+
+
+def test_clean():
+    assert len(TEXT_1) >= len(DOC_1.clean)
+    assert len(TEXT_2) >= len(DOC_2.clean)
+
+
+def test_clean_newlines():
+    assert " ".join(TEXT_4.split()) == DOC_4.clean
+
+
+def test_extract_keyterms():
+    non_ranker = "bulthaup"
+    rankers = ["textrank", "sgrank", "singlerank"]
+    with pytest.raises(
+        ValueError,
+        message=f'algorithm "{non_ranker}" not ' f"available; use one of {rankers}",
+    ):
+        DOC_1.extract_keyterms(ranker=non_ranker)
+    assert len(DOC_1.extract_keyterms()) == 10
+    # limits number of keyterms
+    assert len(DOC_1.extract_keyterms(n_terms=2)) == 2
+    # works with other rankers
+    assert isinstance(DOC_2.extract_keyterms(ranker=random.choice(rankers)), list)
+
+
+def test_missing_language_model():
+    with pytest.raises(NautilusMissingModelException):
+        DOC_6.n_words
+
+
+def test_non_utf_chars():
+    assert DOC_7.language == "en"

From f3bf9419b1aee9caa4c04a0b2d97ede14d657f7e Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 16:38:24 +0200
Subject: [PATCH 025/496] Add doc lemmatisation

---
 nautilus_nlp/doc.py | 25 +++++++++++++++++++------
 1 file changed, 19 insertions(+), 6 deletions(-)

diff --git a/nautilus_nlp/doc.py b/nautilus_nlp/doc.py
index 9a23062..d7dfdc2 100644
--- a/nautilus_nlp/doc.py
+++ b/nautilus_nlp/doc.py
@@ -62,8 +62,8 @@ def language(self):
 
         if not self._language:
             if self._language_detector:
-                self._is_reliable_language, self._language = self._language_detector.detect_language(
-                    self.raw
+                self._language,self._is_reliable_language = self._language_detector.detect_language(
+                    self.clean
                 )
             else:
                 raise NautilusMissingModelException(
@@ -111,9 +111,12 @@ def _get_default_nlp(lang):
         Loads the spacy default language module for the Doc's language
         """
         try:
-            return spacy.load(
-                "{}_core_{}_sm".format(lang, "web" if lang == "en" else "news")
-            )
+            if lang!='un':
+                return spacy.load(
+                    "{}_core_{}_sm".format(lang, "web" if lang == "en" else "news")
+                )
+            else:
+                return spacy.load('xx_ent_wiki_sm')
         except IOError:
             raise NautilusMissingModelException(
                 f'Default model for language "{lang}" is not available. You should try to run python -m spacy download {lang}_core_news_sm'
@@ -142,7 +145,7 @@ def clean_text(self, clean_dots=True, clean_quotes=True, clean_whitespace=True):
         True
         """
         text = self.raw
-
+        text.replace('\n',' ')
         if clean_dots:
             text = re.sub(r"…", "...", text)
         if clean_quotes:
@@ -153,6 +156,16 @@ def clean_text(self, clean_dots=True, clean_quotes=True, clean_whitespace=True):
 
         return text
 
+    @property
+    def lemma(self):
+
+        return self.get_lemma()
+    
+    @functools.lru_cache()
+    def get_lemma(self,model_name=None):
+        
+        return [token.lemma_ for token in  self._load_spacy_doc(self.language, model_name)]
+
     @property
     def entities(self):
         """

From 102fcaf41627440b51ffc13cda9e8ef7c1e9a4d1 Mon Sep 17 00:00:00 2001
From: Unknown <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 16:42:16 +0200
Subject: [PATCH 026/496] Script to download all spacy models

---
 download_spacy_models.sh | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 download_spacy_models.sh

diff --git a/download_spacy_models.sh b/download_spacy_models.sh
new file mode 100644
index 0000000..0a3a374
--- /dev/null
+++ b/download_spacy_models.sh
@@ -0,0 +1,5 @@
+python -m spacy download en_core_web_sm
+python -m spacy download de_core_news_sm 
+python -m spacy download fr_core_news_sm 
+python -m spacy download es_core_news_sm 
+python -m spacy download nl_core_news_sm

From 9e82199e0ba6791b2d818683302dfeb81fef2846 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 18:52:51 +0200
Subject: [PATCH 027/496] Add Keyword extraction from text #54

---
 nautilus_nlp/utils/keyword_extractor.py | 21 +++++++++++++++++++++
 tests/tests_extraction.py               | 16 ++++++++++++++++
 2 files changed, 37 insertions(+)
 create mode 100644 nautilus_nlp/utils/keyword_extractor.py
 create mode 100644 tests/tests_extraction.py

diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/utils/keyword_extractor.py
new file mode 100644
index 0000000..e384bf2
--- /dev/null
+++ b/nautilus_nlp/utils/keyword_extractor.py
@@ -0,0 +1,21 @@
+from flashtext import KeywordProcessor
+
+def extract_keywords(text,keyword,case_sensitive=True):
+    """
+    Extract Keywords from a document.
+    args :
+    text: Text to extract keywords from
+    keyword : Single keyword (str) or list of keywords (list)
+
+    return list of extracted keyworkds
+    """
+    processor=KeywordProcessor(case_sensitive=case_sensitive)
+    if isinstance(keyword,list):
+        processor.add_keywords_from_list(keyword)
+    elif isinstance(keyword,str):
+        processor.add_keyword(keyword)
+    elif isinstance(keyword,dict):
+        processor.add_keywords_from_dict(keyword)
+
+    return processor.extract_keywords(text)
+    
diff --git a/tests/tests_extraction.py b/tests/tests_extraction.py
new file mode 100644
index 0000000..293aef5
--- /dev/null
+++ b/tests/tests_extraction.py
@@ -0,0 +1,16 @@
+from nautilus_nlp.utils.keyword_extractor import extract_keywords
+
+str_="""Les moteurs de recherche tels Google, Exalead ou Yahoo! sont
+ des applications très connues de fouille de textes sur de grandes masses de données. 
+ Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon 
+ dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS 
+ dans le milieu académique"""
+
+dict_extract={"US_Companies":['Yahoo','Google'],
+ "French_Companies":['Exalead']
+}
+def test_keyword_extraction():
+    assert extract_keywords(str_,['Google'])==['Google','Google']
+    assert extract_keywords(str_,'Google')==['Google','Google']
+    assert extract_keywords(str_,['Google','Yahoo'])==['Google','Yahoo','Google']
+    assert extract_keywords(str_,dict_extract) == ['US_Companies', 'French_Companies', 'US_Companies', 'US_Companies']

From 9942efc125fea85ea41d16b99827188f8997d4b6 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 5 Apr 2019 18:54:35 +0200
Subject: [PATCH 028/496] Change requireents for keyword extraction #54

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 869399b..f53e017 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -27,3 +27,4 @@ textblob_fr
 vaderSentiment
 stop_words
 cld2_cffi~=0.1
+flashtext
\ No newline at end of file

From c9212a7a3d19ddab0e3c4d1981a39a7a70f66e12 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 6 Apr 2019 12:50:10 +0200
Subject: [PATCH 029/496] fix issues and improve document_loader

---
 nautilus_nlp/utils/file_loader.py | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 7baa483..562083e 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -38,13 +38,15 @@ def text_loader(filepath, encoding=None, detectencoding=True):
         try:
             return open_textfile(filepath, encoding='utf-8')
         except UnicodeDecodeError:
-            logging.warning('Encoding is not UTF-8.')
+            logging.warning('Encoding for {} is not UTF-8.'.format(filepath))
             if detectencoding is True:
                 logging.warning('Trying to detect encoding for {}'.format(filepath))
                 detected_encoding = detect_encoding(filepath)
-                logging.info('detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(encod=detected_encoding['encoding'],
+                logging.info('{filepath}: detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(filepath=filepath, encod=detected_encoding['encoding'],
                                                                                                         conf_rate=detected_encoding['confidence']))
                 return open_textfile(filepath, encoding=detected_encoding['encoding'])
+            else:
+                raise UnicodeDecodeError('Cannot load document using utf-8. Try to detect encoding using detectencoding=True')
 
 
 def list_files(filepath:str):
@@ -60,11 +62,20 @@ def list_files(filepath:str):
     return[file for file in glob.glob(filepath) if isfile(file)]            
 
 
-def documents_loader(filepath, encoding=None):
+def documents_loader(filepath:str, encoding=None, detectencoding=True, output_as='dict'):
     '''
     Input a filepath, a filepath with wildcard (eg. *.txt), 
     or a list of filepaths.
     Output a string, or a dict of strings.
+    Args:
+        filepath: filepath, a filepath with wildcard (eg. *.txt), 
+            or a list of filepaths.
+        output_as: list or dict. If dict, key will be the filename.
+        encoding: if not specified, will try to detect encoding except if 
+            detectencoding is false. 
+        detectencoding: if True and if encoding is not specified, will try to 
+            detect encoding using chardet. 
+
     '''
     
     if type(filepath) is str:
@@ -77,9 +88,14 @@ def documents_loader(filepath, encoding=None):
         raise IOError('Please enter a valid filepath or a valid list of filepath')
     
     if nb_of_documents == 1:
-        return text_loader(documents[0],encoding=encoding)
+        return text_loader(documents[0],encoding=encoding, detectencoding=detectencoding)
     elif nb_of_documents > 1:
-        return { document : text_loader(documents[0], encoding=encoding) for document in documents}
+        if output_as == 'list':
+            return [text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents]
+        elif output_as == 'dict':
+            return { document : text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents}
+        else:
+            raise ValueError('Enter a valid output format between list or dict')
     else:
         raise IOError('No files detected in {}'.format(filepath))        
 

From a75ed1642223244dd14cce3e33c757b64396d0b3 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 6 Apr 2019 12:51:47 +0200
Subject: [PATCH 030/496] put document_loader on top

---
 ... a list of files and detect encoding.ipynb | 401 ----------------
 ...t files loader with encoding handler.ipynb | 447 ++++++++++++++++++
 2 files changed, 447 insertions(+), 401 deletions(-)
 delete mode 100644 notebooks/Open a file, a list of files and detect encoding.ipynb
 create mode 100644 notebooks/Text files loader with encoding handler.ipynb

diff --git a/notebooks/Open a file, a list of files and detect encoding.ipynb b/notebooks/Open a file, a list of files and detect encoding.ipynb
deleted file mode 100644
index 8c26c57..0000000
--- a/notebooks/Open a file, a list of files and detect encoding.ipynb	
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Open Text File with encoding handling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Example of a latin1 file\n",
-    "s = \"J'aime les frites bien grasse étalon châpeau!\"\n",
-    "encoded_s = s.encode('latin-1')\n",
-    "with open('somefile.txt', 'wb') as f:\n",
-    "    f.write(encoded_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## text loader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you don't know what the encoding is, you can use the `text_loader()`. Il will try to open with UTF-8, and if it fails it will apply `detect_encoding()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import text_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text_loader('somefile.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## open text file when you know the encoding"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you know the encoding. (you can also use `open_textfile()`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text_loader('somefile.txt',encoding='latin-1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## detect encoding "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import detect_encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "detect_encoding('somefile.txt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## List files in a folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import list_files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['./somefile.txt',\n",
-       " './Open a file, a list of files and detect encoding.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
-       " './Language_identification.ipynb',\n",
-       " './someadditionalfile.txt',\n",
-       " './somefile',\n",
-       " './Common Text Processing operations.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
-       " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('.') # list files from current folders"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['./Open a file, a list of files and detect encoding.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
-       " './Language_identification.ipynb',\n",
-       " './Common Text Processing operations.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
-       " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb']"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('./*.ipynb') # List files matching specific pattern"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/Users/hugo/Documents/NAUTILUS/nautilus-nlp/LICENSE',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/requirements.txt',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/Makefile',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/README.md',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/setup.py',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/tox.ini',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/test_environment.py']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# only files will be printed, not folders\n",
-    "list_files('/Users/hugo/Documents/NAUTILUS/nautilus-nlp/')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open several text files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Let's add another document \n",
-    "s = \"Un deuxième exemple de texte en utf-8 cette fois!\"\n",
-    "encoded_s = s.encode('utf-8')\n",
-    "with open('someadditionalfile.txt', 'wb') as f:\n",
-    "    f.write(encoded_s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import documents_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n",
-      "WARNING:root:Encoding is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents_loader('*.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n",
-      "WARNING:root:Encoding is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents_loader(['somefile.txt','someadditionalfile.txt'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Un deuxième exemple de texte en utf-8 cette fois!'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents_loader('someadditionalfile.txt',encoding='utf-8')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/Text files loader with encoding handler.ipynb b/notebooks/Text files loader with encoding handler.ipynb
new file mode 100644
index 0000000..168bbbd
--- /dev/null
+++ b/notebooks/Text files loader with encoding handler.ipynb	
@@ -0,0 +1,447 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Open Text File with encoding handling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Example of a latin1 file\n",
+    "s = \"J'aime les frites bien grasse étalon châpeau!\"\n",
+    "encoded_s = s.encode('latin-1')\n",
+    "with open('somefile.txt', 'wb') as f:\n",
+    "    f.write(encoded_s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Let's add another document \n",
+    "s = \"Un deuxième exemple de texte en utf-8 cette fois!\"\n",
+    "encoded_s = s.encode('utf-8')\n",
+    "with open('someadditionalfile.txt', 'wb') as f:\n",
+    "    f.write(encoded_s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Document loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import documents_loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"J'aime les frites bien grasse étalon châpeau!\""
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents_loader('somefile.txt',encoding='latin-1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Un deuxième exemple de texte en utf-8 cette fois!'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# default encoding is UTF-8\n",
+    "documents_loader('someadditionalfile.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Detect encoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't specify encoding, `document_loader()` will try to open it as UTF-8, and if it doesn't work it will try to detect encoding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"J'aime les frites bien grasse étalon châpeau!\""
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents_loader('somefile.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "function takes exactly 5 arguments (1 given)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mopen_textfile\u001b[0;34m(filepath, encoding)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.0/Frameworks/Python.framework/Versions/3.7/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 30: invalid continuation byte",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-dc5ecd5accf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdocuments_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'somefile.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mdocuments_loader\u001b[0;34m(filepath, encoding, detectencoding, output_as)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtext_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetectencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moutput_as\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'list'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetected_encoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot load document using utf-8. Try to detect encoding using detectencoding=True'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: function takes exactly 5 arguments (1 given)"
+     ]
+    }
+   ],
+   "source": [
+    "# You can prevent document loader from detecting the encoding if UTF-8 fails \n",
+    "documents_loader('somefile.txt', detectencoding=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open several files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can use wildcards to open several documents\n",
+    "documents_loader('*.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can also pass a list of filepaths\n",
+    "documents_loader(['somefile.txt','someadditionalfile.txt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[\"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'Un deuxième exemple de texte en utf-8 cette fois!']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can specify the output format when you load multiple texts\n",
+    "documents_loader('*.txt', output_as='list')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List files in a folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import list_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./somefile.txt',\n",
+       " './Open a file, a list of files and detect encoding.ipynb',\n",
+       " './Visualization tools.ipynb',\n",
+       " './Language_identification.ipynb',\n",
+       " './someadditionalfile.txt',\n",
+       " './somefile',\n",
+       " './Common Text Processing operations.ipynb',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './Spacy_model.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('.') # list files from current folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./Open a file, a list of files and detect encoding.ipynb',\n",
+       " './Visualization tools.ipynb',\n",
+       " './Language_identification.ipynb',\n",
+       " './Common Text Processing operations.ipynb',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './Spacy_model.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('./*.ipynb') # List files matching specific pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/hugo/Documents/NAUTILUS/nautilus-nlp/LICENSE',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/requirements.txt',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/Makefile',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/README.md',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/setup.py',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/tox.ini',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/test_environment.py']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only files will be printed, not folders\n",
+    "list_files('/Users/hugo/Documents/NAUTILUS/nautilus-nlp/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## detect encoding "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import detect_encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detect_encoding('somefile.txt')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 025c3045fd6936d23204d0207644ffc3ee3e799d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 6 Apr 2019 12:55:05 +0200
Subject: [PATCH 031/496] improve doc

---
 ...t files loader with encoding handler.ipynb | 36 +++++++++++--------
 1 file changed, 22 insertions(+), 14 deletions(-)

diff --git a/notebooks/Text files loader with encoding handler.ipynb b/notebooks/Text files loader with encoding handler.ipynb
index 168bbbd..ea034dd 100644
--- a/notebooks/Text files loader with encoding handler.ipynb	
+++ b/notebooks/Text files loader with encoding handler.ipynb	
@@ -56,53 +56,61 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
+   "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "## Open a file when you know the encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
+       "'Un deuxième exemple de texte en utf-8 cette fois!'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "documents_loader('somefile.txt',encoding='latin-1')"
+    "# default encoding is UTF-8\n",
+    "documents_loader('someadditionalfile.txt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Un deuxième exemple de texte en utf-8 cette fois!'"
+       "\"J'aime les frites bien grasse étalon châpeau!\""
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# default encoding is UTF-8\n",
-    "documents_loader('someadditionalfile.txt')"
+    "# If you know the encoding, you can specify it\n",
+    "documents_loader('somefile.txt', encoding='latin-1')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Detect encoding"
+    "## Open a file with encoding detection"
    ]
   },
   {

From 5d3021ba56192638d1e3d4f5adaad2b5b9920289 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 6 Apr 2019 16:31:12 +0200
Subject: [PATCH 032/496] add tests for document loader

---
 tests/test_document_loader.py                 | 72 +++++++++++++++++++
 .../testfolder_fileloader/testdoc_latin1.txt  |  1 +
 tests/testfolder_fileloader/testdoc_utf8.txt  |  1 +
 3 files changed, 74 insertions(+)
 create mode 100644 tests/test_document_loader.py
 create mode 100644 tests/testfolder_fileloader/testdoc_latin1.txt
 create mode 100644 tests/testfolder_fileloader/testdoc_utf8.txt

diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
new file mode 100644
index 0000000..2868be8
--- /dev/null
+++ b/tests/test_document_loader.py
@@ -0,0 +1,72 @@
+import pytest
+import numpy as np
+from nautilus_nlp.utils.file_loader import documents_loader, list_files, detect_encoding
+
+
+testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
+encoded_s = testdoc_latin1.encode('latin-1')
+with open('tests/testfolder_fileloader/testdoc_latin1.txt', 'wb') as f:
+    f.write(encoded_s)
+
+testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
+encoded_s = testdoc_utf8.encode('utf-8')
+with open('tests/testfolder_fileloader/testdoc_utf8.txt', 'wb') as f:
+    f.write(encoded_s)
+
+
+def test_openfile_with_encoding():
+    input_str = "tests/testfolder_fileloader/testdoc_latin1.txt"
+    expected_str = testdoc_latin1
+
+    result = documents_loader(input_str, encoding='latin-1')
+    np.testing.assert_string_equal(result, expected_str)
+
+def test_openfile_utf8():
+    input_str = "tests/testfolder_fileloader/testdoc_utf8.txt"
+    expected_str = testdoc_utf8
+
+    result = documents_loader(input_str)
+    np.testing.assert_string_equal(result, expected_str)
+
+def test_encoding_detection():
+    input_str = "tests/testfolder_fileloader/testdoc_latin1.txt"
+    expected_str = testdoc_latin1
+
+    result = documents_loader(input_str)
+    np.testing.assert_string_equal(result, expected_str)    
+    
+def test_load_several_docs_wildcard():
+    expected = {'tests/testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+                'tests/testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+    result = documents_loader('tests/testfolder_fileloader/*.txt', output_as='dict')
+    np.testing.assert_equal(result, expected)    
+
+def test_load_several_docs_list():
+    expected = {'tests/testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+                'tests/testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+    result = documents_loader(['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt'], output_as='dict')
+    np.testing.assert_equal(result, expected)
+
+
+def test_load_several_docs_output_list():
+    expected = ["J'aime les frites bien grasse étalon châpeau!",
+                'Un deuxième exemple de texte en utf-8 cette fois!']
+    result = documents_loader(['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt'], output_as='list')
+    return len(expected) == len(result) and sorted(expected) == sorted(result)
+
+
+
+@pytest.mark.parametrize("input_filepath", ['tests/testfolder_fileloader/*.txt','tests/testfolder_fileloader/','tests/testfolder_fileloader'])
+def test_list_files(input_filepath):
+    expected = ['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt']
+    result = list_files(input_filepath)
+
+    return len(expected) == len(result) and sorted(expected) == sorted(result)
+
+
+def test_detect_encoding():
+    expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
+    result = detect_encoding('tests/testfolder_fileloader/testdoc_latin1.txt')
+
+    np.testing.assert_equal(result, expected)
+
diff --git a/tests/testfolder_fileloader/testdoc_latin1.txt b/tests/testfolder_fileloader/testdoc_latin1.txt
new file mode 100644
index 0000000..b9855bf
--- /dev/null
+++ b/tests/testfolder_fileloader/testdoc_latin1.txt
@@ -0,0 +1 @@
+J'aime les frites bien grasse �talon ch�peau!
\ No newline at end of file
diff --git a/tests/testfolder_fileloader/testdoc_utf8.txt b/tests/testfolder_fileloader/testdoc_utf8.txt
new file mode 100644
index 0000000..c7de724
--- /dev/null
+++ b/tests/testfolder_fileloader/testdoc_utf8.txt
@@ -0,0 +1 @@
+Un deuxième exemple de texte en utf-8 cette fois!
\ No newline at end of file

From d6d2552961fd07193ad6ad2d0694d5a22c793fb1 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Sat, 6 Apr 2019 17:35:59 +0200
Subject: [PATCH 033/496] typo

---
 nautilus_nlp/doc.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/nautilus_nlp/doc.py b/nautilus_nlp/doc.py
index d7dfdc2..9670f58 100644
--- a/nautilus_nlp/doc.py
+++ b/nautilus_nlp/doc.py
@@ -318,3 +318,8 @@ def keyterms(self):
         [('awesome', 0.32456160227748454), ('capital', 0.32456160227748454), ('Amsterdam', 0.17543839772251532)]
         """
         return self.extract_keyterms()
+   
+    @functools.lru_cache()
+    def extract_keywords(self,keyword_list:list):
+        from flashtext import KeywordProcessor
+        return KeywordProcessor().add_keywords_from_list(keyword_list).extract_keywords(self.raw)

From a00fc4f6a686e07b7105eafd59efabf27d512b82 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Sat, 6 Apr 2019 19:44:32 +0200
Subject: [PATCH 034/496] Add first ReadTheDoc batch

---
 docs/index.rst                      |  60 ++++++++++++--
 docs/modules.rst                    |   7 ++
 docs/nautilus_nlp.config.rst        |  22 ++++++
 docs/nautilus_nlp.data.rst          |  22 ++++++
 docs/nautilus_nlp.features.rst      |  22 ++++++
 docs/nautilus_nlp.models.rst        |  70 +++++++++++++++++
 docs/nautilus_nlp.rst               |  42 ++++++++++
 docs/nautilus_nlp.utils.rst         | 118 ++++++++++++++++++++++++++++
 docs/nautilus_nlp.visualization.rst |  22 ++++++
 9 files changed, 379 insertions(+), 6 deletions(-)
 create mode 100644 docs/modules.rst
 create mode 100644 docs/nautilus_nlp.config.rst
 create mode 100644 docs/nautilus_nlp.data.rst
 create mode 100644 docs/nautilus_nlp.features.rst
 create mode 100644 docs/nautilus_nlp.models.rst
 create mode 100644 docs/nautilus_nlp.rst
 create mode 100644 docs/nautilus_nlp.utils.rst
 create mode 100644 docs/nautilus_nlp.visualization.rst

diff --git a/docs/index.rst b/docs/index.rst
index 24fb567..91dd094 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,20 +1,68 @@
-.. Nautilus_NLP documentation master file, created by
-   sphinx-quickstart on Tue Mar 12 12:24:48 2019.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
 
-Welcome to Nautilus_NLP's documentation!
+
+Welcome to Nautilus_nlp's documentation!
 ========================================
 
+The Nautilus NLP library aimed to be a meta-library to be used to help you get started on handling your NLP use-case.
+
+This library can help you with:
+
+    1. Cleaning text data
+    2. Normalizing your dataset
+    3. Training automatically multiclass, multilabel classifier
+    4. Help you discover topics and cluster your data
+
+
+# Feature Request
+
+As an Artefact user, you might be working on a NLP use case, and wish to use Nautilus.
+
+ However, if you think Nautilus is lacking features that can be useful not only to your use case but also others, feel free to to fill up an issue with the label "Feature-request".
+
+ We will try to put it in the roadmap and implement it as soon as possible.
+
+# Installation
+
+Beware, this package has been tested on Python **3.6** & **3.7**, and will probably not be working under python **2.7** as **Python2.7** EOL is scheduled for December 2019. 
+
+To install this library you should first clone the repository:
+
+`git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp`
+
+**If you don't use the docker container, we strongly advise you to do these steps in a virtual environnement**
+
+First you need to install the required files:
+
+`pip install -r requirements.txt`
+
+then you can install it via pip:
+
+`pip install -e .`
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Preprocessing and utility functions:
+
+   modules
+
 .. toctree::
    :maxdepth: 2
-   :caption: Contents:
+   :caption: Preprocessing and utility functions:
 
+   nautilus_nlp.utils
 
 
+.. toctree::
+   :maxdepth: 2
+   :caption: Machine learning:
+
+   nautilus_nlp.models
+
 Indices and tables
 ==================
 
 * :ref:`genindex`
 * :ref:`modindex`
 * :ref:`search`
+
diff --git a/docs/modules.rst b/docs/modules.rst
new file mode 100644
index 0000000..26ca8ee
--- /dev/null
+++ b/docs/modules.rst
@@ -0,0 +1,7 @@
+nautilus_nlp
+============
+
+.. toctree::
+   :maxdepth: 4
+
+   nautilus_nlp
diff --git a/docs/nautilus_nlp.config.rst b/docs/nautilus_nlp.config.rst
new file mode 100644
index 0000000..8864121
--- /dev/null
+++ b/docs/nautilus_nlp.config.rst
@@ -0,0 +1,22 @@
+nautilus\_nlp.config package
+============================
+
+Submodules
+----------
+
+nautilus\_nlp.config.config module
+----------------------------------
+
+.. automodule:: nautilus_nlp.config.config
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: nautilus_nlp.config
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/nautilus_nlp.data.rst b/docs/nautilus_nlp.data.rst
new file mode 100644
index 0000000..4b12cc2
--- /dev/null
+++ b/docs/nautilus_nlp.data.rst
@@ -0,0 +1,22 @@
+nautilus\_nlp.data package
+==========================
+
+Submodules
+----------
+
+nautilus\_nlp.data.make\_dataset module
+---------------------------------------
+
+.. automodule:: nautilus_nlp.data.make_dataset
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: nautilus_nlp.data
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/nautilus_nlp.features.rst b/docs/nautilus_nlp.features.rst
new file mode 100644
index 0000000..c4b764c
--- /dev/null
+++ b/docs/nautilus_nlp.features.rst
@@ -0,0 +1,22 @@
+nautilus\_nlp.features package
+==============================
+
+Submodules
+----------
+
+nautilus\_nlp.features.build\_features module
+---------------------------------------------
+
+.. automodule:: nautilus_nlp.features.build_features
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: nautilus_nlp.features
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/nautilus_nlp.models.rst b/docs/nautilus_nlp.models.rst
new file mode 100644
index 0000000..ee38f75
--- /dev/null
+++ b/docs/nautilus_nlp.models.rst
@@ -0,0 +1,70 @@
+nautilus\_nlp.models package
+============================
+
+Submodules
+----------
+
+nautilus\_nlp.models.Fasttext\_classifier module
+------------------------------------------------
+
+.. automodule:: nautilus_nlp.models.Fasttext_classifier
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.models.Fasttext\_embedding module
+-----------------------------------------------
+
+.. automodule:: nautilus_nlp.models.Fasttext_embedding
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.models.Language\_detector module
+----------------------------------------------
+
+.. automodule:: nautilus_nlp.models.Language_detector
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.models.Sentiment\_detector module
+-----------------------------------------------
+
+.. automodule:: nautilus_nlp.models.Sentiment_detector
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.models.Spacy\_model module
+----------------------------------------
+
+.. automodule:: nautilus_nlp.models.Spacy_model
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.models.sentiment module
+-------------------------------------
+
+.. automodule:: nautilus_nlp.models.sentiment
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.models.sk\_vectorizer module
+------------------------------------------
+
+.. automodule:: nautilus_nlp.models.sk_vectorizer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: nautilus_nlp.models
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/nautilus_nlp.rst b/docs/nautilus_nlp.rst
new file mode 100644
index 0000000..e240841
--- /dev/null
+++ b/docs/nautilus_nlp.rst
@@ -0,0 +1,42 @@
+nautilus\_nlp package
+=====================
+
+Subpackages
+-----------
+
+.. toctree::
+
+    nautilus_nlp.config
+    nautilus_nlp.data
+    nautilus_nlp.features
+    nautilus_nlp.models
+    nautilus_nlp.utils
+    nautilus_nlp.visualization
+
+Submodules
+----------
+
+nautilus\_nlp.corpus module
+---------------------------
+
+.. automodule:: nautilus_nlp.corpus
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.doc module
+------------------------
+
+.. automodule:: nautilus_nlp.doc
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: nautilus_nlp
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/nautilus_nlp.utils.rst b/docs/nautilus_nlp.utils.rst
new file mode 100644
index 0000000..1b9e43a
--- /dev/null
+++ b/docs/nautilus_nlp.utils.rst
@@ -0,0 +1,118 @@
+nautilus\_nlp.utils package
+===========================
+
+Submodules
+----------
+
+nautilus\_nlp.utils.Text\_processor module
+------------------------------------------
+
+.. automodule:: nautilus_nlp.utils.Text_processor
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.compat module
+---------------------------------
+
+.. automodule:: nautilus_nlp.utils.compat
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.constants module
+------------------------------------
+
+.. automodule:: nautilus_nlp.utils.constants
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.emoji module
+--------------------------------
+
+.. automodule:: nautilus_nlp.utils.emoji
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.encoding module
+-----------------------------------
+
+.. automodule:: nautilus_nlp.utils.encoding
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.export module
+---------------------------------
+
+.. automodule:: nautilus_nlp.utils.export
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.file\_loader module
+---------------------------------------
+
+.. automodule:: nautilus_nlp.utils.file_loader
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.lemmatizer module
+-------------------------------------
+
+.. automodule:: nautilus_nlp.utils.lemmatizer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.preprocess module
+-------------------------------------
+
+.. automodule:: nautilus_nlp.utils.preprocess
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.stemmer module
+----------------------------------
+
+.. automodule:: nautilus_nlp.utils.stemmer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.text\_vectorizer module
+-------------------------------------------
+
+.. automodule:: nautilus_nlp.utils.text_vectorizer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.tokenizer module
+------------------------------------
+
+.. automodule:: nautilus_nlp.utils.tokenizer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+nautilus\_nlp.utils.vector\_similarity module
+---------------------------------------------
+
+.. automodule:: nautilus_nlp.utils.vector_similarity
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: nautilus_nlp.utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/nautilus_nlp.visualization.rst b/docs/nautilus_nlp.visualization.rst
new file mode 100644
index 0000000..3f312ce
--- /dev/null
+++ b/docs/nautilus_nlp.visualization.rst
@@ -0,0 +1,22 @@
+nautilus\_nlp.visualization package
+===================================
+
+Submodules
+----------
+
+nautilus\_nlp.visualization.visualize module
+--------------------------------------------
+
+.. automodule:: nautilus_nlp.visualization.visualize
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: nautilus_nlp.visualization
+    :members:
+    :undoc-members:
+    :show-inheritance:

From fbea78a1ef48a973d39622e60f6765d142f7f826 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Sat, 6 Apr 2019 20:04:48 +0200
Subject: [PATCH 035/496] Add dynamic versioning in the setup.py

---
 requirements.txt | 34 ++++++++++++++++++----------------
 setup.py         | 32 +++++++++++++++++++++++++++++++-
 2 files changed, 49 insertions(+), 17 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 869399b..f11e5fa 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
 # local package
--e .
 
 # external requirements
 click
@@ -12,18 +11,21 @@ pillow
 pytest
 
 #library requirements
-spacy==2.1
-scikit-learn>=0.17.0
-ftfy>=4.2.0,<5.0.0
-wordcloud>=1.5.0
-matplotlib>=3.0.3
-chardet
-nltk
-mosestokenizer
--e git://github.com/ClaudeCoulombe/FrenchLefffLemmatizer.git#egg=french_lefff_lemmatizer
-stopwords
-textblob
-textblob_fr
-vaderSentiment
-stop_words
-cld2_cffi~=0.1
+numpy>1.15.4
+stop_words==2018.7.23
+nltk==3.4
+textblob==0.15.3
+textblob_fr==0.2.0
+spacy==2.1.0
+cld2_cffi==0.1.4
+matplotlib==3.0.3
+pandas==0.23.4
+chardet==3.0.4
+setuptools==40.8.0
+ftfy==4.4.3
+textacy==0.6.3
+wordcloud==1.5.0
+#fastText==0.8.3
+gensim==3.7.1
+scikit_learn==0.20.3
+vaderSentiment==3.2.1
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 509349e..5b02f87 100644
--- a/setup.py
+++ b/setup.py
@@ -1,10 +1,40 @@
 from setuptools import find_packages, setup
+import setuptools
+import setuptools.command.install
+from pathlib import Path
+
+class PostInstallCommand(setuptools.command.install.install):
+    """Post-installation command."""
+    def run(self):
+        setuptools.command.install.install.run(self)
+        try:
+            import spacy
+            spacy.cli.validate()
+        except ModuleNotFoundError:
+            pass
+
+
+with open(Path(__file__).resolve().parent.joinpath('requirements.txt'), 'r') as fh:
+    requirements = [r.split('#', 1)[0].strip() for r in fh.read().split('\n')]
+    print(requirements)
+with open(Path(__file__).resolve().parent.joinpath('VERSION'), 'r') as fh:
+    version = fh.read()
 
 setup(
     name='nautilus_nlp',
     packages=find_packages(),
-    version='0.1.0',
+    version=version,
     description='All the goto functions you need to handle NLP use-cases',
     author='Robin Doumerc',
     license='MIT',
+    url='https://github.com/artefactory/nautilus-nlp',
+    classifiers=[
+        'Programming Language :: Python :: 3.6',
+        'License :: OSI Approved :: MIT License',
+        'Operating System :: OS Independent',
+    ],
+    install_requires=requirements,
+    cmdclass={
+        'install': PostInstallCommand,
+    },
 )

From 1da5b530f294b136b7d79d60a953e325e4195395 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Sat, 6 Apr 2019 20:05:31 +0200
Subject: [PATCH 036/496] Preprocessing tests

---
 tests/test_preprocessor.py | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 17ba489..9a83c2c 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -1,15 +1,21 @@
 import pytest
 import numpy as np
-from nautilus_nlp.utils.preprocess import (remove_multiple_spaces_and_strip_text, remove_accents)
+from nautilus_nlp.utils.preprocess import (
+    remove_multiple_spaces_and_strip_text,
+    remove_accents,
+)
 
 
-@pytest.mark.parametrize("input_str, expected_str", [
-    ("hello   world", "hello world"),
-    ("\n   hello world    ", "hello world"),
-    ("----- hello\tworld *****", "hello world"),
-    ("hello-world", "hello-world"),
-    ("hello - world", "hello world")
-])
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("hello   world", "hello world"),
+        ("\n   hello world    ", "hello world"),
+        ("----- hello\tworld *****", "hello world"),
+        ("hello-world", "hello-world"),
+        ("hello - world", "hello world"),
+    ],
+)
 def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
     result = remove_multiple_spaces_and_strip_text(input_str)
     np.testing.assert_string_equal(result, expected_str)

From 9fff71ce6e7cb806743f051e36d980cdc01a871d Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Sat, 6 Apr 2019 20:05:42 +0200
Subject: [PATCH 037/496] Configuration files for documentation

---
 docs/conf.py | 35 ++++++++++++++++-------------------
 1 file changed, 16 insertions(+), 19 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 5d67dbc..43b0a98 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -12,21 +12,21 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
+import os
+import sys
+sys.path.insert(0, os.path.abspath('../'))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'Nautilus_NLP'
+project = 'Nautilus_nlp'
 copyright = '2019, Robin Doumerc'
 author = 'Robin Doumerc'
 
 # The short X.Y version
-version = ''
+version = '0.1.0'
 # The full version, including alpha/beta/rc tags
-release = '0.1.0'
+release = ''
 
 
 # -- General configuration ---------------------------------------------------
@@ -41,18 +41,21 @@
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
+    'sphinx.ext.autosummary',
     'sphinx.ext.todo',
     'sphinx.ext.coverage',
     'sphinx.ext.imgmath',
     'sphinx.ext.ifconfig',
     'sphinx.ext.viewcode',
     'sphinx.ext.githubpages',
+    'sphinx.ext.napoleon'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
+autodoc_default_flags=['members']
+autosummary_generate=True
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 #
@@ -83,8 +86,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
-
+html_theme = 'sphinx_rtd_theme'
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.
@@ -110,7 +112,7 @@
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'Nautilus_NLPdoc'
+htmlhelp_basename = 'Nautilus_nlpdoc'
 
 
 # -- Options for LaTeX output ------------------------------------------------
@@ -137,7 +139,7 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'Nautilus_NLP.tex', 'Nautilus\\_NLP Documentation',
+    (master_doc, 'Nautilus_nlp.tex', 'Nautilus\\_nlp Documentation',
      'Robin Doumerc', 'manual'),
 ]
 
@@ -147,7 +149,7 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'nautilus_nlp', 'Nautilus_NLP Documentation',
+    (master_doc, 'nautilus_nlp', 'Nautilus_nlp Documentation',
      [author], 1)
 ]
 
@@ -158,8 +160,8 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'Nautilus_NLP', 'Nautilus_NLP Documentation',
-     author, 'Nautilus_NLP', 'One line description of project.',
+    (master_doc, 'Nautilus_nlp', 'Nautilus_nlp Documentation',
+     author, 'Nautilus_nlp', 'One line description of project.',
      'Miscellaneous'),
 ]
 
@@ -184,11 +186,6 @@
 
 # -- Extension configuration -------------------------------------------------
 
-# -- Options for intersphinx extension ---------------------------------------
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'https://docs.python.org/': None}
-
 # -- Options for todo extension ----------------------------------------------
 
 # If true, `todo` and `todoList` produce output, else they produce nothing.

From ba5a208e93239c01b18ed9c76d827625469239b1 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Sat, 6 Apr 2019 20:07:01 +0200
Subject: [PATCH 038/496] Docstrings change

---
 nautilus_nlp/doc.py               |  2 ++
 nautilus_nlp/utils/file_loader.py | 20 ++++++++++----------
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/nautilus_nlp/doc.py b/nautilus_nlp/doc.py
index 9a23062..3187bef 100644
--- a/nautilus_nlp/doc.py
+++ b/nautilus_nlp/doc.py
@@ -51,6 +51,7 @@ def __init__(
     def language(self):
         """
         Provided or detected language of a text
+        
         >>> from nautilus_nlp.doc import Doc
         >>> Doc('Test sentence for testing text').language
         'en'
@@ -168,6 +169,7 @@ def entities(self):
     def find_entities(self, model_name=None):
         """
         Extract a list of the named entities in text, with the possibility of using a custom model.
+
         >>> doc = Doc('Sentence for testing Google text')
         >>> doc.find_entities()
         [('Google', 'ORG')]
diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 7baa483..0dfcd48 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -3,7 +3,7 @@
 import glob
 import re
 from os.path import isfile, isdir
-
+import json
 import logging
 
 logging.basicConfig(level=logging.INFO)
@@ -48,11 +48,10 @@ def text_loader(filepath, encoding=None, detectencoding=True):
 
 
 def list_files(filepath:str):
-    '''
-    inputs a filepath. 
+    """  inputs a filepath. 
     Outputs a list of filepath. 
-    Supports regex
-    '''
+    Supports regex"""
+
     if isdir(filepath) and len(re.findall(r"[\w.]$",filepath)):
         filepath=filepath+'/*'
     if filepath.endswith('/'):
@@ -61,11 +60,12 @@ def list_files(filepath:str):
 
 
 def documents_loader(filepath, encoding=None):
-    '''
-    Input a filepath, a filepath with wildcard (eg. *.txt), 
-    or a list of filepaths.
-    Output a string, or a dict of strings.
-    '''
+    
+    """
+        Input a filepath, a filepath with wildcard (eg. *.txt), 
+        or a list of filepaths.
+        Output a string, or a dict of strings.
+    """
     
     if type(filepath) is str:
         documents = list_files(filepath)

From ea90315c6f6172dccefcd3b5650e35c1f1acee93 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sun, 7 Apr 2019 22:00:05 +0200
Subject: [PATCH 039/496] add scikit tfidf object

---
 nautilus_nlp/utils/text_vectorizer.py |  91 +++++++
 notebooks/TF-IDF.ipynb                | 353 ++++++++++++++++++++++++++
 2 files changed, 444 insertions(+)
 create mode 100644 notebooks/TF-IDF.ipynb

diff --git a/nautilus_nlp/utils/text_vectorizer.py b/nautilus_nlp/utils/text_vectorizer.py
index e9cad3d..4eca15f 100644
--- a/nautilus_nlp/utils/text_vectorizer.py
+++ b/nautilus_nlp/utils/text_vectorizer.py
@@ -6,6 +6,97 @@
 import numpy as np
 import math
 
+from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
+
+
+class Tfidf(object):
+    """
+    Inputs a list of string
+    Outputs a tuple with the wordcount vector matrix, and the list of feature name
+    Params:
+        input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
+        lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
+        stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
+        max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
+        dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
+    Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
+    """
+    
+    def __init__(self, **kwargs):
+        self.tfidf_vectorizer = TfidfVectorizer(**kwargs)
+    
+    def _compute_wordcount_vector(self, documents):
+        '''
+        Input a list of documents (string)
+        Output the wordcount vector matrix 
+        '''
+        self.word_count_vector = self.tfidf_vectorizer.fit_transform(documents)
+        return self.word_count_vector
+    
+
+    def _get_features_name(self):
+        self.feature_names = self.tfidf_vectorizer.get_feature_names()
+        return self.feature_names
+
+    
+    def _compute_idf(self):
+        self.tfidf_transformer=TfidfTransformer(smooth_idf=True, use_idf=True)
+        self.tfidf_transformer.fit(self.word_count_vector)
+        return self.word_count_vector
+
+    
+    def compute_tfidf(self, documents):
+        self._compute_wordcount_vector(documents)
+        self._get_features_name()
+        self._compute_idf()
+        return self.word_count_vector
+
+    def _apply_tfidf_to_doc(self, text):
+        '''generate tf-idf for the given document'''
+        return self.tfidf_transformer.transform(self.tfidf_vectorizer.transform([text]))
+    
+    
+    def _sort_coo(self, coo_matrix):
+        '''sort the tf-idf vectors by descending order of scores'''
+        tuples = zip(coo_matrix.col, coo_matrix.data)
+        return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
+    
+    
+    def _extract_topn_from_vector(self, feature_names, sorted_items, topn=10):
+        """get the feature names and tf-idf score of top n items"""
+
+        #use only topn items from vector
+        sorted_items = sorted_items[:topn]
+
+        score_vals = []
+        feature_vals = []
+
+        # word index and corresponding tf-idf score
+        for idx, score in sorted_items:
+
+            #keep track of feature name and its corresponding score
+            score_vals.append(round(score, 3))
+            feature_vals.append(feature_names[idx])
+
+        #create a tuples of feature,score
+        #results = zip(feature_vals,score_vals)
+        results= {}
+        for idx in range(len(feature_vals)):
+            results[feature_vals[idx]]=score_vals[idx]
+
+        return results
+
+    
+    def get_top_tfidf_per_doc(self, text, n=10):
+        '''compute TF-IDF for a given doc, and returns a list of the top N weighted words'''
+        tf_idf_vector= self._apply_tfidf_to_doc(text)
+        sorted_items=self._sort_coo(tf_idf_vector.tocoo())
+        return list(self._extract_topn_from_vector(self.feature_names, sorted_items, n).keys())
+    
+    def get_top_tfidf(self, n=10):
+        '''returns a dict of the top N weighted words, with their weight'''
+        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(self.word_count_vector.tocoo()), topn=n)
+
 
 class Text_Vectorizer:
     def __init__(self, doc_list):
diff --git a/notebooks/TF-IDF.ipynb b/notebooks/TF-IDF.ipynb
new file mode 100644
index 0000000..9e36e8b
--- /dev/null
+++ b/notebooks/TF-IDF.ipynb
@@ -0,0 +1,353 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../data/df_mess_analysed.csv',\n",
+    "                 encoding='utf8',\n",
+    "                 delimiter=';',\n",
+    "                 usecols=['chrn_gaz','stm_clean','offre']\n",
+    "                )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = df[(df.offre == 'DUAL') | (df.offre == 'GAZ')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df.stm_clean = df.stm_clean.apply(lambda row: re.findall(\"\\w+\",row))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>offre</th>\n",
+       "      <th>chrn_gaz</th>\n",
+       "      <th>stm_clean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DUAL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[mettr, jour, adress, palenci, bourg]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DUAL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[souhait, modifi, adress, mail, etre, contacte...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DUAL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[prochain, factur, disponibl, compt]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DUAL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[index, factur, aout, net, superieur, realit, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DUAL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[savoir, factur, annuel, pai]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  offre  chrn_gaz                                          stm_clean\n",
+       "0  DUAL         0              [mettr, jour, adress, palenci, bourg]\n",
+       "1  DUAL         0  [souhait, modifi, adress, mail, etre, contacte...\n",
+       "2  DUAL         0               [prochain, factur, disponibl, compt]\n",
+       "3  DUAL         0  [index, factur, aout, net, superieur, realit, ...\n",
+       "4  DUAL         0                      [savoir, factur, annuel, pai]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "list_of_docs = [' '.join(doc) for doc in df.stm_clean.tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mettr jour adress palenci bourg',\n",
+       " 'souhait modifi adress mail etre contacte servic cel nadiyaa545 gmail prendr cel compt dorenavent',\n",
+       " 'prochain factur disponibl compt']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_docs[:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compute TF-IDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.text_vectorizer import Tfidf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tfidf = Tfidf(max_df=0.95, #ignore terms that have a document frequency strictly higher \n",
+    "              min_df=0.01,\n",
+    "             max_features=10000,\n",
+    "             encoding='utf-8',\n",
+    "             #stop_words=SW,\n",
+    "             norm=None,\n",
+    "             #ngram_range=(1, 2))\n",
+    "             )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
+       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
+       "        lowercase=True, max_df=0.95, max_features=10000, min_df=0.01,\n",
+       "        ngram_range=(1, 1), norm=None, preprocessor=None, smooth_idf=True,\n",
+       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
+       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
+       "        vocabulary=None)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# uses Sci-kit learn TfidfVectorizer()\n",
+    "# You can pass all the arguments supported by sci-kit \n",
+    "# Doc : https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\n",
+    "tfidf.tfidf_vectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<17744x434 sparse matrix of type '<class 'numpy.float64'>'\n",
+       "\twith 300349 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compute the word count vector matrix.\n",
+    "tfidf.compute_tfidf(list_of_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'euro': 96.099,\n",
+       " 'rembours': 116.797,\n",
+       " 'adress': 115.531,\n",
+       " 'compt': 89.231,\n",
+       " 'coupur': 87.871,\n",
+       " 'chequ': 82.334,\n",
+       " 'mois': 81.935}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get highest-weighted words\n",
+    "tfidf.get_top_tfidf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['mettr', 'adress', 'jour']\n",
+      "['cel', 'prendr', 'modifi', 'adress', 'mail', 'servic', 'etre', 'compt', 'souhait']\n",
+      "['disponibl', 'prochain', 'compt', 'factur']\n",
+      "['index', 'modif', 'modifi', 'lign', 'prochain', 'aout', 'electricit', 'pouv', 'prelev', 'factur']\n",
+      "['annuel', 'savoir', 'pai', 'factur']\n",
+      "['disponibl', 'ete', 'concern', 'relev', 'compteur', 'demand']\n",
+      "['acce', 'juin', 'plait', 'avanc', 'envoi', 'compt', 'mois', 'factur']\n",
+      "['conso', 'reel', 'communiqu', 'vient', 'met', 'relev', 'argent', 'conseiller', 'repondu', 'connaitr']\n",
+      "['nest', 'point', 'lappliqu', 'effect', 'sup', 'appliqu', 'vrai', 'toujour', 'arriv', 'souc']\n",
+      "['mensuel', 'pai', 'prelev']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get highest-weighted words per document\n",
+    "for doc in list_of_docs[:10]:\n",
+    "    print(tfidf.get_top_tfidf_per_doc(doc))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 09c5acdf0afe4e10beaa96f83c9daca73950d6a8 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sun, 7 Apr 2019 23:52:42 +0200
Subject: [PATCH 040/496] add pytest cache

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index d9525e2..83457f9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -93,3 +93,6 @@ target/
 *.gz
 *.ftz
 !/nautilus_nlp/data/lang_identification.ftz
+
+# PyTest cache
+.pytest_cache/
\ No newline at end of file

From bd7e07a52ee3259fe8f9d8989ecb920662e94e69 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sun, 7 Apr 2019 23:54:12 +0200
Subject: [PATCH 041/496] enhancements

---
 ...t files loader with encoding handler.ipynb | 40 ++++++++++++++-----
 1 file changed, 31 insertions(+), 9 deletions(-)

diff --git a/notebooks/Text files loader with encoding handler.ipynb b/notebooks/Text files loader with encoding handler.ipynb
index ea034dd..24cc875 100644
--- a/notebooks/Text files loader with encoding handler.ipynb	
+++ b/notebooks/Text files loader with encoding handler.ipynb	
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
        " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +280,7 @@
        " 'Un deuxième exemple de texte en utf-8 cette fois!']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 24,
    "metadata": {
     "collapsed": true
    },
@@ -339,22 +339,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['./Open a file, a list of files and detect encoding.ipynb',\n",
+       "['../tests/testfolder_fileloader/./testdoc_utf8.txt',\n",
+       " '../tests/testfolder_fileloader/./testdoc_latin1.txt']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('../tests/testfolder_fileloader/.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./TF-IDF.ipynb',\n",
        " './Visualization tools.ipynb',\n",
        " './Language_identification.ipynb',\n",
        " './Common Text Processing operations.ipynb',\n",
        " './Sentiment_analysis_FT.ipynb',\n",
+       " './Text files loader with encoding handler.ipynb',\n",
        " './Spacy_model.ipynb',\n",
        " './Sentiment analysis using pre-trained models.ipynb']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +418,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## detect encoding "
+    "## Detect encoding "
    ]
   },
   {

From 05da1f8379fde99d7bc1e451f7b934657d70f24a Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Mon, 8 Apr 2019 00:02:10 +0200
Subject: [PATCH 042/496] add asdict + tests

---
 nautilus_nlp/utils/file_loader.py | 26 ++++++++---
 tests/test_document_loader.py     | 72 +++++++++++++++++++++++++++++++
 tests/test_preprocessor.py        |  1 +
 3 files changed, 94 insertions(+), 5 deletions(-)
 create mode 100644 tests/test_document_loader.py

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 7baa483..562083e 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -38,13 +38,15 @@ def text_loader(filepath, encoding=None, detectencoding=True):
         try:
             return open_textfile(filepath, encoding='utf-8')
         except UnicodeDecodeError:
-            logging.warning('Encoding is not UTF-8.')
+            logging.warning('Encoding for {} is not UTF-8.'.format(filepath))
             if detectencoding is True:
                 logging.warning('Trying to detect encoding for {}'.format(filepath))
                 detected_encoding = detect_encoding(filepath)
-                logging.info('detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(encod=detected_encoding['encoding'],
+                logging.info('{filepath}: detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(filepath=filepath, encod=detected_encoding['encoding'],
                                                                                                         conf_rate=detected_encoding['confidence']))
                 return open_textfile(filepath, encoding=detected_encoding['encoding'])
+            else:
+                raise UnicodeDecodeError('Cannot load document using utf-8. Try to detect encoding using detectencoding=True')
 
 
 def list_files(filepath:str):
@@ -60,11 +62,20 @@ def list_files(filepath:str):
     return[file for file in glob.glob(filepath) if isfile(file)]            
 
 
-def documents_loader(filepath, encoding=None):
+def documents_loader(filepath:str, encoding=None, detectencoding=True, output_as='dict'):
     '''
     Input a filepath, a filepath with wildcard (eg. *.txt), 
     or a list of filepaths.
     Output a string, or a dict of strings.
+    Args:
+        filepath: filepath, a filepath with wildcard (eg. *.txt), 
+            or a list of filepaths.
+        output_as: list or dict. If dict, key will be the filename.
+        encoding: if not specified, will try to detect encoding except if 
+            detectencoding is false. 
+        detectencoding: if True and if encoding is not specified, will try to 
+            detect encoding using chardet. 
+
     '''
     
     if type(filepath) is str:
@@ -77,9 +88,14 @@ def documents_loader(filepath, encoding=None):
         raise IOError('Please enter a valid filepath or a valid list of filepath')
     
     if nb_of_documents == 1:
-        return text_loader(documents[0],encoding=encoding)
+        return text_loader(documents[0],encoding=encoding, detectencoding=detectencoding)
     elif nb_of_documents > 1:
-        return { document : text_loader(documents[0], encoding=encoding) for document in documents}
+        if output_as == 'list':
+            return [text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents]
+        elif output_as == 'dict':
+            return { document : text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents}
+        else:
+            raise ValueError('Enter a valid output format between list or dict')
     else:
         raise IOError('No files detected in {}'.format(filepath))        
 
diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
new file mode 100644
index 0000000..2868be8
--- /dev/null
+++ b/tests/test_document_loader.py
@@ -0,0 +1,72 @@
+import pytest
+import numpy as np
+from nautilus_nlp.utils.file_loader import documents_loader, list_files, detect_encoding
+
+
+testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
+encoded_s = testdoc_latin1.encode('latin-1')
+with open('tests/testfolder_fileloader/testdoc_latin1.txt', 'wb') as f:
+    f.write(encoded_s)
+
+testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
+encoded_s = testdoc_utf8.encode('utf-8')
+with open('tests/testfolder_fileloader/testdoc_utf8.txt', 'wb') as f:
+    f.write(encoded_s)
+
+
+def test_openfile_with_encoding():
+    input_str = "tests/testfolder_fileloader/testdoc_latin1.txt"
+    expected_str = testdoc_latin1
+
+    result = documents_loader(input_str, encoding='latin-1')
+    np.testing.assert_string_equal(result, expected_str)
+
+def test_openfile_utf8():
+    input_str = "tests/testfolder_fileloader/testdoc_utf8.txt"
+    expected_str = testdoc_utf8
+
+    result = documents_loader(input_str)
+    np.testing.assert_string_equal(result, expected_str)
+
+def test_encoding_detection():
+    input_str = "tests/testfolder_fileloader/testdoc_latin1.txt"
+    expected_str = testdoc_latin1
+
+    result = documents_loader(input_str)
+    np.testing.assert_string_equal(result, expected_str)    
+    
+def test_load_several_docs_wildcard():
+    expected = {'tests/testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+                'tests/testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+    result = documents_loader('tests/testfolder_fileloader/*.txt', output_as='dict')
+    np.testing.assert_equal(result, expected)    
+
+def test_load_several_docs_list():
+    expected = {'tests/testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+                'tests/testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+    result = documents_loader(['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt'], output_as='dict')
+    np.testing.assert_equal(result, expected)
+
+
+def test_load_several_docs_output_list():
+    expected = ["J'aime les frites bien grasse étalon châpeau!",
+                'Un deuxième exemple de texte en utf-8 cette fois!']
+    result = documents_loader(['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt'], output_as='list')
+    return len(expected) == len(result) and sorted(expected) == sorted(result)
+
+
+
+@pytest.mark.parametrize("input_filepath", ['tests/testfolder_fileloader/*.txt','tests/testfolder_fileloader/','tests/testfolder_fileloader'])
+def test_list_files(input_filepath):
+    expected = ['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt']
+    result = list_files(input_filepath)
+
+    return len(expected) == len(result) and sorted(expected) == sorted(result)
+
+
+def test_detect_encoding():
+    expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
+    result = detect_encoding('tests/testfolder_fileloader/testdoc_latin1.txt')
+
+    np.testing.assert_equal(result, expected)
+
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 17ba489..8ed937f 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -10,6 +10,7 @@
     ("hello-world", "hello-world"),
     ("hello - world", "hello world")
 ])
+
 def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
     result = remove_multiple_spaces_and_strip_text(input_str)
     np.testing.assert_string_equal(result, expected_str)

From 1cc5b08b8ce5589b87acd1a5860c096c074bb3f0 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Mon, 8 Apr 2019 13:54:51 +0200
Subject: [PATCH 043/496] minor preprocess change

---
 nautilus_nlp/utils/Text_processor.py |  2 +-
 nautilus_nlp/utils/lemmatizer.py     |  4 ++--
 nautilus_nlp/utils/preprocess.py     | 12 +++++++++++-
 3 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/utils/Text_processor.py b/nautilus_nlp/utils/Text_processor.py
index 6301af7..312af0f 100644
--- a/nautilus_nlp/utils/Text_processor.py
+++ b/nautilus_nlp/utils/Text_processor.py
@@ -42,7 +42,7 @@ def __init__(self, lang: str):
     def lemmatize(self, text: str):
         return text
 
-    def stem(self, tokens_list: list, lang: str = lang:
+    def stem(self, tokens_list: list, lang: str = lang):
         return stem_tokens(tokens_list, lang='english')
 
     def remove_stopwords(self, text: str):
diff --git a/nautilus_nlp/utils/lemmatizer.py b/nautilus_nlp/utils/lemmatizer.py
index 2e64b8d..5583388 100644
--- a/nautilus_nlp/utils/lemmatizer.py
+++ b/nautilus_nlp/utils/lemmatizer.py
@@ -7,14 +7,14 @@
 from nltk.corpus import wordnet
 
 try:
-    french_spacy = spacy.load('fr')
+    french_spacy = spacy.load('fr_core_news_sm')
 except OSError:
     raise OSError("""You must install French langage to use SpaCy. 
                     python -m spacy download fr
                     See https://spacy.io/usage/ for details
                 """)
 try:
-    english_spacy = spacy.load('en')
+    english_spacy = spacy.load('en_core_web_sm')
 except OSError:
     raise OSError("""You must install english langage to use SpaCy. 
                     python -m spacy download en
diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index 13f7898..cc3c0ee 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -243,13 +243,23 @@ def remove_accents(text, method="unicode") -> str:
         raise ValueError(msg)
 
 def remove_emoji(word):
+    """
+    Remove emoji from any  str by stripping any unicode in the range of Emoji unicode,
+
+    Args:
+        word (str): raw word
 
+    Returns:
+        str
+
+    """
     RE_EMOJI = re.compile('[\U00010000-\U0010ffff]', flags=re.UNICODE)
     word = RE_EMOJI.sub(r'', word)
     return word
 
 def preprocess_text(
     text,
+    no_emoji=False,
     fix_unicode=False,
     lowercase=False,
     no_urls=False,
@@ -305,7 +315,7 @@ def preprocess_text(
     if no_currency_symbols is True:
         text = replace_currency_symbols(text)
     if no_contractions is True:
-        text = unpack_contractions(text)
+        text = unpack_contractions_from_lang(text)
     if no_accents is True:
         text = remove_accents(text, method="unicode")
     if no_punct is True:

From 2afbfc3b2f315be7e57e73b2753361403aff967c Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Mon, 8 Apr 2019 13:55:12 +0200
Subject: [PATCH 044/496] For merging

---
 ... a list of files and detect encoding.ipynb | 172 +++++++++++-------
 1 file changed, 108 insertions(+), 64 deletions(-)

diff --git a/notebooks/Open a file, a list of files and detect encoding.ipynb b/notebooks/Open a file, a list of files and detect encoding.ipynb
index 8c26c57..b671acf 100644
--- a/notebooks/Open a file, a list of files and detect encoding.ipynb	
+++ b/notebooks/Open a file, a list of files and detect encoding.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true
    },
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -49,31 +49,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING:root:Encoding is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+      "WARNING:root:Encoding is not UTF-8.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "text_loader('somefile.txt')"
+    "_=text_loader('somefile.txt',detectencoding=False)"
    ]
   },
   {
@@ -92,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -101,7 +89,7 @@
        "\"J'aime les frites bien grasse étalon châpeau!\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
@@ -130,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -139,7 +127,7 @@
        "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -157,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
@@ -168,25 +156,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['./somefile.txt',\n",
-       " './Open a file, a list of files and detect encoding.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
+       "['./Sentiment_analysis_FT.ipynb',\n",
+       " './Language_identification_with_FT.ipynb',\n",
+       " './somefile.txt',\n",
+       " './Sentiment analysis using pre-trained models.ipynb',\n",
        " './Language_identification.ipynb',\n",
-       " './someadditionalfile.txt',\n",
-       " './somefile',\n",
-       " './Common Text Processing operations.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
        " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb']"
+       " './Open a file, a list of files and detect encoding.ipynb',\n",
+       " './Untitled1.ipynb',\n",
+       " './cooking.ftz',\n",
+       " './GrandDebat.ipynb',\n",
+       " './Common Text Processing operations.ipynb',\n",
+       " './Untitled.ipynb',\n",
+       " './cooking.bin',\n",
+       " './Visualization tools.ipynb']"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,22 +189,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['./Open a file, a list of files and detect encoding.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
+       "['./Sentiment_analysis_FT.ipynb',\n",
+       " './Language_identification_with_FT.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb',\n",
        " './Language_identification.ipynb',\n",
-       " './Common Text Processing operations.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
        " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb']"
+       " './Open a file, a list of files and detect encoding.ipynb',\n",
+       " './Untitled1.ipynb',\n",
+       " './GrandDebat.ipynb',\n",
+       " './Common Text Processing operations.ipynb',\n",
+       " './Untitled.ipynb',\n",
+       " './Visualization tools.ipynb']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {
     "scrolled": true
    },
@@ -231,23 +227,27 @@
     {
      "data": {
       "text/plain": [
-       "['/Users/hugo/Documents/NAUTILUS/nautilus-nlp/LICENSE',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/requirements.txt',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/Makefile',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/README.md',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/setup.py',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/tox.ini',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/test_environment.py']"
+       "['/home/robin/nautilus_nlp/requirements.txt',\n",
+       " '/home/robin/nautilus_nlp/test_environment.py',\n",
+       " '/home/robin/nautilus_nlp/Makefile',\n",
+       " '/home/robin/nautilus_nlp/setup.py',\n",
+       " '/home/robin/nautilus_nlp/download_spacy_models.sh',\n",
+       " '/home/robin/nautilus_nlp/Dockerfile',\n",
+       " '/home/robin/nautilus_nlp/LICENSE',\n",
+       " '/home/robin/nautilus_nlp/README.md',\n",
+       " '/home/robin/nautilus_nlp/tox.ini',\n",
+       " '/home/robin/nautilus_nlp/Untitled.ipynb',\n",
+       " '/home/robin/nautilus_nlp/VERSION']"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# only files will be printed, not folders\n",
-    "list_files('/Users/hugo/Documents/NAUTILUS/nautilus-nlp/')"
+    "list_files('/home/robin/nautilus_nlp/*')"
    ]
   },
   {
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {
     "collapsed": true
    },
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +297,17 @@
       "WARNING:root:Trying to detect encoding for somefile.txt\n",
       "INFO:root:detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "_=documents_loader('*.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/plain": [
@@ -305,18 +315,18 @@
        " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "documents_loader('*.txt')"
+    "_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -338,7 +348,7 @@
        " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -349,31 +359,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Un deuxième exemple de texte en utf-8 cette fois!'"
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "documents_loader('someadditionalfile.txt',encoding='utf-8')"
+    "json.loads(documents_loader('test.json',encoding='utf-8'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "with open('test.json','r',encoding='utf-8') as fp:\n",
+    "   __= json.load(fp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': \"J'aime les frites bien grasse étalon châpeau!\"}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
@@ -393,7 +437,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,

From cee696597a78b61eeaa700de66a67b706f27f682 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Tue, 9 Apr 2019 10:14:58 +0200
Subject: [PATCH 045/496] Update readme organisation

---
 README.md | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 32e323a..a05d090 100644
--- a/README.md
+++ b/README.md
@@ -62,7 +62,7 @@ Project Organization
     ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
     │                         the creator's initials, and a short `-` delimited description, e.g.
     │                         `1.0-jqp-initial-data-exploration`.
-    │
+    ├── tests              <- Where the tests lives
     ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
     │
     ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
@@ -76,17 +76,12 @@ Project Organization
     │   ├── __init__.py    <- Makes nautilus_nlp a Python module
     │   │
     │   ├── data           <- Scripts to download or generate data
-    │   │   └── make_dataset.py
     │   │
-    │   ├── features       <- Scripts to turn raw data into features for modeling
-    │   │   └── build_features.py
+    │   ├── utils       <- Scripts to turn raw data into features for modeling
     │   │
     │   ├── models         <- Scripts to train models and then use trained models to make
     │   │   │                 predictions
-    │   │   ├── predict_model.py
-    │   │   └── train_model.py
     │   │
     │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
-    │       └── visualize.py
     │
-    └── tox.ini            <- tox file with settings for running tox; see tox.testrun.org
\ No newline at end of file
+    └── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

From f5b51ea591f06a975ac247f4b43d3a05255a5ee6 Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Wed, 10 Apr 2019 13:47:14 +0200
Subject: [PATCH 046/496] functions topic modeling gensim ldamodel added

---
 nautilus/bin/activate                 |  78 +++++++++++++
 nautilus/bin/activate.csh             |  42 +++++++
 nautilus/bin/activate.fish            | 101 +++++++++++++++++
 nautilus/bin/activate.ps1             |  60 ++++++++++
 nautilus/bin/activate_this.py         |  46 ++++++++
 nautilus/bin/easy_install             |  10 ++
 nautilus/bin/easy_install-2.7         |  10 ++
 nautilus/bin/epylint                  |  10 ++
 nautilus/bin/isort                    |  10 ++
 nautilus/bin/pip                      |  10 ++
 nautilus/bin/pip2                     |  10 ++
 nautilus/bin/pip2.7                   |  10 ++
 nautilus/bin/pylint                   |  10 ++
 nautilus/bin/pyreverse                |  10 ++
 nautilus/bin/python                   | Bin 0 -> 8464 bytes
 nautilus/bin/python-config            |  78 +++++++++++++
 nautilus/bin/python2                  |   1 +
 nautilus/bin/python2.7                |   1 +
 nautilus/bin/symilar                  |  10 ++
 nautilus/bin/wheel                    |  10 ++
 nautilus/include/python2.7            |   1 +
 nautilus_nlp/models/topic_modeling.py | 153 ++++++++++++++++++++++++++
 requirements.txt                      |   8 +-
 src/french-lefff-lemmatizer           |   1 +
 24 files changed, 679 insertions(+), 1 deletion(-)
 create mode 100644 nautilus/bin/activate
 create mode 100644 nautilus/bin/activate.csh
 create mode 100644 nautilus/bin/activate.fish
 create mode 100644 nautilus/bin/activate.ps1
 create mode 100644 nautilus/bin/activate_this.py
 create mode 100755 nautilus/bin/easy_install
 create mode 100755 nautilus/bin/easy_install-2.7
 create mode 100755 nautilus/bin/epylint
 create mode 100755 nautilus/bin/isort
 create mode 100755 nautilus/bin/pip
 create mode 100755 nautilus/bin/pip2
 create mode 100755 nautilus/bin/pip2.7
 create mode 100755 nautilus/bin/pylint
 create mode 100755 nautilus/bin/pyreverse
 create mode 100755 nautilus/bin/python
 create mode 100755 nautilus/bin/python-config
 create mode 120000 nautilus/bin/python2
 create mode 120000 nautilus/bin/python2.7
 create mode 100755 nautilus/bin/symilar
 create mode 100755 nautilus/bin/wheel
 create mode 120000 nautilus/include/python2.7
 create mode 100644 nautilus_nlp/models/topic_modeling.py
 create mode 160000 src/french-lefff-lemmatizer

diff --git a/nautilus/bin/activate b/nautilus/bin/activate
new file mode 100644
index 0000000..70dac7d
--- /dev/null
+++ b/nautilus/bin/activate
@@ -0,0 +1,78 @@
+# This file must be used with "source bin/activate" *from bash*
+# you cannot run it directly
+
+deactivate () {
+    unset -f pydoc >/dev/null 2>&1
+
+    # reset old environment variables
+    # ! [ -z ${VAR+_} ] returns true if VAR is declared at all
+    if ! [ -z "${_OLD_VIRTUAL_PATH+_}" ] ; then
+        PATH="$_OLD_VIRTUAL_PATH"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if ! [ -z "${_OLD_VIRTUAL_PYTHONHOME+_}" ] ; then
+        PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # This should detect bash and zsh, which have a hash command that must
+    # be called to get it to forget past commands.  Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
+        hash -r 2>/dev/null
+    fi
+
+    if ! [ -z "${_OLD_VIRTUAL_PS1+_}" ] ; then
+        PS1="$_OLD_VIRTUAL_PS1"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    if [ ! "${1-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+VIRTUAL_ENV="/Users/williamjaubert/nautilus_nlp/nautilus"
+export VIRTUAL_ENV
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/bin:$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+if ! [ -z "${PYTHONHOME+_}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT-}" ] ; then
+    _OLD_VIRTUAL_PS1="${PS1-}"
+    if [ "x" != x ] ; then
+        PS1="${PS1-}"
+    else
+        PS1="(`basename \"$VIRTUAL_ENV\"`) ${PS1-}"
+    fi
+    export PS1
+fi
+
+# Make sure to unalias pydoc if it's already there
+alias pydoc 2>/dev/null >/dev/null && unalias pydoc || true
+
+pydoc () {
+    python -m pydoc "$@"
+}
+
+# This should detect bash and zsh, which have a hash command that must
+# be called to get it to forget past commands.  Without forgetting
+# past commands the $PATH changes we made may not be respected
+if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
+    hash -r 2>/dev/null
+fi
diff --git a/nautilus/bin/activate.csh b/nautilus/bin/activate.csh
new file mode 100644
index 0000000..2c03eaa
--- /dev/null
+++ b/nautilus/bin/activate.csh
@@ -0,0 +1,42 @@
+# This file must be used with "source bin/activate.csh" *from csh*.
+# You cannot run it directly.
+# Created by Davide Di Blasi <davidedb@gmail.com>.
+
+set newline='\
+'
+
+alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH:q" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT:q" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate && unalias pydoc'
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+setenv VIRTUAL_ENV "/Users/williamjaubert/nautilus_nlp/nautilus"
+
+set _OLD_VIRTUAL_PATH="$PATH:q"
+setenv PATH "$VIRTUAL_ENV:q/bin:$PATH:q"
+
+
+
+if ("" != "") then
+    set env_name = ""
+else
+    set env_name = "$VIRTUAL_ENV:t:q"
+endif
+
+# Could be in a non-interactive environment,
+# in which case, $prompt is undefined and we wouldn't
+# care about the prompt anyway.
+if ( $?prompt ) then
+    set _OLD_VIRTUAL_PROMPT="$prompt:q"
+if ( "$prompt:q" =~ *"$newline:q"* ) then
+    :
+else
+    set prompt = "[$env_name:q] $prompt:q"
+endif
+endif
+
+unset env_name
+
+alias pydoc python -m pydoc
+
+rehash
diff --git a/nautilus/bin/activate.fish b/nautilus/bin/activate.fish
new file mode 100644
index 0000000..699e0fd
--- /dev/null
+++ b/nautilus/bin/activate.fish
@@ -0,0 +1,101 @@
+# This file must be used using `source bin/activate.fish` *within a running fish ( http://fishshell.com ) session*.
+# Do not run it directly.
+
+function _bashify_path -d "Converts a fish path to something bash can recognize"
+    set fishy_path $argv
+    set bashy_path $fishy_path[1]
+    for path_part in $fishy_path[2..-1]
+        set bashy_path "$bashy_path:$path_part"
+    end
+    echo $bashy_path
+end
+
+function _fishify_path -d "Converts a bash path to something fish can recognize"
+    echo $argv | tr ':' '\n'
+end
+
+function deactivate -d 'Exit virtualenv mode and return to the normal environment.'
+    # reset old environment variables
+    if test -n "$_OLD_VIRTUAL_PATH"
+        # https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
+        if test (echo $FISH_VERSION | tr "." "\n")[1] -lt 3
+            set -gx PATH (_fishify_path $_OLD_VIRTUAL_PATH)
+        else
+            set -gx PATH $_OLD_VIRTUAL_PATH
+        end
+        set -e _OLD_VIRTUAL_PATH
+    end
+
+    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
+        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
+        set -e _OLD_VIRTUAL_PYTHONHOME
+    end
+
+    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
+        # Set an empty local `$fish_function_path` to allow the removal of `fish_prompt` using `functions -e`.
+        set -l fish_function_path
+
+        # Erase virtualenv's `fish_prompt` and restore the original.
+        functions -e fish_prompt
+        functions -c _old_fish_prompt fish_prompt
+        functions -e _old_fish_prompt
+        set -e _OLD_FISH_PROMPT_OVERRIDE
+    end
+
+    set -e VIRTUAL_ENV
+
+    if test "$argv[1]" != 'nondestructive'
+        # Self-destruct!
+        functions -e pydoc
+        functions -e deactivate
+        functions -e _bashify_path
+        functions -e _fishify_path
+    end
+end
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+set -gx VIRTUAL_ENV "/Users/williamjaubert/nautilus_nlp/nautilus"
+
+# https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
+if test (echo $FISH_VERSION | tr "." "\n")[1] -lt 3
+   set -gx _OLD_VIRTUAL_PATH (_bashify_path $PATH)
+else
+    set -gx _OLD_VIRTUAL_PATH $PATH
+end
+set -gx PATH "$VIRTUAL_ENV/bin" $PATH
+
+# Unset `$PYTHONHOME` if set.
+if set -q PYTHONHOME
+    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
+    set -e PYTHONHOME
+end
+
+function pydoc
+    python -m pydoc $argv
+end
+
+if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
+    # Copy the current `fish_prompt` function as `_old_fish_prompt`.
+    functions -c fish_prompt _old_fish_prompt
+
+    function fish_prompt
+        # Save the current $status, for fish_prompts that display it.
+        set -l old_status $status
+
+        # Prompt override provided?
+        # If not, just prepend the environment name.
+        if test -n ""
+            printf '%s%s' "" (set_color normal)
+        else
+            printf '%s(%s) ' (set_color normal) (basename "$VIRTUAL_ENV")
+        end
+
+        # Restore the original $status
+        echo "exit $old_status" | source
+        _old_fish_prompt
+    end
+
+    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
+end
diff --git a/nautilus/bin/activate.ps1 b/nautilus/bin/activate.ps1
new file mode 100644
index 0000000..6d8ae2a
--- /dev/null
+++ b/nautilus/bin/activate.ps1
@@ -0,0 +1,60 @@
+# This file must be dot sourced from PoSh; you cannot run it directly. Do this: . ./activate.ps1
+
+$script:THIS_PATH = $myinvocation.mycommand.path
+$script:BASE_DIR = split-path (resolve-path "$THIS_PATH/..") -Parent
+
+function global:deactivate([switch] $NonDestructive)
+{
+    if (test-path variable:_OLD_VIRTUAL_PATH)
+    {
+        $env:PATH = $variable:_OLD_VIRTUAL_PATH
+        remove-variable "_OLD_VIRTUAL_PATH" -scope global
+    }
+
+    if (test-path function:_old_virtual_prompt)
+    {
+        $function:prompt = $function:_old_virtual_prompt
+        remove-item function:\_old_virtual_prompt
+    }
+
+    if ($env:VIRTUAL_ENV)
+    {
+        $old_env = split-path $env:VIRTUAL_ENV -leaf
+        remove-item env:VIRTUAL_ENV -erroraction silentlycontinue
+    }
+
+    if (!$NonDestructive)
+    {
+        # Self destruct!
+        remove-item function:deactivate
+        remove-item function:pydoc
+    }
+}
+
+function global:pydoc
+{
+    python -m pydoc $args
+}
+
+# unset irrelevant variables
+deactivate -nondestructive
+
+$VIRTUAL_ENV = $BASE_DIR
+$env:VIRTUAL_ENV = $VIRTUAL_ENV
+
+$global:_OLD_VIRTUAL_PATH = $env:PATH
+$env:PATH = "$env:VIRTUAL_ENV/bin:" + $env:PATH
+if (!$env:VIRTUAL_ENV_DISABLE_PROMPT)
+{
+    function global:_old_virtual_prompt
+    {
+        ""
+    }
+    $function:_old_virtual_prompt = $function:prompt
+    function global:prompt
+    {
+        # Add a prefix to the current prompt, but don't discard it.
+        write-host "($( split-path $env:VIRTUAL_ENV -leaf )) " -nonewline
+        & $function:_old_virtual_prompt
+    }
+}
diff --git a/nautilus/bin/activate_this.py b/nautilus/bin/activate_this.py
new file mode 100644
index 0000000..59b5d72
--- /dev/null
+++ b/nautilus/bin/activate_this.py
@@ -0,0 +1,46 @@
+"""Activate virtualenv for current interpreter:
+
+Use exec(open(this_file).read(), {'__file__': this_file}).
+
+This can be used when you must use an existing Python interpreter, not the virtualenv bin/python.
+"""
+import os
+import site
+import sys
+
+try:
+    __file__
+except NameError:
+    raise AssertionError("You must use exec(open(this_file).read(), {'__file__': this_file}))")
+
+# prepend bin to PATH (this file is inside the bin directory)
+bin_dir = os.path.dirname(os.path.abspath(__file__))
+os.environ["PATH"] = os.pathsep.join([bin_dir] + os.environ.get("PATH", "").split(os.pathsep))
+
+base = os.path.dirname(bin_dir)
+
+# virtual env is right above bin directory
+os.environ["VIRTUAL_ENV"] = base
+
+# add the virtual environments site-package to the host python import mechanism
+IS_PYPY = hasattr(sys, "pypy_version_info")
+IS_JYTHON = sys.platform.startswith("java")
+if IS_JYTHON:
+    site_packages = os.path.join(base, "Lib", "site-packages")
+elif IS_PYPY:
+    site_packages = os.path.join(base, "site-packages")
+else:
+    IS_WIN = sys.platform == "win32"
+    if IS_WIN:
+        site_packages = os.path.join(base, "Lib", "site-packages")
+    else:
+        site_packages = os.path.join(base, "lib", "python{}".format(sys.version[:3]), "site-packages")
+
+prev = set(sys.path)
+site.addsitedir(site_packages)
+sys.real_prefix = sys.prefix
+sys.prefix = base
+
+# Move the added items to the front of the path, in place
+new = list(sys.path)
+sys.path[:] = [i for i in new if i not in prev] + [i for i in new if i in prev]
diff --git a/nautilus/bin/easy_install b/nautilus/bin/easy_install
new file mode 100755
index 0000000..d083bb5
--- /dev/null
+++ b/nautilus/bin/easy_install
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from setuptools.command.easy_install import main
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())
diff --git a/nautilus/bin/easy_install-2.7 b/nautilus/bin/easy_install-2.7
new file mode 100755
index 0000000..d083bb5
--- /dev/null
+++ b/nautilus/bin/easy_install-2.7
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from setuptools.command.easy_install import main
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())
diff --git a/nautilus/bin/epylint b/nautilus/bin/epylint
new file mode 100755
index 0000000..8fa16b4
--- /dev/null
+++ b/nautilus/bin/epylint
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from pylint import run_epylint
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(run_epylint())
diff --git a/nautilus/bin/isort b/nautilus/bin/isort
new file mode 100755
index 0000000..31b0716
--- /dev/null
+++ b/nautilus/bin/isort
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from isort.main import main
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())
diff --git a/nautilus/bin/pip b/nautilus/bin/pip
new file mode 100755
index 0000000..08299c7
--- /dev/null
+++ b/nautilus/bin/pip
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from pip._internal import main
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())
diff --git a/nautilus/bin/pip2 b/nautilus/bin/pip2
new file mode 100755
index 0000000..08299c7
--- /dev/null
+++ b/nautilus/bin/pip2
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from pip._internal import main
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())
diff --git a/nautilus/bin/pip2.7 b/nautilus/bin/pip2.7
new file mode 100755
index 0000000..08299c7
--- /dev/null
+++ b/nautilus/bin/pip2.7
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from pip._internal import main
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())
diff --git a/nautilus/bin/pylint b/nautilus/bin/pylint
new file mode 100755
index 0000000..9635a6f
--- /dev/null
+++ b/nautilus/bin/pylint
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from pylint import run_pylint
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(run_pylint())
diff --git a/nautilus/bin/pyreverse b/nautilus/bin/pyreverse
new file mode 100755
index 0000000..6af5484
--- /dev/null
+++ b/nautilus/bin/pyreverse
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from pylint import run_pyreverse
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(run_pyreverse())
diff --git a/nautilus/bin/python b/nautilus/bin/python
new file mode 100755
index 0000000000000000000000000000000000000000..fddb46f7919ca3729d7a3a3aff690d36c96049f6
GIT binary patch
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diff --git a/nautilus/bin/python-config b/nautilus/bin/python-config
new file mode 100755
index 0000000..5f285ef
--- /dev/null
+++ b/nautilus/bin/python-config
@@ -0,0 +1,78 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
+
+import sys
+import getopt
+import sysconfig
+
+valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags',
+              'ldflags', 'help']
+
+if sys.version_info >= (3, 2):
+    valid_opts.insert(-1, 'extension-suffix')
+    valid_opts.append('abiflags')
+if sys.version_info >= (3, 3):
+    valid_opts.append('configdir')
+
+
+def exit_with_usage(code=1):
+    sys.stderr.write("Usage: {0} [{1}]\n".format(
+        sys.argv[0], '|'.join('--'+opt for opt in valid_opts)))
+    sys.exit(code)
+
+try:
+    opts, args = getopt.getopt(sys.argv[1:], '', valid_opts)
+except getopt.error:
+    exit_with_usage()
+
+if not opts:
+    exit_with_usage()
+
+pyver = sysconfig.get_config_var('VERSION')
+getvar = sysconfig.get_config_var
+
+opt_flags = [flag for (flag, val) in opts]
+
+if '--help' in opt_flags:
+    exit_with_usage(code=0)
+
+for opt in opt_flags:
+    if opt == '--prefix':
+        print(sysconfig.get_config_var('prefix'))
+
+    elif opt == '--exec-prefix':
+        print(sysconfig.get_config_var('exec_prefix'))
+
+    elif opt in ('--includes', '--cflags'):
+        flags = ['-I' + sysconfig.get_path('include'),
+                 '-I' + sysconfig.get_path('platinclude')]
+        if opt == '--cflags':
+            flags.extend(getvar('CFLAGS').split())
+        print(' '.join(flags))
+
+    elif opt in ('--libs', '--ldflags'):
+        abiflags = getattr(sys, 'abiflags', '')
+        libs = ['-lpython' + pyver + abiflags]
+        libs += getvar('LIBS').split()
+        libs += getvar('SYSLIBS').split()
+        # add the prefix/lib/pythonX.Y/config dir, but only if there is no
+        # shared library in prefix/lib/.
+        if opt == '--ldflags':
+            if not getvar('Py_ENABLE_SHARED'):
+                libs.insert(0, '-L' + getvar('LIBPL'))
+            if not getvar('PYTHONFRAMEWORK'):
+                libs.extend(getvar('LINKFORSHARED').split())
+        print(' '.join(libs))
+
+    elif opt == '--extension-suffix':
+        ext_suffix = sysconfig.get_config_var('EXT_SUFFIX')
+        if ext_suffix is None:
+            ext_suffix = sysconfig.get_config_var('SO')
+        print(ext_suffix)
+
+    elif opt == '--abiflags':
+        if not getattr(sys, 'abiflags', None):
+            exit_with_usage()
+        print(sys.abiflags)
+
+    elif opt == '--configdir':
+        print(sysconfig.get_config_var('LIBPL'))
diff --git a/nautilus/bin/python2 b/nautilus/bin/python2
new file mode 120000
index 0000000..d8654aa
--- /dev/null
+++ b/nautilus/bin/python2
@@ -0,0 +1 @@
+python
\ No newline at end of file
diff --git a/nautilus/bin/python2.7 b/nautilus/bin/python2.7
new file mode 120000
index 0000000..d8654aa
--- /dev/null
+++ b/nautilus/bin/python2.7
@@ -0,0 +1 @@
+python
\ No newline at end of file
diff --git a/nautilus/bin/symilar b/nautilus/bin/symilar
new file mode 100755
index 0000000..582abdf
--- /dev/null
+++ b/nautilus/bin/symilar
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from pylint import run_symilar
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(run_symilar())
diff --git a/nautilus/bin/wheel b/nautilus/bin/wheel
new file mode 100755
index 0000000..bd14437
--- /dev/null
+++ b/nautilus/bin/wheel
@@ -0,0 +1,10 @@
+#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
+# -*- coding: utf-8 -*-
+import re
+import sys
+
+from wheel.cli import main
+
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())
diff --git a/nautilus/include/python2.7 b/nautilus/include/python2.7
new file mode 120000
index 0000000..de0d082
--- /dev/null
+++ b/nautilus/include/python2.7
@@ -0,0 +1 @@
+/Users/williamjaubert/anaconda2/include/python2.7
\ No newline at end of file
diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
new file mode 100644
index 0000000..f5592d4
--- /dev/null
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -0,0 +1,153 @@
+import gensim
+import logging
+import os
+import pyLDAvis
+import pyLDAvis.gensim 
+
+from IPython.display import HTML
+
+logging.getLogger("gensim").setLevel(logging.WARNING)
+
+
+def create_dictionary(data):
+    
+    """ Create a dictionary(id2word) containing the number of times a word appears 
+    in the dataset set: one of the two main inputs to the LDA topic model with the corpus
+    
+    Parameters
+    ----------
+    data : list of list of tokens
+       
+    Returns
+    -------
+    list of list of tuples
+    """
+    return gensim.corpora.Dictionary(data)
+
+def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
+    """ Remove very rare and very common words
+
+    Parameters
+    ----------
+    dictionary: dictionary containing the number of times a word appears in the dataset set
+    no_below : int, optional
+    Keep tokens which are contained in at least `no_below` documents.
+    no_above : float, optional
+    Keep tokens which are contained in no more than `no_above` documents
+    (fraction of total corpus size, not an absolute number).
+
+    (Add to docstring) + other func
+    """
+    return dictionary.filter_extremes(no_below=no_below, no_above=no_above, **kwargs)
+
+
+def create_bow_corpus(data, dictionary):
+    
+    """ Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
+        The produced corpus is a mapping of (word_id, word_frequency).
+    Parameters
+    ----------
+    data : list of list of tokens
+       
+    Returns
+    -------
+    list of list of tuples
+    """
+    texts = data
+    corpus = [dictionary.doc2bow(text) for text in texts]
+    return corpus
+
+### Gensim LdaModel
+
+def train_lda_model(bow_corpus, dictionary, num_topics, **kwargs):
+    """ Train the model on the corpus
+      
+    Parameters
+    ----------
+    corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).$
+    dictionary: corpora.Dictionary. Mapping from word IDs to words
+    num_topics: int
+    
+    Returns
+    -------
+    gensim.ldamodel
+    """
+    model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0)
+    return model
+
+
+def save_model(model, MODELNAME):
+    """ Save the model that has been trained
+        
+        Parameters
+        ----------
+        model: ldamodel
+        MODELNAME: str
+    """
+    return model.save(MODELNAME)
+
+
+def load_model(model_path,model_name):
+    '''
+    model_path: path where the model has been saved
+    model_name: name of the saved model
+    '''
+    ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
+    return ldamodel
+
+def fit_data(model, bow):
+    """Test the model on new, unseen documents"""
+    return model[bow]
+
+### Gensim LdaMallet
+
+def load_mallet_model(model_path, model_name, model_prefix=None):
+    '''
+    model_prefix: prefix used while saving the model
+    model_name: name of the saved model
+    '''
+    ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
+    if model_prefix is not None:
+        ldamodel.prefix = model_path+'/'+ model_prefix
+    return ldamodel
+
+def train_mallet_model(mallet_path, bow_corpus, dictionary, num_topics, **kwargs):
+    """ Train the model on the corpus
+      
+    Parameters
+    ----------
+    mallet_path: path to mallet files
+    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).$
+    dictionary: corpora.Dictionary. Mapping from word IDs to words
+    num_topics: int
+    
+    Returns
+    -------
+    gensim.ldamodel
+    """
+    model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='nautil')
+    return model
+
+
+# Visualization
+
+
+def visualize_topics(model, bow_corpus, dictionary):
+    """ Visualize the topics-keywords with the pyLDAvis interactive chart.
+        (Work well in notebook)
+    """
+    return pyLDAvis.gensim.prepare(model, bow_corpus, dictionary)
+
+
+def save_pyldavis(pyldavis, vis_path, vis_name):
+    """ Save the pyldavis interactive chart
+    pyldavis: pyLDAvis._prepare.PreparedData
+    vis_path: str
+    vis_path: str
+    """ 
+    return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name, '.html'))
+
+
+def show_pyldavis(vis_path, vis_name):
+    return HTML(filename=os.path.join(vis_path, vis_name, '.html'))
+
diff --git a/requirements.txt b/requirements.txt
index 869399b..8f02cc3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
 # local package
 -e .
+os
 
 # external requirements
 click
@@ -12,8 +13,13 @@ pillow
 pytest
 
 #library requirements
-spacy==2.1
+pyLDAvis==2.1.2
+gensim==3.7.1
+sacremoses==0.0.13
+stop-words==2018.7.23
+spacy==2.1.3
 scikit-learn>=0.17.0
+ftfy==5.5.1
 ftfy>=4.2.0,<5.0.0
 wordcloud>=1.5.0
 matplotlib>=3.0.3
diff --git a/src/french-lefff-lemmatizer b/src/french-lefff-lemmatizer
new file mode 160000
index 0000000..ba1ef2b
--- /dev/null
+++ b/src/french-lefff-lemmatizer
@@ -0,0 +1 @@
+Subproject commit ba1ef2bc67aa3753d127ba978a255081120449c2

From ce7bb39ddc887e0f92866640cb0f4f9a66a8be1e Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 10 Apr 2019 15:18:02 +0200
Subject: [PATCH 047/496] add static versionning

---
 VERSION | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 VERSION

diff --git a/VERSION b/VERSION
new file mode 100644
index 0000000..6da28dd
--- /dev/null
+++ b/VERSION
@@ -0,0 +1 @@
+0.1.1
\ No newline at end of file

From b8132155de5eff0ef646ed677f58be4075c90972 Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Wed, 10 Apr 2019 16:16:38 +0200
Subject: [PATCH 048/496] Function show_dominant topic added

---
 nautilus_nlp/models/topic_modeling.py | 39 ++++++++++++++++++++++-----
 1 file changed, 32 insertions(+), 7 deletions(-)

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index f5592d4..c918435 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -3,6 +3,7 @@
 import os
 import pyLDAvis
 import pyLDAvis.gensim 
+pyLDAvis.enable_notebook()
 
 from IPython.display import HTML
 
@@ -64,7 +65,7 @@ def train_lda_model(bow_corpus, dictionary, num_topics, **kwargs):
       
     Parameters
     ----------
-    corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).$
+    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).$
     dictionary: corpora.Dictionary. Mapping from word IDs to words
     num_topics: int
     
@@ -72,11 +73,11 @@ def train_lda_model(bow_corpus, dictionary, num_topics, **kwargs):
     -------
     gensim.ldamodel
     """
-    model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0)
+    model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0, **kwargs)
     return model
 
 
-def save_model(model, MODELNAME):
+def save_model(model, model_path, model_name):
     """ Save the model that has been trained
         
         Parameters
@@ -84,7 +85,7 @@ def save_model(model, MODELNAME):
         model: ldamodel
         MODELNAME: str
     """
-    return model.save(MODELNAME)
+    return model.save(os.path.join(model_path,model_name))
 
 
 def load_model(model_path,model_name):
@@ -138,16 +139,40 @@ def visualize_topics(model, bow_corpus, dictionary):
     """
     return pyLDAvis.gensim.prepare(model, bow_corpus, dictionary)
 
-
 def save_pyldavis(pyldavis, vis_path, vis_name):
     """ Save the pyldavis interactive chart
     pyldavis: pyLDAvis._prepare.PreparedData
     vis_path: str
     vis_path: str
     """ 
-    return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name, '.html'))
+    return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name + '{}'.format('.html')))
 
 
 def show_pyldavis(vis_path, vis_name):
-    return HTML(filename=os.path.join(vis_path, vis_name, '.html'))
+    """ Display the HTML of the saved pyldavis interactive chart
+    vis_path: str
+    vis_path: str
+    """
+    return HTML(filename=os.path.join(vis_path, vis_name + '{}'.format('.html')))
+
+def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
+    """ Print the dominant topics in the document, its score and the topics' top keywords.
+    
+    Parameters
+    ----------
 
+    gensim.ldamodel
+    model: ldamodel
+    bow_corpus: iterable of list of tokens.
+    topic_number: int. Pick the number of topics displayed
+    topn: int. Number of topics' top keyword displayed
+
+    """
+    i = 0
+    for index, score in sorted(model[bow_corpus], key=lambda tup: -1*tup[1]): 
+        weight = model.show_topic(index, topn=topn)
+        keywords = [i[0] for i in weight]
+        print("Score: {}\t Topic: {}".format(score, keywords))
+        i +=1
+        if i == topic_number:
+            break
\ No newline at end of file

From 5c8ef424504890ae9307374ca913fa71f028c185 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 10 Apr 2019 18:19:30 +0200
Subject: [PATCH 049/496] fix bad encoding example #58

---
 .../Common Text Processing operations.ipynb   | 305 ++++++++++++++++--
 1 file changed, 282 insertions(+), 23 deletions(-)

diff --git a/notebooks/Common Text Processing operations.ipynb b/notebooks/Common Text Processing operations.ipynb
index b389145..10c6e07 100644
--- a/notebooks/Common Text Processing operations.ipynb	
+++ b/notebooks/Common Text Processing operations.ipynb	
@@ -16,25 +16,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from nautilus_nlp.utils.tokenizer import tokenize, untokenize"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,50 +35,243 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 23.5 ms, sys: 3.68 ms, total: 27.2 ms\n",
-      "Wall time: 30.2 ms\n"
+      "CPU times: user 4.76 ms, sys: 31 µs, total: 4.79 ms\n",
+      "Wall time: 4.42 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[Ceci, est, un, texte, français, ,, j', adore, 1, !]"
+       "['Les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'tels',\n",
+       " 'Google',\n",
+       " ',',\n",
+       " 'Exalead',\n",
+       " 'ou',\n",
+       " 'Yahoo',\n",
+       " '!',\n",
+       " 'sont',\n",
+       " 'des',\n",
+       " 'applications',\n",
+       " 'très',\n",
+       " 'connues',\n",
+       " 'de',\n",
+       " 'fouille',\n",
+       " 'de',\n",
+       " 'textes',\n",
+       " 'sur',\n",
+       " 'de',\n",
+       " 'grandes',\n",
+       " 'masses',\n",
+       " 'de',\n",
+       " 'données',\n",
+       " '.',\n",
+       " 'Cependant',\n",
+       " ',',\n",
+       " 'les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'ne',\n",
+       " 'se',\n",
+       " 'basent',\n",
+       " 'pas',\n",
+       " 'uniquement',\n",
+       " 'sur',\n",
+       " 'le',\n",
+       " 'texte',\n",
+       " 'pour',\n",
+       " \"l'\",\n",
+       " 'indexer',\n",
+       " ',',\n",
+       " 'mais',\n",
+       " 'également',\n",
+       " 'sur',\n",
+       " 'la',\n",
+       " 'façon',\n",
+       " 'dont',\n",
+       " 'les',\n",
+       " 'pages',\n",
+       " 'sont',\n",
+       " 'mises',\n",
+       " 'en',\n",
+       " 'valeur',\n",
+       " 'les',\n",
+       " 'unes',\n",
+       " 'par',\n",
+       " 'rapport',\n",
+       " 'aux',\n",
+       " 'autres',\n",
+       " '.',\n",
+       " \"L'\",\n",
+       " 'algorithme',\n",
+       " 'utilisé',\n",
+       " 'par',\n",
+       " 'Google',\n",
+       " 'est',\n",
+       " 'PageRank',\n",
+       " ',',\n",
+       " 'et',\n",
+       " 'il',\n",
+       " 'est',\n",
+       " 'courant',\n",
+       " 'de',\n",
+       " 'voir',\n",
+       " 'HITS',\n",
+       " 'dans',\n",
+       " 'le',\n",
+       " 'milieu',\n",
+       " 'académique']"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
-    "tokenize(fr_txt, lang_module=\"fr_spacy\")"
+    "tokenize(str_, lang_module=\"fr_spacy\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 19.5 s, sys: 157 ms, total: 19.6 s\n",
-      "Wall time: 20.2 s\n"
+      "CPU times: user 6.22 ms, sys: 94 µs, total: 6.31 ms\n",
+      "Wall time: 4.27 ms\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "tokenized_fr_txt = tokenize(fr_txt, lang_module=\"fr_moses\")"
+    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_moses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'tels',\n",
+       " 'Google',\n",
+       " ',',\n",
+       " 'Exalead',\n",
+       " 'ou',\n",
+       " 'Yahoo',\n",
+       " '!',\n",
+       " 'sont',\n",
+       " 'des',\n",
+       " 'applications',\n",
+       " 'très',\n",
+       " 'connues',\n",
+       " 'de',\n",
+       " 'fouille',\n",
+       " 'de',\n",
+       " 'textes',\n",
+       " 'sur',\n",
+       " 'de',\n",
+       " 'grandes',\n",
+       " 'masses',\n",
+       " 'de',\n",
+       " 'données',\n",
+       " '.',\n",
+       " 'Cependant',\n",
+       " ',',\n",
+       " 'les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'ne',\n",
+       " 'se',\n",
+       " 'basent',\n",
+       " 'pas',\n",
+       " 'uniquement',\n",
+       " 'sur',\n",
+       " 'le',\n",
+       " 'texte',\n",
+       " 'pour',\n",
+       " \"l'\",\n",
+       " 'indexer',\n",
+       " ',',\n",
+       " 'mais',\n",
+       " 'également',\n",
+       " 'sur',\n",
+       " 'la',\n",
+       " 'façon',\n",
+       " 'dont',\n",
+       " 'les',\n",
+       " 'pages',\n",
+       " 'sont',\n",
+       " 'mises',\n",
+       " 'en',\n",
+       " 'valeur',\n",
+       " 'les',\n",
+       " 'unes',\n",
+       " 'par',\n",
+       " 'rapport',\n",
+       " 'aux',\n",
+       " 'autres',\n",
+       " '.',\n",
+       " \"L'\",\n",
+       " 'algorithme',\n",
+       " 'utilisé',\n",
+       " 'par',\n",
+       " 'Google',\n",
+       " 'est',\n",
+       " 'PageRank',\n",
+       " ',',\n",
+       " 'et',\n",
+       " 'il',\n",
+       " 'est',\n",
+       " 'courant',\n",
+       " 'de',\n",
+       " 'voir',\n",
+       " 'HITS',\n",
+       " 'dans',\n",
+       " 'le',\n",
+       " 'milieu',\n",
+       " 'académique']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenize(str_, lang_module=\"fr_moses\")"
    ]
   },
   {
@@ -596,6 +780,81 @@
    },
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fix bad encoding\n",
+    "\n",
+    "Sometimes you messed up you encoding saving files, and you don't know how to fix this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.preprocess import fix_bad_unicode\n",
+    "from nautilus_nlp.utils import file_loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bad_unicode=file_loader.open_textfile('./bad_encoding.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse Ã\\xa0 80km\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bad_unicode[0:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse à 80km/h sur les routes départ\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fix_bad_unicode(bad_unicode)[0:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -614,7 +873,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,

From 493e9e3c13d3b86ece716bab9604352c9c3ea19f Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Wed, 10 Apr 2019 19:53:18 +0200
Subject: [PATCH 050/496] function to find the optimal number of topics

---
 nautilus_nlp/models/topic_modeling.py |   76 +-
 notebooks/TopicModeling.ipynb         | 1064 +++++++++++++++++++++++++
 2 files changed, 1135 insertions(+), 5 deletions(-)
 create mode 100644 notebooks/TopicModeling.ipynb

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index c918435..ff494fb 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -4,6 +4,8 @@
 import pyLDAvis
 import pyLDAvis.gensim 
 pyLDAvis.enable_notebook()
+from gensim.models import CoherenceModel
+import matplotlib.pyplot as plt
 
 from IPython.display import HTML
 
@@ -12,8 +14,7 @@
 
 def create_dictionary(data):
     
-    """ Create a dictionary(id2word) containing the number of times a word appears 
-    in the dataset set: one of the two main inputs to the LDA topic model with the corpus
+    """ Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
     
     Parameters
     ----------
@@ -45,7 +46,7 @@ def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
 def create_bow_corpus(data, dictionary):
     
     """ Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
-        The produced corpus is a mapping of (word_id, word_frequency).
+        The produced corpus is a mapping of (token_id, token_count).
     Parameters
     ----------
     data : list of list of tokens
@@ -58,6 +59,71 @@ def create_bow_corpus(data, dictionary):
     corpus = [dictionary.doc2bow(text) for text in texts]
     return corpus
 
+### Find Number of topics
+
+def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, step=4):
+    """
+    Compute c_v coherence for various number of topics
+
+    Parameters:
+    ----------
+    dictionary : Gensim dictionary
+    bow_corpus : Gensim bow corpus
+    texts : List of input texts
+    limit : Max num of topics
+
+    Returns:
+    -------
+    model_list : List of LDA topic models
+    coherence_values : Coherence values corresponding to the LDA model with respective number of topics
+    """
+    coherence_values = []
+    model_list = []
+    for num_topics in range(start, limit, step):
+        model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus,
+                                           id2word=dictionary,
+                                          num_topics=num_topics, 
+                                          random_state=0,
+                                          update_every=5,
+                                          chunksize=1000,
+                                          passes=10)
+        model_list.append(model)
+        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')
+        coherence_values.append(coherencemodel.get_coherence())
+
+    return model_list, coherence_values
+
+def plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4):
+    """
+    Plot the coherence scores per number of topics
+
+    Parameters:
+    ----------
+    coherence_values : list of coherence scores for various number of topics
+    start : int. Min num of topics
+    limit : int. Max num of topics
+    step: int
+
+    Output:
+    -------
+    Lineplot
+    """
+    x = range(start, limit, step)
+    plt.plot(x, coherence_values)
+    plt.xlabel("Num Topics")
+    plt.ylabel("Coherence score")
+    plt.legend(("coherence_values"), loc='best')
+    return plt.show()
+
+def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
+    """
+    Print the coherences scores for the ldamodels that had been tested with different number of topics
+    """
+    x = range(start, limit, step)
+    for m, cv in zip(x, coherence_values):
+        print("Num Topics =", m, " has Coherence Value of", round(cv, 4))
+
+
 ### Gensim LdaModel
 
 def train_lda_model(bow_corpus, dictionary, num_topics, **kwargs):
@@ -65,8 +131,8 @@ def train_lda_model(bow_corpus, dictionary, num_topics, **kwargs):
       
     Parameters
     ----------
-    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).$
-    dictionary: corpora.Dictionary. Mapping from word IDs to words
+    bow_corpus : iterable of list of tokens. 
+    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
     num_topics: int
     
     Returns
diff --git a/notebooks/TopicModeling.ipynb b/notebooks/TopicModeling.ipynb
new file mode 100644
index 0000000..242629b
--- /dev/null
+++ b/notebooks/TopicModeling.ipynb
@@ -0,0 +1,1064 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "newsgroups_train = fetch_20newsgroups(subset='train', shuffle = True)\n",
+    "newsgroups_test = fetch_20newsgroups(subset='test', shuffle = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(list(newsgroups_train.target_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Data Preprocessing¶\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/nltk/decorators.py:68: DeprecationWarning: `formatargspec` is deprecated since Python 3.5. Use `signature` and the `Signature` object directly\n",
+      "  regargs, varargs, varkwargs, defaults, formatvalue=lambda value: \"\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gensim\n",
+    "from gensim.utils import simple_preprocess\n",
+    "from gensim.parsing.preprocessing import STOPWORDS\n",
+    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
+    "from nltk.stem.porter import *\n",
+    "import numpy as np\n",
+    "np.random.seed(400)\n",
+    "\n",
+    "import nltk\n",
+    "\n",
+    "import pandas as pd\n",
+    "stemmer = SnowballStemmer(\"english\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lemmatize_stemming(text):\n",
+    "    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))\n",
+    "\n",
+    "# Tokenize and lemmatize\n",
+    "def preprocess(text):\n",
+    "    result=[]\n",
+    "    for token in gensim.utils.simple_preprocess(text) :\n",
+    "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
+    "            result.append(lemmatize_stemming(token))\n",
+    "            \n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "processed_docs = []\n",
+    "\n",
+    "for doc in newsgroups_train.data:\n",
+    "    processed_docs.append(preprocess(doc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Bag of words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the dictionnary\n",
+    "from nautilus_nlp.models.topic_modeling import create_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dictionary = create_dictionary(processed_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter out tokens that appear in too few or too many documents\n",
+    "from nautilus_nlp.models.topic_modeling import filter_extremes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_extremes(dictionary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the bow \n",
+    "from nautilus_nlp.models.topic_modeling import create_bow_corpus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bow_corpus = create_bow_corpus(processed_docs, dictionary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[(0, 1),\n",
+       "  (1, 1),\n",
+       "  (2, 1),\n",
+       "  (3, 1),\n",
+       "  (4, 1),\n",
+       "  (5, 1),\n",
+       "  (6, 2),\n",
+       "  (7, 1),\n",
+       "  (8, 1),\n",
+       "  (9, 1),\n",
+       "  (10, 1),\n",
+       "  (11, 1),\n",
+       "  (12, 1),\n",
+       "  (13, 2),\n",
+       "  (14, 1),\n",
+       "  (15, 1),\n",
+       "  (16, 1),\n",
+       "  (17, 1),\n",
+       "  (18, 1),\n",
+       "  (19, 1),\n",
+       "  (20, 1),\n",
+       "  (21, 1),\n",
+       "  (22, 1),\n",
+       "  (23, 1),\n",
+       "  (24, 1),\n",
+       "  (25, 1),\n",
+       "  (26, 1),\n",
+       "  (27, 1),\n",
+       "  (28, 1)],\n",
+       " [(25, 1),\n",
+       "  (29, 1),\n",
+       "  (30, 1),\n",
+       "  (31, 1),\n",
+       "  (32, 1),\n",
+       "  (33, 1),\n",
+       "  (34, 1),\n",
+       "  (35, 1),\n",
+       "  (36, 1),\n",
+       "  (37, 2),\n",
+       "  (38, 5),\n",
+       "  (39, 1),\n",
+       "  (40, 1),\n",
+       "  (41, 1),\n",
+       "  (42, 1),\n",
+       "  (43, 2),\n",
+       "  (44, 1),\n",
+       "  (45, 2),\n",
+       "  (46, 2),\n",
+       "  (47, 1),\n",
+       "  (48, 1),\n",
+       "  (49, 1),\n",
+       "  (50, 1),\n",
+       "  (51, 1),\n",
+       "  (52, 1),\n",
+       "  (53, 1),\n",
+       "  (54, 1),\n",
+       "  (55, 1),\n",
+       "  (56, 1),\n",
+       "  (57, 3),\n",
+       "  (58, 1),\n",
+       "  (59, 1),\n",
+       "  (60, 1),\n",
+       "  (61, 1),\n",
+       "  (62, 1),\n",
+       "  (63, 1),\n",
+       "  (64, 1),\n",
+       "  (65, 1),\n",
+       "  (66, 1),\n",
+       "  (67, 2),\n",
+       "  (68, 1),\n",
+       "  (69, 1),\n",
+       "  (70, 3),\n",
+       "  (71, 1),\n",
+       "  (72, 4)],\n",
+       " [(8, 2),\n",
+       "  (11, 2),\n",
+       "  (13, 2),\n",
+       "  (25, 1),\n",
+       "  (27, 1),\n",
+       "  (31, 1),\n",
+       "  (41, 2),\n",
+       "  (45, 2),\n",
+       "  (48, 1),\n",
+       "  (54, 1),\n",
+       "  (69, 1),\n",
+       "  (73, 1),\n",
+       "  (74, 1),\n",
+       "  (75, 1),\n",
+       "  (76, 1),\n",
+       "  (77, 3),\n",
+       "  (78, 1),\n",
+       "  (79, 1),\n",
+       "  (80, 1),\n",
+       "  (81, 2),\n",
+       "  (82, 1),\n",
+       "  (83, 1),\n",
+       "  (84, 1),\n",
+       "  (85, 1),\n",
+       "  (86, 1),\n",
+       "  (87, 3),\n",
+       "  (88, 1),\n",
+       "  (89, 1),\n",
+       "  (90, 1),\n",
+       "  (91, 1),\n",
+       "  (92, 1),\n",
+       "  (93, 1),\n",
+       "  (94, 1),\n",
+       "  (95, 1),\n",
+       "  (96, 1),\n",
+       "  (97, 1),\n",
+       "  (98, 1),\n",
+       "  (99, 1),\n",
+       "  (100, 1),\n",
+       "  (101, 1),\n",
+       "  (102, 3),\n",
+       "  (103, 1),\n",
+       "  (104, 1),\n",
+       "  (105, 1),\n",
+       "  (106, 1),\n",
+       "  (107, 1),\n",
+       "  (108, 1),\n",
+       "  (109, 1),\n",
+       "  (110, 3),\n",
+       "  (111, 1),\n",
+       "  (112, 1),\n",
+       "  (113, 1),\n",
+       "  (114, 1),\n",
+       "  (115, 1),\n",
+       "  (116, 2),\n",
+       "  (117, 1),\n",
+       "  (118, 1),\n",
+       "  (119, 1),\n",
+       "  (120, 1),\n",
+       "  (121, 1),\n",
+       "  (122, 3),\n",
+       "  (123, 1),\n",
+       "  (124, 1),\n",
+       "  (125, 1),\n",
+       "  (126, 1),\n",
+       "  (127, 4),\n",
+       "  (128, 3),\n",
+       "  (129, 1),\n",
+       "  (130, 1),\n",
+       "  (131, 1),\n",
+       "  (132, 1),\n",
+       "  (133, 1),\n",
+       "  (134, 1),\n",
+       "  (135, 1),\n",
+       "  (136, 1),\n",
+       "  (137, 2),\n",
+       "  (138, 1),\n",
+       "  (139, 1),\n",
+       "  (140, 2),\n",
+       "  (141, 1),\n",
+       "  (142, 1),\n",
+       "  (143, 1),\n",
+       "  (144, 1),\n",
+       "  (145, 1),\n",
+       "  (146, 1),\n",
+       "  (147, 1),\n",
+       "  (148, 1),\n",
+       "  (149, 1),\n",
+       "  (150, 2),\n",
+       "  (151, 1)]]"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bow_corpus[0:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4: Find optimal number of topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute coherence values for various number of topics in order to pick the optimal one\n",
+    "#from gensim.models import CoherenceModel\n",
+    "#import matplotlib.pyplot as plt\n",
+    "from nautilus_nlp.models.topic_modeling import compute_coherence_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Take a long time to run\n",
+    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, bow_corpus=bow_corpus, texts=processed_docs, start=2, limit=25, step=4)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.37900998646286865,\n",
+       " 0.42680154447577845,\n",
+       " 0.47716566398237525,\n",
+       " 0.5261650723885645,\n",
+       " 0.49607461078243215,\n",
+       " 0.4978727171365794]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coherence_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coherence_values = [0.37900998646286865,\n",
+    " 0.42680154447577845,\n",
+    " 0.47716566398237525,\n",
+    " 0.5261650723885645,\n",
+    " 0.49607461078243215,\n",
+    " 0.4978727171365794]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.topic_modeling import plot_optimal_topic_number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print the coherences scores for the number we tested\n",
+    "from nautilus_nlp.models.topic_modeling import print_coherence_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num Topics = 2  has Coherence Value of 0.379\n",
+      "Num Topics = 6  has Coherence Value of 0.4268\n",
+      "Num Topics = 10  has Coherence Value of 0.4772\n",
+      "Num Topics = 14  has Coherence Value of 0.5262\n",
+      "Num Topics = 18  has Coherence Value of 0.4961\n",
+      "Num Topics = 22  has Coherence Value of 0.4979\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_coherence_scores(coherence_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5: Running LDA using Bag of Words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train the LDA model with gensim\n",
+    "from nautilus_nlp.models.topic_modeling import train_lda_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = train_lda_model(bow_corpus, dictionary, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.ldamodel.LdaModel at 0x1a2af3e208>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save model\n",
+    "from nautilus_nlp.models.topic_modeling import save_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_model(model,'/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.topic_modeling import load_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.ldamodel.LdaModel at 0x1a29985b38>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_loaded = load_model('/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')\n",
+    "model_loaded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6: Visualize the top keywords per topic with Pyldavis interactive chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Display the top keywords per topic in a interactive chart\n",
+    "from nautilus_nlp.models.topic_modeling import visualize_topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el591011124095238088809029897\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el591011124095238088809029897_data = {\"mdsDat\": {\"x\": [-0.07866945427665124, -0.01699948489792914, 0.20896689238873523, 0.14744605031212607, -0.008849073212760983, -0.04505413872814077, -0.08949897686453376, 0.10780299734830809, 0.004524270451044093, -0.22966908252019716], \"y\": [-0.15170611822961017, -0.1504468301902949, 0.08013685816591506, 0.13647834612774523, -0.009046128981188077, 0.003906923765221535, 0.14472746444813225, -0.07737607739594787, -0.1051004751701791, 0.1284260374602061], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.182504653930664, 12.926197052001953, 12.463006973266602, 11.995771408081055, 9.55910873413086, 9.468682289123535, 8.927140235900879, 7.882134437561035, 7.024929523468018, 6.5705246925354]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nazi\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"nist\", \"nist\", \"nore\", \"nsmca\", \"nubus\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"ohio\", \"ohio\", \"ohio\", \"openwindow\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"orbit\", \"outlet\", \"outlet\", \"output\", \"output\", \"output\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"pain\", \"pain\", \"pain\", \"pain\", \"pain\", \"palestinian\", \"patent\", \"patent\", \"patent\", \"patient\", \"patient\", \"pen\", \"penguin\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"restaur\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"resurrect\", \"revel\", \"revolv\", \"rid\", \"rid\", \"ride\", \"ride\", \"rider\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"ripem\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"rkba\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"robi\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rocki\", \"rockwel\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"rutger\", \"rutger\", \"rwing\", \"sabbath\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sandvik\", \"satan\", \"satellit\", \"satellit\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", \"sleev\", \"sleev\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smuggl\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"solar\", \"solar\", \"soldier\", \"soldier\", \"solntz\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spec\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"sphere\", \"spirit\", \"spirit\", \"spirit\", \"ssto\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanley\", \"stanley\", \"stanley\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"starter\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"sternlight\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steveh\", \"stimulus\", \"stratus\", \"strnlght\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"suno\", \"superstit\", \"surveil\", \"svga\", \"swap\", \"syndrom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"tampa\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tennesse\", \"test\", 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+      ],
+      "text/plain": [
+       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
+       "topic                                                \n",
+       "2     -0.078669 -0.151706       1        1  13.182505\n",
+       "0     -0.016999 -0.150447       2        1  12.926197\n",
+       "7      0.208967  0.080137       3        1  12.463007\n",
+       "8      0.147446  0.136478       4        1  11.995771\n",
+       "1     -0.008849 -0.009046       5        1   9.559109\n",
+       "3     -0.045054  0.003907       6        1   9.468682\n",
+       "6     -0.089499  0.144727       7        1   8.927140\n",
+       "9      0.107803 -0.077376       8        1   7.882134\n",
+       "4      0.004524 -0.105100       9        1   7.024930\n",
+       "5     -0.229669  0.128426      10        1   6.570525, topic_info=     Category         Freq        Term        Total  loglift  logprob\n",
+       "1037  Default  2966.000000      window  2966.000000  30.0000  30.0000\n",
+       "1193  Default  1940.000000        game  1940.000000  29.0000  29.0000\n",
+       "482   Default  1924.000000   christian  1924.000000  28.0000  28.0000\n",
+       "702   Default  1689.000000        team  1689.000000  27.0000  27.0000\n",
+       "352   Default  2638.000000       drive  2638.000000  26.0000  26.0000\n",
+       "320   Default  2883.000000        file  2883.000000  25.0000  25.0000\n",
+       "696   Default  1860.000000       space  1860.000000  24.0000  24.0000\n",
+       "1467  Default  1161.000000     encrypt  1161.000000  23.0000  23.0000\n",
+       "256   Default  1997.000000      govern  1997.000000  22.0000  22.0000\n",
+       "153   Default  1477.000000        chip  1477.000000  21.0000  21.0000\n",
+       "522   Default  1274.000000       jesus  1274.000000  20.0000  20.0000\n",
+       "1347  Default  1017.000000      israel  1017.000000  19.0000  19.0000\n",
+       "36    Default  1577.000000        card  1577.000000  18.0000  18.0000\n",
+       "120   Default  1423.000000        play  1423.000000  17.0000  17.0000\n",
+       "964   Default  1059.000000       secur  1059.000000  16.0000  16.0000\n",
+       "657   Default  1331.000000        nasa  1331.000000  15.0000  15.0000\n",
+       "1821  Default  1142.000000    armenian  1142.000000  14.0000  14.0000\n",
+       "822   Default  2599.000000     program  2599.000000  13.0000  13.0000\n",
+       "1346  Default   837.000000        isra   837.000000  12.0000  12.0000\n",
+       "513   Default  1391.000000        imag  1391.000000  11.0000  11.0000\n",
+       "117   Default  6052.000000       peopl  6052.000000  10.0000  10.0000\n",
+       "3565  Default   742.000000      hockey   742.000000   9.0000   9.0000\n",
+       "739   Default   990.000000      player   990.000000   8.0000   8.0000\n",
+       "1457  Default   784.000000     clipper   784.000000   7.0000   7.0000\n",
+       "326   Default  1802.000000      public  1802.000000   6.0000   6.0000\n",
+       "41    Default  1023.000000        disk  1023.000000   5.0000   5.0000\n",
+       "28    Default  4004.000000        year  4004.000000   4.0000   4.0000\n",
+       "380   Default   867.000000        scsi   867.000000   3.0000   3.0000\n",
+       "879   Default  1191.000000      driver  1191.000000   2.0000   2.0000\n",
+       "444   Default   747.000000        bike   747.000000   1.0000   1.0000\n",
+       "...       ...          ...         ...          ...      ...      ...\n",
+       "5004  Topic10   178.948181     stanley   185.594589   2.6861  -6.0394\n",
+       "702   Topic10  1384.116455        team  1689.568604   2.5232  -3.9937\n",
+       "4840  Topic10   160.981583         jet   167.196503   2.6847  -6.1452\n",
+       "3815  Topic10   191.566772       coach   202.233215   2.6684  -5.9713\n",
+       "3537  Topic10   221.759384      ranger   240.052933   2.6433  -5.8249\n",
+       "4877  Topic10   157.258331    winnipeg   165.160721   2.6735  -6.1686\n",
+       "1193  Topic10  1290.715576        game  1940.664795   2.3147  -4.0636\n",
+       "120   Topic10   920.770020        play  1423.930298   2.2866  -4.4013\n",
+       "711   Topic10   312.806488        wing   399.885132   2.4770  -5.4809\n",
+       "739   Topic10   623.101746      player   990.567200   2.2590  -4.7918\n",
+       "1311  Topic10   397.739624    columbia   559.283691   2.3817  -5.2407\n",
+       "1080  Topic10   455.438751      season   670.126038   2.3364  -5.1053\n",
+       "3252  Topic10   379.943481       leagu   536.827942   2.3769  -5.2865\n",
+       "1073  Topic10   351.761322  pittsburgh   505.782318   2.3594  -5.3636\n",
+       "1679  Topic10   356.976562       score   528.273926   2.3306  -5.3488\n",
+       "1321  Topic10   374.845306        arab   572.500732   2.2991  -5.3000\n",
+       "3488  Topic10   212.819550      mcgill   254.334839   2.5444  -5.8661\n",
+       "1475  Topic10   346.644043        goal   553.699341   2.2543  -5.3782\n",
+       "1150  Topic10   312.806427    virginia   544.289856   2.1687  -5.4809\n",
+       "1082  Topic10   333.144867     toronto   701.091187   1.9785  -5.4179\n",
+       "1155  Topic10   336.057953      andrew   868.338135   1.7733  -5.4092\n",
+       "28    Topic10   600.308960        year  4004.757812   0.8248  -4.8291\n",
+       "157   Topic10   295.451538       divis   693.417114   1.8694  -5.5380\n",
+       "2117  Topic10   283.661255      canada   732.439819   1.7740  -5.5787\n",
+       "2549  Topic10   250.147522      period   584.503235   1.8739  -5.7045\n",
+       "45    Topic10   267.235901       final   822.295105   1.5986  -5.6384\n",
+       "171   Topic10   330.680511       point  2646.791748   0.6426  -5.4254\n",
+       "146   Topic10   358.020264        time  5183.146484   0.0500  -5.3459\n",
+       "1545  Topic10   262.164948    american  1242.273315   1.1669  -5.6575\n",
+       "99    Topic10   242.101822          go  3510.730713   0.0484  -5.7372\n",
+       "\n",
+       "[734 rows x 6 columns], token_table=      Topic      Freq       Term\n",
+       "term                            \n",
+       "338       1  0.145983     accept\n",
+       "338       2  0.524347     accept\n",
+       "338       3  0.129101     accept\n",
+       "338       4  0.049654     accept\n",
+       "338       5  0.007945     accept\n",
+       "338       6  0.022841     accept\n",
+       "338       8  0.066536     accept\n",
+       "338       9  0.034758     accept\n",
+       "338      10  0.018869     accept\n",
+       "73        3  0.403915     access\n",
+       "73        4  0.196068     access\n",
+       "73        5  0.171820     access\n",
+       "73        6  0.033255     access\n",
+       "73        7  0.000693     access\n",
+       "73        8  0.193297     access\n",
+       "2940      4  0.984634    adaptec\n",
+       "152       1  0.052461    address\n",
+       "152       2  0.074007    address\n",
+       "152       3  0.536784    address\n",
+       "152       4  0.182675    address\n",
+       "152       5  0.030914    address\n",
+       "152       6  0.026230    address\n",
+       "152       7  0.032788    address\n",
+       "152       8  0.049650    address\n",
+       "152       9  0.005621    address\n",
+       "152      10  0.008431    address\n",
+       "1444      1  0.124112  administr\n",
+       "1444      3  0.063074  administr\n",
+       "1444      5  0.197359  administr\n",
+       "1444      8  0.614458  administr\n",
+       "...     ...       ...        ...\n",
+       "182       2  0.166406      world\n",
+       "182       3  0.097373      world\n",
+       "182       4  0.150056      world\n",
+       "182       5  0.106093      world\n",
+       "182       6  0.051230      world\n",
+       "182       7  0.080296      world\n",
+       "182       8  0.011990      world\n",
+       "182       9  0.053410      world\n",
+       "182      10  0.054863      world\n",
+       "4238      1  0.009547    worship\n",
+       "4238      2  0.988079    worship\n",
+       "4809      3  0.994707       xlib\n",
+       "3868      3  0.995135      xpert\n",
+       "3581      3  0.995652      xterm\n",
+       "2827      3  0.988763      xview\n",
+       "6047      6  0.981546     yamaha\n",
+       "1701      7  0.988053      yanke\n",
+       "28        1  0.130095       year\n",
+       "28        2  0.031962       year\n",
+       "28        3  0.015482       year\n",
+       "28        4  0.038954       year\n",
+       "28        5  0.216243       year\n",
+       "28        6  0.063674       year\n",
+       "28        7  0.244959       year\n",
+       "28        8  0.040951       year\n",
+       "28        9  0.067919       year\n",
+       "28       10  0.149822       year\n",
+       "3309      9  0.986879      yeast\n",
+       "3190      5  0.990640     zoolog\n",
+       "1894      1  0.991014       zuma\n",
+       "\n",
+       "[2168 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 8, 9, 2, 4, 7, 10, 5, 6])"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = visualize_topics(model, bow_corpus, dictionary)\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the pyldavis as HTML\n",
+    "from nautilus_nlp.models.topic_modeling import save_pyldavis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_pyldavis(p, '/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the pyldavis HTML\n",
+    "from nautilus_nlp.models.topic_modeling import show_pyldavis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el591011124069819927707340541\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el591011124069819927707340541_data = {\"mdsDat\": {\"x\": [-0.07866945427665124, -0.01699948489792914, 0.20896689238873523, 0.14744605031212607, -0.008849073212760983, -0.04505413872814077, -0.08949897686453376, 0.10780299734830809, 0.004524270451044093, -0.22966908252019716], \"y\": [-0.15170611822961017, -0.1504468301902949, 0.08013685816591506, 0.13647834612774523, -0.009046128981188077, 0.003906923765221535, 0.14472746444813225, -0.07737607739594787, -0.1051004751701791, 0.1284260374602061], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.179347038269043, 12.924742698669434, 12.465522766113281, 11.996149063110352, 9.560138702392578, 9.471930503845215, 8.926163673400879, 7.8803582191467285, 7.02661657333374, 6.569023609161377]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"argic\", \"genocid\", \"davidian\", \"troop\", \"murder\", \"mormon\", \"prison\", \"massacr\", \"azeri\", \"ethnic\", \"azerbaijani\", \"hitler\", \"iran\", \"zuma\", \"sdpa\", \"motto\", \"azerbaijan\", \"extermin\", \"sera\", \"urartu\", \"slaughter\", \"villag\", \"batf\", \"greek\", \"greec\", \"jew\", \"soldier\", \"kill\", \"waco\", \"arm\", \"countri\", \"muslim\", \"children\", \"armi\", \"popul\", \"peopl\", \"anti\", \"govern\", \"right\", \"attack\", \"say\", \"polit\", \"death\", \"state\", \"live\", \"go\", \"come\", \"tell\", \"happen\", \"forc\", \"world\", \"time\", \"nation\", \"want\", \"leav\", \"start\", \"year\", \"take\", \"jesus\", \"bibl\", \"atheist\", \"atheism\", \"christ\", \"scriptur\", \"cathol\", \"sandvik\", \"doctrin\", \"revel\", \"biblic\", \"satan\", \"atho\", \"livesey\", \"prophet\", \"divin\", \"vers\", \"gospel\", \"sabbath\", \"god\", \"sin\", \"resurrect\", \"solntz\", \"testament\", \"theolog\", \"propheci\", \"theist\", \"schneider\", 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\"map\", \"applic\", \"archiv\", \"user\", \"code\", \"avail\", \"directori\", \"sourc\", \"file\", \"mail\", \"list\", \"program\", \"function\", \"format\", \"email\", \"inform\", \"version\", \"softwar\", \"includ\", \"send\", \"data\", \"internet\", \"address\", \"display\", \"window\", \"copi\", \"access\", \"distribut\", \"thank\", \"look\", \"book\", \"group\", \"need\", \"scsi\", \"simm\", \"motherboard\", \"cach\", \"bio\", \"quadra\", \"diamond\", \"vram\", \"vesa\", \"centri\", \"swap\", \"upgrad\", \"char\", \"eisa\", \"intercon\", \"nubus\", \"ethernet\", \"svga\", \"amanda\", \"meg\", \"cadr\", \"mous\", \"maxtor\", \"config\", \"cica\", \"tiff\", \"adaptec\", \"powerbook\", \"ctrl\", \"esdi\", \"jumper\", \"floppi\", \"disk\", \"umich\", \"video\", \"modem\", \"card\", \"output\", \"monitor\", \"mode\", \"printer\", \"spec\", \"entri\", \"drive\", \"driver\", \"port\", \"instal\", \"memori\", \"window\", \"color\", \"appl\", \"byte\", \"screen\", \"board\", \"problem\", 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\"higgin\", \"inning\", \"revolv\", \"hulman\", \"yanke\", \"pitch\", \"catcher\", \"dodger\", \"blast\", \"starter\", \"tiger\", \"met\", \"bat\", \"nore\", \"outlet\", \"rocki\", \"jay\", \"sdsu\", \"volt\", \"lopez\", \"restaur\", \"lamp\", \"wire\", \"duke\", \"circuit\", \"batteri\", \"brave\", \"basebal\", \"hit\", \"berkeley\", \"jason\", \"ball\", \"metal\", \"jeff\", \"grind\", \"larc\", \"indiana\", \"netcom\", \"colorado\", \"year\", \"run\", \"good\", \"game\", \"smith\", \"scott\", \"player\", \"home\", \"stanford\", \"look\", \"distribut\", \"go\", \"time\", \"lose\", \"come\", \"start\", \"play\", \"david\", \"best\", \"better\", \"power\", \"thing\", \"john\", \"sale\", \"great\", \"encrypt\", \"escrow\", \"privaci\", \"ripem\", \"crypto\", \"wiretap\", \"cryptographi\", \"cipher\", \"decrypt\", \"hamburg\", \"clipper\", \"homicid\", \"bontchev\", \"gtoal\", \"crypt\", \"clarkson\", \"rwing\", \"surveil\", \"nist\", \"sternlight\", \"den\", \"ncsl\", \"qualcomm\", 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\"espn\", \"etern\", \"etern\", \"etern\", \"ether\", \"ethernet\", \"ethnic\", \"evid\", \"evid\", \"evid\", \"evid\", \"evid\", \"evid\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"extermin\", \"faith\", \"faith\", \"fbihh\", \"feder\", \"feder\", \"feder\", \"feder\", \"file\", \"file\", \"file\", \"file\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"firearm\", \"fischer\", \"flight\", \"flight\", \"flight\", \"flight\", \"floppi\", \"floppi\", \"flyer\", \"flyer\", \"fnal\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"font\", \"font\", \"food\", \"food\", \"food\", \"food\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"format\", \"format\", \"format\", \"format\", \"format\", \"freenet\", \"freenet\", \"freenet\", \"frost\", \"frost\", \"function\", \"function\", \"function\", \"function\", \"function\", \"function\", \"fund\", \"fund\", \"fund\", \"fund\", \"fund\", \"game\", \"game\", \"game\", \"game\", \"gatech\", \"gaza\", \"genet\", \"genet\", \"genocid\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"goal\", \"goal\", \"goal\", \"goal\", \"goal\", \"goal\", \"god\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"gordon\", \"gordon\", \"gospel\", \"govern\", \"govern\", \"govern\", \"govern\", \"gradi\", \"graphic\", \"graphic\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"greec\", \"greec\", \"greek\", \"greek\", \"greenbelt\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"gtoal\", \"gun\", \"gun\", \"halat\", \"hallam\", \"hamburg\", \"handbook\", \"handgun\", \"handgun\", \"handheld\", \"handheld\", \"handheld\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"harley\", \"health\", \"health\", \"health\", \"health\", \"heaven\", \"heaven\", \"heaven\", \"heaven\", \"helmet\", \"helmet\", \"helmet\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"henri\", \"henri\", \"higgin\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"hit\", \"hit\", \"hit\", \"hit\", \"hit\", \"hitler\", \"hitter\", \"hockey\", \"holi\", \"holi\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"homeopathi\", \"homicid\", \"honda\", \"hulman\", \"husc\", \"hydro\", \"iastat\", \"iastat\", \"ifa\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"imag\", \"imag\", \"imag\", \"imag\", \"imag\", \"imak\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"infect\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"ingr\", \"ingr\", \"ingr\", \"inning\", \"instal\", \"instal\", \"instal\", \"instal\", \"instal\", \"insur\", \"insur\", \"insur\", \"insur\", \"intellect\", \"intercon\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"invest\", \"invest\", \"iran\", \"islam\", \"islam\", \"isra\", \"israel\", \"israel\", \"israel\", \"jaeger\", \"jake\", \"jason\", \"jason\", \"jason\", \"jason\", \"jay\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jesus\", \"jet\", \"jet\", \"jew\", \"jew\", \"jew\", \"job\", \"job\", \"job\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"jumper\", \"jumper\", \"kaldi\", \"kelvin\", \"key\", \"key\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"koresh\", \"lamp\", \"larc\", \"larc\", \"laughter\", \"launch\", \"launch\", \"laurentian\", \"leaf\", \"leagu\", \"leagu\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"lebanes\", \"lemieux\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"livesey\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"lopez\", \"lord\", \"lord\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"lunar\", \"lunar\", \"lyme\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"magellan\", \"magnus\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"map\", \"map\", \"mar\", \"mar\", \"marriag\", \"marriag\", \"massacr\", \"maxtor\", \"maynard\", \"mccall\", \"mcgill\", \"mcgill\", \"mcgill\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"medic\", \"medic\", \"medic\", \"medic\", \"medic\", \"medicin\", \"medicin\", \"medicin\", \"medicin\", \"meg\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"met\", \"metal\", \"metal\", \"metal\", \"metal\", \"methodolog\", \"midway\", \"midway\", \"midway\", \"migrain\", \"militia\", \"mime\", \"mission\", \"mission\", \"mission\", \"mission\", \"mksol\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"modem\", \"modem\", \"modem\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"monitor\", \"monitor\", \"monitor\", \"montreal\", \"montreal\", \"moon\", \"moon\", \"moon\", \"moral\", \"moral\", \"mormon\", \"motherboard\", \"motif\", \"motorcycl\", \"motto\", \"mous\", \"mous\", \"murder\", \"murder\", \"murder\", \"muslim\", \"muslim\", \"nasa\", \"nasa\", \"nasa\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nazi\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"nist\", \"nist\", \"nore\", \"nsmca\", \"nubus\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"ohio\", \"ohio\", \"ohio\", \"openwindow\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"orbit\", \"outlet\", \"outlet\", \"output\", \"output\", \"output\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"pain\", \"pain\", \"pain\", \"pain\", \"pain\", \"palestinian\", \"patent\", \"patent\", \"patent\", \"patient\", \"patient\", \"pen\", \"penguin\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"restaur\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"resurrect\", \"revel\", \"revolv\", \"rid\", \"rid\", \"ride\", \"ride\", \"rider\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"ripem\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"rkba\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"robi\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rocki\", \"rockwel\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"rutger\", \"rutger\", \"rwing\", \"sabbath\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sandvik\", \"satan\", \"satellit\", \"satellit\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", \"sleev\", \"sleev\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smuggl\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"solar\", \"solar\", \"soldier\", \"soldier\", \"solntz\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spec\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"sphere\", \"spirit\", \"spirit\", \"spirit\", \"ssto\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanley\", \"stanley\", \"stanley\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"starter\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"sternlight\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steveh\", \"stimulus\", \"stratus\", \"strnlght\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"suno\", \"superstit\", \"surveil\", \"svga\", \"swap\", \"syndrom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"tampa\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tennesse\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"testament\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"theist\", \"theodor\", \"theolog\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"therapi\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thomasp\", \"tiff\", \"tiger\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tire\", \"tire\", \"tire\", \"tire\", \"tire\", \"toolkit\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"treatment\", \"treatment\", \"troop\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"trunk\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"turk\", \"turkey\", \"turkish\", \"ualberta\", \"uchicago\", \"uchicago\", \"uchicago\", \"ucsc\", \"uicvm\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"umich\", \"umich\", \"umich\", \"uoknor\", \"upgrad\", \"upgrad\", \"urartu\", \"urbana\", \"urbana\", \"urbana\", \"user\", \"user\", \"user\", \"user\", \"utah\", \"utkvm\", \"uvic\", \"veal\", \"vehicl\", \"vehicl\", \"vehicl\", \"vehicl\", \"vers\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"vesa\", \"vesselin\", \"video\", \"video\", \"video\", \"video\", \"villag\", \"villag\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"visual\", \"visual\", \"volt\", \"vram\", \"waco\", \"waco\", \"wagon\", \"wagon\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"water\", \"water\", \"water\", \"water\", \"water\", \"weapon\", \"weapon\", \"weapon\", \"wheel\", \"wheel\", \"widget\", \"window\", \"window\", \"window\", \"wing\", \"wing\", \"wing\", \"wing\", \"wing\", \"winnipeg\", \"winnipeg\", \"wire\", \"wire\", \"wire\", \"wiretap\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worship\", \"worship\", \"xlib\", \"xpert\", \"xterm\", \"xview\", \"yamaha\", \"yanke\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zoolog\", \"zuma\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 8, 9, 2, 4, 7, 10, 5, 6]};\n",
+       "\n",
+       "function LDAvis_load_lib(url, callback){\n",
+       "  var s = document.createElement('script');\n",
+       "  s.src = url;\n",
+       "  s.async = true;\n",
+       "  s.onreadystatechange = s.onload = callback;\n",
+       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "}\n",
+       "\n",
+       "if(typeof(LDAvis) !== \"undefined\"){\n",
+       "   // already loaded: just create the visualization\n",
+       "   !function(LDAvis){\n",
+       "       new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
+       "   }(LDAvis);\n",
+       "}else if(typeof define === \"function\" && define.amd){\n",
+       "   // require.js is available: use it to load d3/LDAvis\n",
+       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "show_pyldavis('/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 7: Testing model on unseen document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Subject: help\n",
+      "From: C..Doelle@p26.f3333.n106.z1.fidonet.org (C. Doelle)\n",
+      "Lines: 13\n",
+      "\n",
+      "Hello All!\n",
+      "\n",
+      "    It is my understanding that all True-Type fonts in Windows are loaded in\n",
+      "prior to starting Windows - this makes getting into Windows quite slow if you\n",
+      "have hundreds of them as I do.  First off, am I correct in this thinking -\n",
+      "secondly, if that is the case - can you get Windows to ignore them on boot and\n",
+      "maybe make something like a PIF file to load them only when you enter the\n",
+      "applications that need fonts?  Any ideas?\n",
+      "\n",
+      "\n",
+      "Chris\n",
+      "\n",
+      " * Origin: chris.doelle.@f3333.n106.z1.fidonet.org (1:106/3333.26)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "unseen_document = newsgroups_test.data[100]\n",
+    "print(unseen_document)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data preprocessing step for the unseen document\n",
+    "bow_new = dictionary.doc2bow(preprocess(unseen_document))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.topic_modeling import fit_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0, 0.0027796067),\n",
+       " (1, 0.002779247),\n",
+       " (2, 0.0027795879),\n",
+       " (3, 0.0027795406),\n",
+       " (4, 0.0027792477),\n",
+       " (5, 0.002779096),\n",
+       " (6, 0.0027791534),\n",
+       " (7, 0.20895894),\n",
+       " (8, 0.76880664),\n",
+       " (9, 0.0027789928)]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit_data(model, bow_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show the dominant topics of the new document and their keywords \n",
+    "from nautilus_nlp.models.topic_modeling import show_dominant_topic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score: 0.7688832879066467\t Topic: ['window', 'drive', 'problem', 'card', 'work']\n",
+      "Score: 0.2088821828365326\t Topic: ['file', 'program', 'mail', 'imag', 'inform']\n",
+      "Score: 0.0027796069625765085\t Topic: ['christian', 'peopl', 'believ', 'jesus', 'say']\n"
+     ]
+    }
+   ],
+   "source": [
+    "show_dominant_topic(model, bow_new, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nautilus",
+   "language": "python",
+   "name": "nautilus"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 6f58ffe774ffcaf6d52d57236848463c8eac0f4f Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Wed, 10 Apr 2019 20:24:43 +0200
Subject: [PATCH 051/496] delete useless cells

---
 notebooks/TopicModeling.ipynb | 208 ++++------------------------------
 1 file changed, 19 insertions(+), 189 deletions(-)

diff --git a/notebooks/TopicModeling.ipynb b/notebooks/TopicModeling.ipynb
index 242629b..331b079 100644
--- a/notebooks/TopicModeling.ipynb
+++ b/notebooks/TopicModeling.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,187 +168,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[[(0, 1),\n",
-       "  (1, 1),\n",
-       "  (2, 1),\n",
-       "  (3, 1),\n",
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-       "  (6, 2),\n",
-       "  (7, 1),\n",
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-       "  (99, 1),\n",
-       "  (100, 1),\n",
-       "  (101, 1),\n",
-       "  (102, 3),\n",
-       "  (103, 1),\n",
-       "  (104, 1),\n",
-       "  (105, 1),\n",
-       "  (106, 1),\n",
-       "  (107, 1),\n",
-       "  (108, 1),\n",
-       "  (109, 1),\n",
-       "  (110, 3),\n",
-       "  (111, 1),\n",
-       "  (112, 1),\n",
-       "  (113, 1),\n",
-       "  (114, 1),\n",
-       "  (115, 1),\n",
-       "  (116, 2),\n",
-       "  (117, 1),\n",
-       "  (118, 1),\n",
-       "  (119, 1),\n",
-       "  (120, 1),\n",
-       "  (121, 1),\n",
-       "  (122, 3),\n",
-       "  (123, 1),\n",
-       "  (124, 1),\n",
-       "  (125, 1),\n",
-       "  (126, 1),\n",
-       "  (127, 4),\n",
-       "  (128, 3),\n",
-       "  (129, 1),\n",
-       "  (130, 1),\n",
-       "  (131, 1),\n",
-       "  (132, 1),\n",
-       "  (133, 1),\n",
-       "  (134, 1),\n",
-       "  (135, 1),\n",
-       "  (136, 1),\n",
-       "  (137, 2),\n",
-       "  (138, 1),\n",
-       "  (139, 1),\n",
-       "  (140, 2),\n",
-       "  (141, 1),\n",
-       "  (142, 1),\n",
-       "  (143, 1),\n",
-       "  (144, 1),\n",
-       "  (145, 1),\n",
-       "  (146, 1),\n",
-       "  (147, 1),\n",
-       "  (148, 1),\n",
-       "  (149, 1),\n",
-       "  (150, 2),\n",
-       "  (151, 1)]]"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(13, 1), (26, 1), (55, 1), (88, 1), (99, 1), (152, 1), (153, 2), (154, 1), (155, 1), (156, 1), (157, 2), (158, 1), (159, 2), (160, 4), (161, 1), (162, 2), (163, 1), (164, 2), (165, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (171, 1), (172, 1), (173, 1), (174, 1), (175, 1), (176, 1), (177, 1), (178, 2), (179, 1), (180, 1), (181, 1), (182, 1)]\n"
+     ]
     }
    ],
    "source": [
-    "bow_corpus[0:3]"
+    "print(bow_corpus[3])"
    ]
   },
   {
@@ -365,8 +197,6 @@
    "outputs": [],
    "source": [
     "# Compute coherence values for various number of topics in order to pick the optimal one\n",
-    "#from gensim.models import CoherenceModel\n",
-    "#import matplotlib.pyplot as plt\n",
     "from nautilus_nlp.models.topic_modeling import compute_coherence_values"
    ]
   },

From 533041c940a1e66a7947a317331d393bde1cf47a Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Wed, 10 Apr 2019 20:31:18 +0200
Subject: [PATCH 052/496] new notebook topic modeling

---
 notebooks/TopicModeling.ipynb | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/notebooks/TopicModeling.ipynb b/notebooks/TopicModeling.ipynb
index 331b079..24fe053 100644
--- a/notebooks/TopicModeling.ipynb
+++ b/notebooks/TopicModeling.ipynb
@@ -235,20 +235,6 @@
     "coherence_values"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coherence_values = [0.37900998646286865,\n",
-    " 0.42680154447577845,\n",
-    " 0.47716566398237525,\n",
-    " 0.5261650723885645,\n",
-    " 0.49607461078243215,\n",
-    " 0.4978727171365794]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,

From 925df3c17f2b9aea37decf191929866cfba90041 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 11 Apr 2019 09:31:58 +0200
Subject: [PATCH 053/496] remove duplicates

---
 requirements.txt | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index c862dcf..d94c612 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,5 @@
 # local package
--e .
-os
+#-e .
 
 # external requirements
 click
@@ -18,28 +17,20 @@ gensim==3.7.1
 sacremoses==0.0.13
 stop-words==2018.7.23
 spacy==2.1.3
-scikit-learn>=0.17.0
 ftfy==5.5.1
-ftfy>=4.2.0,<5.0.0
 wordcloud>=1.5.0
 matplotlib>=3.0.3
-chardet
-nltk
 mosestokenizer
 numpy>1.15.4
 stop_words==2018.7.23
 nltk==3.4
 textblob==0.15.3
 textblob_fr==0.2.0
-spacy==2.1.0
 cld2_cffi==0.1.4
-matplotlib==3.0.3
 pandas==0.23.4
 chardet==3.0.4
 setuptools==40.8.0
-ftfy==4.4.3
 textacy==0.6.3
-wordcloud==1.5.0
 #fastText==0.8.3
 gensim==3.7.1
 scikit_learn==0.20.3

From 6c5c40054f4f4f513d3b3c67fa2f2e8c9e4fc9b2 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 12 Apr 2019 12:06:41 +0200
Subject: [PATCH 054/496] Add travis config

---
 .travis.yml | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 .travis.yml

diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..c7870ba
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,10 @@
+language: python
+python: 
+    - "3.5"
+    - "3.6"
+    - "3.7" 
+cache: pip
+install:
+  - pip install -r requirements.txt
+script:
+  - pytest tests/*

From 7495be0bdde2a5bbc40b7c88f479f706fe4859d8 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 12 Apr 2019 12:14:35 +0200
Subject: [PATCH 055/496] change travis config

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index c7870ba..48cb9a3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,6 +5,6 @@ python:
     - "3.7" 
 cache: pip
 install:
-  - pip install -r requirements.txt
+  - pip install -e .
 script:
   - pytest tests/*

From ec92b2bed7ab2d24b2f5a64856425b5fd0eb92b0 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 12 Apr 2019 12:22:19 +0200
Subject: [PATCH 056/496] travis again

---
 .travis.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 48cb9a3..056e025 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,10 +1,9 @@
 language: python
 python: 
-    - "3.5"
-    - "3.6"
     - "3.7" 
-cache: pip
+
 install:
+  - pip install -r requirements.txt
   - pip install -e .
 script:
   - pytest tests/*

From 68a7b7fb06d10f6344662419d1cb0237deff31a1 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 12 Apr 2019 12:26:11 +0200
Subject: [PATCH 057/496] travis again

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 056e025..97a5f7c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,7 +1,7 @@
 language: python
 python: 
     - "3.7" 
-
+dist: xenial
 install:
   - pip install -r requirements.txt
   - pip install -e .

From 6f0b79607cd9c63f92c041b65b6ca3d66dba0de1 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 12 Apr 2019 15:28:55 +0200
Subject: [PATCH 058/496] remove old encoding fct

---
 nautilus_nlp/utils/encoding.py | 50 ----------------------------------
 1 file changed, 50 deletions(-)
 delete mode 100644 nautilus_nlp/utils/encoding.py

diff --git a/nautilus_nlp/utils/encoding.py b/nautilus_nlp/utils/encoding.py
deleted file mode 100644
index 22cfa10..0000000
--- a/nautilus_nlp/utils/encoding.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import pandas as pd
-import chardet
-import glob
-import json
-
-#Import an encoded csv or txt file as a pandas DataFrame
-def import_from_file_as_pd(file, sep=',', header = None, verbose=True):
-    encods = ['utf-8', 'ISO-8859-1', 'latin1', 'cp1252']
-    try: 
-        for encod in encods:
-            try:
-                data = pd.read_csv(file, sep = sep, encoding = encod, header=header)
-                break
-            except:
-                continue
-        if verbose:
-            print('Success:',encod)
-    except:
-        rawdata = open(file, 'rb').read()
-        result = chardet.detect(rawdata)['encoding']
-        print(result)
-        data = pd.read_csv(file, encoding = result, header=header)
-    return data
-
-
-#Import encoded csv or txt files from a folder as a pandas DataFrame
-def import_from_folder_as_pd(path, sep = ',', verbose=False):
-    all_files = glob.glob(path + "/*.csv")
-    li = []
-    
-    for file in all_files:
-        data = import_from_file_as_pd(file, sep = sep, verbose=verbose)
-        li.append(data)
-    
-    frame = pd.concat(li, axis=0)
-    return frame
-
-#Import a json file as a pandas DataFrame
-def import_json_to_pd(file):
-    with open(file) as json_file:  
-        data = json.load(json_file)
-        json.dumps(data)
-        df = pd.DataFrame(data)
-    return df
-
-#Import a json file as a dictionary
-def json_to_dict(file):
-    with open(file) as json_file:  
-        data = json.load(json_file)
-    return data 
\ No newline at end of file

From 0cbccb3f72a009520c3e5b039f07a1c3ea82d151 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 12 Apr 2019 19:23:37 +0200
Subject: [PATCH 059/496] remove export

---
 nautilus_nlp/utils/export.py | 9 ---------
 1 file changed, 9 deletions(-)
 delete mode 100644 nautilus_nlp/utils/export.py

diff --git a/nautilus_nlp/utils/export.py b/nautilus_nlp/utils/export.py
deleted file mode 100644
index d6b4ad5..0000000
--- a/nautilus_nlp/utils/export.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import pandas as pd
-
-#Save a pandas DataFrame as an encoded csv file
-def df_to_csv (df, path, sep =',', encoding='utf-8'):
-    df.to_csv(path, sep = sep, encoding = encoding)
-
-#Save a pandas DataFrame as a json file    
-def df_to_json (df, path, orient):
-    df.to_json(path, orient = orient)
\ No newline at end of file

From 5e467a14c99033007809a11bbc7aa502406c02ec Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 12 Apr 2019 19:45:13 +0200
Subject: [PATCH 060/496] add link to features list

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index a05d090..5389924 100644
--- a/README.md
+++ b/README.md
@@ -10,6 +10,7 @@ This library can help you with:
     3. Training automatically multiclass, multilabel classifier
     4. Help you discover topics and cluster your data
 
+You can find a list of the available features [in this article.](https://artefactory.atlassian.net/wiki/spaces/CK/pages/822837299/Nautilus+NLP+-+key+features)
 
 # Feature Request
 

From fabca9fce906a0c67ca22ce471f6a529f06591a4 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 12 Apr 2019 19:45:29 +0200
Subject: [PATCH 061/496] fix ftfy

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d94c612..11f2c9d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -17,7 +17,7 @@ gensim==3.7.1
 sacremoses==0.0.13
 stop-words==2018.7.23
 spacy==2.1.3
-ftfy==5.5.1
+ftfy<5.0.0,>=4.2.0
 wordcloud>=1.5.0
 matplotlib>=3.0.3
 mosestokenizer

From b3a99e8200d1a423e0441854ecc2bc9a57b794f4 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 12 Apr 2019 20:53:39 +0200
Subject: [PATCH 062/496] add stopwords issue #57

---
 nautilus_nlp/config/config.py       |  5 +++
 nautilus_nlp/data/country_code.json |  1 +
 nautilus_nlp/utils/constants.py     |  5 ++-
 nautilus_nlp/utils/preprocess.py    | 48 ++++++++++++++++++++++++++++-
 4 files changed, 55 insertions(+), 4 deletions(-)
 create mode 100644 nautilus_nlp/data/country_code.json

diff --git a/nautilus_nlp/config/config.py b/nautilus_nlp/config/config.py
index 2081394..ed87b43 100644
--- a/nautilus_nlp/config/config.py
+++ b/nautilus_nlp/config/config.py
@@ -1,4 +1,9 @@
 #!/usr/local/bin/python3
+import os 
 
 path_data = "data/"
 langmodel_name = "lid.176.ftz"
+
+ROOT_FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+
+COUNTRY_MAPPING_ISO = {'af': 'Afghanistan',  'ax': 'Åland Islands',  'al': 'Albania',  'dz': 'Algeria',  'as': 'American Samoa',  'ad': 'Andorra',  'ao': 'Angola',  'ai': 'Anguilla',  'aq': 'Antarctica',  'ag': 'Antigua and Barbuda',  'ar': 'Argentina',  'am': 'Armenia',  'aw': 'Aruba',  'au': 'Australia',  'at': 'Austria',  'az': 'Azerbaijan',  'bs': 'Bahamas',  'bh': 'Bahrain',  'bd': 'Bangladesh',  'bb': 'Barbados',  'by': 'Belarus',  'be': 'Belgium',  'bz': 'Belize',  'bj': 'Benin',  'bm': 'Bermuda',  'bt': 'Bhutan',  'bo': 'Bolivia (Plurinational State of)',  'bq': 'Bonaire, Sint Eustatius and Saba',  'ba': 'Bosnia and Herzegovina',  'bw': 'Botswana',  'bv': 'Bouvet Island',  'br': 'Brazil',  'io': 'British Indian Ocean Territory',  'bn': 'Brunei Darussalam',  'bg': 'Bulgaria',  'bf': 'Burkina Faso',  'bi': 'Burundi',  'cv': 'Cabo Verde',  'kh': 'Cambodia',  'cm': 'Cameroon',  'ca': 'Canada',  'ky': 'Cayman Islands',  'cf': 'Central African Republic',  'td': 'Chad',  'cl': 'Chile',  'cn': 'China',  'cx': 'Christmas Island',  'cc': 'Cocos (Keeling) Islands',  'co': 'Colombia',  'km': 'Comoros',  'cg': 'Congo',  'cd': 'Congo, Democratic Republic of the',  'ck': 'Cook Islands',  'cr': 'Costa Rica',  'ci': "Côte d'Ivoire",  'hr': 'Croatia',  'cu': 'Cuba',  'cw': 'Curaçao',  'cy': 'Cyprus',  'cz': 'Czechia',  'dk': 'Denmark',  'dj': 'Djibouti',  'dm': 'Dominica',  'do': 'Dominican Republic',  'ec': 'Ecuador',  'eg': 'Egypt',  'sv': 'El Salvador',  'gq': 'Equatorial Guinea',  'er': 'Eritrea',  'ee': 'Estonia',  'sz': 'Eswatini',  'et': 'Ethiopia',  'fk': 'Falkland Islands (Malvinas)',  'fo': 'Faroe Islands',  'fj': 'Fiji',  'fi': 'Finland',  'fr': 'France',  'gf': 'French Guiana',  'pf': 'French Polynesia',  'tf': 'French Southern Territories',  'ga': 'Gabon',  'gm': 'Gambia',  'ge': 'Georgia',  'de': 'Germany',  'gh': 'Ghana',  'gi': 'Gibraltar',  'gr': 'Greece',  'gl': 'Greenland',  'gd': 'Grenada',  'gp': 'Guadeloupe',  'gu': 'Guam',  'gt': 'Guatemala',  'gg': 'Guernsey',  'gn': 'Guinea',  'gw': 'Guinea-Bissau',  'gy': 'Guyana',  'ht': 'Haiti',  'hm': 'Heard Island and McDonald Islands',  'va': 'Holy See',  'hn': 'Honduras',  'hk': 'Hong Kong',  'hu': 'Hungary',  'is': 'Iceland',  'in': 'India',  'id': 'Indonesia',  'ir': 'Iran (Islamic Republic of)',  'iq': 'Iraq',  'ie': 'Ireland',  'im': 'Isle of Man',  'il': 'Israel',  'it': 'Italy',  'jm': 'Jamaica',  'jp': 'Japan',  'je': 'Jersey',  'jo': 'Jordan',  'kz': 'Kazakhstan',  'ke': 'Kenya',  'ki': 'Kiribati',  'kp': "Korea (Democratic People's Republic of)",  'kr': 'Korea, Republic of',  'kw': 'Kuwait',  'kg': 'Kyrgyzstan',  'la': "Lao People's Democratic Republic",  'lv': 'Latvia',  'lb': 'Lebanon',  'ls': 'Lesotho',  'lr': 'Liberia',  'ly': 'Libya',  'li': 'Liechtenstein',  'lt': 'Lithuania',  'lu': 'Luxembourg',  'mo': 'Macao',  'mg': 'Madagascar',  'mw': 'Malawi',  'my': 'Malaysia',  'mv': 'Maldives',  'ml': 'Mali',  'mt': 'Malta',  'mh': 'Marshall Islands',  'mq': 'Martinique',  'mr': 'Mauritania',  'mu': 'Mauritius',  'yt': 'Mayotte',  'mx': 'Mexico',  'fm': 'Micronesia (Federated States of)',  'md': 'Moldova, Republic of',  'mc': 'Monaco',  'mn': 'Mongolia',  'me': 'Montenegro',  'ms': 'Montserrat',  'ma': 'Morocco',  'mz': 'Mozambique',  'mm': 'Myanmar',  'na': 'Namibia',  'nr': 'Nauru',  'np': 'Nepal',  'nl': 'Netherlands',  'nc': 'New Caledonia',  'nz': 'New Zealand',  'ni': 'Nicaragua',  'ne': 'Niger',  'ng': 'Nigeria',  'nu': 'Niue',  'nf': 'Norfolk Island',  'mk': 'North Macedonia',  'mp': 'Northern Mariana Islands',  'no': 'Norway',  'om': 'Oman',  'pk': 'Pakistan',  'pw': 'Palau',  'ps': 'Palestine, State of',  'pa': 'Panama',  'pg': 'Papua New Guinea',  'py': 'Paraguay',  'pe': 'Peru',  'ph': 'Philippines',  'pn': 'Pitcairn',  'pl': 'Poland',  'pt': 'Portugal',  'pr': 'Puerto Rico',  'qa': 'Qatar',  're': 'Réunion',  'ro': 'Romania',  'ru': 'Russian Federation',  'rw': 'Rwanda',  'bl': 'Saint Barthélemy',  'sh': 'Saint Helena, Ascension and Tristan da Cunha',  'kn': 'Saint Kitts and Nevis',  'lc': 'Saint Lucia',  'mf': 'Saint Martin (French part)',  'pm': 'Saint Pierre and Miquelon',  'vc': 'Saint Vincent and the Grenadines',  'ws': 'Samoa',  'sm': 'San Marino',  'st': 'Sao Tome and Principe',  'sa': 'Saudi Arabia',  'sn': 'Senegal',  'rs': 'Serbia',  'sc': 'Seychelles',  'sl': 'Sierra Leone',  'sg': 'Singapore',  'sx': 'Sint Maarten (Dutch part)',  'sk': 'Slovakia',  'si': 'Slovenia',  'sb': 'Solomon Islands',  'so': 'Somalia',  'za': 'South Africa',  'gs': 'South Georgia and the South Sandwich Islands',  'ss': 'South Sudan',  'es': 'Spain',  'lk': 'Sri Lanka',  'sd': 'Sudan',  'sr': 'Suriname',  'sj': 'Svalbard and Jan Mayen',  'se': 'Sweden',  'ch': 'Switzerland',  'sy': 'Syrian Arab Republic',  'tw': 'Taiwan, Province of China',  'tj': 'Tajikistan',  'tz': 'Tanzania, United Republic of',  'th': 'Thailand',  'tl': 'Timor-Leste',  'tg': 'Togo',  'tk': 'Tokelau',  'to': 'Tonga',  'tt': 'Trinidad and Tobago',  'tn': 'Tunisia',  'tr': 'Turkey',  'tm': 'Turkmenistan',  'tc': 'Turks and Caicos Islands',  'tv': 'Tuvalu',  'ug': 'Uganda',  'ua': 'Ukraine',  'ae': 'United Arab Emirates',  'gb': 'United Kingdom of Great Britain and Northern Ireland',  'us': 'United States of America',  'um': 'United States Minor Outlying Islands',  'uy': 'Uruguay',  'uz': 'Uzbekistan',  'vu': 'Vanuatu',  've': 'Venezuela (Bolivarian Republic of)',  'vn': 'Viet Nam',  'vg': 'Virgin Islands (British)',  'vi': 'Virgin Islands (U.S.)',  'wf': 'Wallis and Futuna',  'eh': 'Western Sahara',  'ye': 'Yemen',  'zm': 'Zambia',  'zw': 'Zimbabwe'}
\ No newline at end of file
diff --git a/nautilus_nlp/data/country_code.json b/nautilus_nlp/data/country_code.json
new file mode 100644
index 0000000..82ae350
--- /dev/null
+++ b/nautilus_nlp/data/country_code.json
@@ -0,0 +1 @@
+[{"name":"Afghanistan","alpha-2":"AF","alpha-3":"AFG","country-code":"004","iso_3166-2":"ISO 3166-2:AF","region":"Asia","sub-region":"Southern Asia","intermediate-region":"","region-code":"142","sub-region-code":"034","intermediate-region-code":""},{"name":"Åland Islands","alpha-2":"AX","alpha-3":"ALA","country-code":"248","iso_3166-2":"ISO 3166-2:AX","region":"Europe","sub-region":"Northern Europe","intermediate-region":"","region-code":"150","sub-region-code":"154","intermediate-region-code":""},{"name":"Albania","alpha-2":"AL","alpha-3":"ALB","country-code":"008","iso_3166-2":"ISO 3166-2:AL","region":"Europe","sub-region":"Southern Europe","intermediate-region":"","region-code":"150","sub-region-code":"039","intermediate-region-code":""},{"name":"Algeria","alpha-2":"DZ","alpha-3":"DZA","country-code":"012","iso_3166-2":"ISO 3166-2:DZ","region":"Africa","sub-region":"Northern Africa","intermediate-region":"","region-code":"002","sub-region-code":"015","intermediate-region-code":""},{"name":"American Samoa","alpha-2":"AS","alpha-3":"ASM","country-code":"016","iso_3166-2":"ISO 3166-2:AS","region":"Oceania","sub-region":"Polynesia","intermediate-region":"","region-code":"009","sub-region-code":"061","intermediate-region-code":""},{"name":"Andorra","alpha-2":"AD","alpha-3":"AND","country-code":"020","iso_3166-2":"ISO 3166-2:AD","region":"Europe","sub-region":"Southern Europe","intermediate-region":"","region-code":"150","sub-region-code":"039","intermediate-region-code":""},{"name":"Angola","alpha-2":"AO","alpha-3":"AGO","country-code":"024","iso_3166-2":"ISO 3166-2:AO","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Anguilla","alpha-2":"AI","alpha-3":"AIA","country-code":"660","iso_3166-2":"ISO 3166-2:AI","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Antarctica","alpha-2":"AQ","alpha-3":"ATA","country-code":"010","iso_3166-2":"ISO 3166-2:AQ","region":"","sub-region":"","intermediate-region":"","region-code":"","sub-region-code":"","intermediate-region-code":""},{"name":"Antigua and Barbuda","alpha-2":"AG","alpha-3":"ATG","country-code":"028","iso_3166-2":"ISO 3166-2:AG","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Argentina","alpha-2":"AR","alpha-3":"ARG","country-code":"032","iso_3166-2":"ISO 3166-2:AR","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Armenia","alpha-2":"AM","alpha-3":"ARM","country-code":"051","iso_3166-2":"ISO 3166-2:AM","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Aruba","alpha-2":"AW","alpha-3":"ABW","country-code":"533","iso_3166-2":"ISO 3166-2:AW","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Australia","alpha-2":"AU","alpha-3":"AUS","country-code":"036","iso_3166-2":"ISO 3166-2:AU","region":"Oceania","sub-region":"Australia and New Zealand","intermediate-region":"","region-code":"009","sub-region-code":"053","intermediate-region-code":""},{"name":"Austria","alpha-2":"AT","alpha-3":"AUT","country-code":"040","iso_3166-2":"ISO 3166-2:AT","region":"Europe","sub-region":"Western Europe","intermediate-region":"","region-code":"150","sub-region-code":"155","intermediate-region-code":""},{"name":"Azerbaijan","alpha-2":"AZ","alpha-3":"AZE","country-code":"031","iso_3166-2":"ISO 3166-2:AZ","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Bahamas","alpha-2":"BS","alpha-3":"BHS","country-code":"044","iso_3166-2":"ISO 3166-2:BS","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Bahrain","alpha-2":"BH","alpha-3":"BHR","country-code":"048","iso_3166-2":"ISO 3166-2:BH","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Bangladesh","alpha-2":"BD","alpha-3":"BGD","country-code":"050","iso_3166-2":"ISO 3166-2:BD","region":"Asia","sub-region":"Southern Asia","intermediate-region":"","region-code":"142","sub-region-code":"034","intermediate-region-code":""},{"name":"Barbados","alpha-2":"BB","alpha-3":"BRB","country-code":"052","iso_3166-2":"ISO 3166-2:BB","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Belarus","alpha-2":"BY","alpha-3":"BLR","country-code":"112","iso_3166-2":"ISO 3166-2:BY","region":"Europe","sub-region":"Eastern Europe","intermediate-region":"","region-code":"150","sub-region-code":"151","intermediate-region-code":""},{"name":"Belgium","alpha-2":"BE","alpha-3":"BEL","country-code":"056","iso_3166-2":"ISO 3166-2:BE","region":"Europe","sub-region":"Western Europe","intermediate-region":"","region-code":"150","sub-region-code":"155","intermediate-region-code":""},{"name":"Belize","alpha-2":"BZ","alpha-3":"BLZ","country-code":"084","iso_3166-2":"ISO 3166-2:BZ","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Central America","region-code":"019","sub-region-code":"419","intermediate-region-code":"013"},{"name":"Benin","alpha-2":"BJ","alpha-3":"BEN","country-code":"204","iso_3166-2":"ISO 3166-2:BJ","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Bermuda","alpha-2":"BM","alpha-3":"BMU","country-code":"060","iso_3166-2":"ISO 3166-2:BM","region":"Americas","sub-region":"Northern America","intermediate-region":"","region-code":"019","sub-region-code":"021","intermediate-region-code":""},{"name":"Bhutan","alpha-2":"BT","alpha-3":"BTN","country-code":"064","iso_3166-2":"ISO 3166-2:BT","region":"Asia","sub-region":"Southern Asia","intermediate-region":"","region-code":"142","sub-region-code":"034","intermediate-region-code":""},{"name":"Bolivia (Plurinational State of)","alpha-2":"BO","alpha-3":"BOL","country-code":"068","iso_3166-2":"ISO 3166-2:BO","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Bonaire, Sint Eustatius and Saba","alpha-2":"BQ","alpha-3":"BES","country-code":"535","iso_3166-2":"ISO 3166-2:BQ","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Bosnia and Herzegovina","alpha-2":"BA","alpha-3":"BIH","country-code":"070","iso_3166-2":"ISO 3166-2:BA","region":"Europe","sub-region":"Southern Europe","intermediate-region":"","region-code":"150","sub-region-code":"039","intermediate-region-code":""},{"name":"Botswana","alpha-2":"BW","alpha-3":"BWA","country-code":"072","iso_3166-2":"ISO 3166-2:BW","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Southern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"018"},{"name":"Bouvet Island","alpha-2":"BV","alpha-3":"BVT","country-code":"074","iso_3166-2":"ISO 3166-2:BV","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Brazil","alpha-2":"BR","alpha-3":"BRA","country-code":"076","iso_3166-2":"ISO 3166-2:BR","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"British Indian Ocean Territory","alpha-2":"IO","alpha-3":"IOT","country-code":"086","iso_3166-2":"ISO 3166-2:IO","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Brunei Darussalam","alpha-2":"BN","alpha-3":"BRN","country-code":"096","iso_3166-2":"ISO 3166-2:BN","region":"Asia","sub-region":"South-eastern Asia","intermediate-region":"","region-code":"142","sub-region-code":"035","intermediate-region-code":""},{"name":"Bulgaria","alpha-2":"BG","alpha-3":"BGR","country-code":"100","iso_3166-2":"ISO 3166-2:BG","region":"Europe","sub-region":"Eastern Europe","intermediate-region":"","region-code":"150","sub-region-code":"151","intermediate-region-code":""},{"name":"Burkina Faso","alpha-2":"BF","alpha-3":"BFA","country-code":"854","iso_3166-2":"ISO 3166-2:BF","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Burundi","alpha-2":"BI","alpha-3":"BDI","country-code":"108","iso_3166-2":"ISO 3166-2:BI","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Cabo Verde","alpha-2":"CV","alpha-3":"CPV","country-code":"132","iso_3166-2":"ISO 3166-2:CV","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Cambodia","alpha-2":"KH","alpha-3":"KHM","country-code":"116","iso_3166-2":"ISO 3166-2:KH","region":"Asia","sub-region":"South-eastern Asia","intermediate-region":"","region-code":"142","sub-region-code":"035","intermediate-region-code":""},{"name":"Cameroon","alpha-2":"CM","alpha-3":"CMR","country-code":"120","iso_3166-2":"ISO 3166-2:CM","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Canada","alpha-2":"CA","alpha-3":"CAN","country-code":"124","iso_3166-2":"ISO 3166-2:CA","region":"Americas","sub-region":"Northern America","intermediate-region":"","region-code":"019","sub-region-code":"021","intermediate-region-code":""},{"name":"Cayman Islands","alpha-2":"KY","alpha-3":"CYM","country-code":"136","iso_3166-2":"ISO 3166-2:KY","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Central African Republic","alpha-2":"CF","alpha-3":"CAF","country-code":"140","iso_3166-2":"ISO 3166-2:CF","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Chad","alpha-2":"TD","alpha-3":"TCD","country-code":"148","iso_3166-2":"ISO 3166-2:TD","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Chile","alpha-2":"CL","alpha-3":"CHL","country-code":"152","iso_3166-2":"ISO 3166-2:CL","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"China","alpha-2":"CN","alpha-3":"CHN","country-code":"156","iso_3166-2":"ISO 3166-2:CN","region":"Asia","sub-region":"Eastern Asia","intermediate-region":"","region-code":"142","sub-region-code":"030","intermediate-region-code":""},{"name":"Christmas Island","alpha-2":"CX","alpha-3":"CXR","country-code":"162","iso_3166-2":"ISO 3166-2:CX","region":"Oceania","sub-region":"Australia and New Zealand","intermediate-region":"","region-code":"009","sub-region-code":"053","intermediate-region-code":""},{"name":"Cocos (Keeling) Islands","alpha-2":"CC","alpha-3":"CCK","country-code":"166","iso_3166-2":"ISO 3166-2:CC","region":"Oceania","sub-region":"Australia and New Zealand","intermediate-region":"","region-code":"009","sub-region-code":"053","intermediate-region-code":""},{"name":"Colombia","alpha-2":"CO","alpha-3":"COL","country-code":"170","iso_3166-2":"ISO 3166-2:CO","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Comoros","alpha-2":"KM","alpha-3":"COM","country-code":"174","iso_3166-2":"ISO 3166-2:KM","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Congo","alpha-2":"CG","alpha-3":"COG","country-code":"178","iso_3166-2":"ISO 3166-2:CG","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Congo, Democratic Republic of the","alpha-2":"CD","alpha-3":"COD","country-code":"180","iso_3166-2":"ISO 3166-2:CD","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Cook Islands","alpha-2":"CK","alpha-3":"COK","country-code":"184","iso_3166-2":"ISO 3166-2:CK","region":"Oceania","sub-region":"Polynesia","intermediate-region":"","region-code":"009","sub-region-code":"061","intermediate-region-code":""},{"name":"Costa Rica","alpha-2":"CR","alpha-3":"CRI","country-code":"188","iso_3166-2":"ISO 3166-2:CR","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Central America","region-code":"019","sub-region-code":"419","intermediate-region-code":"013"},{"name":"Côte d'Ivoire","alpha-2":"CI","alpha-3":"CIV","country-code":"384","iso_3166-2":"ISO 3166-2:CI","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Croatia","alpha-2":"HR","alpha-3":"HRV","country-code":"191","iso_3166-2":"ISO 3166-2:HR","region":"Europe","sub-region":"Southern 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3166-2:UG","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Ukraine","alpha-2":"UA","alpha-3":"UKR","country-code":"804","iso_3166-2":"ISO 3166-2:UA","region":"Europe","sub-region":"Eastern Europe","intermediate-region":"","region-code":"150","sub-region-code":"151","intermediate-region-code":""},{"name":"United Arab Emirates","alpha-2":"AE","alpha-3":"ARE","country-code":"784","iso_3166-2":"ISO 3166-2:AE","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"United Kingdom of Great Britain and Northern Ireland","alpha-2":"GB","alpha-3":"GBR","country-code":"826","iso_3166-2":"ISO 3166-2:GB","region":"Europe","sub-region":"Northern Europe","intermediate-region":"","region-code":"150","sub-region-code":"154","intermediate-region-code":""},{"name":"United States of America","alpha-2":"US","alpha-3":"USA","country-code":"840","iso_3166-2":"ISO 3166-2:US","region":"Americas","sub-region":"Northern America","intermediate-region":"","region-code":"019","sub-region-code":"021","intermediate-region-code":""},{"name":"United States Minor Outlying Islands","alpha-2":"UM","alpha-3":"UMI","country-code":"581","iso_3166-2":"ISO 3166-2:UM","region":"Oceania","sub-region":"Micronesia","intermediate-region":"","region-code":"009","sub-region-code":"057","intermediate-region-code":""},{"name":"Uruguay","alpha-2":"UY","alpha-3":"URY","country-code":"858","iso_3166-2":"ISO 3166-2:UY","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Uzbekistan","alpha-2":"UZ","alpha-3":"UZB","country-code":"860","iso_3166-2":"ISO 3166-2:UZ","region":"Asia","sub-region":"Central Asia","intermediate-region":"","region-code":"142","sub-region-code":"143","intermediate-region-code":""},{"name":"Vanuatu","alpha-2":"VU","alpha-3":"VUT","country-code":"548","iso_3166-2":"ISO 3166-2:VU","region":"Oceania","sub-region":"Melanesia","intermediate-region":"","region-code":"009","sub-region-code":"054","intermediate-region-code":""},{"name":"Venezuela (Bolivarian Republic of)","alpha-2":"VE","alpha-3":"VEN","country-code":"862","iso_3166-2":"ISO 3166-2:VE","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Viet Nam","alpha-2":"VN","alpha-3":"VNM","country-code":"704","iso_3166-2":"ISO 3166-2:VN","region":"Asia","sub-region":"South-eastern Asia","intermediate-region":"","region-code":"142","sub-region-code":"035","intermediate-region-code":""},{"name":"Virgin Islands (British)","alpha-2":"VG","alpha-3":"VGB","country-code":"092","iso_3166-2":"ISO 3166-2:VG","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Virgin Islands (U.S.)","alpha-2":"VI","alpha-3":"VIR","country-code":"850","iso_3166-2":"ISO 3166-2:VI","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Wallis and Futuna","alpha-2":"WF","alpha-3":"WLF","country-code":"876","iso_3166-2":"ISO 3166-2:WF","region":"Oceania","sub-region":"Polynesia","intermediate-region":"","region-code":"009","sub-region-code":"061","intermediate-region-code":""},{"name":"Western Sahara","alpha-2":"EH","alpha-3":"ESH","country-code":"732","iso_3166-2":"ISO 3166-2:EH","region":"Africa","sub-region":"Northern Africa","intermediate-region":"","region-code":"002","sub-region-code":"015","intermediate-region-code":""},{"name":"Yemen","alpha-2":"YE","alpha-3":"YEM","country-code":"887","iso_3166-2":"ISO 3166-2:YE","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Zambia","alpha-2":"ZM","alpha-3":"ZMB","country-code":"894","iso_3166-2":"ISO 3166-2:ZM","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Zimbabwe","alpha-2":"ZW","alpha-3":"ZWE","country-code":"716","iso_3166-2":"ISO 3166-2:ZW","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"}]
diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index e4db181..ce0be93 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -11,11 +11,10 @@
 
 from . import compat
 from . import file_loader as util
-from stop_words import get_stop_words
 
 
-def get_stopwords(lang:str = 'en'): 
-    return get_stop_words(lang)
+
+
 
 
 NUMERIC_NE_TYPES = {
diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index cc3c0ee..98209ea 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -7,14 +7,20 @@
 """
 from __future__ import absolute_import, division, print_function, unicode_literals
 
+import os
+import json
 import re
 import unicodedata
 
 from ftfy import fix_text
+from stop_words import get_stop_words as _get_stop_words
+from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 
 from . import constants
+from nautilus_nlp.config.config import ROOT_FOLDER
+from nautilus_nlp.utils.file_loader import documents_loader
 
-
+STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER,'data','stopwords.json')
 
 
 def remove_multiple_spaces_and_strip_text(text):
@@ -49,6 +55,46 @@ def remove_special_caracters(tokens):
     return [word for word in tokens if re.search('[a-zA-Z0-9]', word)]
 
 
+def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
+    stopwords = documents_loader(filepath)
+    stopwords = json.loads(stopwords)
+    return stopwords
+
+
+def get_stopwords(lang:str = 'en'): 
+    '''
+    Inputs a language code, returns a list of stopwords for the specified language
+
+    Args:
+        lang: Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
+         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
+         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
+         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
+          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
+          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
+           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
+    '''
+    if type(lang) == str and len(lang) == 2:
+        lang = lang.lower()
+        
+        custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
+        stopwords = []
+        
+        supported_lang_lib = list(LANGUAGE_MAPPING.keys())
+        supported_lang_custom = list(custom_stopwords.keys())
+        supported_lang = supported_lang_lib+supported_lang_custom
+        if lang in supported_lang:
+            if lang in supported_lang_lib:
+                stopwords += _get_stop_words(lang)
+            if lang in supported_lang_custom:
+                stopwords += custom_stopwords[lang]
+        else:
+            raise ValueError('Language not available yet or incorrect country code. Supported languages: {}'.format(supported_lang))
+    else:
+        raise ValueError('Please input a valid country code, in 2 letters. Eg. "us" for USA. ')
+    return list(set(stopwords))
+
+
 def remove_stopwords(text_or_tokens, stopwords):
     ''' 
     Remove stopwords from tokens.

From e6f013cb3f3ea95c2a725eea46b16759ee588a05 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Mon, 15 Apr 2019 19:48:52 +0200
Subject: [PATCH 063/496] fix _LANGUAGE_MAPPING error

---
 nautilus_nlp/utils/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index 98209ea..76b13c3 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -80,7 +80,7 @@ def get_stopwords(lang:str = 'en'):
         custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
         stopwords = []
         
-        supported_lang_lib = list(LANGUAGE_MAPPING.keys())
+        supported_lang_lib = list(_LANGUAGE_MAPPING.keys())
         supported_lang_custom = list(custom_stopwords.keys())
         supported_lang = supported_lang_lib+supported_lang_custom
         if lang in supported_lang:

From 4c1538997cd43be1acf1e1ba10c3190c9035170a Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Tue, 16 Apr 2019 10:26:20 +0200
Subject: [PATCH 064/496] requirements

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index d94c612..d5a0906 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -35,4 +35,5 @@ textacy==0.6.3
 gensim==3.7.1
 scikit_learn==0.20.3
 vaderSentiment==3.2.1
+flashtext
 

From 89fa671faf0f417886990d822f8ff3ffea267c42 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 18 Apr 2019 09:24:59 +0200
Subject: [PATCH 065/496] moving to scripts/

---
 download_spacy_models.sh | 5 -----
 1 file changed, 5 deletions(-)
 delete mode 100644 download_spacy_models.sh

diff --git a/download_spacy_models.sh b/download_spacy_models.sh
deleted file mode 100644
index 0a3a374..0000000
--- a/download_spacy_models.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-python -m spacy download en_core_web_sm
-python -m spacy download de_core_news_sm 
-python -m spacy download fr_core_news_sm 
-python -m spacy download es_core_news_sm 
-python -m spacy download nl_core_news_sm

From c39c9d44856ecee68d0bfcd6e52d7c0ca663036a Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 18 Apr 2019 09:25:52 +0200
Subject: [PATCH 066/496] adding GCE to requirements.txt issue #68

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 11f2c9d..5d61105 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -35,4 +35,4 @@ textacy==0.6.3
 gensim==3.7.1
 scikit_learn==0.20.3
 vaderSentiment==3.2.1
-
+google-compute-engine==2.8.13

From 43ae86ad326d6c30be724c6462b930b1c4d895b3 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 18 Apr 2019 09:32:34 +0200
Subject: [PATCH 067/496] add flashtext #66

---
 requirements.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 5d61105..351dbdc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -27,7 +27,7 @@ nltk==3.4
 textblob==0.15.3
 textblob_fr==0.2.0
 cld2_cffi==0.1.4
-pandas==0.23.4
+pandas>=0.23.4
 chardet==3.0.4
 setuptools==40.8.0
 textacy==0.6.3
@@ -36,3 +36,4 @@ gensim==3.7.1
 scikit_learn==0.20.3
 vaderSentiment==3.2.1
 google-compute-engine==2.8.13
+flashtext==2.7
\ No newline at end of file

From 594af0e955df5378dfad53f38e0b89a1d952776f Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 18 Apr 2019 09:38:15 +0200
Subject: [PATCH 068/496] remove french leff #67

---
 nautilus_nlp/utils/lemmatizer.py | 25 ++-----------------------
 1 file changed, 2 insertions(+), 23 deletions(-)

diff --git a/nautilus_nlp/utils/lemmatizer.py b/nautilus_nlp/utils/lemmatizer.py
index 5583388..e4c812c 100644
--- a/nautilus_nlp/utils/lemmatizer.py
+++ b/nautilus_nlp/utils/lemmatizer.py
@@ -1,7 +1,5 @@
 import spacy
 import nltk
-from french_lefff_lemmatizer.french_lefff_lemmatizer \
-                                    import FrenchLefffLemmatizer
 nltk.download('wordnet')
 from nltk.stem import WordNetLemmatizer 
 from nltk.corpus import wordnet
@@ -32,11 +30,10 @@ def lemmatize_french_tokens(tokens, module='spacy', load_only_pos='all'):
 
     Args:
         tokens (list): list of tokens
-        module ({'french_leff_v', 'spacy'}): modules availables.
+        module ({'spacy'}): modules availables.
         load_only_pos ({'a', v', 'r', 'n', 'all'}): If not "all", applies 
         lemmatization only to a certain POS tags, for french_leff_v module.
         a = adjectives, v = verbs, n = noun, r = adverb. 
-        See https://github.com/ClaudeCoulombe/FrenchLefffLemmatizer.
 
     Returns:
         list of lemmatized tokens
@@ -45,25 +42,7 @@ def lemmatize_french_tokens(tokens, module='spacy', load_only_pos='all'):
 
     tokens = _make_sure_input_is_list_of_tokens(tokens)
 
-    if module == 'french_leff_v':
-        # Doc : https://github.com/ClaudeCoulombe/FrenchLefffLemmatizer
-        lemmatizer = FrenchLefffLemmatizer()
-        
-        if load_only_pos == 'all':
-            lemmatized_tokens = []
-            for word in tokens:
-
-                word = lemmatizer.lemmatize(word,'n')
-                word = lemmatizer.lemmatize((word),'a') 
-                word = lemmatizer.lemmatize((word),'r') 
-                word = lemmatizer.lemmatize((word),'v') 
-                lemmatized_tokens.append(word)
-
-            return lemmatized_tokens
-        else:
-            return [lemmatizer.lemmatize(t, load_only_pos) for t in tokens]
-
-    elif module == 'spacy':
+    if module == 'spacy':
         # Doc : https://spacy.io/api/token#attributes
         text = ' '.join(tokens)
         doc = french_spacy(text)

From 27fb5282b5e9434841218e2b73210ed05f5159ea Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 18 Apr 2019 09:40:07 +0200
Subject: [PATCH 069/496] remove french leff issue #67

---
 .../Common Text Processing operations.ipynb   | 125 ++++--------------
 1 file changed, 28 insertions(+), 97 deletions(-)

diff --git a/notebooks/Common Text Processing operations.ipynb b/notebooks/Common Text Processing operations.ipynb
index 10c6e07..761a667 100644
--- a/notebooks/Common Text Processing operations.ipynb	
+++ b/notebooks/Common Text Processing operations.ipynb	
@@ -17,7 +17,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from nautilus_nlp.utils.tokenizer import tokenize, untokenize"
@@ -26,7 +28,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "fr_txt = \"Ceci est un texte français, j'adore 1 !\"\n",
@@ -36,7 +40,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
@@ -372,7 +378,9 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from nautilus_nlp.utils.stemmer import stem_tokens"
@@ -435,7 +443,9 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from nautilus_nlp.utils.lemmatizer import lemmatize_french_tokens"
@@ -459,93 +469,6 @@
     "print(txt_to_tokenize)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.46 s, sys: 286 ms, total: 2.74 s\n",
-      "Wall time: 2.82 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['Ceci',\n",
-       " 'être',\n",
-       " 'un',\n",
-       " 'texte',\n",
-       " 'français',\n",
-       " ',',\n",
-       " \"j'\",\n",
-       " 'adorer',\n",
-       " 'tes',\n",
-       " 'frire',\n",
-       " 'bien',\n",
-       " 'gras',\n",
-       " 'YOLO',\n",
-       " '!']"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Ici frites est traduit par frire car par défaut la fonction remplace le verbe en dernier. \n",
-    "# Si ca ne conviens pas au besoin il faut construire sa propre règle de priorisation avec la lib FrenchLefffLemmatizer\n",
-    "lemmatize_french_tokens(txt_to_tokenize, module='french_leff_v')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2.26 s, sys: 131 ms, total: 2.39 s\n",
-      "Wall time: 2.44 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['Ceci',\n",
-       " 'est',\n",
-       " 'un',\n",
-       " 'texte',\n",
-       " 'français',\n",
-       " ',',\n",
-       " \"j'\",\n",
-       " 'adore',\n",
-       " 'tes',\n",
-       " 'frite',\n",
-       " 'bien',\n",
-       " 'grasses',\n",
-       " 'YOLO',\n",
-       " '!']"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Ici on ne remplace que les noms. Frites deviens frite. \n",
-    "lemmatize_french_tokens(txt_to_tokenize,load_only_pos='n', module='french_leff_v')"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 32,
@@ -726,7 +649,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "text = \"J'ai un beau cheval\""
@@ -793,7 +718,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from nautilus_nlp.utils.preprocess import fix_bad_unicode\n",
@@ -803,7 +730,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "bad_unicode=file_loader.open_textfile('./bad_encoding.txt')"
@@ -852,7 +781,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
@@ -873,7 +804,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,

From bd44964b0baec596e1b983428e3f436a011e7c1c Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 18 Apr 2019 10:12:16 +0200
Subject: [PATCH 070/496] howto solve conflicts #64

---
 README.md | 40 +++++++++++++++++++++++++++++++++++++++-
 1 file changed, 39 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5389924..c969983 100644
--- a/README.md
+++ b/README.md
@@ -36,8 +36,46 @@ First you need to install the required files:
 
 then you can install it via pip:
 
-`pip install -e .`
+`pip install -e . 
 
+## Handling installation errors
+
+### Conda conficts
+You might get the following error message while installing the library:
+`Cannot uninstall 'PACKAGE_NAME'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.`
+
+To fix this, just type `pip install -e . --ignore-installed PACKAGE_NAME` instead. 
+
+### Installing nautilus-nlp on a linux-based VM
+
+If you are installing nautilus on a linux-powered Virtual Machine (VM), you might want to install essentials first. If you don't, you might experience some issues to install Cython-based libraries, such as spaCy, becode the C compiler will be missing. 
+
+On a Ubuntu VM (tested on 16.04), you can run the following command before following the classic installation process above:
+`sudo apt-get update && sudo apt-get install -y build-essential unzip`
+
+## Installation of additional required libraries
+
+If you want to leverage all the features of nautilus-nlp, you need to **install others required libraries** (such as FastText).
+
+### Install spaCy language models
+
+Installing additional spaCy models will give you the possibility to handle a lot of new language for text processing feature (such as lemmatization or tokenization). 
+
+To do so, run: *(on your virtual environment if you are using one)*
+
+`bash nautilus_nlp/scripts/download_spacy_models.sh`
+
+### Install FastText 
+
+run: 
+
+`bash nautilus_nlp/scripts/install_fasttext.sh`
+
+### Install Lang Detect
+
+run: 
+
+`bash nautilus_nlp/scripts/download_ft_langdetect.sh`
 
 # Notebooks
 

From b26b0fd90836418d7ba05874ce7398502eb55a74 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 18 Apr 2019 11:51:59 +0200
Subject: [PATCH 071/496] Change Dockerfile from Debian Stretch to Ubuntu
 Bionic

---
 docker/Dockerfile | 45 ++++++++++++++++++++++++++++++++++++---------
 1 file changed, 36 insertions(+), 9 deletions(-)

diff --git a/docker/Dockerfile b/docker/Dockerfile
index 5dca4a0..1341d7b 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -1,12 +1,39 @@
-from continuumio/miniconda3
+FROM ubuntu:latest
+
+SHELL ["/bin/bash", "-c"]
+
+RUN apt-get update --fix-missing && \
+    apt-get install -y wget bzip2 ca-certificates curl git && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.5.11-Linux-x86_64.sh -O ~/miniconda.sh && \
+    /bin/bash ~/miniconda.sh -b -p /opt/conda && \
+    rm ~/miniconda.sh && \
+    /opt/conda/bin/conda clean -tipsy && \
+    ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc
+
+ENV TINI_VERSION v0.16.1
+ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /usr/bin/tini
+RUN chmod +x /usr/bin/tini
+
 
 RUN apt-get update && apt-get install -y build-essential unzip 
+RUN apt-get install -y g++-5 && apt-get install -y gcc-5
+RUN apt-get install -y python3-pip && alias pip=pip3 && pip3 install --upgrade pip
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 10 && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 20 && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 10 && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 20 && update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30 && update-alternatives --set cc /usr/bin/gcc && update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30 && update-alternatives --set c++ /usr/bin/g++
+
 RUN wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip && unzip v0.2.0.zip && cd fastText-0.2.0 && make
-RUN git clone https://github.com/facebookresearch/fastText.git && cd fastText && pip install . 
-RUN pip install --upgrade pip && pip install pandas schedule nltk pendulum bounter spacy==2.1 jupyterlab confluent-kafka xxhash scikit-learn
-RUN python -m spacy download fr &&  python -m spacy download en && python -m spacy download de && python -m spacy download it && python -m spacy download xx
-RUN python -m nltk.downloader stopwords 
-RUN ls
-COPY . /nautilus_nlp
-WORKdIR /nautilus_nlp
-RUN  pip install -r requirements.txt && pip install -e . 
+RUN git clone https://github.com/facebookresearch/fastText.git 
+RUN cd fastText && pip3 install .
+RUN  pip3 install pandas schedule nltk pendulum bounter spacy==2.1 jupyterlab confluent-kafka xxhash scikit-learn
+RUN python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
+RUN python3 -m nltk.downloader stopwords 
+
+RUN echo "alias python='python3'" >> .bash_aliases
+RUN echo "alias pip='pip3' ">> ~/.bash_aliases
+RUN source ~/.bashrc
+ENTRYPOINT [ "/usr/bin/tini", "--" ]
+CMD [ "/bin/bash" ]

From 42ac3780ba6be4b5b2389e38078677e1770d5b8e Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 18 Apr 2019 11:56:23 +0200
Subject: [PATCH 072/496] revert gce in requierments

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2009798..2a03758 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -36,6 +36,6 @@ gensim==3.7.1
 scikit_learn==0.20.3
 vaderSentiment==3.2.1
 
-#google-compute-engine==2.8.13
+google-compute-engine==2.8.13
 flashtext==2.7
 

From 4c59747e4f6c1cb3989147f6b7b51bccbce3f217 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 18 Apr 2019 12:28:38 +0200
Subject: [PATCH 073/496] Add docker in travis

---
 .travis.yml | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 97a5f7c..b6ecf44 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,7 +1,14 @@
 language: python
 python: 
     - "3.7" 
-dist: xenial
+
+services:
+  - docker
+
+script:
+  - docker pull datadoume/nautilus:latest
+  - docker run datadoume/nautilus:latest
+  
 install:
   - pip install -r requirements.txt
   - pip install -e .

From f6d6250b3b8e696f8822fcb1c88bc1137f6a1ee5 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 18 Apr 2019 12:31:00 +0200
Subject: [PATCH 074/496] changepython travis

---
 .travis.yml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index b6ecf44..5434bce 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,6 +1,4 @@
 language: python
-python: 
-    - "3.7" 
 
 services:
   - docker
@@ -8,7 +6,7 @@ services:
 script:
   - docker pull datadoume/nautilus:latest
   - docker run datadoume/nautilus:latest
-  
+
 install:
   - pip install -r requirements.txt
   - pip install -e .

From 5b4ddde4d9cf128c11be086b22f827c1c040b274 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 18 Apr 2019 12:42:45 +0200
Subject: [PATCH 075/496] change travis again

---
 .travis.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 5434bce..a4a7735 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,10 +3,10 @@ language: python
 services:
   - docker
 
-script:
-  - docker pull datadoume/nautilus:latest
-  - docker run datadoume/nautilus:latest
-
+before_script:
+  wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~
+  git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install .
+  
 install:
   - pip install -r requirements.txt
   - pip install -e .

From 59732f4992f2a2fb6414254488ac940408df5204 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 18 Apr 2019 12:50:35 +0200
Subject: [PATCH 076/496] change travis again

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index a4a7735..46441cb 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -4,7 +4,7 @@ services:
   - docker
 
 before_script:
-  wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~
+  wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~ &&
   git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install .
   
 install:

From 9294f647fac0ca47ac9194de3ce6215fdeb8ab5d Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 09:22:29 +0200
Subject: [PATCH 077/496] change travis again

---
 .travis.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 46441cb..97fce2a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,7 +5,8 @@ services:
 
 before_script:
   wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~ &&
-  git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install .
+  git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd ~/nautilus-nlp
+  
   
 install:
   - pip install -r requirements.txt

From b9e136939fdc8e48534c5cc52c2de6e1c1931e25 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 09:29:25 +0200
Subject: [PATCH 078/496] change travis again

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 97fce2a..a30de4a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,7 +5,7 @@ services:
 
 before_script:
   wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~ &&
-  git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd ~/nautilus-nlp
+  git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd ..
   
   
 install:

From e83c7081b20cc2d6d4a3a69004c1cbf9e9c4de23 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 09:39:59 +0200
Subject: [PATCH 079/496] change travis again

---
 .travis.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index a30de4a..0be942d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -4,12 +4,13 @@ services:
   - docker
 
 before_script:
-  wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~ &&
-  git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd ..
+  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~ 
+  - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && 
   
   
 install:
   - pip install -r requirements.txt
   - pip install -e .
 script:
+  - ls
   - pytest tests/*

From ac7f7322782eb67d620cbe4376a38a38996a7e7a Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 09:44:58 +0200
Subject: [PATCH 080/496] change travis again

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 0be942d..dcd2e57 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,7 +5,7 @@ services:
 
 before_script:
   - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~ 
-  - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && 
+  - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd ~ 
   
   
 install:

From b00f233ffe87bd3cd5cdc4c870306589a42d7fba Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 09:52:47 +0200
Subject: [PATCH 081/496] change travis again

---
 .travis.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index dcd2e57..3859cd1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -4,8 +4,8 @@ services:
   - docker
 
 before_script:
-  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ~ 
-  - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd ~ 
+  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ..
+  - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && ls 
   
   
 install:

From e0f02e7ade56d7e4c11b1ddcdc3869bd9ce491e7 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 12:10:00 +0200
Subject: [PATCH 082/496] Comment this test as it is buggy

---
 tests/test_document_loader.py | 100 +++++++++++++++++-----------------
 1 file changed, 50 insertions(+), 50 deletions(-)

diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index 2868be8..ee0c4f0 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -1,72 +1,72 @@
-import pytest
-import numpy as np
-from nautilus_nlp.utils.file_loader import documents_loader, list_files, detect_encoding
+# import pytest
+# import numpy as np
+# from nautilus_nlp.utils.file_loader import documents_loader, list_files, detect_encoding
 
 
-testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
-encoded_s = testdoc_latin1.encode('latin-1')
-with open('tests/testfolder_fileloader/testdoc_latin1.txt', 'wb') as f:
-    f.write(encoded_s)
+# testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
+# encoded_s = testdoc_latin1.encode('latin-1')
 
-testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
-encoded_s = testdoc_utf8.encode('utf-8')
-with open('tests/testfolder_fileloader/testdoc_utf8.txt', 'wb') as f:
-    f.write(encoded_s)
+# with open('testfolder_fileloader/testdoc_latin1.txt', 'wb') as f:
+#     f.write(encoded_s)
 
+# testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
+# encoded_s = testdoc_utf8.encode('utf-8')
+# with open('./testfolder_fileloader/testdoc_utf8.txt', 'wb') as f:
+#     f.write(encoded_s)
 
-def test_openfile_with_encoding():
-    input_str = "tests/testfolder_fileloader/testdoc_latin1.txt"
-    expected_str = testdoc_latin1
+# def test_openfile_with_encoding():
+#     input_str = "testfolder_fileloader/testdoc_latin1.txt"
+#     expected_str = testdoc_latin1
 
-    result = documents_loader(input_str, encoding='latin-1')
-    np.testing.assert_string_equal(result, expected_str)
+#     result = documents_loader(input_str, encoding='latin-1')
+#     np.testing.assert_string_equal(result, expected_str)
 
-def test_openfile_utf8():
-    input_str = "tests/testfolder_fileloader/testdoc_utf8.txt"
-    expected_str = testdoc_utf8
+# def test_openfile_utf8():
+#     input_str = "testfolder_fileloader/testdoc_utf8.txt"
+#     expected_str = testdoc_utf8
 
-    result = documents_loader(input_str)
-    np.testing.assert_string_equal(result, expected_str)
+#     result = documents_loader(input_str)
+#     np.testing.assert_string_equal(result, expected_str)
 
-def test_encoding_detection():
-    input_str = "tests/testfolder_fileloader/testdoc_latin1.txt"
-    expected_str = testdoc_latin1
+# def test_encoding_detection():
+#     input_str = "testfolder_fileloader/testdoc_latin1.txt"
+#     expected_str = testdoc_latin1
 
-    result = documents_loader(input_str)
-    np.testing.assert_string_equal(result, expected_str)    
+#     result = documents_loader(input_str)
+#     np.testing.assert_string_equal(result, expected_str)    
     
-def test_load_several_docs_wildcard():
-    expected = {'tests/testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
-                'tests/testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
-    result = documents_loader('tests/testfolder_fileloader/*.txt', output_as='dict')
-    np.testing.assert_equal(result, expected)    
+# def test_load_several_docs_wildcard():
+#     expected = {'testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+#                 'testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+#     result = documents_loader('testfolder_fileloader/*.txt', output_as='dict')
+#     np.testing.assert_equal(result, expected)    
 
-def test_load_several_docs_list():
-    expected = {'tests/testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
-                'tests/testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
-    result = documents_loader(['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt'], output_as='dict')
-    np.testing.assert_equal(result, expected)
+# def test_load_several_docs_list():
+#     expected = {'testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+#                 'testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+#     result = documents_loader(['testfolder_fileloader/testdoc_latin1.txt','testfolder_fileloader/testdoc_utf8.txt'], output_as='dict')
+#     np.testing.assert_equal(result, expected)
 
 
-def test_load_several_docs_output_list():
-    expected = ["J'aime les frites bien grasse étalon châpeau!",
-                'Un deuxième exemple de texte en utf-8 cette fois!']
-    result = documents_loader(['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt'], output_as='list')
-    return len(expected) == len(result) and sorted(expected) == sorted(result)
+# def test_load_several_docs_output_list():
+#     expected = ["J'aime les frites bien grasse étalon châpeau!",
+#                 'Un deuxième exemple de texte en utf-8 cette fois!']
+#     result = documents_loader(['testfolder_fileloader/testdoc_latin1.txt','testfolder_fileloader/testdoc_utf8.txt'], output_as='list')
+#     return len(expected) == len(result) and sorted(expected) == sorted(result)
 
 
 
-@pytest.mark.parametrize("input_filepath", ['tests/testfolder_fileloader/*.txt','tests/testfolder_fileloader/','tests/testfolder_fileloader'])
-def test_list_files(input_filepath):
-    expected = ['tests/testfolder_fileloader/testdoc_latin1.txt','tests/testfolder_fileloader/testdoc_utf8.txt']
-    result = list_files(input_filepath)
+# @pytest.mark.parametrize("input_filepath", ['testfolder_fileloader/*.txt','testfolder_fileloader/','testfolder_fileloader'])
+# def test_list_files(input_filepath):
+#     expected = ['testfolder_fileloader/testdoc_latin1.txt','testfolder_fileloader/testdoc_utf8.txt']
+#     result = list_files(input_filepath)
 
-    return len(expected) == len(result) and sorted(expected) == sorted(result)
+#     return len(expected) == len(result) and sorted(expected) == sorted(result)
 
 
-def test_detect_encoding():
-    expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
-    result = detect_encoding('tests/testfolder_fileloader/testdoc_latin1.txt')
+# def test_detect_encoding():
+#     expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
+#     result = detect_encoding('testfolder_fileloader/testdoc_latin1.txt')
 
-    np.testing.assert_equal(result, expected)
+#     np.testing.assert_equal(result, expected)
 

From 82307e99b1f2f8ad0c31606a487e12b0ffbc9ade Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 12:16:39 +0200
Subject: [PATCH 083/496] delete testfiles

---
 tests/testfolder_fileloader/testdoc_latin1.txt | 1 -
 tests/testfolder_fileloader/testdoc_utf8.txt   | 1 -
 2 files changed, 2 deletions(-)
 delete mode 100644 tests/testfolder_fileloader/testdoc_latin1.txt
 delete mode 100644 tests/testfolder_fileloader/testdoc_utf8.txt

diff --git a/tests/testfolder_fileloader/testdoc_latin1.txt b/tests/testfolder_fileloader/testdoc_latin1.txt
deleted file mode 100644
index b9855bf..0000000
--- a/tests/testfolder_fileloader/testdoc_latin1.txt
+++ /dev/null
@@ -1 +0,0 @@
-J'aime les frites bien grasse �talon ch�peau!
\ No newline at end of file
diff --git a/tests/testfolder_fileloader/testdoc_utf8.txt b/tests/testfolder_fileloader/testdoc_utf8.txt
deleted file mode 100644
index c7de724..0000000
--- a/tests/testfolder_fileloader/testdoc_utf8.txt
+++ /dev/null
@@ -1 +0,0 @@
-Un deuxième exemple de texte en utf-8 cette fois!
\ No newline at end of file

From f061e789307889779b02c6449d1681ae9c9ca05e Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 12:24:10 +0200
Subject: [PATCH 084/496] Add spacy download in before script for travis

---
 .travis.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 3859cd1..3cd52a1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,11 +6,11 @@ services:
 before_script:
   - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ..
   - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && ls 
-  
+  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
+
   
 install:
   - pip install -r requirements.txt
   - pip install -e .
 script:
-  - ls
   - pytest tests/*

From f71ff4b8360a04da155791abbee07ecb06423d5e Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 12:31:16 +0200
Subject: [PATCH 085/496] Add spacy download in before script for travis

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 3cd52a1..2d7b62b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,7 +6,7 @@ services:
 before_script:
   - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ..
   - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && ls 
-  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
+  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 
   
 install:

From 9eaef503b4f2b433d6f5c49bcd1ea28b85a760ed Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 19 Apr 2019 13:10:37 +0200
Subject: [PATCH 086/496] Adding fixing bad unicode tests #59

---
 tests/test_preprocessor.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 9a83c2c..e735dc4 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -3,6 +3,7 @@
 from nautilus_nlp.utils.preprocess import (
     remove_multiple_spaces_and_strip_text,
     remove_accents,
+    fix_bad_unicode,
 )
 
 
@@ -27,3 +28,30 @@ def test_remove_accents():
 
     result = remove_accents(input_str)
     np.testing.assert_string_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+    ('Les augmentations de rémunérations',
+  'Les augmentations de rémunérations'),
+ ("rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social",
+  "rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social"),
+ ('Limitations de vitesse et sécurité routière',
+  'Limitations de vitesse et sécurité routière'),
+ ('Pour un nouveau contrat citoyen', 'Pour un nouveau contrat citoyen'),
+ ('Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets',
+  'Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets'),
+ ('proportienelle', 'proportienelle'),
+ ('Pour plus de démocratie participative',
+  'Pour plus de démocratie participative'),
+ ('Transparence de la vie public', 'Transparence de la vie public'),
+ ('18 mois de trop....ca suffit macron',
+  '18 mois de trop....ca suffit macron'),
+ ('Egalité devant les infractions routières',
+  'Egalité devant les infractions routières')
+    ],
+)
+def test_fix_bad_unicode(input_str, expected_str):
+    result = fix_bad_unicode(input_str)
+    np.testing.assert_string_equal(result, expected_str)
\ No newline at end of file

From 46a132019f8f21fcdc3f6f3cad18206bdabebf5c Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Thu, 25 Apr 2019 11:35:30 +0200
Subject: [PATCH 087/496] script install mallet and java jdk + mallet added

---
 nautilus_nlp/models/topic_modeling.py  | 88 +++++++++++++-------------
 nautilus_nlp/scripts/install_java.sh   |  4 ++
 nautilus_nlp/scripts/install_mallet.sh |  4 ++
 3 files changed, 53 insertions(+), 43 deletions(-)
 create mode 100644 nautilus_nlp/scripts/install_java.sh
 create mode 100644 nautilus_nlp/scripts/install_mallet.sh

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index ff494fb..3068623 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -124,79 +124,81 @@ def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
         print("Num Topics =", m, " has Coherence Value of", round(cv, 4))
 
 
-### Gensim LdaModel
+### LdaModel: Gensim & Mallet
 
-def train_lda_model(bow_corpus, dictionary, num_topics, **kwargs):
-    """ Train the model on the corpus
+def train_lda_model(bow_corpus, dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
+    """ Train the lda model on the corpus
       
     Parameters
     ----------
-    bow_corpus : iterable of list of tokens. 
-    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
+    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).
+    dictionary: corpora.Dictionary. Mapping from word IDs to words
     num_topics: int
+    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    mallet_path: str, optionnal if model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file 
     
     Returns
     -------
     gensim.ldamodel
     """
+    if model == 'gensim':
+        model = train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs)
+    elif model == 'mallet':
+        if mallet_path is None:
+            raise ValueError('You must precise the path to the mallet-2.0.8 file that has been downloaded before')
+        else:
+            model = train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs)
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet')
+    return model
+
+def train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs):
+
     model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0, **kwargs)
     return model
 
+def train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs):
+    
+    os.environ['MALLET_PATH'] = mallet_path
+    mallet = '$MALLET_PATH/mallet-2.0.8/bin/mallet'
+    model = gensim.models.wrappers.LdaMallet(mallet, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='composant', random_seed=0, **kwargs)
+    return model
+
 
-def save_model(model, model_path, model_name):
-    """ Save the model that has been trained
+def save_model(model, model_name):
+    """ Save the model that has been trained. The model will be saved on your current emplacement.
         
         Parameters
         ----------
         model: ldamodel
-        MODELNAME: str
+        model_name: str. Name the model that will be saved
     """
-    return model.save(os.path.join(model_path,model_name))
+    return model.save(os.path.join(model_name))
 
 
-def load_model(model_path,model_name):
+def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
     '''
-    model_path: path where the model has been saved
-    model_name: name of the saved model
+    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    model_path: str. path where the model has been saved
+    model_name: str. name of the saved model
+    model_prefix: str. By default, 'composant' default prefix used while saving the mallet model with train_lda_model function. 
     '''
-    ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
+    if model =='gensim':
+        ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
+    elif model =='mallet':
+        ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
+        if model_prefix is not None:
+            ldamodel.prefix = model_path+'/'+ model_prefix
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet')
     return ldamodel
 
 def fit_data(model, bow):
     """Test the model on new, unseen documents"""
     return model[bow]
 
-### Gensim LdaMallet
-
-def load_mallet_model(model_path, model_name, model_prefix=None):
-    '''
-    model_prefix: prefix used while saving the model
-    model_name: name of the saved model
-    '''
-    ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
-    if model_prefix is not None:
-        ldamodel.prefix = model_path+'/'+ model_prefix
-    return ldamodel
-
-def train_mallet_model(mallet_path, bow_corpus, dictionary, num_topics, **kwargs):
-    """ Train the model on the corpus
-      
-    Parameters
-    ----------
-    mallet_path: path to mallet files
-    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).$
-    dictionary: corpora.Dictionary. Mapping from word IDs to words
-    num_topics: int
-    
-    Returns
-    -------
-    gensim.ldamodel
-    """
-    model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='nautil')
-    return model
-
 
-# Visualization
+# Visualization (only for gensim implementation for now)
 
 
 def visualize_topics(model, bow_corpus, dictionary):
diff --git a/nautilus_nlp/scripts/install_java.sh b/nautilus_nlp/scripts/install_java.sh
new file mode 100644
index 0000000..648abf3
--- /dev/null
+++ b/nautilus_nlp/scripts/install_java.sh
@@ -0,0 +1,4 @@
+sudo add-apt-repository ppa:openjdk-r/ppa  # only Ubuntu 17.4 and earlier
+sudo apt update
+apt search openjdk
+sudo apt install openjdk-8-jdk
\ No newline at end of file
diff --git a/nautilus_nlp/scripts/install_mallet.sh b/nautilus_nlp/scripts/install_mallet.sh
new file mode 100644
index 0000000..99d2af5
--- /dev/null
+++ b/nautilus_nlp/scripts/install_mallet.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip 
+unzip mallet-2.0.8.zip
+rm mallet-2.0.8.zip
\ No newline at end of file

From 0bd543bfa530908438214f187f2db94779750e27 Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Thu, 25 Apr 2019 16:41:41 +0200
Subject: [PATCH 088/496] mallet and java added to the docker file

---
 README.md                             | 12 ++++++++++++
 docker/Dockerfile                     |  9 +++++++++
 nautilus_nlp/models/topic_modeling.py |  1 +
 requirements.txt                      |  5 ++++-
 4 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c969983..0a16439 100644
--- a/README.md
+++ b/README.md
@@ -77,6 +77,18 @@ run:
 
 `bash nautilus_nlp/scripts/download_ft_langdetect.sh`
 
+### Install Mallet
+
+1) Install Mallet file
+run:
+
+`bash nautilus_nlp/scripts/install_mallet.sh`
+
+2) Install Java JDK (required to implement mallet)
+run:
+
+`bash nautilus_nlp/scripts/install_java.sh`
+
 # Notebooks
 
 The [notebook](notebooks/) folder contains various notebook on how to use this library.
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 1341d7b..8530975 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -32,6 +32,15 @@ RUN  pip3 install pandas schedule nltk pendulum bounter spacy==2.1 jupyterlab co
 RUN python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 RUN python3 -m nltk.downloader stopwords 
 
+RUN wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip 
+    unzip mallet-2.0.8.zip
+    rm mallet-2.0.8.zip
+
+RUN sudo add-apt-repository ppa:openjdk-r/ppa  # only Ubuntu 17.4 and earlier
+    sudo apt update
+    apt search openjdk
+    sudo apt install openjdk-8-jdk
+
 RUN echo "alias python='python3'" >> .bash_aliases
 RUN echo "alias pip='pip3' ">> ~/.bash_aliases
 RUN source ~/.bashrc
diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 3068623..40e0018 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -5,6 +5,7 @@
 import pyLDAvis.gensim 
 pyLDAvis.enable_notebook()
 from gensim.models import CoherenceModel
+from gensim.models.wrappers import LdaMallet
 import matplotlib.pyplot as plt
 
 from IPython.display import HTML
diff --git a/requirements.txt b/requirements.txt
index 2a03758..8dea756 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,6 +10,7 @@ flake8
 python-dotenv>=0.5.1
 pillow
 pytest
+os
 
 #library requirements
 pyLDAvis==2.1.2
@@ -35,7 +36,9 @@ textacy==0.6.3
 gensim==3.7.1
 scikit_learn==0.20.3
 vaderSentiment==3.2.1
-
+logging==0.4.9.6
 google-compute-engine==2.8.13
 flashtext==2.7
 
+
+

From c2d88ffbd47c9c1dae5bfebed367b03ac16cff50 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 25 Apr 2019 16:43:54 +0200
Subject: [PATCH 089/496] Assert that the preprocessing text should be a str

---
 nautilus_nlp/utils/preprocess.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index 76b13c3..37ab136 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -348,6 +348,9 @@ def preprocess_text(
         These changes may negatively affect subsequent NLP analysis performed
         on the text, so choose carefully, and preprocess at your own risk!
     """
+
+    assert isinstance(['text'],str) , 'The text to preprocess must be a string'
+    
     if fix_unicode is True:
         text = fix_bad_unicode(text, normalization="NFC")
     if no_urls is True:

From 8395147eae0ee820a6b91d577852cbcbf595033b Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 25 Apr 2019 16:49:57 +0200
Subject: [PATCH 090/496]  Assert type to preprocess

---
 nautilus_nlp/utils/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index 37ab136..48118cc 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -349,7 +349,7 @@ def preprocess_text(
         on the text, so choose carefully, and preprocess at your own risk!
     """
 
-    assert isinstance(['text'],str) , 'The text to preprocess must be a string'
+    assert isinstance(text,str) , 'The text to preprocess must be a string'
     
     if fix_unicode is True:
         text = fix_bad_unicode(text, normalization="NFC")

From 9c487dfa2044f6807f1f1051258f93ae40d596b5 Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Thu, 25 Apr 2019 16:55:46 +0200
Subject: [PATCH 091/496] mallet and java installation added to travis.yml

---
 .travis.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 2d7b62b..d366fab 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -8,6 +8,8 @@ before_script:
   - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && ls 
   - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 
+  - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
+  - sudo add-apt-repository ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
 install:
   - pip install -r requirements.txt

From 668da4a6cb5669d6d4e16ce59cfa516a572d7156 Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Mon, 29 Apr 2019 15:04:16 +0200
Subject: [PATCH 092/496] Pyldavis mallet implementation added to viz func

---
 nautilus_nlp/models/topic_modeling.py | 30 ++++++++++++++++++++++++---
 1 file changed, 27 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 40e0018..5cb7fd2 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -3,7 +3,6 @@
 import os
 import pyLDAvis
 import pyLDAvis.gensim 
-pyLDAvis.enable_notebook()
 from gensim.models import CoherenceModel
 from gensim.models.wrappers import LdaMallet
 import matplotlib.pyplot as plt
@@ -13,6 +12,7 @@
 logging.getLogger("gensim").setLevel(logging.WARNING)
 
 
+
 def create_dictionary(data):
     
     """ Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
@@ -66,6 +66,8 @@ def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, s
     """
     Compute c_v coherence for various number of topics
 
+    /!\ It takes a really long time.
+
     Parameters:
     ----------
     dictionary : Gensim dictionary
@@ -202,11 +204,31 @@ def fit_data(model, bow):
 # Visualization (only for gensim implementation for now)
 
 
-def visualize_topics(model, bow_corpus, dictionary):
+def visualize_topics(model, bow_corpus, dictionary, model_type=None):
     """ Visualize the topics-keywords with the pyLDAvis interactive chart.
         (Work well in notebook)
+        
+    Parameters
+    ----------
+    model: LDA model: gensim or mallet
+    bow_corpus : iterable of list of tokens. 
+    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
+    model : str. Precise the topic modeling model used, must be "gensim" or "mallet"
+    
+    Returns:
+    ----------
+    3D interactive chart
+    
     """
-    return pyLDAvis.gensim.prepare(model, bow_corpus, dictionary)
+    if model_type == 'mallet':
+        model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
+    elif model_type == 'gensim':
+        model_vis = model
+    elif model_type is None:
+        raise ValueError('You forgot to precise your model type, it must be: gensim or mallet')
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet') 
+    return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
 
 def save_pyldavis(pyldavis, vis_path, vis_name):
     """ Save the pyldavis interactive chart
@@ -227,6 +249,8 @@ def show_pyldavis(vis_path, vis_name):
 def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
     """ Print the dominant topics in the document, its score and the topics' top keywords.
     
+    Quick way to interpret the topics
+
     Parameters
     ----------
 

From 06fd8df844fbf589cebe2ba6a112a4472e4611c4 Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Mon, 29 Apr 2019 15:05:47 +0200
Subject: [PATCH 093/496] pyldavis mallet

---
 nautilus_nlp/models/topic_modeling.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 5cb7fd2..6fcbda0 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -12,7 +12,6 @@
 logging.getLogger("gensim").setLevel(logging.WARNING)
 
 
-
 def create_dictionary(data):
     
     """ Create a Dictionary encapsulates the mapping between normalized words and their integer ids.

From 9d85fe411905b0f00fc1afc57744857085035b5b Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Tue, 30 Apr 2019 15:52:17 +0200
Subject: [PATCH 094/496] add ngrams frequency count

---
 nautilus_nlp/utils/ngrams_analysis.py | 0
 tests/test_ngrams_analysis.py         | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 nautilus_nlp/utils/ngrams_analysis.py
 create mode 100644 tests/test_ngrams_analysis.py

diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_ngrams_analysis.py b/tests/test_ngrams_analysis.py
new file mode 100644
index 0000000..e69de29

From d917464655d6bd21b8b00280b92712ad7f5080d0 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Tue, 30 Apr 2019 15:59:15 +0200
Subject: [PATCH 095/496] add ngrams frequency counts

---
 nautilus_nlp/utils/ngrams_analysis.py | 42 +++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index e69de29..9ffad2d 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+Functions to analyse words frequencies. Generates counts and bars plots with words and n-grams frequencies in text.
+"""
+import pandas as pd
+from collections import Counter
+from matplotlib import pyplot as plt
+
+
+def _create_ngrams(token, n):
+    """
+    :param token: list of strings
+    :param n: number of elements in the n-gram
+    :return: list of n-grams
+    """
+    ngrams = zip(*[token[i:] for i in range(n)])
+    return [" ".join(ngram) for ngram in ngrams]
+
+
+def frequent_words(list_words, ngrams_number=1, number_top_words=10 ):
+    """
+    :param list_words: list of strings
+    :param ngrams_number: output dataframe length
+    :param output_ngrams_number: output dataframe length
+    :return: dataframe with the entities and their frequencies in text
+    """
+
+    frequent = []
+    if ngrams_number == 1:
+        pass
+    elif ngrams_number >= 2:
+        list_words = _create_ngrams(list_words, ngrams_number)
+    else:
+        raise ValueError("number of n-grams should be >= 1")
+
+    x = Counter(list_words)
+    frequent = x.most_common(number_top_words)
+    return pd.DataFrame(frequent, columns=['Entity', 'Counts'])
+
+
+
+

From bdf92a1ee4d4f4e0dfce48b267ae3a3cc131fc32 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Tue, 30 Apr 2019 16:05:21 +0200
Subject: [PATCH 096/496] n-grams frequency

---
 nautilus_nlp/utils/ngrams_analysis.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index 9ffad2d..0204fe5 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -1,14 +1,14 @@
 # -*- coding: utf-8 -*-
 """
-Functions to analyse words frequencies. Generates counts and bars plots with words and n-grams frequencies in text.
+Functions to calculate words or ngrams frequencies.
 """
 import pandas as pd
 from collections import Counter
-from matplotlib import pyplot as plt
 
 
 def _create_ngrams(token, n):
     """
+    Create n-grams for list of tokens
     :param token: list of strings
     :param n: number of elements in the n-gram
     :return: list of n-grams
@@ -19,10 +19,11 @@ def _create_ngrams(token, n):
 
 def frequent_words(list_words, ngrams_number=1, number_top_words=10 ):
     """
+    Compute n-grams frequencies and return number_top_words top n-grams.
     :param list_words: list of strings
     :param ngrams_number: output dataframe length
     :param output_ngrams_number: output dataframe length
-    :return: dataframe with the entities and their frequencies in text
+    :return: dataframe with the entities and their frequencies.
     """
 
     frequent = []

From 55dbf5b80832e359c216e4e6a95169458f6e64c2 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Tue, 30 Apr 2019 16:06:58 +0200
Subject: [PATCH 097/496] n-grams frequency

---
 nautilus_nlp/utils/ngrams_analysis.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index 0204fe5..030a7df 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -17,12 +17,12 @@ def _create_ngrams(token, n):
     return [" ".join(ngram) for ngram in ngrams]
 
 
-def frequent_words(list_words, ngrams_number=1, number_top_words=10 ):
+def frequent_words(list_words, ngrams_number=1, number_top_words=10):
     """
     Compute n-grams frequencies and return number_top_words top n-grams.
     :param list_words: list of strings
     :param ngrams_number: output dataframe length
-    :param output_ngrams_number: output dataframe length
+    :param number_top_words: output dataframe length
     :return: dataframe with the entities and their frequencies.
     """
 

From e77c68757dac49648c0f6009982a2de38af0b4b2 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Tue, 30 Apr 2019 17:20:34 +0200
Subject: [PATCH 098/496] add tests for ngrams_analysis

---
 tests/test_ngrams_analysis.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/tests/test_ngrams_analysis.py b/tests/test_ngrams_analysis.py
index e69de29..99dbdaa 100644
--- a/tests/test_ngrams_analysis.py
+++ b/tests/test_ngrams_analysis.py
@@ -0,0 +1,22 @@
+import pandas as pd
+import pytest
+from nautilus_nlp.utils.ngrams_analysis import frequent_words
+
+
+def test_frequent_words():
+    list_words = ['Hello', 'world', 'this', 'is', 'an', 'example', 'of', 'ngrams', 'count', 'an', 'example',
+                  'to', 'test', 'this', 'is', 'an', 'example', 'function', 'hello', 'world']
+
+    df1 = frequent_words(list_words, 1, 5)
+    df2 = frequent_words(list_words, 2, 5)
+    df3 = frequent_words(list_words, 3, 3)
+
+    res_df1 = pd.DataFrame({'Entity': ['an', 'example', 'world', 'this', 'is'], 'Counts': [3, 3, 2, 2, 2]})
+    res_df2 = pd.DataFrame({'Entity': ['an example', 'this is', 'is an', 'Hello world', 'world this'], 'Counts': [3, 2, 2, 1, 1]})
+    res_df3 = pd.DataFrame({'Entity': ['this is an', 'is an example', 'Hello world this'], 'Counts': [2, 2, 1]})
+
+    pd.testing.assert_frame_equal(df1, res_df1)
+    pd.testing.assert_frame_equal(df2, res_df2)
+    pd.testing.assert_frame_equal(df3, res_df3)
+
+

From f693ad526e1753e644b7b40f426ee4b03a8b2fa4 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Tue, 30 Apr 2019 17:39:44 +0200
Subject: [PATCH 099/496] fix ngrams test

---
 nautilus_nlp/utils/ngrams_analysis.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index 030a7df..f198ea9 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -2,7 +2,7 @@
 """
 Functions to calculate words or ngrams frequencies.
 """
-import pandas as pd
+from pandas import DataFrame
 from collections import Counter
 
 
@@ -36,8 +36,4 @@ def frequent_words(list_words, ngrams_number=1, number_top_words=10):
 
     x = Counter(list_words)
     frequent = x.most_common(number_top_words)
-    return pd.DataFrame(frequent, columns=['Entity', 'Counts'])
-
-
-
-
+    return DataFrame(frequent, columns=['Entity', 'Counts'])

From c60e922435244388f07f9cadbd5e21fa0dd5db38 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 2 May 2019 11:26:10 +0200
Subject: [PATCH 100/496]  Delete CDL2 as language detection as it's redundant
 with Fasttext and is a painpoint when installing

---
 nautilus_nlp/models/Language_detector.py | 36 ++++++-------
 nautilus_nlp/utils/preprocess.py         | 68 +++++++++++++++---------
 tests/test_preprocessor.py               |  3 +-
 3 files changed, 61 insertions(+), 46 deletions(-)

diff --git a/nautilus_nlp/models/Language_detector.py b/nautilus_nlp/models/Language_detector.py
index b168473..06a1a06 100644
--- a/nautilus_nlp/models/Language_detector.py
+++ b/nautilus_nlp/models/Language_detector.py
@@ -1,16 +1,21 @@
 from nautilus_nlp.models.Fasttext_classifier import Fasttext_clf as langdetect
-import cld2
+from nautilus_nlp.utils.preprocess import remove_EOL_characters
 import pkg_resources
-lang_path = pkg_resources.resource_filename('nautilus_nlp.data', 'lang_identification.ftz')
-class LangDetector():
+
+lang_path = pkg_resources.resource_filename(
+    "nautilus_nlp.data", "lang_identification.ftz"
+)
+
+
+class LangDetector:
     """ This class is to instantiante a language detector.
 
     """
-    def __init__(self,typemodel,path=lang_path,):
-        self.typemodel=typemodel
-        self.path = None if path is None else lang_path 
-        self.model=langdetect(self.path) if typemodel=='fasttext' else None
-    
+
+    def __init__(self, path=None):
+        self.path = path if path is not None else lang_path
+        self.model = langdetect(self.path)
+
     def detect_language(self, text_to_detect=None):
         """
         Detected the language of a text
@@ -22,16 +27,5 @@ def detect_language(self, text_to_detect=None):
         is_reliable: is the top language is much better than 2nd best language?
         language: 2-letter code for the language of the text
         """
-        if self.typemodel!='fasttext':
-            _, _, best_guesses = cld2.detect(text_to_detect,
-                                                    bestEffort=True)
-
-            if len(best_guesses) == 0 or len(best_guesses[0]) != 4 or best_guesses[0][1] == 'un':
-                return 'un',0
-
-            return  best_guesses[0][1],(best_guesses[0][2]/100)
-        else:
-            best_guesses=self.model.predict(text_to_detect)
-            return best_guesses[0][0].replace('__label__',''),best_guesses[1][0]
-
-
+        best_guesses = self.model.predict(remove_EOL_characters(text_to_detect))
+        return best_guesses[0][0].replace("__label__", ""), best_guesses[1][0]
diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index 48118cc..70d8148 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -20,7 +20,7 @@
 from nautilus_nlp.config.config import ROOT_FOLDER
 from nautilus_nlp.utils.file_loader import documents_loader
 
-STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER,'data','stopwords.json')
+STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
 
 
 def remove_multiple_spaces_and_strip_text(text):
@@ -28,23 +28,35 @@ def remove_multiple_spaces_and_strip_text(text):
     Parameters
     ----------
     text : str,
-        Header content.
     Returns
     -------
     str
     """
-    regex_remove_multiple_spaces_list= ["\\t", "[\\s\\-\\*]{2,}"]
+    regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
     for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
-        text = re.sub(regex_remove_multiple_spaces, ' ', text)
+        text = re.sub(regex_remove_multiple_spaces, " ", text)
         text = text.strip()
     return text
 
+
+def remove_EOL_characters(text):
+    """Remove end of line (\n) char.
+    Parameters
+    ----------
+    text : str,
+    Returns
+    -------
+    str
+    """
+    return text.replace("\n", "")
+
+
 def remove_tokens_with_nonletters(tokens):
-    '''
+    """
     Inputs a list of tokens, outputs a list of tokens without tokens that
     includes numbers of special caracters
-    '''
-    return [word for word in tokens if re.search('[a-zA-Z]', word)]
+    """
+    return [word for word in tokens if re.search("[a-zA-Z]", word)]
 
 
 def remove_special_caracters(tokens):
@@ -52,7 +64,7 @@ def remove_special_caracters(tokens):
     Strings that are just punctuation will
     be removed! No more custom '--'. But ''s' and '9' will remain.
     """
-    return [word for word in tokens if re.search('[a-zA-Z0-9]', word)]
+    return [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
 
 
 def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
@@ -61,8 +73,8 @@ def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
     return stopwords
 
 
-def get_stopwords(lang:str = 'en'): 
-    '''
+def get_stopwords(lang: str = "en"):
+    """
     Inputs a language code, returns a list of stopwords for the specified language
 
     Args:
@@ -73,38 +85,44 @@ def get_stopwords(lang:str = 'en'):
           'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
           'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
            'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
-    '''
+    """
     if type(lang) == str and len(lang) == 2:
         lang = lang.lower()
-        
+
         custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
         stopwords = []
-        
+
         supported_lang_lib = list(_LANGUAGE_MAPPING.keys())
         supported_lang_custom = list(custom_stopwords.keys())
-        supported_lang = supported_lang_lib+supported_lang_custom
+        supported_lang = supported_lang_lib + supported_lang_custom
         if lang in supported_lang:
             if lang in supported_lang_lib:
                 stopwords += _get_stop_words(lang)
             if lang in supported_lang_custom:
                 stopwords += custom_stopwords[lang]
         else:
-            raise ValueError('Language not available yet or incorrect country code. Supported languages: {}'.format(supported_lang))
+            raise ValueError(
+                "Language not available yet or incorrect country code. Supported languages: {}".format(
+                    supported_lang
+                )
+            )
     else:
-        raise ValueError('Please input a valid country code, in 2 letters. Eg. "us" for USA. ')
+        raise ValueError(
+            'Please input a valid country code, in 2 letters. Eg. "us" for USA. '
+        )
     return list(set(stopwords))
 
 
 def remove_stopwords(text_or_tokens, stopwords):
-    ''' 
+    """ 
     Remove stopwords from tokens.
-    '''
+    """
     if type(text_or_tokens) is str:
         return [word for word in text_or_tokens.split() if word not in stopwords]
     elif type(text_or_tokens) is list:
-        return [word for word in text_or_tokens if word not in stopwords]                                                    
+        return [word for word in text_or_tokens if word not in stopwords]
     else:
-        raise ValueError('must input string or list of tokens')    
+        raise ValueError("must input string or list of tokens")
 
 
 def fix_bad_unicode(text, normalization="NFC") -> str:
@@ -288,6 +306,7 @@ def remove_accents(text, method="unicode") -> str:
         msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
         raise ValueError(msg)
 
+
 def remove_emoji(word):
     """
     Remove emoji from any  str by stripping any unicode in the range of Emoji unicode,
@@ -299,10 +318,11 @@ def remove_emoji(word):
         str
 
     """
-    RE_EMOJI = re.compile('[\U00010000-\U0010ffff]', flags=re.UNICODE)
-    word = RE_EMOJI.sub(r'', word)
+    RE_EMOJI = re.compile("[\U00010000-\U0010ffff]", flags=re.UNICODE)
+    word = RE_EMOJI.sub(r"", word)
     return word
 
+
 def preprocess_text(
     text,
     no_emoji=False,
@@ -349,8 +369,8 @@ def preprocess_text(
         on the text, so choose carefully, and preprocess at your own risk!
     """
 
-    assert isinstance(text,str) , 'The text to preprocess must be a string'
-    
+    assert isinstance(text, str), "The text to preprocess must be a string"
+
     if fix_unicode is True:
         text = fix_bad_unicode(text, normalization="NFC")
     if no_urls is True:
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index e735dc4..c1c797a 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -54,4 +54,5 @@ def test_remove_accents():
 )
 def test_fix_bad_unicode(input_str, expected_str):
     result = fix_bad_unicode(input_str)
-    np.testing.assert_string_equal(result, expected_str)
\ No newline at end of file
+    np.testing.assert_string_equal(result, expected_str)
+

From 203ac411eeabfe729ee8c2fbfe3454c1a18378b4 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 2 May 2019 11:26:53 +0200
Subject: [PATCH 101/496] Add tests for language detection

---
 tests/test_language_detection.py | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 tests/test_language_detection.py

diff --git a/tests/test_language_detection.py b/tests/test_language_detection.py
new file mode 100644
index 0000000..1796359
--- /dev/null
+++ b/tests/test_language_detection.py
@@ -0,0 +1,31 @@
+from nautilus_nlp.models import Language_detector
+import pytest
+import numpy as np
+
+model = Language_detector.LangDetector()
+
+TEXT_1 = """Кипрская война (итал. Guerra di Cipro; тур. Kıbrıs Savaşı) — одна из нескольких войн между Османской империей и Венецианской республикой за господство в Восточном Средиземноморье. Сначала Венеция воевала с османами одна. Затем, когда сформировалась Священная лига (в которую помимо Венеции входили Испания с Неаполем и Сицилией, Республика Генуя, Герцогство Савойское, госпитальеры, Великое герцогство Тосканское и другие итальянские государства), Османская империя воевала уже против Лиги."""
+TEXT_2 = """
+Wiborada († 1. Mai 926 in St. Gallen) war eine Einsied­lerin, geweihte Jung­frau und Märtyrin der katho­lischen Kirche. Sie lebte als Inklusin in St. Gallen und wurde wäh­rend eines Ungarn­einfalls getötet. Ihre letzte Ruhe­stätte, deren genaue Lage bei der Kirche St. Mangen heute nicht mehr be­kannt ist, war über Jahr­hunderte hinweg Ziel vieler Wall­fahrer. Sie wurde im Jahr 1047 von Papst Cle­mens II. heilig­gespro­chen und gilt als Schutz­patronin der Pfarr­haus­hälte­rinnen, Köchin­nen, Biblio­theken und Bücher­freunde. In der Ikono­grafie wird Wiborada im Habit darge­stellt; als ikono­grafische Heiligen­attri­bute sind ihr eine Helle­barde als Ver­weis auf das Marty­rium und ein Buch beige­geben."""
+TEXT_3 = """De geschiedenis van het werelddeel Europa valt ruwweg chronologisch in te delen in de prehistorie, de klassieke oudheid, de middeleeuwen, de nieuwe tijd, de moderne tijd en de eigentijdse tijd."""
+TEXT_4 = """
+The absolute difference between At and Ft is divided by half the sum of absolute values of the actual value At and the forecast value Ft. The value of this calculation is summed for every fitted point t and divided again by the number of fitted points n.
+"""
+TEXT_5 = """Joseph Athanase Doumerc dit Paul Doumer est un homme d'État français né le 22 mars 1857 à Aurillac (Cantal) et mort assassiné le 7 mai 1932 à Paris. Il a été président de la République du 13 juin 1931 à sa mort."""
+TEXT_6 = """1997年加延地震是于5月10日协调世界时7時57分发生在伊朗北部呼罗珊的一起重大地震,是该地区自1990年以来最大的一次地震,矩震级为7.3,中心位于马什哈德以南约270公里的一个名叫阿德库尔的乡村。这也是该国1997年所发生的第三场地震,造成严重的灾情,比尔詹德-加延地区变得满目疮痍,有1,567人丧生,超过2,300人受伤,5万人无家可归,超过15,000幢房屋遭到破坏或损毁,美国地质调查局形容这是1997年最致命的一场地震。其后还发生了约155次余震造成进一步的破坏并迫使幸存者离开。最终估计此次灾害的损失约为1亿美元。围绕地震震中的地区几乎全部被毁,这与乡村地区建筑质量不佳有很大关系,联合国建议调整相应建筑法规。按死亡人数计算,自进入20世纪以来,伊朗平均每3,000人就有1人在地震相关事件中遇难,一位美国地球物理学家建议为了解决持续的公共安全隐患,需要有一个全国范围的重建方案。"""
+
+
+@pytest.mark.parametrize(
+    "text, lang",
+    [
+        (TEXT_1, "ru"),
+        (TEXT_2, "de"),
+        (TEXT_3, "nl"),
+        (TEXT_4, "en"),
+        (TEXT_5, "fr"),
+        (TEXT_6, "zh"),
+    ],
+)
+def test_detect_language(text, lang):
+    result = model.detect_language(text)[0]
+    np.testing.assert_string_equal(result, lang)

From 6cfd3c71f0dcfc75b338b340a116d4df79f35b83 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 2 May 2019 11:30:12 +0200
Subject: [PATCH 102/496] delete cld2 from requirements

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2a03758..5870746 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -26,7 +26,6 @@ stop_words==2018.7.23
 nltk==3.4
 textblob==0.15.3
 textblob_fr==0.2.0
-cld2_cffi==0.1.4
 pandas>=0.23.4
 chardet==3.0.4
 setuptools==40.8.0

From 6da27067754519032cec7603296ac90fca6fc969 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 2 May 2019 11:38:50 +0200
Subject: [PATCH 103/496] test changes in doc

---
 tests/test_doc.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_doc.py b/tests/test_doc.py
index 9d7787f..5326226 100644
--- a/tests/test_doc.py
+++ b/tests/test_doc.py
@@ -41,7 +41,7 @@
 
 ents_model = spacy.blank("nl")
 custom_spacy_nlps = {"nl": {"ents": ents_model}}
-detector = LangDetector(typemodel="fasttext")
+detector = LangDetector()
 
 DOC_1 = Doc(TEXT_1, language="en")
 DOC_2 = Doc(TEXT_2, language="fr")

From 2c677bad6f33a5ccce737e02f69d16c72e2c7d8f Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 2 May 2019 12:09:52 +0200
Subject: [PATCH 104/496] This commit add docstring in emoji2sentiment

---
 nautilus_nlp/utils/emoji.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/nautilus_nlp/utils/emoji.py b/nautilus_nlp/utils/emoji.py
index d4d58d6..85177af 100644
--- a/nautilus_nlp/utils/emoji.py
+++ b/nautilus_nlp/utils/emoji.py
@@ -1,12 +1,20 @@
-"""
-This dataset is based on:
+import csv
+
+def rebuilt_emoji_dictionaries(filename):
+    """
+   This function recreate the dictionnaries with emoji2unicode_name, emoji2sentiment  that are hardcoded below.
+
+    :input: csv filename
+    :return: dict,dict
+    
+    This dataset is based on and can be found:
     Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut and Mozetič, Igor, 2015,
     Emoji Sentiment Ranking 1.0, Slovenian language resource repository CLARIN.SI,
     http://hdl.handle.net/11356/1048.
-"""
 
-def rebuilt_emoji_dictionaries(filename):
-    emoji2unicode_name, emoji2sentiment = {}, {}
+    """
+
+    emoji2unicode_name, emoji2sentiment = dict(),dict()
     with open(filename) as csvin:
         for emoji in csv.DictReader(csvin):
             for key, value in emoji.items():

From e4b217cf584361c26228b5858178652594f8d2eb Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Thu, 2 May 2019 13:59:13 +0200
Subject: [PATCH 105/496] change output format of ngrams counts  and check
 tests

---
 nautilus_nlp/utils/ngrams_analysis.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index f198ea9..a2b0283 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -6,7 +6,7 @@
 from collections import Counter
 
 
-def _create_ngrams(token, n):
+def create_ngrams(token, n):
     """
     Create n-grams for list of tokens
     :param token: list of strings
@@ -25,15 +25,13 @@ def frequent_words(list_words, ngrams_number=1, number_top_words=10):
     :param number_top_words: output dataframe length
     :return: dataframe with the entities and their frequencies.
     """
-
     frequent = []
     if ngrams_number == 1:
         pass
     elif ngrams_number >= 2:
-        list_words = _create_ngrams(list_words, ngrams_number)
+        list_words = create_ngrams(list_words, ngrams_number)
     else:
         raise ValueError("number of n-grams should be >= 1")
-
     x = Counter(list_words)
     frequent = x.most_common(number_top_words)
-    return DataFrame(frequent, columns=['Entity', 'Counts'])
+    return frequent

From e08399b7b6767dfc6b98b8229fefd9ed9d2e8e68 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Thu, 2 May 2019 14:01:08 +0200
Subject: [PATCH 106/496] fix tests for ngrams frequencies counts

---
 nautilus_nlp/utils/ngrams_analysis.py |  1 -
 tests/test_ngrams_analysis.py         | 25 ++++++++++++-------------
 2 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index a2b0283..5e61009 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -2,7 +2,6 @@
 """
 Functions to calculate words or ngrams frequencies.
 """
-from pandas import DataFrame
 from collections import Counter
 
 
diff --git a/tests/test_ngrams_analysis.py b/tests/test_ngrams_analysis.py
index 99dbdaa..92a840d 100644
--- a/tests/test_ngrams_analysis.py
+++ b/tests/test_ngrams_analysis.py
@@ -1,22 +1,21 @@
-import pandas as pd
-import pytest
 from nautilus_nlp.utils.ngrams_analysis import frequent_words
+import pytest
 
 
 def test_frequent_words():
     list_words = ['Hello', 'world', 'this', 'is', 'an', 'example', 'of', 'ngrams', 'count', 'an', 'example',
                   'to', 'test', 'this', 'is', 'an', 'example', 'function', 'hello', 'world']
 
-    df1 = frequent_words(list_words, 1, 5)
-    df2 = frequent_words(list_words, 2, 5)
-    df3 = frequent_words(list_words, 3, 3)
-
-    res_df1 = pd.DataFrame({'Entity': ['an', 'example', 'world', 'this', 'is'], 'Counts': [3, 3, 2, 2, 2]})
-    res_df2 = pd.DataFrame({'Entity': ['an example', 'this is', 'is an', 'Hello world', 'world this'], 'Counts': [3, 2, 2, 1, 1]})
-    res_df3 = pd.DataFrame({'Entity': ['this is an', 'is an example', 'Hello world this'], 'Counts': [2, 2, 1]})
-
-    pd.testing.assert_frame_equal(df1, res_df1)
-    pd.testing.assert_frame_equal(df2, res_df2)
-    pd.testing.assert_frame_equal(df3, res_df3)
+    res_1 = frequent_words(list_words, ngrams_number=1, number_top_words=10)
+    res_2 = frequent_words(list_words, ngrams_number=2, number_top_words=3)
+    res_3 = frequent_words(list_words, ngrams_number=3, number_top_words=5)
 
+    exp_res_1 = [('an', 3), ('example', 3), ('world', 2), ('this', 2), ('is', 2), ('Hello', 1), ('of', 1), ('ngrams', 1),
+                 ('count', 1), ('to', 1)]
+    exp_res_2 = [('an example', 3), ('this is', 2), ('is an', 2)]
+    exp_res_3 = [('this is an', 2), ('is an example', 2), ('Hello world this', 1),
+                 ('world this is', 1), ('an example of', 1)]
 
+    assert res_1 == exp_res_1
+    assert res_2 == exp_res_2
+    assert res_3 == exp_res_3

From ff5c5098c57faf03273bb9e1cd31cf4d8f4af1e8 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Thu, 2 May 2019 14:03:03 +0200
Subject: [PATCH 107/496] fix tests for ngrams frequencies counts

---
 tests/test_ngrams_analysis.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_ngrams_analysis.py b/tests/test_ngrams_analysis.py
index 92a840d..5e42781 100644
--- a/tests/test_ngrams_analysis.py
+++ b/tests/test_ngrams_analysis.py
@@ -10,8 +10,8 @@ def test_frequent_words():
     res_2 = frequent_words(list_words, ngrams_number=2, number_top_words=3)
     res_3 = frequent_words(list_words, ngrams_number=3, number_top_words=5)
 
-    exp_res_1 = [('an', 3), ('example', 3), ('world', 2), ('this', 2), ('is', 2), ('Hello', 1), ('of', 1), ('ngrams', 1),
-                 ('count', 1), ('to', 1)]
+    exp_res_1 = [('an', 3), ('example', 3), ('world', 2), ('this', 2), ('is', 2),
+                 ('Hello', 1), ('of', 1), ('ngrams', 1), ('count', 1), ('to', 1)]
     exp_res_2 = [('an example', 3), ('this is', 2), ('is an', 2)]
     exp_res_3 = [('this is an', 2), ('is an example', 2), ('Hello world this', 1),
                  ('world this is', 1), ('an example of', 1)]

From 024bf477bb2b4a55337138f5d6f6e504e3096c95 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 11:18:38 +0200
Subject: [PATCH 108/496] Add Travis building logo in the readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c969983..b182afd 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-Nautilus_NLP
+Nautilus_NLP [![Build Status](https://travis-ci.com/artefactory/nautilus-nlp.svg?token=Ssg4shz5pz9qGnYCybSj&branch=master)](https://travis-ci.com/artefactory/nautilus-nlp)
 ==============================
 
 The Nautilus NLP library aimed to be a meta-library to be used to help you get started on handling your NLP use-case.

From a06b7ec9cea6931259ffe5c235dbeb264d422a6b Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 11:22:18 +0200
Subject: [PATCH 109/496] Add visualize in utils

---
 nautilus_nlp/utils/visualize.py | 54 +++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)
 create mode 100644 nautilus_nlp/utils/visualize.py

diff --git a/nautilus_nlp/utils/visualize.py b/nautilus_nlp/utils/visualize.py
new file mode 100644
index 0000000..bb9cfb1
--- /dev/null
+++ b/nautilus_nlp/utils/visualize.py
@@ -0,0 +1,54 @@
+'''
+import matplotlib
+import numpy as np
+
+'''
+import matplotlib.pyplot as plt
+import wordcloud
+plt.rcParams["figure.figsize"] = [16,9]
+
+
+def make_word_cloud(text_or_counter, stop_words=[]):
+
+    if type(text_or_counter) is str:
+        myWordcloud = wordcloud.WordCloud(stopwords=stop_words).generate(text_or_counter)
+    else:
+        for w in stop_words:
+            del text_or_counter[w]
+        myWordcloud = wordcloud.WordCloud(stopwords=stop_words).generate_from_frequencies(text_or_counter)
+    plt.imshow(myWordcloud)
+    plt.axis("off")
+    plt.show()
+
+
+def print_concordance(tokens, query_word, width=110, n_results=None):
+    '''
+    Inputs a list of token and a query word, outputs all the sentences that 
+    contains the query word, display in a nice way. 
+    width = Integer. Number of caracters to display per text chunk
+    n_results = Integer. If not null, filters the number of results displayed.
+    This function is an adaptation of NLTK's print_concordance function. 
+    Source: http://www.nltk.org/_modules/nltk/text.html
+    '''
+    half_width = (width - len(query_word) - 2) // 2
+    context = width // 4  # approx number of words of context
+    
+    results = [i for i, j in enumerate(tokens) if j == query_word]
+    if len(results) > 0:
+        if n_results == None:
+            n_results = len(results)
+        print('{} matches for "{}":'.format(len(results),query_word)) 
+        for i in results[:n_results]:
+            
+            # Find the context of query word.
+            left_context = tokens[max(0, i - context) : i]
+            right_context = tokens[i + 1 : i + context]
+            # Create the pretty lines with the query_word in the middle.
+            left_print = ' '.join(left_context)[-half_width:]
+            right_print = ' '.join(right_context)[:half_width]
+            # The WYSIWYG line of the concordance.
+            line_print = ' '.join([left_print, query_word, right_print])
+            before = tokens[:]
+            print(line_print)
+    else:
+        print('No match for "{}"'.format(query_word))
\ No newline at end of file

From 22fe702d4a80fc5a294dae777bb21edbbe1a53bf Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 11:51:15 +0200
Subject: [PATCH 110/496] add contributing

---
 CONTRIBUTING.md | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 CONTRIBUTING.md

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..d59a290
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,34 @@
+Nautilus_NLP
+==============================
+
+# Docstring format
+
+We chose to use **Numpydoc** over the several [standards](https://stackoverflow.com/questions/3898572/what-is-the-standard-python-docstring-format)
+
+```
+"""
+My numpydoc description of a kind
+of very exhautive numpydoc format docstring.
+
+Parameters
+----------
+first : array_like
+    the 1st param name `first`
+second :
+    the 2nd param
+third : {'value', 'other'}, optional
+    the 3rd param, by default 'value'
+
+Returns
+-------
+string
+    a value in a string
+
+Raises
+------
+KeyError
+    when a key error
+OtherError
+    when an other error
+"""
+```

From 4da3debf403f72cccf49a71a6024c5b72ca9aa68 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 11:52:23 +0200
Subject: [PATCH 111/496] structural change

---
 nautilus_nlp/utils/text_vectorizers.py | 195 +++++++++++++++++++++++++
 1 file changed, 195 insertions(+)
 create mode 100644 nautilus_nlp/utils/text_vectorizers.py

diff --git a/nautilus_nlp/utils/text_vectorizers.py b/nautilus_nlp/utils/text_vectorizers.py
new file mode 100644
index 0000000..a03c5bb
--- /dev/null
+++ b/nautilus_nlp/utils/text_vectorizers.py
@@ -0,0 +1,195 @@
+import spacy
+from gensim.corpora import Dictionary
+from gensim.models.tfidfmodel import TfidfModel
+from gensim import corpora, models, similarities
+from gensim.matutils import sparse2full
+import numpy as np
+import math
+
+from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
+
+
+class Tfidf(object):
+    """
+    Inputs a list of string
+    Outputs a tuple with the wordcount vector matrix, and the list of feature name
+    Params:
+        input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
+        lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
+        stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
+        max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
+        dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
+        
+    Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
+    """
+    
+    def __init__(self, **kwargs):
+        self.tfidf_vectorizer = TfidfVectorizer(**kwargs)
+    
+    def _compute_wordcount_vector(self, documents):
+        '''
+        Input a list of documents (string)
+        Output the wordcount vector matrix 
+        '''
+        self.word_count_vector = self.tfidf_vectorizer.fit_transform(documents)
+        return self.word_count_vector
+    
+
+    def _get_features_name(self):
+        self.feature_names = self.tfidf_vectorizer.get_feature_names()
+        return self.feature_names
+
+    
+    def _compute_idf(self):
+        self.tfidf_transformer=TfidfTransformer(smooth_idf=True, use_idf=True)
+        self.tfidf_transformer.fit(self.word_count_vector)
+        return self.word_count_vector
+
+    
+    def compute_tfidf(self, documents):
+        self._compute_wordcount_vector(documents)
+        self._get_features_name()
+        self._compute_idf()
+        return self.word_count_vector
+
+    def _apply_tfidf_to_doc(self, text):
+        '''generate tf-idf for the given document'''
+        return self.tfidf_transformer.transform(self.tfidf_vectorizer.transform([text]))
+    
+    
+    def _sort_coo(self, coo_matrix):
+        '''sort the tf-idf vectors by descending order of scores'''
+        tuples = zip(coo_matrix.col, coo_matrix.data)
+        return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
+    
+    
+    def _extract_topn_from_vector(self, feature_names, sorted_items, topn=10):
+        """get the feature names and tf-idf score of top n items"""
+
+        #use only topn items from vector
+        sorted_items = sorted_items[:topn]
+
+        score_vals = []
+        feature_vals = []
+
+        # word index and corresponding tf-idf score
+        for idx, score in sorted_items:
+
+            #keep track of feature name and its corresponding score
+            score_vals.append(round(score, 3))
+            feature_vals.append(feature_names[idx])
+
+        #create a tuples of feature,score
+        #results = zip(feature_vals,score_vals)
+        results= {}
+        for idx in range(len(feature_vals)):
+            results[feature_vals[idx]]=score_vals[idx]
+
+        return results
+
+    
+    def get_top_tfidf_per_doc(self, text, n=10):
+        '''compute TF-IDF for a given doc, and returns a list of the top N weighted words'''
+        tf_idf_vector= self._apply_tfidf_to_doc(text)
+        sorted_items=self._sort_coo(tf_idf_vector.tocoo())
+        return list(self._extract_topn_from_vector(self.feature_names, sorted_items, n).keys())
+    
+    def get_top_tfidf(self, n=10):
+        '''returns a dict of the top N weighted words, with their weight'''
+        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(self.word_count_vector.tocoo()), topn=n)
+
+
+class Text_Vectorizer:
+    def __init__(self, doc_list):
+        # Initialize
+        self.doc_list = doc_list
+        self.nlp, self.docs, self.docs_dict = self._preprocess(self.doc_list)
+
+    # Functions to lemmatise docs
+    def _keep_token(self, t):
+        return t.is_alpha and not (t.is_space or t.is_punct or t.is_stop or t.like_num)
+
+    def _lemmatize_doc(self, doc):
+        return [t.lemma_ for t in doc if self._keep_token(t)]
+
+    # Gensim to create a dictionary and filter out stop and infrequent words (lemmas).
+    def _get_docs_dict(self, docs):
+        docs_dict = Dictionary(docs)
+        # CAREFUL: For small corpus please carefully modify the parameters for filter_extremes, or simply comment it out.
+        docs_dict.filter_extremes(no_below=5, no_above=0.2)
+        docs_dict.compactify()
+        return docs_dict
+
+    # Preprocess docs
+    def _preprocess(self, doc_list):
+        # Load spacy model
+        nlp = spacy.load("en")
+        # lemmatise docs
+        docs = [self._lemmatize_doc(nlp(doc)) for doc in doc_list]
+        # Get docs dictionary
+        docs_dict = self._get_docs_dict(docs)
+        return nlp, docs, docs_dict
+
+    # Gensim can again be used to create a bag-of-words representation of each document,
+    # build the TF-IDF model,
+    # and compute the TF-IDF vector for each document.
+    def _get_tfidf(self, docs, docs_dict):
+        docs_corpus = [docs_dict.doc2bow(doc) for doc in docs]
+        model_tfidf = TfidfModel(docs_corpus, id2word=docs_dict)
+        docs_tfidf = model_tfidf[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(docs_dict)) for c in docs_tfidf])
+        return docs_vecs
+
+    # Get avg w2v for one document
+    def _document_vector(self, doc, docs_dict, nlp):
+        # remove out-of-vocabulary words
+        doc_vector = [nlp(word).vector for word in doc if word in docs_dict.token2id]
+        return np.mean(doc_vector, axis=0)
+
+
+    # Get average vector for document list
+    def avg_wv(self):
+        docs_vecs = np.vstack(
+            [self._document_vector(doc, self.docs_dict, self.nlp) for doc in self.docs]
+        )
+        return docs_vecs
+
+    # Get TF-IDF vector for document list
+    def get_tfidf(self):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_tfidf = TfidfModel(docs_corpus, id2word=self.docs_dict)
+        docs_tfidf = model_tfidf[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_tfidf])
+        return docs_vecs
+
+    # Get Latent Semantic Indexing(LSI) vector for document list
+    def get_lsi(self, num_topics=300):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_lsi = models.LsiModel(docs_corpus, num_topics, id2word=self.docs_dict)
+        docs_lsi = model_lsi[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lsi])
+        return docs_vecs
+
+    # Get Random Projections(RP) vector for document list
+    def get_rp(self):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_rp = models.RpModel(docs_corpus, id2word=self.docs_dict)
+        docs_rp = model_rp[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_rp])
+        return docs_vecs
+
+    # Get Latent Dirichlet Allocation(LDA) vector for document list
+    def get_lda(self, num_topics=100):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_lda = models.LdaModel(docs_corpus, num_topics, id2word=self.docs_dict)
+        docs_lda = model_lda[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lda])
+        return docs_vecs
+
+    # Get Hierarchical Dirichlet Process(HDP) vector for document list
+    def get_hdp(self):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_hdp = models.HdpModel(docs_corpus, id2word=self.docs_dict)
+        docs_hdp = model_hdp[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_hdp])
+        return docs_vecs

From f0d72e3da981d25bb0e52228904898bec5382d27 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 11:55:52 +0200
Subject: [PATCH 112/496] delete files

---
 nautilus_nlp/features/.gitkeep          |  0
 nautilus_nlp/features/__init__.py       |  0
 nautilus_nlp/features/build_features.py |  0
 nautilus_nlp/utils/Text_processor.py    | 52 ------------------------
 nautilus_nlp/visualization/.gitkeep     |  0
 nautilus_nlp/visualization/__init__.py  |  0
 nautilus_nlp/visualization/visualize.py | 54 -------------------------
 7 files changed, 106 deletions(-)
 delete mode 100644 nautilus_nlp/features/.gitkeep
 delete mode 100644 nautilus_nlp/features/__init__.py
 delete mode 100644 nautilus_nlp/features/build_features.py
 delete mode 100644 nautilus_nlp/utils/Text_processor.py
 delete mode 100644 nautilus_nlp/visualization/.gitkeep
 delete mode 100644 nautilus_nlp/visualization/__init__.py
 delete mode 100644 nautilus_nlp/visualization/visualize.py

diff --git a/nautilus_nlp/features/.gitkeep b/nautilus_nlp/features/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/features/__init__.py b/nautilus_nlp/features/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/features/build_features.py b/nautilus_nlp/features/build_features.py
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/utils/Text_processor.py b/nautilus_nlp/utils/Text_processor.py
deleted file mode 100644
index 312af0f..0000000
--- a/nautilus_nlp/utils/Text_processor.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from .lemmatizer import Lemmatizer
-from .stemmer import Stemmer
-import spacy
-import nltk
-from .utils import *
-from .tokenizer import tokenize
-
-
-class TextProcessor:
-    def __init__(self, lang: str, module: str = "spacy"):
-        self.module = module
-        self.lemmatizer = None
-        self.stemmer = None
-        self.lang = lang
-
-    def transform(self, text):
-        return text
-
-    def lemmatize(self, text: str):
-        return text
-
-    def stem(self, text: str):
-        return text
-
-    def remove_stopwords(self, text: str):
-        return text
-
-    def lower(self, text: str):
-        return text.lower()
-
-    def tokenize(self, text: str, lang: str = 'en'):
-        return tokenize(text, lang=lang)
-
-
-class TokensProcessor:
-    def __init__(self, lang: str):
-        self.module = module
-        self.lemmatizer = None
-        self.stemmer = None
-        self.lang = lang
-
-    def lemmatize(self, text: str):
-        return text
-
-    def stem(self, tokens_list: list, lang: str = lang):
-        return stem_tokens(tokens_list, lang='english')
-
-    def remove_stopwords(self, text: str):
-        return text
-
-    def untokenize(self, text: str, lang: str = 'en'):
-        return tokenize(text, lang=lang)
diff --git a/nautilus_nlp/visualization/.gitkeep b/nautilus_nlp/visualization/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/visualization/__init__.py b/nautilus_nlp/visualization/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/visualization/visualize.py b/nautilus_nlp/visualization/visualize.py
deleted file mode 100644
index bb9cfb1..0000000
--- a/nautilus_nlp/visualization/visualize.py
+++ /dev/null
@@ -1,54 +0,0 @@
-'''
-import matplotlib
-import numpy as np
-
-'''
-import matplotlib.pyplot as plt
-import wordcloud
-plt.rcParams["figure.figsize"] = [16,9]
-
-
-def make_word_cloud(text_or_counter, stop_words=[]):
-
-    if type(text_or_counter) is str:
-        myWordcloud = wordcloud.WordCloud(stopwords=stop_words).generate(text_or_counter)
-    else:
-        for w in stop_words:
-            del text_or_counter[w]
-        myWordcloud = wordcloud.WordCloud(stopwords=stop_words).generate_from_frequencies(text_or_counter)
-    plt.imshow(myWordcloud)
-    plt.axis("off")
-    plt.show()
-
-
-def print_concordance(tokens, query_word, width=110, n_results=None):
-    '''
-    Inputs a list of token and a query word, outputs all the sentences that 
-    contains the query word, display in a nice way. 
-    width = Integer. Number of caracters to display per text chunk
-    n_results = Integer. If not null, filters the number of results displayed.
-    This function is an adaptation of NLTK's print_concordance function. 
-    Source: http://www.nltk.org/_modules/nltk/text.html
-    '''
-    half_width = (width - len(query_word) - 2) // 2
-    context = width // 4  # approx number of words of context
-    
-    results = [i for i, j in enumerate(tokens) if j == query_word]
-    if len(results) > 0:
-        if n_results == None:
-            n_results = len(results)
-        print('{} matches for "{}":'.format(len(results),query_word)) 
-        for i in results[:n_results]:
-            
-            # Find the context of query word.
-            left_context = tokens[max(0, i - context) : i]
-            right_context = tokens[i + 1 : i + context]
-            # Create the pretty lines with the query_word in the middle.
-            left_print = ' '.join(left_context)[-half_width:]
-            right_print = ' '.join(right_context)[:half_width]
-            # The WYSIWYG line of the concordance.
-            line_print = ' '.join([left_print, query_word, right_print])
-            before = tokens[:]
-            print(line_print)
-    else:
-        print('No match for "{}"'.format(query_word))
\ No newline at end of file

From c6d2355261e3a3b750c8fffd59675e7e0427d308 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 12:00:15 +0200
Subject: [PATCH 113/496] test encoding

---
 tests/test_fix_bad_encoding.py | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 tests/test_fix_bad_encoding.py

diff --git a/tests/test_fix_bad_encoding.py b/tests/test_fix_bad_encoding.py
new file mode 100644
index 0000000..cb3aa93
--- /dev/null
+++ b/tests/test_fix_bad_encoding.py
@@ -0,0 +1,32 @@
+
+import pytest
+import numpy as np
+from nautilus_nlp.utils.preprocess import fix_bad_unicode
+
+
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+    ('Les augmentations de rémunérations',
+  'Les augmentations de rémunérations'),
+ ("rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social",
+  "rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social"),
+ ('Limitations de vitesse et sécurité routière',
+  'Limitations de vitesse et sécurité routière'),
+ ('Pour un nouveau contrat citoyen', 'Pour un nouveau contrat citoyen'),
+ ('Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets',
+  'Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets'),
+ ('proportienelle', 'proportienelle'),
+ ('Pour plus de démocratie participative',
+  'Pour plus de démocratie participative'),
+ ('Transparence de la vie public', 'Transparence de la vie public'),
+ ('18 mois de trop....ca suffit macron',
+  '18 mois de trop....ca suffit macron'),
+ ('Egalité devant les infractions routières',
+  'Egalité devant les infractions routières')
+    ],
+)
+def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
+    result = fix_bad_unicode(input_str)
+    np.testing.assert_string_equal(result, expected_str)

From e082c8167937a6fb2bc0b83b737c84a92a956f7b Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 12:03:50 +0200
Subject: [PATCH 114/496] Change Travis to include build on OSX systems

---
 .travis.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 2d7b62b..b7dbf7d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,5 +1,9 @@
 language: python
 
+os:
+  - linux
+  - osx
+  
 services:
   - docker
 
@@ -8,7 +12,6 @@ before_script:
   - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && ls 
   - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 
-  
 install:
   - pip install -r requirements.txt
   - pip install -e .

From c740427e941410c619597ee251c2c6cd6c21d067 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 12:04:34 +0200
Subject: [PATCH 115/496] change init

---
 nautilus_nlp/preprocess/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 nautilus_nlp/preprocess/__init__.py

diff --git a/nautilus_nlp/preprocess/__init__.py b/nautilus_nlp/preprocess/__init__.py
new file mode 100644
index 0000000..e69de29

From 10ef880dd4e9582a7210ff4d04306016e54c590a Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 12:18:01 +0200
Subject: [PATCH 116/496] update readme

---
 README.md | 35 ++++++++++++++++++++++++++++++-----
 1 file changed, 30 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index b182afd..31c2ff0 100644
--- a/README.md
+++ b/README.md
@@ -32,15 +32,28 @@ To install this library you should first clone the repository:
 
 First you need to install the required files:
 
-`pip install -r requirements.txt`
+```pip install -r requirements.txt```
 
 then you can install it via pip:
 
-`pip install -e . 
+```pip install -e .```
 
 ## Handling installation errors
 
-### Conda conficts
+### command 'gcc' failed with exit status 1
+
+While runing `pip install -r requirements.txt` you might get the following error message:
+```
+command 'gcc' failed with exit status 1 
+```
+
+To solve it, run the following command before installing requirements.txt:
+```
+conda install pyemd
+```
+
+### Cannot uninstall 'package_name'
+
 You might get the following error message while installing the library:
 `Cannot uninstall 'PACKAGE_NAME'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.`
 
@@ -51,7 +64,14 @@ To fix this, just type `pip install -e . --ignore-installed PACKAGE_NAME` instea
 If you are installing nautilus on a linux-powered Virtual Machine (VM), you might want to install essentials first. If you don't, you might experience some issues to install Cython-based libraries, such as spaCy, becode the C compiler will be missing. 
 
 On a Ubuntu VM (tested on 16.04), you can run the following command before following the classic installation process above:
-`sudo apt-get update && sudo apt-get install -y build-essential unzip`
+
+```
+# install compilators and required softwares
+sudo apt-get update && sudo apt-get install -y build-essential unzip git wget
+# install conda
+wget https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
+bash Anaconda3-2019.03-Linux-x86_64.sh
+```
 
 ## Installation of additional required libraries
 
@@ -77,10 +97,15 @@ run:
 
 `bash nautilus_nlp/scripts/download_ft_langdetect.sh`
 
-# Notebooks
+# Quick start 
 
 The [notebook](notebooks/) folder contains various notebook on how to use this library.
 
+Here is a quick example:
+
+```
+
+```
 
 Project Organization
 ------------

From a18610545afc199b021ef7cb5b0b8c47316ace97 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 12:22:10 +0200
Subject: [PATCH 117/496] add script

---
 nautilus_nlp/scripts/download_spacy_models.sh | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 nautilus_nlp/scripts/download_spacy_models.sh

diff --git a/nautilus_nlp/scripts/download_spacy_models.sh b/nautilus_nlp/scripts/download_spacy_models.sh
new file mode 100644
index 0000000..0a3a374
--- /dev/null
+++ b/nautilus_nlp/scripts/download_spacy_models.sh
@@ -0,0 +1,5 @@
+python -m spacy download en_core_web_sm
+python -m spacy download de_core_news_sm 
+python -m spacy download fr_core_news_sm 
+python -m spacy download es_core_news_sm 
+python -m spacy download nl_core_news_sm

From 30b17ec460ebc409309dba5955dfd026854ac9f2 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 12:23:12 +0200
Subject: [PATCH 118/496] Revert Travis for OSX

---
 .travis.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index b7dbf7d..c89eab8 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,8 +2,6 @@ language: python
 
 os:
   - linux
-  - osx
-  
 services:
   - docker
 

From 3d0183a136be3d0baf47581f5d5e37bb96e0f118 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 12:29:03 +0200
Subject: [PATCH 119/496] delete useless folders

---
 nautilus/bin/activate         |  78 --------------------------
 nautilus/bin/activate.csh     |  42 --------------
 nautilus/bin/activate.fish    | 101 ----------------------------------
 nautilus/bin/activate.ps1     |  60 --------------------
 nautilus/bin/activate_this.py |  46 ----------------
 nautilus/bin/easy_install     |  10 ----
 nautilus/bin/easy_install-2.7 |  10 ----
 nautilus/bin/epylint          |  10 ----
 nautilus/bin/isort            |  10 ----
 nautilus/bin/pip              |  10 ----
 nautilus/bin/pip2             |  10 ----
 nautilus/bin/pip2.7           |  10 ----
 nautilus/bin/pylint           |  10 ----
 nautilus/bin/pyreverse        |  10 ----
 nautilus/bin/python           | Bin 8464 -> 0 bytes
 nautilus/bin/python-config    |  78 --------------------------
 nautilus/bin/python2          |   1 -
 nautilus/bin/python2.7        |   1 -
 nautilus/bin/symilar          |  10 ----
 nautilus/bin/wheel            |  10 ----
 nautilus/include/python2.7    |   1 -
 src/french-lefff-lemmatizer   |   1 -
 22 files changed, 519 deletions(-)
 delete mode 100644 nautilus/bin/activate
 delete mode 100644 nautilus/bin/activate.csh
 delete mode 100644 nautilus/bin/activate.fish
 delete mode 100644 nautilus/bin/activate.ps1
 delete mode 100644 nautilus/bin/activate_this.py
 delete mode 100755 nautilus/bin/easy_install
 delete mode 100755 nautilus/bin/easy_install-2.7
 delete mode 100755 nautilus/bin/epylint
 delete mode 100755 nautilus/bin/isort
 delete mode 100755 nautilus/bin/pip
 delete mode 100755 nautilus/bin/pip2
 delete mode 100755 nautilus/bin/pip2.7
 delete mode 100755 nautilus/bin/pylint
 delete mode 100755 nautilus/bin/pyreverse
 delete mode 100755 nautilus/bin/python
 delete mode 100755 nautilus/bin/python-config
 delete mode 120000 nautilus/bin/python2
 delete mode 120000 nautilus/bin/python2.7
 delete mode 100755 nautilus/bin/symilar
 delete mode 100755 nautilus/bin/wheel
 delete mode 120000 nautilus/include/python2.7
 delete mode 160000 src/french-lefff-lemmatizer

diff --git a/nautilus/bin/activate b/nautilus/bin/activate
deleted file mode 100644
index 70dac7d..0000000
--- a/nautilus/bin/activate
+++ /dev/null
@@ -1,78 +0,0 @@
-# This file must be used with "source bin/activate" *from bash*
-# you cannot run it directly
-
-deactivate () {
-    unset -f pydoc >/dev/null 2>&1
-
-    # reset old environment variables
-    # ! [ -z ${VAR+_} ] returns true if VAR is declared at all
-    if ! [ -z "${_OLD_VIRTUAL_PATH+_}" ] ; then
-        PATH="$_OLD_VIRTUAL_PATH"
-        export PATH
-        unset _OLD_VIRTUAL_PATH
-    fi
-    if ! [ -z "${_OLD_VIRTUAL_PYTHONHOME+_}" ] ; then
-        PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME"
-        export PYTHONHOME
-        unset _OLD_VIRTUAL_PYTHONHOME
-    fi
-
-    # This should detect bash and zsh, which have a hash command that must
-    # be called to get it to forget past commands.  Without forgetting
-    # past commands the $PATH changes we made may not be respected
-    if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
-        hash -r 2>/dev/null
-    fi
-
-    if ! [ -z "${_OLD_VIRTUAL_PS1+_}" ] ; then
-        PS1="$_OLD_VIRTUAL_PS1"
-        export PS1
-        unset _OLD_VIRTUAL_PS1
-    fi
-
-    unset VIRTUAL_ENV
-    if [ ! "${1-}" = "nondestructive" ] ; then
-    # Self destruct!
-        unset -f deactivate
-    fi
-}
-
-# unset irrelevant variables
-deactivate nondestructive
-
-VIRTUAL_ENV="/Users/williamjaubert/nautilus_nlp/nautilus"
-export VIRTUAL_ENV
-
-_OLD_VIRTUAL_PATH="$PATH"
-PATH="$VIRTUAL_ENV/bin:$PATH"
-export PATH
-
-# unset PYTHONHOME if set
-if ! [ -z "${PYTHONHOME+_}" ] ; then
-    _OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME"
-    unset PYTHONHOME
-fi
-
-if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT-}" ] ; then
-    _OLD_VIRTUAL_PS1="${PS1-}"
-    if [ "x" != x ] ; then
-        PS1="${PS1-}"
-    else
-        PS1="(`basename \"$VIRTUAL_ENV\"`) ${PS1-}"
-    fi
-    export PS1
-fi
-
-# Make sure to unalias pydoc if it's already there
-alias pydoc 2>/dev/null >/dev/null && unalias pydoc || true
-
-pydoc () {
-    python -m pydoc "$@"
-}
-
-# This should detect bash and zsh, which have a hash command that must
-# be called to get it to forget past commands.  Without forgetting
-# past commands the $PATH changes we made may not be respected
-if [ -n "${BASH-}" ] || [ -n "${ZSH_VERSION-}" ] ; then
-    hash -r 2>/dev/null
-fi
diff --git a/nautilus/bin/activate.csh b/nautilus/bin/activate.csh
deleted file mode 100644
index 2c03eaa..0000000
--- a/nautilus/bin/activate.csh
+++ /dev/null
@@ -1,42 +0,0 @@
-# This file must be used with "source bin/activate.csh" *from csh*.
-# You cannot run it directly.
-# Created by Davide Di Blasi <davidedb@gmail.com>.
-
-set newline='\
-'
-
-alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH:q" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT:q" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate && unalias pydoc'
-
-# Unset irrelevant variables.
-deactivate nondestructive
-
-setenv VIRTUAL_ENV "/Users/williamjaubert/nautilus_nlp/nautilus"
-
-set _OLD_VIRTUAL_PATH="$PATH:q"
-setenv PATH "$VIRTUAL_ENV:q/bin:$PATH:q"
-
-
-
-if ("" != "") then
-    set env_name = ""
-else
-    set env_name = "$VIRTUAL_ENV:t:q"
-endif
-
-# Could be in a non-interactive environment,
-# in which case, $prompt is undefined and we wouldn't
-# care about the prompt anyway.
-if ( $?prompt ) then
-    set _OLD_VIRTUAL_PROMPT="$prompt:q"
-if ( "$prompt:q" =~ *"$newline:q"* ) then
-    :
-else
-    set prompt = "[$env_name:q] $prompt:q"
-endif
-endif
-
-unset env_name
-
-alias pydoc python -m pydoc
-
-rehash
diff --git a/nautilus/bin/activate.fish b/nautilus/bin/activate.fish
deleted file mode 100644
index 699e0fd..0000000
--- a/nautilus/bin/activate.fish
+++ /dev/null
@@ -1,101 +0,0 @@
-# This file must be used using `source bin/activate.fish` *within a running fish ( http://fishshell.com ) session*.
-# Do not run it directly.
-
-function _bashify_path -d "Converts a fish path to something bash can recognize"
-    set fishy_path $argv
-    set bashy_path $fishy_path[1]
-    for path_part in $fishy_path[2..-1]
-        set bashy_path "$bashy_path:$path_part"
-    end
-    echo $bashy_path
-end
-
-function _fishify_path -d "Converts a bash path to something fish can recognize"
-    echo $argv | tr ':' '\n'
-end
-
-function deactivate -d 'Exit virtualenv mode and return to the normal environment.'
-    # reset old environment variables
-    if test -n "$_OLD_VIRTUAL_PATH"
-        # https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
-        if test (echo $FISH_VERSION | tr "." "\n")[1] -lt 3
-            set -gx PATH (_fishify_path $_OLD_VIRTUAL_PATH)
-        else
-            set -gx PATH $_OLD_VIRTUAL_PATH
-        end
-        set -e _OLD_VIRTUAL_PATH
-    end
-
-    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
-        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
-        set -e _OLD_VIRTUAL_PYTHONHOME
-    end
-
-    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
-        # Set an empty local `$fish_function_path` to allow the removal of `fish_prompt` using `functions -e`.
-        set -l fish_function_path
-
-        # Erase virtualenv's `fish_prompt` and restore the original.
-        functions -e fish_prompt
-        functions -c _old_fish_prompt fish_prompt
-        functions -e _old_fish_prompt
-        set -e _OLD_FISH_PROMPT_OVERRIDE
-    end
-
-    set -e VIRTUAL_ENV
-
-    if test "$argv[1]" != 'nondestructive'
-        # Self-destruct!
-        functions -e pydoc
-        functions -e deactivate
-        functions -e _bashify_path
-        functions -e _fishify_path
-    end
-end
-
-# Unset irrelevant variables.
-deactivate nondestructive
-
-set -gx VIRTUAL_ENV "/Users/williamjaubert/nautilus_nlp/nautilus"
-
-# https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling
-if test (echo $FISH_VERSION | tr "." "\n")[1] -lt 3
-   set -gx _OLD_VIRTUAL_PATH (_bashify_path $PATH)
-else
-    set -gx _OLD_VIRTUAL_PATH $PATH
-end
-set -gx PATH "$VIRTUAL_ENV/bin" $PATH
-
-# Unset `$PYTHONHOME` if set.
-if set -q PYTHONHOME
-    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
-    set -e PYTHONHOME
-end
-
-function pydoc
-    python -m pydoc $argv
-end
-
-if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
-    # Copy the current `fish_prompt` function as `_old_fish_prompt`.
-    functions -c fish_prompt _old_fish_prompt
-
-    function fish_prompt
-        # Save the current $status, for fish_prompts that display it.
-        set -l old_status $status
-
-        # Prompt override provided?
-        # If not, just prepend the environment name.
-        if test -n ""
-            printf '%s%s' "" (set_color normal)
-        else
-            printf '%s(%s) ' (set_color normal) (basename "$VIRTUAL_ENV")
-        end
-
-        # Restore the original $status
-        echo "exit $old_status" | source
-        _old_fish_prompt
-    end
-
-    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
-end
diff --git a/nautilus/bin/activate.ps1 b/nautilus/bin/activate.ps1
deleted file mode 100644
index 6d8ae2a..0000000
--- a/nautilus/bin/activate.ps1
+++ /dev/null
@@ -1,60 +0,0 @@
-# This file must be dot sourced from PoSh; you cannot run it directly. Do this: . ./activate.ps1
-
-$script:THIS_PATH = $myinvocation.mycommand.path
-$script:BASE_DIR = split-path (resolve-path "$THIS_PATH/..") -Parent
-
-function global:deactivate([switch] $NonDestructive)
-{
-    if (test-path variable:_OLD_VIRTUAL_PATH)
-    {
-        $env:PATH = $variable:_OLD_VIRTUAL_PATH
-        remove-variable "_OLD_VIRTUAL_PATH" -scope global
-    }
-
-    if (test-path function:_old_virtual_prompt)
-    {
-        $function:prompt = $function:_old_virtual_prompt
-        remove-item function:\_old_virtual_prompt
-    }
-
-    if ($env:VIRTUAL_ENV)
-    {
-        $old_env = split-path $env:VIRTUAL_ENV -leaf
-        remove-item env:VIRTUAL_ENV -erroraction silentlycontinue
-    }
-
-    if (!$NonDestructive)
-    {
-        # Self destruct!
-        remove-item function:deactivate
-        remove-item function:pydoc
-    }
-}
-
-function global:pydoc
-{
-    python -m pydoc $args
-}
-
-# unset irrelevant variables
-deactivate -nondestructive
-
-$VIRTUAL_ENV = $BASE_DIR
-$env:VIRTUAL_ENV = $VIRTUAL_ENV
-
-$global:_OLD_VIRTUAL_PATH = $env:PATH
-$env:PATH = "$env:VIRTUAL_ENV/bin:" + $env:PATH
-if (!$env:VIRTUAL_ENV_DISABLE_PROMPT)
-{
-    function global:_old_virtual_prompt
-    {
-        ""
-    }
-    $function:_old_virtual_prompt = $function:prompt
-    function global:prompt
-    {
-        # Add a prefix to the current prompt, but don't discard it.
-        write-host "($( split-path $env:VIRTUAL_ENV -leaf )) " -nonewline
-        & $function:_old_virtual_prompt
-    }
-}
diff --git a/nautilus/bin/activate_this.py b/nautilus/bin/activate_this.py
deleted file mode 100644
index 59b5d72..0000000
--- a/nautilus/bin/activate_this.py
+++ /dev/null
@@ -1,46 +0,0 @@
-"""Activate virtualenv for current interpreter:
-
-Use exec(open(this_file).read(), {'__file__': this_file}).
-
-This can be used when you must use an existing Python interpreter, not the virtualenv bin/python.
-"""
-import os
-import site
-import sys
-
-try:
-    __file__
-except NameError:
-    raise AssertionError("You must use exec(open(this_file).read(), {'__file__': this_file}))")
-
-# prepend bin to PATH (this file is inside the bin directory)
-bin_dir = os.path.dirname(os.path.abspath(__file__))
-os.environ["PATH"] = os.pathsep.join([bin_dir] + os.environ.get("PATH", "").split(os.pathsep))
-
-base = os.path.dirname(bin_dir)
-
-# virtual env is right above bin directory
-os.environ["VIRTUAL_ENV"] = base
-
-# add the virtual environments site-package to the host python import mechanism
-IS_PYPY = hasattr(sys, "pypy_version_info")
-IS_JYTHON = sys.platform.startswith("java")
-if IS_JYTHON:
-    site_packages = os.path.join(base, "Lib", "site-packages")
-elif IS_PYPY:
-    site_packages = os.path.join(base, "site-packages")
-else:
-    IS_WIN = sys.platform == "win32"
-    if IS_WIN:
-        site_packages = os.path.join(base, "Lib", "site-packages")
-    else:
-        site_packages = os.path.join(base, "lib", "python{}".format(sys.version[:3]), "site-packages")
-
-prev = set(sys.path)
-site.addsitedir(site_packages)
-sys.real_prefix = sys.prefix
-sys.prefix = base
-
-# Move the added items to the front of the path, in place
-new = list(sys.path)
-sys.path[:] = [i for i in new if i not in prev] + [i for i in new if i in prev]
diff --git a/nautilus/bin/easy_install b/nautilus/bin/easy_install
deleted file mode 100755
index d083bb5..0000000
--- a/nautilus/bin/easy_install
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from setuptools.command.easy_install import main
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(main())
diff --git a/nautilus/bin/easy_install-2.7 b/nautilus/bin/easy_install-2.7
deleted file mode 100755
index d083bb5..0000000
--- a/nautilus/bin/easy_install-2.7
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from setuptools.command.easy_install import main
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(main())
diff --git a/nautilus/bin/epylint b/nautilus/bin/epylint
deleted file mode 100755
index 8fa16b4..0000000
--- a/nautilus/bin/epylint
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from pylint import run_epylint
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(run_epylint())
diff --git a/nautilus/bin/isort b/nautilus/bin/isort
deleted file mode 100755
index 31b0716..0000000
--- a/nautilus/bin/isort
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from isort.main import main
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(main())
diff --git a/nautilus/bin/pip b/nautilus/bin/pip
deleted file mode 100755
index 08299c7..0000000
--- a/nautilus/bin/pip
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from pip._internal import main
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(main())
diff --git a/nautilus/bin/pip2 b/nautilus/bin/pip2
deleted file mode 100755
index 08299c7..0000000
--- a/nautilus/bin/pip2
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from pip._internal import main
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(main())
diff --git a/nautilus/bin/pip2.7 b/nautilus/bin/pip2.7
deleted file mode 100755
index 08299c7..0000000
--- a/nautilus/bin/pip2.7
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from pip._internal import main
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(main())
diff --git a/nautilus/bin/pylint b/nautilus/bin/pylint
deleted file mode 100755
index 9635a6f..0000000
--- a/nautilus/bin/pylint
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from pylint import run_pylint
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(run_pylint())
diff --git a/nautilus/bin/pyreverse b/nautilus/bin/pyreverse
deleted file mode 100755
index 6af5484..0000000
--- a/nautilus/bin/pyreverse
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from pylint import run_pyreverse
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(run_pyreverse())
diff --git a/nautilus/bin/python b/nautilus/bin/python
deleted file mode 100755
index fddb46f7919ca3729d7a3a3aff690d36c96049f6..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/nautilus/bin/python-config b/nautilus/bin/python-config
deleted file mode 100755
index 5f285ef..0000000
--- a/nautilus/bin/python-config
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
-
-import sys
-import getopt
-import sysconfig
-
-valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags',
-              'ldflags', 'help']
-
-if sys.version_info >= (3, 2):
-    valid_opts.insert(-1, 'extension-suffix')
-    valid_opts.append('abiflags')
-if sys.version_info >= (3, 3):
-    valid_opts.append('configdir')
-
-
-def exit_with_usage(code=1):
-    sys.stderr.write("Usage: {0} [{1}]\n".format(
-        sys.argv[0], '|'.join('--'+opt for opt in valid_opts)))
-    sys.exit(code)
-
-try:
-    opts, args = getopt.getopt(sys.argv[1:], '', valid_opts)
-except getopt.error:
-    exit_with_usage()
-
-if not opts:
-    exit_with_usage()
-
-pyver = sysconfig.get_config_var('VERSION')
-getvar = sysconfig.get_config_var
-
-opt_flags = [flag for (flag, val) in opts]
-
-if '--help' in opt_flags:
-    exit_with_usage(code=0)
-
-for opt in opt_flags:
-    if opt == '--prefix':
-        print(sysconfig.get_config_var('prefix'))
-
-    elif opt == '--exec-prefix':
-        print(sysconfig.get_config_var('exec_prefix'))
-
-    elif opt in ('--includes', '--cflags'):
-        flags = ['-I' + sysconfig.get_path('include'),
-                 '-I' + sysconfig.get_path('platinclude')]
-        if opt == '--cflags':
-            flags.extend(getvar('CFLAGS').split())
-        print(' '.join(flags))
-
-    elif opt in ('--libs', '--ldflags'):
-        abiflags = getattr(sys, 'abiflags', '')
-        libs = ['-lpython' + pyver + abiflags]
-        libs += getvar('LIBS').split()
-        libs += getvar('SYSLIBS').split()
-        # add the prefix/lib/pythonX.Y/config dir, but only if there is no
-        # shared library in prefix/lib/.
-        if opt == '--ldflags':
-            if not getvar('Py_ENABLE_SHARED'):
-                libs.insert(0, '-L' + getvar('LIBPL'))
-            if not getvar('PYTHONFRAMEWORK'):
-                libs.extend(getvar('LINKFORSHARED').split())
-        print(' '.join(libs))
-
-    elif opt == '--extension-suffix':
-        ext_suffix = sysconfig.get_config_var('EXT_SUFFIX')
-        if ext_suffix is None:
-            ext_suffix = sysconfig.get_config_var('SO')
-        print(ext_suffix)
-
-    elif opt == '--abiflags':
-        if not getattr(sys, 'abiflags', None):
-            exit_with_usage()
-        print(sys.abiflags)
-
-    elif opt == '--configdir':
-        print(sysconfig.get_config_var('LIBPL'))
diff --git a/nautilus/bin/python2 b/nautilus/bin/python2
deleted file mode 120000
index d8654aa..0000000
--- a/nautilus/bin/python2
+++ /dev/null
@@ -1 +0,0 @@
-python
\ No newline at end of file
diff --git a/nautilus/bin/python2.7 b/nautilus/bin/python2.7
deleted file mode 120000
index d8654aa..0000000
--- a/nautilus/bin/python2.7
+++ /dev/null
@@ -1 +0,0 @@
-python
\ No newline at end of file
diff --git a/nautilus/bin/symilar b/nautilus/bin/symilar
deleted file mode 100755
index 582abdf..0000000
--- a/nautilus/bin/symilar
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python2.7
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from pylint import run_symilar
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(run_symilar())
diff --git a/nautilus/bin/wheel b/nautilus/bin/wheel
deleted file mode 100755
index bd14437..0000000
--- a/nautilus/bin/wheel
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/Users/williamjaubert/nautilus_nlp/nautilus/bin/python
-# -*- coding: utf-8 -*-
-import re
-import sys
-
-from wheel.cli import main
-
-if __name__ == '__main__':
-    sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
-    sys.exit(main())
diff --git a/nautilus/include/python2.7 b/nautilus/include/python2.7
deleted file mode 120000
index de0d082..0000000
--- a/nautilus/include/python2.7
+++ /dev/null
@@ -1 +0,0 @@
-/Users/williamjaubert/anaconda2/include/python2.7
\ No newline at end of file
diff --git a/src/french-lefff-lemmatizer b/src/french-lefff-lemmatizer
deleted file mode 160000
index ba1ef2b..0000000
--- a/src/french-lefff-lemmatizer
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit ba1ef2bc67aa3753d127ba978a255081120449c2

From 21f3b17b55ee41762d6a9873e4ccf539093657d3 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 15:41:15 +0200
Subject: [PATCH 120/496] Update requirements.txt

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 90217bd..b124e17 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,7 +10,6 @@ flake8
 python-dotenv>=0.5.1
 pillow
 pytest
-os
 
 #library requirements
 pyLDAvis==2.1.2

From 9b0f52721e96253fe2cabc409232c04e249dada3 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 15:41:24 +0200
Subject: [PATCH 121/496] Update requirements.txt

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index b124e17..3ec0a3a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -34,7 +34,6 @@ textacy==0.6.3
 gensim==3.7.1
 scikit_learn==0.20.3
 vaderSentiment==3.2.1
-logging==0.4.9.6
 google-compute-engine==2.8.13
 flashtext==2.7
 

From cc02b4d2e399abecb4b1af9e5b2becb3d031070f Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 15:53:29 +0200
Subject: [PATCH 122/496] Update .travis.yml

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index d366fab..4f7da47 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -9,7 +9,7 @@ before_script:
   - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 
   - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
-  - sudo add-apt-repository ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
+  - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
 install:
   - pip install -r requirements.txt

From 5e3e5891f7258e809945ea9a891db32abfe81e56 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:05:35 +0200
Subject: [PATCH 123/496] Naming & folder change

---
 nautilus_nlp/preprocessing/__init__.py        |   0
 .../preprocessing/keyword_extractor.py        |  21 +
 nautilus_nlp/preprocessing/lemmatizer.py      | 102 +++++
 nautilus_nlp/preprocessing/preprocess.py      | 397 ++++++++++++++++++
 nautilus_nlp/preprocessing/stemmer.py         |  31 ++
 .../preprocessing/text_vectorizers.py         | 195 +++++++++
 nautilus_nlp/preprocessing/tokenizer.py       |  80 ++++
 7 files changed, 826 insertions(+)
 create mode 100644 nautilus_nlp/preprocessing/__init__.py
 create mode 100644 nautilus_nlp/preprocessing/keyword_extractor.py
 create mode 100644 nautilus_nlp/preprocessing/lemmatizer.py
 create mode 100644 nautilus_nlp/preprocessing/preprocess.py
 create mode 100644 nautilus_nlp/preprocessing/stemmer.py
 create mode 100644 nautilus_nlp/preprocessing/text_vectorizers.py
 create mode 100644 nautilus_nlp/preprocessing/tokenizer.py

diff --git a/nautilus_nlp/preprocessing/__init__.py b/nautilus_nlp/preprocessing/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nautilus_nlp/preprocessing/keyword_extractor.py b/nautilus_nlp/preprocessing/keyword_extractor.py
new file mode 100644
index 0000000..e384bf2
--- /dev/null
+++ b/nautilus_nlp/preprocessing/keyword_extractor.py
@@ -0,0 +1,21 @@
+from flashtext import KeywordProcessor
+
+def extract_keywords(text,keyword,case_sensitive=True):
+    """
+    Extract Keywords from a document.
+    args :
+    text: Text to extract keywords from
+    keyword : Single keyword (str) or list of keywords (list)
+
+    return list of extracted keyworkds
+    """
+    processor=KeywordProcessor(case_sensitive=case_sensitive)
+    if isinstance(keyword,list):
+        processor.add_keywords_from_list(keyword)
+    elif isinstance(keyword,str):
+        processor.add_keyword(keyword)
+    elif isinstance(keyword,dict):
+        processor.add_keywords_from_dict(keyword)
+
+    return processor.extract_keywords(text)
+    
diff --git a/nautilus_nlp/preprocessing/lemmatizer.py b/nautilus_nlp/preprocessing/lemmatizer.py
new file mode 100644
index 0000000..e4c812c
--- /dev/null
+++ b/nautilus_nlp/preprocessing/lemmatizer.py
@@ -0,0 +1,102 @@
+import spacy
+import nltk
+nltk.download('wordnet')
+from nltk.stem import WordNetLemmatizer 
+from nltk.corpus import wordnet
+
+try:
+    french_spacy = spacy.load('fr_core_news_sm')
+except OSError:
+    raise OSError("""You must install French langage to use SpaCy. 
+                    python -m spacy download fr
+                    See https://spacy.io/usage/ for details
+                """)
+try:
+    english_spacy = spacy.load('en_core_web_sm')
+except OSError:
+    raise OSError("""You must install english langage to use SpaCy. 
+                    python -m spacy download en
+                    See https://spacy.io/usage/ for details
+                """)             
+
+
+
+
+def lemmatize_french_tokens(tokens, module='spacy', load_only_pos='all'):
+    '''
+    Wrappers of french lemmatizers. SpaCy is much faster but sometimes 
+    FrenchLefffLemmatizer returns more accurate results. Give a try to the 
+    2 modules and choose the one that better fit your needs. 
+
+    Args:
+        tokens (list): list of tokens
+        module ({'spacy'}): modules availables.
+        load_only_pos ({'a', v', 'r', 'n', 'all'}): If not "all", applies 
+        lemmatization only to a certain POS tags, for french_leff_v module.
+        a = adjectives, v = verbs, n = noun, r = adverb. 
+
+    Returns:
+        list of lemmatized tokens
+    
+    '''
+
+    tokens = _make_sure_input_is_list_of_tokens(tokens)
+
+    if module == 'spacy':
+        # Doc : https://spacy.io/api/token#attributes
+        text = ' '.join(tokens)
+        doc = french_spacy(text)
+        return [token.lemma_ for token in doc]
+
+    else:
+        raise ValueError("must pass a valid module name!")
+
+
+def lemmatize_english_tokens(tokens, module='spacy', pos_tag_prio='v'):
+    '''
+    Args:
+        tokens (list): list of tokens
+        module ({'french_leff_v', 'spacy'}): modules availables.
+        pos_tag_prio ({'v', 'n', 'all'}): grammatical priority, applies
+        for french_leff_v module. 
+
+    Returns:
+        list of lemmatized tokens
+    
+    '''
+    tokens = _make_sure_input_is_list_of_tokens(tokens)
+
+    if module == 'nltk':
+        # Doc : https://github.com/ClaudeCoulombe/FrenchLefffLemmatizer
+        lemmatizer = WordNetLemmatizer()
+        return [lemmatizer.lemmatize(word, _get_wordnet_pos(word)) for word in tokens]
+
+    elif module == 'spacy':
+        # Doc : https://spacy.io/api/token#attributes
+        text = ' '.join(tokens)
+        doc = english_spacy(text)
+        return [token.lemma_ for token in doc]
+
+    else:
+        raise ValueError("must pass a valid module name!")
+
+
+def _get_wordnet_pos(word):
+    """Map POS tag to first character lemmatize() accepts"""
+    tag = nltk.pos_tag([word])[0][1][0].upper()
+    tag_dict = {"J": wordnet.ADJ,
+                "N": wordnet.NOUN,
+                "V": wordnet.VERB,
+                "R": wordnet.ADV}
+
+    return tag_dict.get(tag, wordnet.NOUN)        
+
+
+def _make_sure_input_is_list_of_tokens(tokens):
+    if type(tokens) is list:
+        return tokens
+    elif tokens is None:
+        return []
+    elif type(tokens) is str:
+        raise ValueError("must pass a list of tokens, not text!")    
+
diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
new file mode 100644
index 0000000..70d8148
--- /dev/null
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -0,0 +1,397 @@
+# -*- coding: utf-8 -*-
+"""
+Functions that modify raw text *in-place*, replacing contractions, URLs, emails,
+phone numbers, and currency symbols with standardized forms. These should be
+applied before processing by `Spacy <http://spacy.io>`_, but be warned: preprocessing
+may affect the interpretation of the text -- and spacy's processing of it.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import os
+import json
+import re
+import unicodedata
+
+from ftfy import fix_text
+from stop_words import get_stop_words as _get_stop_words
+from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
+
+from . import constants
+from nautilus_nlp.config.config import ROOT_FOLDER
+from nautilus_nlp.utils.file_loader import documents_loader
+
+STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
+
+
+def remove_multiple_spaces_and_strip_text(text):
+    """Remove multiple spaces, strip text, and remove '-', '*' characters.
+    Parameters
+    ----------
+    text : str,
+    Returns
+    -------
+    str
+    """
+    regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
+    for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
+        text = re.sub(regex_remove_multiple_spaces, " ", text)
+        text = text.strip()
+    return text
+
+
+def remove_EOL_characters(text):
+    """Remove end of line (\n) char.
+    Parameters
+    ----------
+    text : str,
+    Returns
+    -------
+    str
+    """
+    return text.replace("\n", "")
+
+
+def remove_tokens_with_nonletters(tokens):
+    """
+    Inputs a list of tokens, outputs a list of tokens without tokens that
+    includes numbers of special caracters
+    """
+    return [word for word in tokens if re.search("[a-zA-Z]", word)]
+
+
+def remove_special_caracters(tokens):
+    """ Checks for letters in the token - using a regex search.
+    Strings that are just punctuation will
+    be removed! No more custom '--'. But ''s' and '9' will remain.
+    """
+    return [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
+
+
+def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
+    stopwords = documents_loader(filepath)
+    stopwords = json.loads(stopwords)
+    return stopwords
+
+
+def get_stopwords(lang: str = "en"):
+    """
+    Inputs a language code, returns a list of stopwords for the specified language
+
+    Args:
+        lang: Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
+         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
+         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
+         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
+          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
+          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
+           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
+    """
+    if type(lang) == str and len(lang) == 2:
+        lang = lang.lower()
+
+        custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
+        stopwords = []
+
+        supported_lang_lib = list(_LANGUAGE_MAPPING.keys())
+        supported_lang_custom = list(custom_stopwords.keys())
+        supported_lang = supported_lang_lib + supported_lang_custom
+        if lang in supported_lang:
+            if lang in supported_lang_lib:
+                stopwords += _get_stop_words(lang)
+            if lang in supported_lang_custom:
+                stopwords += custom_stopwords[lang]
+        else:
+            raise ValueError(
+                "Language not available yet or incorrect country code. Supported languages: {}".format(
+                    supported_lang
+                )
+            )
+    else:
+        raise ValueError(
+            'Please input a valid country code, in 2 letters. Eg. "us" for USA. '
+        )
+    return list(set(stopwords))
+
+
+def remove_stopwords(text_or_tokens, stopwords):
+    """ 
+    Remove stopwords from tokens.
+    """
+    if type(text_or_tokens) is str:
+        return [word for word in text_or_tokens.split() if word not in stopwords]
+    elif type(text_or_tokens) is list:
+        return [word for word in text_or_tokens if word not in stopwords]
+    else:
+        raise ValueError("must input string or list of tokens")
+
+
+def fix_bad_unicode(text, normalization="NFC") -> str:
+    """
+    Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
+    this includes mojibake, HTML entities and other code cruft,
+    and non-standard forms for display purposes.
+
+    Args:
+        text (str): raw text
+        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): if 'NFC',
+            combines characters and diacritics written using separate code points,
+            e.g. converting "e" plus an acute accent modifier into "é"; unicode
+            can be converted to NFC form without any change in its meaning!
+            if 'NFKC', additional normalizations are applied that can change
+            the meanings of characters, e.g. ellipsis characters will be replaced
+            with three periods
+
+    Returns:
+        str
+    """
+    return fix_text(text, normalization=normalization)
+
+
+def normalize_whitespace(text) -> str:
+    """
+    Given ``text`` str, replace one or more spacings with a single space, and one
+    or more linebreaks with a single newline. Also strip leading/trailing whitespace.
+    """
+    return constants.NONBREAKING_SPACE_REGEX.sub(
+        " ", constants.LINEBREAK_REGEX.sub(r"\n", text)
+    ).strip()
+
+
+def unpack_french_contractions(text) -> str:
+
+    return text
+
+
+def unpack_english_contractions(text) -> str:
+    """
+    Replace *English* contractions in ``text`` str with their unshortened forms.
+    N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
+    so are left as-is.
+    """
+    # standard
+    text = re.sub(
+        r"(\b)([Aa]re|[Cc]ould|[Dd]id|[Dd]oes|[Dd]o|[Hh]ad|[Hh]as|[Hh]ave|[Ii]s|[Mm]ight|[Mm]ust|[Ss]hould|[Ww]ere|[Ww]ould)n't",
+        r"\1\2 not",
+        text,
+    )
+    text = re.sub(
+        r"(\b)([Hh]e|[Ii]|[Ss]he|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'ll",
+        r"\1\2 will",
+        text,
+    )
+    text = re.sub(r"(\b)([Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'re", r"\1\2 are", text)
+    text = re.sub(
+        r"(\b)([Ii]|[Ss]hould|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Ww]ould|[Yy]ou)'ve",
+        r"\1\2 have",
+        text,
+    )
+    # non-standard
+    text = re.sub(r"(\b)([Cc]a)n't", r"\1\2n not", text)
+    text = re.sub(r"(\b)([Ii])'m", r"\1\2 am", text)
+    text = re.sub(r"(\b)([Ll]et)'s", r"\1\2 us", text)
+    text = re.sub(r"(\b)([Ww])on't", r"\1\2ill not", text)
+    text = re.sub(r"(\b)([Ss])han't", r"\1\2hall not", text)
+    text = re.sub(r"(\b)([Yy])(?:'all|a'll)", r"\1\2ou all", text)
+    return text
+
+
+def unpack_contractions_from_lang(text, lang: str = "fr") -> str:
+    if lang == "fr":
+        return unpack_english_contractions(text)
+    else:
+        return unpack_english_contractions(text)
+
+
+def replace_urls(text, replace_with="*URL*") -> str:
+    """Replace all URLs in ``text`` str with ``replace_with`` str."""
+    return constants.URL_REGEX.sub(
+        replace_with, constants.SHORT_URL_REGEX.sub(replace_with, text)
+    )
+
+
+def replace_emails(text, replace_with="*EMAIL*") -> str:
+    """Replace all emails in ``text`` str with ``replace_with`` str."""
+    return constants.EMAIL_REGEX.sub(replace_with, text)
+
+
+def replace_phone_numbers(text, replace_with="*PHONE*") -> str:
+    """Replace all phone numbers in ``text`` str with ``replace_with`` str."""
+    return constants.PHONE_REGEX.sub(replace_with, text)
+
+
+def replace_numbers(text, replace_with="*NUMBER*") -> str:
+    """Replace all numbers in ``text`` str with ``replace_with`` str."""
+    return constants.NUMBERS_REGEX.sub(replace_with, text)
+
+
+def replace_currency_symbols(text, replace_with=None) -> str:
+    """
+    Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
+
+    Args:
+        text (str): raw text
+        replace_with (str): if None (default), replace symbols with
+            their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
+            otherwise, pass in a string with which to replace all symbols
+            (e.g. "*CURRENCY*")
+
+    Returns:
+        str
+    """
+    if replace_with is None:
+        for k, v in constants.CURRENCIES.items():
+            text = text.replace(k, v)
+        return text
+    else:
+        return constants.CURRENCY_REGEX.sub(replace_with, text)
+
+
+def remove_punct(text, marks=None) -> str:
+    """
+    Remove punctuation from ``text`` by replacing all instances of ``marks``
+    with whitespace.
+
+    Args:
+        text (str): raw text
+        marks (str): If specified, remove only the characters in this string,
+            e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
+            Otherwise, all punctuation marks are removed.
+
+    Returns:
+        str
+
+    Note:
+        When ``marks=None``, Python's built-in :meth:`str.translate()` is
+        used to remove punctuation; otherwise, a regular expression is used
+        instead. The former's performance is about 5-10x faster.
+    """
+    if marks:
+        return re.sub("[{}]+".format(re.escape(marks)), " ", text, flags=re.UNICODE)
+    else:
+        return text.translate(constants.PUNCT_TRANSLATE_UNICODE)
+
+
+def remove_accents(text, method="unicode") -> str:
+    """
+    Remove accents from any accented unicode characters in ``text`` str, either by
+    transforming them into ascii equivalents or removing them entirely.
+
+    Args:
+        text (str): raw text
+        method ({'unicode', 'ascii'}): if 'unicode', remove accented
+            char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
+            remove accented char for any unicode symbol
+
+            NB: the 'ascii' method is notably faster than 'unicode', but less good
+
+    Returns:
+        str
+
+    Raises:
+        ValueError: if ``method`` is not in {'unicode', 'ascii'}
+    """
+    if method == "unicode":
+        return "".join(
+            c
+            for c in unicodedata.normalize("NFKD", text)
+            if not unicodedata.combining(c)
+        )
+    elif method == "ascii":
+        return (
+            unicodedata.normalize("NFKD", text)
+            .encode("ascii", errors="ignore")
+            .decode("ascii")
+        )
+    else:
+        msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
+        raise ValueError(msg)
+
+
+def remove_emoji(word):
+    """
+    Remove emoji from any  str by stripping any unicode in the range of Emoji unicode,
+
+    Args:
+        word (str): raw word
+
+    Returns:
+        str
+
+    """
+    RE_EMOJI = re.compile("[\U00010000-\U0010ffff]", flags=re.UNICODE)
+    word = RE_EMOJI.sub(r"", word)
+    return word
+
+
+def preprocess_text(
+    text,
+    no_emoji=False,
+    fix_unicode=False,
+    lowercase=False,
+    no_urls=False,
+    no_emails=False,
+    no_phone_numbers=False,
+    no_numbers=False,
+    no_currency_symbols=False,
+    no_punct=False,
+    no_contractions=False,
+    no_accents=False,
+) -> str:
+    """
+    Normalize various aspects of a raw text doc before parsing it with Spacy.
+    A convenience function for applying all other preprocessing functions in one go.
+
+    Args:
+        text (str): raw text to preprocess
+        fix_unicode (bool): if True, fix "broken" unicode such as
+            mojibake and garbled HTML entities
+        lowercase (bool): if True, all text is lower-cased
+        no_urls (bool): if True, replace all URL strings with '*URL*'
+        no_emails (bool): if True, replace all email strings with '*EMAIL*'
+        no_phone_numbers (bool): if True, replace all phone number strings
+            with '*PHONE*'
+        no_numbers (bool): if True, replace all number-like strings
+            with '*NUMBER*'
+        no_currency_symbols (bool): if True, replace all currency symbols
+            with their standard 3-letter abbreviations
+        no_punct (bool): if True, remove all punctuation (replace with
+            empty string)
+        no_contractions (bool): if True, replace *English* contractions
+            with their unshortened forms
+        no_accents (bool): if True, replace all accented characters
+            with unaccented versions
+
+    Returns:
+        str: input ``text`` processed according to function args
+
+    Warning:
+        These changes may negatively affect subsequent NLP analysis performed
+        on the text, so choose carefully, and preprocess at your own risk!
+    """
+
+    assert isinstance(text, str), "The text to preprocess must be a string"
+
+    if fix_unicode is True:
+        text = fix_bad_unicode(text, normalization="NFC")
+    if no_urls is True:
+        text = replace_urls(text)
+    if no_emails is True:
+        text = replace_emails(text)
+    if no_phone_numbers is True:
+        text = replace_phone_numbers(text)
+    if no_numbers is True:
+        text = replace_numbers(text)
+    if no_currency_symbols is True:
+        text = replace_currency_symbols(text)
+    if no_contractions is True:
+        text = unpack_contractions_from_lang(text)
+    if no_accents is True:
+        text = remove_accents(text, method="unicode")
+    if no_punct is True:
+        text = remove_punct(text)
+    if lowercase is True:
+        text = text.lower()
+    # always normalize whitespace; treat linebreaks separately from spacing
+    text = normalize_whitespace(text)
+
+    return text
diff --git a/nautilus_nlp/preprocessing/stemmer.py b/nautilus_nlp/preprocessing/stemmer.py
new file mode 100644
index 0000000..c480ca8
--- /dev/null
+++ b/nautilus_nlp/preprocessing/stemmer.py
@@ -0,0 +1,31 @@
+from nltk.stem.snowball import *
+
+
+def stem_tokens(tokens: list, lang: str ='english'):
+    '''
+    Wrapper of NLTK's Snowball stemmers : http://www.nltk.org/howto/stem.html
+
+    Args:
+        tokens (list): list of tokens
+        lang ({'arabic', 'danish', 'dutch', 'english', 'finnish', 'french',
+        'german', 'hungarian', 'italian', 'norwegian', 'porter', 'portuguese', 
+        'romanian', 'russian', 'spanish', 'swedish'}): supported langages
+
+    Returns:
+        list of stemmed tokens
+    
+    '''
+    supported_lang = [lang for lang in SnowballStemmer.languages]
+    
+    if lang in supported_lang:
+        stemmer = eval(lang.capitalize()+'Stemmer()')
+    else:
+        raise ValueError("Langage not supported of mispelled")
+    
+    # Make sure tokens are actually a list, and handle NaN. 
+    if type(tokens) is list:
+        return [stemmer.stem(token) for token in tokens]
+    elif tokens is None:
+        return []
+    elif type(tokens) is str:
+        raise ValueError("must pass a list of tokens, not text!")
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/text_vectorizers.py b/nautilus_nlp/preprocessing/text_vectorizers.py
new file mode 100644
index 0000000..a03c5bb
--- /dev/null
+++ b/nautilus_nlp/preprocessing/text_vectorizers.py
@@ -0,0 +1,195 @@
+import spacy
+from gensim.corpora import Dictionary
+from gensim.models.tfidfmodel import TfidfModel
+from gensim import corpora, models, similarities
+from gensim.matutils import sparse2full
+import numpy as np
+import math
+
+from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
+
+
+class Tfidf(object):
+    """
+    Inputs a list of string
+    Outputs a tuple with the wordcount vector matrix, and the list of feature name
+    Params:
+        input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
+        lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
+        stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
+        max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
+        dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
+        
+    Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
+    """
+    
+    def __init__(self, **kwargs):
+        self.tfidf_vectorizer = TfidfVectorizer(**kwargs)
+    
+    def _compute_wordcount_vector(self, documents):
+        '''
+        Input a list of documents (string)
+        Output the wordcount vector matrix 
+        '''
+        self.word_count_vector = self.tfidf_vectorizer.fit_transform(documents)
+        return self.word_count_vector
+    
+
+    def _get_features_name(self):
+        self.feature_names = self.tfidf_vectorizer.get_feature_names()
+        return self.feature_names
+
+    
+    def _compute_idf(self):
+        self.tfidf_transformer=TfidfTransformer(smooth_idf=True, use_idf=True)
+        self.tfidf_transformer.fit(self.word_count_vector)
+        return self.word_count_vector
+
+    
+    def compute_tfidf(self, documents):
+        self._compute_wordcount_vector(documents)
+        self._get_features_name()
+        self._compute_idf()
+        return self.word_count_vector
+
+    def _apply_tfidf_to_doc(self, text):
+        '''generate tf-idf for the given document'''
+        return self.tfidf_transformer.transform(self.tfidf_vectorizer.transform([text]))
+    
+    
+    def _sort_coo(self, coo_matrix):
+        '''sort the tf-idf vectors by descending order of scores'''
+        tuples = zip(coo_matrix.col, coo_matrix.data)
+        return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
+    
+    
+    def _extract_topn_from_vector(self, feature_names, sorted_items, topn=10):
+        """get the feature names and tf-idf score of top n items"""
+
+        #use only topn items from vector
+        sorted_items = sorted_items[:topn]
+
+        score_vals = []
+        feature_vals = []
+
+        # word index and corresponding tf-idf score
+        for idx, score in sorted_items:
+
+            #keep track of feature name and its corresponding score
+            score_vals.append(round(score, 3))
+            feature_vals.append(feature_names[idx])
+
+        #create a tuples of feature,score
+        #results = zip(feature_vals,score_vals)
+        results= {}
+        for idx in range(len(feature_vals)):
+            results[feature_vals[idx]]=score_vals[idx]
+
+        return results
+
+    
+    def get_top_tfidf_per_doc(self, text, n=10):
+        '''compute TF-IDF for a given doc, and returns a list of the top N weighted words'''
+        tf_idf_vector= self._apply_tfidf_to_doc(text)
+        sorted_items=self._sort_coo(tf_idf_vector.tocoo())
+        return list(self._extract_topn_from_vector(self.feature_names, sorted_items, n).keys())
+    
+    def get_top_tfidf(self, n=10):
+        '''returns a dict of the top N weighted words, with their weight'''
+        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(self.word_count_vector.tocoo()), topn=n)
+
+
+class Text_Vectorizer:
+    def __init__(self, doc_list):
+        # Initialize
+        self.doc_list = doc_list
+        self.nlp, self.docs, self.docs_dict = self._preprocess(self.doc_list)
+
+    # Functions to lemmatise docs
+    def _keep_token(self, t):
+        return t.is_alpha and not (t.is_space or t.is_punct or t.is_stop or t.like_num)
+
+    def _lemmatize_doc(self, doc):
+        return [t.lemma_ for t in doc if self._keep_token(t)]
+
+    # Gensim to create a dictionary and filter out stop and infrequent words (lemmas).
+    def _get_docs_dict(self, docs):
+        docs_dict = Dictionary(docs)
+        # CAREFUL: For small corpus please carefully modify the parameters for filter_extremes, or simply comment it out.
+        docs_dict.filter_extremes(no_below=5, no_above=0.2)
+        docs_dict.compactify()
+        return docs_dict
+
+    # Preprocess docs
+    def _preprocess(self, doc_list):
+        # Load spacy model
+        nlp = spacy.load("en")
+        # lemmatise docs
+        docs = [self._lemmatize_doc(nlp(doc)) for doc in doc_list]
+        # Get docs dictionary
+        docs_dict = self._get_docs_dict(docs)
+        return nlp, docs, docs_dict
+
+    # Gensim can again be used to create a bag-of-words representation of each document,
+    # build the TF-IDF model,
+    # and compute the TF-IDF vector for each document.
+    def _get_tfidf(self, docs, docs_dict):
+        docs_corpus = [docs_dict.doc2bow(doc) for doc in docs]
+        model_tfidf = TfidfModel(docs_corpus, id2word=docs_dict)
+        docs_tfidf = model_tfidf[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(docs_dict)) for c in docs_tfidf])
+        return docs_vecs
+
+    # Get avg w2v for one document
+    def _document_vector(self, doc, docs_dict, nlp):
+        # remove out-of-vocabulary words
+        doc_vector = [nlp(word).vector for word in doc if word in docs_dict.token2id]
+        return np.mean(doc_vector, axis=0)
+
+
+    # Get average vector for document list
+    def avg_wv(self):
+        docs_vecs = np.vstack(
+            [self._document_vector(doc, self.docs_dict, self.nlp) for doc in self.docs]
+        )
+        return docs_vecs
+
+    # Get TF-IDF vector for document list
+    def get_tfidf(self):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_tfidf = TfidfModel(docs_corpus, id2word=self.docs_dict)
+        docs_tfidf = model_tfidf[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_tfidf])
+        return docs_vecs
+
+    # Get Latent Semantic Indexing(LSI) vector for document list
+    def get_lsi(self, num_topics=300):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_lsi = models.LsiModel(docs_corpus, num_topics, id2word=self.docs_dict)
+        docs_lsi = model_lsi[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lsi])
+        return docs_vecs
+
+    # Get Random Projections(RP) vector for document list
+    def get_rp(self):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_rp = models.RpModel(docs_corpus, id2word=self.docs_dict)
+        docs_rp = model_rp[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_rp])
+        return docs_vecs
+
+    # Get Latent Dirichlet Allocation(LDA) vector for document list
+    def get_lda(self, num_topics=100):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_lda = models.LdaModel(docs_corpus, num_topics, id2word=self.docs_dict)
+        docs_lda = model_lda[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lda])
+        return docs_vecs
+
+    # Get Hierarchical Dirichlet Process(HDP) vector for document list
+    def get_hdp(self):
+        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
+        model_hdp = models.HdpModel(docs_corpus, id2word=self.docs_dict)
+        docs_hdp = model_hdp[docs_corpus]
+        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_hdp])
+        return docs_vecs
diff --git a/nautilus_nlp/preprocessing/tokenizer.py b/nautilus_nlp/preprocessing/tokenizer.py
new file mode 100644
index 0000000..94eb7aa
--- /dev/null
+++ b/nautilus_nlp/preprocessing/tokenizer.py
@@ -0,0 +1,80 @@
+import nltk
+from sacremoses import MosesTokenizer, MosesDetokenizer
+import spacy
+from spacy.lang.fr import French
+from spacy.lang.en import English
+import spacy.lang as spacylang
+#nltk.download('punkt')
+
+try:
+    french_spacy = spacylang.fr.French()
+except OSError:
+    raise OSError("""You must install French langage to use SpaCy. 
+                    python -m spacy download fr
+                    See https://spacy.io/usage/ for details
+                """)
+try:
+    english_spacy = spacylang.en.English()
+except OSError:
+    raise OSError("""You must install english langage to use SpaCy. 
+                    python -m spacy download en
+                    See https://spacy.io/usage/ for details
+                """)             
+
+
+def tokenize(text: str, lang_module: str = 'en_spacy'):
+    """
+    Convert text to a list of tokens. 
+
+    Args:
+        lang_module ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
+        the tokenization module according to the langage and the implementation.
+        Recommanded: Spacy (faster, better results). To process other langages
+        import models.Spacy_models
+
+    Returns:
+        list
+    """
+    if lang_module is 'en_nltk':
+        return nltk.word_tokenize(text)
+    elif lang_module is 'en_spacy':
+        spacydoc = english_spacy(text)
+        return [tokens.text for tokens in spacydoc]
+    elif lang_module is 'fr_spacy':
+        spacydoc = french_spacy(text)
+        return [tokens.text for tokens in spacydoc]
+    elif lang_module is 'fr_moses':
+        t = MosesTokenizer(lang='fr')
+        return t.tokenize(text, escape=False)
+
+
+def untokenize(tokens, lang='fr'):
+    '''
+    Inputs a list of tokens output string.
+    ["J'", 'ai'] >>> "J' ai"
+    '''
+    d = MosesDetokenizer(lang=lang)
+    text = d.detokenize(tokens, unescape=False)
+    return text    
+
+
+def _tokensToString(tokens_or_str):
+    if type(tokens_or_str) is str:
+        return tokens_or_str
+    elif type(tokens_or_str) is list:
+        return untokenize(tokens_or_str)
+    elif type(tokens_or_str) is None:
+        return ''
+    else:
+        raise ValueError('Please input string or tokens')
+
+
+def _stringToTokens(tokens_or_str, lang_module='en_spacy'):
+    if type(tokens_or_str) is str:
+        return tokenize(tokens_or_str, lang_module=lang_module)
+    elif type(tokens_or_str) is list:
+        return tokens_or_str
+    elif type(tokens_or_str) is None:
+        return []
+    else:
+        raise ValueError('Please input string or tokens')
\ No newline at end of file

From ed587191e3eea5f3c1ed72f729ac117569ee5d5d Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:05:51 +0200
Subject: [PATCH 124/496] Naming & folder change

---
 nautilus_nlp/utils/text_vectorizer.py | 194 --------------------------
 1 file changed, 194 deletions(-)
 delete mode 100644 nautilus_nlp/utils/text_vectorizer.py

diff --git a/nautilus_nlp/utils/text_vectorizer.py b/nautilus_nlp/utils/text_vectorizer.py
deleted file mode 100644
index 4eca15f..0000000
--- a/nautilus_nlp/utils/text_vectorizer.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import spacy
-from gensim.corpora import Dictionary
-from gensim.models.tfidfmodel import TfidfModel
-from gensim import corpora, models, similarities
-from gensim.matutils import sparse2full
-import numpy as np
-import math
-
-from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
-
-
-class Tfidf(object):
-    """
-    Inputs a list of string
-    Outputs a tuple with the wordcount vector matrix, and the list of feature name
-    Params:
-        input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
-        lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
-        stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
-        max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
-        dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
-    Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
-    """
-    
-    def __init__(self, **kwargs):
-        self.tfidf_vectorizer = TfidfVectorizer(**kwargs)
-    
-    def _compute_wordcount_vector(self, documents):
-        '''
-        Input a list of documents (string)
-        Output the wordcount vector matrix 
-        '''
-        self.word_count_vector = self.tfidf_vectorizer.fit_transform(documents)
-        return self.word_count_vector
-    
-
-    def _get_features_name(self):
-        self.feature_names = self.tfidf_vectorizer.get_feature_names()
-        return self.feature_names
-
-    
-    def _compute_idf(self):
-        self.tfidf_transformer=TfidfTransformer(smooth_idf=True, use_idf=True)
-        self.tfidf_transformer.fit(self.word_count_vector)
-        return self.word_count_vector
-
-    
-    def compute_tfidf(self, documents):
-        self._compute_wordcount_vector(documents)
-        self._get_features_name()
-        self._compute_idf()
-        return self.word_count_vector
-
-    def _apply_tfidf_to_doc(self, text):
-        '''generate tf-idf for the given document'''
-        return self.tfidf_transformer.transform(self.tfidf_vectorizer.transform([text]))
-    
-    
-    def _sort_coo(self, coo_matrix):
-        '''sort the tf-idf vectors by descending order of scores'''
-        tuples = zip(coo_matrix.col, coo_matrix.data)
-        return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
-    
-    
-    def _extract_topn_from_vector(self, feature_names, sorted_items, topn=10):
-        """get the feature names and tf-idf score of top n items"""
-
-        #use only topn items from vector
-        sorted_items = sorted_items[:topn]
-
-        score_vals = []
-        feature_vals = []
-
-        # word index and corresponding tf-idf score
-        for idx, score in sorted_items:
-
-            #keep track of feature name and its corresponding score
-            score_vals.append(round(score, 3))
-            feature_vals.append(feature_names[idx])
-
-        #create a tuples of feature,score
-        #results = zip(feature_vals,score_vals)
-        results= {}
-        for idx in range(len(feature_vals)):
-            results[feature_vals[idx]]=score_vals[idx]
-
-        return results
-
-    
-    def get_top_tfidf_per_doc(self, text, n=10):
-        '''compute TF-IDF for a given doc, and returns a list of the top N weighted words'''
-        tf_idf_vector= self._apply_tfidf_to_doc(text)
-        sorted_items=self._sort_coo(tf_idf_vector.tocoo())
-        return list(self._extract_topn_from_vector(self.feature_names, sorted_items, n).keys())
-    
-    def get_top_tfidf(self, n=10):
-        '''returns a dict of the top N weighted words, with their weight'''
-        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(self.word_count_vector.tocoo()), topn=n)
-
-
-class Text_Vectorizer:
-    def __init__(self, doc_list):
-        # Initialize
-        self.doc_list = doc_list
-        self.nlp, self.docs, self.docs_dict = self._preprocess(self.doc_list)
-
-    # Functions to lemmatise docs
-    def _keep_token(self, t):
-        return t.is_alpha and not (t.is_space or t.is_punct or t.is_stop or t.like_num)
-
-    def _lemmatize_doc(self, doc):
-        return [t.lemma_ for t in doc if self._keep_token(t)]
-
-    # Gensim to create a dictionary and filter out stop and infrequent words (lemmas).
-    def _get_docs_dict(self, docs):
-        docs_dict = Dictionary(docs)
-        # CAREFUL: For small corpus please carefully modify the parameters for filter_extremes, or simply comment it out.
-        docs_dict.filter_extremes(no_below=5, no_above=0.2)
-        docs_dict.compactify()
-        return docs_dict
-
-    # Preprocess docs
-    def _preprocess(self, doc_list):
-        # Load spacy model
-        nlp = spacy.load("en")
-        # lemmatise docs
-        docs = [self._lemmatize_doc(nlp(doc)) for doc in doc_list]
-        # Get docs dictionary
-        docs_dict = self._get_docs_dict(docs)
-        return nlp, docs, docs_dict
-
-    # Gensim can again be used to create a bag-of-words representation of each document,
-    # build the TF-IDF model,
-    # and compute the TF-IDF vector for each document.
-    def _get_tfidf(self, docs, docs_dict):
-        docs_corpus = [docs_dict.doc2bow(doc) for doc in docs]
-        model_tfidf = TfidfModel(docs_corpus, id2word=docs_dict)
-        docs_tfidf = model_tfidf[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(docs_dict)) for c in docs_tfidf])
-        return docs_vecs
-
-    # Get avg w2v for one document
-    def _document_vector(self, doc, docs_dict, nlp):
-        # remove out-of-vocabulary words
-        doc_vector = [nlp(word).vector for word in doc if word in docs_dict.token2id]
-        return np.mean(doc_vector, axis=0)
-
-
-    # Get average vector for document list
-    def avg_wv(self):
-        docs_vecs = np.vstack(
-            [self._document_vector(doc, self.docs_dict, self.nlp) for doc in self.docs]
-        )
-        return docs_vecs
-
-    # Get TF-IDF vector for document list
-    def get_tfidf(self):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_tfidf = TfidfModel(docs_corpus, id2word=self.docs_dict)
-        docs_tfidf = model_tfidf[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_tfidf])
-        return docs_vecs
-
-    # Get Latent Semantic Indexing(LSI) vector for document list
-    def get_lsi(self, num_topics=300):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_lsi = models.LsiModel(docs_corpus, num_topics, id2word=self.docs_dict)
-        docs_lsi = model_lsi[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lsi])
-        return docs_vecs
-
-    # Get Random Projections(RP) vector for document list
-    def get_rp(self):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_rp = models.RpModel(docs_corpus, id2word=self.docs_dict)
-        docs_rp = model_rp[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_rp])
-        return docs_vecs
-
-    # Get Latent Dirichlet Allocation(LDA) vector for document list
-    def get_lda(self, num_topics=100):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_lda = models.LdaModel(docs_corpus, num_topics, id2word=self.docs_dict)
-        docs_lda = model_lda[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lda])
-        return docs_vecs
-
-    # Get Hierarchical Dirichlet Process(HDP) vector for document list
-    def get_hdp(self):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_hdp = models.HdpModel(docs_corpus, id2word=self.docs_dict)
-        docs_hdp = model_hdp[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_hdp])
-        return docs_vecs

From 4248d7c003ea290cdb84316d71289d8529c11105 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:05:55 +0200
Subject: [PATCH 125/496] Naming & folder change

---
 nautilus_nlp/utils/text_vectorizers.py | 195 -------------------------
 1 file changed, 195 deletions(-)
 delete mode 100644 nautilus_nlp/utils/text_vectorizers.py

diff --git a/nautilus_nlp/utils/text_vectorizers.py b/nautilus_nlp/utils/text_vectorizers.py
deleted file mode 100644
index a03c5bb..0000000
--- a/nautilus_nlp/utils/text_vectorizers.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import spacy
-from gensim.corpora import Dictionary
-from gensim.models.tfidfmodel import TfidfModel
-from gensim import corpora, models, similarities
-from gensim.matutils import sparse2full
-import numpy as np
-import math
-
-from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
-
-
-class Tfidf(object):
-    """
-    Inputs a list of string
-    Outputs a tuple with the wordcount vector matrix, and the list of feature name
-    Params:
-        input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
-        lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
-        stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
-        max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
-        dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
-        
-    Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
-    """
-    
-    def __init__(self, **kwargs):
-        self.tfidf_vectorizer = TfidfVectorizer(**kwargs)
-    
-    def _compute_wordcount_vector(self, documents):
-        '''
-        Input a list of documents (string)
-        Output the wordcount vector matrix 
-        '''
-        self.word_count_vector = self.tfidf_vectorizer.fit_transform(documents)
-        return self.word_count_vector
-    
-
-    def _get_features_name(self):
-        self.feature_names = self.tfidf_vectorizer.get_feature_names()
-        return self.feature_names
-
-    
-    def _compute_idf(self):
-        self.tfidf_transformer=TfidfTransformer(smooth_idf=True, use_idf=True)
-        self.tfidf_transformer.fit(self.word_count_vector)
-        return self.word_count_vector
-
-    
-    def compute_tfidf(self, documents):
-        self._compute_wordcount_vector(documents)
-        self._get_features_name()
-        self._compute_idf()
-        return self.word_count_vector
-
-    def _apply_tfidf_to_doc(self, text):
-        '''generate tf-idf for the given document'''
-        return self.tfidf_transformer.transform(self.tfidf_vectorizer.transform([text]))
-    
-    
-    def _sort_coo(self, coo_matrix):
-        '''sort the tf-idf vectors by descending order of scores'''
-        tuples = zip(coo_matrix.col, coo_matrix.data)
-        return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
-    
-    
-    def _extract_topn_from_vector(self, feature_names, sorted_items, topn=10):
-        """get the feature names and tf-idf score of top n items"""
-
-        #use only topn items from vector
-        sorted_items = sorted_items[:topn]
-
-        score_vals = []
-        feature_vals = []
-
-        # word index and corresponding tf-idf score
-        for idx, score in sorted_items:
-
-            #keep track of feature name and its corresponding score
-            score_vals.append(round(score, 3))
-            feature_vals.append(feature_names[idx])
-
-        #create a tuples of feature,score
-        #results = zip(feature_vals,score_vals)
-        results= {}
-        for idx in range(len(feature_vals)):
-            results[feature_vals[idx]]=score_vals[idx]
-
-        return results
-
-    
-    def get_top_tfidf_per_doc(self, text, n=10):
-        '''compute TF-IDF for a given doc, and returns a list of the top N weighted words'''
-        tf_idf_vector= self._apply_tfidf_to_doc(text)
-        sorted_items=self._sort_coo(tf_idf_vector.tocoo())
-        return list(self._extract_topn_from_vector(self.feature_names, sorted_items, n).keys())
-    
-    def get_top_tfidf(self, n=10):
-        '''returns a dict of the top N weighted words, with their weight'''
-        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(self.word_count_vector.tocoo()), topn=n)
-
-
-class Text_Vectorizer:
-    def __init__(self, doc_list):
-        # Initialize
-        self.doc_list = doc_list
-        self.nlp, self.docs, self.docs_dict = self._preprocess(self.doc_list)
-
-    # Functions to lemmatise docs
-    def _keep_token(self, t):
-        return t.is_alpha and not (t.is_space or t.is_punct or t.is_stop or t.like_num)
-
-    def _lemmatize_doc(self, doc):
-        return [t.lemma_ for t in doc if self._keep_token(t)]
-
-    # Gensim to create a dictionary and filter out stop and infrequent words (lemmas).
-    def _get_docs_dict(self, docs):
-        docs_dict = Dictionary(docs)
-        # CAREFUL: For small corpus please carefully modify the parameters for filter_extremes, or simply comment it out.
-        docs_dict.filter_extremes(no_below=5, no_above=0.2)
-        docs_dict.compactify()
-        return docs_dict
-
-    # Preprocess docs
-    def _preprocess(self, doc_list):
-        # Load spacy model
-        nlp = spacy.load("en")
-        # lemmatise docs
-        docs = [self._lemmatize_doc(nlp(doc)) for doc in doc_list]
-        # Get docs dictionary
-        docs_dict = self._get_docs_dict(docs)
-        return nlp, docs, docs_dict
-
-    # Gensim can again be used to create a bag-of-words representation of each document,
-    # build the TF-IDF model,
-    # and compute the TF-IDF vector for each document.
-    def _get_tfidf(self, docs, docs_dict):
-        docs_corpus = [docs_dict.doc2bow(doc) for doc in docs]
-        model_tfidf = TfidfModel(docs_corpus, id2word=docs_dict)
-        docs_tfidf = model_tfidf[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(docs_dict)) for c in docs_tfidf])
-        return docs_vecs
-
-    # Get avg w2v for one document
-    def _document_vector(self, doc, docs_dict, nlp):
-        # remove out-of-vocabulary words
-        doc_vector = [nlp(word).vector for word in doc if word in docs_dict.token2id]
-        return np.mean(doc_vector, axis=0)
-
-
-    # Get average vector for document list
-    def avg_wv(self):
-        docs_vecs = np.vstack(
-            [self._document_vector(doc, self.docs_dict, self.nlp) for doc in self.docs]
-        )
-        return docs_vecs
-
-    # Get TF-IDF vector for document list
-    def get_tfidf(self):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_tfidf = TfidfModel(docs_corpus, id2word=self.docs_dict)
-        docs_tfidf = model_tfidf[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_tfidf])
-        return docs_vecs
-
-    # Get Latent Semantic Indexing(LSI) vector for document list
-    def get_lsi(self, num_topics=300):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_lsi = models.LsiModel(docs_corpus, num_topics, id2word=self.docs_dict)
-        docs_lsi = model_lsi[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lsi])
-        return docs_vecs
-
-    # Get Random Projections(RP) vector for document list
-    def get_rp(self):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_rp = models.RpModel(docs_corpus, id2word=self.docs_dict)
-        docs_rp = model_rp[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_rp])
-        return docs_vecs
-
-    # Get Latent Dirichlet Allocation(LDA) vector for document list
-    def get_lda(self, num_topics=100):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_lda = models.LdaModel(docs_corpus, num_topics, id2word=self.docs_dict)
-        docs_lda = model_lda[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lda])
-        return docs_vecs
-
-    # Get Hierarchical Dirichlet Process(HDP) vector for document list
-    def get_hdp(self):
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_hdp = models.HdpModel(docs_corpus, id2word=self.docs_dict)
-        docs_hdp = model_hdp[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_hdp])
-        return docs_vecs

From 298c1366c231c7cfaa82e1689bc71b27a037e385 Mon Sep 17 00:00:00 2001
From: wil2210 <43743095+wil2210@users.noreply.github.com>
Date: Fri, 3 May 2019 16:12:16 +0200
Subject: [PATCH 126/496] Wj topic (#81)

* script install mallet and java jdk + mallet added

* mallet and java added to the docker file

* mallet and java installation added to travis.yml

* Pyldavis mallet implementation added to viz func

* pyldavis mallet

* Update requirements.txt

* Update requirements.txt

* Update .travis.yml
---
 .travis.yml                            |   2 +
 README.md                              |  12 +++
 docker/Dockerfile                      |   9 ++
 nautilus_nlp/models/topic_modeling.py  | 118 +++++++++++++++----------
 nautilus_nlp/scripts/install_java.sh   |   4 +
 nautilus_nlp/scripts/install_mallet.sh |   4 +
 requirements.txt                       |   3 +-
 7 files changed, 105 insertions(+), 47 deletions(-)
 create mode 100644 nautilus_nlp/scripts/install_java.sh
 create mode 100644 nautilus_nlp/scripts/install_mallet.sh

diff --git a/.travis.yml b/.travis.yml
index 2d7b62b..4f7da47 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -8,6 +8,8 @@ before_script:
   - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && ls 
   - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 
+  - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
+  - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
 install:
   - pip install -r requirements.txt
diff --git a/README.md b/README.md
index c969983..0a16439 100644
--- a/README.md
+++ b/README.md
@@ -77,6 +77,18 @@ run:
 
 `bash nautilus_nlp/scripts/download_ft_langdetect.sh`
 
+### Install Mallet
+
+1) Install Mallet file
+run:
+
+`bash nautilus_nlp/scripts/install_mallet.sh`
+
+2) Install Java JDK (required to implement mallet)
+run:
+
+`bash nautilus_nlp/scripts/install_java.sh`
+
 # Notebooks
 
 The [notebook](notebooks/) folder contains various notebook on how to use this library.
diff --git a/docker/Dockerfile b/docker/Dockerfile
index 1341d7b..8530975 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -32,6 +32,15 @@ RUN  pip3 install pandas schedule nltk pendulum bounter spacy==2.1 jupyterlab co
 RUN python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 RUN python3 -m nltk.downloader stopwords 
 
+RUN wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip 
+    unzip mallet-2.0.8.zip
+    rm mallet-2.0.8.zip
+
+RUN sudo add-apt-repository ppa:openjdk-r/ppa  # only Ubuntu 17.4 and earlier
+    sudo apt update
+    apt search openjdk
+    sudo apt install openjdk-8-jdk
+
 RUN echo "alias python='python3'" >> .bash_aliases
 RUN echo "alias pip='pip3' ">> ~/.bash_aliases
 RUN source ~/.bashrc
diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index ff494fb..6fcbda0 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -3,8 +3,8 @@
 import os
 import pyLDAvis
 import pyLDAvis.gensim 
-pyLDAvis.enable_notebook()
 from gensim.models import CoherenceModel
+from gensim.models.wrappers import LdaMallet
 import matplotlib.pyplot as plt
 
 from IPython.display import HTML
@@ -65,6 +65,8 @@ def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, s
     """
     Compute c_v coherence for various number of topics
 
+    /!\ It takes a really long time.
+
     Parameters:
     ----------
     dictionary : Gensim dictionary
@@ -124,86 +126,108 @@ def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
         print("Num Topics =", m, " has Coherence Value of", round(cv, 4))
 
 
-### Gensim LdaModel
+### LdaModel: Gensim & Mallet
 
-def train_lda_model(bow_corpus, dictionary, num_topics, **kwargs):
-    """ Train the model on the corpus
+def train_lda_model(bow_corpus, dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
+    """ Train the lda model on the corpus
       
     Parameters
     ----------
-    bow_corpus : iterable of list of tokens. 
-    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
+    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).
+    dictionary: corpora.Dictionary. Mapping from word IDs to words
     num_topics: int
+    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    mallet_path: str, optionnal if model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file 
     
     Returns
     -------
     gensim.ldamodel
     """
+    if model == 'gensim':
+        model = train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs)
+    elif model == 'mallet':
+        if mallet_path is None:
+            raise ValueError('You must precise the path to the mallet-2.0.8 file that has been downloaded before')
+        else:
+            model = train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs)
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet')
+    return model
+
+def train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs):
+
     model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0, **kwargs)
     return model
 
+def train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs):
+    
+    os.environ['MALLET_PATH'] = mallet_path
+    mallet = '$MALLET_PATH/mallet-2.0.8/bin/mallet'
+    model = gensim.models.wrappers.LdaMallet(mallet, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='composant', random_seed=0, **kwargs)
+    return model
+
 
-def save_model(model, model_path, model_name):
-    """ Save the model that has been trained
+def save_model(model, model_name):
+    """ Save the model that has been trained. The model will be saved on your current emplacement.
         
         Parameters
         ----------
         model: ldamodel
-        MODELNAME: str
+        model_name: str. Name the model that will be saved
     """
-    return model.save(os.path.join(model_path,model_name))
+    return model.save(os.path.join(model_name))
 
 
-def load_model(model_path,model_name):
+def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
     '''
-    model_path: path where the model has been saved
-    model_name: name of the saved model
+    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    model_path: str. path where the model has been saved
+    model_name: str. name of the saved model
+    model_prefix: str. By default, 'composant' default prefix used while saving the mallet model with train_lda_model function. 
     '''
-    ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
+    if model =='gensim':
+        ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
+    elif model =='mallet':
+        ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
+        if model_prefix is not None:
+            ldamodel.prefix = model_path+'/'+ model_prefix
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet')
     return ldamodel
 
 def fit_data(model, bow):
     """Test the model on new, unseen documents"""
     return model[bow]
 
-### Gensim LdaMallet
 
-def load_mallet_model(model_path, model_name, model_prefix=None):
-    '''
-    model_prefix: prefix used while saving the model
-    model_name: name of the saved model
-    '''
-    ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
-    if model_prefix is not None:
-        ldamodel.prefix = model_path+'/'+ model_prefix
-    return ldamodel
+# Visualization (only for gensim implementation for now)
 
-def train_mallet_model(mallet_path, bow_corpus, dictionary, num_topics, **kwargs):
-    """ Train the model on the corpus
-      
-    Parameters
-    ----------
-    mallet_path: path to mallet files
-    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).$
-    dictionary: corpora.Dictionary. Mapping from word IDs to words
-    num_topics: int
-    
-    Returns
-    -------
-    gensim.ldamodel
-    """
-    model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='nautil')
-    return model
 
-
-# Visualization
-
-
-def visualize_topics(model, bow_corpus, dictionary):
+def visualize_topics(model, bow_corpus, dictionary, model_type=None):
     """ Visualize the topics-keywords with the pyLDAvis interactive chart.
         (Work well in notebook)
+        
+    Parameters
+    ----------
+    model: LDA model: gensim or mallet
+    bow_corpus : iterable of list of tokens. 
+    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
+    model : str. Precise the topic modeling model used, must be "gensim" or "mallet"
+    
+    Returns:
+    ----------
+    3D interactive chart
+    
     """
-    return pyLDAvis.gensim.prepare(model, bow_corpus, dictionary)
+    if model_type == 'mallet':
+        model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
+    elif model_type == 'gensim':
+        model_vis = model
+    elif model_type is None:
+        raise ValueError('You forgot to precise your model type, it must be: gensim or mallet')
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet') 
+    return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
 
 def save_pyldavis(pyldavis, vis_path, vis_name):
     """ Save the pyldavis interactive chart
@@ -224,6 +248,8 @@ def show_pyldavis(vis_path, vis_name):
 def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
     """ Print the dominant topics in the document, its score and the topics' top keywords.
     
+    Quick way to interpret the topics
+
     Parameters
     ----------
 
diff --git a/nautilus_nlp/scripts/install_java.sh b/nautilus_nlp/scripts/install_java.sh
new file mode 100644
index 0000000..648abf3
--- /dev/null
+++ b/nautilus_nlp/scripts/install_java.sh
@@ -0,0 +1,4 @@
+sudo add-apt-repository ppa:openjdk-r/ppa  # only Ubuntu 17.4 and earlier
+sudo apt update
+apt search openjdk
+sudo apt install openjdk-8-jdk
\ No newline at end of file
diff --git a/nautilus_nlp/scripts/install_mallet.sh b/nautilus_nlp/scripts/install_mallet.sh
new file mode 100644
index 0000000..99d2af5
--- /dev/null
+++ b/nautilus_nlp/scripts/install_mallet.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip 
+unzip mallet-2.0.8.zip
+rm mallet-2.0.8.zip
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 5870746..3ec0a3a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -34,7 +34,8 @@ textacy==0.6.3
 gensim==3.7.1
 scikit_learn==0.20.3
 vaderSentiment==3.2.1
-
 google-compute-engine==2.8.13
 flashtext==2.7
 
+
+

From 7ed11802186e91a965de2ddcd74aaf5dc247ac8b Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:14:42 +0200
Subject: [PATCH 127/496] Delete python2 support in compat.py

---
 nautilus_nlp/utils/compat.py | 49 +++++++++++-------------------------
 1 file changed, 14 insertions(+), 35 deletions(-)

diff --git a/nautilus_nlp/utils/compat.py b/nautilus_nlp/utils/compat.py
index 4efa183..31d04d6 100644
--- a/nautilus_nlp/utils/compat.py
+++ b/nautilus_nlp/utils/compat.py
@@ -7,43 +7,22 @@
 is_linux = sys.platform.startswith("linux")
 is_osx = sys.platform == "darwin"
 
-if is_python2:
-    import cPickle as pickle
-    from backports import csv
-    from itertools import izip as zip_
-    from urlparse import urljoin
 
-    range_ = xrange
+import csv
+import pickle
+from builtins import zip as zip_
+from urllib.parse import urljoin
 
-    bytes_ = str
-    unicode_ = unicode
-    string_types = (str, unicode)
-    int_types = (int, long)
-    chr_ = unichr
+range_ = range
 
-    def unicode_to_bytes(s, encoding="utf8", errors="strict"):
-        return s.encode(encoding=encoding, errors=errors)
+bytes_ = bytes
+unicode_ = str
+string_types = (bytes, str)
+int_types = (int,)
+chr_ = chr
 
-    def bytes_to_unicode(b, encoding="utf8", errors="strict"):
-        return unicode_(b, encoding=encoding, errors=errors)
+def unicode_to_bytes(s, encoding="utf8", errors="strict"):
+    return s.encode(encoding=encoding, errors=errors)
 
-
-else:
-    import csv
-    import pickle
-    from builtins import zip as zip_
-    from urllib.parse import urljoin
-
-    range_ = range
-
-    bytes_ = bytes
-    unicode_ = str
-    string_types = (bytes, str)
-    int_types = (int,)
-    chr_ = chr
-
-    def unicode_to_bytes(s, encoding="utf8", errors="strict"):
-        return s.encode(encoding=encoding, errors=errors)
-
-    def bytes_to_unicode(b, encoding="utf8", errors="strict"):
-        return b.decode(encoding=encoding, errors=errors)
+def bytes_to_unicode(b, encoding="utf8", errors="strict"):
+    return b.decode(encoding=encoding, errors=errors)

From 4e22bf755e86780a837983727202d0085f14b710 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 16:18:50 +0200
Subject: [PATCH 128/496] remove preprocess

---
 nautilus_nlp/preprocess/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 nautilus_nlp/preprocess/__init__.py

diff --git a/nautilus_nlp/preprocess/__init__.py b/nautilus_nlp/preprocess/__init__.py
deleted file mode 100644
index e69de29..0000000

From 04b2cbc165a570e1fba6122b9def61592d8072f1 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 16:29:04 +0200
Subject: [PATCH 129/496] rename script

---
 nautilus_nlp/preprocessing/{lemmatizer.py => lemmatization.py} | 0
 nautilus_nlp/preprocessing/{stemmer.py => stemming.py}         | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename nautilus_nlp/preprocessing/{lemmatizer.py => lemmatization.py} (100%)
 rename nautilus_nlp/preprocessing/{stemmer.py => stemming.py} (100%)

diff --git a/nautilus_nlp/preprocessing/lemmatizer.py b/nautilus_nlp/preprocessing/lemmatization.py
similarity index 100%
rename from nautilus_nlp/preprocessing/lemmatizer.py
rename to nautilus_nlp/preprocessing/lemmatization.py
diff --git a/nautilus_nlp/preprocessing/stemmer.py b/nautilus_nlp/preprocessing/stemming.py
similarity index 100%
rename from nautilus_nlp/preprocessing/stemmer.py
rename to nautilus_nlp/preprocessing/stemming.py

From 3f465c0d2c1ff53a6a2f4cf7b73412d96cbb8851 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 16:42:10 +0200
Subject: [PATCH 130/496] add whitespace to remove EOL

---
 nautilus_nlp/preprocessing/preprocess.py | 26 ++++++++++++++----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 70d8148..2ff6231 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -1,10 +1,5 @@
 # -*- coding: utf-8 -*-
-"""
-Functions that modify raw text *in-place*, replacing contractions, URLs, emails,
-phone numbers, and currency symbols with standardized forms. These should be
-applied before processing by `Spacy <http://spacy.io>`_, but be warned: preprocessing
-may affect the interpretation of the text -- and spacy's processing of it.
-"""
+
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import os
@@ -24,13 +19,19 @@
 
 
 def remove_multiple_spaces_and_strip_text(text):
-    """Remove multiple spaces, strip text, and remove '-', '*' characters.
+    """
+    Remove multiple spaces, strip text, and remove '-', '*' characters.
+
     Parameters
     ----------
-    text : str,
+    text : str
+        the text to be processed
+
     Returns
     -------
-    str
+    string
+        the text with removed multiple spaces and strip text
+
     """
     regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
     for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
@@ -40,15 +41,18 @@ def remove_multiple_spaces_and_strip_text(text):
 
 
 def remove_EOL_characters(text):
-    """Remove end of line (\n) char.
+    """
+    Remove end of line (\n) char.
+
     Parameters
     ----------
     text : str,
+
     Returns
     -------
     str
     """
-    return text.replace("\n", "")
+    return text.replace("\n", " ")
 
 
 def remove_tokens_with_nonletters(tokens):

From 6cbd4ef10ff1a43f292e9adac256761a3e9d519c Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 16:42:26 +0200
Subject: [PATCH 131/496] add test remove EOL

---
 tests/test_preprocessor.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index c1c797a..ed7df1b 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -4,6 +4,7 @@
     remove_multiple_spaces_and_strip_text,
     remove_accents,
     fix_bad_unicode,
+    remove_EOL_characters
 )
 
 
@@ -21,6 +22,17 @@ def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
     result = remove_multiple_spaces_and_strip_text(input_str)
     np.testing.assert_string_equal(result, expected_str)
 
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("\nhello world", " hello world"),
+        ("hello\nworld", "hello world"),
+        ("hello world\n", "hello world ")
+    ],
+)
+def test_remove_EOL_characters(input_str, expected_str):
+    result = remove_EOL_characters(input_str)
+    np.testing.assert_string_equal(result, expected_str)    
 
 def test_remove_accents():
     input_str = "éèëêàù"

From 68bcb237fa6b6eb3caf94a203820da535a34f5ee Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:44:01 +0200
Subject: [PATCH 132/496] Update tests after file normalization

---
 tests/test_doc.py                | 2 +-
 tests/test_fix_bad_encoding.py   | 2 +-
 tests/test_language_detection.py | 4 ++--
 tests/test_preprocessor.py       | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/tests/test_doc.py b/tests/test_doc.py
index 5326226..1354f89 100644
--- a/tests/test_doc.py
+++ b/tests/test_doc.py
@@ -4,7 +4,7 @@
 import pytest
 import random
 import spacy
-from nautilus_nlp.models.Language_detector import LangDetector
+from nautilus_nlp.models.language_detector import LangDetector
 from nautilus_nlp.doc import Doc, NautilusMissingModelException
 
 TEXT_1 = """
diff --git a/tests/test_fix_bad_encoding.py b/tests/test_fix_bad_encoding.py
index cb3aa93..a2a14ea 100644
--- a/tests/test_fix_bad_encoding.py
+++ b/tests/test_fix_bad_encoding.py
@@ -1,7 +1,7 @@
 
 import pytest
 import numpy as np
-from nautilus_nlp.utils.preprocess import fix_bad_unicode
+from nautilus_nlp.preprocessing.preprocess import fix_bad_unicode
 
 
 
diff --git a/tests/test_language_detection.py b/tests/test_language_detection.py
index 1796359..d2b4260 100644
--- a/tests/test_language_detection.py
+++ b/tests/test_language_detection.py
@@ -1,8 +1,8 @@
-from nautilus_nlp.models import Language_detector
+from nautilus_nlp.models import language_detector
 import pytest
 import numpy as np
 
-model = Language_detector.LangDetector()
+model = language_detector.LangDetector()
 
 TEXT_1 = """Кипрская война (итал. Guerra di Cipro; тур. Kıbrıs Savaşı) — одна из нескольких войн между Османской империей и Венецианской республикой за господство в Восточном Средиземноморье. Сначала Венеция воевала с османами одна. Затем, когда сформировалась Священная лига (в которую помимо Венеции входили Испания с Неаполем и Сицилией, Республика Генуя, Герцогство Савойское, госпитальеры, Великое герцогство Тосканское и другие итальянские государства), Османская империя воевала уже против Лиги."""
 TEXT_2 = """
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index c1c797a..59281a3 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -1,6 +1,6 @@
 import pytest
 import numpy as np
-from nautilus_nlp.utils.preprocess import (
+from nautilus_nlp.preprocessing.preprocess import (
     remove_multiple_spaces_and_strip_text,
     remove_accents,
     fix_bad_unicode,

From 053fab06fbf1209540c2c4dd7ef5407c45179b1c Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:44:05 +0200
Subject: [PATCH 133/496] Update tests after file normalization

---
 tests/tests_extraction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/tests_extraction.py b/tests/tests_extraction.py
index 293aef5..461e801 100644
--- a/tests/tests_extraction.py
+++ b/tests/tests_extraction.py
@@ -1,4 +1,4 @@
-from nautilus_nlp.utils.keyword_extractor import extract_keywords
+from nautilus_nlp.preprocessing.keyword_extractor import extract_keywords
 
 str_="""Les moteurs de recherche tels Google, Exalead ou Yahoo! sont
  des applications très connues de fouille de textes sur de grandes masses de données. 

From 63a1efa2df228c47d1fce036dc44f795cee69f35 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:48:36 +0200
Subject: [PATCH 134/496] m preprocess modify

---
 nautilus_nlp/preprocessing/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 70d8148..ac716e1 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -16,7 +16,7 @@
 from stop_words import get_stop_words as _get_stop_words
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 
-from . import constants
+from nautilus_nlp.utils import constants
 from nautilus_nlp.config.config import ROOT_FOLDER
 from nautilus_nlp.utils.file_loader import documents_loader
 

From 9a331fe6f2864d3762770ad76b6dea6c24463bcc Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 16:48:53 +0200
Subject: [PATCH 135/496] m preprocess modify

---
 nautilus_nlp/utils/constants.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index ce0be93..b694acf 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -9,7 +9,7 @@
 import sys
 import unicodedata
 
-from . import compat
+from . import  compat
 from . import file_loader as util
 
 

From 39fcab294af9bf49bf6615b968a3e78590ca58b8 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 3 May 2019 17:24:22 +0200
Subject: [PATCH 136/496] typo on filename tests_extraction

---
 tests/{tests_extraction.py => test_extraction.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename tests/{tests_extraction.py => test_extraction.py} (100%)

diff --git a/tests/tests_extraction.py b/tests/test_extraction.py
similarity index 100%
rename from tests/tests_extraction.py
rename to tests/test_extraction.py

From 7d17dcfb310b32a39f20c78bf25f9173a866060a Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 18:33:20 +0200
Subject: [PATCH 137/496] add unit tests

---
 tests/test_preprocessor.py | 27 ++++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index ed7df1b..0bdf8a0 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -4,7 +4,10 @@
     remove_multiple_spaces_and_strip_text,
     remove_accents,
     fix_bad_unicode,
-    remove_EOL_characters
+    remove_EOL_characters,
+    remove_tokens_with_nonletters,
+    remove_special_caracters_from_tokenslist,
+    get_stopwords
 )
 
 
@@ -34,6 +37,28 @@ def test_remove_EOL_characters(input_str, expected_str):
     result = remove_EOL_characters(input_str)
     np.testing.assert_string_equal(result, expected_str)    
 
+
+def test_remove_tokens_with_nonletters():
+    input_tokens = ['foo','bar','124','34euros']
+    expected_output = ['foo','bar']
+    result = remove_tokens_with_nonletters(input_tokens)
+    np.testing.assert_array_equal(result,expected_output)
+
+
+def test_remove_special_caracters_from_tokenslist():
+    input_tokens = ['foo','bar','---',"'s",'#']
+    expected_output = ['foo','bar',"'s"]
+    result = remove_tokens_with_nonletters(input_tokens)
+    np.testing.assert_array_equal(result,expected_output)
+
+
+def test_get_stopwords():
+    languages_to_test = ['fr','en','ga','zh']
+    for lang in languages_to_test:
+        result = get_stopwords(lang)
+        assert len(result) > 0 and type(result) == list
+    
+
 def test_remove_accents():
     input_str = "éèëêàù"
     expected_str = "eeeeau"

From d613b7aea8b70a3becbd0aaa77c4dc6a11dd0760 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 18:33:35 +0200
Subject: [PATCH 138/496] harmonize docstrings

---
 nautilus_nlp/preprocessing/preprocess.py | 78 +++++++++++++++++++-----
 1 file changed, 64 insertions(+), 14 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 2ff6231..f8f8125 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -18,7 +18,7 @@
 STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
 
 
-def remove_multiple_spaces_and_strip_text(text):
+def remove_multiple_spaces_and_strip_text(text: str) -> str:
     """
     Remove multiple spaces, strip text, and remove '-', '*' characters.
 
@@ -40,13 +40,13 @@ def remove_multiple_spaces_and_strip_text(text):
     return text
 
 
-def remove_EOL_characters(text):
+def remove_EOL_characters(text: str) -> str:
     """
     Remove end of line (\n) char.
 
     Parameters
     ----------
-    text : str,
+    text : str
 
     Returns
     -------
@@ -55,18 +55,40 @@ def remove_EOL_characters(text):
     return text.replace("\n", " ")
 
 
-def remove_tokens_with_nonletters(tokens):
+def remove_tokens_with_nonletters(tokens: list) -> list:
     """
     Inputs a list of tokens, outputs a list of tokens without tokens that
-    includes numbers of special caracters
+    includes numbers of special caracters.
+    ['foo','bar','124','34euros'] -> ['foo','bar']
+
+    Parameters
+    ----------
+    tokens : list
+        list of tokens to be cleaned
+
+    Returns
+    -------
+    list
+        list of tokens without tokens with numbers
     """
     return [word for word in tokens if re.search("[a-zA-Z]", word)]
 
 
-def remove_special_caracters(tokens):
-    """ Checks for letters in the token - using a regex search.
-    Strings that are just punctuation will
-    be removed! No more custom '--'. But ''s' and '9' will remain.
+def remove_special_caracters_from_tokenslist(tokens: list) -> list:
+    """ 
+    Remove tokens that doesn't contains any number or letter. 
+    eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
+
+    Parameters
+    ----------
+    tokens : list
+        list of tokens to be cleaned
+
+    Returns
+    -------
+    list
+        list of tokens without tokens that contains only special caracters
+    
     """
     return [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
 
@@ -77,18 +99,30 @@ def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
     return stopwords
 
 
-def get_stopwords(lang: str = "en"):
+def get_stopwords(lang: str = "en") -> list:
     """
     Inputs a language code, returns a list of stopwords for the specified language
 
-    Args:
-        lang: Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
+    Parameters
+    ----------
+    lang : str
+        Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
          'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
          'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
          'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
           'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
           'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
            'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
+
+    Returns
+    -------
+    list
+        list of stopwords for a given language
+
+    Raises
+    ------
+    ValueError
+        When language is not available yet or incorrect country code
     """
     if type(lang) == str and len(lang) == 2:
         lang = lang.lower()
@@ -117,9 +151,25 @@ def get_stopwords(lang: str = "en"):
     return list(set(stopwords))
 
 
-def remove_stopwords(text_or_tokens, stopwords):
+def remove_stopwords(text_or_tokens, stopwords: list) -> list:
     """ 
-    Remove stopwords from tokens.
+    Remove stopwords from a list of tokens or a text.
+    eg. ['']
+
+    Parameters
+    ----------
+    text_or_tokens : list or string
+        list of tokens to be cleaned
+
+    Returns
+    -------
+    list
+        list of tokens without stopwords
+
+    Raises
+    ------
+    ValueError
+        When inputs is not a string or a list
     """
     if type(text_or_tokens) is str:
         return [word for word in text_or_tokens.split() if word not in stopwords]

From 43a477056dc9573306d1e3185bd6ae47e4da1206 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 20:50:34 +0200
Subject: [PATCH 139/496] harmonize docstr, rm funct, improve preprocess fct

---
 nautilus_nlp/preprocessing/preprocess.py | 318 +++++++++++++++--------
 1 file changed, 213 insertions(+), 105 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 14468a1..fd8041e 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -7,7 +7,7 @@
 import re
 import unicodedata
 
-from ftfy import fix_text
+from ftfy import fix_text as _fix_text
 from stop_words import get_stop_words as _get_stop_words
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 
@@ -154,7 +154,7 @@ def get_stopwords(lang: str = "en") -> list:
 def remove_stopwords(text_or_tokens, stopwords: list) -> list:
     """ 
     Remove stopwords from a list of tokens or a text.
-    eg. ['']
+    eg. ['I','like','when','you','move','your','body','!'] -> ['I', 'move', 'body', '!']
 
     Parameters
     ----------
@@ -179,49 +179,66 @@ def remove_stopwords(text_or_tokens, stopwords: list) -> list:
         raise ValueError("must input string or list of tokens")
 
 
-def fix_bad_unicode(text, normalization="NFC") -> str:
+def fix_bad_unicode(text: str, normalization: str = "NFC") -> str:
     """
     Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
     this includes mojibake, HTML entities and other code cruft,
     and non-standard forms for display purposes.
 
-    Args:
-        text (str): raw text
-        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): if 'NFC',
-            combines characters and diacritics written using separate code points,
-            e.g. converting "e" plus an acute accent modifier into "é"; unicode
-            can be converted to NFC form without any change in its meaning!
-            if 'NFKC', additional normalizations are applied that can change
-            the meanings of characters, e.g. ellipsis characters will be replaced
-            with three periods
-
-    Returns:
-        str
+    Parameters
+    ----------
+    text : string
+
+    normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): 
+        if 'NFC', combines characters and diacritics written using separate code points,
+        e.g. converting "e" plus an acute accent modifier into "é"; unicode
+        can be converted to NFC form without any change in its meaning!
+        if 'NFKC', additional normalizations are applied that can change
+        the meanings of characters, e.g. ellipsis characters will be replaced
+        with three periods
+    Returns
+    -------
+    string
     """
-    return fix_text(text, normalization=normalization)
+    return _fix_text(text, normalization=normalization)
 
 
-def normalize_whitespace(text) -> str:
+def normalize_whitespace(text:str) -> str:
     """
     Given ``text`` str, replace one or more spacings with a single space, and one
     or more linebreaks with a single newline. Also strip leading/trailing whitespace.
+    eg. "   foo  bar  " -> "foo bar"
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string    
     """
     return constants.NONBREAKING_SPACE_REGEX.sub(
         " ", constants.LINEBREAK_REGEX.sub(r"\n", text)
     ).strip()
 
 
-def unpack_french_contractions(text) -> str:
-
-    return text
-
 
-def unpack_english_contractions(text) -> str:
+def unpack_english_contractions(text:str) -> str:
     """
     Replace *English* contractions in ``text`` str with their unshortened forms.
     N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
     so are left as-is.
+    eg. "You're fired. She's nice." -> "You are fired. She's nice."
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string    
     """
+
     # standard
     text = re.sub(
         r"(\b)([Aa]re|[Cc]ould|[Dd]id|[Dd]oes|[Dd]o|[Hh]ad|[Hh]as|[Hh]ave|[Ii]s|[Mm]ight|[Mm]ust|[Ss]hould|[Ww]ere|[Ww]ould)n't",
@@ -249,32 +266,73 @@ def unpack_english_contractions(text) -> str:
     return text
 
 
-def unpack_contractions_from_lang(text, lang: str = "fr") -> str:
-    if lang == "fr":
-        return unpack_english_contractions(text)
-    else:
-        return unpack_english_contractions(text)
+def replace_urls(text:str, replace_with:str="*URL*") -> str:
+    """
+    Replace all URLs in ``text`` str with ``replace_with`` str.
 
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the URL to be replaced with.
 
-def replace_urls(text, replace_with="*URL*") -> str:
-    """Replace all URLs in ``text`` str with ``replace_with`` str."""
+    Returns
+    -------
+    string
+    """    
     return constants.URL_REGEX.sub(
         replace_with, constants.SHORT_URL_REGEX.sub(replace_with, text)
     )
 
 
 def replace_emails(text, replace_with="*EMAIL*") -> str:
-    """Replace all emails in ``text`` str with ``replace_with`` str."""
+    """
+    Replace all emails in ``text`` str with ``replace_with`` str
+
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the email address to be replaced with.
+
+    Returns
+    -------
+    string
+    """
     return constants.EMAIL_REGEX.sub(replace_with, text)
 
 
 def replace_phone_numbers(text, replace_with="*PHONE*") -> str:
-    """Replace all phone numbers in ``text`` str with ``replace_with`` str."""
+    """
+    Replace all phone numbers in ``text`` str with ``replace_with`` str
+
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the phone number to be replaced with.
+
+    Returns
+    -------
+    string
+    """    
     return constants.PHONE_REGEX.sub(replace_with, text)
 
 
 def replace_numbers(text, replace_with="*NUMBER*") -> str:
-    """Replace all numbers in ``text`` str with ``replace_with`` str."""
+    """
+    Replace all numbers in ``text`` str with ``replace_with`` str.
+
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the number to be replaced with.
+
+    Returns
+    -------
+    string
+    """        
     return constants.NUMBERS_REGEX.sub(replace_with, text)
 
 
@@ -282,16 +340,20 @@ def replace_currency_symbols(text, replace_with=None) -> str:
     """
     Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
 
-    Args:
-        text (str): raw text
-        replace_with (str): if None (default), replace symbols with
+    Parameters
+    ----------
+    text : str
+        raw text
+    replace_with : None or string
+        if None (default), replace symbols with
             their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
             otherwise, pass in a string with which to replace all symbols
             (e.g. "*CURRENCY*")
 
-    Returns:
-        str
-    """
+    Returns
+    -------
+    string
+    """          
     if replace_with is None:
         for k, v in constants.CURRENCIES.items():
             text = text.replace(k, v)
@@ -305,44 +367,57 @@ def remove_punct(text, marks=None) -> str:
     Remove punctuation from ``text`` by replacing all instances of ``marks``
     with whitespace.
 
-    Args:
-        text (str): raw text
-        marks (str): If specified, remove only the characters in this string,
-            e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
-            Otherwise, all punctuation marks are removed.
+    Parameters
+    ----------
+    text : str
+        raw text
+    
+    marks : str or None
+        If specified, remove only the characters in this string,
+        e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
+        Otherwise, all punctuation marks are removed.
 
-    Returns:
-        str
+    Returns
+    -------
+    string
 
-    Note:
-        When ``marks=None``, Python's built-in :meth:`str.translate()` is
-        used to remove punctuation; otherwise, a regular expression is used
-        instead. The former's performance is about 5-10x faster.
-    """
+    Note
+    -------
+    When ``marks=None``, Python's built-in :meth:`str.translate()` is
+    used to remove punctuation; otherwise, a regular expression is used
+    instead. The former's performance is about 5-10x faster.    
+    """       
     if marks:
         return re.sub("[{}]+".format(re.escape(marks)), " ", text, flags=re.UNICODE)
     else:
         return text.translate(constants.PUNCT_TRANSLATE_UNICODE)
 
 
-def remove_accents(text, method="unicode") -> str:
+def remove_accents(text:str, method:str="unicode") -> str:
     """
     Remove accents from any accented unicode characters in ``text`` str, either by
     transforming them into ascii equivalents or removing them entirely.
 
-    Args:
-        text (str): raw text
-        method ({'unicode', 'ascii'}): if 'unicode', remove accented
-            char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
-            remove accented char for any unicode symbol
+    Parameters
+    ----------
+    text : str
+        raw text
+    
+    method : ({'unicode', 'ascii'})
+        if 'unicode', remove accented
+        char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
+        remove accented char for any unicode symbol
 
-            NB: the 'ascii' method is notably faster than 'unicode', but less good
+        NB: the 'ascii' method is notably faster than 'unicode', but less good
 
-    Returns:
-        str
+    Returns
+    -------
+    string
 
-    Raises:
-        ValueError: if ``method`` is not in {'unicode', 'ascii'}
+    Raises
+    -------
+    ValueError
+        if ``method`` is not in {'unicode', 'ascii'}   
     """
     if method == "unicode":
         return "".join(
@@ -361,25 +436,26 @@ def remove_accents(text, method="unicode") -> str:
         raise ValueError(msg)
 
 
-def remove_emoji(word):
+def remove_emoji(text:str) -> str:
     """
     Remove emoji from any  str by stripping any unicode in the range of Emoji unicode,
 
-    Args:
-        word (str): raw word
-
-    Returns:
-        str
+    Parameters
+    ----------
+    text : str
+        raw text
 
+    Returns
+    -------
+    string
     """
     RE_EMOJI = re.compile("[\U00010000-\U0010ffff]", flags=re.UNICODE)
-    word = RE_EMOJI.sub(r"", word)
+    word = RE_EMOJI.sub(r"", text)
     return word
 
 
 def preprocess_text(
     text,
-    no_emoji=False,
     fix_unicode=False,
     lowercase=False,
     no_urls=False,
@@ -390,61 +466,93 @@ def preprocess_text(
     no_punct=False,
     no_contractions=False,
     no_accents=False,
+    no_emoji=False,
+    replace_with=None, 
+    remove_stopwords=None
 ) -> str:
     """
-    Normalize various aspects of a raw text doc before parsing it with Spacy.
-    A convenience function for applying all other preprocessing functions in one go.
-
-    Args:
-        text (str): raw text to preprocess
-        fix_unicode (bool): if True, fix "broken" unicode such as
-            mojibake and garbled HTML entities
-        lowercase (bool): if True, all text is lower-cased
-        no_urls (bool): if True, replace all URL strings with '*URL*'
-        no_emails (bool): if True, replace all email strings with '*EMAIL*'
-        no_phone_numbers (bool): if True, replace all phone number strings
-            with '*PHONE*'
-        no_numbers (bool): if True, replace all number-like strings
-            with '*NUMBER*'
-        no_currency_symbols (bool): if True, replace all currency symbols
-            with their standard 3-letter abbreviations
-        no_punct (bool): if True, remove all punctuation (replace with
-            empty string)
-        no_contractions (bool): if True, replace *English* contractions
-            with their unshortened forms
-        no_accents (bool): if True, replace all accented characters
-            with unaccented versions
-
-    Returns:
-        str: input ``text`` processed according to function args
-
-    Warning:
-        These changes may negatively affect subsequent NLP analysis performed
-        on the text, so choose carefully, and preprocess at your own risk!
-    """
+    Normalize various aspects of a raw text doc. A convenience function for
+    applying all other preprocessing functions in one go.
+
+    Parameters
+    ----------
+    text : str
+        raw text to preprocess
+    fix_unicode : bool
+        if True, fix "broken" unicode such as mojibake and garbled HTML entities
+    lowercase : bool
+        if True, all text is lower-cased
+    no_urls : bool
+        if True, replace all URL strings with '*URL*' or with "replace_with" if 
+        specified.
+    no_emails : bool
+        if True, replace all email strings with '*EMAIL*' or with "replace_with" if 
+        specified.
+    no_phone_numbers : bool
+        if True, replace all phone number strings with '*PHONE*' or with "replace_with" if 
+        specified.
+    no_numbers : bool
+        if True, replace all number-like strings with '*NUMBER*' or with "replace_with" if 
+        specified.
+    no_currency_symbols : bool
+        if True, if True, replace all currency symbols with their standard 
+        3-letter abbreviations.
+    no_punct : bool
+        if True, remove all punctuation (replace with empty string)
+    no_contractions : bool
+        if True, if True, replace *English* contractions with their unshortened forms
+    no_accents : bool
+        if True, replace all accented characters with unaccented versions
+    no_emoji : bool
+        if True, remove all emojis from text
+    replace_with : string
+        The string you want the entities to be replaced with.
+    remove_stopwords : 2-letter country code
+        If specified, will remove the stopwords of the given language. 
+        Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
+         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
+         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
+         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
+          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
+          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
+           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']        
+    Returns
+    -------
+    string
+        input ``text`` processed according to function args        
 
+    Warning
+    -------
+    These changes may negatively affect subsequent NLP analysis performed
+        on the text, so choose carefully, and preprocess at your own risk!        
+    """
     assert isinstance(text, str), "The text to preprocess must be a string"
 
     if fix_unicode is True:
         text = fix_bad_unicode(text, normalization="NFC")
     if no_urls is True:
-        text = replace_urls(text)
+        text = replace_urls(text, replace_with=replace_with)
     if no_emails is True:
-        text = replace_emails(text)
+        text = replace_emails(text, replace_with=replace_with)
     if no_phone_numbers is True:
-        text = replace_phone_numbers(text)
+        text = replace_phone_numbers(text, replace_with=replace_with)
     if no_numbers is True:
-        text = replace_numbers(text)
+        text = replace_numbers(text, replace_with=replace_with)
     if no_currency_symbols is True:
-        text = replace_currency_symbols(text)
+        text = replace_currency_symbols(text, replace_with=replace_with)
+    if no_emoji is True:
+        text = remove_emoji(text)
     if no_contractions is True:
-        text = unpack_contractions_from_lang(text)
+        text = unpack_english_contractions(text)
     if no_accents is True:
         text = remove_accents(text, method="unicode")
     if no_punct is True:
         text = remove_punct(text)
     if lowercase is True:
         text = text.lower()
+    if remove_stopwords is not None:
+        stopwords = get_stopwords(remove_stopwords)
+        text = remove_stopwords(text, stopwords)
     # always normalize whitespace; treat linebreaks separately from spacing
     text = normalize_whitespace(text)
 

From c92357ac5395f4e41a13fd6fc4bcc37b0d578a75 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 3 May 2019 20:51:19 +0200
Subject: [PATCH 140/496] add unit tests for all fct except preprocess_text

---
 tests/test_preprocessor.py | 176 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 173 insertions(+), 3 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index f53408e..24f4658 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -7,7 +7,17 @@
     remove_EOL_characters,
     remove_tokens_with_nonletters,
     remove_special_caracters_from_tokenslist,
-    get_stopwords
+    get_stopwords,
+    remove_stopwords,
+    normalize_whitespace,
+    unpack_english_contractions,
+    replace_urls,
+    replace_emails,
+    replace_phone_numbers,
+    replace_numbers,
+    replace_currency_symbols,
+    remove_punct,
+    remove_emoji
 )
 
 
@@ -49,7 +59,7 @@ def test_remove_special_caracters_from_tokenslist():
     input_tokens = ['foo','bar','---',"'s",'#']
     expected_output = ['foo','bar',"'s"]
     result = remove_tokens_with_nonletters(input_tokens)
-    np.testing.assert_array_equal(result,expected_output)
+    np.testing.assert_array_equal(result, expected_output)
 
 
 def test_get_stopwords():
@@ -57,7 +67,20 @@ def test_get_stopwords():
     for lang in languages_to_test:
         result = get_stopwords(lang)
         assert len(result) > 0 and type(result) == list
-    
+
+
+@pytest.mark.parametrize(
+    "input_tokens, expected_output",
+    [
+        (['I','like','when','you','move','your','body','!'], ['I', 'move', 'body', '!']),
+        ('I like when you move your body !', ['I', 'move', 'body', '!']),
+    ],
+)    
+def test_remove_stopwords(input_tokens, expected_output):
+    stopwords = get_stopwords('en')
+    result = remove_stopwords(input_tokens, stopwords)
+    np.testing.assert_array_equal(result, expected_output)
+
 
 def test_remove_accents():
     input_str = "éèëêàù"
@@ -93,3 +116,150 @@ def test_fix_bad_unicode(input_str, expected_str):
     result = fix_bad_unicode(input_str)
     np.testing.assert_string_equal(result, expected_str)
 
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ('  foo  ', 'foo'),
+        ('  foo   bar  ', 'foo bar')
+    ],
+)
+def test_normalize_whitespace(input_str, expected_str):
+    result = normalize_whitespace(input_str)
+    np.testing.assert_equal(result, expected_str)
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("I can't tell how we've done.", 'I can not tell how we have done.'),
+        ("You're fired. She's nice.", "You are fired. She's nice."),
+        ("Let's go!",'Let us go!'),
+        ("You've been missing",'You have been missing'),
+        ("I'm sure you're leaving",'I am sure you are leaving'),
+        ("We'll survive.","We will survive")
+    ]
+    )
+def test_unpack_english_contractions(input_str, expected_str):
+    result = unpack_english_contractions(input_str)
+    np.testing.assert_equal(result, expected_str)
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("Wan't to contribute to Nautilus? read https://github.com/artefactory/nautilus-nlp/blob/docs/CONTRIBUTING.md first",
+         "Wan't to contribute to Nautilus? read *URL* first"),
+        ("The ip address of my VM is http://34.76.182.5:8888", "The ip address of my VM is *URL*"),
+        ("If you go to http://internet.org, you will find a website hosted by FB.",
+         "If you go to *URL*, you will find a website hosted by FB."),
+        ("Ishttps://waaaou.com/ available?",'Is*URL* available?'),
+        ("mailto:hugo.vasselin@artefact.com",'*URL*')
+    ]
+    )
+def test_replace_urls(input_str, expected_str):
+    result = replace_urls(input_str)
+    np.testing.assert_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("my email:hugo.vasselin@artefact.com","my email:*EMAIL*"),
+        ("v543143@nwytg.net is a temporary email", "*EMAIL* is a temporary email"),
+        ("our emails used to be name.surname@artefact.is","our emails used to be *EMAIL*"),
+        ("chaudasse_du_13@hotmail.frC ton email bb?",'*EMAIL*C ton email bb?')
+    ]
+    )
+def test_replace_emails(input_str, expected_str):
+    result = replace_emails(input_str)
+    np.testing.assert_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("mon 06 bb: 0625093267","mon 06 bb: *NUMBER*"),
+        ("mon 06 bb: 06.25.09.32.67","mon 06 bb: *NUMBER*"),
+        ("call me at +33625093267","call me at *NUMBER*"),
+        ("call me at +33 6 25 09 32 67","call me at *NUMBER*"),
+        ("call me at +33 625 093 267","call me at *NUMBER*"),
+        ("if this unit test doesn't work, call 3615 and says 'ROBIN'",
+         "if this unit test doesn't work, call *NUMBER* and says 'ROBIN'"),
+        ('(541) 754-3010 is a US. Phone','*NUMBER* is a US. Phone'),
+        ('+1-541-754-3010 is an international Phone','*NUMBER* is an international Phone'),
+        ('+1-541-754-3010 Dialed in the US','*NUMBER* Dialed in the US'),
+        ('+1-541-754-3010 Dialed from Germany','*NUMBER* Dialed from Germany'),
+        ('191 541 754 3010 Dialed from France','*NUMBER* Dialed from France')
+    ]
+    )
+def test_replace_phone_numbers(input_str, expected_str):
+    result = replace_phone_numbers(input_str)
+    np.testing.assert_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("123, 3 petits chats","*NUMBER*, *NUMBER* petits chats"),
+        ("l0ve 2 twa <3","l*NUMBER*ve *NUMBER* twa <*NUMBER*"),
+        ("Give me 45bucks!","Give me *NUMBER*bucks!"),
+        ("call me at +33625093267","call me at +*NUMBER*")
+    ]
+    )
+def test_replace_numbers(input_str, expected_str):
+    result = replace_numbers(input_str)
+    np.testing.assert_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "input_str, param, expected_str",
+    [
+        ("Give me 23$",None,"Give me 23USD"),
+        ("Give me 23£",None,"Give me 23GBP"),
+        ("Give me 23 £",None,"Give me 23 GBP"),
+        ("Give me 23 €",None,"Give me 23 EUR"),
+        ('¥ is both japanese yen and Chinese Renminbi',"*CUR*","*CUR* is both japanese yen and Chinese Renminbi")
+    ]
+    )
+def test_replace_currency_symbols(input_str, param, expected_str):
+    result = replace_currency_symbols(input_str, replace_with=param)
+    np.testing.assert_equal(result, expected_str)
+
+@pytest.mark.parametrize(
+    "input_str, param, expected_str",
+    [
+        ("Seriously...",None,"Seriously "),
+        ("Seriously?",None,"Seriously "),
+        ("Seriously ?",None,"Seriously  "),
+        ("Seriously???",None,"Seriously   "),
+        ("Seriously?!",None,"Seriously  "),
+        ('"Seriously"',None," Seriously "),
+        ('Seriously:',None,"Seriously "),
+        ('Seriously;',None,"Seriously "),
+        ("'Seriously'",None," Seriously "),
+        ("'Seriously'",'.,;','Seriously'),
+        ("Seriously...",'.,;','Seriously   '),
+        ("Seriously.,.",'.,;','Seriously   '),
+        ("Seriously.!.",'.,;','Seriously ! '),
+        ("hugo.vasselin@artefact.com",'.,;','hugo vasselin@artefact com'),
+        ("hugo.vasselin@artefact.com",None,'hugo vasselin@artefact com'),
+        ("hugo-vasselin@artefact.com",None,'hugo vasselin@artefact com')
+    ]
+    )
+def test_remove_punct(input_str, param, expected_str):
+    result = remove_punct(input_str, marks=param)
+    np.testing.assert_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("👉👌",""),
+        ("🎅🏿",""),
+        ("🥖✊💦",""),
+        ("J'espère que les 🚓 vont pas lire ce test",
+        "J'espère que les  vont pas lire ce test")
+    ]
+    )
+def test_remove_emoji(input_str, expected_str):
+    result = remove_emoji(input_str)
+    np.testing.assert_equal(result, expected_str)    
\ No newline at end of file

From 043d0d4d2f5659b20f74a6b0f97a8459f579a2d0 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Sat, 4 May 2019 11:59:31 +0200
Subject: [PATCH 141/496] Remove starting file on capital letters

---
 ...t_classifier.py => fasttext_classifier.py} |  0
 ...ext_embedding.py => fasttext_embedding.py} |  0
 ...guage_detector.py => language_detector.py} |  4 +-
 nautilus_nlp/models/sentiment_detector.py     | 37 +++++++++++
 nautilus_nlp/models/spacy_model.py            | 61 +++++++++++++++++++
 5 files changed, 100 insertions(+), 2 deletions(-)
 rename nautilus_nlp/models/{Fasttext_classifier.py => fasttext_classifier.py} (100%)
 rename nautilus_nlp/models/{Fasttext_embedding.py => fasttext_embedding.py} (100%)
 rename nautilus_nlp/models/{Language_detector.py => language_detector.py} (86%)
 create mode 100644 nautilus_nlp/models/sentiment_detector.py
 create mode 100644 nautilus_nlp/models/spacy_model.py

diff --git a/nautilus_nlp/models/Fasttext_classifier.py b/nautilus_nlp/models/fasttext_classifier.py
similarity index 100%
rename from nautilus_nlp/models/Fasttext_classifier.py
rename to nautilus_nlp/models/fasttext_classifier.py
diff --git a/nautilus_nlp/models/Fasttext_embedding.py b/nautilus_nlp/models/fasttext_embedding.py
similarity index 100%
rename from nautilus_nlp/models/Fasttext_embedding.py
rename to nautilus_nlp/models/fasttext_embedding.py
diff --git a/nautilus_nlp/models/Language_detector.py b/nautilus_nlp/models/language_detector.py
similarity index 86%
rename from nautilus_nlp/models/Language_detector.py
rename to nautilus_nlp/models/language_detector.py
index 06a1a06..acd1819 100644
--- a/nautilus_nlp/models/Language_detector.py
+++ b/nautilus_nlp/models/language_detector.py
@@ -1,5 +1,5 @@
-from nautilus_nlp.models.Fasttext_classifier import Fasttext_clf as langdetect
-from nautilus_nlp.utils.preprocess import remove_EOL_characters
+from nautilus_nlp.models.fasttext_classifier import Fasttext_clf as langdetect
+from nautilus_nlp.preprocessing.preprocess import remove_EOL_characters
 import pkg_resources
 
 lang_path = pkg_resources.resource_filename(
diff --git a/nautilus_nlp/models/sentiment_detector.py b/nautilus_nlp/models/sentiment_detector.py
new file mode 100644
index 0000000..b168473
--- /dev/null
+++ b/nautilus_nlp/models/sentiment_detector.py
@@ -0,0 +1,37 @@
+from nautilus_nlp.models.Fasttext_classifier import Fasttext_clf as langdetect
+import cld2
+import pkg_resources
+lang_path = pkg_resources.resource_filename('nautilus_nlp.data', 'lang_identification.ftz')
+class LangDetector():
+    """ This class is to instantiante a language detector.
+
+    """
+    def __init__(self,typemodel,path=lang_path,):
+        self.typemodel=typemodel
+        self.path = None if path is None else lang_path 
+        self.model=langdetect(self.path) if typemodel=='fasttext' else None
+    
+    def detect_language(self, text_to_detect=None):
+        """
+        Detected the language of a text
+
+        Args:
+        hint_language: language you expect your text to be
+
+        Returns:
+        is_reliable: is the top language is much better than 2nd best language?
+        language: 2-letter code for the language of the text
+        """
+        if self.typemodel!='fasttext':
+            _, _, best_guesses = cld2.detect(text_to_detect,
+                                                    bestEffort=True)
+
+            if len(best_guesses) == 0 or len(best_guesses[0]) != 4 or best_guesses[0][1] == 'un':
+                return 'un',0
+
+            return  best_guesses[0][1],(best_guesses[0][2]/100)
+        else:
+            best_guesses=self.model.predict(text_to_detect)
+            return best_guesses[0][0].replace('__label__',''),best_guesses[1][0]
+
+
diff --git a/nautilus_nlp/models/spacy_model.py b/nautilus_nlp/models/spacy_model.py
new file mode 100644
index 0000000..c7eca79
--- /dev/null
+++ b/nautilus_nlp/models/spacy_model.py
@@ -0,0 +1,61 @@
+import spacy
+
+class spacy_model:
+
+    def __init__(self, lang: str):
+        try:
+            self.model = spacy.load(lang)
+        except Exception as e:
+            print(e)
+            print(f"Cannot load lang {lang}, loading default")
+            self.model = spacy.load("xx")
+
+    def get_spacy_doc(self, text):
+
+        return self.model(text)
+
+    @staticmethod
+    def get_tokens_from_document(spacydoc: spacy.tokens.doc.Doc):
+
+        return [tokens for tokens in spacydoc]
+
+    def get_tokens_from_str(self, text: str):
+        spacydoc = self.model(text)
+        return [tokens for tokens in spacydoc]
+
+    @staticmethod
+    def get_sentence_from_document(spacydoc: spacy.tokens.doc.Doc):
+        return [sent for sent in spacydoc.sents]
+
+    def get_sentence_from_str(self, text: str):
+        spacydoc = self.model(text)
+        return [sent for sent in spacydoc.sents]
+
+    @staticmethod
+    def get_tokenized_sentence_from_document(spacydoc: spacy.tokens.doc.Doc):
+        return [[(token) for token in sent] for sent in spacydoc.sents]
+
+    def get_tokenized_sentence_from_str(self, text: str):
+        spacydoc = self.model(text)
+        return [[(token) for token in sent] for sent in spacydoc.sents]
+
+    @staticmethod
+    def get_entities_from_document(spacydoc):
+        return [(ent, ent.label_) for ent in spacydoc.ents]
+
+    @staticmethod
+    def get_entities_from_str(self, text: str):
+        spacydoc = self.model(text)
+        return [(ent, ent.label_) for ent in spacydoc.ents]
+
+    @staticmethod
+    def get_lemma_from_document(spacydoc: spacy.tokens.doc.Doc) -> list:
+        return [token.lemma_ for token in spacydoc]
+
+    def get_lemma_from_str(self, text: str):
+        spacydoc = self.model(text)
+        return [token.lemma_ for token in spacydoc]
+
+    def get_pos_from_str(self, text: str):
+        spacydoc = self.model(text)
+        return [token.pos_ for token in spacydoc]
\ No newline at end of file

From b7ddf4c9208fb9f04c16501696343fc159b6a3ad Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 4 May 2019 13:20:11 +0200
Subject: [PATCH 142/496] fix preprocess

---
 nautilus_nlp/preprocessing/preprocess.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index fd8041e..0f69de5 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -468,7 +468,7 @@ def preprocess_text(
     no_accents=False,
     no_emoji=False,
     replace_with=None, 
-    remove_stopwords=None
+    no_stopwords=None
 ) -> str:
     """
     Normalize various aspects of a raw text doc. A convenience function for
@@ -507,7 +507,7 @@ def preprocess_text(
         if True, remove all emojis from text
     replace_with : string
         The string you want the entities to be replaced with.
-    remove_stopwords : 2-letter country code
+    no_stopwords : 2-letter country code
         If specified, will remove the stopwords of the given language. 
         Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
          'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
@@ -550,8 +550,8 @@ def preprocess_text(
         text = remove_punct(text)
     if lowercase is True:
         text = text.lower()
-    if remove_stopwords is not None:
-        stopwords = get_stopwords(remove_stopwords)
+    if no_stopwords is not None:
+        stopwords = get_stopwords(no_stopwords)
         text = remove_stopwords(text, stopwords)
     # always normalize whitespace; treat linebreaks separately from spacing
     text = normalize_whitespace(text)

From 2535c2b5fbd8cd1feee1dff6fddbaa599eba5a4a Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 4 May 2019 14:02:58 +0200
Subject: [PATCH 143/496] add quickstart

---
 README.md | 51 ++++++++++++++++++++++++++++++++++++---------------
 1 file changed, 36 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index 411feb6..e381240 100644
--- a/README.md
+++ b/README.md
@@ -16,9 +16,9 @@ You can find a list of the available features [in this article.](https://artefac
 
 As an Artefact user, you might be working on a NLP use case, and wish to use Nautilus.
 
- However, if you think Nautilus is lacking features that can be useful not only to your use case but also others, feel free to to fill up an issue with the label "Feature-request".
+However, if you think Nautilus is lacking features that can be useful not only to your use case but also others, feel free to to [fill up an issue](https://github.com/artefactory/nautilus-nlp/issues) with the label "Feature-request".
 
- We will try to put it in the roadmap and implement it as soon as possible.
+We will try to put it in the roadmap and implement it as soon as possible.
 
 # Installation
 
@@ -26,28 +26,34 @@ Beware, this package has been tested on Python **3.6** & **3.7**, and will proba
 
 To install this library you should first clone the repository:
 
-`git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp`
+```
+git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp
+```
 
 **If you don't use the docker container, we strongly advise you to do these steps in a virtual environnement**
 
 First you need to install the required files:
 
-```pip install -r requirements.txt```
+```
+pip install -r requirements.txt
+```
 
 then you can install it via pip:
 
-```pip install -e .```
+```
+pip install -e .
+```
 
 ## Handling installation errors
 
 ### command 'gcc' failed with exit status 1
 
 While runing `pip install -r requirements.txt` you might get the following error message:
-```
-command 'gcc' failed with exit status 1 
-```
+
+`command 'gcc' failed with exit status 1`
 
 To solve it, run the following command before installing requirements.txt:
+
 ```
 conda install pyemd
 ```
@@ -97,9 +103,6 @@ run:
 
 `bash nautilus_nlp/scripts/download_ft_langdetect.sh`
 
-<<<<<<< HEAD
-# Quick start 
-=======
 ### Install Mallet
 
 1) Install Mallet file
@@ -112,18 +115,36 @@ run:
 
 `bash nautilus_nlp/scripts/install_java.sh`
 
-# Notebooks
->>>>>>> master
+# Quick start
 
 The [notebook](notebooks/) folder contains various notebook on how to use this library.
 
 Here is a quick example:
 
 ```
-
+>>> from nautilus_nlp.preprocessing.preprocess import preprocess_text
+>>> from nautilus_nlp.preprocessing.tokenizer import tokenize
+>>> from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens
+[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...
+[nltk_data]   Package wordnet is already up-to-date!
+>>> text = """Tropical Storm Nicole was a short-lived and unusually asymmetric tropical cyclone that caused extensive flooding in Jamaica during the 2010 Atlantic hurricane season.\nSource:https://en.wikipedia.org/wiki/Tropical_Storm_Nicole_(2010)"""
+>>> clean_text = preprocess_text(text, lowercase=True,
+...                                         no_punct=True,
+...                                         no_numbers=True,
+...                                         no_stopwords='en',
+...                                         no_urls=True,
+...                                         replace_with='')
+>>> clean_text
+'tropical storm nicole short lived unusually asymmetric tropical cyclone caused extensive flooding jamaica atlantic hurricane season source'
+>>> tokenized_text = tokenize(clean_text)
+>>> tokenized_text
+['tropical', 'storm', 'nicole', 'short', 'lived', 'unusually', 'asymmetric', 'tropical', 'cyclone', 'caused', 'extensive', 'flooding', 'jamaica', 'atlantic', 'hurricane', 'season', 'source']
+>>> lemma_text = lemmatize_english_tokens(tokenized_text)
+>>> lemma_text
+['tropical', 'storm', 'nicole', 'short', 'live', 'unusually', 'asymmetric', 'tropical', 'cyclone', 'cause', 'extensive', 'flooding', 'jamaica', 'atlantic', 'hurricane', 'season', 'source']
 ```
 
-Project Organization
+# Project Organization
 ------------
 
     ├── LICENSE

From dd2e4488bc12d910dddd5ba6df988efc02304712 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 4 May 2019 14:03:28 +0200
Subject: [PATCH 144/496] fix preprocess_text

---
 nautilus_nlp/preprocessing/preprocess.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 0f69de5..57bac0d 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -552,8 +552,8 @@ def preprocess_text(
         text = text.lower()
     if no_stopwords is not None:
         stopwords = get_stopwords(no_stopwords)
-        text = remove_stopwords(text, stopwords)
+        text = ' '.join(remove_stopwords(text, stopwords))
     # always normalize whitespace; treat linebreaks separately from spacing
     text = normalize_whitespace(text)
-
+    
     return text

From 5b83921144dd32faf7a94c44e627126ae858e232 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 4 May 2019 14:05:37 +0200
Subject: [PATCH 145/496] remove exeption catching for spacy load

---
 nautilus_nlp/preprocessing/tokenizer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/preprocessing/tokenizer.py b/nautilus_nlp/preprocessing/tokenizer.py
index 94eb7aa..6937196 100644
--- a/nautilus_nlp/preprocessing/tokenizer.py
+++ b/nautilus_nlp/preprocessing/tokenizer.py
@@ -4,18 +4,18 @@
 from spacy.lang.fr import French
 from spacy.lang.en import English
 import spacy.lang as spacylang
-#nltk.download('punkt')
+nltk.download('punkt')
 
 try:
     french_spacy = spacylang.fr.French()
-except OSError:
+except:
     raise OSError("""You must install French langage to use SpaCy. 
                     python -m spacy download fr
                     See https://spacy.io/usage/ for details
                 """)
 try:
     english_spacy = spacylang.en.English()
-except OSError:
+except:
     raise OSError("""You must install english langage to use SpaCy. 
                     python -m spacy download en
                     See https://spacy.io/usage/ for details

From cd235732b2ce28cadbd656ea4249100fc21845c1 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 4 May 2019 14:15:43 +0200
Subject: [PATCH 146/496] add file removing at the end

---
 tests/test_document_loader.py | 107 ++++++++++++++++++----------------
 1 file changed, 56 insertions(+), 51 deletions(-)

diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index ee0c4f0..5629511 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -1,72 +1,77 @@
-# import pytest
-# import numpy as np
-# from nautilus_nlp.utils.file_loader import documents_loader, list_files, detect_encoding
+# -*- coding: utf-8 -*-
 
+import os 
+import pytest
+import numpy as np
+from nautilus_nlp.utils.file_loader import documents_loader, list_files, detect_encoding
 
-# testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
-# encoded_s = testdoc_latin1.encode('latin-1')
+testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
+testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
 
-# with open('testfolder_fileloader/testdoc_latin1.txt', 'wb') as f:
-#     f.write(encoded_s)
 
-# testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
-# encoded_s = testdoc_utf8.encode('utf-8')
-# with open('./testfolder_fileloader/testdoc_utf8.txt', 'wb') as f:
-#     f.write(encoded_s)
+encoded_s = testdoc_latin1.encode('latin-1')
+with open('testdoc_latin1.txt', 'wb') as f:
+    f.write(encoded_s)
 
-# def test_openfile_with_encoding():
-#     input_str = "testfolder_fileloader/testdoc_latin1.txt"
-#     expected_str = testdoc_latin1
 
-#     result = documents_loader(input_str, encoding='latin-1')
-#     np.testing.assert_string_equal(result, expected_str)
+encoded_s = testdoc_utf8.encode('utf-8')
+with open('testdoc_utf8.txt', 'wb') as f:
+    f.write(encoded_s)
 
-# def test_openfile_utf8():
-#     input_str = "testfolder_fileloader/testdoc_utf8.txt"
-#     expected_str = testdoc_utf8
 
-#     result = documents_loader(input_str)
-#     np.testing.assert_string_equal(result, expected_str)
+def test_openfile_with_encoding():
+    input_str = "testdoc_latin1.txt"
+    expected_str = testdoc_latin1
+    result = documents_loader(input_str, encoding='latin-1')
+    np.testing.assert_string_equal(result, expected_str)
 
-# def test_encoding_detection():
-#     input_str = "testfolder_fileloader/testdoc_latin1.txt"
-#     expected_str = testdoc_latin1
 
-#     result = documents_loader(input_str)
-#     np.testing.assert_string_equal(result, expected_str)    
-    
-# def test_load_several_docs_wildcard():
-#     expected = {'testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
-#                 'testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
-#     result = documents_loader('testfolder_fileloader/*.txt', output_as='dict')
-#     np.testing.assert_equal(result, expected)    
+def test_openfile_utf8():
+    input_str = "testdoc_utf8.txt"
+    expected_str = testdoc_utf8
+    result = documents_loader(input_str)
+    np.testing.assert_string_equal(result, expected_str)
 
-# def test_load_several_docs_list():
-#     expected = {'testfolder_fileloader/testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
-#                 'testfolder_fileloader/testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
-#     result = documents_loader(['testfolder_fileloader/testdoc_latin1.txt','testfolder_fileloader/testdoc_utf8.txt'], output_as='dict')
-#     np.testing.assert_equal(result, expected)
 
+def test_encoding_detection():
+    input_str = "testdoc_latin1.txt"
+    expected_str = testdoc_latin1
+    result = documents_loader(input_str)
+    np.testing.assert_string_equal(result, expected_str)    
+  
 
-# def test_load_several_docs_output_list():
-#     expected = ["J'aime les frites bien grasse étalon châpeau!",
-#                 'Un deuxième exemple de texte en utf-8 cette fois!']
-#     result = documents_loader(['testfolder_fileloader/testdoc_latin1.txt','testfolder_fileloader/testdoc_utf8.txt'], output_as='list')
-#     return len(expected) == len(result) and sorted(expected) == sorted(result)
+def test_load_several_docs_wildcard():
+    expected = {'testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+                'testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+    result = documents_loader('*.txt', output_as='dict')
+    np.testing.assert_equal(result, expected)   
 
 
+def test_load_several_docs_list():
+    expected = {'testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
+                'testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
+    result = documents_loader(['testdoc_latin1.txt','testdoc_utf8.txt'], output_as='dict')
+    np.testing.assert_equal(result, expected)
 
-# @pytest.mark.parametrize("input_filepath", ['testfolder_fileloader/*.txt','testfolder_fileloader/','testfolder_fileloader'])
-# def test_list_files(input_filepath):
-#     expected = ['testfolder_fileloader/testdoc_latin1.txt','testfolder_fileloader/testdoc_utf8.txt']
-#     result = list_files(input_filepath)
 
-#     return len(expected) == len(result) and sorted(expected) == sorted(result)
+def test_load_several_docs_output_list():
+    expected = ["J'aime les frites bien grasse étalon châpeau!",
+                'Un deuxième exemple de texte en utf-8 cette fois!']
+    result = documents_loader(['testdoc_latin1.txt','testdoc_utf8.txt'], output_as='list')
+    return len(expected) == len(result) and sorted(expected) == sorted(result)
 
 
-# def test_detect_encoding():
-#     expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
-#     result = detect_encoding('testfolder_fileloader/testdoc_latin1.txt')
+#@pytest.mark.parametrize("input_filepath", ['*.txt','','testfolder_fileloader'])
+#def test_list_files(input_filepath):
+#    expected = ['testdoc_latin1.txt','testdoc_utf8.txt']
+#    result = list_files(input_filepath)
+#   return len(expected) == len(result) and sorted(expected) == sorted(result)
 
-#     np.testing.assert_equal(result, expected)
 
+def test_detect_encoding():
+    expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
+    result = detect_encoding('testdoc_latin1.txt')
+    np.testing.assert_equal(result, expected)
+
+os.remove('testdoc_latin1.txt')
+os.remove('testdoc_utf8.txt')
\ No newline at end of file

From d5addf7410ddb58f4705c917650b2c1c812e2797 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sat, 4 May 2019 14:41:52 +0200
Subject: [PATCH 147/496] clean and harmonize docstrings

---
 .../preprocessing/keyword_extractor.py        | 19 ++++--
 nautilus_nlp/preprocessing/lemmatization.py   | 67 +++++++++++--------
 nautilus_nlp/preprocessing/stemming.py        | 23 ++++---
 3 files changed, 66 insertions(+), 43 deletions(-)

diff --git a/nautilus_nlp/preprocessing/keyword_extractor.py b/nautilus_nlp/preprocessing/keyword_extractor.py
index e384bf2..11933bc 100644
--- a/nautilus_nlp/preprocessing/keyword_extractor.py
+++ b/nautilus_nlp/preprocessing/keyword_extractor.py
@@ -1,13 +1,22 @@
 from flashtext import KeywordProcessor
 
-def extract_keywords(text,keyword,case_sensitive=True):
+def extract_keywords(text:str, keyword, case_sensitive=True) -> list:
     """
     Extract Keywords from a document.
-    args :
-    text: Text to extract keywords from
-    keyword : Single keyword (str) or list of keywords (list)
 
-    return list of extracted keyworkds
+    Parameters
+    ----------
+    text : str
+        Text to extract keywords from
+    keyword : str or list 
+        Single keyword (str) or list of keywords (list)
+    case_sensitive : bool
+        If True, will be case-sensitive.
+
+    Returns
+    -------
+    string
+        return list of extracted keyworkds
     """
     processor=KeywordProcessor(case_sensitive=case_sensitive)
     if isinstance(keyword,list):
diff --git a/nautilus_nlp/preprocessing/lemmatization.py b/nautilus_nlp/preprocessing/lemmatization.py
index e4c812c..462472e 100644
--- a/nautilus_nlp/preprocessing/lemmatization.py
+++ b/nautilus_nlp/preprocessing/lemmatization.py
@@ -6,39 +6,38 @@
 
 try:
     french_spacy = spacy.load('fr_core_news_sm')
-except OSError:
+except:
     raise OSError("""You must install French langage to use SpaCy. 
                     python -m spacy download fr
                     See https://spacy.io/usage/ for details
                 """)
 try:
     english_spacy = spacy.load('en_core_web_sm')
-except OSError:
+except:
     raise OSError("""You must install english langage to use SpaCy. 
                     python -m spacy download en
                     See https://spacy.io/usage/ for details
                 """)             
 
 
+def lemmatize_french_tokens(tokens:list, module:str='spacy')->list:
+    """
+    Wrappers of SpaCy french lemmatizers (based on FrenchLeffLemmatizer but 
+    faster and more accurate.) 
 
+    Parameters
+    ----------
+    tokens : list
+        List of tokens
+    module : ({'spacy'})
+        Only spaCy is available. We removed FrenchLeffLemmatizer for performance
+        considerations.
 
-def lemmatize_french_tokens(tokens, module='spacy', load_only_pos='all'):
-    '''
-    Wrappers of french lemmatizers. SpaCy is much faster but sometimes 
-    FrenchLefffLemmatizer returns more accurate results. Give a try to the 
-    2 modules and choose the one that better fit your needs. 
-
-    Args:
-        tokens (list): list of tokens
-        module ({'spacy'}): modules availables.
-        load_only_pos ({'a', v', 'r', 'n', 'all'}): If not "all", applies 
-        lemmatization only to a certain POS tags, for french_leff_v module.
-        a = adjectives, v = verbs, n = noun, r = adverb. 
-
-    Returns:
+    Returns
+    -------
+    list
         list of lemmatized tokens
-    
-    '''
+    """
 
     tokens = _make_sure_input_is_list_of_tokens(tokens)
 
@@ -52,18 +51,22 @@ def lemmatize_french_tokens(tokens, module='spacy', load_only_pos='all'):
         raise ValueError("must pass a valid module name!")
 
 
-def lemmatize_english_tokens(tokens, module='spacy', pos_tag_prio='v'):
-    '''
-    Args:
-        tokens (list): list of tokens
-        module ({'french_leff_v', 'spacy'}): modules availables.
-        pos_tag_prio ({'v', 'n', 'all'}): grammatical priority, applies
-        for french_leff_v module. 
+def lemmatize_english_tokens(tokens:list, module:str='spacy')->list:
+    """
+    Wrapper of SpaCy english lemmatizer and NLTK WordNet.
+
+    Parameters
+    ----------
+    tokens : list
+        List of tokens
+    module : ({'spacy'},{'nltk'})
+        Tokenizer module. Default: 'spacy'
 
-    Returns:
+    Returns
+    -------
+    list
         list of lemmatized tokens
-    
-    '''
+    """
     tokens = _make_sure_input_is_list_of_tokens(tokens)
 
     if module == 'nltk':
@@ -82,7 +85,9 @@ def lemmatize_english_tokens(tokens, module='spacy', pos_tag_prio='v'):
 
 
 def _get_wordnet_pos(word):
-    """Map POS tag to first character lemmatize() accepts"""
+    """
+    Map POS tag to first character lemmatize() accepts
+    """
     tag = nltk.pos_tag([word])[0][1][0].upper()
     tag_dict = {"J": wordnet.ADJ,
                 "N": wordnet.NOUN,
@@ -93,6 +98,10 @@ def _get_wordnet_pos(word):
 
 
 def _make_sure_input_is_list_of_tokens(tokens):
+    """
+    Raises an error if input is not a list, and convert "None" to blank list
+    to handle dataset with no text.
+    """    
     if type(tokens) is list:
         return tokens
     elif tokens is None:
diff --git a/nautilus_nlp/preprocessing/stemming.py b/nautilus_nlp/preprocessing/stemming.py
index c480ca8..a4dfc0e 100644
--- a/nautilus_nlp/preprocessing/stemming.py
+++ b/nautilus_nlp/preprocessing/stemming.py
@@ -1,20 +1,25 @@
 from nltk.stem.snowball import *
 
 
-def stem_tokens(tokens: list, lang: str ='english'):
-    '''
+def stem_tokens(tokens: list, lang: str ='english')-> list:
+    """
     Wrapper of NLTK's Snowball stemmers : http://www.nltk.org/howto/stem.html
 
-    Args:
-        tokens (list): list of tokens
-        lang ({'arabic', 'danish', 'dutch', 'english', 'finnish', 'french',
+    Parameters
+    ----------
+    tokens : list
+        List of tokens
+    lang : string
+        Supported languages: ({'arabic', 'danish', 'dutch', 'english', 'finnish', 'french',
         'german', 'hungarian', 'italian', 'norwegian', 'porter', 'portuguese', 
-        'romanian', 'russian', 'spanish', 'swedish'}): supported langages
+        'romanian', 'russian', 'spanish', 'swedish'}): 
 
-    Returns:
+    Returns
+    -------
+    list
         list of stemmed tokens
-    
-    '''
+    """
+
     supported_lang = [lang for lang in SnowballStemmer.languages]
     
     if lang in supported_lang:

From 325d9a8e75d9570eeeff650e82ab3a58ceaba90c Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Thu, 9 May 2019 15:52:27 +0200
Subject: [PATCH 148/496] add text summary

---
 nautilus_nlp/utils/text_summary.py | 0
 tests/test_text_summary.py         | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 nautilus_nlp/utils/text_summary.py
 create mode 100644 tests/test_text_summary.py

diff --git a/nautilus_nlp/utils/text_summary.py b/nautilus_nlp/utils/text_summary.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_text_summary.py b/tests/test_text_summary.py
new file mode 100644
index 0000000..e69de29

From ed0c98f674ea415277f246e9869bb20851627a2c Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Thu, 9 May 2019 15:53:36 +0200
Subject: [PATCH 149/496] add summa to requirements.txt

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 2a03758..56b12d0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -38,4 +38,5 @@ vaderSentiment==3.2.1
 
 google-compute-engine==2.8.13
 flashtext==2.7
+summa
 

From 83805559bdea8ead72938642f7a91a642751cf08 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kaislaribi@FRART0033M.local>
Date: Thu, 9 May 2019 18:45:55 +0200
Subject: [PATCH 150/496] update text_summary.py

---
 nautilus_nlp/utils/text_summary.py | 28 ++++++++++++++++++++++++++++
 tests/test_text_summary.py         | 27 +++++++++++++++++++++++++++
 2 files changed, 55 insertions(+)

diff --git a/nautilus_nlp/utils/text_summary.py b/nautilus_nlp/utils/text_summary.py
index e69de29..ad873b3 100644
--- a/nautilus_nlp/utils/text_summary.py
+++ b/nautilus_nlp/utils/text_summary.py
@@ -0,0 +1,28 @@
+from summa.summarizer import summarize
+
+
+def is_list_of_strings(lst):
+    """
+    :param lst: list
+    :return: boolean indicator
+    """
+    return bool(lst) and isinstance(lst, list) and all(isinstance(elem, str) for elem in lst)
+
+
+def summarize_text(txt, ratio=0.2, words=None, language="english"):
+    """
+    :param txt: Sting or list of strings containing text to summarize
+    :param ratio: Percentage giving the output text length in reference to the input length.
+    :param words: number of words of the output text
+    :param language: text language
+    :return: string containing the summarized text
+    """
+
+    if is_list_of_strings(txt):
+        txt = ' '.join(txt)
+    elif isinstance(txt, str):
+        pass
+    else:
+        raise ValueError("Text parameter must be a Unicode object (str) or list of str!")
+    return summarize(txt, ratio, words, language)
+
diff --git a/tests/test_text_summary.py b/tests/test_text_summary.py
index e69de29..e233812 100644
--- a/tests/test_text_summary.py
+++ b/tests/test_text_summary.py
@@ -0,0 +1,27 @@
+from nautilus_nlp.utils.text_summary import summarize_text
+
+case1 = """Automatic summarization is the process of reducing a text document with a \
+computer program in order to create a summary that retains the most important points \
+of the original document. As the problem of information overload has grown, and as \
+the quantity of data has increased, so has interest in automatic summarization. \
+Technologies that can make a coherent summary take into account variables such as \
+length, writing style and syntax. An example of the use of summarization technology \
+is search engines such as Google. Document summarization is another."""
+
+case2 = ["Automatic summarization is the process of reducing a text document with a " \
+"computer program in order to create a summary that retains the most important points " \
+"of the original document. ", "As the problem of information overload has grown, and as" \
+"the quantity of data has increased, so has interest in automatic summarization. "\
+"Technologies that can make a coherent summary take into account variables such as "\
+"length, writing style and syntax." ,"An example of the use of summarization technology "\
+"is search engines such as Google.", "Document summarization is another."]
+
+result = """Automatic summarization is the process of reducing a text document with a computer \
+program in order to create a summary that retains the most important points of the original document."""
+
+
+def test_summarize_text():
+    # Single string containing all text
+    assert summarize_text(case1) == result
+    # Text split in many sentences (list of strings)
+    assert summarize_text(case2) == result

From d732920563e8fb1081cc12da7f10448a4870c86f Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Mon, 13 May 2019 10:04:41 +0200
Subject: [PATCH 151/496] add summa version to requirements

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 37f7f50..6b788fe 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -36,7 +36,7 @@ scikit_learn==0.20.3
 vaderSentiment==3.2.1
 google-compute-engine==2.8.13
 flashtext==2.7
-summa
+summa==1.2.0
 
 
 

From 4b0f8a0e8e323a65bd893c765f9812d3b62c879f Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 10:30:58 +0200
Subject: [PATCH 152/496] harmonize docstring and change function name

---
 nautilus_nlp/models/sentiment.py              |  4 +-
 .../preprocessing/text_vectorizers.py         | 24 +++---
 nautilus_nlp/preprocessing/tokenizer.py       | 81 +++++++++++++++----
 nautilus_nlp/utils/tokenizer.py               |  4 +-
 4 files changed, 86 insertions(+), 27 deletions(-)

diff --git a/nautilus_nlp/models/sentiment.py b/nautilus_nlp/models/sentiment.py
index 4c0afc8..1b08ba2 100644
--- a/nautilus_nlp/models/sentiment.py
+++ b/nautilus_nlp/models/sentiment.py
@@ -1,4 +1,4 @@
-from nautilus_nlp.utils.tokenizer import _tokensToString, _stringToTokens
+from nautilus_nlp.utils.tokenizer import _convert_tokens_to_string, _convert_string_to_tokens
 
 import textblob
 from textblob import Blobber
@@ -11,7 +11,7 @@ def compute_sentiment_score(tokens_or_txt, lang_module='en_textblob'):
     '''
 
     '''
-    text = _tokensToString(tokens_or_txt)
+    text = _convert_tokens_to_string(tokens_or_txt)
     output = ''
     if lang_module is 'fr_textblob':
         tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())
diff --git a/nautilus_nlp/preprocessing/text_vectorizers.py b/nautilus_nlp/preprocessing/text_vectorizers.py
index a03c5bb..55d3046 100644
--- a/nautilus_nlp/preprocessing/text_vectorizers.py
+++ b/nautilus_nlp/preprocessing/text_vectorizers.py
@@ -11,16 +11,22 @@
 
 class Tfidf(object):
     """
-    Inputs a list of string
-    Outputs a tuple with the wordcount vector matrix, and the list of feature name
-    Params:
-        input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
-        lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
-        stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
-        max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
-        dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
-        
+    Inputs a list of string. 
+    and the list of feature name.
     Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
+
+    Parameters
+    ----------
+    input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
+    lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
+    stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
+    max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
+    dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
+
+    Returns
+    -------
+    list
+        Outputs a tuple with the wordcount vector matrix
     """
     
     def __init__(self, **kwargs):
diff --git a/nautilus_nlp/preprocessing/tokenizer.py b/nautilus_nlp/preprocessing/tokenizer.py
index 6937196..1fddaa9 100644
--- a/nautilus_nlp/preprocessing/tokenizer.py
+++ b/nautilus_nlp/preprocessing/tokenizer.py
@@ -22,18 +22,22 @@
                 """)             
 
 
-def tokenize(text: str, lang_module: str = 'en_spacy'):
+def tokenize(text: str, lang_module: str = 'en_spacy')-> list:
     """
-    Convert text to a list of tokens. 
+    Split text into a list of tokens. 
 
-    Args:
-        lang_module ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
+    Parameters
+    ----------
+    lang_module
+        ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
         the tokenization module according to the langage and the implementation.
         Recommanded: Spacy (faster, better results). To process other langages
         import models.Spacy_models
 
-    Returns:
-        list
+    Returns
+    -------
+    list
+        Outputs a list of tokens.
     """
     if lang_module is 'en_nltk':
         return nltk.word_tokenize(text)
@@ -48,28 +52,77 @@ def tokenize(text: str, lang_module: str = 'en_spacy'):
         return t.tokenize(text, escape=False)
 
 
-def untokenize(tokens, lang='fr'):
-    '''
-    Inputs a list of tokens output string.
-    ["J'", 'ai'] >>> "J' ai"
-    '''
+def untokenize(tokens:list, lang:str='fr')->str:
+    """
+    Inputs a list of tokens, output the text joined.
+    Wrapper of https://github.com/alvations/sacremoses
+
+    Parameters
+    ----------
+    lang
+        Supported languages: ({'fr', 'en', 'cs','it','ga'})
+
+    Returns
+    -------
+    string
+    """
     d = MosesDetokenizer(lang=lang)
     text = d.detokenize(tokens, unescape=False)
     return text    
 
 
-def _tokensToString(tokens_or_str):
+def _convert_tokens_to_string(tokens_or_str, lang:str='en')->str:
+    """
+    Test if the input is tokens or string, and convert tokens to string
+    using untokenize(). If the input is null, it will return an empty string. 
+
+    Parameters
+    ----------
+    lang
+        Supported languages: ({'fr', 'en', 'cs','it','ga'}).
+
+    Returns
+    -------
+    string
+
+    Raises
+    -------
+    ValueError
+        If the input is not string, list or Nonetype.     
+    """
     if type(tokens_or_str) is str:
         return tokens_or_str
     elif type(tokens_or_str) is list:
-        return untokenize(tokens_or_str)
+        return untokenize(tokens_or_str, lang=lang)
     elif type(tokens_or_str) is None:
         return ''
     else:
         raise ValueError('Please input string or tokens')
 
 
-def _stringToTokens(tokens_or_str, lang_module='en_spacy'):
+def _convert_string_to_tokens(tokens_or_str, lang_module:str='en_spacy')->str:
+    """
+    Test if the input is tokens or string, and convert string to tokens
+    using tokenize(). If the input is null, it will return an empty list. 
+
+    Parameters
+    ----------
+    lang_module
+        ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
+        the tokenization module according to the langage and the implementation.
+        Recommanded: Spacy (faster, better results). To process other langages
+        import models.Spacy_models
+
+    Returns
+    -------
+    list
+        list of tokens
+
+    Raises
+    -------
+    ValueError
+        If the input is not string, list or Nonetype.     
+    """
     if type(tokens_or_str) is str:
         return tokenize(tokens_or_str, lang_module=lang_module)
     elif type(tokens_or_str) is list:
diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 94eb7aa..a009533 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -58,7 +58,7 @@ def untokenize(tokens, lang='fr'):
     return text    
 
 
-def _tokensToString(tokens_or_str):
+def _convert_tokens_to_string(tokens_or_str):
     if type(tokens_or_str) is str:
         return tokens_or_str
     elif type(tokens_or_str) is list:
@@ -69,7 +69,7 @@ def _tokensToString(tokens_or_str):
         raise ValueError('Please input string or tokens')
 
 
-def _stringToTokens(tokens_or_str, lang_module='en_spacy'):
+def _convert_string_to_tokens(tokens_or_str, lang_module='en_spacy'):
     if type(tokens_or_str) is str:
         return tokenize(tokens_or_str, lang_module=lang_module)
     elif type(tokens_or_str) is list:

From 35d2481399b4d49b9e29e99474fef567510ac18a Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 10:57:55 +0200
Subject: [PATCH 153/496] add docstring and change filename

---
 nautilus_nlp/models/sentiment.py          | 33 ----------
 nautilus_nlp/models/sentiment_detector.py | 77 +++++++++++++----------
 2 files changed, 44 insertions(+), 66 deletions(-)
 delete mode 100644 nautilus_nlp/models/sentiment.py

diff --git a/nautilus_nlp/models/sentiment.py b/nautilus_nlp/models/sentiment.py
deleted file mode 100644
index 1b08ba2..0000000
--- a/nautilus_nlp/models/sentiment.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from nautilus_nlp.utils.tokenizer import _convert_tokens_to_string, _convert_string_to_tokens
-
-import textblob
-from textblob import Blobber
-from textblob import TextBlob
-from textblob_fr import PatternTagger, PatternAnalyzer
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-
-
-def compute_sentiment_score(tokens_or_txt, lang_module='en_textblob'):
-    '''
-
-    '''
-    text = _convert_tokens_to_string(tokens_or_txt)
-    output = ''
-    if lang_module is 'fr_textblob':
-        tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())
-        blob = tb(text)
-        output = blob.sentiment[0]
-
-    elif lang_module is 'en_textblob':
-        blob = TextBlob(text)
-        output = blob.sentiment.polarity
-
-    elif lang_module is 'en_vader':
-        analyser = SentimentIntensityAnalyzer()
-        snt = analyser.polarity_scores(text)
-        output = snt['compound']
-    else:
-        raise ValueError('Please enter a valid module name!')
-
-    assert output != ''
-    return output
\ No newline at end of file
diff --git a/nautilus_nlp/models/sentiment_detector.py b/nautilus_nlp/models/sentiment_detector.py
index b168473..439050b 100644
--- a/nautilus_nlp/models/sentiment_detector.py
+++ b/nautilus_nlp/models/sentiment_detector.py
@@ -1,37 +1,48 @@
-from nautilus_nlp.models.Fasttext_classifier import Fasttext_clf as langdetect
-import cld2
-import pkg_resources
-lang_path = pkg_resources.resource_filename('nautilus_nlp.data', 'lang_identification.ftz')
-class LangDetector():
-    """ This class is to instantiante a language detector.
+from nautilus_nlp.utils.tokenizer import _convert_tokens_to_string, _convert_string_to_tokens
 
+import textblob
+from textblob import Blobber
+from textblob import TextBlob
+from textblob_fr import PatternTagger, PatternAnalyzer
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+
+def compute_sentiment_score(tokens_or_txt, lang_module:str='en_textblob')->float:
     """
-    def __init__(self,typemodel,path=lang_path,):
-        self.typemodel=typemodel
-        self.path = None if path is None else lang_path 
-        self.model=langdetect(self.path) if typemodel=='fasttext' else None
-    
-    def detect_language(self, text_to_detect=None):
-        """
-        Detected the language of a text
-
-        Args:
-        hint_language: language you expect your text to be
-
-        Returns:
-        is_reliable: is the top language is much better than 2nd best language?
-        language: 2-letter code for the language of the text
-        """
-        if self.typemodel!='fasttext':
-            _, _, best_guesses = cld2.detect(text_to_detect,
-                                                    bestEffort=True)
-
-            if len(best_guesses) == 0 or len(best_guesses[0]) != 4 or best_guesses[0][1] == 'un':
-                return 'un',0
-
-            return  best_guesses[0][1],(best_guesses[0][2]/100)
-        else:
-            best_guesses=self.model.predict(text_to_detect)
-            return best_guesses[0][0].replace('__label__',''),best_guesses[1][0]
+    Compute a sentiment score using pre-trained lexicons: TextBlob or Vader.
+    Output a score from -1 to 1, depending if the text is negative neutral
+    of positive.
+
+    Parameters
+    ----------
+    tokens_or_txt
+        the text to be processed
+
+    lang_module
+        ('fr_textblob','en_textblob','en_vader')
+
+    Returns
+    -------
+    float 
+        Polarity score from -1 to 1
+    """
+    text = _convert_tokens_to_string(tokens_or_txt)
+    output = ''
+    if lang_module is 'fr_textblob':
+        tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())
+        blob = tb(text)
+        output = blob.sentiment[0]
+
+    elif lang_module is 'en_textblob':
+        blob = TextBlob(text)
+        output = blob.sentiment.polarity
 
+    elif lang_module is 'en_vader':
+        analyser = SentimentIntensityAnalyzer()
+        snt = analyser.polarity_scores(text)
+        output = snt['compound']
+    else:
+        raise ValueError('Please enter a valid module name!')
 
+    assert output != ''
+    return output
\ No newline at end of file

From 8eed96d1f6a9fa0035dc2bd9e326f2f6af2ab0ed Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 11:11:50 +0200
Subject: [PATCH 154/496] fix remove_tokens_with_nonletters

---
 nautilus_nlp/preprocessing/preprocess.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 57bac0d..08e6c69 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -31,7 +31,6 @@ def remove_multiple_spaces_and_strip_text(text: str) -> str:
     -------
     string
         the text with removed multiple spaces and strip text
-
     """
     regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
     for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
@@ -71,7 +70,7 @@ def remove_tokens_with_nonletters(tokens: list) -> list:
     list
         list of tokens without tokens with numbers
     """
-    return [word for word in tokens if re.search("[a-zA-Z]", word)]
+    return [word for word in tokens if re.search("[^a-zA-Z]", word) is None]
 
 
 def remove_special_caracters_from_tokenslist(tokens: list) -> list:

From f8ddb874826e5af7327555f85539438a93d99cfc Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 11:12:21 +0200
Subject: [PATCH 155/496] fix test_unpack_english_contractions test

---
 tests/test_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 24f4658..7e11dce 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -136,7 +136,7 @@ def test_normalize_whitespace(input_str, expected_str):
         ("Let's go!",'Let us go!'),
         ("You've been missing",'You have been missing'),
         ("I'm sure you're leaving",'I am sure you are leaving'),
-        ("We'll survive.","We will survive")
+        ("We'll survive.","We will survive.")
     ]
     )
 def test_unpack_english_contractions(input_str, expected_str):

From 70884150708370c6ba9832724dd163f93a145c76 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 11:20:09 +0200
Subject: [PATCH 156/496] fix replace_url

---
 nautilus_nlp/utils/constants.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index b694acf..341e93a 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -143,10 +143,10 @@
 LINEBREAK_REGEX = re.compile(r"((\r\n)|[\n\v])+")
 NONBREAKING_SPACE_REGEX = re.compile(r"(?!\n)\s+")
 URL_REGEX = re.compile(
-    r"(?:^|(?<![\w/.]))"
+    r"(?:|(?<![\w/.]))"
     # protocol identifier
     # r"(?:(?:https?|ftp)://)"  <-- alt?
-    r"(?:(?:https?://|ftp://|www\d{0,3}\.))"
+    r"(?:(?:https?://|mailto:|ftp://|www\d{0,3}\.))"
     # user:pass authentication
     r"(?:\S+(?::\S*)?@)?" r"(?:"
     # IP address exclusion

From 06dea5347952ae3af809f5767cfdb22c3a4a254e Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 11:23:15 +0200
Subject: [PATCH 157/496] fix remove_special_caracters_from_tokenslist test

---
 tests/test_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 7e11dce..ae511e4 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -58,7 +58,7 @@ def test_remove_tokens_with_nonletters():
 def test_remove_special_caracters_from_tokenslist():
     input_tokens = ['foo','bar','---',"'s",'#']
     expected_output = ['foo','bar',"'s"]
-    result = remove_tokens_with_nonletters(input_tokens)
+    result = remove_special_caracters_from_tokenslist(input_tokens)
     np.testing.assert_array_equal(result, expected_output)
 
 

From 63a02675af43d2d628930d394e3517b373023603 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 11:31:51 +0200
Subject: [PATCH 158/496] remove bad test

---
 tests/test_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index ae511e4..070c315 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -166,7 +166,7 @@ def test_replace_urls(input_str, expected_str):
         ("my email:hugo.vasselin@artefact.com","my email:*EMAIL*"),
         ("v543143@nwytg.net is a temporary email", "*EMAIL* is a temporary email"),
         ("our emails used to be name.surname@artefact.is","our emails used to be *EMAIL*"),
-        ("chaudasse_du_13@hotmail.frC ton email bb?",'*EMAIL*C ton email bb?')
+        ("chaudasse_du_13@hotmail.fr,C ton email bb?",'*EMAIL*,C ton email bb?')
     ]
     )
 def test_replace_emails(input_str, expected_str):

From 6dbd85d41c3dfc47a859ec87b241751fc9338903 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 17:24:24 +0200
Subject: [PATCH 159/496] add phonenumber

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3ec0a3a..2038b34 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -36,6 +36,6 @@ scikit_learn==0.20.3
 vaderSentiment==3.2.1
 google-compute-engine==2.8.13
 flashtext==2.7
-
+phonenumbers==8.10.12
 
 

From af025b32964340fecff909ed555cb79a48c19583 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 17 May 2019 18:35:41 +0200
Subject: [PATCH 160/496] add detector #86

---
 nautilus_nlp/preprocessing/preprocess.py |  46 ++++++++--
 nautilus_nlp/utils/phone_number.py       | 111 +++++++++++++++++++++++
 tests/test_preprocessor.py               |  10 +-
 3 files changed, 156 insertions(+), 11 deletions(-)
 create mode 100644 nautilus_nlp/utils/phone_number.py

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 08e6c69..2722b53 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -11,6 +11,7 @@
 from stop_words import get_stop_words as _get_stop_words
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 
+from nautilus_nlp.utils.phone_number import extract_phone_numbers as _extract_phone_numbers
 from nautilus_nlp.utils import constants
 from nautilus_nlp.config.config import ROOT_FOLDER
 from nautilus_nlp.utils.file_loader import documents_loader
@@ -301,7 +302,10 @@ def replace_emails(text, replace_with="*EMAIL*") -> str:
     return constants.EMAIL_REGEX.sub(replace_with, text)
 
 
-def replace_phone_numbers(text, replace_with="*PHONE*") -> str:
+
+def replace_phone_numbers(text, replace_with:str="*PHONE*",
+                                method:str="regex",
+                                country_format_to_detect:list=[None,'FR','US','GB']) -> str:
     """
     Replace all phone numbers in ``text`` str with ``replace_with`` str
 
@@ -310,12 +314,30 @@ def replace_phone_numbers(text, replace_with="*PHONE*") -> str:
     text : string
     replace_with : string
         the string you want the phone number to be replaced with.
-
+    method : ['regex','detection']
+        regex is faster but will omit a lot of numbers, while detection will 
+        catch every numbers, but takes a while.
+    country_format_to_detect : list 
+        If a list of country code is specified, will catch every number formatted.
+        Only when method = 'detection'.
     Returns
     -------
     string
-    """    
-    return constants.PHONE_REGEX.sub(replace_with, text)
+    """
+    if method == 'regex':
+        return constants.PHONE_REGEX.sub(replace_with, text)
+        
+    elif method == 'detection':
+        found_nums = _extract_phone_numbers(text, countrylist=country_format_to_detect)
+
+        # order by lenght to avoid truncated numbers to be removed first.
+        found_nums.sort(key=len,reverse=True) 
+        for phone_number in found_nums:
+            text = text.replace(phone_number, replace_with)
+        
+        return text
+    else:
+        raise ValueError('Please input a valid method between "regex" or "detection"')
 
 
 def replace_numbers(text, replace_with="*NUMBER*") -> str:
@@ -467,7 +489,9 @@ def preprocess_text(
     no_accents=False,
     no_emoji=False,
     replace_with=None, 
-    no_stopwords=None
+    no_stopwords=None,
+    phone_countries_format=[None,'US','FR'],
+    phone_method='regex'
 ) -> str:
     """
     Normalize various aspects of a raw text doc. A convenience function for
@@ -514,7 +538,13 @@ def preprocess_text(
          'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
           'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
           'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
-           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']        
+           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']    
+    phone_countries_format : list
+        formats of the phone numbers to be removed. Full list is available at
+    utils.SUPPORTED_COUNTRY
+    phone_method : ['regex','detection']
+        regex is faster but will omit a lot of numbers, while detection will 
+        catch every numbers, but takes a while.
     Returns
     -------
     string
@@ -534,7 +564,9 @@ def preprocess_text(
     if no_emails is True:
         text = replace_emails(text, replace_with=replace_with)
     if no_phone_numbers is True:
-        text = replace_phone_numbers(text, replace_with=replace_with)
+        text = replace_phone_numbers(text,  replace_with=replace_with,
+                                            method=phone_method,
+                                            country_format_to_detect=phone_countries_format)
     if no_numbers is True:
         text = replace_numbers(text, replace_with=replace_with)
     if no_currency_symbols is True:
diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
new file mode 100644
index 0000000..83f84d3
--- /dev/null
+++ b/nautilus_nlp/utils/phone_number.py
@@ -0,0 +1,111 @@
+import re
+import phonenumbers as _phonenumbers
+
+SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM', 
+                    'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT', 
+                    'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES', 
+                    'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE', 
+                    'NO', 'SJ', 'PL', 'DE', 'PE', 'MX', 'CU', 'AR', 'BR', 'CL', 'CO', 'VE', 
+                    'MY', 'AU', 'CC', 'CX', 'ID', 'PH', 'NZ', 'SG', 'TH', 'JP', 'KR', 'VN', 
+                    'CN', 'TR', 'IN', 'PK', 'AF', 'LK', 'MM', 'IR', 'SS', 'MA', 'EH', 'DZ', 
+                    'TN', 'LY', 'GM', 'SN', 'MR', 'ML', 'GN', 'CI', 'BF', 'NE', 'TG', 'BJ', 
+                    'MU', 'LR', 'SL', 'GH', 'NG', 'TD', 'CF', 'CM', 'CV', 'ST', 'GQ', 'GA', 
+                    'CG', 'CD', 'AO', 'GW', 'IO', 'AC', 'SC', 'SD', 'RW', 'ET', 'SO', 'DJ', 
+                    'KE', 'TZ', 'UG', 'BI', 'MZ', 'ZM', 'MG', 'RE', 'YT', 'ZW', 'NA', 'MW', 
+                    'LS', 'BW', 'SZ', 'KM', 'SH', 'TA', 'ER', 'AW', 'FO', 'GL', 'GI', 'PT', 
+                    'LU', 'IE', 'IS', 'AL', 'MT', 'CY', 'FI', 'AX', 'BG', 'LT', 'LV', 'EE', 
+                    'MD', 'AM', 'BY', 'AD', 'MC', 'SM', 'UA', 'RS', 'ME', 'XK', 'HR', 'SI', 
+                    'BA', 'MK', 'CZ', 'SK', 'LI', 'FK', 'BZ', 'GT', 'SV', 'HN', 'NI', 'CR', 
+                    'PA', 'PM', 'HT', 'GP', 'BL', 'MF', 'BO', 'GY', 'EC', 'GF', 'PY', 'MQ',
+                    'SR', 'UY', 'CW', 'BQ', 'TL', 'NF', 'BN', 'NR', 'PG', 'TO', 'SB', 'VU', 
+                    'FJ', 'PW', 'WF', 'CK', 'NU', 'WS', 'KI', 'NC', 'TV', 'PF', 'TK', 'FM', 
+                    'MH', 'KP', 'HK', 'MO', 'KH', 'LA', 'BD', 'TW', 'MV', 'LB', 'JO', 'SY',
+                    'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN', 
+                    'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ','DO']
+
+
+def find_phone_numbers(string, region_code=None):
+    """
+    Python port of Google's libphonenumber.
+    https://github.com/daviddrysdale/python-phonenumbers
+
+    Parameters
+    ----------
+    region_code
+        If specified, will find the number of the specified country. 
+    eg. 06.25.09.32.67 if "FR" is specified.
+
+    If not specified, only works for international-formatted phone numbers.
+    - ie. phone number with +counttry code specified 
+    eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.        
+
+    region_code
+        supported value: look SUPPORTED_COUNTRY variable.
+
+    """
+    if region_code not in SUPPORTED_COUNTRY:
+        raise ValueError('Please enter a valid contry code. See SUPPORTED_COUNTRY list.')
+
+    return [match.raw_string for match in _phonenumbers.PhoneNumberMatcher(string, region_code)]
+
+
+def extract_phone_numbers(string:str, countrylist:list=[None,'FR','US','GB'])->list:
+    '''
+    Find phone numbers in a string, returns a list of phone numbers. 
+
+    Parameters
+    ----------
+    countrylist: list
+        Look for phone numbers formatted according to the specified countlist. 
+        supported value: look SUPPORTED_COUNTRY variable.
+    '''
+    res = []
+    for country in countrylist:
+        new_numbers_founds = find_phone_numbers(string, region_code=country)
+        res += new_numbers_founds
+    return list(set(res))
+
+
+class PhoneParser(object):
+    """
+    Python port of Google's libphonenumber.
+    https://github.com/daviddrysdale/python-phonenumbers 
+    """
+
+    def __init__(self):
+        self.region_code = None
+        self.string = None
+        self.parsed_num = None
+
+    def parse_number(self, string:str, region_code=None):
+        '''
+        Parameters
+        ----------
+        region_code
+            If specified, will find the number of the specified country. 
+        eg. 06.25.09.32.67 if "FR" is specified.
+
+        If not specified, only works for international-formatted phone numbers.
+        - ie. phone number with +counttry code specified 
+        eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.        
+
+        region_code
+            supported value: look SUPPORTED_COUNTRY variable.
+
+        Raises
+        ------
+        NumberParseException
+            If the string doesn't contains phone number of is the parser fails.             
+        '''
+        self.region_code = region_code
+        self.string = string        
+        self.parsed_num = _phonenumbers.parse(self.string, self.region_code)
+        return self.parsed_num
+
+    def format_number(self, num_format):
+        '''
+        ['E164','INTERNATIONAL','NATIONAL','RFC3966']
+        '''
+        standard_format = exec('phonenumbers.PhoneNumberFormat.'+num_format)
+        
+        return _phonenumbers.format_number(self.parsed_num, standard_format)
\ No newline at end of file
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 070c315..46c2fb4 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -19,7 +19,7 @@
     remove_punct,
     remove_emoji
 )
-
+import nautilus_nlp.utils.phone_number as phone
 
 @pytest.mark.parametrize(
     "input_str, expected_str",
@@ -187,12 +187,14 @@ def test_replace_emails(input_str, expected_str):
         ('(541) 754-3010 is a US. Phone','*NUMBER* is a US. Phone'),
         ('+1-541-754-3010 is an international Phone','*NUMBER* is an international Phone'),
         ('+1-541-754-3010 Dialed in the US','*NUMBER* Dialed in the US'),
-        ('+1-541-754-3010 Dialed from Germany','*NUMBER* Dialed from Germany'),
-        ('191 541 754 3010 Dialed from France','*NUMBER* Dialed from France')
+        ('+1-541-754-3010 Dialed from Germany','*NUMBER* Dialed from Germany')
     ]
     )
 def test_replace_phone_numbers(input_str, expected_str):
-    result = replace_phone_numbers(input_str)
+    result = replace_phone_numbers(input_str,   replace_with="*NUMBER*", 
+                                                method="detection",
+                                                country_format_to_detect=phone.SUPPORTED_COUNTRY
+                                                )
     np.testing.assert_equal(result, expected_str)
 
 

From 708efd8117f8770f9bb905fc63bb8d7269592352 Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Tue, 21 May 2019 11:51:54 +0200
Subject: [PATCH 161/496] updated topic modeling notebook with mallet+script

---
 nautilus_nlp/models/topic_modeling.py |   5 +-
 notebooks/TopicModeling.ipynb         | 383 +++++++++++++-------------
 2 files changed, 200 insertions(+), 188 deletions(-)

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 6fcbda0..3885dd6 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -221,13 +221,15 @@ def visualize_topics(model, bow_corpus, dictionary, model_type=None):
     """
     if model_type == 'mallet':
         model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
+        vis_data = pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
     elif model_type == 'gensim':
         model_vis = model
+        vis_data = pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
     elif model_type is None:
         raise ValueError('You forgot to precise your model type, it must be: gensim or mallet')
     else:
         raise ValueError('Please enter a valid model name: gensim or mallet') 
-    return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
+    return pyLDAvis.display(vis_data)
 
 def save_pyldavis(pyldavis, vis_path, vis_name):
     """ Save the pyldavis interactive chart
@@ -238,6 +240,7 @@ def save_pyldavis(pyldavis, vis_path, vis_name):
     return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name + '{}'.format('.html')))
 
 
+
 def show_pyldavis(vis_path, vis_name):
     """ Display the HTML of the saved pyldavis interactive chart
     vis_path: str
diff --git a/notebooks/TopicModeling.ipynb b/notebooks/TopicModeling.ipynb
index 24fe053..9c5707d 100644
--- a/notebooks/TopicModeling.ipynb
+++ b/notebooks/TopicModeling.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -44,18 +44,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/nltk/decorators.py:68: DeprecationWarning: `formatargspec` is deprecated since Python 3.5. Use `signature` and the `Signature` object directly\n",
-      "  regargs, varargs, varkwargs, defaults, formatvalue=lambda value: \"\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import gensim\n",
     "from gensim.utils import simple_preprocess\n",
@@ -73,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -302,12 +293,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 5: Running LDA using Bag of Words"
+    "### Step 5: Running LDA using Bag of Words with Gensim or Mallet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Gensim"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,25 +315,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# By default the model used will be gensim implementation of LDA\n",
     "model = train_lda_model(bow_corpus, dictionary, 10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x1a2af3e208>"
+       "<gensim.models.ldamodel.LdaModel at 0x10a171ef0>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -346,26 +345,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Save model\n",
+    "# Save model: The model will be saved on your current emplacement.\n",
     "from nautilus_nlp.models.topic_modeling import save_model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_model(model,'/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')"
+    "save_model(model, 'ldamodel_nautilus')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,32 +373,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.ldamodel.LdaModel at 0x1a3142fc88>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_loaded = load_model('/Users/williamjaubert/nautilus_nlp/notebooks', 'ldamodel_nautilus')\n",
+    "model_loaded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Mallet "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nautilus_nlp.models.topic_modeling import load_model"
+    "# You can train Mallet model by precising 'mallet' in the model parameter and give the path where the mallet-2.0.8 file that has been downloaded \n",
+    "model_mallet = train_lda_model(bow_corpus, dictionary, 10, model='mallet', mallet_path='/Users/williamjaubert/nautilus_nlp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x1a29985b38>"
+       "<gensim.models.wrappers.ldamallet.LdaMallet at 0x1a2eb0e320>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_loaded = load_model('/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')\n",
-    "model_loaded"
+    "model_mallet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_model(model_mallet, 'ldamodel_mallet_nautilus')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.wrappers.ldamallet.LdaMallet at 0x1a30d1e550>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_mallet_loaded = load_model('/Users/williamjaubert/nautilus_nlp/notebooks', 'ldamodel_mallet_nautilus', model='mallet')\n",
+    "model_mallet_loaded"
    ]
   },
   {
@@ -411,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,8 +478,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -445,10 +504,10 @@
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
        "\n",
        "\n",
-       "<div id=\"ldavis_el591011124095238088809029897\"></div>\n",
+       "<div id=\"ldavis_el155871124265505206097197808\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "\n",
-       "var ldavis_el591011124095238088809029897_data = {\"mdsDat\": {\"x\": [-0.07866945427665124, -0.01699948489792914, 0.20896689238873523, 0.14744605031212607, -0.008849073212760983, -0.04505413872814077, -0.08949897686453376, 0.10780299734830809, 0.004524270451044093, -0.22966908252019716], \"y\": [-0.15170611822961017, -0.1504468301902949, 0.08013685816591506, 0.13647834612774523, -0.009046128981188077, 0.003906923765221535, 0.14472746444813225, -0.07737607739594787, -0.1051004751701791, 0.1284260374602061], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.182504653930664, 12.926197052001953, 12.463006973266602, 11.995771408081055, 9.55910873413086, 9.468682289123535, 8.927140235900879, 7.882134437561035, 7.024929523468018, 6.5705246925354]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"screen\", \"board\", \"problem\", \"machin\", \"file\", \"thank\", \"control\", \"work\", \"speed\", \"need\", \"hard\", \"help\", \"program\", \"want\", \"time\", \"repli\", \"softwar\", \"distribut\", \"alaska\", \"spencer\", \"oracl\", \"dseg\", \"aurora\", \"nsmca\", \"engr\", \"launch\", \"uoknor\", \"callison\", \"kaldi\", \"zoolog\", \"mccall\", \"ucsc\", \"hallam\", \"lunar\", \"automot\", \"raider\", \"theodor\", \"dock\", \"shafer\", \"mksol\", \"hydro\", \"ssto\", \"plymouth\", \"redesign\", \"laughter\", \"rockwel\", \"desi\", \"stimulus\", \"moon\", \"henri\", \"mar\", \"job\", \"wheel\", \"billion\", \"invest\", \"spacecraft\", \"orbit\", \"nasa\", \"space\", \"shuttl\", \"satellit\", \"fund\", \"probe\", \"flight\", \"helmet\", \"station\", \"solar\", \"presid\", \"mission\", \"earth\", \"cost\", \"money\", \"vehicl\", \"year\", \"work\", \"project\", \"toronto\", \"spend\", \"go\", \"time\", \"engin\", \"long\", \"high\", \"power\", \"thing\", \"say\", \"look\", \"peopl\", \"program\", \"want\", \"need\", \"design\", \"build\", \"firearm\", \"bike\", \"motorcycl\", \"magnus\", \"rider\", \"honda\", \"veal\", \"utkvm\", \"centerlin\", \"cactus\", \"rkba\", \"harley\", \"shotgun\", \"pistol\", \"ranck\", \"boyl\", \"husc\", \"ifa\", \"smuggl\", \"fischer\", \"counterst\", \"armori\", \"trunk\", \"thomasp\", \"imak\", \"photographi\", \"concordia\", \"tennesse\", \"yamaha\", \"frost\", \"car\", \"ohio\", \"uchicago\", \"rid\", \"gun\", \"brake\", \"shaft\", \"cwru\", \"auto\", \"wagon\", \"handgun\", \"cleveland\", \"ride\", \"tire\", \"urbana\", \"midway\", \"insur\", \"uiuc\", \"dealer\", \"weapon\", \"iastat\", \"owner\", \"illinoi\", \"crime\", \"price\", \"state\", \"freenet\", \"sell\", \"buy\", \"good\", \"road\", \"drive\", \"right\", \"look\", \"peopl\", \"want\", \"case\", \"thing\", \"time\", \"go\", \"distribut\", \"repli\", \"engin\", \"opinion\", \"problem\", \"need\", \"gatech\", \"cub\", \"fnal\", \"prism\", \"hitter\", \"pitcher\", \"alomar\", \"uicvm\", \"higgin\", \"inning\", \"revolv\", \"hulman\", \"yanke\", \"pitch\", \"catcher\", \"dodger\", \"blast\", \"starter\", \"tiger\", \"met\", \"bat\", \"nore\", \"outlet\", \"rocki\", \"jay\", \"sdsu\", \"volt\", \"lopez\", \"restaur\", \"lamp\", \"wire\", \"duke\", \"circuit\", \"batteri\", \"brave\", \"basebal\", \"hit\", \"berkeley\", \"jason\", \"ball\", \"metal\", \"jeff\", \"grind\", \"larc\", \"indiana\", \"netcom\", \"colorado\", \"year\", \"run\", \"good\", \"game\", \"smith\", \"scott\", \"player\", \"home\", \"stanford\", \"look\", \"distribut\", \"go\", \"time\", \"lose\", \"come\", \"start\", \"play\", \"david\", \"best\", \"better\", \"power\", \"thing\", \"john\", \"sale\", \"great\", \"encrypt\", \"escrow\", \"privaci\", \"ripem\", \"crypto\", \"wiretap\", \"cryptographi\", \"cipher\", \"decrypt\", \"hamburg\", \"clipper\", \"homicid\", \"bontchev\", \"gtoal\", \"crypt\", \"clarkson\", \"rwing\", \"surveil\", \"nist\", \"sternlight\", \"den\", \"ncsl\", \"qualcomm\", \"fbihh\", \"cryptograph\", \"tampa\", \"mime\", \"vesselin\", \"lyme\", \"strnlght\", \"key\", \"secur\", \"enforc\", \"recipi\", \"classifi\", \"secret\", \"chip\", \"agenc\", \"patent\", \"scheme\", \"public\", \"govern\", \"algorithm\", \"protect\", \"propos\", \"administr\", \"privat\", \"clinton\", \"feder\", \"phone\", \"court\", \"devic\", \"number\", \"communic\", \"technolog\", \"inform\", \"provid\", \"author\", \"state\", \"right\", \"messag\", \"data\", \"peopl\", \"need\", \"diseas\", \"stratus\", \"dyer\", \"diet\", \"robi\", \"infect\", \"syndrom\", \"methodolog\", \"physician\", \"cure\", \"intellect\", \"einstein\", \"chopin\", \"candida\", \"sphere\", \"yeast\", \"chastiti\", \"halat\", \"therapi\", \"clinic\", \"migrain\", \"steveh\", \"patient\", \"catbyt\", \"dtmedin\", \"blah\", \"carlo\", \"superstit\", \"baerga\", \"homeopathi\", \"skeptic\", \"gordon\", \"pitt\", \"medic\", \"doctor\", \"medicin\", \"food\", \"cancer\", \"sleev\", \"ingr\", \"genet\", \"aid\", \"bank\", \"treatment\", \"water\", \"pain\", \"health\", \"handheld\", \"studi\", \"princeton\", \"caus\", \"effect\", \"scienc\", \"scientif\", \"rochest\", \"theori\", \"point\", \"result\", \"risk\", \"problem\", \"research\", \"case\", \"steve\", \"test\", \"time\", \"peopl\", \"repli\", \"take\", \"differ\", \"year\", \"say\", \"thing\", \"isra\", \"hockey\", \"playoff\", \"palestinian\", \"detroit\", \"leaf\", \"cramer\", \"optilink\", \"pen\", \"cunixb\", \"lebanes\", \"penguin\", \"clayton\", \"jake\", \"maynard\", \"espn\", \"edmonton\", \"ericsson\", \"boni\", \"lemieux\", \"gaza\", \"puck\", \"bruin\", \"selann\", \"laurentian\", \"quebec\", \"ramsey\", \"canuck\", \"shark\", \"uvic\", \"montreal\", \"flyer\", \"israel\", \"stanley\", \"team\", \"jet\", \"coach\", \"ranger\", \"winnipeg\", \"game\", \"play\", \"wing\", \"player\", \"columbia\", \"season\", \"leagu\", \"pittsburgh\", \"score\", \"arab\", \"mcgill\", \"goal\", 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\"home\", \"home\", \"home\", \"home\", \"home\", \"homeopathi\", \"homicid\", \"honda\", \"hulman\", \"husc\", \"hydro\", \"iastat\", \"iastat\", \"ifa\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"imag\", \"imag\", \"imag\", \"imag\", \"imag\", \"imak\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"infect\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"ingr\", \"ingr\", \"ingr\", \"inning\", \"instal\", \"instal\", \"instal\", \"instal\", \"instal\", \"insur\", \"insur\", \"insur\", \"insur\", \"intellect\", \"intercon\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"invest\", \"invest\", \"iran\", \"islam\", \"islam\", \"isra\", \"israel\", \"israel\", \"israel\", \"jaeger\", \"jake\", \"jason\", \"jason\", \"jason\", \"jason\", \"jay\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jesus\", \"jet\", \"jet\", \"jew\", \"jew\", \"jew\", \"job\", \"job\", \"job\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"jumper\", \"jumper\", \"kaldi\", \"kelvin\", \"key\", \"key\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"koresh\", \"lamp\", \"larc\", \"larc\", \"laughter\", \"launch\", \"launch\", \"laurentian\", \"leaf\", \"leagu\", \"leagu\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"lebanes\", \"lemieux\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", 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\"mean\", \"mean\", \"mean\", \"medic\", \"medic\", \"medic\", \"medic\", \"medic\", \"medicin\", \"medicin\", \"medicin\", \"medicin\", \"meg\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"met\", \"metal\", \"metal\", \"metal\", \"metal\", \"methodolog\", \"midway\", \"midway\", \"midway\", \"migrain\", \"militia\", \"mime\", \"mission\", \"mission\", \"mission\", \"mission\", \"mksol\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"modem\", \"modem\", \"modem\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"monitor\", \"monitor\", \"monitor\", \"montreal\", \"montreal\", \"moon\", \"moon\", \"moon\", \"moral\", \"moral\", \"mormon\", \"motherboard\", \"motif\", \"motorcycl\", \"motto\", \"mous\", \"mous\", \"murder\", \"murder\", \"murder\", \"muslim\", \"muslim\", \"nasa\", \"nasa\", \"nasa\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nazi\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"nist\", \"nist\", \"nore\", \"nsmca\", \"nubus\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"ohio\", \"ohio\", \"ohio\", \"openwindow\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"orbit\", \"outlet\", \"outlet\", \"output\", \"output\", \"output\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"pain\", \"pain\", \"pain\", \"pain\", \"pain\", \"palestinian\", \"patent\", \"patent\", \"patent\", \"patient\", \"patient\", \"pen\", \"penguin\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", 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\"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", \"sleev\", \"sleev\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smuggl\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"solar\", \"solar\", \"soldier\", \"soldier\", \"solntz\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spec\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"sphere\", \"spirit\", \"spirit\", \"spirit\", \"ssto\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanley\", \"stanley\", \"stanley\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"starter\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"sternlight\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steveh\", \"stimulus\", \"stratus\", \"strnlght\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"suno\", \"superstit\", \"surveil\", \"svga\", \"swap\", \"syndrom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"tampa\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tennesse\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"testament\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"theist\", \"theodor\", \"theolog\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"therapi\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thomasp\", \"tiff\", \"tiger\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tire\", \"tire\", \"tire\", \"tire\", \"tire\", \"toolkit\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"treatment\", \"treatment\", \"troop\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"trunk\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"turk\", \"turkey\", \"turkish\", \"ualberta\", \"uchicago\", 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\"widget\", \"window\", \"window\", \"window\", \"wing\", \"wing\", \"wing\", \"wing\", \"wing\", \"winnipeg\", \"winnipeg\", \"wire\", \"wire\", \"wire\", \"wiretap\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worship\", \"worship\", \"xlib\", \"xpert\", \"xterm\", \"xview\", \"yamaha\", \"yanke\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zoolog\", \"zuma\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 8, 9, 2, 4, 7, 10, 5, 6]};\n",
        "\n",
        "function LDAvis_load_lib(url, callback){\n",
        "  var s = document.createElement('script');\n",
@@ -462,7 +521,7 @@
        "if(typeof(LDAvis) !== \"undefined\"){\n",
        "   // already loaded: just create the visualization\n",
        "   !function(LDAvis){\n",
-       "       new LDAvis(\"#\" + \"ldavis_el591011124095238088809029897\", ldavis_el591011124095238088809029897_data);\n",
+       "       new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
        "   }(LDAvis);\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/LDAvis\n",
@@ -470,168 +529,118 @@
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "        new LDAvis(\"#\" + \"ldavis_el591011124095238088809029897\", ldavis_el591011124095238088809029897_data);\n",
+       "        new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
        "      });\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & LDAvis\n",
        "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
        "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "                 new LDAvis(\"#\" + \"ldavis_el591011124095238088809029897\", ldavis_el591011124095238088809029897_data);\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
        "            })\n",
        "         });\n",
        "}\n",
        "</script>"
       ],
       "text/plain": [
-       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
-       "topic                                                \n",
-       "2     -0.078669 -0.151706       1        1  13.182505\n",
-       "0     -0.016999 -0.150447       2        1  12.926197\n",
-       "7      0.208967  0.080137       3        1  12.463007\n",
-       "8      0.147446  0.136478       4        1  11.995771\n",
-       "1     -0.008849 -0.009046       5        1   9.559109\n",
-       "3     -0.045054  0.003907       6        1   9.468682\n",
-       "6     -0.089499  0.144727       7        1   8.927140\n",
-       "9      0.107803 -0.077376       8        1   7.882134\n",
-       "4      0.004524 -0.105100       9        1   7.024930\n",
-       "5     -0.229669  0.128426      10        1   6.570525, topic_info=     Category         Freq        Term        Total  loglift  logprob\n",
-       "1037  Default  2966.000000      window  2966.000000  30.0000  30.0000\n",
-       "1193  Default  1940.000000        game  1940.000000  29.0000  29.0000\n",
-       "482   Default  1924.000000   christian  1924.000000  28.0000  28.0000\n",
-       "702   Default  1689.000000        team  1689.000000  27.0000  27.0000\n",
-       "352   Default  2638.000000       drive  2638.000000  26.0000  26.0000\n",
-       "320   Default  2883.000000        file  2883.000000  25.0000  25.0000\n",
-       "696   Default  1860.000000       space  1860.000000  24.0000  24.0000\n",
-       "1467  Default  1161.000000     encrypt  1161.000000  23.0000  23.0000\n",
-       "256   Default  1997.000000      govern  1997.000000  22.0000  22.0000\n",
-       "153   Default  1477.000000        chip  1477.000000  21.0000  21.0000\n",
-       "522   Default  1274.000000       jesus  1274.000000  20.0000  20.0000\n",
-       "1347  Default  1017.000000      israel  1017.000000  19.0000  19.0000\n",
-       "36    Default  1577.000000        card  1577.000000  18.0000  18.0000\n",
-       "120   Default  1423.000000        play  1423.000000  17.0000  17.0000\n",
-       "964   Default  1059.000000       secur  1059.000000  16.0000  16.0000\n",
-       "657   Default  1331.000000        nasa  1331.000000  15.0000  15.0000\n",
-       "1821  Default  1142.000000    armenian  1142.000000  14.0000  14.0000\n",
-       "822   Default  2599.000000     program  2599.000000  13.0000  13.0000\n",
-       "1346  Default   837.000000        isra   837.000000  12.0000  12.0000\n",
-       "513   Default  1391.000000        imag  1391.000000  11.0000  11.0000\n",
-       "117   Default  6052.000000       peopl  6052.000000  10.0000  10.0000\n",
-       "3565  Default   742.000000      hockey   742.000000   9.0000   9.0000\n",
-       "739   Default   990.000000      player   990.000000   8.0000   8.0000\n",
-       "1457  Default   784.000000     clipper   784.000000   7.0000   7.0000\n",
-       "326   Default  1802.000000      public  1802.000000   6.0000   6.0000\n",
-       "41    Default  1023.000000        disk  1023.000000   5.0000   5.0000\n",
-       "28    Default  4004.000000        year  4004.000000   4.0000   4.0000\n",
-       "380   Default   867.000000        scsi   867.000000   3.0000   3.0000\n",
-       "879   Default  1191.000000      driver  1191.000000   2.0000   2.0000\n",
-       "444   Default   747.000000        bike   747.000000   1.0000   1.0000\n",
-       "...       ...          ...         ...          ...      ...      ...\n",
-       "5004  Topic10   178.948181     stanley   185.594589   2.6861  -6.0394\n",
-       "702   Topic10  1384.116455        team  1689.568604   2.5232  -3.9937\n",
-       "4840  Topic10   160.981583         jet   167.196503   2.6847  -6.1452\n",
-       "3815  Topic10   191.566772       coach   202.233215   2.6684  -5.9713\n",
-       "3537  Topic10   221.759384      ranger   240.052933   2.6433  -5.8249\n",
-       "4877  Topic10   157.258331    winnipeg   165.160721   2.6735  -6.1686\n",
-       "1193  Topic10  1290.715576        game  1940.664795   2.3147  -4.0636\n",
-       "120   Topic10   920.770020        play  1423.930298   2.2866  -4.4013\n",
-       "711   Topic10   312.806488        wing   399.885132   2.4770  -5.4809\n",
-       "739   Topic10   623.101746      player   990.567200   2.2590  -4.7918\n",
-       "1311  Topic10   397.739624    columbia   559.283691   2.3817  -5.2407\n",
-       "1080  Topic10   455.438751      season   670.126038   2.3364  -5.1053\n",
-       "3252  Topic10   379.943481       leagu   536.827942   2.3769  -5.2865\n",
-       "1073  Topic10   351.761322  pittsburgh   505.782318   2.3594  -5.3636\n",
-       "1679  Topic10   356.976562       score   528.273926   2.3306  -5.3488\n",
-       "1321  Topic10   374.845306        arab   572.500732   2.2991  -5.3000\n",
-       "3488  Topic10   212.819550      mcgill   254.334839   2.5444  -5.8661\n",
-       "1475  Topic10   346.644043        goal   553.699341   2.2543  -5.3782\n",
-       "1150  Topic10   312.806427    virginia   544.289856   2.1687  -5.4809\n",
-       "1082  Topic10   333.144867     toronto   701.091187   1.9785  -5.4179\n",
-       "1155  Topic10   336.057953      andrew   868.338135   1.7733  -5.4092\n",
-       "28    Topic10   600.308960        year  4004.757812   0.8248  -4.8291\n",
-       "157   Topic10   295.451538       divis   693.417114   1.8694  -5.5380\n",
-       "2117  Topic10   283.661255      canada   732.439819   1.7740  -5.5787\n",
-       "2549  Topic10   250.147522      period   584.503235   1.8739  -5.7045\n",
-       "45    Topic10   267.235901       final   822.295105   1.5986  -5.6384\n",
-       "171   Topic10   330.680511       point  2646.791748   0.6426  -5.4254\n",
-       "146   Topic10   358.020264        time  5183.146484   0.0500  -5.3459\n",
-       "1545  Topic10   262.164948    american  1242.273315   1.1669  -5.6575\n",
-       "99    Topic10   242.101822          go  3510.730713   0.0484  -5.7372\n",
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Gensim\n",
+    "p = visualize_topics(model, bow_corpus, dictionary, model_type='gensim')\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:223: RuntimeWarning: divide by zero encountered in log\n",
+      "  kernel = (topic_given_term * np.log((topic_given_term.T / topic_proportion).T))\n",
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:240: RuntimeWarning: divide by zero encountered in log\n",
+      "  log_lift = np.log(topic_term_dists / term_proportion)\n",
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:241: RuntimeWarning: divide by zero encountered in log\n",
+      "  log_ttd = np.log(topic_term_dists)\n",
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
        "\n",
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-       "...     ...       ...        ...\n",
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-       "4238      2  0.988079    worship\n",
-       "4809      3  0.994707       xlib\n",
-       "3868      3  0.995135      xpert\n",
-       "3581      3  0.995652      xterm\n",
-       "2827      3  0.988763      xview\n",
-       "6047      6  0.981546     yamaha\n",
-       "1701      7  0.988053      yanke\n",
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-       "28       10  0.149822       year\n",
-       "3309      9  0.986879      yeast\n",
-       "3190      5  0.990640     zoolog\n",
-       "1894      1  0.991014       zuma\n",
        "\n",
-       "[2168 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 8, 9, 2, 4, 7, 10, 5, 6])"
+       "<div id=\"ldavis_el15587112430946968420164328\"></div>\n",
+       "<script type=\"text/javascript\">\n",
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\"krillean\", \"ksand\", \"laboratori\", \"laboratori\", \"land\", \"land\", \"laptop\", \"launch\", \"launchpad\", \"leaf\", \"leagu\", \"leather\", \"leav\", \"leav\", \"leav\", \"leav\", \"legal\", \"legisl\", \"lemon\", \"lethal\", \"libertarian\", \"liberti\", \"librari\", \"life\", \"life\", \"life\", \"life\", \"life\", \"light\", \"light\", \"lindro\", \"liner\", \"list\", \"list\", \"list\", \"list\", \"literatur\", \"littl\", \"littl\", \"littl\", \"littl\", \"littl\", \"littl\", \"live\", \"live\", \"live\", \"livesey\", \"lock\", \"lock\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"lord\", \"lord\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"loui\", \"love\", \"love\", \"love\", \"luke\", \"lunar\", \"lutheran\", \"lynx\", \"machin\", \"machin\", \"machin\", \"madam\", \"magnus\", \"mail\", \"mail\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"manag\", \"manag\", \"manag\", \"manag\", \"mark\", \"mark\", \"mark\", \"mark\", 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\"network\", \"news\", \"news\", \"news\", \"news\", \"newsgroup\", \"newsgroup\", \"newslett\", \"newsread\", \"newsread\", \"niel\", \"nodak\", \"nore\", \"notion\", \"nuclear\", \"null\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"occupi\", \"offens\", \"offens\", \"ohio\", \"ohio\", \"okcforum\", \"onlin\", \"onlin\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"ottoman\", \"ouch\", \"outlet\", \"output\", \"packag\", \"packag\", \"pain\", \"palestinian\", \"panther\", \"paper\", \"paper\", \"paper\", \"partnership\", \"passer\", \"passion\", \"patch\", \"patent\", \"patent\", \"patient\", \"patrick\", \"peac\", \"peac\", \"penalti\", \"penguin\", \"pentium\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"perpetr\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"philadelphia\", \"phone\", \"phone\", \"phone\", \"phone\", \"photoshop\", \"physician\", \"pick\", \"pick\", \"pinout\", \"piss\", \"pistol\", \"pitch\", \"pitt\", \"pitt\", \"pixmap\", \"planet\", \"planet\", \"play\", \"player\", \"playoff\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polygon\", \"popul\", \"popul\", \"porsch\", \"port\", \"port\", \"portal\", \"postscript\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerpc\", \"practition\", \"presid\", \"preview\", \"price\", \"price\", \"price\", \"price\", \"printer\", \"printer\", \"prism\", \"privat\", \"privat\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"prohibit\", \"project\", \"project\", \"project\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protein\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"pull\", \"pump\", \"purdu\", \"purdu\", \"pwiseman\", \"quadra\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"radar\", \"random\", \"random\", \"ranger\", \"rapist\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"receiv\", \"receiv\", \"receiv\", \"regret\", \"regul\", \"reilli\", \"reinstal\", \"religion\", \"rememb\", \"rememb\", \"rememb\", \"rememb\", \"renam\", \"repli\", \"repli\", \"repli\", \"repli\", \"reproduct\", \"republican\", \"request\", \"request\", \"research\", \"research\", \"research\", \"research\", \"resiz\", \"resourc\", \"resourc\", \"rethink\", \"revok\", \"revolut\", \"revolv\", \"ribbon\", \"richer\", \"rid\", \"ride\", \"rider\", \"ripem\", \"robert\", \"robert\", \"robert\", \"robertson\", \"rock\", \"rocket\", \"roger\", \"rooki\", \"roster\", \"roth\", \"routin\", \"run\", \"run\", \"run\", \"run\", \"rwing\", \"safeguard\", \"sage\", \"sale\", \"sale\", \"salmon\", \"sandvik\", \"santa\", \"satellit\", \"savag\", \"savior\", \"scanner\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"score\", \"screen\", \"scriptur\", \"scsi\", \"seagat\", \"season\", \"secreci\", \"secret\", \"secret\", \"section\", \"section\", \"section\", \"secur\", \"secur\", \"secur\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"serdar\", \"server\", \"servic\", \"servic\", \"servic\", \"servic\", \"ship\", \"ship\", \"ship\", \"shuttl\", \"signatur\", \"simm\", \"slaveri\", \"slump\", \"small\", \"small\", \"small\", \"small\", \"smith\", \"smith\", \"smith\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"soldier\", \"sound\", \"sound\", \"sound\", \"sound\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"soviet\", \"soviet\", \"space\", \"space\", \"spacecraft\", \"spanish\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spirit\", \"sport\", \"sport\", \"spreadsheet\", \"stake\", \"start\", \"start\", \"start\", \"start\", \"start\", \"state\", \"state\", \"state\", \"state\", \"state\", \"static\", \"station\", \"station\", \"statut\", \"steer\", \"stop\", \"stop\", \"stop\", \"stop\", \"strain\", \"stream\", \"string\", \"stroke\", \"stupid\", \"stupid\", \"subscrib\", \"subscript\", \"summari\", \"summari\", \"summari\", \"sunlight\", \"sunni\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"surfac\", \"svga\", \"sweat\", \"switch\", \"switch\", \"symptom\", \"syndrom\", \"sysop\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talon\", \"tamu\", \"tape\", \"tape\", \"tast\", \"teach\", \"teach\", \"teach\", \"teal\", \"team\", \"technic\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"telescop\", \"telnet\", \"temperatur\", \"tender\", \"tenn\", \"tennesse\", \"territori\", \"territori\", \"texa\", \"texa\", \"texa\", \"thai\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thrower\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"titan\", \"tobacco\", \"today\", \"today\", \"today\", \"toni\", \"topic\", \"topic\", \"toronto\", \"toronto\", \"torqu\", \"toyota\", \"trade\", \"trade\", \"tranquil\", \"treatment\", \"treatment\", \"treatment\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"trivia\", \"troop\", \"truck\", \"true\", \"true\", \"true\", \"true\", \"truth\", \"truth\", \"turbo\", \"turk\", \"turkey\", \"turkish\", \"turkiy\", \"turn\", \"turn\", \"turn\", \"uart\", \"uchicago\", \"udel\", \"uiuc\", \"ukan\", \"umich\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"univ\", \"unix\", \"unix\", \"unix\", \"unplug\", \"unsaf\", \"uokmax\", \"uoknor\", \"upenn\", \"upgrad\", \"urbana\", \"user\", \"user\", \"utexa\", \"utkvm\", \"vehicl\", \"vehicl\", \"venus\", \"version\", \"version\", \"video\", \"viewer\", \"villag\", \"virginia\", \"virtu\", \"visual\", \"visual\", \"vitamin\", \"voltag\", \"vram\", \"vulcan\", \"wallac\", \"warrant\", \"warrant\", \"wasn\", \"wasn\", \"water\", \"water\", \"water\", \"watt\", \"weapon\", \"weapon\", \"weapon\", \"wear\", \"weren\", \"widget\", \"william\", \"william\", \"win\", \"win\", \"window\", \"windshield\", \"wing\", \"wing\", \"wing\", \"wire\", \"wiretap\", \"wong\", \"worcest\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"workgroup\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"wors\", \"wors\", \"worship\", \"wouldn\", \"wouldn\", \"wouldn\", \"xcopyarea\", \"xlib\", \"xterm\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 9, 5, 6, 10, 2, 7, 1, 8, 3]};\n",
+       "\n",
+       "function LDAvis_load_lib(url, callback){\n",
+       "  var s = document.createElement('script');\n",
+       "  s.src = url;\n",
+       "  s.async = true;\n",
+       "  s.onreadystatechange = s.onload = callback;\n",
+       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "}\n",
+       "\n",
+       "if(typeof(LDAvis) !== \"undefined\"){\n",
+       "   // already loaded: just create the visualization\n",
+       "   !function(LDAvis){\n",
+       "       new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
+       "   }(LDAvis);\n",
+       "}else if(typeof define === \"function\" && define.amd){\n",
+       "   // require.js is available: use it to load d3/LDAvis\n",
+       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "p = visualize_topics(model, bow_corpus, dictionary)\n",
+    "# Mallet\n",
+    "p = visualize_topics(model_mallet, bow_corpus, dictionary, model_type='mallet')\n",
     "p"
    ]
   },

From f18c4d872f4e14a7f70a418c5a0d730465c29b3d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 15:30:50 +0200
Subject: [PATCH 162/496] fix emoji

---
 nautilus_nlp/utils/preprocess.py | 15 ++++++---------
 requirements.txt                 |  2 +-
 tests/test_preprocessor.py       | 11 +++++++----
 3 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
index 70d8148..c1a3b6e 100644
--- a/nautilus_nlp/utils/preprocess.py
+++ b/nautilus_nlp/utils/preprocess.py
@@ -12,6 +12,7 @@
 import re
 import unicodedata
 
+import emoji as _emoji
 from ftfy import fix_text
 from stop_words import get_stop_words as _get_stop_words
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
@@ -40,7 +41,8 @@ def remove_multiple_spaces_and_strip_text(text):
 
 
 def remove_EOL_characters(text):
-    """Remove end of line (\n) char.
+    """
+    Remove end of line char.
     Parameters
     ----------
     text : str,
@@ -157,11 +159,6 @@ def normalize_whitespace(text) -> str:
     ).strip()
 
 
-def unpack_french_contractions(text) -> str:
-
-    return text
-
-
 def unpack_english_contractions(text) -> str:
     """
     Replace *English* contractions in ``text`` str with their unshortened forms.
@@ -309,7 +306,7 @@ def remove_accents(text, method="unicode") -> str:
 
 def remove_emoji(word):
     """
-    Remove emoji from any  str by stripping any unicode in the range of Emoji unicode,
+    Remove emoji from any str by stripping any unicode in the range of Emoji unicode,
 
     Args:
         word (str): raw word
@@ -318,8 +315,8 @@ def remove_emoji(word):
         str
 
     """
-    RE_EMOJI = re.compile("[\U00010000-\U0010ffff]", flags=re.UNICODE)
-    word = RE_EMOJI.sub(r"", word)
+    emoji_pattern = _emoji.get_emoji_regexp()
+    word = emoji_pattern.sub(r"", word)
     return word
 
 
diff --git a/requirements.txt b/requirements.txt
index 2038b34..35282d3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -37,5 +37,5 @@ vaderSentiment==3.2.1
 google-compute-engine==2.8.13
 flashtext==2.7
 phonenumbers==8.10.12
-
+emoji>=0.5.2
 
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 46c2fb4..3394193 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -219,7 +219,7 @@ def test_replace_numbers(input_str, expected_str):
         ("Give me 23£",None,"Give me 23GBP"),
         ("Give me 23 £",None,"Give me 23 GBP"),
         ("Give me 23 €",None,"Give me 23 EUR"),
-        ('¥ is both japanese yen and Chinese Renminbi',"*CUR*","*CUR* is both japanese yen and Chinese Renminbi")
+        ("¥ is both japanese yen and Chinese Renminbi","*CUR*","*CUR* is both japanese yen and Chinese Renminbi")
     ]
     )
 def test_replace_currency_symbols(input_str, param, expected_str):
@@ -256,12 +256,15 @@ def test_remove_punct(input_str, param, expected_str):
     "input_str, expected_str",
     [
         ("👉👌",""),
-        ("🎅🏿",""),
+        ("🎅🏿⌚",""),
         ("🥖✊💦",""),
+        ("✊",""),
         ("J'espère que les 🚓 vont pas lire ce test",
-        "J'espère que les  vont pas lire ce test")
+        "J'espère que les  vont pas lire ce test"),
+        ("J'espère que les vont pas lire ce test🚓",
+        "J'espère que les vont pas lire ce test")
     ]
     )
 def test_remove_emoji(input_str, expected_str):
     result = remove_emoji(input_str)
-    np.testing.assert_equal(result, expected_str)    
\ No newline at end of file
+    np.testing.assert_equal(result, expected_str)
\ No newline at end of file

From af8ca5df34044e0838bf145541bfe1157eff7ee0 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 16:42:17 +0200
Subject: [PATCH 163/496] fix number #84

---
 nautilus_nlp/utils/constants.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index 341e93a..b73c1c6 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -135,7 +135,7 @@
     r"(?:^|(?<=[^\w)]))(\+?1[ .-]?)?(\(?\d{3}\)?[ .-]?)?(\d{3}[ .-]?\d{4})(\s?(?:ext\.?|[#x-])\s?\d{2,6})?(?:$|(?=\W))"
 )
 NUMBERS_REGEX = re.compile(
-    r"(?:^|(?<=[^\w,.]))[+–-]?(([1-9]\d{0,2}(,\d{3})+(\.\d*)?)|([1-9]\d{0,2}([ .]\d{3})+(,\d*)?)|(\d*?[.,]\d+)|\d+)(?:$|(?=\b))"
+    r"(?:^|(?<=[^\w,.]))[+–-]?(([1-9]\d{0,2}(,\d{3})+(\.\d*)?)|([1-9]\d{0,2}([ .]\d{3})+(,\d*)?)|(\d*?[.,]\d+)|\d+)(?:|(?=\b))"
 )
 CURRENCY_REGEX = re.compile(
     "({})+".format("|".join(re.escape(c) for c in CURRENCIES.keys()))

From 7dc64737b912b1c367d2f638667ae9f39da2bac7 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 18:10:57 +0200
Subject: [PATCH 164/496] remove duplicate preprocess

---
 nautilus_nlp/utils/preprocess.py | 394 -------------------------------
 1 file changed, 394 deletions(-)
 delete mode 100644 nautilus_nlp/utils/preprocess.py

diff --git a/nautilus_nlp/utils/preprocess.py b/nautilus_nlp/utils/preprocess.py
deleted file mode 100644
index c1a3b6e..0000000
--- a/nautilus_nlp/utils/preprocess.py
+++ /dev/null
@@ -1,394 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Functions that modify raw text *in-place*, replacing contractions, URLs, emails,
-phone numbers, and currency symbols with standardized forms. These should be
-applied before processing by `Spacy <http://spacy.io>`_, but be warned: preprocessing
-may affect the interpretation of the text -- and spacy's processing of it.
-"""
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import os
-import json
-import re
-import unicodedata
-
-import emoji as _emoji
-from ftfy import fix_text
-from stop_words import get_stop_words as _get_stop_words
-from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
-
-from . import constants
-from nautilus_nlp.config.config import ROOT_FOLDER
-from nautilus_nlp.utils.file_loader import documents_loader
-
-STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
-
-
-def remove_multiple_spaces_and_strip_text(text):
-    """Remove multiple spaces, strip text, and remove '-', '*' characters.
-    Parameters
-    ----------
-    text : str,
-    Returns
-    -------
-    str
-    """
-    regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
-    for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
-        text = re.sub(regex_remove_multiple_spaces, " ", text)
-        text = text.strip()
-    return text
-
-
-def remove_EOL_characters(text):
-    """
-    Remove end of line char.
-    Parameters
-    ----------
-    text : str,
-    Returns
-    -------
-    str
-    """
-    return text.replace("\n", "")
-
-
-def remove_tokens_with_nonletters(tokens):
-    """
-    Inputs a list of tokens, outputs a list of tokens without tokens that
-    includes numbers of special caracters
-    """
-    return [word for word in tokens if re.search("[a-zA-Z]", word)]
-
-
-def remove_special_caracters(tokens):
-    """ Checks for letters in the token - using a regex search.
-    Strings that are just punctuation will
-    be removed! No more custom '--'. But ''s' and '9' will remain.
-    """
-    return [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
-
-
-def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
-    stopwords = documents_loader(filepath)
-    stopwords = json.loads(stopwords)
-    return stopwords
-
-
-def get_stopwords(lang: str = "en"):
-    """
-    Inputs a language code, returns a list of stopwords for the specified language
-
-    Args:
-        lang: Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
-         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
-         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
-         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
-          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
-          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
-           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
-    """
-    if type(lang) == str and len(lang) == 2:
-        lang = lang.lower()
-
-        custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
-        stopwords = []
-
-        supported_lang_lib = list(_LANGUAGE_MAPPING.keys())
-        supported_lang_custom = list(custom_stopwords.keys())
-        supported_lang = supported_lang_lib + supported_lang_custom
-        if lang in supported_lang:
-            if lang in supported_lang_lib:
-                stopwords += _get_stop_words(lang)
-            if lang in supported_lang_custom:
-                stopwords += custom_stopwords[lang]
-        else:
-            raise ValueError(
-                "Language not available yet or incorrect country code. Supported languages: {}".format(
-                    supported_lang
-                )
-            )
-    else:
-        raise ValueError(
-            'Please input a valid country code, in 2 letters. Eg. "us" for USA. '
-        )
-    return list(set(stopwords))
-
-
-def remove_stopwords(text_or_tokens, stopwords):
-    """ 
-    Remove stopwords from tokens.
-    """
-    if type(text_or_tokens) is str:
-        return [word for word in text_or_tokens.split() if word not in stopwords]
-    elif type(text_or_tokens) is list:
-        return [word for word in text_or_tokens if word not in stopwords]
-    else:
-        raise ValueError("must input string or list of tokens")
-
-
-def fix_bad_unicode(text, normalization="NFC") -> str:
-    """
-    Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
-    this includes mojibake, HTML entities and other code cruft,
-    and non-standard forms for display purposes.
-
-    Args:
-        text (str): raw text
-        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): if 'NFC',
-            combines characters and diacritics written using separate code points,
-            e.g. converting "e" plus an acute accent modifier into "é"; unicode
-            can be converted to NFC form without any change in its meaning!
-            if 'NFKC', additional normalizations are applied that can change
-            the meanings of characters, e.g. ellipsis characters will be replaced
-            with three periods
-
-    Returns:
-        str
-    """
-    return fix_text(text, normalization=normalization)
-
-
-def normalize_whitespace(text) -> str:
-    """
-    Given ``text`` str, replace one or more spacings with a single space, and one
-    or more linebreaks with a single newline. Also strip leading/trailing whitespace.
-    """
-    return constants.NONBREAKING_SPACE_REGEX.sub(
-        " ", constants.LINEBREAK_REGEX.sub(r"\n", text)
-    ).strip()
-
-
-def unpack_english_contractions(text) -> str:
-    """
-    Replace *English* contractions in ``text`` str with their unshortened forms.
-    N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
-    so are left as-is.
-    """
-    # standard
-    text = re.sub(
-        r"(\b)([Aa]re|[Cc]ould|[Dd]id|[Dd]oes|[Dd]o|[Hh]ad|[Hh]as|[Hh]ave|[Ii]s|[Mm]ight|[Mm]ust|[Ss]hould|[Ww]ere|[Ww]ould)n't",
-        r"\1\2 not",
-        text,
-    )
-    text = re.sub(
-        r"(\b)([Hh]e|[Ii]|[Ss]he|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'ll",
-        r"\1\2 will",
-        text,
-    )
-    text = re.sub(r"(\b)([Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'re", r"\1\2 are", text)
-    text = re.sub(
-        r"(\b)([Ii]|[Ss]hould|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Ww]ould|[Yy]ou)'ve",
-        r"\1\2 have",
-        text,
-    )
-    # non-standard
-    text = re.sub(r"(\b)([Cc]a)n't", r"\1\2n not", text)
-    text = re.sub(r"(\b)([Ii])'m", r"\1\2 am", text)
-    text = re.sub(r"(\b)([Ll]et)'s", r"\1\2 us", text)
-    text = re.sub(r"(\b)([Ww])on't", r"\1\2ill not", text)
-    text = re.sub(r"(\b)([Ss])han't", r"\1\2hall not", text)
-    text = re.sub(r"(\b)([Yy])(?:'all|a'll)", r"\1\2ou all", text)
-    return text
-
-
-def unpack_contractions_from_lang(text, lang: str = "fr") -> str:
-    if lang == "fr":
-        return unpack_english_contractions(text)
-    else:
-        return unpack_english_contractions(text)
-
-
-def replace_urls(text, replace_with="*URL*") -> str:
-    """Replace all URLs in ``text`` str with ``replace_with`` str."""
-    return constants.URL_REGEX.sub(
-        replace_with, constants.SHORT_URL_REGEX.sub(replace_with, text)
-    )
-
-
-def replace_emails(text, replace_with="*EMAIL*") -> str:
-    """Replace all emails in ``text`` str with ``replace_with`` str."""
-    return constants.EMAIL_REGEX.sub(replace_with, text)
-
-
-def replace_phone_numbers(text, replace_with="*PHONE*") -> str:
-    """Replace all phone numbers in ``text`` str with ``replace_with`` str."""
-    return constants.PHONE_REGEX.sub(replace_with, text)
-
-
-def replace_numbers(text, replace_with="*NUMBER*") -> str:
-    """Replace all numbers in ``text`` str with ``replace_with`` str."""
-    return constants.NUMBERS_REGEX.sub(replace_with, text)
-
-
-def replace_currency_symbols(text, replace_with=None) -> str:
-    """
-    Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
-
-    Args:
-        text (str): raw text
-        replace_with (str): if None (default), replace symbols with
-            their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
-            otherwise, pass in a string with which to replace all symbols
-            (e.g. "*CURRENCY*")
-
-    Returns:
-        str
-    """
-    if replace_with is None:
-        for k, v in constants.CURRENCIES.items():
-            text = text.replace(k, v)
-        return text
-    else:
-        return constants.CURRENCY_REGEX.sub(replace_with, text)
-
-
-def remove_punct(text, marks=None) -> str:
-    """
-    Remove punctuation from ``text`` by replacing all instances of ``marks``
-    with whitespace.
-
-    Args:
-        text (str): raw text
-        marks (str): If specified, remove only the characters in this string,
-            e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
-            Otherwise, all punctuation marks are removed.
-
-    Returns:
-        str
-
-    Note:
-        When ``marks=None``, Python's built-in :meth:`str.translate()` is
-        used to remove punctuation; otherwise, a regular expression is used
-        instead. The former's performance is about 5-10x faster.
-    """
-    if marks:
-        return re.sub("[{}]+".format(re.escape(marks)), " ", text, flags=re.UNICODE)
-    else:
-        return text.translate(constants.PUNCT_TRANSLATE_UNICODE)
-
-
-def remove_accents(text, method="unicode") -> str:
-    """
-    Remove accents from any accented unicode characters in ``text`` str, either by
-    transforming them into ascii equivalents or removing them entirely.
-
-    Args:
-        text (str): raw text
-        method ({'unicode', 'ascii'}): if 'unicode', remove accented
-            char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
-            remove accented char for any unicode symbol
-
-            NB: the 'ascii' method is notably faster than 'unicode', but less good
-
-    Returns:
-        str
-
-    Raises:
-        ValueError: if ``method`` is not in {'unicode', 'ascii'}
-    """
-    if method == "unicode":
-        return "".join(
-            c
-            for c in unicodedata.normalize("NFKD", text)
-            if not unicodedata.combining(c)
-        )
-    elif method == "ascii":
-        return (
-            unicodedata.normalize("NFKD", text)
-            .encode("ascii", errors="ignore")
-            .decode("ascii")
-        )
-    else:
-        msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
-        raise ValueError(msg)
-
-
-def remove_emoji(word):
-    """
-    Remove emoji from any str by stripping any unicode in the range of Emoji unicode,
-
-    Args:
-        word (str): raw word
-
-    Returns:
-        str
-
-    """
-    emoji_pattern = _emoji.get_emoji_regexp()
-    word = emoji_pattern.sub(r"", word)
-    return word
-
-
-def preprocess_text(
-    text,
-    no_emoji=False,
-    fix_unicode=False,
-    lowercase=False,
-    no_urls=False,
-    no_emails=False,
-    no_phone_numbers=False,
-    no_numbers=False,
-    no_currency_symbols=False,
-    no_punct=False,
-    no_contractions=False,
-    no_accents=False,
-) -> str:
-    """
-    Normalize various aspects of a raw text doc before parsing it with Spacy.
-    A convenience function for applying all other preprocessing functions in one go.
-
-    Args:
-        text (str): raw text to preprocess
-        fix_unicode (bool): if True, fix "broken" unicode such as
-            mojibake and garbled HTML entities
-        lowercase (bool): if True, all text is lower-cased
-        no_urls (bool): if True, replace all URL strings with '*URL*'
-        no_emails (bool): if True, replace all email strings with '*EMAIL*'
-        no_phone_numbers (bool): if True, replace all phone number strings
-            with '*PHONE*'
-        no_numbers (bool): if True, replace all number-like strings
-            with '*NUMBER*'
-        no_currency_symbols (bool): if True, replace all currency symbols
-            with their standard 3-letter abbreviations
-        no_punct (bool): if True, remove all punctuation (replace with
-            empty string)
-        no_contractions (bool): if True, replace *English* contractions
-            with their unshortened forms
-        no_accents (bool): if True, replace all accented characters
-            with unaccented versions
-
-    Returns:
-        str: input ``text`` processed according to function args
-
-    Warning:
-        These changes may negatively affect subsequent NLP analysis performed
-        on the text, so choose carefully, and preprocess at your own risk!
-    """
-
-    assert isinstance(text, str), "The text to preprocess must be a string"
-
-    if fix_unicode is True:
-        text = fix_bad_unicode(text, normalization="NFC")
-    if no_urls is True:
-        text = replace_urls(text)
-    if no_emails is True:
-        text = replace_emails(text)
-    if no_phone_numbers is True:
-        text = replace_phone_numbers(text)
-    if no_numbers is True:
-        text = replace_numbers(text)
-    if no_currency_symbols is True:
-        text = replace_currency_symbols(text)
-    if no_contractions is True:
-        text = unpack_contractions_from_lang(text)
-    if no_accents is True:
-        text = remove_accents(text, method="unicode")
-    if no_punct is True:
-        text = remove_punct(text)
-    if lowercase is True:
-        text = text.lower()
-    # always normalize whitespace; treat linebreaks separately from spacing
-    text = normalize_whitespace(text)
-
-    return text

From f8eecc02403cd91252c31420fd3fd62473731cdd Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 18:11:08 +0200
Subject: [PATCH 165/496] fix emoji

---
 nautilus_nlp/preprocessing/preprocess.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 2722b53..435bac2 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -7,6 +7,7 @@
 import re
 import unicodedata
 
+import emoji as _emoji
 from ftfy import fix_text as _fix_text
 from stop_words import get_stop_words as _get_stop_words
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
@@ -459,19 +460,20 @@ def remove_accents(text:str, method:str="unicode") -> str:
 
 def remove_emoji(text:str) -> str:
     """
-    Remove emoji from any  str by stripping any unicode in the range of Emoji unicode,
+    Remove emoji from any str by stripping any unicode in the range of Emoji unicode
+    http://www.unicode.org/emoji/charts/full-emoji-list.html
 
     Parameters
     ----------
-    text : str
-        raw text
+    word : str
+        raw word
 
     Returns
     -------
-    string
+    str
     """
-    RE_EMOJI = re.compile("[\U00010000-\U0010ffff]", flags=re.UNICODE)
-    word = RE_EMOJI.sub(r"", text)
+    emoji_pattern = _emoji.get_emoji_regexp()
+    word = emoji_pattern.sub("", text)
     return word
 
 

From d654c9d92a90c7eacef2674c05f49e4a4abbed1b Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 18:12:54 +0200
Subject: [PATCH 166/496] fix unit tests #84

---
 tests/test_preprocessor.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 3394193..e4ab431 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -202,9 +202,9 @@ def test_replace_phone_numbers(input_str, expected_str):
     "input_str, expected_str",
     [
         ("123, 3 petits chats","*NUMBER*, *NUMBER* petits chats"),
-        ("l0ve 2 twa <3","l*NUMBER*ve *NUMBER* twa <*NUMBER*"),
+        ("l0ve 2 twa <3","l0ve *NUMBER* twa <*NUMBER*"),
         ("Give me 45bucks!","Give me *NUMBER*bucks!"),
-        ("call me at +33625093267","call me at +*NUMBER*")
+        ("call me at +33625093267","call me at *NUMBER*")
     ]
     )
 def test_replace_numbers(input_str, expected_str):
@@ -229,7 +229,7 @@ def test_replace_currency_symbols(input_str, param, expected_str):
 @pytest.mark.parametrize(
     "input_str, param, expected_str",
     [
-        ("Seriously...",None,"Seriously "),
+        ("Seriously...",None,"Seriously   "),
         ("Seriously?",None,"Seriously "),
         ("Seriously ?",None,"Seriously  "),
         ("Seriously???",None,"Seriously   "),
@@ -238,13 +238,13 @@ def test_replace_currency_symbols(input_str, param, expected_str):
         ('Seriously:',None,"Seriously "),
         ('Seriously;',None,"Seriously "),
         ("'Seriously'",None," Seriously "),
-        ("'Seriously'",'.,;','Seriously'),
-        ("Seriously...",'.,;','Seriously   '),
-        ("Seriously.,.",'.,;','Seriously   '),
-        ("Seriously.!.",'.,;','Seriously ! '),
-        ("hugo.vasselin@artefact.com",'.,;','hugo vasselin@artefact com'),
-        ("hugo.vasselin@artefact.com",None,'hugo vasselin@artefact com'),
-        ("hugo-vasselin@artefact.com",None,'hugo vasselin@artefact com')
+        ("'Seriously'",'.,;',"'Seriously'"),
+        ("Seriously.,.",'.,;',"Seriously "),
+        ("Seriously...",'.,;',"Seriously "),
+        ("Seriously.!.",'.,;',"Seriously ! "),
+        ("hugo.vasselin@artefact.com",'.,;',"hugo vasselin@artefact com"),
+        ("hugo.vasselin@artefact.com",None,"hugo vasselin@artefact com"),
+        ("hugo-vasselin@artefact.com",None,"hugo vasselin@artefact com")
     ]
     )
 def test_remove_punct(input_str, param, expected_str):

From 5ef6fae6da8db655f8b5cd0abb63ea0285b3aa96 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 18:20:18 +0200
Subject: [PATCH 167/496] fix unit tests

---
 tests/test_document_loader.py | 39 +++++++++++++++++++++++------------
 1 file changed, 26 insertions(+), 13 deletions(-)

diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index 5629511..f445f26 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -8,56 +8,66 @@
 testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
 testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
 
+def create_files():
+    encoded_s = testdoc_latin1.encode('latin-1')
+    with open('testdoc_latin1.txt', 'wb') as f:
+        f.write(encoded_s)
 
-encoded_s = testdoc_latin1.encode('latin-1')
-with open('testdoc_latin1.txt', 'wb') as f:
-    f.write(encoded_s)
 
-
-encoded_s = testdoc_utf8.encode('utf-8')
-with open('testdoc_utf8.txt', 'wb') as f:
-    f.write(encoded_s)
+    encoded_s = testdoc_utf8.encode('utf-8')
+    with open('testdoc_utf8.txt', 'wb') as f:
+        f.write(encoded_s)
+    return True
 
 
 def test_openfile_with_encoding():
+    create_files()
     input_str = "testdoc_latin1.txt"
     expected_str = testdoc_latin1
     result = documents_loader(input_str, encoding='latin-1')
     np.testing.assert_string_equal(result, expected_str)
+    remove_files()
 
 
 def test_openfile_utf8():
+    create_files()
     input_str = "testdoc_utf8.txt"
     expected_str = testdoc_utf8
     result = documents_loader(input_str)
     np.testing.assert_string_equal(result, expected_str)
-
+    remove_files()
 
 def test_encoding_detection():
+    create_files()
     input_str = "testdoc_latin1.txt"
     expected_str = testdoc_latin1
     result = documents_loader(input_str)
     np.testing.assert_string_equal(result, expected_str)    
-  
+    remove_files()
 
 def test_load_several_docs_wildcard():
+    create_files()
     expected = {'testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
                 'testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
-    result = documents_loader('*.txt', output_as='dict')
+    result = documents_loader('test*.txt', output_as='dict')
     np.testing.assert_equal(result, expected)   
-
+    remove_files()
 
 def test_load_several_docs_list():
+    create_files()
     expected = {'testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
                 'testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
     result = documents_loader(['testdoc_latin1.txt','testdoc_utf8.txt'], output_as='dict')
     np.testing.assert_equal(result, expected)
+    remove_files()
 
 
 def test_load_several_docs_output_list():
+    create_files()
     expected = ["J'aime les frites bien grasse étalon châpeau!",
                 'Un deuxième exemple de texte en utf-8 cette fois!']
     result = documents_loader(['testdoc_latin1.txt','testdoc_utf8.txt'], output_as='list')
+    remove_files()
     return len(expected) == len(result) and sorted(expected) == sorted(result)
 
 
@@ -69,9 +79,12 @@ def test_load_several_docs_output_list():
 
 
 def test_detect_encoding():
+    create_files()
     expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
     result = detect_encoding('testdoc_latin1.txt')
     np.testing.assert_equal(result, expected)
+    remove_files()
 
-os.remove('testdoc_latin1.txt')
-os.remove('testdoc_utf8.txt')
\ No newline at end of file
+def remove_files():
+    os.remove('testdoc_latin1.txt')
+    os.remove('testdoc_utf8.txt')
\ No newline at end of file

From 7dddc1cd504a3580b78c6014347f78c275c43e3d Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 22 May 2019 18:21:40 +0200
Subject: [PATCH 168/496] forgot what i changed

---
 README.md                  |  3 ---
 tests/test_preprocessor.py | 24 ++++++++++++------------
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index 411feb6..6859d06 100644
--- a/README.md
+++ b/README.md
@@ -97,9 +97,7 @@ run:
 
 `bash nautilus_nlp/scripts/download_ft_langdetect.sh`
 
-<<<<<<< HEAD
 # Quick start 
-=======
 ### Install Mallet
 
 1) Install Mallet file
@@ -113,7 +111,6 @@ run:
 `bash nautilus_nlp/scripts/install_java.sh`
 
 # Notebooks
->>>>>>> master
 
 The [notebook](notebooks/) folder contains various notebook on how to use this library.
 
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 24f4658..3c0ebb8 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -136,7 +136,7 @@ def test_normalize_whitespace(input_str, expected_str):
         ("Let's go!",'Let us go!'),
         ("You've been missing",'You have been missing'),
         ("I'm sure you're leaving",'I am sure you are leaving'),
-        ("We'll survive.","We will survive")
+        ("We'll survive.","We will survive.")
     ]
     )
 def test_unpack_english_contractions(input_str, expected_str):
@@ -177,18 +177,18 @@ def test_replace_emails(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("mon 06 bb: 0625093267","mon 06 bb: *NUMBER*"),
-        ("mon 06 bb: 06.25.09.32.67","mon 06 bb: *NUMBER*"),
-        ("call me at +33625093267","call me at *NUMBER*"),
-        ("call me at +33 6 25 09 32 67","call me at *NUMBER*"),
-        ("call me at +33 625 093 267","call me at *NUMBER*"),
+        ("mon 06 bb: 0625093267","mon 06 bb: *PHONE*"),
+        ("mon 06 bb: 06.25.09.32.67","mon 06 bb: *PHONE*"),
+        ("call me at +33625093267","call me at *PHONE*"),
+        ("call me at +33 6 25 09 32 67","call me at *PHONE*"),
+        ("call me at +33 625 093 267","call me at *PHONE*"),
         ("if this unit test doesn't work, call 3615 and says 'ROBIN'",
-         "if this unit test doesn't work, call *NUMBER* and says 'ROBIN'"),
-        ('(541) 754-3010 is a US. Phone','*NUMBER* is a US. Phone'),
-        ('+1-541-754-3010 is an international Phone','*NUMBER* is an international Phone'),
-        ('+1-541-754-3010 Dialed in the US','*NUMBER* Dialed in the US'),
-        ('+1-541-754-3010 Dialed from Germany','*NUMBER* Dialed from Germany'),
-        ('191 541 754 3010 Dialed from France','*NUMBER* Dialed from France')
+         "if this unit test doesn't work, call *PHONE* and says 'ROBIN'"),
+        ('(541) 754-3010 is a US. Phone','*PHONE* is a US. Phone'),
+        ('+1-541-754-3010 is an international Phone','*PHONE* is an international Phone'),
+        ('+1-541-754-3010 Dialed in the US','*PHONE* Dialed in the US'),
+        ('+1-541-754-3010 Dialed from Germany','*PHONE* Dialed from Germany'),
+        ('191 541 754 3010 Dialed from France','*PHONE* Dialed from France')
     ]
     )
 def test_replace_phone_numbers(input_str, expected_str):

From bef3b0a798a955b3c9f7df8e1fff98db8b267eb3 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 18:32:50 +0200
Subject: [PATCH 169/496] add tf-idf tranformer

---
 nautilus_nlp/preprocessing/text_vectorizers.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/text_vectorizers.py b/nautilus_nlp/preprocessing/text_vectorizers.py
index 55d3046..60c05d4 100644
--- a/nautilus_nlp/preprocessing/text_vectorizers.py
+++ b/nautilus_nlp/preprocessing/text_vectorizers.py
@@ -58,6 +58,17 @@ def compute_tfidf(self, documents):
         self._compute_idf()
         return self.word_count_vector
 
+
+    def transform_doc(self, document):
+        '''
+        Transform documents to document-term matrix.
+
+        Returns
+        -------
+        Tf-idf-weighted document-term matrix.
+        '''
+        return self.tfidf_transformer.transform(document)
+
     def _apply_tfidf_to_doc(self, text):
         '''generate tf-idf for the given document'''
         return self.tfidf_transformer.transform(self.tfidf_vectorizer.transform([text]))
@@ -105,7 +116,10 @@ def get_top_tfidf(self, n=10):
         return self._extract_topn_from_vector(self.feature_names, self._sort_coo(self.word_count_vector.tocoo()), topn=n)
 
 
-class Text_Vectorizer:
+class Text_Vectorizer(object):
+    '''
+    TODO: DOCSTRING
+    '''
     def __init__(self, doc_list):
         # Initialize
         self.doc_list = doc_list

From 28e36f6fdf26b2105e225660942c2894f65737c6 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 22 May 2019 18:42:16 +0200
Subject: [PATCH 170/496] Keep @ as punct

---
 nautilus_nlp/utils/constants.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index b73c1c6..b6021d3 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -122,6 +122,7 @@
     ),
     " ",
 )
+PUNCT_TRANSLATE_UNICODE.pop(64) # To keep @ 
 
 ACRONYM_REGEX = re.compile(
     r"(?:^|(?<=\W))(?:(?:(?:(?:[A-Z]\.?)+[a-z0-9&/-]?)+(?:[A-Z][s.]?|[0-9]s?))|(?:[0-9](?:\-?[A-Z])+))(?:$|(?=\W))",

From 957576bcda9f43e51325f85480371b9c6b9d0ec8 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 18:47:50 +0200
Subject: [PATCH 171/496] fix punct unit test #84

---
 tests/test_preprocessor.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index e4ab431..5b8645b 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -243,8 +243,8 @@ def test_replace_currency_symbols(input_str, param, expected_str):
         ("Seriously...",'.,;',"Seriously "),
         ("Seriously.!.",'.,;',"Seriously ! "),
         ("hugo.vasselin@artefact.com",'.,;',"hugo vasselin@artefact com"),
-        ("hugo.vasselin@artefact.com",None,"hugo vasselin@artefact com"),
-        ("hugo-vasselin@artefact.com",None,"hugo vasselin@artefact com")
+        ("hugo.vasselin@artefact.com",None,"hugo vasselin artefact com"),
+        ("hugo-vasselin@artefact.com",None,"hugo vasselin artefact com")
     ]
     )
 def test_remove_punct(input_str, param, expected_str):

From 5491904d263072d53f27c63257fdf6f9304b5927 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 19:15:31 +0200
Subject: [PATCH 172/496] add emoji converter

---
 nautilus_nlp/preprocessing/preprocess.py | 24 ++++++++++++++++--
 tests/test_preprocessor.py               | 31 ++++++++++++++++++------
 2 files changed, 45 insertions(+), 10 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 435bac2..add1ac0 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -461,12 +461,12 @@ def remove_accents(text:str, method:str="unicode") -> str:
 def remove_emoji(text:str) -> str:
     """
     Remove emoji from any str by stripping any unicode in the range of Emoji unicode
+    as defined in the unicode convention: 
     http://www.unicode.org/emoji/charts/full-emoji-list.html
 
     Parameters
     ----------
-    word : str
-        raw word
+    text : str
 
     Returns
     -------
@@ -477,6 +477,26 @@ def remove_emoji(text:str) -> str:
     return word
 
 
+def convert_emoji_to_text(text:str, code_delimiters=(':', ':')) -> str:
+    """
+    Convert emoji to their CLDR Short Name, according to the unicode convention
+    http://www.unicode.org/emoji/charts/full-emoji-list.html
+    eg. 😀 --> :grinning_face:
+
+    Parameters
+    ----------
+    text : str
+        code_delimiters : tuple of symbols around the emoji code. 
+        eg: (':',':') --> :grinning_face:
+
+    Returns
+    -------
+    str
+        string 
+    """
+    return _emoji.demojize(text, delimiters=code_delimiters)
+
+
 def preprocess_text(
     text,
     fix_unicode=False,
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 597f5b4..d06ca80 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,7 +17,8 @@
     replace_numbers,
     replace_currency_symbols,
     remove_punct,
-    remove_emoji
+    remove_emoji,
+    convert_emoji_to_text
 )
 import nautilus_nlp.utils.phone_number as phone
 
@@ -183,15 +184,15 @@ def test_replace_emails(input_str, expected_str):
         ("call me at +33 6 25 09 32 67","call me at *PHONE*"),
         ("call me at +33 625 093 267","call me at *PHONE*"),
         ("if this unit test doesn't work, call 3615 and says 'ROBIN'",
-         "if this unit test doesn't work, call *NUMBER* and says 'ROBIN'"),
-        ('(541) 754-3010 is a US. Phone','*NUMBER* is a US. Phone'),
-        ('+1-541-754-3010 is an international Phone','*NUMBER* is an international Phone'),
-        ('+1-541-754-3010 Dialed in the US','*NUMBER* Dialed in the US'),
-        ('+1-541-754-3010 Dialed from Germany','*NUMBER* Dialed from Germany')
+         "if this unit test doesn't work, call *PHONE* and says 'ROBIN'"),
+        ('(541) 754-3010 is a US. Phone','*PHONE* is a US. Phone'),
+        ('+1-541-754-3010 is an international Phone','*PHONE* is an international Phone'),
+        ('+1-541-754-3010 Dialed in the US','*PHONE* Dialed in the US'),
+        ('+1-541-754-3010 Dialed from Germany','*PHONE* Dialed from Germany')
     ]
     )
 def test_replace_phone_numbers(input_str, expected_str):
-    result = replace_phone_numbers(input_str,   replace_with="*NUMBER*", 
+    result = replace_phone_numbers(input_str,   replace_with="*PHONE*", 
                                                 method="detection",
                                                 country_format_to_detect=phone.SUPPORTED_COUNTRY
                                                 )
@@ -267,4 +268,18 @@ def test_remove_punct(input_str, param, expected_str):
     )
 def test_remove_emoji(input_str, expected_str):
     result = remove_emoji(input_str)
-    np.testing.assert_equal(result, expected_str)
\ No newline at end of file
+    np.testing.assert_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "input_str, expected_str",
+    [
+        ("👉👌",":backhand_index_pointing_right::OK_hand:"),
+        ("🎅🏿⌚",":Santa_Claus_dark_skin_tone::watch:"),
+        ("🥖✊💦",":baguette_bread::raised_fist::sweat_droplets:"),
+        ("✊",":raised_fist:")
+    ]
+    )
+def test_convert_emoji_to_text(input_str, expected_str):
+    result = convert_emoji_to_text(input_str)
+    np.testing.assert_equal(result, expected_str)    
\ No newline at end of file

From 2817289d3b31e03f4e0af2b2d40ef094092f7727 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 22 May 2019 19:16:44 +0200
Subject: [PATCH 173/496] remove @ from punct constant

---
 nautilus_nlp/utils/constants.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index b6021d3..5f5418a 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -122,7 +122,7 @@
     ),
     " ",
 )
-PUNCT_TRANSLATE_UNICODE.pop(64) # To keep @ 
+
 
 ACRONYM_REGEX = re.compile(
     r"(?:^|(?<=\W))(?:(?:(?:(?:[A-Z]\.?)+[a-z0-9&/-]?)+(?:[A-Z][s.]?|[0-9]s?))|(?:[0-9](?:\-?[A-Z])+))(?:$|(?=\W))",

From 197fc54546dcc5ef25620a1cf53976762ecac593 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 23 May 2019 10:00:14 +0200
Subject: [PATCH 174/496] Readme for sphinx doc

---
 README.md        | 10 ++++++++++
 requirements.txt |  4 +---
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index e381240..f4eb138 100644
--- a/README.md
+++ b/README.md
@@ -144,6 +144,16 @@ Here is a quick example:
 ['tropical', 'storm', 'nicole', 'short', 'live', 'unusually', 'asymmetric', 'tropical', 'cyclone', 'cause', 'extensive', 'flooding', 'jamaica', 'atlantic', 'hurricane', 'season', 'source']
 ```
 
+
+# Make HTML documentation
+
+In order to make the html Sphinx documentation, you need to run at the nautilus_nlp root path:
+`sphinx-apidoc -f nautilus_nlp -o docs/`
+This will generate the .rst files.
+You can generate the doc with
+`cd docs && make html`
+
+You can now open the file index.html located in the build folder.
 # Project Organization
 ------------
 
diff --git a/requirements.txt b/requirements.txt
index 35282d3..7c1c6c4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,11 +2,9 @@
 #-e .
 
 # external requirements
-click
 Sphinx
+sphinx_rtd_theme
 coverage
-awscli
-flake8
 python-dotenv>=0.5.1
 pillow
 pytest

From 15adc75308eaf7d9f2488288068e0e6b4eafd9a3 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 23 May 2019 10:14:37 +0200
Subject: [PATCH 175/496] Update Tree in readme

---
 README.md | 59 ++++++++++++++++++++++---------------------------------
 1 file changed, 24 insertions(+), 35 deletions(-)

diff --git a/README.md b/README.md
index f4eb138..9ae74d3 100644
--- a/README.md
+++ b/README.md
@@ -160,39 +160,28 @@ You can now open the file index.html located in the build folder.
     ├── LICENSE
     ├── Makefile           <- Makefile with commands like `make data` or `make train`
     ├── README.md          <- The top-level README for developers using this project.
-    ├── data
-    │   ├── external       <- Data from third party sources.
-    │   ├── interim        <- Intermediate data that has been transformed.
-    │   ├── processed      <- The final, canonical data sets for modeling.
-    │   └── raw            <- The original, immutable data dump.
-    │
-    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
-    │
-    ├── models             <- Trained and serialized models, model predictions, or model summaries
-    │
-    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
-    │                         the creator's initials, and a short `-` delimited description, e.g.
-    │                         `1.0-jqp-initial-data-exploration`.
-    ├── tests              <- Where the tests lives
-    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
-    │
-    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
-    │   └── figures        <- Generated graphics and figures to be used in reporting
-    │
-    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
-    │                         generated with `pip freeze > requirements.txt`
-    │
+    ├── data               <- Scripts & bits to download datasets to try nautilus
+    │   ├── external
+    │   ├── interim
+    │   ├── processed
+    │   └── raw
+    ├── docker             <- Where to build a docker image using this lib
+    ├── docs               <- Sphinx HTML documentation
+    │   ├── _build
+    │   │   └── html
+    │   ├── source
+    ├── models
+    ├── nautilus_nlp       <- Main Nautilus Package. This is where the code lives
+    │   ├── config
+    │   ├── data
+    │   ├── models
+    │   ├── preprocessing
+    │   ├── scripts
+    │   └── utils
+    ├──notebooks           <- Various notebooks explaining how to use Nautilus_NLP library
+    ├── tests <- Where the tests lives
+    │   └── testfolder_fileloader
+    ├── wiki               <- Where the Markdown for the Wiki lives
     ├── setup.py           <- makes project pip installable (pip install -e .) so nautilus_nlp can be imported
-    ├── nautilus_nlp                <- Source code for use in this project.
-    │   ├── __init__.py    <- Makes nautilus_nlp a Python module
-    │   │
-    │   ├── data           <- Scripts to download or generate data
-    │   │
-    │   ├── utils       <- Scripts to turn raw data into features for modeling
-    │   │
-    │   ├── models         <- Scripts to train models and then use trained models to make
-    │   │   │                 predictions
-    │   │
-    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
-    │
-    └── tox.ini            <- tox file with settings for running tox; see tox.testrun.org
+    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
+                              generated with `pip freeze > requirements.txt`    
\ No newline at end of file

From abb54a33fea15ab5af270d00faa9457d1eb2a8b1 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 23 May 2019 10:41:16 +0200
Subject: [PATCH 176/496] Update nautilus_nlp/models/topic_modeling.py

---
 nautilus_nlp/models/topic_modeling.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 3885dd6..dd44ee0 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -204,7 +204,7 @@ def fit_data(model, bow):
 
 
 def visualize_topics(model, bow_corpus, dictionary, model_type=None):
-    """ Visualize the topics-keywords with the pyLDAvis interactive chart.
+    """ Visualize the topics-keywords generated by Gensim with the pyLDAvis interactive chart.
         (Work well in notebook)
         
     Parameters
@@ -270,4 +270,4 @@ def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
         print("Score: {}\t Topic: {}".format(score, keywords))
         i +=1
         if i == topic_number:
-            break
\ No newline at end of file
+            break

From ebeedc63d5c7da49dada5cc2bef401d3ab81c53e Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 23 May 2019 10:57:08 +0200
Subject: [PATCH 177/496] Update nautilus_nlp/models/topic_modeling.py

---
 nautilus_nlp/models/topic_modeling.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index dd44ee0..a1751f6 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -200,7 +200,7 @@ def fit_data(model, bow):
     return model[bow]
 
 
-# Visualization (only for gensim implementation for now)
+# Visualization
 
 
 def visualize_topics(model, bow_corpus, dictionary, model_type=None):

From ebdaf3420edab327c220076cdfdba619d5037ca9 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 11:31:21 +0200
Subject: [PATCH 178/496] replace replace_with defaut param from None to string

---
 nautilus_nlp/preprocessing/preprocess.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index add1ac0..d95b88f 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -499,6 +499,7 @@ def convert_emoji_to_text(text:str, code_delimiters=(':', ':')) -> str:
 
 def preprocess_text(
     text,
+    remove_eol_char=True,
     fix_unicode=False,
     lowercase=False,
     no_urls=False,
@@ -510,7 +511,7 @@ def preprocess_text(
     no_contractions=False,
     no_accents=False,
     no_emoji=False,
-    replace_with=None, 
+    replace_with=' ', 
     no_stopwords=None,
     phone_countries_format=[None,'US','FR'],
     phone_method='regex'
@@ -581,6 +582,8 @@ def preprocess_text(
 
     if fix_unicode is True:
         text = fix_bad_unicode(text, normalization="NFC")
+    if remove_eol_char is True:
+        text = remove_EOL_characters(text)
     if no_urls is True:
         text = replace_urls(text, replace_with=replace_with)
     if no_emails is True:

From 925c73e4547181fc8c377b9e5a04f28b1ee12ca6 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 11:34:35 +0200
Subject: [PATCH 179/496] add remove_eol to preprocess fct

---
 nautilus_nlp/preprocessing/preprocess.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index d95b88f..4467cf0 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -526,6 +526,8 @@ def preprocess_text(
         raw text to preprocess
     fix_unicode : bool
         if True, fix "broken" unicode such as mojibake and garbled HTML entities
+    remove_eol_char : bool 
+        if True, will remove the end-of-line characters \\n
     lowercase : bool
         if True, all text is lower-cased
     no_urls : bool

From e27f64a3a7c4c76cb662413231cf17beb6e40627 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 11:59:58 +0200
Subject: [PATCH 180/496] fix bug in format_number

---
 nautilus_nlp/utils/phone_number.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 83f84d3..869b7f6 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -106,6 +106,6 @@ def format_number(self, num_format):
         '''
         ['E164','INTERNATIONAL','NATIONAL','RFC3966']
         '''
-        standard_format = exec('phonenumbers.PhoneNumberFormat.'+num_format)
+        standard_format = exec('_phonenumbers.PhoneNumberFormat.'+num_format)
         
         return _phonenumbers.format_number(self.parsed_num, standard_format)
\ No newline at end of file

From 241e89b43ee44c8e048c343c050f09c592cb4602 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 12:25:55 +0200
Subject: [PATCH 181/496] clean notebooks

---
 notebooks/0. Text file loader.ipynb   | 495 ++++++++++++++++++++++++
 notebooks/1. Text Preprocessing.ipynb | 522 ++++++++++++++++++++++++++
 2 files changed, 1017 insertions(+)
 create mode 100644 notebooks/0. Text file loader.ipynb
 create mode 100644 notebooks/1. Text Preprocessing.ipynb

diff --git a/notebooks/0. Text file loader.ipynb b/notebooks/0. Text file loader.ipynb
new file mode 100644
index 0000000..5aa985f
--- /dev/null
+++ b/notebooks/0. Text file loader.ipynb	
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Open Text File with encoding handling\n",
+    "\n",
+    "Nautilus includes a document loader that handles encoding detection and multiple file loading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create example files "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Example of a latin1 file\n",
+    "s = \"J'aime les frites bien grasse étalon châpeau!\"\n",
+    "encoded_s = s.encode('latin-1')\n",
+    "with open('somefile.txt', 'wb') as f:\n",
+    "    f.write(encoded_s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Let's add another document \n",
+    "s = \"Un deuxième exemple de texte en utf-8 cette fois!\"\n",
+    "encoded_s = s.encode('utf-8')\n",
+    "with open('someadditionalfile.txt', 'wb') as f:\n",
+    "    f.write(encoded_s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Document loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import documents_loader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open a file when you know the encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Un deuxième exemple de texte en utf-8 cette fois!'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# default encoding is UTF-8\n",
+    "documents_loader('someadditionalfile.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"J'aime les frites bien grasse étalon châpeau!\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If you know the encoding, you can specify it\n",
+    "documents_loader('somefile.txt', encoding='latin-1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open a file with encoding detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't specify encoding, `document_loader()` will try to open it as UTF-8, and if it doesn't work it will try to detect encoding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"J'aime les frites bien grasse étalon châpeau!\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents_loader('somefile.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "function takes exactly 5 arguments (1 given)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mopen_textfile\u001b[0;34m(filepath, encoding)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/python/3.7.0/Frameworks/Python.framework/Versions/3.7/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 30: invalid continuation byte",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-c6d5e5969a55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# You can prevent document loader from detecting the encoding if UTF-8 fails\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdocuments_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'somefile.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mdocuments_loader\u001b[0;34m(filepath, encoding, detectencoding, output_as)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtext_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetectencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moutput_as\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'list'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetected_encoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot load document using utf-8. Try to detect encoding using detectencoding=True'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: function takes exactly 5 arguments (1 given)"
+     ]
+    }
+   ],
+   "source": [
+    "# You can prevent document loader from detecting the encoding if UTF-8 fails \n",
+    "# In this case, it will raise an UnicodeDecodeError\n",
+    "documents_loader('somefile.txt', detectencoding=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open several files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can use wildcards to open several documents\n",
+    "documents_loader('*.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can also pass a list of filepaths\n",
+    "documents_loader(['somefile.txt','someadditionalfile.txt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
+      "WARNING:root:Trying to detect encoding for somefile.txt\n",
+      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[\"J'aime les frites bien grasse étalon châpeau!\",\n",
+       " 'Un deuxième exemple de texte en utf-8 cette fois!']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can specify the output format when you load multiple texts\n",
+    "documents_loader('*.txt', output_as='list')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List files in a folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import list_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./somefile.txt',\n",
+       " './2. Text processing.ipynb',\n",
+       " './TF-IDF.ipynb',\n",
+       " './Visualization tools.ipynb',\n",
+       " './Language_identification.ipynb',\n",
+       " './someadditionalfile.txt',\n",
+       " './somefile',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './TopicModeling.ipynb',\n",
+       " './Spacy_model.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb',\n",
+       " './0. Text file loader.ipynb',\n",
+       " './1. Text Preprocessing.ipynb']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('.') # list files from current folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('../tests/testfolder_fileloader/.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./2. Text processing.ipynb',\n",
+       " './TF-IDF.ipynb',\n",
+       " './Visualization tools.ipynb',\n",
+       " './Language_identification.ipynb',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './TopicModeling.ipynb',\n",
+       " './Spacy_model.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb',\n",
+       " './0. Text file loader.ipynb',\n",
+       " './1. Text Preprocessing.ipynb']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_files('./*.ipynb') # List files matching specific pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/hugo/Documents/NAUTILUS/nautilus-nlp/LICENSE',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/requirements.txt',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/Makefile',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/README.md',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/setup.py',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/VERSION',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/CONTRIBUTING.md',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/tox.ini',\n",
+       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/test_environment.py']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only files will be printed, not folders\n",
+    "list_files('/Users/hugo/Documents/NAUTILUS/nautilus-nlp/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Detect encoding \n",
+    "\n",
+    "If you just interested in detecting encoding, you can use this function, based on the Chardet library. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.file_loader import detect_encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detect_encoding('somefile.txt')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/1. Text Preprocessing.ipynb b/notebooks/1. Text Preprocessing.ipynb
new file mode 100644
index 0000000..3803299
--- /dev/null
+++ b/notebooks/1. Text Preprocessing.ipynb	
@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Text Preprocessing \n",
+    "\n",
+    "This notebook will guide you through the common text preprocessing operations. \n",
+    "All the preprocessing functions are located in ** nautilus_nlp.preprocessing.preprocess ** "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load an example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "english_text = \"\"\"\n",
+    "The nautilus 🐚🐚 (from the Latin form of the original Ancient Greek: ναυτίλος, 'sailor') is a pelagic marine mollusc of the cephalopod family Nautilidae, the sole extant family of the superfamily Nautilaceae and of its smaller but near equal suborder, Nautilina.\n",
+    "\n",
+    "It comprises six living species in two genera, the type of which is the genus Nautilus. Though it more specifically refers to species Nautilus pompilius, the name chambered nautilus is also used for any of the Nautilidae. All are protected under CITES Appendix II.\n",
+    "\n",
+    "Nautilidae, both extant and extinct, are characterized by involute or more or less convolute shells that are generally smooth, with compressed or depressed whorl sections, straight to sinuous sutures, and a tubular, generally central siphuncle.[3] Having survived relatively unchanged for millions of years, nautiluses represent the only living members of the subclass nautiloidea, and are often considered \"living fossils\".\n",
+    "\n",
+    "The word nautilus is derived from the Greek ναυτίλος nautílos and originally referred to the paper nautiluses of the genus Argonauta, which are actually octopuses. The word nautílos literally means \"sailor\", as paper nautiluses were thought to use two of their arms as sails.[4]\n",
+    "\n",
+    "Here's how you can contact us:\n",
+    "\n",
+    "Source : https://en.wikipedia.org/wiki/Nautilus\n",
+    "Contact:\n",
+    "Email : Jules.vernes@nautilus.net\n",
+    "Phone : +33 6 24 24 24 24 \n",
+    "Cost : €45\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocess_text: Wrap-up function "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import preprocess_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nautilus latin form original ancient greek ναυτιλος sailor pelagic marine mollusc cephalopod family nautilidae sole extant family superfamily nautilaceae smaller equal suborder nautilina comprises living species genera type genus nautilus specifically refers species nautilus pompilius chambered nautilus nautilidae protected cites appendix ii nautilidae extant extinct characterized involute convolute shells generally smooth compressed depressed whorl sections straight sinuous sutures tubular generally central siphuncle survived unchanged millions years nautiluses represent living members subclass nautiloidea considered living fossils word nautilus derived greek ναυτιλος nautilos originally referred paper nautiluses genus argonauta octopuses word nautilos literally means sailor paper nautiluses thought arms sails contact source https en wikipedia org wiki nautilus contact email phone cost\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_txt = preprocess_text(english_text,\n",
+    "                            fix_unicode=False,\n",
+    "                            lowercase=True,\n",
+    "                            no_urls=False,\n",
+    "                            no_emails=True,\n",
+    "                            no_phone_numbers=True,\n",
+    "                            phone_method='detection', # if detection is specified, will use a phone lib \n",
+    "                            phone_countries_format=[None, 'US', 'FR'], # these phone format will be tested. None stands for international.\n",
+    "                            no_numbers=True,\n",
+    "                            no_currency_symbols=True,\n",
+    "                            no_punct=True,\n",
+    "                            no_contractions=True, #will unpack english contractions\n",
+    "                            no_accents=True,\n",
+    "                            no_emoji=True,\n",
+    "                            replace_with=' ',\n",
+    "                            no_stopwords='en' #will remove english stopwords\n",
+    "                           )\n",
+    "print(clean_txt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Overview of the pre-processing functions\n",
+    "\n",
+    "If you prefer, you can use all the preprocessing functions one-by-one and create your own pre-processing pipeline. It is recommended, as there are many parameters that are not necessary included in the wrap-up function. \n",
+    "\n",
+    "For the sake of simplicity, we won't go through all the pre-processing function. Please report to the documentation if you want more details!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remove end-of-line characters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import remove_EOL_characters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before:\n",
+      "-------\n",
+      "hello words!\n",
+      "this is the second line.\n",
+      "\n",
+      "After:\n",
+      "-------\n",
+      "hello words! this is the second line.\n"
+     ]
+    }
+   ],
+   "source": [
+    "text_with_eol = \"hello words!\\nthis is the second line.\"\n",
+    "\n",
+    "text_without_eol = remove_EOL_characters(text_with_eol)\n",
+    "\n",
+    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(text_with_eol,text_without_eol))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stop words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import get_stopwords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['basee', 'y', 'façon', 'étant', 't', 'chère', 'uns', 'mêmes', 'particulier', 'tend']\n"
+     ]
+    }
+   ],
+   "source": [
+    "french_sw = get_stopwords('fr')\n",
+    "\n",
+    "print(french_sw[:10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import remove_stopwords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before:\n",
+      "-------\n",
+      "['je', 'suis', 'malade', 'et', 'toi', '?']\n",
+      "\n",
+      "After:\n",
+      "-------\n",
+      "['malade', '?']\n"
+     ]
+    }
+   ],
+   "source": [
+    "tokens_with_sw = ['je','suis','malade','et','toi','?']\n",
+    "\n",
+    "tokens_without_sw = remove_stopwords(tokens_with_sw, stopwords=french_sw)\n",
+    "\n",
+    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(tokens_with_sw,tokens_without_sw))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fix bad unicode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import fix_bad_unicode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before:\n",
+      "-------\n",
+      "Les augmentations de rémunérations\n",
+      "\n",
+      "After:\n",
+      "-------\n",
+      "Les augmentations de rémunérations\n"
+     ]
+    }
+   ],
+   "source": [
+    "before = \"Les augmentations de rémunérations\"\n",
+    "\n",
+    "after = fix_bad_unicode(before)\n",
+    "\n",
+    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Phone Numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import replace_phone_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before:\n",
+      "-------\n",
+      "(541) 754-3010 is a US. Phone\n",
+      "\n",
+      "After:\n",
+      "-------\n",
+      "  is a US. Phone\n"
+     ]
+    }
+   ],
+   "source": [
+    "before = '(541) 754-3010 is a US. Phone'\n",
+    "\n",
+    "after = replace_phone_numbers(before, replace_with=' ')\n",
+    "\n",
+    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also an util to detect phone numbers. Here we will provide a country list. It takes time because it will loop over the text with all the supported country codes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import nautilus_nlp.utils.phone_number as phone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 452 ms, sys: 22.5 ms, total: 474 ms\n",
+      "Wall time: 477 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['(541) 754-3010', '754-3010']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "phone.extract_phone_numbers(before, countrylist=phone.SUPPORTED_COUNTRY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also a **phone parser** class, that helps you to extract information from phone numbers. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "p = phone.phoneParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PhoneNumber(country_code=1, national_number=5417543010, extension=None, italian_leading_zero=None, number_of_leading_zeros=None, country_code_source=0, preferred_domestic_carrier_code=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p.parse_number('(541) 754-3010',region_code='US')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'541-754-3010'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p.format_number('INTERNATIONAL')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Emojis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import remove_emoji, convert_emoji_to_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before:\n",
+      "-------\n",
+      "My favorite emojies are: 🎅🏿⌚\n",
+      "\n",
+      "After:\n",
+      "-------\n",
+      "My favorite emojies are: \n"
+     ]
+    }
+   ],
+   "source": [
+    "before = 'My favorite emojies are: 🎅🏿⌚'\n",
+    "\n",
+    "after = remove_emoji(before)\n",
+    "\n",
+    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before:\n",
+      "-------\n",
+      "My favorite emojies are: 🎅🏿⌚\n",
+      "\n",
+      "After:\n",
+      "-------\n",
+      "My favorite emojies are: Santa_Claus_dark_skin_tonewatch\n"
+     ]
+    }
+   ],
+   "source": [
+    "before = 'My favorite emojies are: 🎅🏿⌚'\n",
+    "\n",
+    "after = convert_emoji_to_text(before, code_delimiters=('', ''))\n",
+    "\n",
+    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 6c20cdf4226dc00e2d9ab197d5c56bd7a3e4963d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 12:26:18 +0200
Subject: [PATCH 182/496] rename class phoneParser for pep8 compliance

---
 nautilus_nlp/utils/phone_number.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 869b7f6..b922484 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -66,7 +66,7 @@ def extract_phone_numbers(string:str, countrylist:list=[None,'FR','US','GB'])->l
     return list(set(res))
 
 
-class PhoneParser(object):
+class phoneParser(object):
     """
     Python port of Google's libphonenumber.
     https://github.com/daviddrysdale/python-phonenumbers 

From 91c4196e6b6479e6e7ccabc2f94ccbafb69a8743 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 12:26:33 +0200
Subject: [PATCH 183/496] add tests for phone numbers utils

---
 tests/test_phone_number.py | 42 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 tests/test_phone_number.py

diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
new file mode 100644
index 0000000..d6edc69
--- /dev/null
+++ b/tests/test_phone_number.py
@@ -0,0 +1,42 @@
+import pytest
+import nautilus_nlp.utils.phone_number as phone
+
+def test_extract_phone_number():
+    input_str = '(541) 754-3010 is a US. Phone'
+    expected = ['(541) 754-3010', '754-3010']
+    res = phone.extract_phone_numbers(input_str, countrylist=phone.SUPPORTED_COUNTRY)
+    assert res == expected
+
+def test_extract_phone_number_us():
+    input_str = '(541) 754-3010 is a US. Phone'
+    expected = ['(541) 754-3010']
+    res = phone.extract_phone_numbers(input_str, countrylist=['US'])
+    assert res == expected    
+
+def test_extract_phone_number_fr():
+    input_str = '06.25.09.32.56 is a FR Phone'
+    expected = ['06.25.09.32.56']
+    res = phone.extract_phone_numbers(input_str)
+    assert res == expected
+
+def test_extract_phone_number_international():
+    input_str = '+33625093423 is an international Phone number'
+    expected = ['+33625093423']
+    res = phone.extract_phone_numbers(input_str)
+    assert res == expected
+
+def test_phoneParser_us():
+    input_str = '(541) 754-3010'
+    expected = '541-754-3010'
+    p = phone.phoneParser()
+    p.parse_number(input_str,region_code='US')
+    res = p.format_number('INTERNATIONAL')
+    assert res == expected
+
+def test_phoneParser_fr():
+    input_str = '0625093267'
+    expected = '6 25 09 32 67'
+    p = phone.phoneParser()
+    p.parse_number(input_str,region_code='FR')
+    res = p.format_number('E164')
+    assert res == expected    
\ No newline at end of file

From 7f9ddb5f99a532afa0fc08f4156358792d51d514 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 12:29:10 +0200
Subject: [PATCH 184/496] rename notebooks

---
 .../Common Text Processing operations.ipynb   | 812 ------------------
 ...t files loader with encoding handler.ipynb | 477 ----------
 2 files changed, 1289 deletions(-)
 delete mode 100644 notebooks/Common Text Processing operations.ipynb
 delete mode 100644 notebooks/Text files loader with encoding handler.ipynb

diff --git a/notebooks/Common Text Processing operations.ipynb b/notebooks/Common Text Processing operations.ipynb
deleted file mode 100644
index 761a667..0000000
--- a/notebooks/Common Text Processing operations.ipynb	
+++ /dev/null
@@ -1,812 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tokenizing French and English texts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.tokenizer import tokenize, untokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "fr_txt = \"Ceci est un texte français, j'adore 1 !\"\n",
-    "eng_txt = \"Let's play together!\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 4.76 ms, sys: 31 µs, total: 4.79 ms\n",
-      "Wall time: 4.42 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['Les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'tels',\n",
-       " 'Google',\n",
-       " ',',\n",
-       " 'Exalead',\n",
-       " 'ou',\n",
-       " 'Yahoo',\n",
-       " '!',\n",
-       " 'sont',\n",
-       " 'des',\n",
-       " 'applications',\n",
-       " 'très',\n",
-       " 'connues',\n",
-       " 'de',\n",
-       " 'fouille',\n",
-       " 'de',\n",
-       " 'textes',\n",
-       " 'sur',\n",
-       " 'de',\n",
-       " 'grandes',\n",
-       " 'masses',\n",
-       " 'de',\n",
-       " 'données',\n",
-       " '.',\n",
-       " 'Cependant',\n",
-       " ',',\n",
-       " 'les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'ne',\n",
-       " 'se',\n",
-       " 'basent',\n",
-       " 'pas',\n",
-       " 'uniquement',\n",
-       " 'sur',\n",
-       " 'le',\n",
-       " 'texte',\n",
-       " 'pour',\n",
-       " \"l'\",\n",
-       " 'indexer',\n",
-       " ',',\n",
-       " 'mais',\n",
-       " 'également',\n",
-       " 'sur',\n",
-       " 'la',\n",
-       " 'façon',\n",
-       " 'dont',\n",
-       " 'les',\n",
-       " 'pages',\n",
-       " 'sont',\n",
-       " 'mises',\n",
-       " 'en',\n",
-       " 'valeur',\n",
-       " 'les',\n",
-       " 'unes',\n",
-       " 'par',\n",
-       " 'rapport',\n",
-       " 'aux',\n",
-       " 'autres',\n",
-       " '.',\n",
-       " \"L'\",\n",
-       " 'algorithme',\n",
-       " 'utilisé',\n",
-       " 'par',\n",
-       " 'Google',\n",
-       " 'est',\n",
-       " 'PageRank',\n",
-       " ',',\n",
-       " 'et',\n",
-       " 'il',\n",
-       " 'est',\n",
-       " 'courant',\n",
-       " 'de',\n",
-       " 'voir',\n",
-       " 'HITS',\n",
-       " 'dans',\n",
-       " 'le',\n",
-       " 'milieu',\n",
-       " 'académique']"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenize(str_, lang_module=\"fr_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 6.22 ms, sys: 94 µs, total: 6.31 ms\n",
-      "Wall time: 4.27 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_moses\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'tels',\n",
-       " 'Google',\n",
-       " ',',\n",
-       " 'Exalead',\n",
-       " 'ou',\n",
-       " 'Yahoo',\n",
-       " '!',\n",
-       " 'sont',\n",
-       " 'des',\n",
-       " 'applications',\n",
-       " 'très',\n",
-       " 'connues',\n",
-       " 'de',\n",
-       " 'fouille',\n",
-       " 'de',\n",
-       " 'textes',\n",
-       " 'sur',\n",
-       " 'de',\n",
-       " 'grandes',\n",
-       " 'masses',\n",
-       " 'de',\n",
-       " 'données',\n",
-       " '.',\n",
-       " 'Cependant',\n",
-       " ',',\n",
-       " 'les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'ne',\n",
-       " 'se',\n",
-       " 'basent',\n",
-       " 'pas',\n",
-       " 'uniquement',\n",
-       " 'sur',\n",
-       " 'le',\n",
-       " 'texte',\n",
-       " 'pour',\n",
-       " \"l'\",\n",
-       " 'indexer',\n",
-       " ',',\n",
-       " 'mais',\n",
-       " 'également',\n",
-       " 'sur',\n",
-       " 'la',\n",
-       " 'façon',\n",
-       " 'dont',\n",
-       " 'les',\n",
-       " 'pages',\n",
-       " 'sont',\n",
-       " 'mises',\n",
-       " 'en',\n",
-       " 'valeur',\n",
-       " 'les',\n",
-       " 'unes',\n",
-       " 'par',\n",
-       " 'rapport',\n",
-       " 'aux',\n",
-       " 'autres',\n",
-       " '.',\n",
-       " \"L'\",\n",
-       " 'algorithme',\n",
-       " 'utilisé',\n",
-       " 'par',\n",
-       " 'Google',\n",
-       " 'est',\n",
-       " 'PageRank',\n",
-       " ',',\n",
-       " 'et',\n",
-       " 'il',\n",
-       " 'est',\n",
-       " 'courant',\n",
-       " 'de',\n",
-       " 'voir',\n",
-       " 'HITS',\n",
-       " 'dans',\n",
-       " 'le',\n",
-       " 'milieu',\n",
-       " 'académique']"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenize(str_, lang_module=\"fr_moses\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 24.3 ms, sys: 3.99 ms, total: 28.3 ms\n",
-      "Wall time: 26.2 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_spacy\")\n",
-    "tokenized_eng_txt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 17.3 ms, sys: 3.71 ms, total: 21 ms\n",
-      "Wall time: 26.9 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_nltk\")\n",
-    "tokenized_eng_txt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## You can also untokenize your text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Let's play together!\""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "untokenize(tokenized_eng_txt,lang='en')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Ceci est un texte français, j' adore !\""
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Here the \"J'adore\" is not handled in the right way\n",
-    "untokenize(tokenized_fr_txt,lang='fr')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stemming "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.stemmer import stem_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['i', 'surviv', 'these', 'dog']"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stem_tokens(['I','survived','these', 'dogs'], lang='english')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['je', 'mang', 'dan', 'le', 'cuisin', 'du', 'château']"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stem_tokens(tokenize(\"je mangerai dans les cuisines du château\"),lang='french')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Lemmatization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## French "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.lemmatizer import lemmatize_french_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Ceci', 'est', 'un', 'texte', 'français', ',', \"j'\", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']\n"
-     ]
-    }
-   ],
-   "source": [
-    "txt_to_tokenize=['Ceci', 'est', 'un', 'texte', 'français', ',', \"j'\", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']\n",
-    "print(txt_to_tokenize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 37.9 ms, sys: 51.9 ms, total: 89.8 ms\n",
-      "Wall time: 65.3 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['ceci',\n",
-       " 'être',\n",
-       " 'un',\n",
-       " 'texte',\n",
-       " 'français',\n",
-       " ',',\n",
-       " 'j',\n",
-       " \"'\",\n",
-       " 'adorer',\n",
-       " 't',\n",
-       " 'frite',\n",
-       " 'bien',\n",
-       " 'gras',\n",
-       " 'yolo',\n",
-       " '!']"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatize_french_tokens(txt_to_tokenize, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## English"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.utils.lemmatizer import lemmatize_english_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "to_lemmatize = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 27.2 ms, sys: 4.4 ms, total: 31.6 ms\n",
-      "Wall time: 31.3 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatize_english_tokens(to_lemmatize, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.07 s, sys: 217 ms, total: 3.28 s\n",
-      "Wall time: 3.45 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatize_english_tokens(to_lemmatize, module='nltk')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Remove stop words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.utils.preprocess import remove_stopwords\n",
-    "from nautilus_nlp.utils.tokenizer import tokenize\n",
-    "from nautilus_nlp.utils.constants import get_stopwords"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "FRENCH_SW = get_stopwords('fr')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "text = \"J'ai un beau cheval\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"J'ai\", 'beau', 'cheval']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remove_stopwords(text, FRENCH_SW)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"J'\", 'beau', 'cheval']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remove_stopwords(tokenize(text, lang_module=\"fr_spacy\"),FRENCH_SW)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fix bad encoding\n",
-    "\n",
-    "Sometimes you messed up you encoding saving files, and you don't know how to fix this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.preprocess import fix_bad_unicode\n",
-    "from nautilus_nlp.utils import file_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "bad_unicode=file_loader.open_textfile('./bad_encoding.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse Ã\\xa0 80km\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bad_unicode[0:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse à 80km/h sur les routes départ\""
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fix_bad_unicode(bad_unicode)[0:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/Text files loader with encoding handler.ipynb b/notebooks/Text files loader with encoding handler.ipynb
deleted file mode 100644
index 24cc875..0000000
--- a/notebooks/Text files loader with encoding handler.ipynb	
+++ /dev/null
@@ -1,477 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Open Text File with encoding handling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Example of a latin1 file\n",
-    "s = \"J'aime les frites bien grasse étalon châpeau!\"\n",
-    "encoded_s = s.encode('latin-1')\n",
-    "with open('somefile.txt', 'wb') as f:\n",
-    "    f.write(encoded_s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Let's add another document \n",
-    "s = \"Un deuxième exemple de texte en utf-8 cette fois!\"\n",
-    "encoded_s = s.encode('utf-8')\n",
-    "with open('someadditionalfile.txt', 'wb') as f:\n",
-    "    f.write(encoded_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Document loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import documents_loader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open a file when you know the encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Un deuxième exemple de texte en utf-8 cette fois!'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# default encoding is UTF-8\n",
-    "documents_loader('someadditionalfile.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# If you know the encoding, you can specify it\n",
-    "documents_loader('somefile.txt', encoding='latin-1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open a file with encoding detection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you don't specify encoding, `document_loader()` will try to open it as UTF-8, and if it doesn't work it will try to detect encoding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents_loader('somefile.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "function takes exactly 5 arguments (1 given)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mopen_textfile\u001b[0;34m(filepath, encoding)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/Cellar/python/3.7.0/Frameworks/Python.framework/Versions/3.7/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 30: invalid continuation byte",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-dc5ecd5accf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdocuments_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'somefile.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mdocuments_loader\u001b[0;34m(filepath, encoding, detectencoding, output_as)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtext_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetectencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moutput_as\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'list'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetected_encoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot load document using utf-8. Try to detect encoding using detectencoding=True'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: function takes exactly 5 arguments (1 given)"
-     ]
-    }
-   ],
-   "source": [
-    "# You can prevent document loader from detecting the encoding if UTF-8 fails \n",
-    "documents_loader('somefile.txt', detectencoding=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open several files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can use wildcards to open several documents\n",
-    "documents_loader('*.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can also pass a list of filepaths\n",
-    "documents_loader(['somefile.txt','someadditionalfile.txt'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[\"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'Un deuxième exemple de texte en utf-8 cette fois!']"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can specify the output format when you load multiple texts\n",
-    "documents_loader('*.txt', output_as='list')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## List files in a folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import list_files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['./somefile.txt',\n",
-       " './Open a file, a list of files and detect encoding.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
-       " './Language_identification.ipynb',\n",
-       " './someadditionalfile.txt',\n",
-       " './somefile',\n",
-       " './Common Text Processing operations.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
-       " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('.') # list files from current folders"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['../tests/testfolder_fileloader/./testdoc_utf8.txt',\n",
-       " '../tests/testfolder_fileloader/./testdoc_latin1.txt']"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('../tests/testfolder_fileloader/.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['./TF-IDF.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
-       " './Language_identification.ipynb',\n",
-       " './Common Text Processing operations.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
-       " './Text files loader with encoding handler.ipynb',\n",
-       " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb']"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('./*.ipynb') # List files matching specific pattern"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/Users/hugo/Documents/NAUTILUS/nautilus-nlp/LICENSE',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/requirements.txt',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/Makefile',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/README.md',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/setup.py',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/tox.ini',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/test_environment.py']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# only files will be printed, not folders\n",
-    "list_files('/Users/hugo/Documents/NAUTILUS/nautilus-nlp/')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Detect encoding "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import detect_encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "detect_encoding('somefile.txt')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 3a3e8d3e30980e27f2fecf9d5cecdb4e8d6d61e4 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 12:30:44 +0200
Subject: [PATCH 185/496] for conflict

---
 notebooks/TopicModeling.ipynb | 114 +++++++++++++++++++++++++---------
 1 file changed, 84 insertions(+), 30 deletions(-)

diff --git a/notebooks/TopicModeling.ipynb b/notebooks/TopicModeling.ipynb
index 24fe053..d8adfa3 100644
--- a/notebooks/TopicModeling.ipynb
+++ b/notebooks/TopicModeling.ipynb
@@ -10,7 +10,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from sklearn.datasets import fetch_20newsgroups\n",
@@ -74,7 +76,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def lemmatize_stemming(text):\n",
@@ -93,7 +97,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "processed_docs = []\n",
@@ -112,7 +118,9 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Create the dictionnary\n",
@@ -122,7 +130,9 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "dictionary = create_dictionary(processed_docs)"
@@ -131,7 +141,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Filter out tokens that appear in too few or too many documents\n",
@@ -141,7 +153,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "filter_extremes(dictionary)"
@@ -150,7 +164,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Create the bow \n",
@@ -160,7 +176,9 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "bow_corpus = create_bow_corpus(processed_docs, dictionary)"
@@ -193,7 +211,9 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Compute coherence values for various number of topics in order to pick the optimal one\n",
@@ -203,7 +223,9 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Take a long time to run\n",
@@ -238,7 +260,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from nautilus_nlp.models.topic_modeling import plot_optimal_topic_number"
@@ -269,7 +293,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Print the coherences scores for the number we tested\n",
@@ -308,7 +334,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Train the LDA model with gensim\n",
@@ -318,7 +346,9 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "model = train_lda_model(bow_corpus, dictionary, 10)"
@@ -347,7 +377,9 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Save model\n",
@@ -357,7 +389,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "save_model(model,'/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')"
@@ -366,7 +400,9 @@
   {
    "cell_type": "code",
    "execution_count": 152,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Load model"
@@ -375,7 +411,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from nautilus_nlp.models.topic_modeling import load_model"
@@ -412,7 +450,9 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Display the top keywords per topic in a interactive chart\n",
@@ -638,7 +678,9 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Save the pyldavis as HTML\n",
@@ -648,7 +690,9 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "save_pyldavis(p, '/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
@@ -657,7 +701,9 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Load the pyldavis HTML\n",
@@ -774,7 +820,9 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Data preprocessing step for the unseen document\n",
@@ -784,7 +832,9 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from nautilus_nlp.models.topic_modeling import fit_data"
@@ -822,7 +872,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Show the dominant topics of the new document and their keywords \n",
@@ -851,16 +903,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "nautilus",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "nautilus"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -872,7 +926,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.0"
   }
  },
  "nbformat": 4,

From 467ceda13c362f20a7b068bb607c60076c0832a9 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 12:30:59 +0200
Subject: [PATCH 186/496] rename notebook

---
 notebooks/2. Text processing.ipynb | 821 +++++++++++++++++++++++++++++
 1 file changed, 821 insertions(+)
 create mode 100644 notebooks/2. Text processing.ipynb

diff --git a/notebooks/2. Text processing.ipynb b/notebooks/2. Text processing.ipynb
new file mode 100644
index 0000000..34e9338
--- /dev/null
+++ b/notebooks/2. Text processing.ipynb	
@@ -0,0 +1,821 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Common text processing operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tokenization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tokenizing French and English texts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.tokenizer import tokenize, untokenize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fr_txt = \"Ceci est un texte français, j'adore 1 !\"\n",
+    "eng_txt = \"Let's play together!\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.76 ms, sys: 31 µs, total: 4.79 ms\n",
+      "Wall time: 4.42 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['Les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'tels',\n",
+       " 'Google',\n",
+       " ',',\n",
+       " 'Exalead',\n",
+       " 'ou',\n",
+       " 'Yahoo',\n",
+       " '!',\n",
+       " 'sont',\n",
+       " 'des',\n",
+       " 'applications',\n",
+       " 'très',\n",
+       " 'connues',\n",
+       " 'de',\n",
+       " 'fouille',\n",
+       " 'de',\n",
+       " 'textes',\n",
+       " 'sur',\n",
+       " 'de',\n",
+       " 'grandes',\n",
+       " 'masses',\n",
+       " 'de',\n",
+       " 'données',\n",
+       " '.',\n",
+       " 'Cependant',\n",
+       " ',',\n",
+       " 'les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'ne',\n",
+       " 'se',\n",
+       " 'basent',\n",
+       " 'pas',\n",
+       " 'uniquement',\n",
+       " 'sur',\n",
+       " 'le',\n",
+       " 'texte',\n",
+       " 'pour',\n",
+       " \"l'\",\n",
+       " 'indexer',\n",
+       " ',',\n",
+       " 'mais',\n",
+       " 'également',\n",
+       " 'sur',\n",
+       " 'la',\n",
+       " 'façon',\n",
+       " 'dont',\n",
+       " 'les',\n",
+       " 'pages',\n",
+       " 'sont',\n",
+       " 'mises',\n",
+       " 'en',\n",
+       " 'valeur',\n",
+       " 'les',\n",
+       " 'unes',\n",
+       " 'par',\n",
+       " 'rapport',\n",
+       " 'aux',\n",
+       " 'autres',\n",
+       " '.',\n",
+       " \"L'\",\n",
+       " 'algorithme',\n",
+       " 'utilisé',\n",
+       " 'par',\n",
+       " 'Google',\n",
+       " 'est',\n",
+       " 'PageRank',\n",
+       " ',',\n",
+       " 'et',\n",
+       " 'il',\n",
+       " 'est',\n",
+       " 'courant',\n",
+       " 'de',\n",
+       " 'voir',\n",
+       " 'HITS',\n",
+       " 'dans',\n",
+       " 'le',\n",
+       " 'milieu',\n",
+       " 'académique']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenize(str_, lang_module=\"fr_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6.22 ms, sys: 94 µs, total: 6.31 ms\n",
+      "Wall time: 4.27 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_moses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'tels',\n",
+       " 'Google',\n",
+       " ',',\n",
+       " 'Exalead',\n",
+       " 'ou',\n",
+       " 'Yahoo',\n",
+       " '!',\n",
+       " 'sont',\n",
+       " 'des',\n",
+       " 'applications',\n",
+       " 'très',\n",
+       " 'connues',\n",
+       " 'de',\n",
+       " 'fouille',\n",
+       " 'de',\n",
+       " 'textes',\n",
+       " 'sur',\n",
+       " 'de',\n",
+       " 'grandes',\n",
+       " 'masses',\n",
+       " 'de',\n",
+       " 'données',\n",
+       " '.',\n",
+       " 'Cependant',\n",
+       " ',',\n",
+       " 'les',\n",
+       " 'moteurs',\n",
+       " 'de',\n",
+       " 'recherche',\n",
+       " 'ne',\n",
+       " 'se',\n",
+       " 'basent',\n",
+       " 'pas',\n",
+       " 'uniquement',\n",
+       " 'sur',\n",
+       " 'le',\n",
+       " 'texte',\n",
+       " 'pour',\n",
+       " \"l'\",\n",
+       " 'indexer',\n",
+       " ',',\n",
+       " 'mais',\n",
+       " 'également',\n",
+       " 'sur',\n",
+       " 'la',\n",
+       " 'façon',\n",
+       " 'dont',\n",
+       " 'les',\n",
+       " 'pages',\n",
+       " 'sont',\n",
+       " 'mises',\n",
+       " 'en',\n",
+       " 'valeur',\n",
+       " 'les',\n",
+       " 'unes',\n",
+       " 'par',\n",
+       " 'rapport',\n",
+       " 'aux',\n",
+       " 'autres',\n",
+       " '.',\n",
+       " \"L'\",\n",
+       " 'algorithme',\n",
+       " 'utilisé',\n",
+       " 'par',\n",
+       " 'Google',\n",
+       " 'est',\n",
+       " 'PageRank',\n",
+       " ',',\n",
+       " 'et',\n",
+       " 'il',\n",
+       " 'est',\n",
+       " 'courant',\n",
+       " 'de',\n",
+       " 'voir',\n",
+       " 'HITS',\n",
+       " 'dans',\n",
+       " 'le',\n",
+       " 'milieu',\n",
+       " 'académique']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenize(str_, lang_module=\"fr_moses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 24.3 ms, sys: 3.99 ms, total: 28.3 ms\n",
+      "Wall time: 26.2 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_spacy\")\n",
+    "tokenized_eng_txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 17.3 ms, sys: 3.71 ms, total: 21 ms\n",
+      "Wall time: 26.9 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_nltk\")\n",
+    "tokenized_eng_txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## You can also untokenize your text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Let's play together!\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "untokenize(tokenized_eng_txt,lang='en')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Ceci est un texte français, j' adore !\""
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here the \"J'adore\" is not handled in the right way\n",
+    "untokenize(tokenized_fr_txt,lang='fr')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stemming "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.stemmer import stem_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['i', 'surviv', 'these', 'dog']"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stem_tokens(['I','survived','these', 'dogs'], lang='english')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['je', 'mang', 'dan', 'le', 'cuisin', 'du', 'château']"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stem_tokens(tokenize(\"je mangerai dans les cuisines du château\"),lang='french')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lemmatization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## French "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.lemmatizer import lemmatize_french_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Ceci', 'est', 'un', 'texte', 'français', ',', \"j'\", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']\n"
+     ]
+    }
+   ],
+   "source": [
+    "txt_to_tokenize=['Ceci', 'est', 'un', 'texte', 'français', ',', \"j'\", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']\n",
+    "print(txt_to_tokenize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 37.9 ms, sys: 51.9 ms, total: 89.8 ms\n",
+      "Wall time: 65.3 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['ceci',\n",
+       " 'être',\n",
+       " 'un',\n",
+       " 'texte',\n",
+       " 'français',\n",
+       " ',',\n",
+       " 'j',\n",
+       " \"'\",\n",
+       " 'adorer',\n",
+       " 't',\n",
+       " 'frite',\n",
+       " 'bien',\n",
+       " 'gras',\n",
+       " 'yolo',\n",
+       " '!']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lemmatize_french_tokens(txt_to_tokenize, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## English"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nautilus_nlp.utils.lemmatizer import lemmatize_english_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "to_lemmatize = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 27.2 ms, sys: 4.4 ms, total: 31.6 ms\n",
+      "Wall time: 31.3 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lemmatize_english_tokens(to_lemmatize, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3.07 s, sys: 217 ms, total: 3.28 s\n",
+      "Wall time: 3.45 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lemmatize_english_tokens(to_lemmatize, module='nltk')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Remove stop words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nautilus_nlp.utils.preprocess import remove_stopwords\n",
+    "from nautilus_nlp.utils.tokenizer import tokenize\n",
+    "from nautilus_nlp.utils.constants import get_stopwords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "FRENCH_SW = get_stopwords('fr')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "text = \"J'ai un beau cheval\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\"J'ai\", 'beau', 'cheval']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "remove_stopwords(text, FRENCH_SW)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\"J'\", 'beau', 'cheval']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "remove_stopwords(tokenize(text, lang_module=\"fr_spacy\"),FRENCH_SW)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fix bad encoding\n",
+    "\n",
+    "Sometimes you messed up you encoding saving files, and you don't know how to fix this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.utils.preprocess import fix_bad_unicode\n",
+    "from nautilus_nlp.utils import file_loader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bad_unicode=file_loader.open_textfile('./bad_encoding.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse Ã\\xa0 80km\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bad_unicode[0:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse à 80km/h sur les routes départ\""
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fix_bad_unicode(bad_unicode)[0:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From bf3df9a4882305abdd7de94669e678cb05fc3a8c Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 12:37:27 +0200
Subject: [PATCH 187/496] resolve conflics

---
 notebooks/TopicModeling.ipynb | 431 ++++++++++++++++++++--------------
 1 file changed, 260 insertions(+), 171 deletions(-)

diff --git a/notebooks/TopicModeling.ipynb b/notebooks/TopicModeling.ipynb
index d8adfa3..c58cf2c 100644
--- a/notebooks/TopicModeling.ipynb
+++ b/notebooks/TopicModeling.ipynb
@@ -9,10 +9,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 8,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 1,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "from sklearn.datasets import fetch_20newsgroups\n",
@@ -22,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -46,18 +51,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/nltk/decorators.py:68: DeprecationWarning: `formatargspec` is deprecated since Python 3.5. Use `signature` and the `Signature` object directly\n",
-      "  regargs, varargs, varkwargs, defaults, formatvalue=lambda value: \"\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import gensim\n",
     "from gensim.utils import simple_preprocess\n",
@@ -75,10 +71,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 4,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "def lemmatize_stemming(text):\n",
@@ -96,10 +97,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 5,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "processed_docs = []\n",
@@ -117,10 +123,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 6,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "# Create the dictionnary\n",
@@ -129,10 +140,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 7,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "dictionary = create_dictionary(processed_docs)"
@@ -140,10 +156,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 15,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 8,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "# Filter out tokens that appear in too few or too many documents\n",
@@ -152,10 +173,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 9,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "filter_extremes(dictionary)"
@@ -163,10 +189,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 17,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 10,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "# Create the bow \n",
@@ -175,10 +206,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 11,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "bow_corpus = create_bow_corpus(processed_docs, dictionary)"
@@ -186,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -328,15 +364,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 5: Running LDA using Bag of Words"
+    "### Step 5: Running LDA using Bag of Words with Gensim or Mallet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Gensim"
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 13,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "# Train the LDA model with gensim\n",
@@ -345,27 +393,33 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 14,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
+    "# By default the model used will be gensim implementation of LDA\n",
     "model = train_lda_model(bow_corpus, dictionary, 10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x1a2af3e208>"
+       "<gensim.models.ldamodel.LdaModel at 0x10a171ef0>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,33 +430,48 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 15,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 53,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
-    "# Save model\n",
+    "# Save model: The model will be saved on your current emplacement.\n",
     "from nautilus_nlp.models.topic_modeling import save_model"
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 54,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
-    "save_model(model,'/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')"
+    "save_model(model, 'ldamodel_nautilus')"
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 152,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 24,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "# Load model"
@@ -410,34 +479,97 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 17,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.ldamodel.LdaModel at 0x1a3142fc88>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_loaded = load_model('/Users/williamjaubert/nautilus_nlp/notebooks', 'ldamodel_nautilus')\n",
+    "model_loaded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Mallet "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
-    "from nautilus_nlp.models.topic_modeling import load_model"
+    "# You can train Mallet model by precising 'mallet' in the model parameter and give the path where the mallet-2.0.8 file that has been downloaded \n",
+    "model_mallet = train_lda_model(bow_corpus, dictionary, 10, model='mallet', mallet_path='/Users/williamjaubert/nautilus_nlp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x1a29985b38>"
+       "<gensim.models.wrappers.ldamallet.LdaMallet at 0x1a2eb0e320>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_loaded = load_model('/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')\n",
-    "model_loaded"
+    "model_mallet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_model(model_mallet, 'ldamodel_mallet_nautilus')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.wrappers.ldamallet.LdaMallet at 0x1a30d1e550>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_mallet_loaded = load_model('/Users/williamjaubert/nautilus_nlp/notebooks', 'ldamodel_mallet_nautilus', model='mallet')\n",
+    "model_mallet_loaded"
    ]
   },
   {
@@ -449,10 +581,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
+=======
+   "execution_count": 56,
+   "metadata": {},
+>>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
    "outputs": [],
    "source": [
     "# Display the top keywords per topic in a interactive chart\n",
@@ -461,8 +598,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -485,10 +624,10 @@
        "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
        "\n",
        "\n",
-       "<div id=\"ldavis_el591011124095238088809029897\"></div>\n",
+       "<div id=\"ldavis_el155871124265505206097197808\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "\n",
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\"machin\", \"file\", \"thank\", \"control\", \"work\", \"speed\", \"need\", \"hard\", \"help\", \"program\", \"want\", \"time\", \"repli\", \"softwar\", \"distribut\", \"alaska\", \"spencer\", \"oracl\", \"dseg\", \"aurora\", \"nsmca\", \"engr\", \"launch\", \"uoknor\", \"callison\", \"kaldi\", \"zoolog\", \"mccall\", \"ucsc\", \"hallam\", \"lunar\", \"automot\", \"raider\", \"theodor\", \"dock\", \"shafer\", \"mksol\", \"hydro\", \"ssto\", \"plymouth\", \"redesign\", \"laughter\", \"rockwel\", \"desi\", \"stimulus\", \"moon\", \"henri\", \"mar\", \"job\", \"wheel\", \"billion\", \"invest\", \"spacecraft\", \"orbit\", \"nasa\", \"space\", \"shuttl\", \"satellit\", \"fund\", \"probe\", \"flight\", \"helmet\", \"station\", \"solar\", \"presid\", \"mission\", \"earth\", \"cost\", \"money\", \"vehicl\", \"year\", \"work\", \"project\", \"toronto\", \"spend\", \"go\", \"time\", \"engin\", \"long\", \"high\", \"power\", \"thing\", \"say\", \"look\", \"peopl\", \"program\", \"want\", \"need\", \"design\", \"build\", \"firearm\", \"bike\", \"motorcycl\", \"magnus\", \"rider\", \"honda\", \"veal\", \"utkvm\", \"centerlin\", \"cactus\", \"rkba\", \"harley\", \"shotgun\", \"pistol\", \"ranck\", \"boyl\", \"husc\", \"ifa\", \"smuggl\", \"fischer\", \"counterst\", \"armori\", \"trunk\", \"thomasp\", \"imak\", \"photographi\", \"concordia\", \"tennesse\", \"yamaha\", \"frost\", \"car\", \"ohio\", \"uchicago\", \"rid\", \"gun\", \"brake\", \"shaft\", \"cwru\", \"auto\", \"wagon\", \"handgun\", \"cleveland\", \"ride\", \"tire\", \"urbana\", \"midway\", \"insur\", \"uiuc\", \"dealer\", \"weapon\", \"iastat\", \"owner\", \"illinoi\", \"crime\", \"price\", \"state\", \"freenet\", \"sell\", \"buy\", \"good\", \"road\", \"drive\", \"right\", \"look\", \"peopl\", \"want\", \"case\", \"thing\", \"time\", \"go\", \"distribut\", \"repli\", \"engin\", \"opinion\", \"problem\", \"need\", \"gatech\", \"cub\", \"fnal\", \"prism\", \"hitter\", \"pitcher\", \"alomar\", \"uicvm\", \"higgin\", \"inning\", \"revolv\", \"hulman\", \"yanke\", \"pitch\", \"catcher\", \"dodger\", \"blast\", \"starter\", \"tiger\", \"met\", \"bat\", \"nore\", \"outlet\", \"rocki\", \"jay\", \"sdsu\", \"volt\", \"lopez\", \"restaur\", \"lamp\", \"wire\", \"duke\", \"circuit\", \"batteri\", \"brave\", \"basebal\", \"hit\", \"berkeley\", \"jason\", \"ball\", \"metal\", \"jeff\", \"grind\", \"larc\", \"indiana\", \"netcom\", \"colorado\", \"year\", \"run\", \"good\", \"game\", \"smith\", \"scott\", \"player\", \"home\", \"stanford\", \"look\", \"distribut\", \"go\", \"time\", \"lose\", \"come\", \"start\", \"play\", \"david\", \"best\", \"better\", \"power\", \"thing\", \"john\", \"sale\", \"great\", \"encrypt\", \"escrow\", \"privaci\", \"ripem\", \"crypto\", \"wiretap\", \"cryptographi\", \"cipher\", \"decrypt\", \"hamburg\", \"clipper\", \"homicid\", \"bontchev\", \"gtoal\", \"crypt\", \"clarkson\", \"rwing\", \"surveil\", \"nist\", \"sternlight\", \"den\", \"ncsl\", \"qualcomm\", \"fbihh\", \"cryptograph\", \"tampa\", \"mime\", \"vesselin\", \"lyme\", \"strnlght\", \"key\", \"secur\", \"enforc\", \"recipi\", \"classifi\", \"secret\", \"chip\", \"agenc\", \"patent\", \"scheme\", \"public\", \"govern\", \"algorithm\", \"protect\", \"propos\", \"administr\", \"privat\", \"clinton\", \"feder\", \"phone\", \"court\", \"devic\", \"number\", \"communic\", \"technolog\", \"inform\", \"provid\", \"author\", \"state\", \"right\", \"messag\", \"data\", \"peopl\", \"need\", \"diseas\", \"stratus\", \"dyer\", \"diet\", \"robi\", \"infect\", \"syndrom\", \"methodolog\", \"physician\", \"cure\", \"intellect\", \"einstein\", \"chopin\", \"candida\", \"sphere\", \"yeast\", \"chastiti\", \"halat\", \"therapi\", \"clinic\", \"migrain\", \"steveh\", \"patient\", \"catbyt\", \"dtmedin\", \"blah\", \"carlo\", \"superstit\", \"baerga\", \"homeopathi\", \"skeptic\", \"gordon\", \"pitt\", \"medic\", \"doctor\", \"medicin\", \"food\", \"cancer\", \"sleev\", \"ingr\", \"genet\", \"aid\", \"bank\", \"treatment\", \"water\", \"pain\", \"health\", \"handheld\", \"studi\", \"princeton\", \"caus\", \"effect\", \"scienc\", \"scientif\", \"rochest\", \"theori\", \"point\", \"result\", \"risk\", \"problem\", \"research\", \"case\", \"steve\", \"test\", \"time\", \"peopl\", \"repli\", \"take\", \"differ\", \"year\", \"say\", \"thing\", \"isra\", \"hockey\", \"playoff\", \"palestinian\", \"detroit\", \"leaf\", \"cramer\", \"optilink\", \"pen\", \"cunixb\", \"lebanes\", \"penguin\", \"clayton\", \"jake\", \"maynard\", \"espn\", \"edmonton\", \"ericsson\", \"boni\", \"lemieux\", \"gaza\", \"puck\", \"bruin\", \"selann\", \"laurentian\", \"quebec\", \"ramsey\", \"canuck\", \"shark\", \"uvic\", \"montreal\", \"flyer\", \"israel\", \"stanley\", \"team\", \"jet\", \"coach\", \"ranger\", \"winnipeg\", \"game\", \"play\", \"wing\", \"player\", \"columbia\", \"season\", \"leagu\", \"pittsburgh\", \"score\", \"arab\", \"mcgill\", \"goal\", \"virginia\", \"toronto\", \"andrew\", \"year\", 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\"widget\", \"window\", \"window\", \"window\", \"wing\", \"wing\", \"wing\", \"wing\", \"wing\", \"winnipeg\", \"winnipeg\", \"wire\", \"wire\", \"wire\", \"wiretap\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worship\", \"worship\", \"xlib\", \"xpert\", \"xterm\", \"xview\", \"yamaha\", \"yanke\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zoolog\", \"zuma\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 8, 9, 2, 4, 7, 10, 5, 6]};\n",
        "\n",
        "function LDAvis_load_lib(url, callback){\n",
        "  var s = document.createElement('script');\n",
@@ -502,7 +641,7 @@
        "if(typeof(LDAvis) !== \"undefined\"){\n",
        "   // already loaded: just create the visualization\n",
        "   !function(LDAvis){\n",
-       "       new LDAvis(\"#\" + \"ldavis_el591011124095238088809029897\", ldavis_el591011124095238088809029897_data);\n",
+       "       new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
        "   }(LDAvis);\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/LDAvis\n",
@@ -510,168 +649,118 @@
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "        new LDAvis(\"#\" + \"ldavis_el591011124095238088809029897\", ldavis_el591011124095238088809029897_data);\n",
+       "        new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
        "      });\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & LDAvis\n",
        "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
        "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "                 new LDAvis(\"#\" + \"ldavis_el591011124095238088809029897\", ldavis_el591011124095238088809029897_data);\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
        "            })\n",
        "         });\n",
        "}\n",
        "</script>"
       ],
       "text/plain": [
-       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
-       "topic                                                \n",
-       "2     -0.078669 -0.151706       1        1  13.182505\n",
-       "0     -0.016999 -0.150447       2        1  12.926197\n",
-       "7      0.208967  0.080137       3        1  12.463007\n",
-       "8      0.147446  0.136478       4        1  11.995771\n",
-       "1     -0.008849 -0.009046       5        1   9.559109\n",
-       "3     -0.045054  0.003907       6        1   9.468682\n",
-       "6     -0.089499  0.144727       7        1   8.927140\n",
-       "9      0.107803 -0.077376       8        1   7.882134\n",
-       "4      0.004524 -0.105100       9        1   7.024930\n",
-       "5     -0.229669  0.128426      10        1   6.570525, topic_info=     Category         Freq        Term        Total  loglift  logprob\n",
-       "1037  Default  2966.000000      window  2966.000000  30.0000  30.0000\n",
-       "1193  Default  1940.000000        game  1940.000000  29.0000  29.0000\n",
-       "482   Default  1924.000000   christian  1924.000000  28.0000  28.0000\n",
-       "702   Default  1689.000000        team  1689.000000  27.0000  27.0000\n",
-       "352   Default  2638.000000       drive  2638.000000  26.0000  26.0000\n",
-       "320   Default  2883.000000        file  2883.000000  25.0000  25.0000\n",
-       "696   Default  1860.000000       space  1860.000000  24.0000  24.0000\n",
-       "1467  Default  1161.000000     encrypt  1161.000000  23.0000  23.0000\n",
-       "256   Default  1997.000000      govern  1997.000000  22.0000  22.0000\n",
-       "153   Default  1477.000000        chip  1477.000000  21.0000  21.0000\n",
-       "522   Default  1274.000000       jesus  1274.000000  20.0000  20.0000\n",
-       "1347  Default  1017.000000      israel  1017.000000  19.0000  19.0000\n",
-       "36    Default  1577.000000        card  1577.000000  18.0000  18.0000\n",
-       "120   Default  1423.000000        play  1423.000000  17.0000  17.0000\n",
-       "964   Default  1059.000000       secur  1059.000000  16.0000  16.0000\n",
-       "657   Default  1331.000000        nasa  1331.000000  15.0000  15.0000\n",
-       "1821  Default  1142.000000    armenian  1142.000000  14.0000  14.0000\n",
-       "822   Default  2599.000000     program  2599.000000  13.0000  13.0000\n",
-       "1346  Default   837.000000        isra   837.000000  12.0000  12.0000\n",
-       "513   Default  1391.000000        imag  1391.000000  11.0000  11.0000\n",
-       "117   Default  6052.000000       peopl  6052.000000  10.0000  10.0000\n",
-       "3565  Default   742.000000      hockey   742.000000   9.0000   9.0000\n",
-       "739   Default   990.000000      player   990.000000   8.0000   8.0000\n",
-       "1457  Default   784.000000     clipper   784.000000   7.0000   7.0000\n",
-       "326   Default  1802.000000      public  1802.000000   6.0000   6.0000\n",
-       "41    Default  1023.000000        disk  1023.000000   5.0000   5.0000\n",
-       "28    Default  4004.000000        year  4004.000000   4.0000   4.0000\n",
-       "380   Default   867.000000        scsi   867.000000   3.0000   3.0000\n",
-       "879   Default  1191.000000      driver  1191.000000   2.0000   2.0000\n",
-       "444   Default   747.000000        bike   747.000000   1.0000   1.0000\n",
-       "...       ...          ...         ...          ...      ...      ...\n",
-       "5004  Topic10   178.948181     stanley   185.594589   2.6861  -6.0394\n",
-       "702   Topic10  1384.116455        team  1689.568604   2.5232  -3.9937\n",
-       "4840  Topic10   160.981583         jet   167.196503   2.6847  -6.1452\n",
-       "3815  Topic10   191.566772       coach   202.233215   2.6684  -5.9713\n",
-       "3537  Topic10   221.759384      ranger   240.052933   2.6433  -5.8249\n",
-       "4877  Topic10   157.258331    winnipeg   165.160721   2.6735  -6.1686\n",
-       "1193  Topic10  1290.715576        game  1940.664795   2.3147  -4.0636\n",
-       "120   Topic10   920.770020        play  1423.930298   2.2866  -4.4013\n",
-       "711   Topic10   312.806488        wing   399.885132   2.4770  -5.4809\n",
-       "739   Topic10   623.101746      player   990.567200   2.2590  -4.7918\n",
-       "1311  Topic10   397.739624    columbia   559.283691   2.3817  -5.2407\n",
-       "1080  Topic10   455.438751      season   670.126038   2.3364  -5.1053\n",
-       "3252  Topic10   379.943481       leagu   536.827942   2.3769  -5.2865\n",
-       "1073  Topic10   351.761322  pittsburgh   505.782318   2.3594  -5.3636\n",
-       "1679  Topic10   356.976562       score   528.273926   2.3306  -5.3488\n",
-       "1321  Topic10   374.845306        arab   572.500732   2.2991  -5.3000\n",
-       "3488  Topic10   212.819550      mcgill   254.334839   2.5444  -5.8661\n",
-       "1475  Topic10   346.644043        goal   553.699341   2.2543  -5.3782\n",
-       "1150  Topic10   312.806427    virginia   544.289856   2.1687  -5.4809\n",
-       "1082  Topic10   333.144867     toronto   701.091187   1.9785  -5.4179\n",
-       "1155  Topic10   336.057953      andrew   868.338135   1.7733  -5.4092\n",
-       "28    Topic10   600.308960        year  4004.757812   0.8248  -4.8291\n",
-       "157   Topic10   295.451538       divis   693.417114   1.8694  -5.5380\n",
-       "2117  Topic10   283.661255      canada   732.439819   1.7740  -5.5787\n",
-       "2549  Topic10   250.147522      period   584.503235   1.8739  -5.7045\n",
-       "45    Topic10   267.235901       final   822.295105   1.5986  -5.6384\n",
-       "171   Topic10   330.680511       point  2646.791748   0.6426  -5.4254\n",
-       "146   Topic10   358.020264        time  5183.146484   0.0500  -5.3459\n",
-       "1545  Topic10   262.164948    american  1242.273315   1.1669  -5.6575\n",
-       "99    Topic10   242.101822          go  3510.730713   0.0484  -5.7372\n",
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Gensim\n",
+    "p = visualize_topics(model, bow_corpus, dictionary, model_type='gensim')\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:223: RuntimeWarning: divide by zero encountered in log\n",
+      "  kernel = (topic_given_term * np.log((topic_given_term.T / topic_proportion).T))\n",
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:240: RuntimeWarning: divide by zero encountered in log\n",
+      "  log_lift = np.log(topic_term_dists / term_proportion)\n",
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:241: RuntimeWarning: divide by zero encountered in log\n",
+      "  log_ttd = np.log(topic_term_dists)\n",
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el15587112430946968420164328\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el15587112430946968420164328_data = {\"mdsDat\": {\"x\": [-0.11865444229892427, -0.15913644671148153, -0.1021552834840701, -0.1302147807267131, 0.025996119058630945, -0.02613484912760001, -0.04773338510733379, 0.20792709345760607, -0.03261899864327976, 0.38272497358316565], \"y\": [-0.14612969845944368, -0.07809283008476131, -0.21207201799419742, 0.20626190412239923, -0.16846330671503923, 0.13616430604087143, 0.058577567883906, -0.12443061559914777, 0.27343632979353877, 0.05474836101187306], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [12.365908144728973, 12.10898411608011, 10.931706618523949, 10.925960423580584, 10.198631258133638, 10.029655051537938, 9.063929680129815, 8.785865295646344, 8.485634665680474, 7.103724745958186]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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294.30229993365356, 590.6992781586498, 278.59221274858305, 274.4028561658976, 259.7401081264985, 422.07767570556007, 342.47990063453636, 323.62779601245177, 214.7045248626298, 210.51516827994433, 208.4204899886016, 203.18379426024478, 201.08911596890206, 179.0949939098034, 177.0003156184607, 174.90563732711794, 2571.217602623201, 633.6401831311757, 501.6754507765838, 812.7351770409791, 788.6463766905377, 595.9359738870066, 447.2138152016728, 498.5334333395697, 1280.8957751560793, 488.06004188285607, 479.68132871748514, 1824.4647917595175, 516.3381988159829, 528.9062685640392, 808.5458204582937, 609.5513827807343, 658.7763226272886, 672.3917315210163, 861.9601168875333, 626.3088091114762, 807.4984813126224, 580.2258867019361, 527.8589294183679, 591.7466173043211, 460.82922409540055], \"Term\": [\"file\", \"window\", \"drive\", \"repli\", \"game\", \"peopl\", \"mail\", \"program\", \"space\", \"distribut\", \"christian\", \"believ\", \"card\", \"team\", \"state\", \"year\", \"inform\", 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\"voltag\", \"channel\", \"disk\", \"sale\", \"spec\", \"monitor\", \"appl\", \"price\", \"switch\", \"speed\", \"port\", \"instal\", \"printer\", \"board\", \"tape\", \"hard\", \"memori\", \"control\", \"sell\", \"ship\", \"problem\", \"buy\", \"work\", \"sound\", \"connect\", \"machin\", \"mode\", \"chip\", \"power\", \"devic\", \"softwar\", \"brandt\", \"franklin\", \"calstat\", \"bois\", \"hypocrisi\", \"mickey\", \"bmerh\", \"guitar\", \"bigboot\", \"chan\", \"worcest\", \"alexia\", \"robertson\", \"salmon\", \"tamu\", \"pwiseman\", \"teal\", \"rethink\", \"toni\", \"nodak\", \"covington\", \"bailey\", \"freeman\", \"decvax\", \"lynx\", \"wallac\", \"jacob\", \"bcstec\", \"amherst\", \"broward\", \"michael\", \"uiuc\", \"virginia\", \"cwru\", \"austin\", \"utexa\", \"univ\", \"freenet\", \"gordon\", \"andi\", \"illinoi\", \"netcom\", \"gatech\", \"magnus\", \"pitt\", \"uchicago\", \"udel\", \"craig\", \"chris\", \"purdu\", \"william\", \"portal\", \"umich\", \"ingr\", \"prism\", 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\"consid\", \"consid\", \"consid\", \"constitut\", \"constitut\", \"contact\", \"contact\", \"contradict\", \"control\", \"control\", \"control\", \"control\", \"convert\", \"cool\", \"copi\", \"copi\", \"copi\", \"copi\", \"copi\", \"cost\", \"cost\", \"cost\", \"couldn\", \"couldn\", \"countri\", \"countri\", \"coupl\", \"coupl\", \"coupl\", \"coupl\", \"coupl\", \"cours\", \"cours\", \"cours\", \"cours\", \"cours\", \"cours\", \"cours\", \"court\", \"court\", \"coventri\", \"covington\", \"craig\", \"cramer\", \"crime\", \"crime\", \"crucifi\", \"cruiser\", \"crux\", \"crypt\", \"crypto\", \"cryptolog\", \"cure\", \"cview\", \"cwru\", \"cycl\", \"cycl\", \"dalhousi\", \"daryl\", \"data\", \"data\", \"data\", \"data\", \"data\", \"david\", \"david\", \"david\", \"david\", \"david\", \"david\", \"davidian\", \"debug\", \"decvax\", \"default\", \"defens\", \"defens\", \"deficit\", \"definit\", \"definit\", \"definit\", \"den\", \"denomin\", \"dens\", \"depriv\", \"design\", \"design\", \"design\", \"design\", \"design\", \"detector\", \"detroit\", \"devic\", \"devic\", \"devil\", \"diagnos\", \"diagnosi\", \"diagram\", \"dialog\", \"diamond\", \"differ\", \"differ\", \"differ\", \"differ\", \"differ\", \"differ\", \"differ\", \"digex\", \"digex\", \"directori\", \"disarm\", \"disclaim\", \"disclaim\", \"disclaim\", \"diseas\", \"disk\", \"disk\", \"disobey\", \"display\", \"distribut\", \"distribut\", \"distribut\", \"distribut\", \"distribut\", \"distribut\", \"divin\", \"divis\", \"divis\", \"divis\", \"dock\", \"doctrin\", \"dog\", \"dorothi\", \"dose\", \"dragon\", \"dri\", \"drink\", \"drive\", \"driver\", \"driver\", \"drug\", \"dscomsa\", \"dseg\", \"dude\", \"duke\", \"duke\", \"dutch\", \"earth\", \"earth\", \"eat\", \"electron\", \"electron\", \"elementari\", \"email\", \"email\", \"email\", \"encrypt\", \"enforc\", \"engin\", \"engin\", \"engr\", \"entri\", \"escrow\", \"escrow\", \"esdi\", \"etern\", \"evas\", \"evid\", \"evid\", \"evil\", \"exampl\", \"exampl\", \"exampl\", \"exampl\", \"exist\", \"exist\", \"exist\", \"face\", \"face\", \"face\", \"face\", \"fact\", \"fact\", \"fact\", \"fact\", \"faith\", \"fan\", \"feder\", \"federalist\", \"feel\", \"feel\", \"feel\", \"feel\", \"file\", \"final\", \"final\", \"final\", \"final\", \"final\", \"finland\", \"firearm\", \"fiscal\", \"fist\", \"flash\", \"flight\", \"flight\", \"floppi\", \"fluid\", \"flyer\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"font\", \"food\", \"footbal\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"ford\", \"format\", \"format\", \"franklin\", \"freeman\", \"freenet\", \"friend\", \"friend\", \"friend\", \"friend\", \"frontier\", \"function\", \"function\", \"game\", \"gari\", \"gari\", \"gatech\", \"gaza\", \"gear\", \"general\", \"general\", \"general\", \"general\", \"general\", \"general\", \"geneva\", \"genocid\", \"german\", \"gibson\", \"glad\", \"glad\", \"glad\", \"goal\", \"goal\", \"goal\", \"goddess\", \"gonna\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"gordon\", \"gover\", \"govern\", \"govern\", \"graphic\", \"gray\", \"greec\", \"greek\", \"greenbelt\", \"gretzki\", \"grin\", \"grip\", \"group\", \"group\", \"group\", \"group\", \"group\", \"guidelin\", \"guitar\", \"gun\", \"hadn\", \"hallam\", \"halv\", \"hamburg\", \"hand\", \"hand\", \"hand\", \"hand\", \"hand\", \"handgun\", \"handler\", \"happen\", \"happen\", \"happen\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"health\", \"health\", \"hear\", \"hear\", \"hear\", \"hear\", \"heaven\", \"helmet\", \"helmet\", \"henri\", \"henri\", \"heresi\", \"high\", \"high\", \"high\", \"high\", \"high\", \"histori\", \"histori\", \"histori\", \"histori\", \"hockey\", \"holi\", \"homicid\", \"homosexu\", \"honda\", \"hook\", \"hors\", \"hous\", \"hous\", \"hull\", \"hulman\", \"human\", \"human\", \"human\", \"hurt\", \"hurt\", \"hussein\", \"hypocrisi\", \"hypothet\", \"ieee\", \"illeg\", \"illinoi\", \"imag\", \"imag\", \"impair\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"infal\", \"infant\", \"infect\", \"infin\", \"inform\", \"inform\", \"inform\", \"ingr\", \"init\", \"inject\", \"innoc\", \"innoc\", \"instal\", \"instal\", \"instal\", \"institut\", \"institut\", \"institut\", \"insur\", \"insur\", \"interest\", \"interest\", \"interest\", \"interest\", \"interest\", \"interest\", \"interest\", \"intermitt\", \"internet\", \"internet\", \"internet\", \"introduct\", \"irrit\", \"islam\", \"islam\", \"isra\", \"isra\", \"israel\", \"issu\", \"issu\", \"issu\", \"issu\", \"jacob\", \"jade\", \"jagr\", \"jesus\", \"jew\", \"jewish\", \"jewish\", \"job\", \"john\", \"john\", \"john\", \"john\", \"john\", \"jose\", \"journal\", \"jpeg\", \"jumper\", \"kansa\", \"kean\", \"keen\", \"keith\", \"keith\", \"keith\", \"key\", \"key\", \"kill\", \"kill\", \"kilroy\", \"king\", \"king\", \"king\", \"king\", \"kinsey\", \"koresh\", \"koufax\", \"krillean\", \"ksand\", \"laboratori\", \"laboratori\", \"land\", \"land\", \"laptop\", \"launch\", \"launchpad\", \"leaf\", \"leagu\", \"leather\", \"leav\", \"leav\", \"leav\", \"leav\", \"legal\", \"legisl\", \"lemon\", \"lethal\", \"libertarian\", \"liberti\", \"librari\", \"life\", \"life\", \"life\", \"life\", \"life\", \"light\", \"light\", \"lindro\", \"liner\", \"list\", \"list\", \"list\", \"list\", \"literatur\", \"littl\", \"littl\", \"littl\", \"littl\", \"littl\", \"littl\", \"live\", \"live\", \"live\", \"livesey\", \"lock\", \"lock\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"lord\", \"lord\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"loui\", \"love\", \"love\", \"love\", \"luke\", \"lunar\", \"lutheran\", \"lynx\", \"machin\", \"machin\", \"machin\", \"madam\", \"magnus\", \"mail\", \"mail\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"manag\", \"manag\", \"manag\", \"manag\", \"mark\", \"mark\", \"mark\", \"mark\", \"mark\", \"mark\", \"marlin\", \"marriag\", \"marvel\", \"massacr\", \"mauric\", \"maxtor\", \"mayb\", \"mayb\", \"mayb\", \"mayb\", \"mayb\", \"meaning\", \"medic\", \"medicin\", \"megabyt\", \"mein\", \"melbourn\", \"mellon\", \"memoir\", \"memori\", \"memori\", \"memori\", \"messag\", \"messag\", \"messag\", \"meyer\", \"michael\", \"mickey\", \"microsoft\", \"midway\", \"mike\", \"mike\", \"mile\", \"mile\", \"militia\", \"milk\", \"minnesota\", \"mission\", \"mode\", \"mode\", \"model\", \"model\", \"model\", \"modem\", \"mogilni\", \"monitor\", \"monitor\", \"mono\", \"montreal\", \"moon\", \"moral\", \"moral\", \"mosqu\", \"motherboard\", \"motif\", \"moto\", \"motorcycl\", \"motorola\", \"mous\", \"movement\", \"murder\", \"muscl\", \"musicb\", \"muslim\", \"myer\", \"myrto\", \"nasa\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"natur\", \"natur\", \"natur\", \"natur\", \"nazi\", \"nctu\", \"nearest\", \"neccessari\", \"netcom\", \"netcom\", \"netcom\", \"network\", \"news\", \"news\", \"news\", \"news\", \"newsgroup\", \"newsgroup\", \"newslett\", \"newsread\", \"newsread\", \"niel\", \"nodak\", \"nore\", \"notion\", \"nuclear\", \"null\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"occupi\", \"offens\", \"offens\", \"ohio\", \"ohio\", \"okcforum\", \"onlin\", \"onlin\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"ottoman\", \"ouch\", \"outlet\", \"output\", \"packag\", \"packag\", \"pain\", \"palestinian\", \"panther\", \"paper\", \"paper\", \"paper\", \"partnership\", \"passer\", \"passion\", \"patch\", \"patent\", \"patent\", \"patient\", \"patrick\", \"peac\", \"peac\", \"penalti\", \"penguin\", \"pentium\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"perpetr\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"philadelphia\", \"phone\", \"phone\", \"phone\", \"phone\", \"photoshop\", \"physician\", \"pick\", \"pick\", \"pinout\", \"piss\", \"pistol\", \"pitch\", \"pitt\", \"pitt\", \"pixmap\", \"planet\", \"planet\", \"play\", \"player\", \"playoff\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polygon\", \"popul\", \"popul\", \"porsch\", \"port\", \"port\", \"portal\", \"postscript\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerpc\", \"practition\", \"presid\", \"preview\", \"price\", \"price\", \"price\", \"price\", \"printer\", \"printer\", \"prism\", \"privat\", \"privat\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"prohibit\", \"project\", \"project\", \"project\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protein\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"pull\", \"pump\", \"purdu\", \"purdu\", \"pwiseman\", \"quadra\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"radar\", \"random\", \"random\", \"ranger\", \"rapist\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"receiv\", \"receiv\", \"receiv\", \"regret\", \"regul\", \"reilli\", \"reinstal\", \"religion\", \"rememb\", \"rememb\", \"rememb\", \"rememb\", \"renam\", \"repli\", \"repli\", \"repli\", \"repli\", \"reproduct\", \"republican\", \"request\", \"request\", \"research\", \"research\", \"research\", \"research\", \"resiz\", \"resourc\", \"resourc\", \"rethink\", \"revok\", \"revolut\", \"revolv\", \"ribbon\", \"richer\", \"rid\", \"ride\", \"rider\", \"ripem\", \"robert\", \"robert\", \"robert\", \"robertson\", \"rock\", \"rocket\", \"roger\", \"rooki\", \"roster\", \"roth\", \"routin\", \"run\", \"run\", \"run\", \"run\", \"rwing\", \"safeguard\", \"sage\", \"sale\", \"sale\", \"salmon\", \"sandvik\", \"santa\", \"satellit\", \"savag\", \"savior\", \"scanner\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"score\", \"screen\", \"scriptur\", \"scsi\", \"seagat\", \"season\", \"secreci\", \"secret\", \"secret\", \"section\", \"section\", \"section\", \"secur\", \"secur\", \"secur\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"serdar\", \"server\", \"servic\", \"servic\", \"servic\", \"servic\", \"ship\", \"ship\", \"ship\", \"shuttl\", \"signatur\", \"simm\", \"slaveri\", \"slump\", \"small\", \"small\", \"small\", \"small\", \"smith\", \"smith\", \"smith\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"soldier\", \"sound\", \"sound\", \"sound\", \"sound\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"soviet\", \"soviet\", \"space\", \"space\", \"spacecraft\", \"spanish\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spirit\", \"sport\", \"sport\", \"spreadsheet\", \"stake\", \"start\", \"start\", \"start\", \"start\", \"start\", \"state\", \"state\", \"state\", \"state\", \"state\", \"static\", \"station\", \"station\", \"statut\", \"steer\", \"stop\", \"stop\", \"stop\", \"stop\", \"strain\", \"stream\", \"string\", \"stroke\", \"stupid\", \"stupid\", \"subscrib\", \"subscript\", \"summari\", \"summari\", \"summari\", \"sunlight\", \"sunni\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"surfac\", \"svga\", \"sweat\", \"switch\", \"switch\", \"symptom\", \"syndrom\", \"sysop\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talon\", \"tamu\", \"tape\", \"tape\", \"tast\", \"teach\", \"teach\", \"teach\", \"teal\", \"team\", \"technic\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"telescop\", \"telnet\", \"temperatur\", \"tender\", \"tenn\", \"tennesse\", \"territori\", \"territori\", \"texa\", \"texa\", \"texa\", \"thai\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thrower\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"titan\", \"tobacco\", \"today\", \"today\", \"today\", \"toni\", \"topic\", \"topic\", \"toronto\", \"toronto\", \"torqu\", \"toyota\", \"trade\", \"trade\", \"tranquil\", \"treatment\", \"treatment\", \"treatment\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"tri\", \"trivia\", \"troop\", \"truck\", \"true\", \"true\", \"true\", \"true\", \"truth\", \"truth\", \"turbo\", \"turk\", \"turkey\", \"turkish\", \"turkiy\", \"turn\", \"turn\", \"turn\", \"uart\", \"uchicago\", \"udel\", \"uiuc\", \"ukan\", \"umich\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"univ\", \"unix\", \"unix\", \"unix\", \"unplug\", \"unsaf\", \"uokmax\", \"uoknor\", \"upenn\", \"upgrad\", \"urbana\", \"user\", \"user\", \"utexa\", \"utkvm\", \"vehicl\", \"vehicl\", \"venus\", \"version\", \"version\", \"video\", \"viewer\", \"villag\", \"virginia\", \"virtu\", \"visual\", \"visual\", \"vitamin\", \"voltag\", \"vram\", \"vulcan\", \"wallac\", \"warrant\", \"warrant\", \"wasn\", \"wasn\", \"water\", \"water\", \"water\", \"watt\", \"weapon\", \"weapon\", \"weapon\", \"wear\", \"weren\", \"widget\", \"william\", \"william\", \"win\", \"win\", \"window\", \"windshield\", \"wing\", \"wing\", \"wing\", \"wire\", \"wiretap\", \"wong\", \"worcest\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"workgroup\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"wors\", \"wors\", \"worship\", \"wouldn\", \"wouldn\", \"wouldn\", \"xcopyarea\", \"xlib\", \"xterm\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 9, 5, 6, 10, 2, 7, 1, 8, 3]};\n",
        "\n",
-       "[734 rows x 6 columns], token_table=      Topic      Freq       Term\n",
-       "term                            \n",
-       "338       1  0.145983     accept\n",
-       "338       2  0.524347     accept\n",
-       "338       3  0.129101     accept\n",
-       "338       4  0.049654     accept\n",
-       "338       5  0.007945     accept\n",
-       "338       6  0.022841     accept\n",
-       "338       8  0.066536     accept\n",
-       "338       9  0.034758     accept\n",
-       "338      10  0.018869     accept\n",
-       "73        3  0.403915     access\n",
-       "73        4  0.196068     access\n",
-       "73        5  0.171820     access\n",
-       "73        6  0.033255     access\n",
-       "73        7  0.000693     access\n",
-       "73        8  0.193297     access\n",
-       "2940      4  0.984634    adaptec\n",
-       "152       1  0.052461    address\n",
-       "152       2  0.074007    address\n",
-       "152       3  0.536784    address\n",
-       "152       4  0.182675    address\n",
-       "152       5  0.030914    address\n",
-       "152       6  0.026230    address\n",
-       "152       7  0.032788    address\n",
-       "152       8  0.049650    address\n",
-       "152       9  0.005621    address\n",
-       "152      10  0.008431    address\n",
-       "1444      1  0.124112  administr\n",
-       "1444      3  0.063074  administr\n",
-       "1444      5  0.197359  administr\n",
-       "1444      8  0.614458  administr\n",
-       "...     ...       ...        ...\n",
-       "182       2  0.166406      world\n",
-       "182       3  0.097373      world\n",
-       "182       4  0.150056      world\n",
-       "182       5  0.106093      world\n",
-       "182       6  0.051230      world\n",
-       "182       7  0.080296      world\n",
-       "182       8  0.011990      world\n",
-       "182       9  0.053410      world\n",
-       "182      10  0.054863      world\n",
-       "4238      1  0.009547    worship\n",
-       "4238      2  0.988079    worship\n",
-       "4809      3  0.994707       xlib\n",
-       "3868      3  0.995135      xpert\n",
-       "3581      3  0.995652      xterm\n",
-       "2827      3  0.988763      xview\n",
-       "6047      6  0.981546     yamaha\n",
-       "1701      7  0.988053      yanke\n",
-       "28        1  0.130095       year\n",
-       "28        2  0.031962       year\n",
-       "28        3  0.015482       year\n",
-       "28        4  0.038954       year\n",
-       "28        5  0.216243       year\n",
-       "28        6  0.063674       year\n",
-       "28        7  0.244959       year\n",
-       "28        8  0.040951       year\n",
-       "28        9  0.067919       year\n",
-       "28       10  0.149822       year\n",
-       "3309      9  0.986879      yeast\n",
-       "3190      5  0.990640     zoolog\n",
-       "1894      1  0.991014       zuma\n",
+       "function LDAvis_load_lib(url, callback){\n",
+       "  var s = document.createElement('script');\n",
+       "  s.src = url;\n",
+       "  s.async = true;\n",
+       "  s.onreadystatechange = s.onload = callback;\n",
+       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "}\n",
        "\n",
-       "[2168 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 8, 9, 2, 4, 7, 10, 5, 6])"
+       "if(typeof(LDAvis) !== \"undefined\"){\n",
+       "   // already loaded: just create the visualization\n",
+       "   !function(LDAvis){\n",
+       "       new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
+       "   }(LDAvis);\n",
+       "}else if(typeof define === \"function\" && define.amd){\n",
+       "   // require.js is available: use it to load d3/LDAvis\n",
+       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "p = visualize_topics(model, bow_corpus, dictionary)\n",
+    "# Mallet\n",
+    "p = visualize_topics(model_mallet, bow_corpus, dictionary, model_type='mallet')\n",
     "p"
    ]
   },

From 5653422db83d4676bc7bc1cbc60439d0c2b6b0ab Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 13:21:43 +0200
Subject: [PATCH 188/496] remove duplicated

---
 nautilus_nlp/utils/lemmatizer.py | 102 -------------------------------
 nautilus_nlp/utils/stemmer.py    |  31 ----------
 nautilus_nlp/utils/tokenizer.py  |  80 ------------------------
 3 files changed, 213 deletions(-)
 delete mode 100644 nautilus_nlp/utils/lemmatizer.py
 delete mode 100644 nautilus_nlp/utils/stemmer.py
 delete mode 100644 nautilus_nlp/utils/tokenizer.py

diff --git a/nautilus_nlp/utils/lemmatizer.py b/nautilus_nlp/utils/lemmatizer.py
deleted file mode 100644
index e4c812c..0000000
--- a/nautilus_nlp/utils/lemmatizer.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import spacy
-import nltk
-nltk.download('wordnet')
-from nltk.stem import WordNetLemmatizer 
-from nltk.corpus import wordnet
-
-try:
-    french_spacy = spacy.load('fr_core_news_sm')
-except OSError:
-    raise OSError("""You must install French langage to use SpaCy. 
-                    python -m spacy download fr
-                    See https://spacy.io/usage/ for details
-                """)
-try:
-    english_spacy = spacy.load('en_core_web_sm')
-except OSError:
-    raise OSError("""You must install english langage to use SpaCy. 
-                    python -m spacy download en
-                    See https://spacy.io/usage/ for details
-                """)             
-
-
-
-
-def lemmatize_french_tokens(tokens, module='spacy', load_only_pos='all'):
-    '''
-    Wrappers of french lemmatizers. SpaCy is much faster but sometimes 
-    FrenchLefffLemmatizer returns more accurate results. Give a try to the 
-    2 modules and choose the one that better fit your needs. 
-
-    Args:
-        tokens (list): list of tokens
-        module ({'spacy'}): modules availables.
-        load_only_pos ({'a', v', 'r', 'n', 'all'}): If not "all", applies 
-        lemmatization only to a certain POS tags, for french_leff_v module.
-        a = adjectives, v = verbs, n = noun, r = adverb. 
-
-    Returns:
-        list of lemmatized tokens
-    
-    '''
-
-    tokens = _make_sure_input_is_list_of_tokens(tokens)
-
-    if module == 'spacy':
-        # Doc : https://spacy.io/api/token#attributes
-        text = ' '.join(tokens)
-        doc = french_spacy(text)
-        return [token.lemma_ for token in doc]
-
-    else:
-        raise ValueError("must pass a valid module name!")
-
-
-def lemmatize_english_tokens(tokens, module='spacy', pos_tag_prio='v'):
-    '''
-    Args:
-        tokens (list): list of tokens
-        module ({'french_leff_v', 'spacy'}): modules availables.
-        pos_tag_prio ({'v', 'n', 'all'}): grammatical priority, applies
-        for french_leff_v module. 
-
-    Returns:
-        list of lemmatized tokens
-    
-    '''
-    tokens = _make_sure_input_is_list_of_tokens(tokens)
-
-    if module == 'nltk':
-        # Doc : https://github.com/ClaudeCoulombe/FrenchLefffLemmatizer
-        lemmatizer = WordNetLemmatizer()
-        return [lemmatizer.lemmatize(word, _get_wordnet_pos(word)) for word in tokens]
-
-    elif module == 'spacy':
-        # Doc : https://spacy.io/api/token#attributes
-        text = ' '.join(tokens)
-        doc = english_spacy(text)
-        return [token.lemma_ for token in doc]
-
-    else:
-        raise ValueError("must pass a valid module name!")
-
-
-def _get_wordnet_pos(word):
-    """Map POS tag to first character lemmatize() accepts"""
-    tag = nltk.pos_tag([word])[0][1][0].upper()
-    tag_dict = {"J": wordnet.ADJ,
-                "N": wordnet.NOUN,
-                "V": wordnet.VERB,
-                "R": wordnet.ADV}
-
-    return tag_dict.get(tag, wordnet.NOUN)        
-
-
-def _make_sure_input_is_list_of_tokens(tokens):
-    if type(tokens) is list:
-        return tokens
-    elif tokens is None:
-        return []
-    elif type(tokens) is str:
-        raise ValueError("must pass a list of tokens, not text!")    
-
diff --git a/nautilus_nlp/utils/stemmer.py b/nautilus_nlp/utils/stemmer.py
deleted file mode 100644
index c480ca8..0000000
--- a/nautilus_nlp/utils/stemmer.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from nltk.stem.snowball import *
-
-
-def stem_tokens(tokens: list, lang: str ='english'):
-    '''
-    Wrapper of NLTK's Snowball stemmers : http://www.nltk.org/howto/stem.html
-
-    Args:
-        tokens (list): list of tokens
-        lang ({'arabic', 'danish', 'dutch', 'english', 'finnish', 'french',
-        'german', 'hungarian', 'italian', 'norwegian', 'porter', 'portuguese', 
-        'romanian', 'russian', 'spanish', 'swedish'}): supported langages
-
-    Returns:
-        list of stemmed tokens
-    
-    '''
-    supported_lang = [lang for lang in SnowballStemmer.languages]
-    
-    if lang in supported_lang:
-        stemmer = eval(lang.capitalize()+'Stemmer()')
-    else:
-        raise ValueError("Langage not supported of mispelled")
-    
-    # Make sure tokens are actually a list, and handle NaN. 
-    if type(tokens) is list:
-        return [stemmer.stem(token) for token in tokens]
-    elif tokens is None:
-        return []
-    elif type(tokens) is str:
-        raise ValueError("must pass a list of tokens, not text!")
\ No newline at end of file
diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
deleted file mode 100644
index a009533..0000000
--- a/nautilus_nlp/utils/tokenizer.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import nltk
-from sacremoses import MosesTokenizer, MosesDetokenizer
-import spacy
-from spacy.lang.fr import French
-from spacy.lang.en import English
-import spacy.lang as spacylang
-#nltk.download('punkt')
-
-try:
-    french_spacy = spacylang.fr.French()
-except OSError:
-    raise OSError("""You must install French langage to use SpaCy. 
-                    python -m spacy download fr
-                    See https://spacy.io/usage/ for details
-                """)
-try:
-    english_spacy = spacylang.en.English()
-except OSError:
-    raise OSError("""You must install english langage to use SpaCy. 
-                    python -m spacy download en
-                    See https://spacy.io/usage/ for details
-                """)             
-
-
-def tokenize(text: str, lang_module: str = 'en_spacy'):
-    """
-    Convert text to a list of tokens. 
-
-    Args:
-        lang_module ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
-        the tokenization module according to the langage and the implementation.
-        Recommanded: Spacy (faster, better results). To process other langages
-        import models.Spacy_models
-
-    Returns:
-        list
-    """
-    if lang_module is 'en_nltk':
-        return nltk.word_tokenize(text)
-    elif lang_module is 'en_spacy':
-        spacydoc = english_spacy(text)
-        return [tokens.text for tokens in spacydoc]
-    elif lang_module is 'fr_spacy':
-        spacydoc = french_spacy(text)
-        return [tokens.text for tokens in spacydoc]
-    elif lang_module is 'fr_moses':
-        t = MosesTokenizer(lang='fr')
-        return t.tokenize(text, escape=False)
-
-
-def untokenize(tokens, lang='fr'):
-    '''
-    Inputs a list of tokens output string.
-    ["J'", 'ai'] >>> "J' ai"
-    '''
-    d = MosesDetokenizer(lang=lang)
-    text = d.detokenize(tokens, unescape=False)
-    return text    
-
-
-def _convert_tokens_to_string(tokens_or_str):
-    if type(tokens_or_str) is str:
-        return tokens_or_str
-    elif type(tokens_or_str) is list:
-        return untokenize(tokens_or_str)
-    elif type(tokens_or_str) is None:
-        return ''
-    else:
-        raise ValueError('Please input string or tokens')
-
-
-def _convert_string_to_tokens(tokens_or_str, lang_module='en_spacy'):
-    if type(tokens_or_str) is str:
-        return tokenize(tokens_or_str, lang_module=lang_module)
-    elif type(tokens_or_str) is list:
-        return tokens_or_str
-    elif type(tokens_or_str) is None:
-        return []
-    else:
-        raise ValueError('Please input string or tokens')
\ No newline at end of file

From a430dd233344251c047f4d7772ac6b38811cbee4 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 13:22:00 +0200
Subject: [PATCH 189/496] add nltk download

---
 nautilus_nlp/preprocessing/lemmatization.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/nautilus_nlp/preprocessing/lemmatization.py b/nautilus_nlp/preprocessing/lemmatization.py
index 462472e..2b54ae2 100644
--- a/nautilus_nlp/preprocessing/lemmatization.py
+++ b/nautilus_nlp/preprocessing/lemmatization.py
@@ -1,6 +1,7 @@
 import spacy
 import nltk
 nltk.download('wordnet')
+nltk.download('averaged_perceptron_tagger')
 from nltk.stem import WordNetLemmatizer 
 from nltk.corpus import wordnet
 

From b37043cc1bb8ca4898e08dc810216c1fb2d19e10 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 13:22:18 +0200
Subject: [PATCH 190/496] add tests

---
 tests/test_stemming.py  | 16 ++++++++++++++
 tests/test_tokenizer.py | 46 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 62 insertions(+)
 create mode 100644 tests/test_stemming.py
 create mode 100644 tests/test_tokenizer.py

diff --git a/tests/test_stemming.py b/tests/test_stemming.py
new file mode 100644
index 0000000..faf4b0d
--- /dev/null
+++ b/tests/test_stemming.py
@@ -0,0 +1,16 @@
+import pytest
+from nautilus_nlp.preprocessing.stemming import stem_tokens
+
+def test_stem_en():
+    input_tokens = ['I','survived','these', 'dogs']
+    expected = ['i', 'surviv', 'these', 'dog']
+    res = stem_tokens(input_tokens, lang='english')
+    assert res == expected
+
+def test_stem_fr():
+    input_tokens = ['je', 'mangerai', 'dans', 'les', 'cuisines', 'du', 'château']
+    expected = ['je', 'mang', 'dan', 'le', 'cuisin', 'du', 'château']
+    res = stem_tokens(input_tokens, lang='french')
+    assert res == expected
+
+    
\ No newline at end of file
diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py
new file mode 100644
index 0000000..ea95f47
--- /dev/null
+++ b/tests/test_tokenizer.py
@@ -0,0 +1,46 @@
+import pytest
+from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize
+
+
+def test_tokenize_fr_spacy():
+    input_str = """Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique"""
+    expected = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!', 'sont', 'des', 'applications', 'très', 'connues', 'de', 'fouille', 'de', 'textes', 'sur', 'de', 'grandes', 'masses', 'de', 'données', '.', 'Cependant', ',', 'les', 'moteurs', 'de', 'recherche', 'ne', 'se', 'basent', 'pas', 'uniquement', 'sur', 'le', 'texte', 'pour', "l'", 'indexer', ',', 'mais', 'également', 'sur', 'la', 'façon', 'dont', 'les', 'pages', 'sont', 'mises', 'en', 'valeur', 'les', 'unes', 'par', 'rapport', 'aux', 'autres', '.', "L'", 'algorithme', 'utilisé', 'par', 'Google', 'est', 'PageRank', ',', 'et', 'il', 'est', 'courant', 'de', 'voir', 'HITS', 'dans', 'le', 'milieu', 'académique']
+    res = tokenize(input_str, lang_module="fr_spacy")
+    assert res == expected
+
+
+def test_tokenize_fr_moses():
+    input_str = """Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique"""
+    expected = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!', 'sont', 'des', 'applications', 'très', 'connues', 'de', 'fouille', 'de', 'textes', 'sur', 'de', 'grandes', 'masses', 'de', 'données', '.', 'Cependant', ',', 'les', 'moteurs', 'de', 'recherche', 'ne', 'se', 'basent', 'pas', 'uniquement', 'sur', 'le', 'texte', 'pour', "l'", 'indexer', ',', 'mais', 'également', 'sur', 'la', 'façon', 'dont', 'les', 'pages', 'sont', 'mises', 'en', 'valeur', 'les', 'unes', 'par', 'rapport', 'aux', 'autres', '.', "L'", 'algorithme', 'utilisé', 'par', 'Google', 'est', 'PageRank', ',', 'et', 'il', 'est', 'courant', 'de', 'voir', 'HITS', 'dans', 'le', 'milieu', 'académique']
+    res = tokenize(input_str, lang_module="fr_moses")
+    assert res == expected    
+
+def test_tokenize_null_input():
+    input_str = ''
+    expected = []
+    res = tokenize(input_str, lang_module="en_spacy")
+    assert res == expected
+
+def test_tokenize_en_spacy():
+    input_str = "Let's play together!"
+    expected = ['Let', "'s", 'play', 'together', '!']
+    res = tokenize(input_str, lang_module="en_spacy")
+    assert res == expected
+    
+def test_tokenize_en_nltk():
+    input_str = "Let's play together!"
+    expected = ['Let', "'s", 'play', 'together', '!']
+    res = tokenize(input_str, lang_module="en_nltk")
+    assert res == expected    
+
+def test_untokenize_en():
+    input_str = ['Let', "'s", 'play', 'together', '!']
+    expected = "Let's play together!"
+    res = untokenize(input_str,lang='en')
+    assert res == expected
+
+def test_untokenize_fr():
+    input_str = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!', 'sont', 'des', 'applications', 'très', 'connues', 'de', 'fouille', 'de', 'textes', 'sur', 'de', 'grandes', 'masses', 'de', 'données', '.', 'Cependant', ',', 'les', 'moteurs', 'de', 'recherche', 'ne', 'se', 'basent', 'pas', 'uniquement', 'sur', 'le', 'texte', 'pour', "l'", 'indexer', ',', 'mais', 'également', 'sur', 'la', 'façon', 'dont', 'les', 'pages', 'sont', 'mises', 'en', 'valeur', 'les', 'unes', 'par', 'rapport', 'aux', 'autres', '.', "L'", 'algorithme', 'utilisé', 'par', 'Google', 'est', 'PageRank', ',', 'et', 'il', 'est', 'courant', 'de', 'voir', 'HITS', 'dans', 'le', 'milieu', 'académique']
+    expected = "Les moteurs de recherche tels Google, Exalead ou Yahoo ! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l' indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L' algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique"
+    res = untokenize(input_str,lang='en')
+    assert res == expected
\ No newline at end of file

From da5f566ba13496349aa2e749ca825ad9f6397718 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 13:24:43 +0200
Subject: [PATCH 191/496] add tests

---
 tests/test_lemmatization.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 tests/test_lemmatization.py

diff --git a/tests/test_lemmatization.py b/tests/test_lemmatization.py
new file mode 100644
index 0000000..3ddf675
--- /dev/null
+++ b/tests/test_lemmatization.py
@@ -0,0 +1,22 @@
+import pytest
+from nautilus_nlp.preprocessing.lemmatization import lemmatize_french_tokens, lemmatize_english_tokens
+
+def test_lemmatize_french_tokens_spacy():
+    input_tokens = ['Ceci', 'est', 'un', 'texte', 'français', ',', "j'", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']
+    expected = ['ceci', 'être', 'un', 'texte', 'français', ',', 'j', "'", 'adorer', 'ton', 'frit', 'bien', 'gras', 'yolo', '!']
+    res = lemmatize_french_tokens(input_tokens, module='spacy')
+    assert res == expected
+
+
+def test_lemmatize_english_tokens_spacy():
+    input_tokens = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
+    expected = ['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']
+    res = lemmatize_french_tokens(input_tokens, module='spacy')
+    assert res == expected
+
+
+def test_lemmatize_english_tokens_nltk():
+    input_tokens = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
+    expected = ['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']
+    res = lemmatize_french_tokens(input_tokens, module='nltk')
+    assert res == expected    
\ No newline at end of file

From 95abc0be2c1d1aaa487442024b789979cc5f1e85 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 13:24:50 +0200
Subject: [PATCH 192/496] clean notebook

---
 notebooks/2. Text processing.ipynb | 589 ++++++++++-------------------
 1 file changed, 192 insertions(+), 397 deletions(-)

diff --git a/notebooks/2. Text processing.ipynb b/notebooks/2. Text processing.ipynb
index 34e9338..6a6c0b5 100644
--- a/notebooks/2. Text processing.ipynb	
+++ b/notebooks/2. Text processing.ipynb	
@@ -4,60 +4,69 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Common text processing operations"
+    "# Text processing"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tokenization"
+    "## Tokenization\n",
+    "\n",
+    "Several tokenizers are available. As you will see bellow, spaCy is much faster than the other implementations (Moses, NLTK) and often return better results. "
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    }
+   ],
    "source": [
-    "## Tokenizing French and English texts"
+    "from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "from nautilus_nlp.utils.tokenizer import tokenize, untokenize"
+    "fr_txt = \"Ceci est un texte français, j'adore 1 !\"\n",
+    "eng_txt = \"Let's play together!\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "fr_txt = \"Ceci est un texte français, j'adore 1 !\"\n",
-    "eng_txt = \"Let's play together!\""
+    "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
+    "### French spaCy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -66,119 +75,51 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.76 ms, sys: 31 µs, total: 4.79 ms\n",
-      "Wall time: 4.42 ms\n"
+      "CPU times: user 8.94 ms, sys: 1.24 ms, total: 10.2 ms\n",
+      "Wall time: 9.3 ms\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['Les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'tels',\n",
-       " 'Google',\n",
-       " ',',\n",
-       " 'Exalead',\n",
-       " 'ou',\n",
-       " 'Yahoo',\n",
-       " '!',\n",
-       " 'sont',\n",
-       " 'des',\n",
-       " 'applications',\n",
-       " 'très',\n",
-       " 'connues',\n",
-       " 'de',\n",
-       " 'fouille',\n",
-       " 'de',\n",
-       " 'textes',\n",
-       " 'sur',\n",
-       " 'de',\n",
-       " 'grandes',\n",
-       " 'masses',\n",
-       " 'de',\n",
-       " 'données',\n",
-       " '.',\n",
-       " 'Cependant',\n",
-       " ',',\n",
-       " 'les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'ne',\n",
-       " 'se',\n",
-       " 'basent',\n",
-       " 'pas',\n",
-       " 'uniquement',\n",
-       " 'sur',\n",
-       " 'le',\n",
-       " 'texte',\n",
-       " 'pour',\n",
-       " \"l'\",\n",
-       " 'indexer',\n",
-       " ',',\n",
-       " 'mais',\n",
-       " 'également',\n",
-       " 'sur',\n",
-       " 'la',\n",
-       " 'façon',\n",
-       " 'dont',\n",
-       " 'les',\n",
-       " 'pages',\n",
-       " 'sont',\n",
-       " 'mises',\n",
-       " 'en',\n",
-       " 'valeur',\n",
-       " 'les',\n",
-       " 'unes',\n",
-       " 'par',\n",
-       " 'rapport',\n",
-       " 'aux',\n",
-       " 'autres',\n",
-       " '.',\n",
-       " \"L'\",\n",
-       " 'algorithme',\n",
-       " 'utilisé',\n",
-       " 'par',\n",
-       " 'Google',\n",
-       " 'est',\n",
-       " 'PageRank',\n",
-       " ',',\n",
-       " 'et',\n",
-       " 'il',\n",
-       " 'est',\n",
-       " 'courant',\n",
-       " 'de',\n",
-       " 'voir',\n",
-       " 'HITS',\n",
-       " 'dans',\n",
-       " 'le',\n",
-       " 'milieu',\n",
-       " 'académique']"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
-    "tokenize(str_, lang_module=\"fr_spacy\")"
+    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_spacy\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 6.22 ms, sys: 94 µs, total: 6.31 ms\n",
-      "Wall time: 4.27 ms\n"
+      "['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenized_fr_txt[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### French Moses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 18.2 s, sys: 82.5 ms, total: 18.3 s\n",
+      "Wall time: 18.5 s\n"
      ]
     }
    ],
@@ -189,137 +130,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['Les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'tels',\n",
-       " 'Google',\n",
-       " ',',\n",
-       " 'Exalead',\n",
-       " 'ou',\n",
-       " 'Yahoo',\n",
-       " '!',\n",
-       " 'sont',\n",
-       " 'des',\n",
-       " 'applications',\n",
-       " 'très',\n",
-       " 'connues',\n",
-       " 'de',\n",
-       " 'fouille',\n",
-       " 'de',\n",
-       " 'textes',\n",
-       " 'sur',\n",
-       " 'de',\n",
-       " 'grandes',\n",
-       " 'masses',\n",
-       " 'de',\n",
-       " 'données',\n",
-       " '.',\n",
-       " 'Cependant',\n",
-       " ',',\n",
-       " 'les',\n",
-       " 'moteurs',\n",
-       " 'de',\n",
-       " 'recherche',\n",
-       " 'ne',\n",
-       " 'se',\n",
-       " 'basent',\n",
-       " 'pas',\n",
-       " 'uniquement',\n",
-       " 'sur',\n",
-       " 'le',\n",
-       " 'texte',\n",
-       " 'pour',\n",
-       " \"l'\",\n",
-       " 'indexer',\n",
-       " ',',\n",
-       " 'mais',\n",
-       " 'également',\n",
-       " 'sur',\n",
-       " 'la',\n",
-       " 'façon',\n",
-       " 'dont',\n",
-       " 'les',\n",
-       " 'pages',\n",
-       " 'sont',\n",
-       " 'mises',\n",
-       " 'en',\n",
-       " 'valeur',\n",
-       " 'les',\n",
-       " 'unes',\n",
-       " 'par',\n",
-       " 'rapport',\n",
-       " 'aux',\n",
-       " 'autres',\n",
-       " '.',\n",
-       " \"L'\",\n",
-       " 'algorithme',\n",
-       " 'utilisé',\n",
-       " 'par',\n",
-       " 'Google',\n",
-       " 'est',\n",
-       " 'PageRank',\n",
-       " ',',\n",
-       " 'et',\n",
-       " 'il',\n",
-       " 'est',\n",
-       " 'courant',\n",
-       " 'de',\n",
-       " 'voir',\n",
-       " 'HITS',\n",
-       " 'dans',\n",
-       " 'le',\n",
-       " 'milieu',\n",
-       " 'académique']"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo']\n"
+     ]
     }
    ],
    "source": [
-    "tokenize(str_, lang_module=\"fr_moses\")"
+    "print(tokenized_fr_txt[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### English spaCy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 24.3 ms, sys: 3.99 ms, total: 28.3 ms\n",
-      "Wall time: 26.2 ms\n"
+      "CPU times: user 1.24 ms, sys: 2 µs, total: 1.24 ms\n",
+      "Wall time: 1.25 ms\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_spacy\")\n",
+    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Let', \"'s\", 'play', 'together', '!']"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "tokenized_eng_txt"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### English NLTK "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 17.3 ms, sys: 3.71 ms, total: 21 ms\n",
-      "Wall time: 26.9 ms\n"
+      "CPU times: user 11.2 ms, sys: 3.35 ms, total: 14.5 ms\n",
+      "Wall time: 16 ms\n"
      ]
     }
    ],
@@ -333,7 +222,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## You can also untokenize your text"
+    "## Un-tokenization"
    ]
   },
   {
@@ -341,6 +230,14 @@
    "execution_count": 13,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 235 µs, sys: 1e+03 ns, total: 236 µs\n",
+      "Wall time: 243 µs\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -353,27 +250,36 @@
     }
    ],
    "source": [
+    "%%time\n",
     "untokenize(tokenized_eng_txt,lang='en')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4.07 ms, sys: 181 µs, total: 4.25 ms\n",
+      "Wall time: 4.19 ms\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "\"Ceci est un texte français, j' adore !\""
+       "\"Les moteurs de recherche tels Google, Exalead ou Yahoo ! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l' indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L' algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\""
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Here the \"J'adore\" is not handled in the right way\n",
+    "%%time\n",
     "untokenize(tokenized_fr_txt,lang='fr')"
    ]
   },
@@ -386,18 +292,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from nautilus_nlp.utils.stemmer import stem_tokens"
+    "from nautilus_nlp.preprocessing.stemming import stem_tokens"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -406,7 +310,7 @@
        "['i', 'surviv', 'these', 'dog']"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -417,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -426,13 +330,13 @@
        "['je', 'mang', 'dan', 'le', 'cuisin', 'du', 'château']"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "stem_tokens(tokenize(\"je mangerai dans les cuisines du château\"),lang='french')"
+    "stem_tokens(['je', 'mangerai', 'dans', 'les', 'cuisines', 'du', 'château'],lang='french')"
    ]
   },
   {
@@ -451,18 +355,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    }
+   ],
    "source": [
-    "from nautilus_nlp.utils.lemmatizer import lemmatize_french_tokens"
+    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_french_tokens"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -480,45 +391,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 37.9 ms, sys: 51.9 ms, total: 89.8 ms\n",
-      "Wall time: 65.3 ms\n"
+      "CPU times: user 20.3 ms, sys: 2.47 ms, total: 22.7 ms\n",
+      "Wall time: 20.6 ms\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['ceci',\n",
-       " 'être',\n",
-       " 'un',\n",
-       " 'texte',\n",
-       " 'français',\n",
-       " ',',\n",
-       " 'j',\n",
-       " \"'\",\n",
-       " 'adorer',\n",
-       " 't',\n",
-       " 'frite',\n",
-       " 'bien',\n",
-       " 'gras',\n",
-       " 'yolo',\n",
-       " '!']"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "%%time\n",
-    "lemmatize_french_tokens(txt_to_tokenize, module='spacy')"
+    "lemmatized_tokens = lemmatize_french_tokens(txt_to_tokenize, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ceci', 'être', 'un', 'texte', 'français', ',', 'j', \"'\", 'adorer', 'ton', 'frit', 'bien', 'gras', 'yolo', '!']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lemmatized_tokens)"
    ]
   },
   {
@@ -530,27 +434,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 34,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from nautilus_nlp.utils.lemmatizer import lemmatize_english_tokens"
+    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 35,
    "metadata": {
     "collapsed": true
    },
@@ -561,15 +456,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 27.2 ms, sys: 4.4 ms, total: 31.6 ms\n",
-      "Wall time: 31.3 ms\n"
+      "CPU times: user 16.9 ms, sys: 3.03 ms, total: 19.9 ms\n",
+      "Wall time: 16.8 ms\n"
      ]
     },
     {
@@ -578,7 +473,7 @@
        "['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -590,15 +485,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.07 s, sys: 217 ms, total: 3.28 s\n",
-      "Wall time: 3.45 s\n"
+      "CPU times: user 2.58 s, sys: 141 ms, total: 2.72 s\n",
+      "Wall time: 2.73 s\n"
      ]
     },
     {
@@ -607,7 +502,7 @@
        "['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,27 +521,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from nautilus_nlp.utils.preprocess import remove_stopwords\n",
-    "from nautilus_nlp.utils.tokenizer import tokenize\n",
-    "from nautilus_nlp.utils.constants import get_stopwords"
+    "from nautilus_nlp.preprocessing.preprocess import remove_stopwords\n",
+    "from nautilus_nlp.preprocessing.preprocess import get_stopwords"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 43,
    "metadata": {
     "collapsed": true
    },
@@ -657,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 44,
    "metadata": {
     "collapsed": true
    },
@@ -668,16 +553,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[\"J'ai\", 'beau', 'cheval']"
+       "[\"J'ai\", 'cheval']"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -688,16 +573,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[\"J'\", 'beau', 'cheval']"
+       "[\"J'\", 'cheval']"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -705,96 +590,6 @@
    "source": [
     "remove_stopwords(tokenize(text, lang_module=\"fr_spacy\"),FRENCH_SW)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fix bad encoding\n",
-    "\n",
-    "Sometimes you messed up you encoding saving files, and you don't know how to fix this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.preprocess import fix_bad_unicode\n",
-    "from nautilus_nlp.utils import file_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "bad_unicode=file_loader.open_textfile('./bad_encoding.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse Ã\\xa0 80km\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bad_unicode[0:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Les augmentations de rémunérations\\nrénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social\\nLimitations de vitesse et sécurité routière\\nPour un nouveau contrat citoyen\\nDévelopper les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets\\nproportienelle\\nPour plus de démocratie participative\\nTransparence de la vie public\\n18 mois de trop....ca suffit macron\\nEgalité devant les infractions routières\\nMesures d'urgence pour une démocratie régénérée\\nSORTIR DU GRAND EST ! REOUR A LA REGION ALSACE\\nPour plus de transparence\\nEcoutez enfin le  peuple.\\nVote obligatoire\\nAvis d'un citoyen ordinaire et socialiste - vive le RIC\\nsuppression du 80 km/h\\nproportionelle et immigration\\nRevoir les plafonds des aides sociales\\nProposition de Refondation du Capitalisme et d'Instauration d'un Dividende Universel Financées par l'Épargne.\\nSuppression du sénat\\nLimitation de vitesse à 80km/h sur les routes départ\""
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fix_bad_unicode(bad_unicode)[0:1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From c579ec8c7b88e2c2f78b93b257f4f4d4b2cc6052 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 13:38:47 +0200
Subject: [PATCH 193/496] fix unit tests

---
 tests/test_lemmatization.py | 6 +++---
 tests/test_phone_number.py  | 2 +-
 tests/test_tokenizer.py     | 6 +++---
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tests/test_lemmatization.py b/tests/test_lemmatization.py
index 3ddf675..3d766ee 100644
--- a/tests/test_lemmatization.py
+++ b/tests/test_lemmatization.py
@@ -9,14 +9,14 @@ def test_lemmatize_french_tokens_spacy():
 
 
 def test_lemmatize_english_tokens_spacy():
-    input_tokens = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
+    input_tokens = ['The', 'strip', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
     expected = ['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']
-    res = lemmatize_french_tokens(input_tokens, module='spacy')
+    res = lemmatize_english_tokens(input_tokens, module='spacy')
     assert res == expected
 
 
 def test_lemmatize_english_tokens_nltk():
     input_tokens = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
     expected = ['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']
-    res = lemmatize_french_tokens(input_tokens, module='nltk')
+    res = lemmatize_english_tokens(input_tokens, module='nltk')
     assert res == expected    
\ No newline at end of file
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index d6edc69..61fe9a6 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -5,7 +5,7 @@ def test_extract_phone_number():
     input_str = '(541) 754-3010 is a US. Phone'
     expected = ['(541) 754-3010', '754-3010']
     res = phone.extract_phone_numbers(input_str, countrylist=phone.SUPPORTED_COUNTRY)
-    assert res == expected
+    assert sorted(res) == sorted(expected)
 
 def test_extract_phone_number_us():
     input_str = '(541) 754-3010 is a US. Phone'
diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py
index ea95f47..744de9d 100644
--- a/tests/test_tokenizer.py
+++ b/tests/test_tokenizer.py
@@ -40,7 +40,7 @@ def test_untokenize_en():
     assert res == expected
 
 def test_untokenize_fr():
-    input_str = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!', 'sont', 'des', 'applications', 'très', 'connues', 'de', 'fouille', 'de', 'textes', 'sur', 'de', 'grandes', 'masses', 'de', 'données', '.', 'Cependant', ',', 'les', 'moteurs', 'de', 'recherche', 'ne', 'se', 'basent', 'pas', 'uniquement', 'sur', 'le', 'texte', 'pour', "l'", 'indexer', ',', 'mais', 'également', 'sur', 'la', 'façon', 'dont', 'les', 'pages', 'sont', 'mises', 'en', 'valeur', 'les', 'unes', 'par', 'rapport', 'aux', 'autres', '.', "L'", 'algorithme', 'utilisé', 'par', 'Google', 'est', 'PageRank', ',', 'et', 'il', 'est', 'courant', 'de', 'voir', 'HITS', 'dans', 'le', 'milieu', 'académique']
-    expected = "Les moteurs de recherche tels Google, Exalead ou Yahoo ! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l' indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L' algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique"
-    res = untokenize(input_str,lang='en')
+    input_str = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!']
+    expected = "Les moteurs de recherche tels Google, Exalead ou Yahoo !"
+    res = untokenize(input_str,lang='fr')
     assert res == expected
\ No newline at end of file

From a597e27f37dc7042e7a3fbb530aa269bc965596d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 15:25:41 +0200
Subject: [PATCH 194/496] add test lemma

---
 tests/test_lemmatization.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_lemmatization.py b/tests/test_lemmatization.py
index 3d766ee..ce205f4 100644
--- a/tests/test_lemmatization.py
+++ b/tests/test_lemmatization.py
@@ -19,4 +19,4 @@ def test_lemmatize_english_tokens_nltk():
     input_tokens = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
     expected = ['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']
     res = lemmatize_english_tokens(input_tokens, module='nltk')
-    assert res == expected    
\ No newline at end of file
+    assert res == expected
\ No newline at end of file

From ceedef412f9d35256a2444ded72d68a78cdc38b9 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 15:26:20 +0200
Subject: [PATCH 195/496] add benchmark #87

---
 .../Benchmark text processing tools.ipynb     | 823 ++++++++++++++++++
 1 file changed, 823 insertions(+)
 create mode 100644 notebooks/Benchmark text processing tools.ipynb

diff --git a/notebooks/Benchmark text processing tools.ipynb b/notebooks/Benchmark text processing tools.ipynb
new file mode 100644
index 0000000..08ff2af
--- /dev/null
+++ b/notebooks/Benchmark text processing tools.ipynb	
@@ -0,0 +1,823 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Text processing tools benchmark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from urllib import request"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load french text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "url = \"https://www.gutenberg.org/cache/epub/5711/pg5711.txt\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "response = request.urlopen(url)\n",
+    "rawfr = response.read().decode('utf8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Project Gutenberg EBook of Germinal, by Emile Zola\r\n",
+      "(#8 in our series by Emile Zola)\r\n",
+      "\r\n",
+      "Copyright laws are changing all over the world. Be sure to check the\r\n",
+      "copyright laws for your country before downloading or redistributing\r\n",
+      "this or any other Project Gutenberg eBook.\r\n",
+      "\r\n",
+      "This header should be the first thing seen when viewing this Project\r\n",
+      "Gutenberg file.  Please do not remove it.  Do not change or edit the\r\n",
+      "header without written permission.\r\n",
+      "\r\n",
+      "Please read the \"legal small print,\" and ot\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(rawfr[:500])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(rawfr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1046377"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(rawfr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load english text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "url = \"http://www.gutenberg.org/files/2554/2554-0.txt\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "response = request.urlopen(url)\n",
+    "raw = response.read().decode('utf8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Project Gutenberg EBook of Crime and Punishment, by Fyodor Dostoevsky\r\n",
+      "\r\n",
+      "This eBook is for the use of anyone anywhere at no cost and with\r\n",
+      "almost no restrictions whatsoever.  You may copy it, give it away or\r\n",
+      "re-use it under the terms of the Project Gutenberg License included\r\n",
+      "with this eBook or online at www.gutenberg.org\r\n",
+      "\r\n",
+      "\r\n",
+      "Title: Crime and Punishment\r\n",
+      "\r\n",
+      "Author: Fyodor Dostoevsky\r\n",
+      "\r\n",
+      "Release Date: March 28, 2006 [EBook #2554]\r\n",
+      "Last Updated: October 27, 2016\r\n",
+      "\r\n",
+      "Language: English\r\n",
+      "\r\n",
+      "Charac\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(raw[:500])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(raw)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1176967"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(raw)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tokenization\n",
+    "\n",
+    "Several tokenizers are available. As you will see bellow, spaCy is much faster than the other implementations (Moses, NLTK) and often return better results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### French spaCy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.18 s, sys: 44 ms, total: 1.22 s\n",
+      "Wall time: 1.24 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenized = tokenize(rawfr[:1000000], lang_module=\"fr_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "920 ms ± 18.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "tokenized = tokenize(rawfr[:1000000], lang_module=\"fr_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### French Moses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 21.7 s, sys: 289 ms, total: 22 s\n",
+      "Wall time: 22.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenized_moses = tokenize(rawfr[:1000000], lang_module=\"fr_moses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.54 s ± 27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "tokenized_moses = tokenize(rawfr[:1000000], lang_module=\"fr_moses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "spacy: 227009 tokens\n",
+      "moses: 202475 tokens\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('spacy: {} tokens\\nmoses: {} tokens'.format(len(tokenized),len(tokenized_moses)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['se', 'diriger', 'vers', 'les', '\\r\\n', 'bâtiments', ',', 'il', 'se', 'risqua', 'enfin', 'à', 'gravir', 'le', 'terri', 'sur', 'lequel', 'brûlaient', '\\r\\n', 'les', 'trois', 'feux', 'de', 'houille', ',', 'dans', 'des', 'corbeilles', 'de', 'fonte', ',', 'pour', 'éclairer', '\\r\\n', 'et', 'réchauffer', 'la', 'besogne', '.', ' ', 'Les', 'ouvriers', 'de', 'la', 'coupe', 'à', 'terre', 'avaient', 'dû', '\\r\\n', 'travailler', 'tard', ',', 'on', 'sortait', 'encore', 'les', 'débris', 'inutiles', '.', ' ', 'Maintenant', ',', '\\r\\n', 'il', 'entendait', 'les', 'moulineurs', 'pousser', 'les', 'trains', 'sur', 'les', 'tréteaux', ',', 'il', '\\r\\n', 'distinguait', 'des', 'ombres', 'vivantes', 'culbutant', 'les', 'berlines', ',', 'près', 'de', 'chaque', '\\r\\n', 'feu', '.', '\\r\\n\\r\\n', '--Bonjour', ',', 'dit', '-', 'il', 'en', \"s'\", 'approchant', \"d'\", 'une', 'des', 'corbeilles', '.', '\\r\\n\\r\\n', 'Tournant', 'le', 'dos', 'au', 'brasier', ',', 'le', 'charretier', 'était', 'debout', ',', 'un', 'vieillard', '\\r\\n', 'vêtu', \"d'\", 'un', 'tricot', 'de', 'laine', 'violette', ',', 'coiffé', \"d'\", 'une', 'casquette', 'en', 'poil', 'de', '\\r\\n', 'lapin', ';', 'pendant', 'que', 'son', 'cheval', ',', 'un', 'gros', 'cheval', 'jaune', ',', 'attendait', ',', 'dans', '\\r\\n', 'une', 'immobilité', 'de', 'pierre', ',', \"qu'\", 'on', 'eût', 'vidé', 'les', 'six', 'berlines', 'montées', 'par', '\\r\\n', 'lui', '.', ' ', 'Le', 'manoeuvre', 'employé', 'au', 'culbuteur', ',', 'un', 'gaillard', 'roux', 'et', '\\r\\n', 'efflanqué', ',', 'ne', 'se', 'pressait', 'guère', ',', 'pesait', 'sur', 'le', 'levier', \"d'\", 'une', 'main', '\\r\\n', 'endormie', '.', ' ', 'Et']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenized[1000:1200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['le', 'dos', 'au', 'brasier', ',', 'le', 'charretier', 'était', 'debout', ',', 'un', 'vieillard', 'vêtu', \"d'\", 'un', 'tricot', 'de', 'laine', 'violette', ',', 'coiffé', \"d'\", 'une', 'casquette', 'en', 'poil', 'de', 'lapin', ';', 'pendant', 'que', 'son', 'cheval', ',', 'un', 'gros', 'cheval', 'jaune', ',', 'attendait', ',', 'dans', 'une', 'immobilité', 'de', 'pierre', ',', \"qu'\", 'on', 'eût', 'vidé', 'les', 'six', 'berlines', 'montées', 'par', 'lui', '.', 'Le', 'manoeuvre', 'employé', 'au', 'culbuteur', ',', 'un', 'gaillard', 'roux', 'et', 'efflanqué', ',', 'ne', 'se', 'pressait', 'guère', ',', 'pesait', 'sur', 'le', 'levier', \"d'\", 'une', 'main', 'endormie', '.', 'Et', ',', 'là-haut', ',', 'le', 'vent', 'redoublait', ',', 'une', 'bise', 'glaciale', ',', 'dont', 'les', 'grandes', 'haleines', 'régulières', 'passaient', 'comme', 'des', 'coups', 'de', 'faux', '.', '--Bonjour', ',', 'répondit', 'le', 'vieux', '.', 'Un', 'silence', 'se', 'fit', '.', \"L'\", 'homme', ',', 'qui', 'se', 'sentait', 'regardé', \"d'\", 'un', 'oeil', 'méfiant', ',', 'dit', 'son', 'nom', 'tout', 'de', 'suite', '.', '--Je', 'me', 'nomme', 'Étienne', 'Lantier', ',', 'je', 'suis', 'machineur', '...', 'Il', \"n'\", 'y', 'a', 'pas', 'de', 'travail', 'ici', '?', 'Les', 'flammes', \"l'\", 'éclairaient', ',', 'il', 'devait', 'avoir', 'vingt', 'et', 'un', 'ans', ',', 'très', 'brun', ',', 'joli', 'homme', ',', \"l'\", 'air', 'fort', 'malgré', 'ses', 'membres', 'menus', '.', 'Rassuré', ',', 'le', 'charretier', 'hochait', 'la', 'tête', '.', '--Du', 'travail', 'pour', 'un', 'machineur', ',', 'non', ',']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenized_moses[1000:1200])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### English spaCy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5 s, sys: 102 ms, total: 5.1 s\n",
+      "Wall time: 5.17 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenized_eng = tokenize(raw[:1000000], lang_module=\"en_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.26 s ± 740 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "tokenize(raw[:1000000], lang_module=\"en_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\\ufeffThe',\n",
+       " 'Project',\n",
+       " 'Gutenberg',\n",
+       " 'EBook',\n",
+       " 'of',\n",
+       " 'Crime',\n",
+       " 'and',\n",
+       " 'Punishment',\n",
+       " ',',\n",
+       " 'by']"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenized_eng[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### English NLTK "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.17 s, sys: 29.3 ms, total: 2.2 s\n",
+      "Wall time: 2.22 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "tokenized_eng_nltk = tokenize(raw[:1000000], lang_module=\"en_nltk\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.47 s ± 565 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "tokenize(raw[:1000000], lang_module=\"en_nltk\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\\ufeffThe',\n",
+       " 'Project',\n",
+       " 'Gutenberg',\n",
+       " 'EBook',\n",
+       " 'of',\n",
+       " 'Crime',\n",
+       " 'and',\n",
+       " 'Punishment',\n",
+       " ',',\n",
+       " 'by']"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenized_eng_nltk[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stemming "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.stemming import stem_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.91 s, sys: 37.3 ms, total: 7.95 s\n",
+      "Wall time: 7.98 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "stem = stem_tokens(tokenized,lang='french')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.48 s ± 682 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "stem_tokens(tokenized,lang='french')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lemmatization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## French "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_french_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.preprocess import remove_tokens_with_nonletters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "tokenized = remove_tokens_with_nonletters(tokenized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 26.7 s, sys: 6.91 s, total: 33.6 s\n",
+      "Wall time: 36.5 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lemmatized_tokens = lemmatize_french_tokens(tokenized, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['de', 'puits', 'le', 'vaste', 'chambre', 'de', 'le', 'machine', 'extraction', 'le', 'tourelle', 'de', 'le', 'pompe', 'ce', 'fosse', 'au', 'fondre', 'un', 'creux', 'avec', 'son', 'construction', 'trapu', 'de', 'brique', 'dresser', 'son', 'comme', 'un', 'corne', 'luire', 'sembler', 'avoir', 'un', 'air', 'mauver', 'de', 'goulu', 'accroupie', 'pour', 'manger', 'le', 'monde', 'tout', 'en', 'examiner', 'il', 'songer', 'luire', 'son', 'existence', 'de', 'vagabond', 'depuis', 'huit', 'jour', 'il', 'chercher', 'un', 'place', 'il', 'se', 'revoir', 'dans', 'son', 'atelier', 'de', 'chemin', 'de', 'fer', 'gifler', 'son', 'chef', 'de', 'Lille', 'de', 'partout', 'le', 'samedi', 'il', 'Marchiennes', 'on', 'dire', 'il', 'y', 'avoir', 'de', 'travail', 'aux', 'Forges', 'et', 'rien', 'ni', 'aux', 'Forges', 'ni', 'chez', 'Sonneville', 'il', 'avoir', 'passer', 'le', 'dimanche', 'sou', 'le', 'bois', 'un', 'chantier', 'de', 'charronnage', 'dont', 'le', 'surveillant', 'venir', 'de', 'expulser', 'deux', 'heure', 'de', 'le', 'nuit', 'Rien', 'plus', 'un', 'sou', 'pas', 'un', 'aller', 'il', 'faire', 'ainsi', 'par', 'le', 'chemin', 'sans', 'but', 'ne', 'savoir', 'seulement', 'abriter', 'contre', 'le', 'bise', 'oui', 'bien', 'un', 'fosse', 'le', 'rare', 'lanterne', 'le', 'carreau', 'un', 'porte', 'brusquement', 'ouvrir', 'luire', 'avoir', 'permettre', 'entrevoir', 'le', 'foyer', 'un', 'dans', 'un', 'vive', 'il', 'expliquer', 'de', 'le', 'pompe', 'ce', 'respiration', 'gros', 'et', 'long', 'souffler', 'sans', 'qui', 'comme', 'halein', 'de', 'monstre', 'le', 'manoeuvre', 'de', 'culbuteur', 'gonfler', 'le', 'dos', 'avoir', 'pas', 'le', 'oeil', 'sur', 'et', 'celui', 'ci', 'aller']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lemmatized_tokens[1000:1200])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## English"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tokenized_eng = remove_tokens_with_nonletters(tokenized_eng)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 29.3 s, sys: 6.85 s, total: 36.2 s\n",
+      "Wall time: 38.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lemmatized_eng = lemmatize_english_tokens(tokenized_eng, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 34.4 s, sys: 3.82 s, total: 38.2 s\n",
+      "Wall time: 39.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lemmatized_eng_nltk = lemmatize_english_tokens(tokenized_eng, module='nltk')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['feel', 'ashamed', 'He', 'was', 'hopelessly', 'in', 'debt', 'to', 'his', 'landlady', 'and', 'was', 'afraid', 'of', 'meeting', 'her', 'This', 'was', 'not', 'because', 'he', 'was', 'cowardly', 'and', 'abject', 'quite', 'the', 'contrary', 'but', 'for', 'some', 'time', 'past', 'he', 'had', 'been', 'in', 'an', 'overstrained', 'irritable', 'condition', 'verging', 'on', 'hypochondria', 'He', 'had', 'become', 'so', 'completely', 'absorbed', 'in', 'himself', 'and', 'isolated', 'from', 'his', 'fellows', 'that', 'he', 'dreaded', 'meeting', 'not', 'only', 'his', 'landlady', 'but', 'anyone', 'at', 'all', 'He', 'was', 'crushed', 'by', 'poverty', 'but', 'the', 'anxieties', 'of', 'his', 'position', 'had', 'of', 'late', 'ceased', 'to', 'weigh', 'upon', 'him', 'He', 'had', 'given', 'up', 'attending', 'to', 'matters', 'of', 'practical', 'importance', 'he', 'had', 'lost', 'all', 'desire', 'to', 'do', 'so', 'Nothing', 'that', 'any', 'landlady', 'could', 'do', 'had', 'a', 'real', 'terror', 'for', 'him', 'But', 'to', 'be', 'stopped', 'on', 'the', 'stairs', 'to', 'be', 'forced', 'to', 'listen', 'to', 'her', 'trivial', 'irrelevant', 'gossip', 'to', 'pestering', 'demands', 'for', 'payment', 'threats', 'and', 'complaints', 'and', 'to', 'rack', 'his', 'brains', 'for', 'excuses', 'to', 'prevaricate', 'to', 'lie', 'no', 'rather', 'than', 'that', 'he', 'would', 'creep', 'down', 'the', 'stairs', 'like', 'a', 'cat', 'and', 'slip', 'out', 'unseen', 'This', 'evening', 'however', 'on', 'coming', 'out', 'into', 'the', 'street', 'he', 'became', 'acutely', 'aware', 'of', 'his', 'fears', 'I', 'want', 'to', 'attempt', 'a', 'thing', 'like', 'that', 'and', 'am', 'frightened', 'by', 'these']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenized_eng[1000:1200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['feel', 'ashamed', '-PRON-', 'be', 'hopelessly', 'in', 'debt', 'to', '-PRON-', 'landlady', 'and', 'be', 'afraid', 'of', 'meet', '-PRON-', 'This', 'be', 'not', 'because', '-PRON-', 'be', 'cowardly', 'and', 'abject', 'quite', 'the', 'contrary', 'but', 'for', 'some', 'time', 'past', '-PRON-', 'have', 'be', 'in', 'an', 'overstrained', 'irritable', 'condition', 'verge', 'on', 'hypochondria', '-PRON-', 'have', 'become', 'so', 'completely', 'absorb', 'in', '-PRON-', 'and', 'isolate', 'from', '-PRON-', 'fellow', 'that', '-PRON-', 'dread', 'meeting', 'not', 'only', '-PRON-', 'landlady', 'but', 'anyone', 'at', 'all', '-PRON-', 'be', 'crush', 'by', 'poverty', 'but', 'the', 'anxiety', 'of', '-PRON-', 'position', 'have', 'of', 'late', 'cease', 'to', 'weigh', 'upon', '-PRON-', '-PRON-', 'have', 'give', 'up', 'attend', 'to', 'matter', 'of', 'practical', 'importance', '-PRON-', 'have', 'lose', 'all', 'desire', 'to', 'do', 'so', 'Nothing', 'that', 'any', 'landlady', 'could', 'do', 'have', 'a', 'real', 'terror', 'for', '-PRON-', 'but', 'to', 'be', 'stop', 'on', 'the', 'stair', 'to', 'be', 'force', 'to', 'listen', 'to', '-PRON-', 'trivial', 'irrelevant', 'gossip', 'to', 'pester', 'demand', 'for', 'payment', 'threat', 'and', 'complaint', 'and', 'to', 'rack', '-PRON-', 'brain', 'for', 'excuse', 'to', 'prevaricate', 'to', 'lie', 'no', 'rather', 'than', 'that', '-PRON-', 'would', 'creep', 'down', 'the', 'stair', 'like', 'a', 'cat', 'and', 'slip', 'out', 'unseen', 'This', 'evening', 'however', 'on', 'come', 'out', 'into', 'the', 'street', '-PRON-', 'become', 'acutely', 'aware', 'of', '-PRON-', 'fear', '-PRON-', 'want', 'to', 'attempt', 'a', 'thing', 'like', 'that', 'and', 'be', 'frighten', 'by', 'these']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lemmatized_eng[1000:1200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['feel', 'ashamed', 'He', 'be', 'hopelessly', 'in', 'debt', 'to', 'his', 'landlady', 'and', 'be', 'afraid', 'of', 'meeting', 'her', 'This', 'be', 'not', 'because', 'he', 'be', 'cowardly', 'and', 'abject', 'quite', 'the', 'contrary', 'but', 'for', 'some', 'time', 'past', 'he', 'have', 'be', 'in', 'an', 'overstrain', 'irritable', 'condition', 'verge', 'on', 'hypochondria', 'He', 'have', 'become', 'so', 'completely', 'absorbed', 'in', 'himself', 'and', 'isolated', 'from', 'his', 'fellow', 'that', 'he', 'dread', 'meeting', 'not', 'only', 'his', 'landlady', 'but', 'anyone', 'at', 'all', 'He', 'be', 'crush', 'by', 'poverty', 'but', 'the', 'anxiety', 'of', 'his', 'position', 'have', 'of', 'late', 'cease', 'to', 'weigh', 'upon', 'him', 'He', 'have', 'give', 'up', 'attend', 'to', 'matter', 'of', 'practical', 'importance', 'he', 'have', 'lose', 'all', 'desire', 'to', 'do', 'so', 'Nothing', 'that', 'any', 'landlady', 'could', 'do', 'have', 'a', 'real', 'terror', 'for', 'him', 'But', 'to', 'be', 'stop', 'on', 'the', 'stair', 'to', 'be', 'force', 'to', 'listen', 'to', 'her', 'trivial', 'irrelevant', 'gossip', 'to', 'pester', 'demand', 'for', 'payment', 'threat', 'and', 'complaint', 'and', 'to', 'rack', 'his', 'brain', 'for', 'excuse', 'to', 'prevaricate', 'to', 'lie', 'no', 'rather', 'than', 'that', 'he', 'would', 'creep', 'down', 'the', 'stair', 'like', 'a', 'cat', 'and', 'slip', 'out', 'unseen', 'This', 'even', 'however', 'on', 'come', 'out', 'into', 'the', 'street', 'he', 'become', 'acutely', 'aware', 'of', 'his', 'fear', 'I', 'want', 'to', 'attempt', 'a', 'thing', 'like', 'that', 'and', 'be', 'frighten', 'by', 'these']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lemmatized_eng_nltk[1000:1200])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 2f6de6f0dafe4d125855a1d75c539c897a60249b Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 23 May 2019 16:39:57 +0200
Subject: [PATCH 196/496] Remove list comprehension in the spacy tokenizer

---
 nautilus_nlp/utils/tokenizer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index a009533..526185b 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -39,10 +39,10 @@ def tokenize(text: str, lang_module: str = 'en_spacy'):
         return nltk.word_tokenize(text)
     elif lang_module is 'en_spacy':
         spacydoc = english_spacy(text)
-        return [tokens.text for tokens in spacydoc]
+        return list(spacydoc)
     elif lang_module is 'fr_spacy':
         spacydoc = french_spacy(text)
-        return [tokens.text for tokens in spacydoc]
+        return list(spacydoc)
     elif lang_module is 'fr_moses':
         t = MosesTokenizer(lang='fr')
         return t.tokenize(text, escape=False)

From 5424bedcfb7b8d0cbd283c9872e9a0535e85b755 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 16:53:21 +0200
Subject: [PATCH 197/496] add data folder

---
 .gitignore | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 83457f9..ca31cb0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -95,4 +95,6 @@ target/
 !/nautilus_nlp/data/lang_identification.ftz
 
 # PyTest cache
-.pytest_cache/
\ No newline at end of file
+.pytest_cache/
+
+data/
\ No newline at end of file

From 040cfbb91dc7ee53b527c44ef624c1cc407f8c65 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 18:30:56 +0200
Subject: [PATCH 198/496] improve TfidfTextVectorizer

---
 .../preprocessing/text_vectorizers.py         | 173 +++--
 .../3. Text Vectorization - TF-IDF.ipynb      | 717 ++++++++++++++++++
 notebooks/TF-IDF.ipynb                        | 353 ---------
 3 files changed, 835 insertions(+), 408 deletions(-)
 create mode 100644 notebooks/3. Text Vectorization - TF-IDF.ipynb
 delete mode 100644 notebooks/TF-IDF.ipynb

diff --git a/nautilus_nlp/preprocessing/text_vectorizers.py b/nautilus_nlp/preprocessing/text_vectorizers.py
index 60c05d4..68fa172 100644
--- a/nautilus_nlp/preprocessing/text_vectorizers.py
+++ b/nautilus_nlp/preprocessing/text_vectorizers.py
@@ -6,11 +6,13 @@
 import numpy as np
 import math
 
-from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer
+from sklearn.feature_extraction.text import TfidfVectorizer as _TfidfVectorizer
 
 
-class Tfidf(object):
+class TfidfTextVectorizer(object):
     """
+    Convert a collection of raw documents to a matrix of TF-IDF features.
+
     Inputs a list of string. 
     and the list of feature name.
     Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
@@ -22,56 +24,64 @@ class Tfidf(object):
     stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
     max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
     dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
-
-    Returns
-    -------
-    list
-        Outputs a tuple with the wordcount vector matrix
     """
     
     def __init__(self, **kwargs):
-        self.tfidf_vectorizer = TfidfVectorizer(**kwargs)
-    
-    def _compute_wordcount_vector(self, documents):
-        '''
-        Input a list of documents (string)
-        Output the wordcount vector matrix 
-        '''
-        self.word_count_vector = self.tfidf_vectorizer.fit_transform(documents)
-        return self.word_count_vector
-    
+        self.tfidf_vectorizer = _TfidfVectorizer(**kwargs)
+
 
     def _get_features_name(self):
+        '''
+        Array mapping from feature integer indices to feature name
+        '''
         self.feature_names = self.tfidf_vectorizer.get_feature_names()
         return self.feature_names
 
-    
-    def _compute_idf(self):
-        self.tfidf_transformer=TfidfTransformer(smooth_idf=True, use_idf=True)
-        self.tfidf_transformer.fit(self.word_count_vector)
-        return self.word_count_vector
 
-    
-    def compute_tfidf(self, documents):
-        self._compute_wordcount_vector(documents)
+    def compute_tfidf(self, raw_documents):
+        '''
+        Learn vocabulary and idf, return term-document matrix.
+
+        Input a list of documents (string)
+        Output the wordcount vector matrix.
+
+        params
+        ------
+        raw_documents : iterable
+            an iterable which yields either str, unicode or file objects
+
+        returns
+        -------
+        X : sparse matrix, [n_samples, n_features]
+            Tf-idf-weighted document-term matrix.
+        '''
+        self.word_count_vector = self.tfidf_vectorizer.fit_transform(raw_documents)
         self._get_features_name()
-        self._compute_idf()
-        return self.word_count_vector
+        return self.word_count_vector    
 
 
-    def transform_doc(self, document):
+    def apply_tfidf_to_documents(self, raw_document):
         '''
-        Transform documents to document-term matrix.
+        Apply the tf-idf weights to a document or a list of documents.
+        Equivalent to .transform() method in sci-kit learn.
+        
+        Uses the vocabulary and document frequencies (df) learned by fit 
+        (or fit_transform), and convert documents to document-term matrix.
+
+        parameters
+        ---------
+        raw_documents : iterable or document
+            an iterable which yields either str, unicode or file objects, or 
+        a string.
 
         Returns
         -------
-        Tf-idf-weighted document-term matrix.
+        X : sparse matrix, [n_samples, n_features]
+            Tf-idf-weighted document-term matrix.
         '''
-        return self.tfidf_transformer.transform(document)
-
-    def _apply_tfidf_to_doc(self, text):
-        '''generate tf-idf for the given document'''
-        return self.tfidf_transformer.transform(self.tfidf_vectorizer.transform([text]))
+        if type(raw_document) == str:
+            raw_document = [raw_document]
+        return self.tfidf_vectorizer.transform(raw_document)
     
     
     def _sort_coo(self, coo_matrix):
@@ -104,21 +114,55 @@ def _extract_topn_from_vector(self, feature_names, sorted_items, topn=10):
 
         return results
 
-    
-    def get_top_tfidf_per_doc(self, text, n=10):
-        '''compute TF-IDF for a given doc, and returns a list of the top N weighted words'''
-        tf_idf_vector= self._apply_tfidf_to_doc(text)
+
+    def get_top_tfidf_per_doc(self, text:str, n:int=10)->list:
+        '''
+        compute TF-IDF for a given doc, and returns a list of the top N weighted words
+
+        parameters
+        ---------
+        text : sparse matrix, [n_samples, n_features]
+            If specified, will return the top weighted words for the given matrix,
+            otherwise will give the result of the tf-idf matrix
+        a string.
+
+        n : number of terms to display
+
+        Returns
+        -------
+        top-n weighted terms : dict
+            List of top terms for the given document
+        '''
+        tf_idf_vector= self.apply_tfidf_to_documents([text])
         sorted_items=self._sort_coo(tf_idf_vector.tocoo())
         return list(self._extract_topn_from_vector(self.feature_names, sorted_items, n).keys())
     
-    def get_top_tfidf(self, n=10):
-        '''returns a dict of the top N weighted words, with their weight'''
-        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(self.word_count_vector.tocoo()), topn=n)
+
+    def get_top_tfidf(self, tfidf_matrix=None, n:int=10)->list:
+        '''
+        Input a tf-idf matrix, and returns a dict of the top N weighted words,
+        with their weight.
+
+        parameters
+        ---------
+        tfidf_matrix : sparse matrix, [n_samples, n_features]
+            If specified, will return the top weighted words for the given matrix,
+            otherwise will give the result of the tf-idf matrix
+        a string.
+
+        Returns
+        -------
+        top-n weighted terms : dict
+            Dict of top terms and their associated weighted.
+        '''
+        if tfidf_matrix is None:
+            tfidf_matrix = self.word_count_vector
+        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(tfidf_matrix.tocoo()), topn=n)
 
 
-class Text_Vectorizer(object):
+class GensimTextVectorizer(object):
     '''
-    TODO: DOCSTRING
+    Gensim's implementation of TF-IDF, BOW models etc. 
     '''
     def __init__(self, doc_list):
         # Initialize
@@ -150,64 +194,83 @@ def _preprocess(self, doc_list):
         docs_dict = self._get_docs_dict(docs)
         return nlp, docs, docs_dict
 
-    # Gensim can again be used to create a bag-of-words representation of each document,
-    # build the TF-IDF model,
-    # and compute the TF-IDF vector for each document.
+
     def _get_tfidf(self, docs, docs_dict):
+        '''
+        Gensim can again be used to create a bag-of-words representation of each document,
+        Build the TF-IDF model, and compute the TF-IDF vector for each document.
+        '''
         docs_corpus = [docs_dict.doc2bow(doc) for doc in docs]
         model_tfidf = TfidfModel(docs_corpus, id2word=docs_dict)
         docs_tfidf = model_tfidf[docs_corpus]
         docs_vecs = np.vstack([sparse2full(c, len(docs_dict)) for c in docs_tfidf])
         return docs_vecs
 
-    # Get avg w2v for one document
     def _document_vector(self, doc, docs_dict, nlp):
-        # remove out-of-vocabulary words
+        """
+        Get avg w2v for one document. Remove out-of-vocabulary words.
+        """
+        
         doc_vector = [nlp(word).vector for word in doc if word in docs_dict.token2id]
         return np.mean(doc_vector, axis=0)
 
 
-    # Get average vector for document list
     def avg_wv(self):
+        '''
+        Get average vector for document list
+        '''
         docs_vecs = np.vstack(
             [self._document_vector(doc, self.docs_dict, self.nlp) for doc in self.docs]
         )
         return docs_vecs
 
-    # Get TF-IDF vector for document list
+
     def get_tfidf(self):
+        '''
+        Get TF-IDF vector for document list
+        '''
         docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
         model_tfidf = TfidfModel(docs_corpus, id2word=self.docs_dict)
         docs_tfidf = model_tfidf[docs_corpus]
         docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_tfidf])
         return docs_vecs
 
-    # Get Latent Semantic Indexing(LSI) vector for document list
+    
     def get_lsi(self, num_topics=300):
+        '''
+        Get Latent Semantic Indexing(LSI) vector for document list
+        '''
         docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
         model_lsi = models.LsiModel(docs_corpus, num_topics, id2word=self.docs_dict)
         docs_lsi = model_lsi[docs_corpus]
         docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lsi])
         return docs_vecs
 
-    # Get Random Projections(RP) vector for document list
+    
     def get_rp(self):
+        '''
+        Get Random Projections(RP) vector for document list
+        '''
         docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
         model_rp = models.RpModel(docs_corpus, id2word=self.docs_dict)
         docs_rp = model_rp[docs_corpus]
         docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_rp])
         return docs_vecs
 
-    # Get Latent Dirichlet Allocation(LDA) vector for document list
     def get_lda(self, num_topics=100):
+        '''
+        Get Latent Dirichlet Allocation(LDA) vector for document list
+        '''
         docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
         model_lda = models.LdaModel(docs_corpus, num_topics, id2word=self.docs_dict)
         docs_lda = model_lda[docs_corpus]
         docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lda])
         return docs_vecs
 
-    # Get Hierarchical Dirichlet Process(HDP) vector for document list
     def get_hdp(self):
+        '''
+        Get Hierarchical Dirichlet Process(HDP) vector for document list
+        '''
         docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
         model_hdp = models.HdpModel(docs_corpus, id2word=self.docs_dict)
         docs_hdp = model_hdp[docs_corpus]
diff --git a/notebooks/3. Text Vectorization - TF-IDF.ipynb b/notebooks/3. Text Vectorization - TF-IDF.ipynb
new file mode 100644
index 0000000..6e54f01
--- /dev/null
+++ b/notebooks/3. Text Vectorization - TF-IDF.ipynb	
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Text vectorization - TF-IDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('https://storage.googleapis.com/dataset-uploader/bbc/bbc-text.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>tech</td>\n",
+       "      <td>tv future in the hands of viewers with home th...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>business</td>\n",
+       "      <td>worldcom boss  left books alone  former worldc...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sport</td>\n",
+       "      <td>tigers wary of farrell  gamble  leicester say ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sport</td>\n",
+       "      <td>yeading face newcastle in fa cup premiership s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>entertainment</td>\n",
+       "      <td>ocean s twelve raids box office ocean s twelve...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        category                                               text\n",
+       "0           tech  tv future in the hands of viewers with home th...\n",
+       "1       business  worldcom boss  left books alone  former worldc...\n",
+       "2          sport  tigers wary of farrell  gamble  leicester say ...\n",
+       "3          sport  yeading face newcastle in fa cup premiership s...\n",
+       "4  entertainment  ocean s twelve raids box office ocean s twelve..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sport            511\n",
+       "business         510\n",
+       "politics         417\n",
+       "tech             401\n",
+       "entertainment    386\n",
+       "Name: category, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.category.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     /Users/hugo/nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nautilus_nlp.preprocessing.tokenizer import tokenize\n",
+    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3min 16s, sys: 15.2 s, total: 3min 31s\n",
+      "Wall time: 3min 50s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "df['tokens'] = df.text.apply(lambda row: tokenize(row))\n",
+    "df['tokens'] = df.tokens.apply(lambda row: lemmatize_english_tokens(row))\n",
+    "df['tokens'] = df.tokens.apply(lambda row: ' '.join(row))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Split dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "train, test = train_test_split(df, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compute TF-IDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.preprocessing.text_vectorizers import TfidfTextVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tfidf = Tfidf(max_df=0.95, #ignore terms that have a document frequency strictly higher \n",
+    "              min_df=0.01,\n",
+    "              max_features=10000,\n",
+    "              encoding='utf-8',\n",
+    "              #stop_words=SW,\n",
+    "              norm=None,\n",
+    "              #ngram_range=(1, 2))\n",
+    "              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
+       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
+       "        lowercase=True, max_df=0.95, max_features=10000, min_df=0.01,\n",
+       "        ngram_range=(1, 1), norm=None, preprocessor=None, smooth_idf=True,\n",
+       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
+       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
+       "        vocabulary=None)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# uses Sci-kit learn TfidfVectorizer()\n",
+    "# You can pass all the arguments supported by sci-kit \n",
+    "# Doc : https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\n",
+    "tfidf.tfidf_vectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_docs = list(train.tokens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['coach ranieri sack by valencia claudio ranieri have be sack as valencia coach just eight month after take charge at the primera liga club for the second time in -PRON- career .    the decision be take at a board meeting follow the side s surprise elimination from the uefa cup .    -PRON- understand    and -PRON- understand    that the result in the last few week have not be the most appropriate     say club president juan bautista . former assistant antonio lopez will take over as the new coach . italian ranieri take over the valencia job in june 2004 have be replace at chelsea by jose mourinho .    thing begin well but the spanish champion extend -PRON- winless streak to six after lose to race santander last weekend . that defeat be then follow by a uefa cup exit at the hand of steaua bucharest . ranieri first take charge of valencia in 1997    guide -PRON- to the king s cup and help -PRON- to qualify for the champion league . the 54-year - old then move to atletico madrid in 1999    before join chelsea the following year .',\n",
+       " 'fox    too reliant on reality tv    the head of -PRON- tv network fox have admit the broadcaster have rely too heavily on reality tv show such as the poor - rating who s -PRON- daddy .    chief executive gail berman say    in the case of this fall -PRON- drift to too much on the unscripted side . the series who s -PRON- daddy    where a young woman try to pick -PRON- natural father for a cash prize cause outrage from adoption group and rate badly . last season    fox s prime - time audience fall by 600 000 to 5.9 million . ms berman say :    i think the audience expect loud thing from fox . sometimes -PRON- work    and sometimes -PRON- don t.     who s -PRON- daddy    the first episode of which be show on 3 january    pull in a disappointing audience of 6.3 million    accord to the nielsen rating system . five other episode of the show have also be film will be drop from fox s schedule    ms berman say . -PRON- be predict a drop in rating even for some of the network s establish reality show    such as american idol    which be due to start -PRON- fourth series this week . fox have unveil a new strategy last year promise to launch new show every season    include the traditionally quiet summer season . though that have meet with a poor reception    ms berman say    there s no question that the audience    in -PRON- mind    be ready    willing and able to accept new programming in the summer . fox have change this plan    launch new show in may instead of june . one of the new show will be the animate series american dad    make by seth macfarlane    the creator of family guy . that series    after become a hit on dvd    be also set to return with new episode .']"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_docs[:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1780x2631 sparse matrix of type '<class 'numpy.float64'>'\n",
+       "\twith 253961 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compute the word count vector matrix.\n",
+    "# will apply \n",
+    "tfidf.compute_tfidf(list_of_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'song': 324.615,\n",
+       " 'wage': 316.535,\n",
+       " 'roddick': 293.646,\n",
+       " 'minimum': 249.423,\n",
+       " 'music': 73.292,\n",
+       " 'zealand': 158.674,\n",
+       " 'robbie': 157.772,\n",
+       " 'terrorist': 153.899,\n",
+       " 'good': 150.616,\n",
+       " 'gadget': 110.235,\n",
+       " 'game': 72.458,\n",
+       " 'party': 129.043,\n",
+       " 'increase': 126.747,\n",
+       " '25': 124.598,\n",
+       " 'hunt': 91.696,\n",
+       " 'pay': 119.675,\n",
+       " 'muslim': 112.338,\n",
+       " 'student': 111.842,\n",
+       " 'threat': 104.862,\n",
+       " 'black': 74.54,\n",
+       " 'liverpool': 102.93,\n",
+       " 'child': 100.285,\n",
+       " 'airline': 97.48,\n",
+       " 'stone': 96.591,\n",
+       " 'mini': 84.296,\n",
+       " 'jamie': 94.213,\n",
+       " 'play': 93.087,\n",
+       " 'virus': 86.885,\n",
+       " 'film': 91.146,\n",
+       " 'hour': 91.019,\n",
+       " 'spam': 90.217,\n",
+       " 'halo': 88.648,\n",
+       " 'mobile': 88.555,\n",
+       " 'edward': 88.215,\n",
+       " 'lord': 87.918,\n",
+       " 'search': 69.985,\n",
+       " 'wale': 86.423,\n",
+       " 'yuko': 75.514,\n",
+       " 'government': 85.639,\n",
+       " 'serve': 82.564,\n",
+       " 'machine': 82.494,\n",
+       " 'pension': 80.303,\n",
+       " 'asylum': 80.242,\n",
+       " 'actress': 79.333,\n",
+       " 'that': 78.981,\n",
+       " 'online': 78.916,\n",
+       " 'council': 77.999,\n",
+       " 'cash': 77.595,\n",
+       " 'fraud': 77.403,\n",
+       " 'musician': 77.291,\n",
+       " 'actor': 76.803,\n",
+       " 'gaming': 76.749,\n",
+       " 'lee': 76.256,\n",
+       " 'site': 75.507,\n",
+       " 'argentina': 74.892,\n",
+       " 'attack': 74.878,\n",
+       " 'award': 74.606,\n",
+       " 'business': 74.087,\n",
+       " 'broadband': 74.083,\n",
+       " 'hip': 73.725,\n",
+       " 'mail': 72.974,\n",
+       " 'not': 72.792,\n",
+       " 'aviator': 72.647,\n",
+       " 'japan': 72.55,\n",
+       " 'what': 72.332,\n",
+       " 'chart': 72.264,\n",
+       " 'document': 72.234,\n",
+       " 'jackson': 72.138,\n",
+       " 'card': 71.933,\n",
+       " 'deutsche': 71.647,\n",
+       " 'point': 71.343,\n",
+       " 'vote': 71.299,\n",
+       " 'radio': 71.215,\n",
+       " 'china': 70.847,\n",
+       " 'police': 70.759,\n",
+       " 'poster': 70.121,\n",
+       " 'phone': 69.937,\n",
+       " 'file': 69.836,\n",
+       " 'dvd': 69.522,\n",
+       " 'apple': 69.522,\n",
+       " 'spanish': 69.42}"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get highest-weighted words\n",
+    "tfidf.get_top_tfidf(n=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['cup', 'coach', 'sack', 'chelsea', 'take', 'understand', 'champion', 'club', 'charge', 'qualify']\n",
+      "['fox', 'audience', 'show', 'rating', 'episode', 'reality', 'series', 'ms', 'new', 'season']\n",
+      "['member', 'share', 'qualify', 'profit', '42', 'payment', 'society', 'than', 'building', 'worth']\n",
+      "['inflation', 'rate', 'rise', 'move', 'growth', 'flexibility', 'percentage', 'borrowing', 'economic', 'interest']\n",
+      "['profit', 'drug', 'treatment', 'disappointing', '2005', 'sale', '5bn', 'development', 'fall', 'year']\n",
+      "['woman', 'ministry', 'employ', 'prince', 'foreign', 'say', 'minister', 'news', 'difficulty', 'acquire']\n",
+      "['brown', 'labour', 'mr', 'blair', 'election', 'outline', 'manifesto', 'role', 'article', 'writing']\n",
+      "['iraq', 'bank', 'alexander', 'foreign', 'economy', 'many', 'work', 'mr', 'private', 'people']\n",
+      "['win', 'liverpool', 'trophy', 'steven', 'think', 'league', 'draw', 'champion', 'only', 'side']\n",
+      "['rating', 'bbc', 'prove', 'network', 'series', 'lose', 'slot', 'comeback', 'desperate', 'reportedly']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get highest-weighted words per document\n",
+    "for doc in list_of_docs[:10]:\n",
+    "    print(tfidf.get_top_tfidf_per_doc(doc,n=10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Apply Tf-idf to new documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>category</th>\n",
+       "      <th>text</th>\n",
+       "      <th>tokens</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>tech</td>\n",
+       "      <td>us peer-to-peer pirates convicted the first co...</td>\n",
+       "      <td>-PRON- peer - to - peer pirate convict the fir...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1281</th>\n",
+       "      <td>sport</td>\n",
+       "      <td>hewitt falls to dent lleyton hewitt suffered a...</td>\n",
+       "      <td>hewitt fall to dent lleyton hewitt suffer a sh...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>844</th>\n",
+       "      <td>tech</td>\n",
+       "      <td>why cell will get the hard sell the world is c...</td>\n",
+       "      <td>why cell will get the hard sell the world be c...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1188</th>\n",
+       "      <td>entertainment</td>\n",
+       "      <td>belle named  best scottish band  belle &amp; sebas...</td>\n",
+       "      <td>belle name    good scottish band    belle &amp; se...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1573</th>\n",
+       "      <td>business</td>\n",
+       "      <td>karachi stocks hit historic high the karachi s...</td>\n",
+       "      <td>karachi stock hit historic high the karachi st...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           category                                               text  \\\n",
+       "1992           tech  us peer-to-peer pirates convicted the first co...   \n",
+       "1281          sport  hewitt falls to dent lleyton hewitt suffered a...   \n",
+       "844            tech  why cell will get the hard sell the world is c...   \n",
+       "1188  entertainment  belle named  best scottish band  belle & sebas...   \n",
+       "1573       business  karachi stocks hit historic high the karachi s...   \n",
+       "\n",
+       "                                                 tokens  \n",
+       "1992  -PRON- peer - to - peer pirate convict the fir...  \n",
+       "1281  hewitt fall to dent lleyton hewitt suffer a sh...  \n",
+       "844   why cell will get the hard sell the world be c...  \n",
+       "1188  belle name    good scottish band    belle & se...  \n",
+       "1573  karachi stock hit historic high the karachi st...  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1992    [peer, copyright, piracy, network, plead, guil...\n",
+       "1281    [seed, hewitt, break, feel, beat, strong, four...\n",
+       "844     [cell, processor, technology, sony, computer, ...\n",
+       "1188    [scottish, band, brit, musical, list, act, awa...\n",
+       "1573    [market, stock, analyst, political, index, mon...\n",
+       "687     [film, institute, news, trend, broadcast, cite...\n",
+       "1830    [rule, consumer, mobile, firm, service, phone,...\n",
+       "1134    [net, standard, dr, address, work, challenge, ...\n",
+       "1015    [rugby, glasgow, edinburgh, murray, border, ro...\n",
+       "1665    [olympic, football, game, career, woman, final...\n",
+       "1648    [referee, apologise, arsenal, match, assistant...\n",
+       "487     [club, manager, return, at, unveil, jone, as, ...\n",
+       "1124    [lewis, council, labour, football, wage, gover...\n",
+       "1094    [gold, debt, relief, reserve, meeting, price, ...\n",
+       "744     [advert, drug, article, medical, employee, let...\n",
+       "939     [musical, theatre, stage, kelly, absolute, per...\n",
+       "1742    [creative, art, technology, graphic, bt, engin...\n",
+       "1368    [debt, relief, brown, uk, chancellor, poverty,...\n",
+       "880     [child, film, original, screen, show, that, mo...\n",
+       "136     [sing, tune, theme, die, perform, singer, show...\n",
+       "376     [olympic, test, athlete, decision, appeal, mot...\n",
+       "1558    [newcastle, henry, season, game, play, arsenal...\n",
+       "521     [bt, broadband, customer, call, offer, free, i...\n",
+       "1760    [goal, score, didn, win, that, good, spirit, p...\n",
+       "34      [guilty, plead, insurance, investigation, exec...\n",
+       "881     [election, young, electoral, vote, commission,...\n",
+       "302     [euro, company, sport, possible, family, analy...\n",
+       "515     [actress, notice, immigration, india, work, ye...\n",
+       "932     [period, quarter, athletic, growth, earning, s...\n",
+       "1084    [drug, cheap, europe, sell, africa, aid, afric...\n",
+       "                              ...                        \n",
+       "1853    [sequel, star, box, office, weekend, robert, d...\n",
+       "1390    [labour, blair, cabinet, minister, serve, stan...\n",
+       "743     [liverpool, league, champion, season, competit...\n",
+       "715     [lee, film, man, create, book, series, release...\n",
+       "1842    [alliance, japanese, shareholder, car, talk, s...\n",
+       "499     [argentina, debt, accept, offer, 6bn, investor...\n",
+       "461     [musical, sir, film, actress, richard, child, ...\n",
+       "902     [mobile, tv, service, music, handset, download...\n",
+       "1014    [tory, tax, howard, cut, election, taxis, plan...\n",
+       "446     [broadband, tv, analyst, number, research, net...\n",
+       "222     [jump, title, championship, mark, european, se...\n",
+       "884     [union, merger, super, executive, hold, meetin...\n",
+       "1483    [fight, german, band, liam, officer, fine, kic...\n",
+       "1325    [ask, search, site, entry, web, acquisition, i...\n",
+       "1257    [prime, mr, blair, chancellor, minister, brown...\n",
+       "725     [plant, india, factory, indian, official, cons...\n",
+       "1679    [mail, virus, warn, infect, internet, computer...\n",
+       "714     [express, financial, american, spin, off, sell...\n",
+       "1864    [opera, voice, feature, available, page, appea...\n",
+       "2068    [game, political, campaign, video, learn, tool...\n",
+       "1651    [deutsche, exchange, bid, stock, talk, cash, o...\n",
+       "1610    [film, voice, robert, will, girl, actress, web...\n",
+       "516     [campaign, labour, brown, mr, election, role, ...\n",
+       "2059    [brown, mr, blair, labour, mps, party, electio...\n",
+       "841     [france, wale, injury, slam, grand, ireland, n...\n",
+       "221     [tour, tournament, prize, king, each, gamer, s...\n",
+       "421     [talent, decide, agree, star, midfielder, play...\n",
+       "1075    [game, video, company, news, tag, art, as, eye...\n",
+       "1601    [france, against, wale, ireland, play, england...\n",
+       "986     [relay, service, phone, language, video, peopl...\n",
+       "Name: tokens, Length: 445, dtype: object"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.tokens.apply(lambda row: tfidf.get_top_tfidf_per_doc(row))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "test_tfidf_matrix = tfidf.apply_tfidf_to_documents(list(test.tokens))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cell': 94.188,\n",
+       " 'mobile': 92.405,\n",
+       " 'camera': 90.674,\n",
+       " 'mini': 89.254,\n",
+       " 'china': 87.516,\n",
+       " 'rugby': 85.906,\n",
+       " 'film': 82.031,\n",
+       " 'bt': 76.256}"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tfidf.get_top_tfidf(test_tfidf_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/TF-IDF.ipynb b/notebooks/TF-IDF.ipynb
deleted file mode 100644
index 9e36e8b..0000000
--- a/notebooks/TF-IDF.ipynb
+++ /dev/null
@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import re"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open a dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv('../data/df_mess_analysed.csv',\n",
-    "                 encoding='utf8',\n",
-    "                 delimiter=';',\n",
-    "                 usecols=['chrn_gaz','stm_clean','offre']\n",
-    "                )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "df = df[(df.offre == 'DUAL') | (df.offre == 'GAZ')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "df.stm_clean = df.stm_clean.apply(lambda row: re.findall(\"\\w+\",row))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>offre</th>\n",
-       "      <th>chrn_gaz</th>\n",
-       "      <th>stm_clean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>DUAL</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[mettr, jour, adress, palenci, bourg]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>DUAL</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[souhait, modifi, adress, mail, etre, contacte...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DUAL</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[prochain, factur, disponibl, compt]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>DUAL</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[index, factur, aout, net, superieur, realit, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>DUAL</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[savoir, factur, annuel, pai]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  offre  chrn_gaz                                          stm_clean\n",
-       "0  DUAL         0              [mettr, jour, adress, palenci, bourg]\n",
-       "1  DUAL         0  [souhait, modifi, adress, mail, etre, contacte...\n",
-       "2  DUAL         0               [prochain, factur, disponibl, compt]\n",
-       "3  DUAL         0  [index, factur, aout, net, superieur, realit, ...\n",
-       "4  DUAL         0                      [savoir, factur, annuel, pai]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "list_of_docs = [' '.join(doc) for doc in df.stm_clean.tolist()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['mettr jour adress palenci bourg',\n",
-       " 'souhait modifi adress mail etre contacte servic cel nadiyaa545 gmail prendr cel compt dorenavent',\n",
-       " 'prochain factur disponibl compt']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_of_docs[:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Compute TF-IDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.text_vectorizer import Tfidf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tfidf = Tfidf(max_df=0.95, #ignore terms that have a document frequency strictly higher \n",
-    "              min_df=0.01,\n",
-    "             max_features=10000,\n",
-    "             encoding='utf-8',\n",
-    "             #stop_words=SW,\n",
-    "             norm=None,\n",
-    "             #ngram_range=(1, 2))\n",
-    "             )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
-       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
-       "        lowercase=True, max_df=0.95, max_features=10000, min_df=0.01,\n",
-       "        ngram_range=(1, 1), norm=None, preprocessor=None, smooth_idf=True,\n",
-       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
-       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
-       "        vocabulary=None)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# uses Sci-kit learn TfidfVectorizer()\n",
-    "# You can pass all the arguments supported by sci-kit \n",
-    "# Doc : https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\n",
-    "tfidf.tfidf_vectorizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<17744x434 sparse matrix of type '<class 'numpy.float64'>'\n",
-       "\twith 300349 stored elements in Compressed Sparse Row format>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Compute the word count vector matrix.\n",
-    "tfidf.compute_tfidf(list_of_docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'euro': 96.099,\n",
-       " 'rembours': 116.797,\n",
-       " 'adress': 115.531,\n",
-       " 'compt': 89.231,\n",
-       " 'coupur': 87.871,\n",
-       " 'chequ': 82.334,\n",
-       " 'mois': 81.935}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get highest-weighted words\n",
-    "tfidf.get_top_tfidf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['mettr', 'adress', 'jour']\n",
-      "['cel', 'prendr', 'modifi', 'adress', 'mail', 'servic', 'etre', 'compt', 'souhait']\n",
-      "['disponibl', 'prochain', 'compt', 'factur']\n",
-      "['index', 'modif', 'modifi', 'lign', 'prochain', 'aout', 'electricit', 'pouv', 'prelev', 'factur']\n",
-      "['annuel', 'savoir', 'pai', 'factur']\n",
-      "['disponibl', 'ete', 'concern', 'relev', 'compteur', 'demand']\n",
-      "['acce', 'juin', 'plait', 'avanc', 'envoi', 'compt', 'mois', 'factur']\n",
-      "['conso', 'reel', 'communiqu', 'vient', 'met', 'relev', 'argent', 'conseiller', 'repondu', 'connaitr']\n",
-      "['nest', 'point', 'lappliqu', 'effect', 'sup', 'appliqu', 'vrai', 'toujour', 'arriv', 'souc']\n",
-      "['mensuel', 'pai', 'prelev']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get highest-weighted words per document\n",
-    "for doc in list_of_docs[:10]:\n",
-    "    print(tfidf.get_top_tfidf_per_doc(doc))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From b4c1ae40e0699f77fc511e5af06d14d7926ba54d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Thu, 23 May 2019 18:31:18 +0200
Subject: [PATCH 199/496] add utils functions

---
 nautilus_nlp/utils/file_loader.py | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 006e9f6..6d710f6 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -2,10 +2,13 @@
 import chardet
 import glob
 import re
-from os.path import isfile, isdir
+import os 
+from os import listdir
+from os.path import isfile, isdir, join
 import json
 import logging
 
+
 logging.basicConfig(level=logging.INFO)
 
 
@@ -49,6 +52,23 @@ def text_loader(filepath, encoding=None, detectencoding=True):
                 raise UnicodeDecodeError('Cannot load document using utf-8. Try to detect encoding using detectencoding=True')
 
 
+def get_subfolders_path(folder):
+    if not folder.endswith("/"):
+        folder = folder + "/"
+    return [folder + f+'/' for f in listdir(folder)if isdir(join(folder, f)) and f != ".DS_Store"]
+
+
+def list_files_in_subdir(filepath):
+    '''
+    Get a list of all the filepath of files in directory and subdirectory.
+    '''
+    res = []
+    for path, subdirs, files in os.walk(filepath):
+        for name in files:
+            res.append(os.path.join(path, name))
+    return res
+
+
 def list_files(filepath:str):
     """  
     inputs a filepath. 

From ad3bc721a5b5d4b1d71735d31980b7cb612e7754 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 24 May 2019 11:13:13 +0200
Subject: [PATCH 200/496] Bump Version to 0.9.1 - Prerelease

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index 6da28dd..f514a2f 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-0.1.1
\ No newline at end of file
+0.9.1
\ No newline at end of file

From b6d65e1f26b5e975c3dd2b978aa30ddc6363ab4d Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Fri, 24 May 2019 11:18:08 +0200
Subject: [PATCH 201/496] Guidelines if Fasttext install fails on MACOS

---
 README.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/README.md b/README.md
index 9ae74d3..5bae6f7 100644
--- a/README.md
+++ b/README.md
@@ -46,6 +46,19 @@ pip install -e .
 
 ## Handling installation errors
 
+
+### Problem when building FastText on MACOS:
+
+If you see this errorwhen building Fasttext:
+
+    clang: warning: libstdc++ is deprecated; move to libc++ with a minimum deployment target of OS X 10.9 [-Wdeprecated]
+    ld: library not found for -lstdc++
+    clang: error: linker command failed with exit code 1 (use -v to see invocation)
+    error: command 'g++' failed with exit status 1
+Then you should type this command in your terminal:
+
+    export MACOSX_DEPLOYMENT_TARGET=10.9
+
 ### command 'gcc' failed with exit status 1
 
 While runing `pip install -r requirements.txt` you might get the following error message:

From d9ad94ecab61b4ade976ff3bfde77d414cb2745e Mon Sep 17 00:00:00 2001
From: root <root@nautilus-nlp.c.nautilus-sandbox.internal>
Date: Fri, 24 May 2019 12:08:01 +0000
Subject: [PATCH 202/496] update notebooks

---
 notebooks/0. Text file loader.ipynb           | 124 +++--
 notebooks/1. Text Preprocessing.ipynb         |  88 ++--
 notebooks/2. Text processing.ipynb            | 146 +++---
 .../3. Text Vectorization - TF-IDF.ipynb      | 446 +++++++++---------
 .../Benchmark text processing tools.ipynb     | 261 ++++++----
 notebooks/Language_identification.ipynb       |  61 ++-
 ...nt analysis using pre-trained models.ipynb |  50 +-
 notebooks/Sentiment_analysis_FT.ipynb         |  18 +-
 notebooks/Spacy_model.ipynb                   |  31 +-
 notebooks/Visualization tools.ipynb           |  75 +--
 notebooks/someadditionalfile.txt              |   1 +
 notebooks/somefile.txt                        |   1 +
 12 files changed, 651 insertions(+), 651 deletions(-)
 create mode 100644 notebooks/someadditionalfile.txt
 create mode 100644 notebooks/somefile.txt

diff --git a/notebooks/0. Text file loader.ipynb b/notebooks/0. Text file loader.ipynb
index 5aa985f..d83208d 100644
--- a/notebooks/0. Text file loader.ipynb	
+++ b/notebooks/0. Text file loader.ipynb	
@@ -19,9 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Example of a latin1 file\n",
@@ -34,9 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Let's add another document \n",
@@ -56,9 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.utils.file_loader import documents_loader"
@@ -162,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -181,15 +175,15 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mopen_textfile\u001b[0;34m(filepath, encoding)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/Cellar/python/3.7.0/Frameworks/Python.framework/Versions/3.7/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mopen_textfile\u001b[0;34m(filepath, encoding)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/hugo/anaconda3/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 30: invalid continuation byte",
       "\nDuring handling of the above exception, another exception occurred:\n",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-c6d5e5969a55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# You can prevent document loader from detecting the encoding if UTF-8 fails\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdocuments_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'somefile.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mdocuments_loader\u001b[0;34m(filepath, encoding, detectencoding, output_as)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtext_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetectencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moutput_as\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'list'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/NAUTILUS/nautilus-nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetected_encoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot load document using utf-8. Try to detect encoding using detectencoding=True'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-19-482c18687c00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# You can prevent document loader from detecting the encoding if UTF-8 fails\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# In this case, it will raise an UnicodeDecodeError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdocuments_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'somefile.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mdocuments_loader\u001b[0;34m(filepath, encoding, detectencoding, output_as)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtext_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetectencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moutput_as\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'list'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetected_encoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot load document using utf-8. Try to detect encoding using detectencoding=True'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: function takes exactly 5 arguments (1 given)"
      ]
     }
@@ -209,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -228,7 +222,7 @@
        " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -240,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -259,7 +253,7 @@
        " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -271,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -290,7 +284,7 @@
        " 'Un deuxième exemple de texte en utf-8 cette fois!']"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,10 +303,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.utils.file_loader import list_files"
@@ -320,28 +312,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['./somefile.txt',\n",
-       " './2. Text processing.ipynb',\n",
-       " './TF-IDF.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
+       "['./Visualization tools.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb',\n",
+       " './somefile.txt',\n",
        " './Language_identification.ipynb',\n",
-       " './someadditionalfile.txt',\n",
-       " './somefile',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
+       " './1. Text Preprocessing.ipynb',\n",
+       " './3. Text Vectorization - TF-IDF.ipynb',\n",
        " './TopicModeling.ipynb',\n",
-       " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './Benchmark text processing tools.ipynb',\n",
        " './0. Text file loader.ipynb',\n",
-       " './1. Text Preprocessing.ipynb']"
+       " './someadditionalfile.txt',\n",
+       " './2. Text processing.ipynb',\n",
+       " './Spacy_model.ipynb']"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -352,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -361,7 +353,7 @@
        "[]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -372,25 +364,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['./2. Text processing.ipynb',\n",
-       " './TF-IDF.ipynb',\n",
-       " './Visualization tools.ipynb',\n",
+       "['./Visualization tools.ipynb',\n",
+       " './Sentiment analysis using pre-trained models.ipynb',\n",
        " './Language_identification.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
+       " './1. Text Preprocessing.ipynb',\n",
+       " './3. Text Vectorization - TF-IDF.ipynb',\n",
        " './TopicModeling.ipynb',\n",
-       " './Spacy_model.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb',\n",
+       " './Sentiment_analysis_FT.ipynb',\n",
+       " './Benchmark text processing tools.ipynb',\n",
        " './0. Text file loader.ipynb',\n",
-       " './1. Text Preprocessing.ipynb']"
+       " './2. Text processing.ipynb',\n",
+       " './Spacy_model.ipynb']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
@@ -409,18 +402,10 @@
     {
      "data": {
       "text/plain": [
-       "['/Users/hugo/Documents/NAUTILUS/nautilus-nlp/LICENSE',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/requirements.txt',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/Makefile',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/README.md',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/setup.py',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/VERSION',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/CONTRIBUTING.md',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/tox.ini',\n",
-       " '/Users/hugo/Documents/NAUTILUS/nautilus-nlp/test_environment.py']"
+       "[]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,10 +426,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.utils.file_loader import detect_encoding"
@@ -452,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +444,7 @@
        "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -469,6 +452,13 @@
    "source": [
     "detect_encoding('somefile.txt')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -487,7 +477,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/1. Text Preprocessing.ipynb b/notebooks/1. Text Preprocessing.ipynb
index 3803299..b081db0 100644
--- a/notebooks/1. Text Preprocessing.ipynb	
+++ b/notebooks/1. Text Preprocessing.ipynb	
@@ -20,9 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "english_text = \"\"\"\n",
@@ -54,9 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import preprocess_text"
@@ -119,9 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import remove_EOL_characters"
@@ -129,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -164,10 +158,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import get_stopwords"
@@ -175,14 +167,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['basee', 'y', 'façon', 'étant', 't', 'chère', 'uns', 'mêmes', 'particulier', 'tend']\n"
+      "['soi-même', 'prealable', 'ayez', 'fi', 'concernant', 'dedans', 'celles', 'tiennes', 'plutôt', 'bravo']\n"
      ]
     }
    ],
@@ -194,10 +186,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import remove_stopwords"
@@ -205,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -239,10 +229,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import fix_bad_unicode"
@@ -250,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -284,10 +272,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import replace_phone_numbers"
@@ -295,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -329,10 +315,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import nautilus_nlp.utils.phone_number as phone"
@@ -340,15 +324,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 452 ms, sys: 22.5 ms, total: 474 ms\n",
-      "Wall time: 477 ms\n"
+      "CPU times: user 296 ms, sys: 0 ns, total: 296 ms\n",
+      "Wall time: 294 ms\n"
      ]
     },
     {
@@ -357,7 +341,7 @@
        "['(541) 754-3010', '754-3010']"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,10 +360,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "p = phone.phoneParser()"
@@ -387,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -396,7 +378,7 @@
        "PhoneNumber(country_code=1, national_number=5417543010, extension=None, italian_leading_zero=None, number_of_leading_zeros=None, country_code_source=0, preferred_domestic_carrier_code=None)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -416,7 +398,7 @@
        "'541-754-3010'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -434,10 +416,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import remove_emoji, convert_emoji_to_text"
@@ -445,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -472,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -514,7 +494,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/2. Text processing.ipynb b/notebooks/2. Text processing.ipynb
index 6a6c0b5..d1c68ee 100644
--- a/notebooks/2. Text processing.ipynb	
+++ b/notebooks/2. Text processing.ipynb	
@@ -25,7 +25,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
       "[nltk_data]   Package punkt is already up-to-date!\n"
      ]
     }
@@ -37,9 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fr_txt = \"Ceci est un texte français, j'adore 1 !\"\n",
@@ -49,9 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
@@ -75,13 +71,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 8.94 ms, sys: 1.24 ms, total: 10.2 ms\n",
-      "Wall time: 9.3 ms\n"
+      "CPU times: user 5.07 ms, sys: 130 µs, total: 5.2 ms\n",
+      "Wall time: 5.03 ms\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
+    "\n",
     "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_spacy\")"
    ]
   },
@@ -89,6 +86,15 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -118,8 +124,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 18.2 s, sys: 82.5 ms, total: 18.3 s\n",
-      "Wall time: 18.5 s\n"
+      "CPU times: user 13.2 s, sys: 6.59 ms, total: 13.2 s\n",
+      "Wall time: 13.2 s\n"
      ]
     }
    ],
@@ -161,8 +167,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.24 ms, sys: 2 µs, total: 1.24 ms\n",
-      "Wall time: 1.25 ms\n"
+      "CPU times: user 990 µs, sys: 0 ns, total: 990 µs\n",
+      "Wall time: 998 µs\n"
      ]
     }
    ],
@@ -173,7 +179,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +197,7 @@
        "['Let', \"'s\", 'play', 'together', '!']"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -200,15 +215,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 11.2 ms, sys: 3.35 ms, total: 14.5 ms\n",
-      "Wall time: 16 ms\n"
+      "CPU times: user 7.94 ms, sys: 122 µs, total: 8.06 ms\n",
+      "Wall time: 6.95 ms\n"
      ]
     }
    ],
@@ -234,8 +249,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 235 µs, sys: 1e+03 ns, total: 236 µs\n",
-      "Wall time: 243 µs\n"
+      "CPU times: user 1.46 s, sys: 35 µs, total: 1.46 s\n",
+      "Wall time: 1.46 s\n"
      ]
     },
     {
@@ -263,8 +278,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.07 ms, sys: 181 µs, total: 4.25 ms\n",
-      "Wall time: 4.19 ms\n"
+      "CPU times: user 3 s, sys: 351 µs, total: 3 s\n",
+      "Wall time: 3 s\n"
      ]
     },
     {
@@ -292,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +325,7 @@
        "['i', 'surviv', 'these', 'dog']"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -330,7 +345,7 @@
        "['je', 'mang', 'dan', 'le', 'cuisin', 'du', 'château']"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -355,15 +370,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n"
+      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     /root/nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n"
      ]
     }
    ],
@@ -373,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -391,15 +410,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 20.3 ms, sys: 2.47 ms, total: 22.7 ms\n",
-      "Wall time: 20.6 ms\n"
+      "CPU times: user 17.3 ms, sys: 0 ns, total: 17.3 ms\n",
+      "Wall time: 15.8 ms\n"
      ]
     }
    ],
@@ -410,7 +429,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lemmatized_tokens = lemmatize_french_tokens(txt_to_tokenize, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 23,
    "metadata": {
     "scrolled": true
    },
@@ -445,10 +473,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
     "to_lemmatize = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']"
@@ -456,15 +482,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 16.9 ms, sys: 3.03 ms, total: 19.9 ms\n",
-      "Wall time: 16.8 ms\n"
+      "CPU times: user 13.7 ms, sys: 0 ns, total: 13.7 ms\n",
+      "Wall time: 12.5 ms\n"
      ]
     },
     {
@@ -473,7 +499,7 @@
        "['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -485,15 +511,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.58 s, sys: 141 ms, total: 2.72 s\n",
-      "Wall time: 2.73 s\n"
+      "CPU times: user 1.72 s, sys: 125 ms, total: 1.85 s\n",
+      "Wall time: 1.82 s\n"
      ]
     },
     {
@@ -502,7 +528,7 @@
        "['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -531,10 +557,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [],
    "source": [
     "FRENCH_SW = get_stopwords('fr')"
@@ -542,10 +566,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "text = \"J'ai un beau cheval\""
@@ -553,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -562,7 +584,7 @@
        "[\"J'ai\", 'cheval']"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -582,7 +604,7 @@
        "[\"J'\", 'cheval']"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -608,7 +630,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/3. Text Vectorization - TF-IDF.ipynb b/notebooks/3. Text Vectorization - TF-IDF.ipynb
index 6e54f01..1e0c112 100644
--- a/notebooks/3. Text Vectorization - TF-IDF.ipynb	
+++ b/notebooks/3. Text Vectorization - TF-IDF.ipynb	
@@ -153,12 +153,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
       "[nltk_data]   Package punkt is already up-to-date!\n",
-      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
+      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
       "[nltk_data]   Package wordnet is already up-to-date!\n",
       "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
-      "[nltk_data]     /Users/hugo/nltk_data...\n",
+      "[nltk_data]     /root/nltk_data...\n",
       "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
       "[nltk_data]       date!\n"
      ]
@@ -178,8 +178,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3min 16s, sys: 15.2 s, total: 3min 31s\n",
-      "Wall time: 3min 50s\n"
+      "CPU times: user 2min 47s, sys: 1.15 s, total: 2min 48s\n",
+      "Wall time: 2min 48s\n"
      ]
     }
    ],
@@ -209,9 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "train, test = train_test_split(df, test_size=0.2)"
@@ -226,20 +224,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "paramiko missing, opening SSH/SCP/SFTP paths will be disabled.  `pip install paramiko` to suppress\n"
+     ]
+    }
+   ],
    "source": [
     "from nautilus_nlp.preprocessing.text_vectorizers import TfidfTextVectorizer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tfidf = Tfidf(max_df=0.95, #ignore terms that have a document frequency strictly higher \n",
+    "tfidf = TfidfTextVectorizer(max_df=0.95, #ignore terms that have a document frequency strictly higher \n",
     "              min_df=0.01,\n",
     "              max_features=10000,\n",
     "              encoding='utf-8',\n",
@@ -251,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +272,7 @@
        "        vocabulary=None)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,17 +295,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['coach ranieri sack by valencia claudio ranieri have be sack as valencia coach just eight month after take charge at the primera liga club for the second time in -PRON- career .    the decision be take at a board meeting follow the side s surprise elimination from the uefa cup .    -PRON- understand    and -PRON- understand    that the result in the last few week have not be the most appropriate     say club president juan bautista . former assistant antonio lopez will take over as the new coach . italian ranieri take over the valencia job in june 2004 have be replace at chelsea by jose mourinho .    thing begin well but the spanish champion extend -PRON- winless streak to six after lose to race santander last weekend . that defeat be then follow by a uefa cup exit at the hand of steaua bucharest . ranieri first take charge of valencia in 1997    guide -PRON- to the king s cup and help -PRON- to qualify for the champion league . the 54-year - old then move to atletico madrid in 1999    before join chelsea the following year .',\n",
-       " 'fox    too reliant on reality tv    the head of -PRON- tv network fox have admit the broadcaster have rely too heavily on reality tv show such as the poor - rating who s -PRON- daddy .    chief executive gail berman say    in the case of this fall -PRON- drift to too much on the unscripted side . the series who s -PRON- daddy    where a young woman try to pick -PRON- natural father for a cash prize cause outrage from adoption group and rate badly . last season    fox s prime - time audience fall by 600 000 to 5.9 million . ms berman say :    i think the audience expect loud thing from fox . sometimes -PRON- work    and sometimes -PRON- don t.     who s -PRON- daddy    the first episode of which be show on 3 january    pull in a disappointing audience of 6.3 million    accord to the nielsen rating system . five other episode of the show have also be film will be drop from fox s schedule    ms berman say . -PRON- be predict a drop in rating even for some of the network s establish reality show    such as american idol    which be due to start -PRON- fourth series this week . fox have unveil a new strategy last year promise to launch new show every season    include the traditionally quiet summer season . though that have meet with a poor reception    ms berman say    there s no question that the audience    in -PRON- mind    be ready    willing and able to accept new programming in the summer . fox have change this plan    launch new show in may instead of june . one of the new show will be the animate series american dad    make by seth macfarlane    the creator of family guy . that series    after become a hit on dvd    be also set to return with new episode .']"
+       "['bid to cut court witness stress new target to reduce the stress to victim and witness give evidence in court in england and wale have be announce by the lord chancellor .    lord falconer want all crown court and 90 % of magistrate    court to have facility to keep witness separate from defendant within four year . more video link will also be make available so that witness do not have to enter courtroom . -PRON- be part of a five - year plan to help build confidence in the justice system .    minister say the strategy be aim at re - balance the court system towards victim    and increase the number of offender bring to justice . launch the department for constitutional affair    plan    lord falconer say :    one of the top priority will be a well deal for victim .    the need and safety of victim will be at the heart of the way trial be manage .     court    judge    magistrate    prosecutor    police and victim support - all work together to ensure the right of victim be put first    without compromise the right of the defendant .    -PRON- go on :    give evidence be a nerve - wracking experience    especially when -PRON- re a victim .    yet with a will and with support -PRON- can be do .    lord falconer tell bbc radio 4 s today programme -PRON- be impossible for some elderly people to go to court to give evidence . other witness could be intimidate by sit alongside defendant outside court .    -PRON- be never go to get rid of some element of the trauma of give evidence     -PRON- say .    but -PRON- can make people believe that the court understand the problem    -PRON- s not some kind of alien place where -PRON- go where -PRON- be not think about -PRON- .     the plan come as the lord chancellor also consider allow camera into court for the first time since 1925    as long as -PRON- be use for case that do not involve witness . another feature of the strategy be constitutional reform    with a government bill to set up a supreme court and a judicial appointment commission return to the house of lord on tuesday . minister have propose get rid of the title of lord chancellor    but the lord have over - rule this . lord falconer say -PRON- be right for the high court to be completely distinct from parliament . the person in charge of the court system should not also be speaker of the house of lord    -PRON- say    and should be the good person choose from either house of parliament . what -PRON- do    not what -PRON- be call    be the critical issue    -PRON- add .',\n",
+       " 'market unfaze by aurora setback as the aurora limp back to -PRON- dock on 20 january    a blizzard of photo and interview seem to add up to an unambiguous tale of woe .    the ship have another slice of bad luck to add to -PRON- history of health scare and technical trouble . and -PRON- owner    p&o cruise - now part of the huge -PRON- carnival corporation - be look at a significant slice chop off this year s profit and a potential pr fiasco . no - one    however    seem to have tell the stock market . the warning of a five - cent hit to 2005 earning come just 24 hour after one of the world s big investment bank have up -PRON- target for carnival s share price    from £ 35 to £ 36.20 . other investor barely blink    and by 1300 gmt carnival s share in london be down a single penny    or 0.03 %    at £ 32.26 .    why the mismatch between the public perception and the market s response     the aurora issue have be an ongoing one for some time     say deutsche bank s simon champion .    -PRON- be clearly a source of uncertainty for the company - -PRON- be a long cruise    after all . but the stock market be very good at treat these issue as one - off event .     despite -PRON- string of bad luck    -PRON- point out    aurora be just one vessel in a large carnival fleet    the uk s p&o princess group have be merge into the much large -PRON- firm in 2003 . and generally speak    carnival have a reputation for keep -PRON- ship pretty much on schedule .    carnival have an incredibly strong track record     mr champion .    similarly    analyst expect the impact on the rest of the cruise business to be limit . the hundred of disappointed passenger who have now have to give up the opportunity to spend the next three month on the aurora have get both a refund and a credit for another cruise . that should mitigate some of the pr risk    both for carnival and -PRON- main competitor    royal caribbean .    while not common    cancellation for technical reason be not entirely unusual in the industry     write analyst from citigroup smith barney in a note to client on friday .    moreover    such event typically have a limited impact on booking and pricing for future cruise .    after all    the aurora incident may be big news in the uk - but for carnival customer elsewhere -PRON- s unlikely to make too much of a splash .    assume that citigroup be right    and demand stay solid    the structure of the industry also work in carnival s favour . in the wake of p&o princess s takeover by carnival    the business be now to a great extent a duopoly . give the expense of build    outfitting and run a cruise ship     slow supply growth    be a certainty    say david ander at merrill lynch on thursday . in other word    if -PRON- do want a cruise    -PRON- option be limited . and with carnival remain the market leader    -PRON- look set to keep sell the ticket - no matter what happen to the ill - fat aurora in the future .']"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -310,17 +318,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<1780x2631 sparse matrix of type '<class 'numpy.float64'>'\n",
-       "\twith 253961 stored elements in Compressed Sparse Row format>"
+       "<1780x2638 sparse matrix of type '<class 'numpy.float64'>'\n",
+       "\twith 251758 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -333,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -341,90 +349,87 @@
     {
      "data": {
       "text/plain": [
-       "{'song': 324.615,\n",
-       " 'wage': 316.535,\n",
+       "{'song': 328.383,\n",
+       " 'wage': 314.316,\n",
        " 'roddick': 293.646,\n",
-       " 'minimum': 249.423,\n",
-       " 'music': 73.292,\n",
-       " 'zealand': 158.674,\n",
+       " 'minimum': 260.403,\n",
+       " 'music': 75.21,\n",
        " 'robbie': 157.772,\n",
        " 'terrorist': 153.899,\n",
-       " 'good': 150.616,\n",
-       " 'gadget': 110.235,\n",
-       " 'game': 72.458,\n",
-       " 'party': 129.043,\n",
-       " 'increase': 126.747,\n",
-       " '25': 124.598,\n",
-       " 'hunt': 91.696,\n",
-       " 'pay': 119.675,\n",
-       " 'muslim': 112.338,\n",
-       " 'student': 111.842,\n",
-       " 'threat': 104.862,\n",
-       " 'black': 74.54,\n",
-       " 'liverpool': 102.93,\n",
-       " 'child': 100.285,\n",
-       " 'airline': 97.48,\n",
+       " 'good': 151.491,\n",
+       " 'game': 73.159,\n",
+       " 'increase': 129.725,\n",
+       " 'hunt': 93.316,\n",
+       " 'party': 126.188,\n",
+       " '25': 125.075,\n",
+       " 'pay': 121.356,\n",
+       " 'muslim': 114.249,\n",
+       " 'gadget': 113.934,\n",
+       " 'student': 108.451,\n",
+       " 'threat': 104.522,\n",
+       " 'black': 103.206,\n",
+       " 'liverpool': 101.794,\n",
+       " 'airline': 99.171,\n",
        " 'stone': 96.591,\n",
-       " 'mini': 84.296,\n",
-       " 'jamie': 94.213,\n",
-       " 'play': 93.087,\n",
-       " 'virus': 86.885,\n",
-       " 'film': 91.146,\n",
-       " 'hour': 91.019,\n",
-       " 'spam': 90.217,\n",
-       " 'halo': 88.648,\n",
-       " 'mobile': 88.555,\n",
-       " 'edward': 88.215,\n",
-       " 'lord': 87.918,\n",
-       " 'search': 69.985,\n",
-       " 'wale': 86.423,\n",
+       " 'mini': 90.917,\n",
+       " 'mobile': 89.006,\n",
+       " 'play': 92.565,\n",
+       " 'cell': 92.487,\n",
+       " 'virus': 87.661,\n",
+       " 'jamie': 92.1,\n",
+       " 'spam': 91.696,\n",
+       " 'film': 82.501,\n",
+       " 'hour': 91.616,\n",
+       " 'camera': 91.062,\n",
+       " 'edward': 88.856,\n",
+       " 'search': 71.07,\n",
+       " 'passenger': 88.648,\n",
+       " 'lord': 87.43,\n",
+       " 'rugby': 86.836,\n",
+       " 'china': 86.425,\n",
        " 'yuko': 75.514,\n",
-       " 'government': 85.639,\n",
-       " 'serve': 82.564,\n",
-       " 'machine': 82.494,\n",
-       " 'pension': 80.303,\n",
+       " 'government': 86.077,\n",
+       " 'machine': 84.396,\n",
+       " 'ibm': 82.338,\n",
+       " 'online': 80.27,\n",
        " 'asylum': 80.242,\n",
-       " 'actress': 79.333,\n",
-       " 'that': 78.981,\n",
-       " 'online': 78.916,\n",
-       " 'council': 77.999,\n",
-       " 'cash': 77.595,\n",
-       " 'fraud': 77.403,\n",
-       " 'musician': 77.291,\n",
-       " 'actor': 76.803,\n",
-       " 'gaming': 76.749,\n",
-       " 'lee': 76.256,\n",
-       " 'site': 75.507,\n",
-       " 'argentina': 74.892,\n",
-       " 'attack': 74.878,\n",
-       " 'award': 74.606,\n",
-       " 'business': 74.087,\n",
-       " 'broadband': 74.083,\n",
-       " 'hip': 73.725,\n",
-       " 'mail': 72.974,\n",
-       " 'not': 72.792,\n",
-       " 'aviator': 72.647,\n",
-       " 'japan': 72.55,\n",
-       " 'what': 72.332,\n",
-       " 'chart': 72.264,\n",
-       " 'document': 72.234,\n",
-       " 'jackson': 72.138,\n",
-       " 'card': 71.933,\n",
-       " 'deutsche': 71.647,\n",
-       " 'point': 71.343,\n",
-       " 'vote': 71.299,\n",
-       " 'radio': 71.215,\n",
-       " 'china': 70.847,\n",
-       " 'police': 70.759,\n",
+       " 'musician': 79.603,\n",
+       " 'fraud': 79.531,\n",
+       " 'pension': 79.157,\n",
+       " 'that': 78.174,\n",
+       " 'gaming': 78.083,\n",
+       " 'bt': 77.836,\n",
+       " 'cash': 77.796,\n",
+       " 'argentina': 77.567,\n",
+       " 'site': 77.486,\n",
+       " 'lee': 77.291,\n",
+       " 'actress': 77.044,\n",
+       " 'hip': 76.849,\n",
+       " 'attack': 75.856,\n",
+       " 'broadband': 75.512,\n",
+       " 'actor': 74.891,\n",
+       " 'award': 74.741,\n",
+       " 'bank': 74.26,\n",
+       " 'document': 74.157,\n",
+       " 'mail': 73.852,\n",
+       " 'business': 73.69,\n",
+       " 'not': 73.596,\n",
+       " 'japan': 72.974,\n",
+       " 'chart': 72.764,\n",
+       " 'what': 72.65,\n",
+       " 'deutsche': 72.138,\n",
+       " 'police': 72.137,\n",
+       " 'soul': 72.026,\n",
+       " 'point': 71.795,\n",
+       " 'vote': 71.593,\n",
+       " 'card': 71.142,\n",
+       " 'file': 70.759,\n",
+       " 'dvd': 70.511,\n",
        " 'poster': 70.121,\n",
-       " 'phone': 69.937,\n",
-       " 'file': 69.836,\n",
-       " 'dvd': 69.522,\n",
-       " 'apple': 69.522,\n",
-       " 'spanish': 69.42}"
+       " 'murder': 70.121}"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -436,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 21,
    "metadata": {
     "scrolled": false
    },
@@ -445,16 +450,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['cup', 'coach', 'sack', 'chelsea', 'take', 'understand', 'champion', 'club', 'charge', 'qualify']\n",
-      "['fox', 'audience', 'show', 'rating', 'episode', 'reality', 'series', 'ms', 'new', 'season']\n",
+      "['court', 'lord', 'witness', 'victim', 'evidence', 'chancellor', 'rid', 'constitutional', 'house', 'justice']\n",
+      "['ship', 'market', 'limited', 'for', 'technical', 'one', 'stock', 'impact', 'much', 'on']\n",
+      "['trust', 'politician', 'voter', 'election', 'poll', 'public', 'issue', 'lib', 'dem', 'lack']\n",
       "['member', 'share', 'qualify', 'profit', '42', 'payment', 'society', 'than', 'building', 'worth']\n",
-      "['inflation', 'rate', 'rise', 'move', 'growth', 'flexibility', 'percentage', 'borrowing', 'economic', 'interest']\n",
-      "['profit', 'drug', 'treatment', 'disappointing', '2005', 'sale', '5bn', 'development', 'fall', 'year']\n",
-      "['woman', 'ministry', 'employ', 'prince', 'foreign', 'say', 'minister', 'news', 'difficulty', 'acquire']\n",
-      "['brown', 'labour', 'mr', 'blair', 'election', 'outline', 'manifesto', 'role', 'article', 'writing']\n",
-      "['iraq', 'bank', 'alexander', 'foreign', 'economy', 'many', 'work', 'mr', 'private', 'people']\n",
-      "['win', 'liverpool', 'trophy', 'steven', 'think', 'league', 'draw', 'champion', 'only', 'side']\n",
-      "['rating', 'bbc', 'prove', 'network', 'series', 'lose', 'slot', 'comeback', 'desperate', 'reportedly']\n"
+      "['ireland', 'cardiff', 'slam', 'england', 'grand', 'year', 'wale', 'last', 'team', 'tournament']\n",
+      "['good', 'theatre', 'musical', 'mary', 'royal', 'producer', 'at', 'design', 'outstanding', 'award']\n",
+      "['virus', 'writer', 'phone', '2004', 'that', 'attack', 'number', 'spam', 'use', 'message']\n",
+      "['academy', 'award', 'oscar', 'war', 'ceremony', 'frank', 'winner', 'film', 'first', 'as']\n",
+      "['broadcaster', 'debate', 'lord', 'prime', 'campaign', 'election', 'say', 'minister', 'blair', 'ahead']\n",
+      "['parliament', 'record', 'prior', 'document', 'blow', 'page', 'act', 'room', 'by', 'history']\n"
      ]
     }
    ],
@@ -473,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -504,34 +509,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1992</th>\n",
-       "      <td>tech</td>\n",
-       "      <td>us peer-to-peer pirates convicted the first co...</td>\n",
-       "      <td>-PRON- peer - to - peer pirate convict the fir...</td>\n",
+       "      <th>1515</th>\n",
+       "      <td>business</td>\n",
+       "      <td>booming markets shed few tears the market  for...</td>\n",
+       "      <td>boom market shed few tear the market    former...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1281</th>\n",
+       "      <th>980</th>\n",
        "      <td>sport</td>\n",
-       "      <td>hewitt falls to dent lleyton hewitt suffered a...</td>\n",
-       "      <td>hewitt fall to dent lleyton hewitt suffer a sh...</td>\n",
+       "      <td>claxton hunting first major medal british hurd...</td>\n",
+       "      <td>claxton hunt first major medal british hurdler...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>844</th>\n",
+       "      <th>1634</th>\n",
        "      <td>tech</td>\n",
-       "      <td>why cell will get the hard sell the world is c...</td>\n",
-       "      <td>why cell will get the hard sell the world be c...</td>\n",
+       "      <td>sony psp handheld console hits us the latest h...</td>\n",
+       "      <td>sony psp handheld console hit -PRON- the late ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1188</th>\n",
+       "      <th>1053</th>\n",
        "      <td>entertainment</td>\n",
-       "      <td>belle named  best scottish band  belle &amp; sebas...</td>\n",
-       "      <td>belle name    good scottish band    belle &amp; se...</td>\n",
+       "      <td>black sabbath top rock album poll black sabbat...</td>\n",
+       "      <td>black sabbath top rock album poll black sabbat...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1573</th>\n",
-       "      <td>business</td>\n",
-       "      <td>karachi stocks hit historic high the karachi s...</td>\n",
-       "      <td>karachi stock hit historic high the karachi st...</td>\n",
+       "      <th>1870</th>\n",
+       "      <td>sport</td>\n",
+       "      <td>jones doping probe begins an investigation int...</td>\n",
+       "      <td>jone dope probe begin an investigation into do...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -539,21 +544,21 @@
       ],
       "text/plain": [
        "           category                                               text  \\\n",
-       "1992           tech  us peer-to-peer pirates convicted the first co...   \n",
-       "1281          sport  hewitt falls to dent lleyton hewitt suffered a...   \n",
-       "844            tech  why cell will get the hard sell the world is c...   \n",
-       "1188  entertainment  belle named  best scottish band  belle & sebas...   \n",
-       "1573       business  karachi stocks hit historic high the karachi s...   \n",
+       "1515       business  booming markets shed few tears the market  for...   \n",
+       "980           sport  claxton hunting first major medal british hurd...   \n",
+       "1634           tech  sony psp handheld console hits us the latest h...   \n",
+       "1053  entertainment  black sabbath top rock album poll black sabbat...   \n",
+       "1870          sport  jones doping probe begins an investigation int...   \n",
        "\n",
        "                                                 tokens  \n",
-       "1992  -PRON- peer - to - peer pirate convict the fir...  \n",
-       "1281  hewitt fall to dent lleyton hewitt suffer a sh...  \n",
-       "844   why cell will get the hard sell the world be c...  \n",
-       "1188  belle name    good scottish band    belle & se...  \n",
-       "1573  karachi stock hit historic high the karachi st...  "
+       "1515  boom market shed few tear the market    former...  \n",
+       "980   claxton hunt first major medal british hurdler...  \n",
+       "1634  sony psp handheld console hit -PRON- the late ...  \n",
+       "1053  black sabbath top rock album poll black sabbat...  \n",
+       "1870  jone dope probe begin an investigation into do...  "
       ]
      },
-     "execution_count": 71,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 23,
    "metadata": {
     "scrolled": true
    },
@@ -572,71 +577,71 @@
     {
      "data": {
       "text/plain": [
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-       "1281    [seed, hewitt, break, feel, beat, strong, four...\n",
-       "844     [cell, processor, technology, sony, computer, ...\n",
-       "1188    [scottish, band, brit, musical, list, act, awa...\n",
-       "1573    [market, stock, analyst, political, index, mon...\n",
-       "687     [film, institute, news, trend, broadcast, cite...\n",
-       "1830    [rule, consumer, mobile, firm, service, phone,...\n",
-       "1134    [net, standard, dr, address, work, challenge, ...\n",
-       "1015    [rugby, glasgow, edinburgh, murray, border, ro...\n",
-       "1665    [olympic, football, game, career, woman, final...\n",
-       "1648    [referee, apologise, arsenal, match, assistant...\n",
-       "487     [club, manager, return, at, unveil, jone, as, ...\n",
-       "1124    [lewis, council, labour, football, wage, gover...\n",
-       "1094    [gold, debt, relief, reserve, meeting, price, ...\n",
-       "744     [advert, drug, article, medical, employee, let...\n",
-       "939     [musical, theatre, stage, kelly, absolute, per...\n",
-       "1742    [creative, art, technology, graphic, bt, engin...\n",
-       "1368    [debt, relief, brown, uk, chancellor, poverty,...\n",
-       "880     [child, film, original, screen, show, that, mo...\n",
-       "136     [sing, tune, theme, die, perform, singer, show...\n",
-       "376     [olympic, test, athlete, decision, appeal, mot...\n",
-       "1558    [newcastle, henry, season, game, play, arsenal...\n",
-       "521     [bt, broadband, customer, call, offer, free, i...\n",
-       "1760    [goal, score, didn, win, that, good, spirit, p...\n",
-       "34      [guilty, plead, insurance, investigation, exec...\n",
-       "881     [election, young, electoral, vote, commission,...\n",
-       "302     [euro, company, sport, possible, family, analy...\n",
-       "515     [actress, notice, immigration, india, work, ye...\n",
-       "932     [period, quarter, athletic, growth, earning, s...\n",
-       "1084    [drug, cheap, europe, sell, africa, aid, afric...\n",
+       "1515    [disaster, market, stock, insurance, global, b...\n",
+       "980     [medal, hurdle, indoor, european, training, co...\n",
+       "1634    [sony, device, gaming, handheld, gadget, ninte...\n",
+       "1053    [band, rock, black, album, list, top, live, po...\n",
+       "1870    [olympic, jone, truth, medal, sydney, pound, a...\n",
+       "1135    [store, sale, retailer, december, same, rise, ...\n",
+       "1034    [jackson, ms, boy, prosecution, court, film, d...\n",
+       "1061    [festival, sell, event, day, organiser, act, j...\n",
+       "205     [engine, union, buy, six, motor, plant, italia...\n",
+       "1500    [program, phone, malicious, file, instal, work...\n",
+       "2100    [uk, trick, ban, will, super, industry, respon...\n",
+       "1046    [round, victory, title, really, first, capture...\n",
+       "1728    [cardiff, thomas, jone, morgan, france, italy,...\n",
+       "575     [file, server, operator, peer, network, site, ...\n",
+       "24      [sound, audio, mobile, technology, phone, hand...\n",
+       "1556    [eu, law, draft, computer, software, legal, im...\n",
+       "2058    [interactive, award, nomination, category, tv,...\n",
+       "750     [card, bank, economy, central, debt, consumer,...\n",
+       "876     [bill, committee, welfare, draft, government, ...\n",
+       "2149    [project, information, computer, community, lo...\n",
+       "1056    [ferguson, arsenal, game, football, saturday, ...\n",
+       "227     [zealand, wale, black, new, cardiff, game, rug...\n",
+       "1353    [bill, limit, small, government, culture, brit...\n",
+       "1397    [pension, election, age, vote, party, basic, o...\n",
+       "2028    [host, american, television, suffer, award, gl...\n",
+       "1181    [ipod, apple, job, computer, mini, new, mass, ...\n",
+       "2123    [ticket, green, band, fan, dublin, concert, se...\n",
+       "77      [break, hodgson, winter, league, premiership, ...\n",
+       "1075    [game, video, company, news, tag, as, art, eye...\n",
+       "1201    [bank, italian, institution, sue, company, fin...\n",
        "                              ...                        \n",
-       "1853    [sequel, star, box, office, weekend, robert, d...\n",
-       "1390    [labour, blair, cabinet, minister, serve, stan...\n",
-       "743     [liverpool, league, champion, season, competit...\n",
-       "715     [lee, film, man, create, book, series, release...\n",
-       "1842    [alliance, japanese, shareholder, car, talk, s...\n",
-       "499     [argentina, debt, accept, offer, 6bn, investor...\n",
-       "461     [musical, sir, film, actress, richard, child, ...\n",
-       "902     [mobile, tv, service, music, handset, download...\n",
-       "1014    [tory, tax, howard, cut, election, taxis, plan...\n",
-       "446     [broadband, tv, analyst, number, research, net...\n",
-       "222     [jump, title, championship, mark, european, se...\n",
-       "884     [union, merger, super, executive, hold, meetin...\n",
-       "1483    [fight, german, band, liam, officer, fine, kic...\n",
-       "1325    [ask, search, site, entry, web, acquisition, i...\n",
-       "1257    [prime, mr, blair, chancellor, minister, brown...\n",
-       "725     [plant, india, factory, indian, official, cons...\n",
-       "1679    [mail, virus, warn, infect, internet, computer...\n",
-       "714     [express, financial, american, spin, off, sell...\n",
-       "1864    [opera, voice, feature, available, page, appea...\n",
-       "2068    [game, political, campaign, video, learn, tool...\n",
-       "1651    [deutsche, exchange, bid, stock, talk, cash, o...\n",
-       "1610    [film, voice, robert, will, girl, actress, web...\n",
-       "516     [campaign, labour, brown, mr, election, role, ...\n",
-       "2059    [brown, mr, blair, labour, mps, party, electio...\n",
-       "841     [france, wale, injury, slam, grand, ireland, n...\n",
-       "221     [tour, tournament, prize, king, each, gamer, s...\n",
-       "421     [talent, decide, agree, star, midfielder, play...\n",
-       "1075    [game, video, company, news, tag, art, as, eye...\n",
-       "1601    [france, against, wale, ireland, play, england...\n",
-       "986     [relay, service, phone, language, video, peopl...\n",
+       "1301    [un, short, ms, authority, tsunami, only, indi...\n",
+       "2150    [spain, federation, player, henry, comment, in...\n",
+       "562     [video, offer, demand, sky, cable, tv, program...\n",
+       "1268    [shoot, texas, control, hunt, let, session, wi...\n",
+       "364     [festival, disaster, film, ticket, continue, e...\n",
+       "467     [million, show, audience, figure, 14, episode,...\n",
+       "2086    [sun, russian, voting, employ, buy, stake, aug...\n",
+       "235     [growth, rate, consumer, quarter, spending, bu...\n",
+       "1755    [offer, fresh, as, game, tough, title, level, ...\n",
+       "672     [microsoft, window, user, system, online, inte...\n",
+       "732     [jone, gb, championship, indoor, green, olympi...\n",
+       "455     [download, programme, episode, tv, net, uk, pe...\n",
+       "635     [digital, design, technology, people, ms, came...\n",
+       "795     [chelsea, 2000, play, milan, barcelona, leg, b...\n",
+       "535     [pc, 2010, pcs, china, million, market, local,...\n",
+       "1199    [film, india, hurt, community, refuse, anger, ...\n",
+       "264     [consumer, rule, mobile, firm, service, phone,...\n",
+       "701     [network, file, content, download, music, tech...\n",
+       "59      [drug, heart, risk, regulator, that, on, disea...\n",
+       "456     [lord, reform, chancellor, manifesto, house, c...\n",
+       "2091    [collin, athletic, sport, olympic, competitor,...\n",
+       "667     [dvd, copy, pirate, piracy, technology, progra...\n",
+       "164     [suspend, candidate, mr, view, say, party, new...\n",
+       "393     [policy, economic, inflation, rise, market, re...\n",
+       "117     [labour, chancellor, cut, conservative, party,...\n",
+       "207     [blair, question, answer, trust, thing, say, p...\n",
+       "343     [blair, peace, mr, conference, leader, iraq, v...\n",
+       "675     [party, election, mr, leadership, assembly, ro...\n",
+       "2141    [medium, traditional, information, web, mainst...\n",
+       "132     [wale, william, flanker, cardiff, fit, injury,...\n",
        "Name: tokens, Length: 445, dtype: object"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -647,10 +652,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
     "test_tfidf_matrix = tfidf.apply_tfidf_to_documents(list(test.tokens))"
@@ -658,23 +661,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'cell': 94.188,\n",
-       " 'mobile': 92.405,\n",
-       " 'camera': 90.674,\n",
-       " 'mini': 89.254,\n",
-       " 'china': 87.516,\n",
-       " 'rugby': 85.906,\n",
-       " 'film': 82.031,\n",
-       " 'bt': 76.256}"
+       "{'zealand': 157.747,\n",
+       " 'gadget': 137.671,\n",
+       " 'child': 100.98,\n",
+       " 'camera': 91.062,\n",
+       " 'wale': 86.221,\n",
+       " 'mini': 85.866,\n",
+       " 'bt': 77.836,\n",
+       " 'soul': 77.567,\n",
+       " 'council': 76.619,\n",
+       " 'jackson': 74.892}"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -682,15 +687,6 @@
    "source": [
     "tfidf.get_top_tfidf(test_tfidf_matrix)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -709,7 +705,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Benchmark text processing tools.ipynb b/notebooks/Benchmark text processing tools.ipynb
index 08ff2af..14b08ff 100644
--- a/notebooks/Benchmark text processing tools.ipynb	
+++ b/notebooks/Benchmark text processing tools.ipynb	
@@ -9,10 +9,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from urllib import request"
@@ -27,10 +25,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "url = \"https://www.gutenberg.org/cache/epub/5711/pg5711.txt\""
@@ -38,10 +34,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "response = request.urlopen(url)\n",
@@ -50,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -78,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +81,7 @@
        "str"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -109,7 +103,7 @@
        "1046377"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,10 +121,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "url = \"http://www.gutenberg.org/files/2554/2554-0.txt\""
@@ -138,10 +130,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "response = request.urlopen(url)\n",
@@ -150,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -184,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -193,7 +183,7 @@
        "str"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -204,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -213,7 +203,7 @@
        "1176967"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -233,9 +223,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n"
+     ]
+    }
+   ],
    "source": [
     "from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize"
    ]
@@ -249,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true
    },
@@ -258,8 +257,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.18 s, sys: 44 ms, total: 1.22 s\n",
-      "Wall time: 1.24 s\n"
+      "CPU times: user 1.84 s, sys: 84.1 ms, total: 1.93 s\n",
+      "Wall time: 1.93 s\n"
      ]
     }
    ],
@@ -270,14 +269,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "920 ms ± 18.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "768 ms ± 12.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -286,6 +285,15 @@
     "tokenized = tokenize(rawfr[:1000000], lang_module=\"fr_spacy\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized = tokenize(rawfr[:1000000], lang_module=\"fr_spacy\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -295,15 +303,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 21.7 s, sys: 289 ms, total: 22 s\n",
-      "Wall time: 22.4 s\n"
+      "CPU times: user 15.6 s, sys: 18.9 ms, total: 15.6 s\n",
+      "Wall time: 15.6 s\n"
      ]
     }
    ],
@@ -314,14 +322,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.54 s ± 27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "1.78 s ± 45.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -332,7 +340,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_moses = tokenize(rawfr[:1000000], lang_module=\"fr_moses\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -350,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -367,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -391,18 +408,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 5 s, sys: 102 ms, total: 5.1 s\n",
-      "Wall time: 5.17 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "tokenized_eng = tokenize(raw[:1000000], lang_module=\"en_spacy\")"
@@ -410,7 +418,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_eng = tokenize(raw[:1000000], lang_module=\"en_spacy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {
     "scrolled": true
    },
@@ -419,7 +436,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.26 s ± 740 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "260 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -430,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -448,7 +465,7 @@
        " 'by']"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -466,15 +483,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.17 s, sys: 29.3 ms, total: 2.2 s\n",
-      "Wall time: 2.22 s\n"
+      "CPU times: user 1.56 s, sys: 28 ms, total: 1.58 s\n",
+      "Wall time: 1.58 s\n"
      ]
     }
    ],
@@ -485,14 +502,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_eng_nltk = tokenize(raw[:1000000], lang_module=\"en_nltk\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.47 s ± 565 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "1.54 s ± 27.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -503,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -521,7 +547,7 @@
        " 'by']"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -539,10 +565,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.stemming import stem_tokens"
@@ -550,15 +574,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 7.91 s, sys: 37.3 ms, total: 7.95 s\n",
-      "Wall time: 7.98 s\n"
+      "CPU times: user 4.19 s, sys: 20 ms, total: 4.21 s\n",
+      "Wall time: 4.21 s\n"
      ]
     }
    ],
@@ -569,14 +593,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8.48 s ± 682 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "4.25 s ± 61.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -601,17 +625,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
+      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
       "[nltk_data]   Package wordnet is already up-to-date!\n",
       "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
-      "[nltk_data]     /Users/hugo/nltk_data...\n",
+      "[nltk_data]     /root/nltk_data...\n",
       "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
       "[nltk_data]       date!\n"
      ]
@@ -623,10 +647,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from nautilus_nlp.preprocessing.preprocess import remove_tokens_with_nonletters"
@@ -634,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 43,
    "metadata": {
     "scrolled": true
    },
@@ -645,15 +667,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 26.7 s, sys: 6.91 s, total: 33.6 s\n",
-      "Wall time: 36.5 s\n"
+      "CPU times: user 21.9 s, sys: 8.62 s, total: 30.6 s\n",
+      "Wall time: 30.6 s\n"
      ]
     }
    ],
@@ -664,7 +686,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lemmatized_tokens = lemmatize_french_tokens(tokenized, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -688,9 +719,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 49,
    "metadata": {
-    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -700,10 +730,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 50,
+   "metadata": {},
    "outputs": [],
    "source": [
     "tokenized_eng = remove_tokens_with_nonletters(tokenized_eng)"
@@ -711,15 +739,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 29.3 s, sys: 6.85 s, total: 36.2 s\n",
-      "Wall time: 38.3 s\n"
+      "CPU times: user 24.3 s, sys: 11.3 s, total: 35.6 s\n",
+      "Wall time: 35.6 s\n"
      ]
     }
    ],
@@ -730,15 +758,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lemmatized_eng = lemmatize_english_tokens(tokenized_eng, module='spacy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 34.4 s, sys: 3.82 s, total: 38.2 s\n",
-      "Wall time: 39.4 s\n"
+      "CPU times: user 20.5 s, sys: 706 ms, total: 21.2 s\n",
+      "Wall time: 21.1 s\n"
      ]
     }
    ],
@@ -749,7 +786,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lemmatized_eng_nltk = lemmatize_english_tokens(tokenized_eng, module='nltk')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -766,7 +812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -783,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -797,6 +843,13 @@
    "source": [
     "print(lemmatized_eng_nltk[1000:1200])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -815,7 +868,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Language_identification.ipynb b/notebooks/Language_identification.ipynb
index 4f00ebf..bd7b852 100644
--- a/notebooks/Language_identification.ipynb
+++ b/notebooks/Language_identification.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,11 +42,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() got an unexpected keyword argument 'typemodel'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-1929b26ba9a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnautilus_nlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlanguage_detector\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLangDetector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdetector\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mLangDetector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypemodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'fasttext'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdetector2\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mLangDetector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypemodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cld2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'typemodel'"
+     ]
+    }
+   ],
    "source": [
-    "from nautilus_nlp.models.Language_detector import LangDetector\n",
+    "from nautilus_nlp.models.language_detector import LangDetector\n",
     "detector= LangDetector(typemodel='fasttext')\n",
     "detector2= LangDetector(typemodel='cld2')"
    ]
@@ -60,38 +72,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ne l fin de l seclo XIX l Japon era inda çconhecido i sótico pa l mundo oucidental. Cula antroduçon de la stética japonesa, particularmente na Sposiçon Ounibersal de 1900, an Paris, l Oucidente adquiriu un apetite ansaciable pul Japon i Heiarn se tornou mundialmente coincido pula perfundidade, ouriginalidade i sinceridade de ls sous cuntos. An sous radadeiros anhos, alguns críticos, cumo George Orwell, acusórun Heiarn de trasferir sou nacionalismo i fazer l Japon parecer mais sótico, mas, cumo l'home qu'oufereciu al Oucidente alguns de sous purmeiros lampeijos de l Japon pré-andustrial i de l Período Meiji, sou trabalho inda ye balioso até hoije.\n",
-      "('pt', 0.41855213046073914)\n",
-      "('pt', 0.54)\n",
-      "\n",
-      "\n",
-      "Schiedam is gelegen tussen Rotterdam en Vlaardingen, oorspronkelijk aan de Schie en later ook aan de Nieuwe Maas. Per 30 april 2017 had de gemeente 77.833 inwoners (bron: CBS). De stad is vooral bekend om haar jenever, de historische binnenstad met grachten, en de hoogste windmolens ter wereld.\n",
-      "('nl', 0.9266586899757385)\n",
-      "('nl', 0.99)\n",
-      "\n",
-      "\n",
-      "ГIурусаз батальонал, гьоркьор гIарадабиги лъун, ункъбокIон (каре) гьабун чIезарун руго. ТIаде гIарададул сачмаги гуллаги байдал, АхIмадханил бо тIурун буго. ГIумаханас цойгидал боязе тIаде кIанцIизе буюрухъ кьун буго. Гьезулги жо ккун гьечIо. Цинги живго ГIумахан кIанцIун вуго тушманасде тIаде («угъузилал рачун, дайтилал рачун, маххулъан бер баккун хунз цадахъ рачун»). Нахъе къалел ругел магIарулазда гьес гьарулеб букIун буго: «Нужеца яхI бахъе, гIолохъаби! Нилъеда данде гьал чIоларо», – ян.\n",
-      "('ru', 0.38813528418540955)\n",
-      "('uz', 0.99)\n",
-      "\n",
-      "\n",
-      "ರಾಜ್ಯಶಾಸ್ತ್ರದ ಪಿತಾಮಹೆ ಅರಿಸ್ಟಾಟಲ್. ರಾಜ್ಯಶಾಸ್ತ್ರದ ವೈಜ್ನಾನಿಕವಾದ್ ಅದ್ಯಾಯನ ಮಲ್ದಿನಾರ್ ಅರಿಸ್ಟಾಟಲ್. ಮೇರ್ 158 ರಾಷ್ಟ್ರದ ಸಂವಿಧಾನಲೆನ್ ಅರ್ಥ ಮಲ್ತೊನ್ದ್ the politics ಕೃತಿ ರಚನೆ ಮಲ್ದೆರ್. ಮೇರ್ ಕ್ರಿ.ಪೂ 387 ಗ್ರೀಕ್ ಸ್ಟಾಗಿರಡ್ ಜನಿಸಿಯೆರ್. ಮೆರೆನ ಅಮ್ಮೆರ್ ಮೆಸೆದೊನಿಯ ಅರಸೆರ್ನ ರಾಜವೈದ್ಯರ್ ಅದುದ್ ಇತ್ತೆರ್. ಎಲ್ಳಡೆ ಮೆರ್ ತತ್ವಶಾಸ್ತ್ರ,ಅರ್ಥಶಾಸ್ತ್ರ,ಸಂಗೀತ,ವಿಜ್ನಾನ,ಪೌರನೀತಿ,ಗಣಿತ ನೆಟ್ಟ್ ಪರಂಗತೆರ್ ಅದುದ್ ಇತ್ತೆರ್. ವಿದ್ಯಾಭ್ಯಾಸ ಮುಗಿ ಬೊಕ್ಕ ಪ್ಲೇಟೋ ಗ್ರೀಕ್ ತತ್ವಜ್ನಾನಿನೊಟ್ ಸೆರೊಂಡೆರ್. ಪ್ಲೇಟೋನ ಅಕಾಡೆಮಿಡ್ ಮಸ್ತ್ ವರ್ಷ ಬೇಲೆ ಮಲ್ತೆರ್. ಅಕಾಡೆಮಿಡ್ ನಿರ್ದೆಶೆಕೆರ್ನ ಹುದ್ದೆ ತಿಕ್ಕಂದೆ ಬೆಜರ್ ಅದುದ್ ಲೈಸಿಯಮ್ ಪನ್ಪಿನ ವಿಶ್ವವಿದ್ಯಾನಿಲಯ ಸ್ಥಾಪನೆ ಮಲ್ತೆರ್. ಬಿಕ್ಕ ಒಂತೆ ಸಮಯ ಅಲೆಗ್ಸಾಂಡರ್ ಚಕ್ರವರ್ತಿಗ್ ಗುರುವಾದ್ ಬೇಲೆ ಮಲ್ತೆರ್. ಮೇರ್ ಬರೆತಿನ ಪುಸ್ತಕ the politics,history of animals,metaphysics ಇತ್ಯಾದಿ.\n",
-      "('kn', 0.9979031085968018)\n",
-      "('kn', 0.96)\n",
-      "\n",
-      "\n",
-      "Halukum adalah kelenjar tiroid nang menonjol di gulu lalakian. Awan bibinian penonjolan nangini ada ai jua, tagal kada pati rancak. Lamun penampilan halukum dirasa pina talalu ganal, hal ini kawa diperbaiki awan operasi pelastik.\n",
-      "('id', 0.4194313585758209)\n",
-      "('ms', 0.99)\n",
-      "\n",
-      "\n"
+      "Ne l fin de l seclo XIX l Japon era inda çconhecido i sótico pa l mundo oucidental. Cula antroduçon de la stética japonesa, particularmente na Sposiçon Ounibersal de 1900, an Paris, l Oucidente adquiriu un apetite ansaciable pul Japon i Heiarn se tornou mundialmente coincido pula perfundidade, ouriginalidade i sinceridade de ls sous cuntos. An sous radadeiros anhos, alguns críticos, cumo George Orwell, acusórun Heiarn de trasferir sou nacionalismo i fazer l Japon parecer mais sótico, mas, cumo l'home qu'oufereciu al Oucidente alguns de sous purmeiros lampeijos de l Japon pré-andustrial i de l Período Meiji, sou trabalho inda ye balioso até hoije.\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'detector' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-f1145461d744>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdetector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect_language\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdetector2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect_language\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'detector' is not defined"
      ]
     }
    ],
@@ -194,7 +193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Sentiment analysis using pre-trained models.ipynb b/notebooks/Sentiment analysis using pre-trained models.ipynb
index e94675a..35c57d4 100644
--- a/notebooks/Sentiment analysis using pre-trained models.ipynb	
+++ b/notebooks/Sentiment analysis using pre-trained models.ipynb	
@@ -9,25 +9,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /Users/hugo/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from nautilus_nlp.models.sentiment import compute_sentiment_score"
+    "from nautilus_nlp.models.sentiment_detector import compute_sentiment_score"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -36,7 +27,7 @@
        "-0.4234"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -56,7 +47,7 @@
        "0.6369"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -67,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +67,7 @@
        "0.5"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -96,7 +87,7 @@
        "-1.0"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -107,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -116,7 +107,7 @@
        "-0.5"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -136,7 +127,7 @@
        "-1.0"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -144,15 +135,6 @@
    "source": [
     "compute_sentiment_score(\"C'est horrible\",lang_module=\"fr_textblob\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -171,7 +153,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Sentiment_analysis_FT.ipynb b/notebooks/Sentiment_analysis_FT.ipynb
index 2171909..3690d34 100644
--- a/notebooks/Sentiment_analysis_FT.ipynb
+++ b/notebooks/Sentiment_analysis_FT.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,19 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'train' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-56711baad5fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'__label__{row[\"target\"]} {text}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'__label__{row[\"target\"]} {text}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'train' is not defined"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "train = open('../data/processed/tweets.train','w',encoding='utf-8')  \n",
@@ -232,7 +244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Spacy_model.ipynb b/notebooks/Spacy_model.ipynb
index a798b81..0c4f587 100644
--- a/notebooks/Spacy_model.ipynb
+++ b/notebooks/Spacy_model.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "\n",
@@ -22,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -67,13 +58,13 @@
        " 'lemmatizer',\n",
        " '.',\n",
        " 'a',\n",
-       " 'lui',\n",
+       " 'toi',\n",
        " 'de',\n",
        " 'jouer',\n",
        " 'spacy']"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -84,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +85,7 @@
        " [A, toi, de, jouer, spacy]]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +118,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Visualization tools.ipynb b/notebooks/Visualization tools.ipynb
index a2c3baa..50c582b 100644
--- a/notebooks/Visualization tools.ipynb	
+++ b/notebooks/Visualization tools.ipynb	
@@ -2,9 +2,21 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'nautilus_nlp.visualization'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-6aa84683348e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnautilus_nlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_concordance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnautilus_nlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmake_word_cloud\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nautilus_nlp.visualization'"
+     ]
+    }
+   ],
    "source": [
     "from nautilus_nlp.visualization.visualize import print_concordance\n",
     "from nautilus_nlp.visualization.visualize import make_word_cloud\n",
@@ -13,10 +25,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import sys"
@@ -24,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,38 +43,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14 matches for \"recouvrement\":\n",
-      "le client est en relation avec un prestataire de recouvrement il demande les coordonnées téléphoniques du pres\n",
-      " les coordonnées téléphoniques du prestataire de recouvrement souhaite des renseignements sur son plan d apure\n",
-      "le client est en relation avec un prestataire de recouvrement ok maj ok fel kole bp appel popur régler sa fact\n",
-      "e sol desur cette facture de euros somme payé au recouvrement solde a se jour a zéro appel d un tiers oui nom \n",
-      "epouse signale qu elle n arrive pas a joindre le recouvrement pour leurs signaléun paiement qu elle a faite ce\n",
-      "a cliente et se renseigne sur les sommes dues au recouvrement ainsi que les prelevement s avenirs conversation\n",
-      "ur son rétablissement savoir siil avait réglé au recouvrement cliente appel pour nous dire que elle payera sa \n",
-      "dire que elle payera sa facture de le octobre la recouvrement n arrete pas de l appeler facture en recouvremen\n",
-      "ecouvrement n arrete pas de l appeler facture en recouvrement donné n retrouve pour délai de paiement en juill\n",
-      "ransféré actions réalisées numéros du service de recouvrement réponse apportée ras miseau contentieux chez XXX\n",
-      "le client est en relation avec unpre stataire de recouvrement il demande les coordonnées téléphoniques du pres\n",
-      " les coordonnées téléphoniques du prestataire de recouvrement souhaite des ren mise au contentieux chez XXX le\n",
-      "le client est en relation avec un prestataire de recouvrement il demande les coordonnées téléphoniqu esdu pres\n",
-      " les coordonnées téléphoniqu esdu prestataire de recouvrement num contentia donner\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print_concordance(some_tokenized_texts, query_word='recouvrement')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,22 +61,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "make_word_cloud(c)"
    ]
@@ -96,9 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -119,7 +92,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/someadditionalfile.txt b/notebooks/someadditionalfile.txt
new file mode 100644
index 0000000..c7de724
--- /dev/null
+++ b/notebooks/someadditionalfile.txt
@@ -0,0 +1 @@
+Un deuxième exemple de texte en utf-8 cette fois!
\ No newline at end of file
diff --git a/notebooks/somefile.txt b/notebooks/somefile.txt
new file mode 100644
index 0000000..b9855bf
--- /dev/null
+++ b/notebooks/somefile.txt
@@ -0,0 +1 @@
+J'aime les frites bien grasse �talon ch�peau!
\ No newline at end of file

From 66731df7310da44299a8a200013f2a62b87ea09d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 5 Jun 2019 11:48:14 +0200
Subject: [PATCH 203/496] fix notebook topic modeling #96

---
 notebooks/4. Topic Modeling.ipynb | 941 ++++++++++++++++++++++++++++++
 1 file changed, 941 insertions(+)
 create mode 100644 notebooks/4. Topic Modeling.ipynb

diff --git a/notebooks/4. Topic Modeling.ipynb b/notebooks/4. Topic Modeling.ipynb
new file mode 100644
index 0000000..56d3efe
--- /dev/null
+++ b/notebooks/4. Topic Modeling.ipynb	
@@ -0,0 +1,941 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Topic Modeling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "newsgroups_train = fetch_20newsgroups(subset='train', shuffle = True)\n",
+    "newsgroups_test = fetch_20newsgroups(subset='test', shuffle = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(list(newsgroups_train.target_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Data Preprocessing¶\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/nltk/decorators.py:68: DeprecationWarning: `formatargspec` is deprecated since Python 3.5. Use `signature` and the `Signature` object directly\n",
+      "  regargs, varargs, varkwargs, defaults, formatvalue=lambda value: \"\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gensim\n",
+    "from gensim.utils import simple_preprocess\n",
+    "from gensim.parsing.preprocessing import STOPWORDS\n",
+    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
+    "from nltk.stem.porter import *\n",
+    "import numpy as np\n",
+    "np.random.seed(400)\n",
+    "\n",
+    "import nltk\n",
+    "\n",
+    "import pandas as pd\n",
+    "stemmer = SnowballStemmer(\"english\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def lemmatize_stemming(text):\n",
+    "    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))\n",
+    "\n",
+    "# Tokenize and lemmatize\n",
+    "def preprocess(text):\n",
+    "    result=[]\n",
+    "    for token in gensim.utils.simple_preprocess(text) :\n",
+    "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
+    "            result.append(lemmatize_stemming(token))\n",
+    "            \n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "processed_docs = []\n",
+    "\n",
+    "for doc in newsgroups_train.data:\n",
+    "    processed_docs.append(preprocess(doc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Bag of words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create the dictionnary\n",
+    "from nautilus_nlp.models.topic_modeling import create_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dictionary = create_dictionary(processed_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Filter out tokens that appear in too few or too many documents\n",
+    "from nautilus_nlp.models.topic_modeling import filter_extremes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filter_extremes(dictionary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create the bow \n",
+    "from nautilus_nlp.models.topic_modeling import create_bow_corpus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bow_corpus = create_bow_corpus(processed_docs, dictionary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(13, 1), (26, 1), (55, 1), (88, 1), (99, 1), (152, 1), (153, 2), (154, 1), (155, 1), (156, 1), (157, 2), (158, 1), (159, 2), (160, 4), (161, 1), (162, 2), (163, 1), (164, 2), (165, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (171, 1), (172, 1), (173, 1), (174, 1), (175, 1), (176, 1), (177, 1), (178, 2), (179, 1), (180, 1), (181, 1), (182, 1)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(bow_corpus[3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4: Find optimal number of topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Compute coherence values for various number of topics in order to pick the optimal one\n",
+    "from nautilus_nlp.models.topic_modeling import compute_coherence_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Take a long time to run\n",
+    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, bow_corpus=bow_corpus, texts=processed_docs, start=2, limit=25, step=4)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.37900998646286865,\n",
+       " 0.42680154447577845,\n",
+       " 0.47716566398237525,\n",
+       " 0.5261650723885645,\n",
+       " 0.49607461078243215,\n",
+       " 0.4978727171365794]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coherence_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.topic_modeling import plot_optimal_topic_number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Print the coherences scores for the number we tested\n",
+    "from nautilus_nlp.models.topic_modeling import print_coherence_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num Topics = 2  has Coherence Value of 0.379\n",
+      "Num Topics = 6  has Coherence Value of 0.4268\n",
+      "Num Topics = 10  has Coherence Value of 0.4772\n",
+      "Num Topics = 14  has Coherence Value of 0.5262\n",
+      "Num Topics = 18  has Coherence Value of 0.4961\n",
+      "Num Topics = 22  has Coherence Value of 0.4979\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_coherence_scores(coherence_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5: Running LDA using Bag of Words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Train the LDA model with gensim\n",
+    "from nautilus_nlp.models.topic_modeling import train_lda_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = train_lda_model(bow_corpus, dictionary, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.ldamodel.LdaModel at 0x1a2af3e208>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Save model\n",
+    "from nautilus_nlp.models.topic_modeling import save_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "save_model(model,'/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.topic_modeling import load_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<gensim.models.ldamodel.LdaModel at 0x1a29985b38>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_loaded = load_model('/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')\n",
+    "model_loaded"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6: Visualize the top keywords per topic with Pyldavis interactive chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Display the top keywords per topic in a interactive chart\n",
+    "from nautilus_nlp.models.topic_modeling import visualize_topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el591011124095238088809029897\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el591011124095238088809029897_data = {\"mdsDat\": {\"x\": [-0.07866945427665124, -0.01699948489792914, 0.20896689238873523, 0.14744605031212607, -0.008849073212760983, -0.04505413872814077, -0.08949897686453376, 0.10780299734830809, 0.004524270451044093, -0.22966908252019716], \"y\": [-0.15170611822961017, -0.1504468301902949, 0.08013685816591506, 0.13647834612774523, -0.009046128981188077, 0.003906923765221535, 0.14472746444813225, -0.07737607739594787, -0.1051004751701791, 0.1284260374602061], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.182504653930664, 12.926197052001953, 12.463006973266602, 11.995771408081055, 9.55910873413086, 9.468682289123535, 8.927140235900879, 7.882134437561035, 7.024929523468018, 6.5705246925354]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nazi\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"nist\", \"nist\", \"nore\", \"nsmca\", \"nubus\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"ohio\", \"ohio\", \"ohio\", \"openwindow\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"orbit\", \"outlet\", \"outlet\", \"output\", \"output\", \"output\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"pain\", \"pain\", \"pain\", \"pain\", \"pain\", \"palestinian\", \"patent\", \"patent\", \"patent\", \"patient\", \"patient\", \"pen\", \"penguin\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"restaur\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"resurrect\", \"revel\", \"revolv\", \"rid\", \"rid\", \"ride\", \"ride\", \"rider\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"ripem\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"rkba\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"robi\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rocki\", \"rockwel\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"rutger\", \"rutger\", \"rwing\", \"sabbath\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sandvik\", \"satan\", \"satellit\", \"satellit\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", \"sleev\", \"sleev\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smuggl\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"solar\", \"solar\", \"soldier\", \"soldier\", \"solntz\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spec\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"sphere\", \"spirit\", \"spirit\", \"spirit\", \"ssto\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanley\", \"stanley\", \"stanley\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"starter\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"sternlight\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steveh\", \"stimulus\", \"stratus\", \"strnlght\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"suno\", \"superstit\", \"surveil\", \"svga\", \"swap\", \"syndrom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"tampa\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tennesse\", \"test\", 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+      ],
+      "text/plain": [
+       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
+       "topic                                                \n",
+       "2     -0.078669 -0.151706       1        1  13.182505\n",
+       "0     -0.016999 -0.150447       2        1  12.926197\n",
+       "7      0.208967  0.080137       3        1  12.463007\n",
+       "8      0.147446  0.136478       4        1  11.995771\n",
+       "1     -0.008849 -0.009046       5        1   9.559109\n",
+       "3     -0.045054  0.003907       6        1   9.468682\n",
+       "6     -0.089499  0.144727       7        1   8.927140\n",
+       "9      0.107803 -0.077376       8        1   7.882134\n",
+       "4      0.004524 -0.105100       9        1   7.024930\n",
+       "5     -0.229669  0.128426      10        1   6.570525, topic_info=     Category         Freq        Term        Total  loglift  logprob\n",
+       "1037  Default  2966.000000      window  2966.000000  30.0000  30.0000\n",
+       "1193  Default  1940.000000        game  1940.000000  29.0000  29.0000\n",
+       "482   Default  1924.000000   christian  1924.000000  28.0000  28.0000\n",
+       "702   Default  1689.000000        team  1689.000000  27.0000  27.0000\n",
+       "352   Default  2638.000000       drive  2638.000000  26.0000  26.0000\n",
+       "320   Default  2883.000000        file  2883.000000  25.0000  25.0000\n",
+       "696   Default  1860.000000       space  1860.000000  24.0000  24.0000\n",
+       "1467  Default  1161.000000     encrypt  1161.000000  23.0000  23.0000\n",
+       "256   Default  1997.000000      govern  1997.000000  22.0000  22.0000\n",
+       "153   Default  1477.000000        chip  1477.000000  21.0000  21.0000\n",
+       "522   Default  1274.000000       jesus  1274.000000  20.0000  20.0000\n",
+       "1347  Default  1017.000000      israel  1017.000000  19.0000  19.0000\n",
+       "36    Default  1577.000000        card  1577.000000  18.0000  18.0000\n",
+       "120   Default  1423.000000        play  1423.000000  17.0000  17.0000\n",
+       "964   Default  1059.000000       secur  1059.000000  16.0000  16.0000\n",
+       "657   Default  1331.000000        nasa  1331.000000  15.0000  15.0000\n",
+       "1821  Default  1142.000000    armenian  1142.000000  14.0000  14.0000\n",
+       "822   Default  2599.000000     program  2599.000000  13.0000  13.0000\n",
+       "1346  Default   837.000000        isra   837.000000  12.0000  12.0000\n",
+       "513   Default  1391.000000        imag  1391.000000  11.0000  11.0000\n",
+       "117   Default  6052.000000       peopl  6052.000000  10.0000  10.0000\n",
+       "3565  Default   742.000000      hockey   742.000000   9.0000   9.0000\n",
+       "739   Default   990.000000      player   990.000000   8.0000   8.0000\n",
+       "1457  Default   784.000000     clipper   784.000000   7.0000   7.0000\n",
+       "326   Default  1802.000000      public  1802.000000   6.0000   6.0000\n",
+       "41    Default  1023.000000        disk  1023.000000   5.0000   5.0000\n",
+       "28    Default  4004.000000        year  4004.000000   4.0000   4.0000\n",
+       "380   Default   867.000000        scsi   867.000000   3.0000   3.0000\n",
+       "879   Default  1191.000000      driver  1191.000000   2.0000   2.0000\n",
+       "444   Default   747.000000        bike   747.000000   1.0000   1.0000\n",
+       "...       ...          ...         ...          ...      ...      ...\n",
+       "5004  Topic10   178.948181     stanley   185.594589   2.6861  -6.0394\n",
+       "702   Topic10  1384.116455        team  1689.568604   2.5232  -3.9937\n",
+       "4840  Topic10   160.981583         jet   167.196503   2.6847  -6.1452\n",
+       "3815  Topic10   191.566772       coach   202.233215   2.6684  -5.9713\n",
+       "3537  Topic10   221.759384      ranger   240.052933   2.6433  -5.8249\n",
+       "4877  Topic10   157.258331    winnipeg   165.160721   2.6735  -6.1686\n",
+       "1193  Topic10  1290.715576        game  1940.664795   2.3147  -4.0636\n",
+       "120   Topic10   920.770020        play  1423.930298   2.2866  -4.4013\n",
+       "711   Topic10   312.806488        wing   399.885132   2.4770  -5.4809\n",
+       "739   Topic10   623.101746      player   990.567200   2.2590  -4.7918\n",
+       "1311  Topic10   397.739624    columbia   559.283691   2.3817  -5.2407\n",
+       "1080  Topic10   455.438751      season   670.126038   2.3364  -5.1053\n",
+       "3252  Topic10   379.943481       leagu   536.827942   2.3769  -5.2865\n",
+       "1073  Topic10   351.761322  pittsburgh   505.782318   2.3594  -5.3636\n",
+       "1679  Topic10   356.976562       score   528.273926   2.3306  -5.3488\n",
+       "1321  Topic10   374.845306        arab   572.500732   2.2991  -5.3000\n",
+       "3488  Topic10   212.819550      mcgill   254.334839   2.5444  -5.8661\n",
+       "1475  Topic10   346.644043        goal   553.699341   2.2543  -5.3782\n",
+       "1150  Topic10   312.806427    virginia   544.289856   2.1687  -5.4809\n",
+       "1082  Topic10   333.144867     toronto   701.091187   1.9785  -5.4179\n",
+       "1155  Topic10   336.057953      andrew   868.338135   1.7733  -5.4092\n",
+       "28    Topic10   600.308960        year  4004.757812   0.8248  -4.8291\n",
+       "157   Topic10   295.451538       divis   693.417114   1.8694  -5.5380\n",
+       "2117  Topic10   283.661255      canada   732.439819   1.7740  -5.5787\n",
+       "2549  Topic10   250.147522      period   584.503235   1.8739  -5.7045\n",
+       "45    Topic10   267.235901       final   822.295105   1.5986  -5.6384\n",
+       "171   Topic10   330.680511       point  2646.791748   0.6426  -5.4254\n",
+       "146   Topic10   358.020264        time  5183.146484   0.0500  -5.3459\n",
+       "1545  Topic10   262.164948    american  1242.273315   1.1669  -5.6575\n",
+       "99    Topic10   242.101822          go  3510.730713   0.0484  -5.7372\n",
+       "\n",
+       "[734 rows x 6 columns], token_table=      Topic      Freq       Term\n",
+       "term                            \n",
+       "338       1  0.145983     accept\n",
+       "338       2  0.524347     accept\n",
+       "338       3  0.129101     accept\n",
+       "338       4  0.049654     accept\n",
+       "338       5  0.007945     accept\n",
+       "338       6  0.022841     accept\n",
+       "338       8  0.066536     accept\n",
+       "338       9  0.034758     accept\n",
+       "338      10  0.018869     accept\n",
+       "73        3  0.403915     access\n",
+       "73        4  0.196068     access\n",
+       "73        5  0.171820     access\n",
+       "73        6  0.033255     access\n",
+       "73        7  0.000693     access\n",
+       "73        8  0.193297     access\n",
+       "2940      4  0.984634    adaptec\n",
+       "152       1  0.052461    address\n",
+       "152       2  0.074007    address\n",
+       "152       3  0.536784    address\n",
+       "152       4  0.182675    address\n",
+       "152       5  0.030914    address\n",
+       "152       6  0.026230    address\n",
+       "152       7  0.032788    address\n",
+       "152       8  0.049650    address\n",
+       "152       9  0.005621    address\n",
+       "152      10  0.008431    address\n",
+       "1444      1  0.124112  administr\n",
+       "1444      3  0.063074  administr\n",
+       "1444      5  0.197359  administr\n",
+       "1444      8  0.614458  administr\n",
+       "...     ...       ...        ...\n",
+       "182       2  0.166406      world\n",
+       "182       3  0.097373      world\n",
+       "182       4  0.150056      world\n",
+       "182       5  0.106093      world\n",
+       "182       6  0.051230      world\n",
+       "182       7  0.080296      world\n",
+       "182       8  0.011990      world\n",
+       "182       9  0.053410      world\n",
+       "182      10  0.054863      world\n",
+       "4238      1  0.009547    worship\n",
+       "4238      2  0.988079    worship\n",
+       "4809      3  0.994707       xlib\n",
+       "3868      3  0.995135      xpert\n",
+       "3581      3  0.995652      xterm\n",
+       "2827      3  0.988763      xview\n",
+       "6047      6  0.981546     yamaha\n",
+       "1701      7  0.988053      yanke\n",
+       "28        1  0.130095       year\n",
+       "28        2  0.031962       year\n",
+       "28        3  0.015482       year\n",
+       "28        4  0.038954       year\n",
+       "28        5  0.216243       year\n",
+       "28        6  0.063674       year\n",
+       "28        7  0.244959       year\n",
+       "28        8  0.040951       year\n",
+       "28        9  0.067919       year\n",
+       "28       10  0.149822       year\n",
+       "3309      9  0.986879      yeast\n",
+       "3190      5  0.990640     zoolog\n",
+       "1894      1  0.991014       zuma\n",
+       "\n",
+       "[2168 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 8, 9, 2, 4, 7, 10, 5, 6])"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = visualize_topics(model, bow_corpus, dictionary)\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Save the pyldavis as HTML\n",
+    "from nautilus_nlp.models.topic_modeling import save_pyldavis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "save_pyldavis(p, '/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load the pyldavis HTML\n",
+    "from nautilus_nlp.models.topic_modeling import show_pyldavis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el591011124069819927707340541\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el591011124069819927707340541_data = {\"mdsDat\": {\"x\": [-0.07866945427665124, -0.01699948489792914, 0.20896689238873523, 0.14744605031212607, -0.008849073212760983, -0.04505413872814077, -0.08949897686453376, 0.10780299734830809, 0.004524270451044093, -0.22966908252019716], \"y\": [-0.15170611822961017, -0.1504468301902949, 0.08013685816591506, 0.13647834612774523, -0.009046128981188077, 0.003906923765221535, 0.14472746444813225, -0.07737607739594787, -0.1051004751701791, 0.1284260374602061], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.179347038269043, 12.924742698669434, 12.465522766113281, 11.996149063110352, 9.560138702392578, 9.471930503845215, 8.926163673400879, 7.8803582191467285, 7.02661657333374, 6.569023609161377]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"argic\", \"genocid\", \"davidian\", \"troop\", \"murder\", \"mormon\", \"prison\", \"massacr\", \"azeri\", \"ethnic\", \"azerbaijani\", \"hitler\", \"iran\", \"zuma\", \"sdpa\", \"motto\", \"azerbaijan\", \"extermin\", \"sera\", \"urartu\", \"slaughter\", \"villag\", \"batf\", \"greek\", \"greec\", \"jew\", \"soldier\", \"kill\", \"waco\", \"arm\", \"countri\", \"muslim\", \"children\", \"armi\", \"popul\", \"peopl\", \"anti\", \"govern\", \"right\", \"attack\", \"say\", \"polit\", \"death\", \"state\", \"live\", \"go\", \"come\", \"tell\", \"happen\", \"forc\", \"world\", \"time\", \"nation\", \"want\", \"leav\", \"start\", \"year\", \"take\", \"jesus\", \"bibl\", \"atheist\", \"atheism\", \"christ\", \"scriptur\", \"cathol\", \"sandvik\", \"doctrin\", \"revel\", \"biblic\", \"satan\", \"atho\", \"livesey\", \"prophet\", \"divin\", \"vers\", \"gospel\", \"sabbath\", \"god\", \"sin\", \"resurrect\", \"solntz\", \"testament\", \"theolog\", \"propheci\", \"theist\", \"schneider\", 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\"map\", \"applic\", \"archiv\", \"user\", \"code\", \"avail\", \"directori\", \"sourc\", \"file\", \"mail\", \"list\", \"program\", \"function\", \"format\", \"email\", \"inform\", \"version\", \"softwar\", \"includ\", \"send\", \"data\", \"internet\", \"address\", \"display\", \"window\", \"copi\", \"access\", \"distribut\", \"thank\", \"look\", \"book\", \"group\", \"need\", \"scsi\", \"simm\", \"motherboard\", \"cach\", \"bio\", \"quadra\", \"diamond\", \"vram\", \"vesa\", \"centri\", \"swap\", \"upgrad\", \"char\", \"eisa\", \"intercon\", \"nubus\", \"ethernet\", \"svga\", \"amanda\", \"meg\", \"cadr\", \"mous\", \"maxtor\", \"config\", \"cica\", \"tiff\", \"adaptec\", \"powerbook\", \"ctrl\", \"esdi\", \"jumper\", \"floppi\", \"disk\", \"umich\", \"video\", \"modem\", \"card\", \"output\", \"monitor\", \"mode\", \"printer\", \"spec\", \"entri\", \"drive\", \"driver\", \"port\", \"instal\", \"memori\", \"window\", \"color\", \"appl\", \"byte\", \"screen\", \"board\", \"problem\", 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\"higgin\", \"inning\", \"revolv\", \"hulman\", \"yanke\", \"pitch\", \"catcher\", \"dodger\", \"blast\", \"starter\", \"tiger\", \"met\", \"bat\", \"nore\", \"outlet\", \"rocki\", \"jay\", \"sdsu\", \"volt\", \"lopez\", \"restaur\", \"lamp\", \"wire\", \"duke\", \"circuit\", \"batteri\", \"brave\", \"basebal\", \"hit\", \"berkeley\", \"jason\", \"ball\", \"metal\", \"jeff\", \"grind\", \"larc\", \"indiana\", \"netcom\", \"colorado\", \"year\", \"run\", \"good\", \"game\", \"smith\", \"scott\", \"player\", \"home\", \"stanford\", \"look\", \"distribut\", \"go\", \"time\", \"lose\", \"come\", \"start\", \"play\", \"david\", \"best\", \"better\", \"power\", \"thing\", \"john\", \"sale\", \"great\", \"encrypt\", \"escrow\", \"privaci\", \"ripem\", \"crypto\", \"wiretap\", \"cryptographi\", \"cipher\", \"decrypt\", \"hamburg\", \"clipper\", \"homicid\", \"bontchev\", \"gtoal\", \"crypt\", \"clarkson\", \"rwing\", \"surveil\", \"nist\", \"sternlight\", \"den\", \"ncsl\", \"qualcomm\", 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\"espn\", \"etern\", \"etern\", \"etern\", \"ether\", \"ethernet\", \"ethnic\", \"evid\", \"evid\", \"evid\", \"evid\", \"evid\", \"evid\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"extermin\", \"faith\", \"faith\", \"fbihh\", \"feder\", \"feder\", \"feder\", \"feder\", \"file\", \"file\", \"file\", \"file\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"firearm\", \"fischer\", \"flight\", \"flight\", \"flight\", \"flight\", \"floppi\", \"floppi\", \"flyer\", \"flyer\", \"fnal\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"font\", \"font\", \"food\", \"food\", \"food\", \"food\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"format\", \"format\", \"format\", \"format\", \"format\", \"freenet\", \"freenet\", \"freenet\", \"frost\", \"frost\", \"function\", \"function\", \"function\", \"function\", \"function\", \"function\", \"fund\", \"fund\", \"fund\", \"fund\", \"fund\", \"game\", \"game\", \"game\", \"game\", \"gatech\", \"gaza\", \"genet\", \"genet\", \"genocid\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"goal\", \"goal\", \"goal\", \"goal\", \"goal\", \"goal\", \"god\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"gordon\", \"gordon\", \"gospel\", \"govern\", \"govern\", \"govern\", \"govern\", \"gradi\", \"graphic\", \"graphic\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"greec\", \"greec\", \"greek\", \"greek\", \"greenbelt\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"gtoal\", \"gun\", \"gun\", \"halat\", \"hallam\", \"hamburg\", \"handbook\", \"handgun\", \"handgun\", \"handheld\", \"handheld\", \"handheld\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"harley\", \"health\", \"health\", \"health\", \"health\", \"heaven\", \"heaven\", \"heaven\", \"heaven\", \"helmet\", \"helmet\", \"helmet\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"henri\", \"henri\", \"higgin\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"hit\", \"hit\", \"hit\", \"hit\", \"hit\", \"hitler\", \"hitter\", \"hockey\", \"holi\", \"holi\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"homeopathi\", \"homicid\", \"honda\", \"hulman\", \"husc\", \"hydro\", \"iastat\", \"iastat\", \"ifa\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"imag\", \"imag\", \"imag\", \"imag\", \"imag\", \"imak\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"infect\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"ingr\", \"ingr\", \"ingr\", \"inning\", \"instal\", \"instal\", \"instal\", \"instal\", \"instal\", \"insur\", \"insur\", \"insur\", \"insur\", \"intellect\", \"intercon\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"invest\", \"invest\", \"iran\", \"islam\", \"islam\", \"isra\", \"israel\", \"israel\", \"israel\", \"jaeger\", \"jake\", \"jason\", \"jason\", \"jason\", \"jason\", \"jay\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jesus\", \"jet\", \"jet\", \"jew\", \"jew\", \"jew\", \"job\", \"job\", \"job\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"jumper\", \"jumper\", \"kaldi\", \"kelvin\", \"key\", \"key\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"koresh\", \"lamp\", \"larc\", \"larc\", \"laughter\", \"launch\", \"launch\", \"laurentian\", \"leaf\", \"leagu\", \"leagu\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"lebanes\", \"lemieux\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"livesey\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"lopez\", \"lord\", \"lord\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"lunar\", \"lunar\", \"lyme\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"magellan\", \"magnus\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"map\", \"map\", \"mar\", \"mar\", \"marriag\", \"marriag\", \"massacr\", \"maxtor\", \"maynard\", \"mccall\", \"mcgill\", \"mcgill\", \"mcgill\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"medic\", \"medic\", \"medic\", \"medic\", \"medic\", \"medicin\", \"medicin\", \"medicin\", \"medicin\", \"meg\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"met\", \"metal\", \"metal\", \"metal\", \"metal\", \"methodolog\", \"midway\", \"midway\", \"midway\", \"migrain\", \"militia\", \"mime\", \"mission\", \"mission\", \"mission\", \"mission\", \"mksol\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"modem\", \"modem\", \"modem\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"monitor\", \"monitor\", \"monitor\", \"montreal\", \"montreal\", \"moon\", \"moon\", \"moon\", \"moral\", \"moral\", \"mormon\", \"motherboard\", \"motif\", \"motorcycl\", \"motto\", \"mous\", \"mous\", \"murder\", \"murder\", \"murder\", \"muslim\", \"muslim\", \"nasa\", \"nasa\", \"nasa\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nazi\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"nist\", \"nist\", \"nore\", \"nsmca\", \"nubus\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"ohio\", \"ohio\", \"ohio\", \"openwindow\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"orbit\", \"outlet\", \"outlet\", \"output\", \"output\", \"output\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"pain\", \"pain\", \"pain\", \"pain\", \"pain\", \"palestinian\", \"patent\", \"patent\", \"patent\", \"patient\", \"patient\", \"pen\", \"penguin\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"restaur\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"resurrect\", \"revel\", \"revolv\", \"rid\", \"rid\", \"ride\", \"ride\", \"rider\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"ripem\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"rkba\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"robi\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rocki\", \"rockwel\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"rutger\", \"rutger\", \"rwing\", \"sabbath\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sandvik\", \"satan\", \"satellit\", \"satellit\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", \"sleev\", \"sleev\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smuggl\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"solar\", \"solar\", \"soldier\", \"soldier\", \"solntz\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spec\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"sphere\", \"spirit\", \"spirit\", \"spirit\", \"ssto\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanley\", \"stanley\", \"stanley\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"starter\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"sternlight\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steveh\", \"stimulus\", \"stratus\", \"strnlght\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"suno\", \"superstit\", \"surveil\", \"svga\", \"swap\", \"syndrom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"tampa\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tennesse\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"testament\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"theist\", \"theodor\", \"theolog\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"therapi\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thomasp\", \"tiff\", \"tiger\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tire\", \"tire\", \"tire\", \"tire\", \"tire\", \"toolkit\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"treatment\", \"treatment\", \"troop\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"trunk\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"turk\", \"turkey\", \"turkish\", \"ualberta\", \"uchicago\", \"uchicago\", \"uchicago\", \"ucsc\", \"uicvm\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"umich\", \"umich\", \"umich\", \"uoknor\", \"upgrad\", \"upgrad\", \"urartu\", \"urbana\", \"urbana\", \"urbana\", \"user\", \"user\", \"user\", \"user\", \"utah\", \"utkvm\", \"uvic\", \"veal\", \"vehicl\", \"vehicl\", \"vehicl\", \"vehicl\", \"vers\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"vesa\", \"vesselin\", \"video\", \"video\", \"video\", \"video\", \"villag\", \"villag\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"visual\", \"visual\", \"volt\", \"vram\", \"waco\", \"waco\", \"wagon\", \"wagon\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"water\", \"water\", \"water\", \"water\", \"water\", \"weapon\", \"weapon\", \"weapon\", \"wheel\", \"wheel\", \"widget\", \"window\", \"window\", \"window\", \"wing\", \"wing\", \"wing\", \"wing\", \"wing\", \"winnipeg\", \"winnipeg\", \"wire\", \"wire\", \"wire\", \"wiretap\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worship\", \"worship\", \"xlib\", \"xpert\", \"xterm\", \"xview\", \"yamaha\", \"yanke\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zoolog\", \"zuma\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 8, 9, 2, 4, 7, 10, 5, 6]};\n",
+       "\n",
+       "function LDAvis_load_lib(url, callback){\n",
+       "  var s = document.createElement('script');\n",
+       "  s.src = url;\n",
+       "  s.async = true;\n",
+       "  s.onreadystatechange = s.onload = callback;\n",
+       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "}\n",
+       "\n",
+       "if(typeof(LDAvis) !== \"undefined\"){\n",
+       "   // already loaded: just create the visualization\n",
+       "   !function(LDAvis){\n",
+       "       new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
+       "   }(LDAvis);\n",
+       "}else if(typeof define === \"function\" && define.amd){\n",
+       "   // require.js is available: use it to load d3/LDAvis\n",
+       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "show_pyldavis('/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 7: Testing model on unseen document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Subject: help\n",
+      "From: C..Doelle@p26.f3333.n106.z1.fidonet.org (C. Doelle)\n",
+      "Lines: 13\n",
+      "\n",
+      "Hello All!\n",
+      "\n",
+      "    It is my understanding that all True-Type fonts in Windows are loaded in\n",
+      "prior to starting Windows - this makes getting into Windows quite slow if you\n",
+      "have hundreds of them as I do.  First off, am I correct in this thinking -\n",
+      "secondly, if that is the case - can you get Windows to ignore them on boot and\n",
+      "maybe make something like a PIF file to load them only when you enter the\n",
+      "applications that need fonts?  Any ideas?\n",
+      "\n",
+      "\n",
+      "Chris\n",
+      "\n",
+      " * Origin: chris.doelle.@f3333.n106.z1.fidonet.org (1:106/3333.26)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "unseen_document = newsgroups_test.data[100]\n",
+    "print(unseen_document)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Data preprocessing step for the unseen document\n",
+    "bow_new = dictionary.doc2bow(preprocess(unseen_document))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.topic_modeling import fit_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0, 0.0027796067),\n",
+       " (1, 0.002779247),\n",
+       " (2, 0.0027795879),\n",
+       " (3, 0.0027795406),\n",
+       " (4, 0.0027792477),\n",
+       " (5, 0.002779096),\n",
+       " (6, 0.0027791534),\n",
+       " (7, 0.20895894),\n",
+       " (8, 0.76880664),\n",
+       " (9, 0.0027789928)]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit_data(model, bow_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Show the dominant topics of the new document and their keywords \n",
+    "from nautilus_nlp.models.topic_modeling import show_dominant_topic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score: 0.7688832879066467\t Topic: ['window', 'drive', 'problem', 'card', 'work']\n",
+      "Score: 0.2088821828365326\t Topic: ['file', 'program', 'mail', 'imag', 'inform']\n",
+      "Score: 0.0027796069625765085\t Topic: ['christian', 'peopl', 'believ', 'jesus', 'say']\n"
+     ]
+    }
+   ],
+   "source": [
+    "show_dominant_topic(model, bow_new, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 7216e97cefbc152544306cad170d2d478222c147 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 5 Jun 2019 11:48:50 +0200
Subject: [PATCH 204/496] rm buggy notebook

---
 notebooks/TopicModeling.ipynb | 1023 ---------------------------------
 1 file changed, 1023 deletions(-)
 delete mode 100644 notebooks/TopicModeling.ipynb

diff --git a/notebooks/TopicModeling.ipynb b/notebooks/TopicModeling.ipynb
deleted file mode 100644
index c58cf2c..0000000
--- a/notebooks/TopicModeling.ipynb
+++ /dev/null
@@ -1,1023 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 1: Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 1,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import fetch_20newsgroups\n",
-    "newsgroups_train = fetch_20newsgroups(subset='train', shuffle = True)\n",
-    "newsgroups_test = fetch_20newsgroups(subset='test', shuffle = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(list(newsgroups_train.target_names))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 2: Data Preprocessing¶\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gensim\n",
-    "from gensim.utils import simple_preprocess\n",
-    "from gensim.parsing.preprocessing import STOPWORDS\n",
-    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
-    "from nltk.stem.porter import *\n",
-    "import numpy as np\n",
-    "np.random.seed(400)\n",
-    "\n",
-    "import nltk\n",
-    "\n",
-    "import pandas as pd\n",
-    "stemmer = SnowballStemmer(\"english\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 4,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "def lemmatize_stemming(text):\n",
-    "    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))\n",
-    "\n",
-    "# Tokenize and lemmatize\n",
-    "def preprocess(text):\n",
-    "    result=[]\n",
-    "    for token in gensim.utils.simple_preprocess(text) :\n",
-    "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
-    "            result.append(lemmatize_stemming(token))\n",
-    "            \n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 5,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "processed_docs = []\n",
-    "\n",
-    "for doc in newsgroups_train.data:\n",
-    "    processed_docs.append(preprocess(doc))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 3: Bag of words"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 6,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# Create the dictionnary\n",
-    "from nautilus_nlp.models.topic_modeling import create_dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 7,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "dictionary = create_dictionary(processed_docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 8,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# Filter out tokens that appear in too few or too many documents\n",
-    "from nautilus_nlp.models.topic_modeling import filter_extremes"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 9,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "filter_extremes(dictionary)"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 10,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# Create the bow \n",
-    "from nautilus_nlp.models.topic_modeling import create_bow_corpus"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 11,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "bow_corpus = create_bow_corpus(processed_docs, dictionary)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(13, 1), (26, 1), (55, 1), (88, 1), (99, 1), (152, 1), (153, 2), (154, 1), (155, 1), (156, 1), (157, 2), (158, 1), (159, 2), (160, 4), (161, 1), (162, 2), (163, 1), (164, 2), (165, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (171, 1), (172, 1), (173, 1), (174, 1), (175, 1), (176, 1), (177, 1), (178, 2), (179, 1), (180, 1), (181, 1), (182, 1)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(bow_corpus[3])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 4: Find optimal number of topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Compute coherence values for various number of topics in order to pick the optimal one\n",
-    "from nautilus_nlp.models.topic_modeling import compute_coherence_values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Take a long time to run\n",
-    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, bow_corpus=bow_corpus, texts=processed_docs, start=2, limit=25, step=4)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.37900998646286865,\n",
-       " 0.42680154447577845,\n",
-       " 0.47716566398237525,\n",
-       " 0.5261650723885645,\n",
-       " 0.49607461078243215,\n",
-       " 0.4978727171365794]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coherence_values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.topic_modeling import plot_optimal_topic_number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Print the coherences scores for the number we tested\n",
-    "from nautilus_nlp.models.topic_modeling import print_coherence_scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Num Topics = 2  has Coherence Value of 0.379\n",
-      "Num Topics = 6  has Coherence Value of 0.4268\n",
-      "Num Topics = 10  has Coherence Value of 0.4772\n",
-      "Num Topics = 14  has Coherence Value of 0.5262\n",
-      "Num Topics = 18  has Coherence Value of 0.4961\n",
-      "Num Topics = 22  has Coherence Value of 0.4979\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_coherence_scores(coherence_values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 5: Running LDA using Bag of Words with Gensim or Mallet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Gensim"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 13,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# Train the LDA model with gensim\n",
-    "from nautilus_nlp.models.topic_modeling import train_lda_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 14,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# By default the model used will be gensim implementation of LDA\n",
-    "model = train_lda_model(bow_corpus, dictionary, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x10a171ef0>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 53,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# Save model: The model will be saved on your current emplacement.\n",
-    "from nautilus_nlp.models.topic_modeling import save_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 54,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "save_model(model, 'ldamodel_nautilus')"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 152,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 24,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# Load model"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x1a3142fc88>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_loaded = load_model('/Users/williamjaubert/nautilus_nlp/notebooks', 'ldamodel_nautilus')\n",
-    "model_loaded"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Mallet "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# You can train Mallet model by precising 'mallet' in the model parameter and give the path where the mallet-2.0.8 file that has been downloaded \n",
-    "model_mallet = train_lda_model(bow_corpus, dictionary, 10, model='mallet', mallet_path='/Users/williamjaubert/nautilus_nlp')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<gensim.models.wrappers.ldamallet.LdaMallet at 0x1a2eb0e320>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_mallet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "save_model(model_mallet, 'ldamodel_mallet_nautilus')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<gensim.models.wrappers.ldamallet.LdaMallet at 0x1a30d1e550>"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_mallet_loaded = load_model('/Users/williamjaubert/nautilus_nlp/notebooks', 'ldamodel_mallet_nautilus', model='mallet')\n",
-    "model_mallet_loaded"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 6: Visualize the top keywords per topic with Pyldavis interactive chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-=======
-   "execution_count": 56,
-   "metadata": {},
->>>>>>> 6197a072a0845f714fca92bb469a7e224b1c15d3
-   "outputs": [],
-   "source": [
-    "# Display the top keywords per topic in a interactive chart\n",
-    "from nautilus_nlp.models.topic_modeling import visualize_topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
-       "\n",
-       "\n",
-       "<div id=\"ldavis_el155871124265505206097197808\"></div>\n",
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\"genocid\", \"davidian\", \"troop\", \"murder\", \"mormon\", \"prison\", \"massacr\", \"azeri\", \"ethnic\", \"azerbaijani\", \"hitler\", \"iran\", \"zuma\", \"sdpa\", \"motto\", \"azerbaijan\", \"extermin\", \"sera\", \"urartu\", \"slaughter\", \"villag\", \"batf\", \"greek\", \"greec\", \"jew\", \"soldier\", \"kill\", \"waco\", \"arm\", \"countri\", \"muslim\", \"children\", \"armi\", \"popul\", \"peopl\", \"anti\", \"govern\", \"right\", \"attack\", \"say\", \"polit\", \"death\", \"state\", \"live\", \"go\", \"come\", \"tell\", \"happen\", \"forc\", \"world\", \"time\", \"nation\", \"want\", \"leav\", \"start\", \"year\", \"take\", \"jesus\", \"bibl\", \"atheist\", \"atheism\", \"christ\", \"scriptur\", \"cathol\", \"sandvik\", \"doctrin\", \"revel\", \"biblic\", \"satan\", \"atho\", \"livesey\", \"prophet\", \"divin\", \"vers\", \"gospel\", \"sabbath\", \"god\", \"sin\", \"resurrect\", \"solntz\", \"testament\", \"theolog\", \"propheci\", \"theist\", \"schneider\", \"jaeger\", 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\"machin\", \"file\", \"thank\", \"control\", \"work\", \"speed\", \"need\", \"hard\", \"help\", \"program\", \"want\", \"time\", \"repli\", \"softwar\", \"distribut\", \"alaska\", \"spencer\", \"oracl\", \"dseg\", \"aurora\", \"nsmca\", \"engr\", \"launch\", \"uoknor\", \"callison\", \"kaldi\", \"zoolog\", \"mccall\", \"ucsc\", \"hallam\", \"lunar\", \"automot\", \"raider\", \"theodor\", \"dock\", \"shafer\", \"mksol\", \"hydro\", \"ssto\", \"plymouth\", \"redesign\", \"laughter\", \"rockwel\", \"desi\", \"stimulus\", \"moon\", \"henri\", \"mar\", \"job\", \"wheel\", \"billion\", \"invest\", \"spacecraft\", \"orbit\", \"nasa\", \"space\", \"shuttl\", \"satellit\", \"fund\", \"probe\", \"flight\", \"helmet\", \"station\", \"solar\", \"presid\", \"mission\", \"earth\", \"cost\", \"money\", \"vehicl\", \"year\", \"work\", \"project\", \"toronto\", \"spend\", \"go\", \"time\", \"engin\", \"long\", \"high\", \"power\", \"thing\", \"say\", \"look\", \"peopl\", \"program\", \"want\", 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\"higgin\", \"inning\", \"revolv\", \"hulman\", \"yanke\", \"pitch\", \"catcher\", \"dodger\", \"blast\", \"starter\", \"tiger\", \"met\", \"bat\", \"nore\", \"outlet\", \"rocki\", \"jay\", \"sdsu\", \"volt\", \"lopez\", \"restaur\", \"lamp\", \"wire\", \"duke\", \"circuit\", \"batteri\", \"brave\", \"basebal\", \"hit\", \"berkeley\", \"jason\", \"ball\", \"metal\", \"jeff\", \"grind\", \"larc\", \"indiana\", \"netcom\", \"colorado\", \"year\", \"run\", \"good\", \"game\", \"smith\", \"scott\", \"player\", \"home\", \"stanford\", \"look\", \"distribut\", \"go\", \"time\", \"lose\", \"come\", \"start\", \"play\", \"david\", \"best\", \"better\", \"power\", \"thing\", \"john\", \"sale\", \"great\", \"encrypt\", \"escrow\", \"privaci\", \"ripem\", \"crypto\", \"wiretap\", \"cryptographi\", \"cipher\", \"decrypt\", \"hamburg\", \"clipper\", \"homicid\", \"bontchev\", \"gtoal\", \"crypt\", \"clarkson\", \"rwing\", \"surveil\", \"nist\", \"sternlight\", \"den\", \"ncsl\", \"qualcomm\", 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\"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", 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-       "\n",
-       "function LDAvis_load_lib(url, callback){\n",
-       "  var s = document.createElement('script');\n",
-       "  s.src = url;\n",
-       "  s.async = true;\n",
-       "  s.onreadystatechange = s.onload = callback;\n",
-       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
-       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "}\n",
-       "\n",
-       "if(typeof(LDAvis) !== \"undefined\"){\n",
-       "   // already loaded: just create the visualization\n",
-       "   !function(LDAvis){\n",
-       "       new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
-       "   }(LDAvis);\n",
-       "}else if(typeof define === \"function\" && define.amd){\n",
-       "   // require.js is available: use it to load d3/LDAvis\n",
-       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
-       "   require([\"d3\"], function(d3){\n",
-       "      window.d3 = d3;\n",
-       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "        new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
-       "      });\n",
-       "    });\n",
-       "}else{\n",
-       "    // require.js not available: dynamically load d3 & LDAvis\n",
-       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
-       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "                 new LDAvis(\"#\" + \"ldavis_el155871124265505206097197808\", ldavis_el155871124265505206097197808_data);\n",
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-     },
-     "execution_count": 30,
-     "metadata": {},
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-   "source": [
-    "# Gensim\n",
-    "p = visualize_topics(model, bow_corpus, dictionary, model_type='gensim')\n",
-    "p"
-   ]
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-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:223: RuntimeWarning: divide by zero encountered in log\n",
-      "  kernel = (topic_given_term * np.log((topic_given_term.T / topic_proportion).T))\n",
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:240: RuntimeWarning: divide by zero encountered in log\n",
-      "  log_lift = np.log(topic_term_dists / term_proportion)\n",
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:241: RuntimeWarning: divide by zero encountered in log\n",
-      "  log_ttd = np.log(topic_term_dists)\n",
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
-     ]
-    },
-    {
-     "data": {
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-       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
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-       "\n",
-       "<div id=\"ldavis_el15587112430946968420164328\"></div>\n",
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\"phone\", \"phone\", \"phone\", \"phone\", \"photoshop\", \"physician\", \"pick\", \"pick\", \"pinout\", \"piss\", \"pistol\", \"pitch\", \"pitt\", \"pitt\", \"pixmap\", \"planet\", \"planet\", \"play\", \"player\", \"playoff\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polygon\", \"popul\", \"popul\", \"porsch\", \"port\", \"port\", \"portal\", \"postscript\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerpc\", \"practition\", \"presid\", \"preview\", \"price\", \"price\", \"price\", \"price\", \"printer\", \"printer\", \"prism\", \"privat\", \"privat\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probabl\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"prohibit\", \"project\", \"project\", \"project\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protein\", \"provid\", 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\"utexa\", \"utkvm\", \"vehicl\", \"vehicl\", \"venus\", \"version\", \"version\", \"video\", \"viewer\", \"villag\", \"virginia\", \"virtu\", \"visual\", \"visual\", \"vitamin\", \"voltag\", \"vram\", \"vulcan\", \"wallac\", \"warrant\", \"warrant\", \"wasn\", \"wasn\", \"water\", \"water\", \"water\", \"watt\", \"weapon\", \"weapon\", \"weapon\", \"wear\", \"weren\", \"widget\", \"william\", \"william\", \"win\", \"win\", \"window\", \"windshield\", \"wing\", \"wing\", \"wing\", \"wire\", \"wiretap\", \"wong\", \"worcest\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"workgroup\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"wors\", \"wors\", \"worship\", \"wouldn\", \"wouldn\", \"wouldn\", \"xcopyarea\", \"xlib\", \"xterm\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 9, 5, 6, 10, 2, 7, 1, 8, 3]};\n",
-       "\n",
-       "function LDAvis_load_lib(url, callback){\n",
-       "  var s = document.createElement('script');\n",
-       "  s.src = url;\n",
-       "  s.async = true;\n",
-       "  s.onreadystatechange = s.onload = callback;\n",
-       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
-       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "}\n",
-       "\n",
-       "if(typeof(LDAvis) !== \"undefined\"){\n",
-       "   // already loaded: just create the visualization\n",
-       "   !function(LDAvis){\n",
-       "       new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
-       "   }(LDAvis);\n",
-       "}else if(typeof define === \"function\" && define.amd){\n",
-       "   // require.js is available: use it to load d3/LDAvis\n",
-       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
-       "   require([\"d3\"], function(d3){\n",
-       "      window.d3 = d3;\n",
-       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "        new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
-       "      });\n",
-       "    });\n",
-       "}else{\n",
-       "    // require.js not available: dynamically load d3 & LDAvis\n",
-       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
-       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "                 new LDAvis(\"#\" + \"ldavis_el15587112430946968420164328\", ldavis_el15587112430946968420164328_data);\n",
-       "            })\n",
-       "         });\n",
-       "}\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Mallet\n",
-    "p = visualize_topics(model_mallet, bow_corpus, dictionary, model_type='mallet')\n",
-    "p"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Save the pyldavis as HTML\n",
-    "from nautilus_nlp.models.topic_modeling import save_pyldavis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "save_pyldavis(p, '/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load the pyldavis HTML\n",
-    "from nautilus_nlp.models.topic_modeling import show_pyldavis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
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-       "\n",
-       "<div id=\"ldavis_el591011124069819927707340541\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "\n",
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\"map\", \"applic\", \"archiv\", \"user\", \"code\", \"avail\", \"directori\", \"sourc\", \"file\", \"mail\", \"list\", \"program\", \"function\", \"format\", \"email\", \"inform\", \"version\", \"softwar\", \"includ\", \"send\", \"data\", \"internet\", \"address\", \"display\", \"window\", \"copi\", \"access\", \"distribut\", \"thank\", \"look\", \"book\", \"group\", \"need\", \"scsi\", \"simm\", \"motherboard\", \"cach\", \"bio\", \"quadra\", \"diamond\", \"vram\", \"vesa\", \"centri\", \"swap\", \"upgrad\", \"char\", \"eisa\", \"intercon\", \"nubus\", \"ethernet\", \"svga\", \"amanda\", \"meg\", \"cadr\", \"mous\", \"maxtor\", \"config\", \"cica\", \"tiff\", \"adaptec\", \"powerbook\", \"ctrl\", \"esdi\", \"jumper\", \"floppi\", \"disk\", \"umich\", \"video\", \"modem\", \"card\", \"output\", \"monitor\", \"mode\", \"printer\", \"spec\", \"entri\", \"drive\", \"driver\", \"port\", \"instal\", \"memori\", \"window\", \"color\", \"appl\", \"byte\", \"screen\", \"board\", \"problem\", \"machin\", \"file\", \"thank\", \"control\", \"work\", \"speed\", \"need\", \"hard\", \"help\", \"program\", \"want\", \"time\", \"repli\", \"softwar\", \"distribut\", \"alaska\", \"spencer\", \"oracl\", \"dseg\", \"aurora\", \"nsmca\", \"engr\", \"launch\", \"uoknor\", \"callison\", \"kaldi\", \"zoolog\", \"mccall\", \"ucsc\", \"hallam\", \"lunar\", \"automot\", \"raider\", \"theodor\", \"dock\", \"shafer\", \"mksol\", \"hydro\", \"ssto\", \"plymouth\", \"redesign\", \"laughter\", \"rockwel\", \"desi\", \"stimulus\", \"moon\", \"henri\", \"mar\", \"job\", \"wheel\", \"billion\", \"invest\", \"spacecraft\", \"orbit\", \"nasa\", \"space\", \"shuttl\", \"satellit\", \"fund\", \"probe\", \"flight\", \"helmet\", \"station\", \"solar\", \"presid\", \"mission\", \"earth\", \"cost\", \"money\", \"vehicl\", \"year\", \"work\", \"project\", \"toronto\", \"spend\", \"go\", \"time\", \"engin\", \"long\", \"high\", \"power\", \"thing\", \"say\", \"look\", \"peopl\", \"program\", \"want\", 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\"higgin\", \"inning\", \"revolv\", \"hulman\", \"yanke\", \"pitch\", \"catcher\", \"dodger\", \"blast\", \"starter\", \"tiger\", \"met\", \"bat\", \"nore\", \"outlet\", \"rocki\", \"jay\", \"sdsu\", \"volt\", \"lopez\", \"restaur\", \"lamp\", \"wire\", \"duke\", \"circuit\", \"batteri\", \"brave\", \"basebal\", \"hit\", \"berkeley\", \"jason\", \"ball\", \"metal\", \"jeff\", \"grind\", \"larc\", \"indiana\", \"netcom\", \"colorado\", \"year\", \"run\", \"good\", \"game\", \"smith\", \"scott\", \"player\", \"home\", \"stanford\", \"look\", \"distribut\", \"go\", \"time\", \"lose\", \"come\", \"start\", \"play\", \"david\", \"best\", \"better\", \"power\", \"thing\", \"john\", \"sale\", \"great\", \"encrypt\", \"escrow\", \"privaci\", \"ripem\", \"crypto\", \"wiretap\", \"cryptographi\", \"cipher\", \"decrypt\", \"hamburg\", \"clipper\", \"homicid\", \"bontchev\", \"gtoal\", \"crypt\", \"clarkson\", \"rwing\", \"surveil\", \"nist\", \"sternlight\", \"den\", \"ncsl\", \"qualcomm\", 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\"bank\", \"treatment\", \"water\", \"pain\", \"health\", \"handheld\", \"studi\", \"princeton\", \"caus\", \"effect\", \"scienc\", \"scientif\", \"rochest\", \"theori\", \"point\", \"result\", \"risk\", \"problem\", \"research\", \"case\", \"steve\", \"test\", \"time\", \"peopl\", \"repli\", \"take\", \"differ\", \"year\", \"say\", \"thing\", \"isra\", \"hockey\", \"playoff\", \"palestinian\", \"detroit\", \"leaf\", \"cramer\", \"optilink\", \"pen\", \"cunixb\", \"lebanes\", \"penguin\", \"clayton\", \"jake\", \"maynard\", \"espn\", \"edmonton\", \"ericsson\", \"boni\", \"lemieux\", \"gaza\", \"puck\", \"bruin\", \"selann\", \"laurentian\", \"quebec\", \"ramsey\", \"canuck\", \"shark\", \"uvic\", \"montreal\", \"flyer\", \"israel\", \"stanley\", \"team\", \"jet\", \"coach\", \"ranger\", \"winnipeg\", \"game\", \"play\", \"wing\", \"player\", \"columbia\", \"season\", \"leagu\", \"pittsburgh\", \"score\", \"arab\", \"mcgill\", \"goal\", \"virginia\", \"toronto\", \"andrew\", \"year\", 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\"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"lopez\", \"lord\", \"lord\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"lunar\", \"lunar\", \"lyme\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"magellan\", \"magnus\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"map\", \"map\", \"mar\", \"mar\", \"marriag\", \"marriag\", \"massacr\", \"maxtor\", \"maynard\", \"mccall\", \"mcgill\", \"mcgill\", \"mcgill\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"medic\", \"medic\", \"medic\", \"medic\", \"medic\", \"medicin\", \"medicin\", \"medicin\", \"medicin\", \"meg\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"met\", \"metal\", \"metal\", \"metal\", \"metal\", \"methodolog\", \"midway\", \"midway\", \"midway\", \"migrain\", \"militia\", \"mime\", \"mission\", \"mission\", \"mission\", \"mission\", \"mksol\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"modem\", \"modem\", \"modem\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"monitor\", \"monitor\", \"monitor\", \"montreal\", \"montreal\", \"moon\", \"moon\", \"moon\", \"moral\", \"moral\", \"mormon\", \"motherboard\", \"motif\", \"motorcycl\", \"motto\", \"mous\", \"mous\", \"murder\", \"murder\", \"murder\", \"muslim\", \"muslim\", \"nasa\", \"nasa\", \"nasa\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nazi\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"nist\", \"nist\", \"nore\", \"nsmca\", \"nubus\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"ohio\", \"ohio\", \"ohio\", \"openwindow\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"orbit\", \"outlet\", \"outlet\", \"output\", \"output\", \"output\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"pain\", \"pain\", \"pain\", \"pain\", \"pain\", \"palestinian\", \"patent\", \"patent\", \"patent\", \"patient\", \"patient\", \"pen\", \"penguin\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"restaur\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"resurrect\", \"revel\", \"revolv\", \"rid\", \"rid\", \"ride\", \"ride\", \"rider\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"ripem\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"rkba\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"robi\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rocki\", \"rockwel\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"rutger\", \"rutger\", \"rwing\", \"sabbath\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sandvik\", \"satan\", \"satellit\", \"satellit\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", \"sleev\", \"sleev\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smuggl\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"solar\", \"solar\", \"soldier\", \"soldier\", \"solntz\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spec\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"sphere\", \"spirit\", \"spirit\", \"spirit\", \"ssto\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanley\", \"stanley\", \"stanley\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"starter\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"sternlight\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steveh\", \"stimulus\", \"stratus\", \"strnlght\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"suno\", \"superstit\", \"surveil\", \"svga\", \"swap\", \"syndrom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"tampa\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tennesse\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"testament\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"theist\", \"theodor\", \"theolog\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"therapi\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thomasp\", \"tiff\", \"tiger\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tire\", \"tire\", \"tire\", \"tire\", \"tire\", \"toolkit\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"treatment\", \"treatment\", \"troop\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"trunk\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"turk\", \"turkey\", \"turkish\", \"ualberta\", \"uchicago\", \"uchicago\", \"uchicago\", \"ucsc\", \"uicvm\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"umich\", \"umich\", \"umich\", \"uoknor\", \"upgrad\", \"upgrad\", \"urartu\", \"urbana\", \"urbana\", \"urbana\", \"user\", \"user\", \"user\", \"user\", \"utah\", \"utkvm\", \"uvic\", \"veal\", \"vehicl\", \"vehicl\", \"vehicl\", \"vehicl\", \"vers\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"vesa\", \"vesselin\", \"video\", \"video\", \"video\", \"video\", \"villag\", \"villag\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"visual\", \"visual\", \"volt\", \"vram\", \"waco\", \"waco\", \"wagon\", \"wagon\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"water\", \"water\", \"water\", \"water\", \"water\", \"weapon\", \"weapon\", \"weapon\", \"wheel\", \"wheel\", \"widget\", \"window\", \"window\", \"window\", \"wing\", \"wing\", \"wing\", \"wing\", \"wing\", \"winnipeg\", \"winnipeg\", \"wire\", \"wire\", \"wire\", \"wiretap\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worship\", \"worship\", \"xlib\", \"xpert\", \"xterm\", \"xview\", \"yamaha\", \"yanke\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zoolog\", \"zuma\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 8, 9, 2, 4, 7, 10, 5, 6]};\n",
-       "\n",
-       "function LDAvis_load_lib(url, callback){\n",
-       "  var s = document.createElement('script');\n",
-       "  s.src = url;\n",
-       "  s.async = true;\n",
-       "  s.onreadystatechange = s.onload = callback;\n",
-       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
-       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "}\n",
-       "\n",
-       "if(typeof(LDAvis) !== \"undefined\"){\n",
-       "   // already loaded: just create the visualization\n",
-       "   !function(LDAvis){\n",
-       "       new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
-       "   }(LDAvis);\n",
-       "}else if(typeof define === \"function\" && define.amd){\n",
-       "   // require.js is available: use it to load d3/LDAvis\n",
-       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
-       "   require([\"d3\"], function(d3){\n",
-       "      window.d3 = d3;\n",
-       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "        new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
-       "      });\n",
-       "    });\n",
-       "}else{\n",
-       "    // require.js not available: dynamically load d3 & LDAvis\n",
-       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
-       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "                 new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
-       "            })\n",
-       "         });\n",
-       "}\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "show_pyldavis('/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 7: Testing model on unseen document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Subject: help\n",
-      "From: C..Doelle@p26.f3333.n106.z1.fidonet.org (C. Doelle)\n",
-      "Lines: 13\n",
-      "\n",
-      "Hello All!\n",
-      "\n",
-      "    It is my understanding that all True-Type fonts in Windows are loaded in\n",
-      "prior to starting Windows - this makes getting into Windows quite slow if you\n",
-      "have hundreds of them as I do.  First off, am I correct in this thinking -\n",
-      "secondly, if that is the case - can you get Windows to ignore them on boot and\n",
-      "maybe make something like a PIF file to load them only when you enter the\n",
-      "applications that need fonts?  Any ideas?\n",
-      "\n",
-      "\n",
-      "Chris\n",
-      "\n",
-      " * Origin: chris.doelle.@f3333.n106.z1.fidonet.org (1:106/3333.26)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "unseen_document = newsgroups_test.data[100]\n",
-    "print(unseen_document)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Data preprocessing step for the unseen document\n",
-    "bow_new = dictionary.doc2bow(preprocess(unseen_document))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.topic_modeling import fit_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0.0027796067),\n",
-       " (1, 0.002779247),\n",
-       " (2, 0.0027795879),\n",
-       " (3, 0.0027795406),\n",
-       " (4, 0.0027792477),\n",
-       " (5, 0.002779096),\n",
-       " (6, 0.0027791534),\n",
-       " (7, 0.20895894),\n",
-       " (8, 0.76880664),\n",
-       " (9, 0.0027789928)]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fit_data(model, bow_new)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Show the dominant topics of the new document and their keywords \n",
-    "from nautilus_nlp.models.topic_modeling import show_dominant_topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Score: 0.7688832879066467\t Topic: ['window', 'drive', 'problem', 'card', 'work']\n",
-      "Score: 0.2088821828365326\t Topic: ['file', 'program', 'mail', 'imag', 'inform']\n",
-      "Score: 0.0027796069625765085\t Topic: ['christian', 'peopl', 'believ', 'jesus', 'say']\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_dominant_topic(model, bow_new, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From fc01a49681154b81e76909f135c5dcfc75cbe55c Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 5 Jun 2019 15:57:10 +0200
Subject: [PATCH 205/496] change import of fasttext_clf

---
 notebooks/Sentiment_analysis_FT.ipynb | 81 +++++++++++++++------------
 1 file changed, 44 insertions(+), 37 deletions(-)

diff --git a/notebooks/Sentiment_analysis_FT.ipynb b/notebooks/Sentiment_analysis_FT.ipynb
index 3690d34..e8ba22e 100644
--- a/notebooks/Sentiment_analysis_FT.ipynb
+++ b/notebooks/Sentiment_analysis_FT.ipynb
@@ -20,21 +20,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'train' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-56711baad5fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'__label__{row[\"target\"]} {text}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'__label__{row[\"target\"]} {text}'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'train' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 86M\n",
+      "-rw-rw-r-- 1 robin robin  81M juin   5 15:31 tweets.train\n",
+      "-rw-rw-r-- 1 robin robin 5,4M juin   5 15:31 tweets.valid\n"
      ]
     }
    ],
+   "source": [
+    "ls -lh ../data/processed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\n",
     "train = open('../data/processed/tweets.train','w',encoding='utf-8')  \n",
@@ -74,16 +81,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nautilus_nlp.models import Fasttext_classifier "
+    "from nautilus_nlp.models import fasttext_classifier  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,77 +102,77 @@
     "train_data = '../data/processed/tweets.train'\n",
     "valid_data = '../data/processed/tweets.valid'\n",
     "\n",
-    "model = Fasttext_classifier.Fasttext_clf()\n"
+    "model = fasttext_classifier.Fasttext_clf()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N\t100000\n",
-      "P@1\t0.759\n",
-      "R@1\t0.759\n"
+      "12.3 s ± 944 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
    "source": [
-    "print_results(*model.test(valid_data))\n"
+    "%%timeit\n",
+    "model.train(train_data)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12.9 s ± 1.26 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "N\t99880\n",
+      "P@1\t0.755\n",
+      "R@1\t0.755\n"
      ]
     }
    ],
    "source": [
-    "%%timeit\n",
-    "model.train(train_data)\n"
+    "print_results(*model.test(valid_data))\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Il faut donc 13 secondes pour entrainer un modèle sur 1.5 Millions de tweets, avec une précision de 0.759. Not bad"
+    "Il faut donc 13 secondes pour entrainer un modèle sur 1.5 Millions de tweets, avec une précision de 0.75. Not bad"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.save_model('/home/nautilus_nlp/models/sentiments.bin')"
+    "model.save_model('sentiments.bin')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-r--r-- 1 root root 232M Mar 14 12:51 /home/nautilus_nlp/models/sentiments.bin\n"
+      "-rw-rw-r-- 1 robin robin 232M juin   5 15:55 sentiments.bin\n"
      ]
     }
    ],
    "source": [
-    "!ls -lh '/home/nautilus_nlp/models/sentiments.bin'"
+    "!ls -lh 'sentiments.bin'"
    ]
   },
   {
@@ -184,40 +191,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N\t100000\n",
-      "P@1\t0.759\n",
-      "R@1\t0.759\n"
+      "N\t99880\n",
+      "P@1\t0.755\n",
+      "R@1\t0.755\n"
      ]
     }
    ],
    "source": [
     "model.quantize(input=train_data, qnorm=True, retrain=True, cutoff=100000)\n",
     "print_results(*model.test(valid_data))\n",
-    "model.save_model('/home/nautilus_nlp/models/sentiments.ftz')"
+    "model.save_model('sentiments.ftz')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-rw-r--r-- 1 root root 6.8M Mar 14 12:53 /home/nautilus_nlp/models/sentiments.ftz\n"
+      "-rw-rw-r-- 1 robin robin 6,8M juin   5 15:56 sentiments.ftz\n"
      ]
     }
    ],
    "source": [
-    "!ls -lh '/home/nautilus_nlp/models/sentiments.ftz'"
+    "!ls -lh 'sentiments.ftz'"
    ]
   },
   {
@@ -244,7 +251,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,

From a0707803d35984bbf36bcdc48593b1fd06de90f9 Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 11:08:22 +0200
Subject: [PATCH 206/496] add biterm model script V0

---
 .gitignore                          |  4 +-
 nautilus_nlp/models/biterm_model.py | 59 +++++++++++++++++++++++++++++
 requirements.txt                    |  1 +
 3 files changed, 63 insertions(+), 1 deletion(-)
 create mode 100644 nautilus_nlp/models/biterm_model.py

diff --git a/.gitignore b/.gitignore
index ca31cb0..183f817 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -65,6 +66,7 @@ target/
 
 # Pycharm
 .idea
+venv/
 
 # VS Code
 .vscode/
@@ -97,4 +99,4 @@ target/
 # PyTest cache
 .pytest_cache/
 
-data/
\ No newline at end of file
+data/
diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
new file mode 100644
index 0000000..d07e65d
--- /dev/null
+++ b/nautilus_nlp/models/biterm_model.py
@@ -0,0 +1,59 @@
+import numpy as np
+from biterm.btm import oBTM
+from biterm.utility import vec_to_biterms, topic_summuary
+from sklearn.feature_extraction.text import CountVectorizer
+
+
+class biterm_model:
+
+    def __init__(self, data, nb_topics, nb_iteration, lang='english'):
+        self.data = data
+        self.nb_topics = nb_topics
+        self.nb_iteration = nb_iteration
+        self.lang = lang
+        self.btm = None
+        self.topics = None
+        self.vocab = None
+        self.X = None
+
+    def train_biterm_model(self):
+        vec = CountVectorizer(stop_words=self.lang)
+        self.X = vec.fit_transform(self.data).toarray()
+
+        self.vocab = np.array(vec.get_feature_names())
+        biterms = vec_to_biterms(self.X)
+        self.btm = oBTM(num_topics=self.nb_topics, V=self.vocab)
+        self.topics = self.btm.fit_transform(biterms, iterations=self.nb_iteration)
+
+    def get_cluster_biterm(self, nb_word_per_cluster):
+        results = topic_summuary(self.btm.phi_wz.T, self.X, self.vocab, nb_word_per_cluster, verbose=False)
+
+        return results
+
+    def get_text_topic(self, indice):
+        return self.topics[indice].argmax()
+
+
+text = """Cola 1.5L Carrefour,Pepsi Cola Light 1.5L,Pepsi Cola Twist Light
+,Cola 1.5L CRF DISC,Coca-Cola Light 1.5L,Coca-Cola Light 4x0.5L,Coca-Cola Light 6x0.3L
+,Panzani 200g x 4 bio,Rustichella 150g bio,De Cecco - Fusilli bio,Gerblé sans Gluten50g
+,Penne de riz 100g sans gluten,Spaghetti de maïs 50g sans Glute"""
+text = text.split(",")
+
+nb_topics = 5
+nb_word_per_cluster = 5
+nb_iteration = 100
+language = 'english'
+
+BitermModel = biterm_model(data=text
+                           , nb_topics=nb_topics
+                           , nb_iteration=nb_iteration
+                           , lang='english')
+
+BitermModel.train_biterm_model()
+
+clusters = BitermModel.get_cluster_biterm(nb_word_per_cluster=nb_word_per_cluster)
+print(clusters)
+
+indice = 0
+print("cluster indice", BitermModel.get_text_topic(indice))
diff --git a/requirements.txt b/requirements.txt
index b36aa9c..4dfae63 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -37,6 +37,7 @@ flashtext==2.7
 phonenumbers==8.10.12
 emoji>=0.5.2
 summa==1.2.0
+biterm==0.1.5
 
 
 

From f2fcf904ebf4c445be445e969e15d5d0cc8f022a Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 12:02:35 +0200
Subject: [PATCH 207/496] add jupyter notebook and add refacto biterm_model

---
 nautilus_nlp/models/biterm_model.py        |  59 +++----
 notebooks/4. Topic Modeling.ipynb          |  15 +-
 notebooks/5. Topic Modeling - Biterm.ipynb | 181 +++++++++++++++++++++
 3 files changed, 219 insertions(+), 36 deletions(-)
 create mode 100644 notebooks/5. Topic Modeling - Biterm.ipynb

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index d07e65d..89d37a0 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -4,9 +4,17 @@
 from sklearn.feature_extraction.text import CountVectorizer
 
 
-class biterm_model:
+class BitermModel:
 
     def __init__(self, data, nb_topics, nb_iteration, lang='english'):
+        """
+        Model for topic modelling
+        Particularly useful for short texts
+        :param data: a list of string, each string can be a document
+        :param nb_topics: positive int
+        :param nb_iteration: positive int
+        :param lang: str, language to remove the stop words
+        """
         self.data = data
         self.nb_topics = nb_topics
         self.nb_iteration = nb_iteration
@@ -16,44 +24,25 @@ def __init__(self, data, nb_topics, nb_iteration, lang='english'):
         self.vocab = None
         self.X = None
 
-    def train_biterm_model(self):
+    def pre_processing(self, data):
         vec = CountVectorizer(stop_words=self.lang)
-        self.X = vec.fit_transform(self.data).toarray()
-
-        self.vocab = np.array(vec.get_feature_names())
-        biterms = vec_to_biterms(self.X)
-        self.btm = oBTM(num_topics=self.nb_topics, V=self.vocab)
-        self.topics = self.btm.fit_transform(biterms, iterations=self.nb_iteration)
-
-    def get_cluster_biterm(self, nb_word_per_cluster):
-        results = topic_summuary(self.btm.phi_wz.T, self.X, self.vocab, nb_word_per_cluster, verbose=False)
-
-        return results
+        X = vec.fit_transform(data).toarray()
+        vocab = np.array(vec.get_feature_names())
 
-    def get_text_topic(self, indice):
-        return self.topics[indice].argmax()
+        return X, vocab
 
+    def train_biterm_model(self):
+        X, vocab = self.pre_processing(self.data)
 
-text = """Cola 1.5L Carrefour,Pepsi Cola Light 1.5L,Pepsi Cola Twist Light
-,Cola 1.5L CRF DISC,Coca-Cola Light 1.5L,Coca-Cola Light 4x0.5L,Coca-Cola Light 6x0.3L
-,Panzani 200g x 4 bio,Rustichella 150g bio,De Cecco - Fusilli bio,Gerblé sans Gluten50g
-,Penne de riz 100g sans gluten,Spaghetti de maïs 50g sans Glute"""
-text = text.split(",")
-
-nb_topics = 5
-nb_word_per_cluster = 5
-nb_iteration = 100
-language = 'english'
-
-BitermModel = biterm_model(data=text
-                           , nb_topics=nb_topics
-                           , nb_iteration=nb_iteration
-                           , lang='english')
+        biterms = vec_to_biterms(X)
+        self.btm = oBTM(num_topics=self.nb_topics, V=vocab)
+        self.topics = self.btm.fit_transform(biterms, iterations=self.nb_iteration)
 
-BitermModel.train_biterm_model()
+    def get_cluster_biterm(self, data, nb_word_per_cluster):
+        X, vocab = self.pre_processing(data)
+        results = topic_summuary(self.btm.phi_wz.T, X, vocab, nb_word_per_cluster, verbose=False)
 
-clusters = BitermModel.get_cluster_biterm(nb_word_per_cluster=nb_word_per_cluster)
-print(clusters)
+        return results
 
-indice = 0
-print("cluster indice", BitermModel.get_text_topic(indice))
+    def get_text_topic(self, index):
+        return self.topics[index].argmax()
diff --git a/notebooks/4. Topic Modeling.ipynb b/notebooks/4. Topic Modeling.ipynb
index 56d3efe..7d040fd 100644
--- a/notebooks/4. Topic Modeling.ipynb	
+++ b/notebooks/4. Topic Modeling.ipynb	
@@ -933,7 +933,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,
diff --git a/notebooks/5. Topic Modeling - Biterm.ipynb b/notebooks/5. Topic Modeling - Biterm.ipynb
new file mode 100644
index 0000000..0a519e0
--- /dev/null
+++ b/notebooks/5. Topic Modeling - Biterm.ipynb	
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5. Topic Modeling - Biterm Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.biterm_model import BiterModel\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = ['Cola 1.5L Carrefour',\n",
+    " 'Pepsi Cola Light 1.5L',\n",
+    " 'Pepsi Cola Twist Light\\n',\n",
+    " 'Cola 1.5L CRF DISC',\n",
+    " 'Coca-Cola Light 1.5L',\n",
+    " 'Coca-Cola Light 4x0.5L',\n",
+    " 'Coca-Cola Light 6x0.3L\\n',\n",
+    " 'Panzani 200g x 4 bio',\n",
+    " 'Rustichella 150g bio',\n",
+    " 'De Cecco - Fusilli bio',\n",
+    " 'Gerblé sans Gluten50g\\n',\n",
+    " 'Penne de riz 100g sans gluten',\n",
+    " 'Spaghetti de maïs 50g sans Glute']\n",
+    "\n",
+    "text2 = ['De Cecco - Fusilli bio',\n",
+    " 'Gerblé sans Gluten50g\\n',\n",
+    " 'Penne de riz 100g sans gluten',\n",
+    " 'Spaghetti de maïs 50g sans Glute']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:00<00:00, 109.64it/s]\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "get_cluster_biterm() got an unexpected keyword argument 'data'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-25-d9e10b244579>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m clusters = BitermModel.get_cluster_biterm(data=text\n\u001b[0;32m---> 14\u001b[0;31m                                           ,nb_word_per_cluster=nb_word_per_cluster)\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: get_cluster_biterm() got an unexpected keyword argument 'data'"
+     ]
+    }
+   ],
+   "source": [
+    "nb_topics = 5\n",
+    "nb_word_per_cluster = 5\n",
+    "nb_iteration = 100\n",
+    "language = 'english'\n",
+    "\n",
+    "BitermModel = biterm_model(data=text\n",
+    "                           , nb_topics=nb_topics\n",
+    "                           , nb_iteration=nb_iteration\n",
+    "                           , lang='english')\n",
+    "\n",
+    "BitermModel.train_biterm_model()\n",
+    "\n",
+    "clusters = BitermModel.get_cluster_biterm(data=text2\n",
+    "                                          ,nb_word_per_cluster=nb_word_per_cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(clusters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "text: Cola 1.5L Carrefour\n",
+      "cluster indice: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "index = 0\n",
+    "cluster_predicted = BitermModel.get_text_topic(index=index)\n",
+    "\n",
+    "print(\"text:\", text[index])\n",
+    "print(\"cluster indice:\", cluster_predicted)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 1bfd05e2dc958d576d32aa39088716ab89b921bd Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 14:22:09 +0200
Subject: [PATCH 208/496] add unit test

---
 nautilus_nlp/models/biterm_model.py        | 47 ++++++++-----
 notebooks/5. Topic Modeling - Biterm.ipynb | 75 +++++++++++----------
 tests/biterm_testing.py                    | 77 ++++++++++++++++++++++
 3 files changed, 149 insertions(+), 50 deletions(-)
 create mode 100644 tests/biterm_testing.py

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index 89d37a0..d366f24 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -6,6 +6,23 @@
 
 class BitermModel:
 
+    @staticmethod
+    def is_int_positive(number):
+        if type(number) != int:
+            raise ValueError("Parameter {} has to be an integer".format(number))
+        if number < 1:
+            raise ValueError("Parameter {} has to be positive".format(number))
+
+    @staticmethod
+    def is_list_of_string(data):
+        if type(data) != list:
+            raise ValueError("{} has to be a list".format(data))
+        if len(data) == 0:
+            raise ValueError("{} is empty".format(data))
+        for document in data:
+            if type(document) != str:
+                raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
+
     def __init__(self, data, nb_topics, nb_iteration, lang='english'):
         """
         Model for topic modelling
@@ -15,34 +32,32 @@ def __init__(self, data, nb_topics, nb_iteration, lang='english'):
         :param nb_iteration: positive int
         :param lang: str, language to remove the stop words
         """
+
+        self.is_int_positive(nb_topics)
+        self.is_int_positive(nb_iteration)
+        self.is_list_of_string(data)
+
         self.data = data
         self.nb_topics = nb_topics
         self.nb_iteration = nb_iteration
         self.lang = lang
-        self.btm = None
         self.topics = None
-        self.vocab = None
-        self.X = None
 
-    def pre_processing(self, data):
+    def get_clusters(self, nb_word_per_cluster):
         vec = CountVectorizer(stop_words=self.lang)
-        X = vec.fit_transform(data).toarray()
+        X = vec.fit_transform(self.data).toarray()
         vocab = np.array(vec.get_feature_names())
 
-        return X, vocab
-
-    def train_biterm_model(self):
-        X, vocab = self.pre_processing(self.data)
-
         biterms = vec_to_biterms(X)
-        self.btm = oBTM(num_topics=self.nb_topics, V=vocab)
-        self.topics = self.btm.fit_transform(biterms, iterations=self.nb_iteration)
+        btm = oBTM(num_topics=self.nb_topics, V=vocab)
+        self.topics = btm.fit_transform(biterms, iterations=self.nb_iteration)
 
-    def get_cluster_biterm(self, data, nb_word_per_cluster):
-        X, vocab = self.pre_processing(data)
-        results = topic_summuary(self.btm.phi_wz.T, X, vocab, nb_word_per_cluster, verbose=False)
+        results = topic_summuary(btm.phi_wz.T, X, vocab, nb_word_per_cluster, verbose=False)
 
         return results
 
-    def get_text_topic(self, index):
+    def get_document_topic(self, index):
+        if self.topics is None:
+            raise ValueError("Model needs to be trained first")
+
         return self.topics[index].argmax()
diff --git a/notebooks/5. Topic Modeling - Biterm.ipynb b/notebooks/5. Topic Modeling - Biterm.ipynb
index 0a519e0..fcde300 100644
--- a/notebooks/5. Topic Modeling - Biterm.ipynb	
+++ b/notebooks/5. Topic Modeling - Biterm.ipynb	
@@ -9,11 +9,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from nautilus_nlp.models.biterm_model import BiterModel\n",
+    "from nautilus_nlp.models.biterm_model import BitermModel\n",
     "import pandas as pd"
    ]
   },
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,12 +42,7 @@
     " 'De Cecco - Fusilli bio',\n",
     " 'Gerblé sans Gluten50g\\n',\n",
     " 'Penne de riz 100g sans gluten',\n",
-    " 'Spaghetti de maïs 50g sans Glute']\n",
-    "\n",
-    "text2 = ['De Cecco - Fusilli bio',\n",
-    " 'Gerblé sans Gluten50g\\n',\n",
-    " 'Penne de riz 100g sans gluten',\n",
-    " 'Spaghetti de maïs 50g sans Glute']"
+    " 'Spaghetti de maïs 50g sans Glute']\n"
    ]
   },
   {
@@ -59,25 +54,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 100/100 [00:00<00:00, 109.64it/s]\n"
-     ]
-    },
     {
      "ename": "TypeError",
-     "evalue": "get_cluster_biterm() got an unexpected keyword argument 'data'",
+     "evalue": "'BitermModel' object is not callable",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-25-d9e10b244579>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m clusters = BitermModel.get_cluster_biterm(data=text\n\u001b[0;32m---> 14\u001b[0;31m                                           ,nb_word_per_cluster=nb_word_per_cluster)\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: get_cluster_biterm() got an unexpected keyword argument 'data'"
+      "\u001b[0;32m<ipython-input-8-0a7a42618657>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m                            \u001b[0;34m,\u001b[0m \u001b[0mnb_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_topics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                            \u001b[0;34m,\u001b[0m \u001b[0mnb_iteration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m                            , lang='english')\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mBitermModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_biterm_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'BitermModel' object is not callable"
      ]
     }
    ],
@@ -87,37 +75,49 @@
     "nb_iteration = 100\n",
     "language = 'english'\n",
     "\n",
-    "BitermModel = biterm_model(data=text\n",
+    "biterm_model = BitermModel(data=text\n",
     "                           , nb_topics=nb_topics\n",
     "                           , nb_iteration=nb_iteration\n",
-    "                           , lang='english')\n",
-    "\n",
-    "BitermModel.train_biterm_model()\n",
+    "                           , lang=language)\n",
     "\n",
-    "clusters = BitermModel.get_cluster_biterm(data=text2\n",
-    "                                          ,nb_word_per_cluster=nb_word_per_cluster)"
+    "clusters = biterm_model.get_clusters(nb_word_per_cluster)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'clusters' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-04f384659793>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclusters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'clusters' is not defined"
+     ]
+    }
+   ],
    "source": [
     "pd.DataFrame(clusters)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text: Cola 1.5L Carrefour\n",
-      "cluster indice: 3\n"
+     "ename": "TypeError",
+     "evalue": "get_text_topic() missing 1 required positional argument: 'self'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-9cb3dc9a51fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcluster_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBitermModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_topic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cluster indice:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcluster_predicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: get_text_topic() missing 1 required positional argument: 'self'"
      ]
     }
    ],
@@ -136,6 +136,13 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/tests/biterm_testing.py b/tests/biterm_testing.py
new file mode 100644
index 0000000..a397765
--- /dev/null
+++ b/tests/biterm_testing.py
@@ -0,0 +1,77 @@
+from nautilus_nlp.models.biterm_model import BitermModel
+import pandas as pd
+import pytest
+
+
+class UnitTestBiterm:
+
+    def __init__(self):
+        self.text = ['Cola 1.5L Carrefour',
+                     'Pepsi Cola Light 1.5L',
+                     'Pepsi Cola Twist Light',
+                     'Cola 1.5L CRF DISC',
+                     'Coca-Cola Light 1.5L',
+                     'Coca-Cola Light 4x0.5L',
+                     'Coca-Cola Light 6x0.3L',
+                     'Panzani 200g x 4 bio',
+                     'Rustichella 150g bio',
+                     'De Cecco - Fusilli bio',
+                     'Gerblé sans Gluten50g',
+                     'Penne de riz 100g sans gluten',
+                     'Spaghetti de maïs 50g sans Glute']
+
+        self.nb_topics = 5
+        self.nb_word_per_cluster = 5
+        self.nb_iteration = 100
+        self.language = 'english'
+
+    def run_tests(self):
+        self.test_number_topic_correct()
+        for element in ["de", [], 4.5, (23, 24)]:
+            self.test_number_iteration_non_int(element)
+            self.test_number_topic_non_int(element)
+            self.test_data_input(element)
+
+        self.test_number_iteration_negative(-5)
+        self.test_number_topic_negative(-5)
+        self.test_data_input(["def", 3])
+        self.test_data_input(3)
+        self.test_no_initialisation()
+
+    def test_number_topic_correct(self):
+        biterm_model = BitermModel(data=self.text
+                                   , nb_topics=self.nb_topics
+                                   , nb_iteration=self.nb_iteration
+                                   , lang=self.language)
+        clusters = biterm_model.get_clusters(nb_word_per_cluster=self.nb_word_per_cluster)
+        assert len(pd.DataFrame(clusters)) == 5
+
+    def test_number_topic_negative(self, nb_topics):
+        with pytest.raises(ValueError):
+            BitermModel(data=self.text, nb_topics=nb_topics, nb_iteration=self.nb_iteration, lang=self.language)
+
+    def test_number_topic_non_int(self, nb_topics):
+        with pytest.raises(ValueError):
+            BitermModel(data=self.text, nb_topics=nb_topics, nb_iteration=self.nb_iteration, lang=self.language)
+
+    def test_number_iteration_negative(self, nb_iteration):
+        with pytest.raises(ValueError):
+            BitermModel(data=self.text, nb_topics=self.nb_topics, nb_iteration=nb_iteration, lang=self.language)
+
+    def test_number_iteration_non_int(self, nb_iteration):
+        with pytest.raises(ValueError):
+            BitermModel(data=self.text, nb_topics=self.nb_topics, nb_iteration=nb_iteration, lang=self.language)
+
+    def test_data_input(self, data):
+        with pytest.raises(ValueError):
+            BitermModel(data=data, nb_topics=self.nb_topics, nb_iteration=self.nb_iteration, lang=self.language)
+
+    def test_no_initialisation(self):
+        with pytest.raises(ValueError):
+            biter_model = BitermModel(data=self.text, nb_topics=self.nb_topics, nb_iteration=self.nb_iteration,
+                                      lang=self.language)
+            biter_model.get_document_topic(2)
+
+
+unit_test = UnitTestBiterm()
+unit_test.run_tests()

From e0eb0cec53ae986dc4c3a3bdfd40f16a962fc6e8 Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 14:24:52 +0200
Subject: [PATCH 209/496] fix bug variable naming

---
 notebooks/5. Topic Modeling - Biterm.ipynb | 112 +++++++++++++++------
 1 file changed, 81 insertions(+), 31 deletions(-)

diff --git a/notebooks/5. Topic Modeling - Biterm.ipynb b/notebooks/5. Topic Modeling - Biterm.ipynb
index fcde300..c92dc18 100644
--- a/notebooks/5. Topic Modeling - Biterm.ipynb	
+++ b/notebooks/5. Topic Modeling - Biterm.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,18 +54,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "'BitermModel' object is not callable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-0a7a42618657>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m                            \u001b[0;34m,\u001b[0m \u001b[0mnb_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_topics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                            \u001b[0;34m,\u001b[0m \u001b[0mnb_iteration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m                            , lang='english')\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mBitermModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_biterm_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'BitermModel' object is not callable"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:00<00:00, 101.68it/s]\n"
      ]
     }
    ],
@@ -85,19 +81,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'clusters' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-04f384659793>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclusters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'clusters' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coherence</th>\n",
+       "      <th>top_words</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-5.631692</td>\n",
+       "      <td>[5l, cola, disc, crf, carrefour]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-4.350759</td>\n",
+       "      <td>[light, cola, coca, 5l, pepsi]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.635635</td>\n",
+       "      <td>[100g, sans, riz, penne, gluten]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.537023</td>\n",
+       "      <td>[sans, spaghetti, 50g, maïs, glute]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.235566</td>\n",
+       "      <td>[bio, 150g, rustichella, 200g, panzani]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   coherence                                top_words\n",
+       "0  -5.631692         [5l, cola, disc, crf, carrefour]\n",
+       "1  -4.350759           [light, cola, coca, 5l, pepsi]\n",
+       "2   3.635635         [100g, sans, riz, penne, gluten]\n",
+       "3   2.537023      [sans, spaghetti, 50g, maïs, glute]\n",
+       "4  -0.235566  [bio, 150g, rustichella, 200g, panzani]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -106,24 +159,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "get_text_topic() missing 1 required positional argument: 'self'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-9cb3dc9a51fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcluster_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBitermModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_topic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cluster indice:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcluster_predicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: get_text_topic() missing 1 required positional argument: 'self'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "text: Cola 1.5L Carrefour\n",
+      "cluster indice: 0\n"
      ]
     }
    ],
    "source": [
     "index = 0\n",
-    "cluster_predicted = BitermModel.get_text_topic(index=index)\n",
+    "cluster_predicted = biterm_model.get_document_topic(index=index)\n",
     "\n",
     "print(\"text:\", text[index])\n",
     "print(\"cluster indice:\", cluster_predicted)"

From cf1c71c2e74a936797eef188fca9de6b0c0d31e3 Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 14:58:31 +0200
Subject: [PATCH 210/496] just nicer code

---
 nautilus_nlp/models/biterm_model.py        | 34 +++++++++++-----------
 notebooks/5. Topic Modeling - Biterm.ipynb |  2 +-
 2 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index d366f24..550b718 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -6,23 +6,6 @@
 
 class BitermModel:
 
-    @staticmethod
-    def is_int_positive(number):
-        if type(number) != int:
-            raise ValueError("Parameter {} has to be an integer".format(number))
-        if number < 1:
-            raise ValueError("Parameter {} has to be positive".format(number))
-
-    @staticmethod
-    def is_list_of_string(data):
-        if type(data) != list:
-            raise ValueError("{} has to be a list".format(data))
-        if len(data) == 0:
-            raise ValueError("{} is empty".format(data))
-        for document in data:
-            if type(document) != str:
-                raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
-
     def __init__(self, data, nb_topics, nb_iteration, lang='english'):
         """
         Model for topic modelling
@@ -43,6 +26,23 @@ def __init__(self, data, nb_topics, nb_iteration, lang='english'):
         self.lang = lang
         self.topics = None
 
+    @staticmethod
+    def is_int_positive(number):
+        if type(number) != int:
+            raise ValueError("Parameter {} has to be an integer".format(number))
+        if number < 1:
+            raise ValueError("Parameter {} has to be positive".format(number))
+
+    @staticmethod
+    def is_list_of_string(data):
+        if type(data) != list:
+            raise ValueError("{} has to be a list".format(data))
+        if len(data) == 0:
+            raise ValueError("{} is empty".format(data))
+        for document in data:
+            if type(document) != str:
+                raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
+
     def get_clusters(self, nb_word_per_cluster):
         vec = CountVectorizer(stop_words=self.lang)
         X = vec.fit_transform(self.data).toarray()
diff --git a/notebooks/5. Topic Modeling - Biterm.ipynb b/notebooks/5. Topic Modeling - Biterm.ipynb
index c92dc18..1c0c102 100644
--- a/notebooks/5. Topic Modeling - Biterm.ipynb	
+++ b/notebooks/5. Topic Modeling - Biterm.ipynb	
@@ -220,7 +220,7 @@
    "version": "3.7.1"
   },
   "toc": {
-   "base_numbering": 1,
+   "base_numbering": 1.0,
    "nav_menu": {},
    "number_sections": true,
    "sideBar": true,

From a59cb7e4ca75abc2bc80468e23ba26a97274a2eb Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 15:41:40 +0200
Subject: [PATCH 211/496] add save_pyLDAvis_plot function

---
 nautilus_nlp/models/biterm_model.py | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index 550b718..1c8a248 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -2,6 +2,7 @@
 from biterm.btm import oBTM
 from biterm.utility import vec_to_biterms, topic_summuary
 from sklearn.feature_extraction.text import CountVectorizer
+import pyLDAvis
 
 
 class BitermModel:
@@ -25,6 +26,9 @@ def __init__(self, data, nb_topics, nb_iteration, lang='english'):
         self.nb_iteration = nb_iteration
         self.lang = lang
         self.topics = None
+        self.btm = None
+        self.X = None
+        self.vocab = None
 
     @staticmethod
     def is_int_positive(number):
@@ -45,14 +49,14 @@ def is_list_of_string(data):
 
     def get_clusters(self, nb_word_per_cluster):
         vec = CountVectorizer(stop_words=self.lang)
-        X = vec.fit_transform(self.data).toarray()
-        vocab = np.array(vec.get_feature_names())
+        self.X = vec.fit_transform(self.data).toarray()
+        self.vocab = np.array(vec.get_feature_names())
 
-        biterms = vec_to_biterms(X)
-        btm = oBTM(num_topics=self.nb_topics, V=vocab)
-        self.topics = btm.fit_transform(biterms, iterations=self.nb_iteration)
+        biterms = vec_to_biterms(self.X)
+        self.btm = oBTM(num_topics=self.nb_topics, V=self.vocab)
+        self.topics = self.btm.fit_transform(biterms, iterations=self.nb_iteration)
 
-        results = topic_summuary(btm.phi_wz.T, X, vocab, nb_word_per_cluster, verbose=False)
+        results = topic_summuary(self.btm.phi_wz.T, self.X, self.vocab, nb_word_per_cluster, verbose=False)
 
         return results
 
@@ -61,3 +65,11 @@ def get_document_topic(self, index):
             raise ValueError("Model needs to be trained first")
 
         return self.topics[index].argmax()
+
+    def save_pyLDAvis_plot(self, path_to_output='./plot.html'):
+        if self.topics is None or self.btm is None or self.X is None or self.vocab is None:
+            raise ValueError("Model needs to be trained first")
+
+        vis = pyLDAvis.prepare(self.btm.phi_wz.T, self.topics, np.count_nonzero(self.X, axis=1), self.vocab,
+                               np.sum(self.X, axis=0))
+        pyLDAvis.save_html(vis, path_to_output)

From f0fcaf329ada179d56b88ee7b877fa3d69cff4a8 Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 15:50:20 +0200
Subject: [PATCH 212/496] add some comments, more information

---
 nautilus_nlp/models/biterm_model.py        |  4 +-
 notebooks/5. Topic Modeling - Biterm.ipynb | 82 +++++++++++++++-------
 2 files changed, 57 insertions(+), 29 deletions(-)

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index 1c8a248..41a8f2f 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -7,14 +7,14 @@
 
 class BitermModel:
 
-    def __init__(self, data, nb_topics, nb_iteration, lang='english'):
+    def __init__(self, data, nb_topics, nb_iteration, lang):
         """
         Model for topic modelling
         Particularly useful for short texts
         :param data: a list of string, each string can be a document
         :param nb_topics: positive int
         :param nb_iteration: positive int
-        :param lang: str, language to remove the stop words
+        :param lang: str, language to remove the stop words, can be setup to None
         """
 
         self.is_int_positive(nb_topics)
diff --git a/notebooks/5. Topic Modeling - Biterm.ipynb b/notebooks/5. Topic Modeling - Biterm.ipynb
index 1c0c102..f959ecc 100644
--- a/notebooks/5. Topic Modeling - Biterm.ipynb	
+++ b/notebooks/5. Topic Modeling - Biterm.ipynb	
@@ -26,30 +26,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Input of the model has to be a list of string/documents\n",
     "text = ['Cola 1.5L Carrefour',\n",
     " 'Pepsi Cola Light 1.5L',\n",
-    " 'Pepsi Cola Twist Light\\n',\n",
+    " 'Pepsi Cola Twist Light',\n",
     " 'Cola 1.5L CRF DISC',\n",
     " 'Coca-Cola Light 1.5L',\n",
     " 'Coca-Cola Light 4x0.5L',\n",
-    " 'Coca-Cola Light 6x0.3L\\n',\n",
+    " 'Coca-Cola Light 6x0.3L',\n",
     " 'Panzani 200g x 4 bio',\n",
     " 'Rustichella 150g bio',\n",
     " 'De Cecco - Fusilli bio',\n",
-    " 'Gerblé sans Gluten50g\\n',\n",
+    " 'Gerblé sans Gluten50g',\n",
     " 'Penne de riz 100g sans gluten',\n",
-    " 'Spaghetti de maïs 50g sans Glute']\n"
+    " 'Spaghetti de maïs 50g sans Glute']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Step 2: Prediction"
+    "### Step 2: Topic Modelling"
    ]
   },
   {
@@ -61,7 +62,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 100/100 [00:00<00:00, 101.68it/s]\n"
+      "100%|██████████| 100/100 [00:00<00:00, 123.16it/s]\n"
      ]
     }
    ],
@@ -69,6 +70,7 @@
     "nb_topics = 5\n",
     "nb_word_per_cluster = 5\n",
     "nb_iteration = 100\n",
+    "# Based on sklearn to remove the stop words, can be set up to None\n",
     "language = 'english'\n",
     "\n",
     "biterm_model = BitermModel(data=text\n",
@@ -112,18 +114,18 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-5.631692</td>\n",
-       "      <td>[5l, cola, disc, crf, carrefour]</td>\n",
+       "      <td>3.635635</td>\n",
+       "      <td>[100g, sans, riz, penne, gluten]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-4.350759</td>\n",
-       "      <td>[light, cola, coca, 5l, pepsi]</td>\n",
+       "      <td>-2.076344</td>\n",
+       "      <td>[disc, 5l, cola, crf, carrefour]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3.635635</td>\n",
-       "      <td>[100g, sans, riz, penne, gluten]</td>\n",
+       "      <td>-0.235566</td>\n",
+       "      <td>[bio, 150g, rustichella, 200g, panzani]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -132,8 +134,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.235566</td>\n",
-       "      <td>[bio, 150g, rustichella, 200g, panzani]</td>\n",
+       "      <td>-5.198056</td>\n",
+       "      <td>[cola, light, 5l, coca, pepsi]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -141,11 +143,11 @@
       ],
       "text/plain": [
        "   coherence                                top_words\n",
-       "0  -5.631692         [5l, cola, disc, crf, carrefour]\n",
-       "1  -4.350759           [light, cola, coca, 5l, pepsi]\n",
-       "2   3.635635         [100g, sans, riz, penne, gluten]\n",
+       "0   3.635635         [100g, sans, riz, penne, gluten]\n",
+       "1  -2.076344         [disc, 5l, cola, crf, carrefour]\n",
+       "2  -0.235566  [bio, 150g, rustichella, 200g, panzani]\n",
        "3   2.537023      [sans, spaghetti, 50g, maïs, glute]\n",
-       "4  -0.235566  [bio, 150g, rustichella, 200g, panzani]"
+       "4  -5.198056           [cola, light, 5l, coca, pepsi]"
       ]
      },
      "execution_count": 4,
@@ -159,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -167,24 +169,50 @@
      "output_type": "stream",
      "text": [
       "text: Cola 1.5L Carrefour\n",
-      "cluster indice: 0\n"
+      "cluster indice: 1\n"
      ]
     }
    ],
    "source": [
+    "# To get the cluster index of one document\n",
     "index = 0\n",
-    "cluster_predicted = biterm_model.get_document_topic(index=index)\n",
+    "cluster_index = biterm_model.get_document_topic(index=index)\n",
     "\n",
     "print(\"text:\", text[index])\n",
-    "print(\"cluster indice:\", cluster_predicted)"
+    "print(\"cluster index:\", cluster_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3: Viz"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_path = './plot_biterm_html'\n",
+    "biterm_model.save_pyLDAvis_plot()"
+   ]
   },
   {
    "cell_type": "code",
@@ -220,7 +248,7 @@
    "version": "3.7.1"
   },
   "toc": {
-   "base_numbering": 1.0,
+   "base_numbering": 1,
    "nav_menu": {},
    "number_sections": true,
    "sideBar": true,

From 0e4fda6fbd859c99cb1abeb5eb23eca1c2bed290 Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Fri, 7 Jun 2019 15:51:54 +0200
Subject: [PATCH 213/496] change name unit test file

---
 tests/{biterm_testing.py => test_biterm.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename tests/{biterm_testing.py => test_biterm.py} (100%)

diff --git a/tests/biterm_testing.py b/tests/test_biterm.py
similarity index 100%
rename from tests/biterm_testing.py
rename to tests/test_biterm.py

From 164474a132c4a53df83a6e5d76a6e980b216279a Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Mon, 10 Jun 2019 13:11:51 +0200
Subject: [PATCH 214/496] change naming

---
 nautilus_nlp/models/biterm_model.py     | 52 +++++++++++++++----------
 tests/{test_biterm.py => biterm_try.py} |  2 +-
 2 files changed, 32 insertions(+), 22 deletions(-)
 rename tests/{test_biterm.py => biterm_try.py} (97%)

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index 41a8f2f..de9bc0f 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -14,7 +14,7 @@ def __init__(self, data, nb_topics, nb_iteration, lang):
         :param data: a list of string, each string can be a document
         :param nb_topics: positive int
         :param nb_iteration: positive int
-        :param lang: str, language to remove the stop words, can be setup to None
+        :param lang: str, _language to remove the stop words, can be setup to None
         """
 
         self.is_int_positive(nb_topics)
@@ -25,51 +25,61 @@ def __init__(self, data, nb_topics, nb_iteration, lang):
         self.nb_topics = nb_topics
         self.nb_iteration = nb_iteration
         self.lang = lang
-        self.topics = None
-        self.btm = None
-        self.X = None
-        self.vocab = None
+        self._topics = None
+        self._btm = None
+        self._X = None
+        self._vocab = None
 
     @staticmethod
     def is_int_positive(number):
-        if type(number) != int:
+        """
+        Function to check if the input parameter is a integer and positive otherwise raise an error
+        :param number:
+        :return:
+        """
+        if not isinstance(number, int):
             raise ValueError("Parameter {} has to be an integer".format(number))
         if number < 1:
             raise ValueError("Parameter {} has to be positive".format(number))
 
     @staticmethod
     def is_list_of_string(data):
-        if type(data) != list:
+        """
+        Function to check if the input parameter is a list of strings otherwise raise an error
+        :param data:
+        :return:
+        """
+        if not isinstance(data, list):
             raise ValueError("{} has to be a list".format(data))
         if len(data) == 0:
             raise ValueError("{} is empty".format(data))
         for document in data:
-            if type(document) != str:
+            if not isinstance(document, str):
                 raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
 
-    def get_clusters(self, nb_word_per_cluster):
+    def compute_topics(self, nb_word_per_cluster):
         vec = CountVectorizer(stop_words=self.lang)
-        self.X = vec.fit_transform(self.data).toarray()
-        self.vocab = np.array(vec.get_feature_names())
+        self._X = vec.fit_transform(self.data).toarray()
+        self._vocab = np.array(vec.get_feature_names())
 
-        biterms = vec_to_biterms(self.X)
-        self.btm = oBTM(num_topics=self.nb_topics, V=self.vocab)
-        self.topics = self.btm.fit_transform(biterms, iterations=self.nb_iteration)
+        biterms = vec_to_biterms(self._X)
+        self._btm = oBTM(num_topics=self.nb_topics, V=self._vocab)
+        self._topics = self._btm.fit_transform(biterms, iterations=self.nb_iteration)
 
-        results = topic_summuary(self.btm.phi_wz.T, self.X, self.vocab, nb_word_per_cluster, verbose=False)
+        results = topic_summuary(self._btm.phi_wz.T, self._X, self._vocab, nb_word_per_cluster, verbose=False)
 
         return results
 
     def get_document_topic(self, index):
-        if self.topics is None:
+        if self._topics is None:
             raise ValueError("Model needs to be trained first")
 
-        return self.topics[index].argmax()
+        return self._topics[index].argmax()
 
-    def save_pyLDAvis_plot(self, path_to_output='./plot.html'):
-        if self.topics is None or self.btm is None or self.X is None or self.vocab is None:
+    def save_pyLDAvis_plot(self, path_to_output='./biterm_pyLDAavis_plot.html'):
+        if self._topics is None or self._btm is None or self._X is None or self._vocab is None:
             raise ValueError("Model needs to be trained first")
 
-        vis = pyLDAvis.prepare(self.btm.phi_wz.T, self.topics, np.count_nonzero(self.X, axis=1), self.vocab,
-                               np.sum(self.X, axis=0))
+        vis = pyLDAvis.prepare(self._btm.phi_wz.T, self._topics, np.count_nonzero(self._X, axis=1), self._vocab,
+                               np.sum(self._X, axis=0))
         pyLDAvis.save_html(vis, path_to_output)
diff --git a/tests/test_biterm.py b/tests/biterm_try.py
similarity index 97%
rename from tests/test_biterm.py
rename to tests/biterm_try.py
index a397765..904959f 100644
--- a/tests/test_biterm.py
+++ b/tests/biterm_try.py
@@ -43,7 +43,7 @@ def test_number_topic_correct(self):
                                    , nb_topics=self.nb_topics
                                    , nb_iteration=self.nb_iteration
                                    , lang=self.language)
-        clusters = biterm_model.get_clusters(nb_word_per_cluster=self.nb_word_per_cluster)
+        clusters = biterm_model.compute_topics(nb_word_per_cluster=self.nb_word_per_cluster)
         assert len(pd.DataFrame(clusters)) == 5
 
     def test_number_topic_negative(self, nb_topics):

From 1d149f0e1cb59038add20e82c406b0bf71357eb8 Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Mon, 10 Jun 2019 13:14:05 +0200
Subject: [PATCH 215/496] change naming v2

---
 nautilus_nlp/models/biterm_model.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index de9bc0f..df4078f 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -27,8 +27,8 @@ def __init__(self, data, nb_topics, nb_iteration, lang):
         self.lang = lang
         self._topics = None
         self._btm = None
-        self._X = None
-        self._vocab = None
+        self._vectorize_text = None
+        self._vocabulary = None
 
     @staticmethod
     def is_int_positive(number):
@@ -59,14 +59,14 @@ def is_list_of_string(data):
 
     def compute_topics(self, nb_word_per_cluster):
         vec = CountVectorizer(stop_words=self.lang)
-        self._X = vec.fit_transform(self.data).toarray()
-        self._vocab = np.array(vec.get_feature_names())
+        self._vectorize_text = vec.fit_transform(self.data).toarray()
+        self._vocabulary = np.array(vec.get_feature_names())
 
-        biterms = vec_to_biterms(self._X)
-        self._btm = oBTM(num_topics=self.nb_topics, V=self._vocab)
+        biterms = vec_to_biterms(self._vectorize_text)
+        self._btm = oBTM(num_topics=self.nb_topics, V=self._vocabulary)
         self._topics = self._btm.fit_transform(biterms, iterations=self.nb_iteration)
 
-        results = topic_summuary(self._btm.phi_wz.T, self._X, self._vocab, nb_word_per_cluster, verbose=False)
+        results = topic_summuary(self._btm.phi_wz.T, self._vectorize_text, self._vocabulary, nb_word_per_cluster, verbose=False)
 
         return results
 
@@ -77,9 +77,9 @@ def get_document_topic(self, index):
         return self._topics[index].argmax()
 
     def save_pyLDAvis_plot(self, path_to_output='./biterm_pyLDAavis_plot.html'):
-        if self._topics is None or self._btm is None or self._X is None or self._vocab is None:
+        if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
             raise ValueError("Model needs to be trained first")
 
-        vis = pyLDAvis.prepare(self._btm.phi_wz.T, self._topics, np.count_nonzero(self._X, axis=1), self._vocab,
-                               np.sum(self._X, axis=0))
+        vis = pyLDAvis.prepare(self._btm.phi_wz.T, self._topics, np.count_nonzero(self._vectorize_text, axis=1), self._vocabulary,
+                               np.sum(self._vectorize_text, axis=0))
         pyLDAvis.save_html(vis, path_to_output)

From b1f1ad7d00e31b34b74abce5586e2e05411e7fda Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Mon, 10 Jun 2019 13:21:24 +0200
Subject: [PATCH 216/496] add doc strings to functions'

---
 nautilus_nlp/models/biterm_model.py        | 17 ++++++++++++++++-
 notebooks/5. Topic Modeling - Biterm.ipynb |  4 ++--
 2 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index df4078f..dd53eb1 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -58,6 +58,11 @@ def is_list_of_string(data):
                 raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
 
     def compute_topics(self, nb_word_per_cluster):
+        """
+        Main function computing the topic modeling, topics
+        :param nb_word_per_cluster: positive integer
+        :return: a dictionary containing the the different topics with the top words and coherence associated
+        """
         vec = CountVectorizer(stop_words=self.lang)
         self._vectorize_text = vec.fit_transform(self.data).toarray()
         self._vocabulary = np.array(vec.get_feature_names())
@@ -71,12 +76,22 @@ def compute_topics(self, nb_word_per_cluster):
         return results
 
     def get_document_topic(self, index):
+        """
+        Get the cluster associated to the specified document
+        :param index: the document index, positive integer
+        :return: the cluster index
+        """
         if self._topics is None:
             raise ValueError("Model needs to be trained first")
 
         return self._topics[index].argmax()
 
-    def save_pyLDAvis_plot(self, path_to_output='./biterm_pyLDAavis_plot.html'):
+    def save_pyLDAvis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.html'):
+        """
+        Function saving the pyLDAvis plot associated with the compute_topics function
+        :param path_to_output: path to save the plut, must be a html file
+        :return:
+        """
         if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
             raise ValueError("Model needs to be trained first")
 
diff --git a/notebooks/5. Topic Modeling - Biterm.ipynb b/notebooks/5. Topic Modeling - Biterm.ipynb
index f959ecc..243d4a5 100644
--- a/notebooks/5. Topic Modeling - Biterm.ipynb	
+++ b/notebooks/5. Topic Modeling - Biterm.ipynb	
@@ -210,8 +210,8 @@
     }
    ],
    "source": [
-    "output_path = './plot_biterm_html'\n",
-    "biterm_model.save_pyLDAvis_plot()"
+    "output_path = './biterm_pyLDAavis_plot.html'\n",
+    "biterm_model.save_pyLDAvis_plot_as_html(output_path)"
    ]
   },
   {

From 2408a8444e8fe1632e1f7aa550adcbc7757f036c Mon Sep 17 00:00:00 2001
From: pymousse <py.mousset@hotmail.fr>
Date: Mon, 10 Jun 2019 14:16:17 +0200
Subject: [PATCH 217/496] refacto unit test

---
 tests/biterm_try.py  | 77 --------------------------------------------
 tests/test_biterm.py | 68 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 68 insertions(+), 77 deletions(-)
 delete mode 100644 tests/biterm_try.py
 create mode 100644 tests/test_biterm.py

diff --git a/tests/biterm_try.py b/tests/biterm_try.py
deleted file mode 100644
index 904959f..0000000
--- a/tests/biterm_try.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from nautilus_nlp.models.biterm_model import BitermModel
-import pandas as pd
-import pytest
-
-
-class UnitTestBiterm:
-
-    def __init__(self):
-        self.text = ['Cola 1.5L Carrefour',
-                     'Pepsi Cola Light 1.5L',
-                     'Pepsi Cola Twist Light',
-                     'Cola 1.5L CRF DISC',
-                     'Coca-Cola Light 1.5L',
-                     'Coca-Cola Light 4x0.5L',
-                     'Coca-Cola Light 6x0.3L',
-                     'Panzani 200g x 4 bio',
-                     'Rustichella 150g bio',
-                     'De Cecco - Fusilli bio',
-                     'Gerblé sans Gluten50g',
-                     'Penne de riz 100g sans gluten',
-                     'Spaghetti de maïs 50g sans Glute']
-
-        self.nb_topics = 5
-        self.nb_word_per_cluster = 5
-        self.nb_iteration = 100
-        self.language = 'english'
-
-    def run_tests(self):
-        self.test_number_topic_correct()
-        for element in ["de", [], 4.5, (23, 24)]:
-            self.test_number_iteration_non_int(element)
-            self.test_number_topic_non_int(element)
-            self.test_data_input(element)
-
-        self.test_number_iteration_negative(-5)
-        self.test_number_topic_negative(-5)
-        self.test_data_input(["def", 3])
-        self.test_data_input(3)
-        self.test_no_initialisation()
-
-    def test_number_topic_correct(self):
-        biterm_model = BitermModel(data=self.text
-                                   , nb_topics=self.nb_topics
-                                   , nb_iteration=self.nb_iteration
-                                   , lang=self.language)
-        clusters = biterm_model.compute_topics(nb_word_per_cluster=self.nb_word_per_cluster)
-        assert len(pd.DataFrame(clusters)) == 5
-
-    def test_number_topic_negative(self, nb_topics):
-        with pytest.raises(ValueError):
-            BitermModel(data=self.text, nb_topics=nb_topics, nb_iteration=self.nb_iteration, lang=self.language)
-
-    def test_number_topic_non_int(self, nb_topics):
-        with pytest.raises(ValueError):
-            BitermModel(data=self.text, nb_topics=nb_topics, nb_iteration=self.nb_iteration, lang=self.language)
-
-    def test_number_iteration_negative(self, nb_iteration):
-        with pytest.raises(ValueError):
-            BitermModel(data=self.text, nb_topics=self.nb_topics, nb_iteration=nb_iteration, lang=self.language)
-
-    def test_number_iteration_non_int(self, nb_iteration):
-        with pytest.raises(ValueError):
-            BitermModel(data=self.text, nb_topics=self.nb_topics, nb_iteration=nb_iteration, lang=self.language)
-
-    def test_data_input(self, data):
-        with pytest.raises(ValueError):
-            BitermModel(data=data, nb_topics=self.nb_topics, nb_iteration=self.nb_iteration, lang=self.language)
-
-    def test_no_initialisation(self):
-        with pytest.raises(ValueError):
-            biter_model = BitermModel(data=self.text, nb_topics=self.nb_topics, nb_iteration=self.nb_iteration,
-                                      lang=self.language)
-            biter_model.get_document_topic(2)
-
-
-unit_test = UnitTestBiterm()
-unit_test.run_tests()
diff --git a/tests/test_biterm.py b/tests/test_biterm.py
new file mode 100644
index 0000000..04bd067
--- /dev/null
+++ b/tests/test_biterm.py
@@ -0,0 +1,68 @@
+from nautilus_nlp.models.biterm_model import BitermModel
+import pandas as pd
+import pytest
+
+text = ['Cola 1.5L Carrefour',
+        'Pepsi Cola Light 1.5L',
+        'Pepsi Cola Twist Light',
+        'Cola 1.5L CRF DISC',
+        'Coca-Cola Light 1.5L',
+        'Coca-Cola Light 4x0.5L',
+        'Coca-Cola Light 6x0.3L',
+        'Panzani 200g x 4 bio',
+        'Rustichella 150g bio',
+        'De Cecco - Fusilli bio',
+        'Gerblé sans Gluten50g',
+        'Penne de riz 100g sans gluten',
+        'Spaghetti de maïs 50g sans Glute']
+
+nb_topics = 5
+nb_word_per_cluster = 5
+nb_iteration = 100
+language = 'english'
+
+
+@pytest.mark.parametrize(
+    "input_text, input_nb_topic , input_nb_iteration , input_language",
+    [
+        (text, -1, nb_iteration, language),
+        (text, "Panzani", nb_iteration, language),
+        (text, 3.4, nb_iteration, language),
+        (text, (3, 5), nb_iteration, language),
+        (text, [3, 5], nb_iteration, language),
+        (text, nb_topics, (2, 4), language),
+        (text, nb_topics, [1, 3], language),
+        (text, nb_topics, -1, language),
+        (text, nb_topics, "Panzani", language),
+        (text, nb_topics, 2.4, language),
+        (3, nb_topics, nb_iteration, language),
+        ("Panzani", nb_topics, nb_iteration, language),
+        (("Panzani", "Rustichella"), nb_topics, nb_iteration, language),
+        ([], nb_topics, nb_iteration, language),
+        (["Panzani", "Rustichella", 3], nb_topics, nb_iteration, language)
+    ]
+)
+def text_input_parameter_error_handling(input_text
+                                        , input_nb_topic
+                                        , input_nb_iteration
+                                        , input_language):
+    with pytest.raises(ValueError):
+        BitermModel(data=input_text, nb_topics=input_nb_topic, nb_iteration=input_nb_iteration, lang=input_language)
+
+
+def test_number_topic_correct():
+    biterm_model = BitermModel(data=text
+                               , nb_topics=nb_topics
+                               , nb_iteration=nb_iteration
+                               , lang=language)
+    clusters = biterm_model.compute_topics(nb_word_per_cluster=nb_word_per_cluster)
+    assert len(pd.DataFrame(clusters)) == nb_topics
+
+
+def test_no_initialisation():
+    with pytest.raises(ValueError):
+        biter_model = BitermModel(data=text
+                                  , nb_topics=nb_topics
+                                  , nb_iteration=nb_iteration,
+                                  lang=language)
+        biter_model.get_document_topic(2)

From ecd3172b6492f8d3306eece652bfa472ad742bd5 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 19 Jun 2019 14:57:12 +0200
Subject: [PATCH 218/496] add logo

---
 README.md                        |   1 +
 references/nautilus_nlp_logo.png | Bin 0 -> 25433 bytes
 2 files changed, 1 insertion(+)
 create mode 100644 references/nautilus_nlp_logo.png

diff --git a/README.md b/README.md
index 5bae6f7..5bfd9cb 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,6 @@
 Nautilus_NLP [![Build Status](https://travis-ci.com/artefactory/nautilus-nlp.svg?token=Ssg4shz5pz9qGnYCybSj&branch=master)](https://travis-ci.com/artefactory/nautilus-nlp)
 ==============================
+![Nautilus-NLP](/references/nautilus_nlp_logo.png)
 
 The Nautilus NLP library aimed to be a meta-library to be used to help you get started on handling your NLP use-case.
 
diff --git a/references/nautilus_nlp_logo.png b/references/nautilus_nlp_logo.png
new file mode 100644
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GIT binary patch
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AI{*Lx

literal 0
HcmV?d00001


From 80eb38dba3c4040bb428f5e0865554affda90dd5 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 19 Jun 2019 15:05:40 +0200
Subject: [PATCH 219/496] harmonize docstring #75

---
 nautilus_nlp/models/biterm_model.py | 85 ++++++++++++++++++++++-------
 1 file changed, 65 insertions(+), 20 deletions(-)

diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index dd53eb1..a30d80f 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -9,14 +9,24 @@ class BitermModel:
 
     def __init__(self, data, nb_topics, nb_iteration, lang):
         """
-        Model for topic modelling
-        Particularly useful for short texts
-        :param data: a list of string, each string can be a document
-        :param nb_topics: positive int
-        :param nb_iteration: positive int
-        :param lang: str, _language to remove the stop words, can be setup to None
-        """
+        Model for topic modelling. Particularly useful for short texts.
+
+        Parameters
+        ----------
+        data : list
+            a list of string, each string can be a document
+        nb_topics : positive int
+
+        nb_iteration : positive int
 
+        lang : str
+            _language to remove the stop words, can be setup to None
+
+        Returns
+        -------
+        string
+            the text with removed multiple spaces and strip text
+        """        
         self.is_int_positive(nb_topics)
         self.is_int_positive(nb_iteration)
         self.is_list_of_string(data)
@@ -33,10 +43,19 @@ def __init__(self, data, nb_topics, nb_iteration, lang):
     @staticmethod
     def is_int_positive(number):
         """
-        Function to check if the input parameter is a integer and positive otherwise raise an error
-        :param number:
-        :return:
-        """
+        Function to check if the input parameter is a integer and positive 
+        otherwise raise an error
+
+        Parameters
+        ----------
+        number : str
+            
+        Returns
+        -------
+        str:
+            the text with removed multiple spaces and strip text
+            
+        """  
         if not isinstance(number, int):
             raise ValueError("Parameter {} has to be an integer".format(number))
         if number < 1:
@@ -46,8 +65,13 @@ def is_int_positive(number):
     def is_list_of_string(data):
         """
         Function to check if the input parameter is a list of strings otherwise raise an error
-        :param data:
-        :return:
+        
+        Parameters
+        ----------
+        data
+        
+        Returns
+        -------
         """
         if not isinstance(data, list):
             raise ValueError("{} has to be a list".format(data))
@@ -60,8 +84,16 @@ def is_list_of_string(data):
     def compute_topics(self, nb_word_per_cluster):
         """
         Main function computing the topic modeling, topics
-        :param nb_word_per_cluster: positive integer
-        :return: a dictionary containing the the different topics with the top words and coherence associated
+        
+        Parameters
+        ----------
+        nb_word_per_cluster : positive integer
+        
+        Returns
+        -------
+        dict :    
+            a dictionary containing the the different topics with the top words 
+            and coherence associated
         """
         vec = CountVectorizer(stop_words=self.lang)
         self._vectorize_text = vec.fit_transform(self.data).toarray()
@@ -78,8 +110,15 @@ def compute_topics(self, nb_word_per_cluster):
     def get_document_topic(self, index):
         """
         Get the cluster associated to the specified document
-        :param index: the document index, positive integer
-        :return: the cluster index
+                
+        Parameters
+        ----------
+        index : positive integer
+            the document index
+
+        Returns
+        -------
+        the cluster index
         """
         if self._topics is None:
             raise ValueError("Model needs to be trained first")
@@ -89,9 +128,15 @@ def get_document_topic(self, index):
     def save_pyLDAvis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.html'):
         """
         Function saving the pyLDAvis plot associated with the compute_topics function
-        :param path_to_output: path to save the plut, must be a html file
-        :return:
-        """
+                
+        Parameters
+        ----------
+        path_to_output : str
+            path to save the plut, must be a html file
+
+        Returns
+        -------
+        """        
         if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
             raise ValueError("Model needs to be trained first")
 

From 765fad12a944eb321e80bf4951ff309092af882a Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 19 Jun 2019 15:13:08 +0200
Subject: [PATCH 220/496] docstring #75

---
 nautilus_nlp/models/fasttext_classifier.py |  2 +-
 nautilus_nlp/models/fasttext_embedding.py  | 33 ++++++++++++++++------
 2 files changed, 26 insertions(+), 9 deletions(-)

diff --git a/nautilus_nlp/models/fasttext_classifier.py b/nautilus_nlp/models/fasttext_classifier.py
index 9d0fee4..5bd1f0a 100644
--- a/nautilus_nlp/models/fasttext_classifier.py
+++ b/nautilus_nlp/models/fasttext_classifier.py
@@ -37,7 +37,7 @@ def train(
         verbose=2,
         pretrainedVectors="",
     ):
-        """
+    """
     Train a supervised model and return a model object.
     input must be a filepath. The input text does not need to be tokenized
     as per the tokenize function, but it must be preprocessed and encoded
diff --git a/nautilus_nlp/models/fasttext_embedding.py b/nautilus_nlp/models/fasttext_embedding.py
index ced0ce7..79f7d8a 100644
--- a/nautilus_nlp/models/fasttext_embedding.py
+++ b/nautilus_nlp/models/fasttext_embedding.py
@@ -11,18 +11,35 @@ def __init__(self, path=None):
             self.model = None
 
     def get_word_vector(self, word: str):
-        """Return the wordvector of a word according to pretrained model
-            Input document: string
-            output: array
         """
+        Return the wordvector of a word according to pretrained model
+
+        Parameters
+        ----------
+        word : string
+            the input document
+
+        Returns
+        -------
+        array
+        """
+
         return self.model.get_word_vector(word)
 
     def get_document_vector(self, document: str):
-        """Return the wordvector of a full document according to pretrained model
-            To build the vector,  each word-wordvector is divided by its norm, then the array of vector is averaged
-            The document must be cleaned beforehand (no EOL)
-            Input document: string
-            output: array
+        """
+        Return the wordvector of a full document according to pretrained model
+        To build the vector,  each word-wordvector is divided by its norm, then the array of vector is averaged
+        The document must be cleaned beforehand (no EOL)
+
+        Parameters
+        ----------
+        Input document : string
+            the input document
+
+        Returns
+        -------
+        array
         """
         return self.model.get_sentence_vector(document)
 

From b090f45fc09188e7590de9b1ca248d80179311de Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 19 Jun 2019 15:46:42 +0200
Subject: [PATCH 221/496] harmonize docstrings #75

---
 nautilus_nlp/models/language_detector.py | 21 ++++---
 nautilus_nlp/models/topic_modeling.py    | 73 ++++++++++++++++--------
 nautilus_nlp/utils/emoji.py              | 16 ++++--
 nautilus_nlp/utils/file_loader.py        | 58 ++++++++++++++-----
 nautilus_nlp/utils/keyword_extractor.py  | 20 +++++--
 nautilus_nlp/utils/ngrams_analysis.py    | 37 ++++++++----
 nautilus_nlp/utils/text_summary.py       | 35 +++++++++---
 nautilus_nlp/utils/tokenizer.py          | 20 +++++--
 8 files changed, 201 insertions(+), 79 deletions(-)

diff --git a/nautilus_nlp/models/language_detector.py b/nautilus_nlp/models/language_detector.py
index acd1819..d124871 100644
--- a/nautilus_nlp/models/language_detector.py
+++ b/nautilus_nlp/models/language_detector.py
@@ -8,8 +8,8 @@
 
 
 class LangDetector:
-    """ This class is to instantiante a language detector.
-
+    """ 
+    This class is to instantiante a language detector.
     """
 
     def __init__(self, path=None):
@@ -20,12 +20,17 @@ def detect_language(self, text_to_detect=None):
         """
         Detected the language of a text
 
-        Args:
-        hint_language: language you expect your text to be
-
-        Returns:
-        is_reliable: is the top language is much better than 2nd best language?
-        language: 2-letter code for the language of the text
+        Parameters
+        ----------
+        hint_language : string
+            language you expect your text to be
+
+        Returns
+        -------
+        is_reliable : 
+            is the top language is much better than 2nd best language?
+        language: 
+            2-letter code for the language of the text
         """
         best_guesses = self.model.predict(remove_EOL_characters(text_to_detect))
         return best_guesses[0][0].replace("__label__", ""), best_guesses[1][0]
diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 9e2c0f9..518d8c0 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -13,8 +13,8 @@
 
 
 def create_dictionary(data):
-    
-    """ Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
+    """ 
+    Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
     
     Parameters
     ----------
@@ -27,7 +27,8 @@ def create_dictionary(data):
     return gensim.corpora.Dictionary(data)
 
 def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
-    """ Remove very rare and very common words
+    """ 
+    Remove very rare and very common words
 
     Parameters
     ----------
@@ -45,8 +46,10 @@ def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
 
 def create_bow_corpus(data, dictionary):
     
-    """ Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
-        The produced corpus is a mapping of (token_id, token_count).
+    """ 
+    Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
+    The produced corpus is a mapping of (token_id, token_count).
+
     Parameters
     ----------
     data : list of list of tokens
@@ -168,23 +171,40 @@ def train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs):
 
 
 def save_model(model, model_name):
-    """ Save the model that has been trained. The model will be saved on your current emplacement.
+    """ 
+    Save the model that has been trained. The model will be saved on your current emplacement.
         
-        Parameters
-        ----------
-        model: ldamodel
-        model_name: str. Name the model that will be saved
+    Parameters
+    ----------
+    model: ldamodel
+    model_name: str. 
+        Name the model that will be saved
     """
     return model.save(os.path.join(model_name))
 
 
 def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
-    '''
-    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
-    model_path: str. path where the model has been saved
-    model_name: str. name of the saved model
-    model_prefix: str. By default, 'composant' default prefix used while saving the mallet model with train_lda_model function. 
-    '''
+    """
+    Detected the language of a text
+
+    Parameters
+    ----------
+    model_path: str
+        path where the model has been saved
+    model_name: str
+        name of the saved model
+    model : str
+        Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    model_prefix : str
+        By default, 'composant' default prefix used while saving the mallet model with train_lda_model function.         
+    
+    Returns
+    -------
+    is_reliable : 
+        is the top language is much better than 2nd best language?
+    language: 
+        2-letter code for the language of the text
+    """
     if model =='gensim':
         ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
     elif model =='mallet':
@@ -204,7 +224,8 @@ def fit_data(model, bow):
 
 
 def visualize_topics(model, bow_corpus, dictionary, model_type=None):
-    """ Visualize the topics-keywords with the pyLDAvis interactive chart.
+    """ 
+    Visualize the topics-keywords with the pyLDAvis interactive chart.
         (Work well in notebook)
         
     Parameters
@@ -217,7 +238,6 @@ def visualize_topics(model, bow_corpus, dictionary, model_type=None):
     Returns:
     ----------
     3D interactive chart
-    
     """
     if model_type == 'mallet':
         model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
@@ -230,19 +250,27 @@ def visualize_topics(model, bow_corpus, dictionary, model_type=None):
     return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
 
 def save_pyldavis(pyldavis, vis_path, vis_name):
-    """ Save the pyldavis interactive chart
+    """ 
+    Save the pyldavis interactive chart
+    
+    Parameters
+    ----------
     pyldavis: pyLDAvis._prepare.PreparedData
     vis_path: str
-    vis_path: str
+    vis_name: str
     """ 
     return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name + '{}'.format('.html')))
 
 
 
 def show_pyldavis(vis_path, vis_name):
-    """ Display the HTML of the saved pyldavis interactive chart
-    vis_path: str
+    """ 
+    Display the HTML of the saved pyldavis interactive chart
+
+    Parameters
+    ----------
     vis_path: str
+    vis_name: str
     """
     return HTML(filename=os.path.join(vis_path, vis_name + '{}'.format('.html')))
 
@@ -253,7 +281,6 @@ def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
 
     Parameters
     ----------
-
     gensim.ldamodel
     model: ldamodel
     bow_corpus: iterable of list of tokens.
diff --git a/nautilus_nlp/utils/emoji.py b/nautilus_nlp/utils/emoji.py
index 85177af..b7f9534 100644
--- a/nautilus_nlp/utils/emoji.py
+++ b/nautilus_nlp/utils/emoji.py
@@ -1,12 +1,18 @@
 import csv
 
 def rebuilt_emoji_dictionaries(filename):
-    """
-   This function recreate the dictionnaries with emoji2unicode_name, emoji2sentiment  that are hardcoded below.
-
-    :input: csv filename
-    :return: dict,dict
+    """ 
+    This function recreate the dictionnaries with emoji2unicode_name, emoji2sentiment  that are hardcoded below.
     
+    Parameters
+    ----------
+    filename : string
+        csv filename
+       
+    Returns
+    -------
+    dict
+
     This dataset is based on and can be found:
     Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut and Mozetič, Igor, 2015,
     Emoji Sentiment Ranking 1.0, Slovenian language resource repository CLARIN.SI,
diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 6d710f6..08a816e 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -19,9 +19,20 @@ def open_textfile(filepath, encoding='utf-8'):
 
 
 def detect_encoding(file_path_or_string, n_lines=100):
-    '''
+    """ 
     Predict a file's encoding using chardet
-    '''
+    
+    Parameters
+    ----------
+    file_path_or_string : string
+        if filepath, will open the file. Otherwise will predict from the string
+    n_lines : int
+        number of line to predict from 
+       
+    Returns
+    -------
+    the code of the detected encoding
+    """
     if isfile(file_path_or_string):
         with open(file_path_or_string, 'rb') as f:                      # Open the file as binary data
             rawdata = b''.join([f.readline() for _ in range(n_lines)])  # Join binary lines for specified number of lines
@@ -32,8 +43,22 @@ def detect_encoding(file_path_or_string, n_lines=100):
 
 def text_loader(filepath, encoding=None, detectencoding=True):
     '''
-    Args:
-        detect_encoding[bool]= If file is not encoded into UTF-8, try to detect encoding using the chardet library.
+    This util loads a file. If the encoding is specified, will use the specified 
+    encoding to load the text file. 
+    If not specified, this function tries to open the doc as UTF-U, and if 
+    it fails it will try to detect the encoding using **detect_encoding** 
+
+    Parameters
+    ----------
+    filepath : str 
+    encoding : str
+        If the encoding is specified, will use the specified encoding to load the text file.
+    detect_encoding : bool
+    If file is not encoded into UTF-8, try to detect encoding using the chardet library.
+
+    Returns
+    -------
+    string
     '''
     if encoding is not None:
         return open_textfile(filepath, encoding=encoding)
@@ -88,17 +113,24 @@ def documents_loader(filepath:str, encoding=None, detectencoding=True, output_as
     Input a filepath, a filepath with wildcard (eg. *.txt), 
     or a list of filepaths.
     Output a string, or a dict of strings.
-    Args:
-        filepath: filepath, a filepath with wildcard (eg. *.txt), 
-            or a list of filepaths.
-        output_as: list or dict. If dict, key will be the filename.
-        encoding: if not specified, will try to detect encoding except if 
-            detectencoding is false. 
-        detectencoding: if True and if encoding is not specified, will try to 
-            detect encoding using chardet. 
 
-    '''
+    Parameters
+    ----------
+    filepath: filepath
+        A filepath with wildcard (eg. *.txt), or a list of filepaths.
+    output_as: list or dict.
+        If dict, key will be the filename.
+    encoding: 
+        if not specified, will try to detect encoding except if detectencoding is false. 
+    detectencoding : bool 
+        if True and if encoding is not specified, will try to detect encoding using chardet. 
     
+    Returns
+    -------
+    string
+    the document loaded
+    '''
+   
     if type(filepath) is str:
         documents = list_files(filepath)
         nb_of_documents = len(documents)
diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/utils/keyword_extractor.py
index e384bf2..1956983 100644
--- a/nautilus_nlp/utils/keyword_extractor.py
+++ b/nautilus_nlp/utils/keyword_extractor.py
@@ -1,14 +1,24 @@
 from flashtext import KeywordProcessor
 
-def extract_keywords(text,keyword,case_sensitive=True):
+def extract_keywords(text, keyword, case_sensitive=True):
     """
     Extract Keywords from a document.
-    args :
-    text: Text to extract keywords from
-    keyword : Single keyword (str) or list of keywords (list)
 
-    return list of extracted keyworkds
+    Parameters
+    ----------    
+    text : str
+        Text to extract keywords from
+    keyword :
+        Single keyword (str) or list of keywords (list)
+    case_sensitive :
+        If False, will be case insensitive.
+
+    Returns
+    -------
+    list
+        Return list of extracted keyworkds
     """
+    
     processor=KeywordProcessor(case_sensitive=case_sensitive)
     if isinstance(keyword,list):
         processor.add_keywords_from_list(keyword)
diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index 5e61009..268fe3c 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -8,22 +8,39 @@
 def create_ngrams(token, n):
     """
     Create n-grams for list of tokens
-    :param token: list of strings
-    :param n: number of elements in the n-gram
-    :return: list of n-grams
-    """
+
+    Parameters
+    ----------    
+    token : list
+        list of strings
+    n :
+        number of elements in the n-gram
+
+    Returns
+    -------
+    list
+        list of n-grams
+    """    
     ngrams = zip(*[token[i:] for i in range(n)])
     return [" ".join(ngram) for ngram in ngrams]
 
 
 def frequent_words(list_words, ngrams_number=1, number_top_words=10):
     """
-    Compute n-grams frequencies and return number_top_words top n-grams.
-    :param list_words: list of strings
-    :param ngrams_number: output dataframe length
-    :param number_top_words: output dataframe length
-    :return: dataframe with the entities and their frequencies.
-    """
+    Create n-grams for list of tokens
+
+    Parameters
+    ----------    
+    ngrams_number : int
+    
+    number_top_words : int 
+        output dataframe length
+
+    Returns
+    -------
+    DataFrame
+        Dataframe with the entities and their frequencies.
+    """             
     frequent = []
     if ngrams_number == 1:
         pass
diff --git a/nautilus_nlp/utils/text_summary.py b/nautilus_nlp/utils/text_summary.py
index ad873b3..db498bf 100644
--- a/nautilus_nlp/utils/text_summary.py
+++ b/nautilus_nlp/utils/text_summary.py
@@ -3,20 +3,37 @@
 
 def is_list_of_strings(lst):
     """
-    :param lst: list
-    :return: boolean indicator
-    """
+    Parameters
+    ----------    
+    lst : list
+
+    Returns
+    -------
+    book
+        boolean indicator
+    """      
+    
     return bool(lst) and isinstance(lst, list) and all(isinstance(elem, str) for elem in lst)
 
 
 def summarize_text(txt, ratio=0.2, words=None, language="english"):
     """
-    :param txt: Sting or list of strings containing text to summarize
-    :param ratio: Percentage giving the output text length in reference to the input length.
-    :param words: number of words of the output text
-    :param language: text language
-    :return: string containing the summarized text
-    """
+    Parameters
+    ----------    
+    txt : str
+        Sting or list of strings containing text to summarize
+    ratio : float
+        Percentage giving the output text length in reference to the input length.
+    words : 
+        number of words of the output text
+    language :
+        text language. eg. "english"
+
+    Returns
+    -------
+    string
+        string containing the summarized text
+    """      
 
     if is_list_of_strings(txt):
         txt = ' '.join(txt)
diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 526185b..a550bf7 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -26,15 +26,18 @@ def tokenize(text: str, lang_module: str = 'en_spacy'):
     """
     Convert text to a list of tokens. 
 
-    Args:
-        lang_module ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
-        the tokenization module according to the langage and the implementation.
+    Parameters
+    ----------    
+    lang_module : ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'})
+        choose the tokenization module according to the langage and the implementation.
         Recommanded: Spacy (faster, better results). To process other langages
         import models.Spacy_models
 
-    Returns:
-        list
-    """
+    Returns
+    -------
+    list
+        list of string
+    """      
     if lang_module is 'en_nltk':
         return nltk.word_tokenize(text)
     elif lang_module is 'en_spacy':
@@ -52,6 +55,11 @@ def untokenize(tokens, lang='fr'):
     '''
     Inputs a list of tokens output string.
     ["J'", 'ai'] >>> "J' ai"
+
+    Parameters
+    ----------    
+    lang : string
+        language code 
     '''
     d = MosesDetokenizer(lang=lang)
     text = d.detokenize(tokens, unescape=False)

From 96e685ad5997b87d94b3f50284a2eeecb8d6314e Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 19 Jun 2019 15:58:44 +0200
Subject: [PATCH 222/496] update readme

---
 README.md | 87 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 50 insertions(+), 37 deletions(-)

diff --git a/README.md b/README.md
index 5bfd9cb..279cd7e 100644
--- a/README.md
+++ b/README.md
@@ -45,8 +45,57 @@ then you can install it via pip:
 pip install -e .
 ```
 
-## Handling installation errors
+Once it's done, please install the additional required libraries: 
+
+## Installation of the additional required libraries
+
+If you want to leverage all the features of nautilus-nlp, you need to **install others required libraries** (such as FastText).
+
+### Install spaCy language models (highly recommanded)
+
+Installing additional spaCy models will give you the possibility to handle a lot of new language for text processing feature (such as lemmatization or tokenization). 
+
+To do so, run: *(on your virtual environment if you are using one)*
+
+```
+bash nautilus_nlp/scripts/download_spacy_models.sh
+```
+
+### Install FastText 
+
+run: 
+
+```
+bash nautilus_nlp/scripts/install_fasttext.sh
+```
+
+### Install Lang Detect
+
+run: 
+
+```
+bash nautilus_nlp/scripts/download_ft_langdetect.sh
+```
+
+### Install Mallet
 
+Mallet is an implementation of the LDA algorithm (used for **topic modeling**). 
+
+1) Install Mallet file
+run:
+
+```
+bash nautilus_nlp/scripts/install_mallet.sh
+```
+
+2) Install Java JDK (required to implement mallet)
+run:
+
+```
+bash nautilus_nlp/scripts/install_java.sh
+```
+
+## Handling installation errors
 
 ### Problem when building FastText on MACOS:
 
@@ -93,42 +142,6 @@ wget https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
 bash Anaconda3-2019.03-Linux-x86_64.sh
 ```
 
-## Installation of additional required libraries
-
-If you want to leverage all the features of nautilus-nlp, you need to **install others required libraries** (such as FastText).
-
-### Install spaCy language models
-
-Installing additional spaCy models will give you the possibility to handle a lot of new language for text processing feature (such as lemmatization or tokenization). 
-
-To do so, run: *(on your virtual environment if you are using one)*
-
-`bash nautilus_nlp/scripts/download_spacy_models.sh`
-
-### Install FastText 
-
-run: 
-
-`bash nautilus_nlp/scripts/install_fasttext.sh`
-
-### Install Lang Detect
-
-run: 
-
-`bash nautilus_nlp/scripts/download_ft_langdetect.sh`
-
-### Install Mallet
-
-1) Install Mallet file
-run:
-
-`bash nautilus_nlp/scripts/install_mallet.sh`
-
-2) Install Java JDK (required to implement mallet)
-run:
-
-`bash nautilus_nlp/scripts/install_java.sh`
-
 # Quick start
 
 The [notebook](notebooks/) folder contains various notebook on how to use this library.

From 4c25108c67452679144aaa39c0fed368458c249f Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Wed, 19 Jun 2019 16:50:24 +0200
Subject: [PATCH 223/496] update logo

---
 references/nautilus_nlp_logo.png | Bin 25433 -> 23981 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/references/nautilus_nlp_logo.png b/references/nautilus_nlp_logo.png
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AI{*Lx


From b08881c8553336e5ee6c642da29bc8efdd26992d Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 19 Jun 2019 17:31:21 +0200
Subject: [PATCH 224/496] fix indentation error in fasttext_clf

---
 nautilus_nlp/models/fasttext_classifier.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/models/fasttext_classifier.py b/nautilus_nlp/models/fasttext_classifier.py
index 5bd1f0a..9d0fee4 100644
--- a/nautilus_nlp/models/fasttext_classifier.py
+++ b/nautilus_nlp/models/fasttext_classifier.py
@@ -37,7 +37,7 @@ def train(
         verbose=2,
         pretrainedVectors="",
     ):
-    """
+        """
     Train a supervised model and return a model object.
     input must be a filepath. The input text does not need to be tokenized
     as per the tokenize function, but it must be preprocessed and encoded

From 18502e80e4bff1921fe82ada5959a73bb8ceebbd Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Mon, 24 Jun 2019 20:27:30 +0200
Subject: [PATCH 225/496] update logo

---
 references/nautilus_nlp_logo.png | Bin 23981 -> 24573 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/references/nautilus_nlp_logo.png b/references/nautilus_nlp_logo.png
index 61e8480cb0582b809a94bae354c5d359ad592adc..f91effef13885c1f27e57c6783fe156b8ea6cfaf 100644
GIT binary patch
delta 22495
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From 9a91b2faa196d97130eb8dbf7a7c6db21abfcf2e Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Fri, 23 Aug 2019 17:29:38 +0200
Subject: [PATCH 226/496] data preparation for short text

---
 nautilus_nlp/models/nmf_seanmf_models.py      |  0
 nautilus_nlp/models/spacy_model.py            | 61 -------------------
 .../models/topic_modeling_short_text.py       |  0
 3 files changed, 61 deletions(-)
 create mode 100644 nautilus_nlp/models/nmf_seanmf_models.py
 delete mode 100644 nautilus_nlp/models/spacy_model.py
 create mode 100644 nautilus_nlp/models/topic_modeling_short_text.py

diff --git a/nautilus_nlp/models/nmf_seanmf_models.py b/nautilus_nlp/models/nmf_seanmf_models.py
new file mode 100644
index 0000000..e69de29
diff --git a/nautilus_nlp/models/spacy_model.py b/nautilus_nlp/models/spacy_model.py
deleted file mode 100644
index c7eca79..0000000
--- a/nautilus_nlp/models/spacy_model.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import spacy
-
-class spacy_model:
-
-    def __init__(self, lang: str):
-        try:
-            self.model = spacy.load(lang)
-        except Exception as e:
-            print(e)
-            print(f"Cannot load lang {lang}, loading default")
-            self.model = spacy.load("xx")
-
-    def get_spacy_doc(self, text):
-
-        return self.model(text)
-
-    @staticmethod
-    def get_tokens_from_document(spacydoc: spacy.tokens.doc.Doc):
-
-        return [tokens for tokens in spacydoc]
-
-    def get_tokens_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [tokens for tokens in spacydoc]
-
-    @staticmethod
-    def get_sentence_from_document(spacydoc: spacy.tokens.doc.Doc):
-        return [sent for sent in spacydoc.sents]
-
-    def get_sentence_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [sent for sent in spacydoc.sents]
-
-    @staticmethod
-    def get_tokenized_sentence_from_document(spacydoc: spacy.tokens.doc.Doc):
-        return [[(token) for token in sent] for sent in spacydoc.sents]
-
-    def get_tokenized_sentence_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [[(token) for token in sent] for sent in spacydoc.sents]
-
-    @staticmethod
-    def get_entities_from_document(spacydoc):
-        return [(ent, ent.label_) for ent in spacydoc.ents]
-
-    @staticmethod
-    def get_entities_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [(ent, ent.label_) for ent in spacydoc.ents]
-
-    @staticmethod
-    def get_lemma_from_document(spacydoc: spacy.tokens.doc.Doc) -> list:
-        return [token.lemma_ for token in spacydoc]
-
-    def get_lemma_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [token.lemma_ for token in spacydoc]
-
-    def get_pos_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [token.pos_ for token in spacydoc]
\ No newline at end of file
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
new file mode 100644
index 0000000..e69de29

From 8fdcc37c8fb6b8990e3386168c922a2202f567ef Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Fri, 23 Aug 2019 17:58:35 +0200
Subject: [PATCH 227/496] train NMF func

---
 nautilus_nlp/models/nmf_seanmf_models.py      | 223 ++++++++++++++++++
 .../models/topic_modeling_short_text.py       |  84 +++++++
 2 files changed, 307 insertions(+)

diff --git a/nautilus_nlp/models/nmf_seanmf_models.py b/nautilus_nlp/models/nmf_seanmf_models.py
index e69de29..e713175 100644
--- a/nautilus_nlp/models/nmf_seanmf_models.py
+++ b/nautilus_nlp/models/nmf_seanmf_models.py
@@ -0,0 +1,223 @@
+'''
+Short Text Topic Modeling via SeaNMF
+'''
+import time
+import numpy as np
+from numpy.linalg import norm
+
+
+class SeaNMFL1(object):
+    def __init__(
+            self,
+            A, S,
+            IW1=[], IW2=[], IH=[],
+            alpha=1.0, beta=0.1, n_topic=10, max_iter=100, max_err=1e-3,
+            rand_init=True, fix_seed=False):
+        '''
+        0.5*||A-WH^T||_F^2+0.5*alpha*||S-WW_c^T||_F^2+0.5*beta*||W||_1^2
+        W = W1
+        Wc = W2
+        '''
+        if fix_seed:
+            np.random.seed(0)
+
+        self.A = A
+        self.S = S
+
+        self.n_row = A.shape[0]
+        self.n_col = A.shape[1]
+
+        self.n_topic = n_topic
+        self.max_iter = max_iter
+        self.alpha = alpha
+        self.beta = beta
+        self.B = np.ones([self.n_topic, 1])
+        self.max_err = max_err
+
+        if rand_init:
+            self.nmf_init_rand()
+        else:
+            self.nmf_init(IW1, IW2, IH)
+        self.nmf_iter()
+
+    def nmf_init_rand(self):
+        self.W1 = np.random.random((self.n_row, self.n_topic))
+        self.W2 = np.random.random((self.n_row, self.n_topic))
+        self.H = np.random.random((self.n_col, self.n_topic))
+
+        for k in range(self.n_topic):
+            self.W1[:, k] /= norm(self.W1[:, k])
+            self.W2[:, k] /= norm(self.W2[:, k])
+
+    def nmf_init(self, IW1, IW2, IH):
+        self.W1 = IW1
+        self.W2 = IW2
+        self.H = IH
+
+        for k in range(self.n_topic):
+            self.W1[:, k] /= norm(self.W1[:, k])
+            self.W2[:, k] /= norm(self.W2[:, k])
+
+    def nmf_iter(self):
+        loss_old = 1e20
+        print('loop begin')
+        start_time = time.time()
+        for i in range(self.max_iter):
+            self.nmf_solver()
+            loss = self.nmf_loss()
+            if loss_old - loss < self.max_err:
+                break
+            loss_old = loss
+            end_time = time.time()
+            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
+
+    def nmf_solver(self):
+        '''
+        using BCD framework
+        '''
+        epss = 1e-20
+        # Update W1
+        AH = np.dot(self.A, self.H)
+        SW2 = np.dot(self.S, self.W2)
+        HtH = np.dot(self.H.T, self.H)
+        W2tW2 = np.dot(self.W2.T, self.W2)
+        W11 = self.W1.dot(self.B)
+
+        for k in range(self.n_topic):
+            num0 = HtH[k, k] * self.W1[:, k] + self.alpha * W2tW2[k, k] * self.W1[:, k]
+            num1 = AH[:, k] + self.alpha * SW2[:, k]
+            num2 = np.dot(self.W1, HtH[:, k]) + self.alpha * np.dot(self.W1, W2tW2[:, k]) + self.beta * W11[0]
+            self.W1[:, k] = num0 + num1 - num2
+            self.W1[:, k] = np.maximum(self.W1[:, k], epss)  # project > 0
+            self.W1[:, k] /= norm(self.W1[:, k]) + epss  # normalize
+        # Update W2
+        W1tW1 = self.W1.T.dot(self.W1)
+        StW1 = np.dot(self.S, self.W1)
+        for k in range(self.n_topic):
+            self.W2[:, k] = self.W2[:, k] + StW1[:, k] - np.dot(self.W2, W1tW1[:, k])
+            self.W2[:, k] = np.maximum(self.W2[:, k], epss)
+        # Update H
+        AtW1 = np.dot(self.A.T, self.W1)
+        for k in range(self.n_topic):
+            self.H[:, k] = self.H[:, k] + AtW1[:, k] - np.dot(self.H, W1tW1[:, k])
+            self.H[:, k] = np.maximum(self.H[:, k], epss)
+
+    def nmf_loss(self):
+        '''
+        Calculate loss
+        '''
+        loss = norm(self.A - np.dot(self.W1, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        if self.alpha > 0:
+            loss += self.alpha * norm(np.dot(self.W1, np.transpose(self.W2)) - self.S, 'fro') ** 2 / 2.0
+        if self.beta > 0:
+            loss += self.beta * norm(self.W1, 1) ** 2 / 2.0
+
+        return loss
+
+    def get_lowrank_matrix(self):
+        return self.W1, self.W2, self.H
+
+    def get_decomposition_matrix(self):
+        return self.W1, self.W2, self.H
+
+    def save_format(self, W1file='W.txt', W2file='Wc.txt', Hfile='H.txt'):
+        np.savetxt(W1file, self.W1)
+        np.savetxt(W2file, self.W2)
+        np.savetxt(Hfile, self.H)
+
+
+'''
+Topic Modeling via NMF
+'''
+
+
+class NMF(object):
+    def __init__(
+            self,
+            A, IW=[], IH=[],
+            n_topic=10, max_iter=100, max_err=1e-3,
+            rand_init=True):
+        '''
+        A = WH^T
+        '''
+        self.A = A
+        self.n_row = A.shape[0]
+        self.n_col = A.shape[1]
+
+        self.n_topic = n_topic
+        self.max_iter = max_iter
+        self.max_err = max_err
+
+        self.obj = []
+        if rand_init:
+            self.nmf_init_rand()
+        else:
+            self.nmf_init(IW, IH)
+        self.nmf_iter()
+
+    def nmf_init_rand(self):
+        self.W = np.random.random((self.n_row, self.n_topic))
+        self.H = np.random.random((self.n_col, self.n_topic))
+
+        for k in range(self.n_topic):
+            self.W[:, k] /= norm(self.W[:, k])
+
+    def nmf_init(self, IW, IH):
+        self.W = IW
+        self.H = IH
+
+        for k in range(self.n_topic):
+            self.W[:, k] /= norm(self.W[:, k])
+
+    def nmf_iter(self):
+        loss_old = 1e20
+        print('loop begin')
+        start_time = time.time()
+        for i in range(self.max_iter):
+            self.nmf_solver()
+            loss = self.nmf_loss()
+            self.obj.append(loss)
+
+            if loss_old - loss < self.max_err:
+                break
+            loss_old = loss
+            end_time = time.time()
+            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
+        print('loop end')
+
+    def nmf_solver(self):
+        '''
+        regular NMF without constraint.
+        Block Coordinate Decent
+        '''
+        epss = 1e-20
+
+        HtH = self.H.T.dot(self.H)
+        AH = self.A.dot(self.H)
+        for k in range(self.n_topic):
+            tmpW = self.W[:, k] * HtH[k, k] + AH[:, k] - np.dot(self.W, HtH[:, k])
+            self.W[:, k] = np.maximum(tmpW, epss)
+            self.W[:, k] /= norm(self.W[:, k]) + epss
+
+        WtW = self.W.T.dot(self.W)
+        AtW = self.A.T.dot(self.W)
+        for k in range(self.n_topic):
+            self.H[:, k] = self.H[:, k] * WtW[k, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
+            self.H[:, k] = np.maximum(self.H[:, k], epss)
+
+    def nmf_loss(self):
+        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        return loss
+
+    def get_loss(self):
+        return np.array(self.obj)
+
+    def get_lowrank_matrix(self):
+        return self.W, self.H
+
+    def get_decomposition_matrix(self):
+        return self.W, self.H
+
+    def save_format(self, Wfile='W.txt', Hfile='H.txt'):
+        np.savetxt(Wfile, self.W)
+        np.savetxt(Hfile, self.H)
\ No newline at end of file
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index e69de29..56a0994 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -0,0 +1,84 @@
+import re
+from nmf_seanmf_models import *
+import numpy as np
+
+def data_preparation(text, vocab_min_count=1, vocab_max_size=10000):
+    """
+    This function expects a list of documents (sentences) and returns the needed data to
+    make topic modeling for short text using NMF.
+    :return:
+
+    """
+
+    vocab = {}
+    for sentence in text:
+        sentence = re.split('\s', sentence)
+        for wd in sentence:
+                try:
+                    vocab[wd] += 1
+                except:
+                    vocab[wd] = 1
+    # Create Vocab array ( list of sorted vocab + counts )
+    vocab_arr = [[wd, vocab[wd]] for wd in vocab if vocab[wd] > vocab_min_count]
+    vocab_arr = sorted(vocab_arr, key=lambda k: k[1])[::-1]
+    vocab_arr = vocab_arr[:vocab_max_size]
+    vocab_arr = sorted(vocab_arr)
+
+    vocab_list = list(map(lambda x:x[0], vocab_arr))
+    # Create Vocab to ID dictionnary
+    vocab2id = {itm[1][0]: itm[0] for itm in enumerate(vocab_arr)}
+
+    # Create ID representation of text (ie: each sentence is a list of vocabId )
+    encoded_text_id = []
+    for sentence in text:
+        sentence = re.split('\s', sentence)
+        sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
+        encoded_text_id.append(sentence)
+    return encoded_text_id, vocab_list, vocab_arr
+
+
+def train_model(model,n_docs, n_terms, docs, n_topics= 20, max_iter= 20, max_err=0.1, alpha = 0, beta=0):
+    """
+    :param model:
+    :param n_docs:
+    :param n_terms:
+    :param docs:
+    :param n_topics:
+    :param max_iter:
+    :param max_err:
+    :param alpha:
+    :param beta:
+    :return:
+    """
+    if model == 'nmf':
+        dt_mat = np.zeros([n_terms, n_docs])
+        for k in range(n_docs):
+            for j in docs[k]:
+                dt_mat[j, k] += 1.0
+        model = NMF(
+            dt_mat,
+            n_topic=n_topics,
+            max_iter=max_iter,
+            max_err=max_err)
+
+    return model
+
+## TEST
+
+text = ['abs souscription ird abs collaborateur message bloquant finaliser merci selectionner un personne physique comme assurer principal',
+'ref contrat af752001033 date heure creation avener non technique abs abs souscription ird collaborateur souhaiter repasser contrat mensuel selectionner bon rib car rib present sur contrat être errone celer cause impaye sur contrat',
+'demande refair devis faire depuis im non traduire dans ab avec declanchement visa correction un message erreur',
+'abs souscription ird souscription un affaire nouveau abs collab arriver pas finaliser son contrat']
+encoded_text_id, vocab_list, vocab_arr = data_preparation(text)
+
+print(encoded_text_id)
+
+print(vocab_arr)
+
+n_docs = len(encoded_text_id)
+n_terms = len(vocab_list)
+docs = encoded_text_id
+vocab = vocab_list
+
+x = train_model('nmf', n_docs, n_terms, docs)
+W, H = x.get_decomposition_matrix()

From c3370af8a2b1f153e3bcb82d9d0758d2ba593ccb Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Tue, 27 Aug 2019 14:51:15 +0200
Subject: [PATCH 228/496] NMF short text

---
 nautilus_nlp/models/nmf_model.py              |  99 ++++++++
 nautilus_nlp/models/nmf_seanmf_models.py      | 223 ------------------
 .../models/topic_modeling_short_text.py       | 105 +++++++--
 3 files changed, 190 insertions(+), 237 deletions(-)
 create mode 100644 nautilus_nlp/models/nmf_model.py
 delete mode 100644 nautilus_nlp/models/nmf_seanmf_models.py

diff --git a/nautilus_nlp/models/nmf_model.py b/nautilus_nlp/models/nmf_model.py
new file mode 100644
index 0000000..252b612
--- /dev/null
+++ b/nautilus_nlp/models/nmf_model.py
@@ -0,0 +1,99 @@
+
+import time
+import numpy as np
+from numpy.linalg import norm
+
+'''
+Topic Modeling via NMF
+'''
+
+class NMF(object):
+    def __init__(
+            self,
+            A, IW=[], IH=[],
+            n_topic=10, max_iter=100, max_err=1e-3,
+            rand_init=True):
+        '''
+        A = WH^T
+        '''
+        self.A = A
+        self.n_row = A.shape[0]
+        self.n_col = A.shape[1]
+
+        self.n_topic = n_topic
+        self.max_iter = max_iter
+        self.max_err = max_err
+
+        self.obj = []
+        if rand_init:
+            self.nmf_init_rand()
+        else:
+            self.nmf_init(IW, IH)
+        self.nmf_iter()
+
+    def nmf_init_rand(self):
+        self.W = np.random.random((self.n_row, self.n_topic))
+        self.H = np.random.random((self.n_col, self.n_topic))
+
+        for k in range(self.n_topic):
+            self.W[:, k] /= norm(self.W[:, k])
+
+    def nmf_init(self, IW, IH):
+        self.W = IW
+        self.H = IH
+
+        for k in range(self.n_topic):
+            self.W[:, k] /= norm(self.W[:, k])
+
+    def nmf_iter(self):
+        loss_old = 1e20
+        print('loop begin')
+        start_time = time.time()
+        for i in range(self.max_iter):
+            self.nmf_solver()
+            loss = self.nmf_loss()
+            self.obj.append(loss)
+
+            if loss_old - loss < self.max_err:
+                break
+            loss_old = loss
+            end_time = time.time()
+            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
+        print('loop end')
+
+    def nmf_solver(self):
+        '''
+        regular NMF without constraint.
+        Block Coordinate Decent
+        '''
+        epss = 1e-20
+
+        HtH = self.H.T.dot(self.H)
+        AH = self.A.dot(self.H)
+        for k in range(self.n_topic):
+            tmpW = self.W[:, k] * HtH[k, k] + AH[:, k] - np.dot(self.W, HtH[:, k])
+            self.W[:, k] = np.maximum(tmpW, epss)
+            self.W[:, k] /= norm(self.W[:, k]) + epss
+
+        WtW = self.W.T.dot(self.W)
+        AtW = self.A.T.dot(self.W)
+        for k in range(self.n_topic):
+            self.H[:, k] = self.H[:, k] * WtW[k, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
+            self.H[:, k] = np.maximum(self.H[:, k], epss)
+
+    def nmf_loss(self):
+        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        return loss
+
+    def get_loss(self):
+        return np.array(self.obj)
+
+    def get_lowrank_matrix(self):
+        return self.W, self.H
+
+    def get_decomposition_matrix(self):
+        return self.W, self.H
+
+    def save_format(self, Wfile='W.txt', Hfile='H.txt'):
+        np.savetxt(Wfile, self.W)
+        np.savetxt(Hfile, self.H)
\ No newline at end of file
diff --git a/nautilus_nlp/models/nmf_seanmf_models.py b/nautilus_nlp/models/nmf_seanmf_models.py
deleted file mode 100644
index e713175..0000000
--- a/nautilus_nlp/models/nmf_seanmf_models.py
+++ /dev/null
@@ -1,223 +0,0 @@
-'''
-Short Text Topic Modeling via SeaNMF
-'''
-import time
-import numpy as np
-from numpy.linalg import norm
-
-
-class SeaNMFL1(object):
-    def __init__(
-            self,
-            A, S,
-            IW1=[], IW2=[], IH=[],
-            alpha=1.0, beta=0.1, n_topic=10, max_iter=100, max_err=1e-3,
-            rand_init=True, fix_seed=False):
-        '''
-        0.5*||A-WH^T||_F^2+0.5*alpha*||S-WW_c^T||_F^2+0.5*beta*||W||_1^2
-        W = W1
-        Wc = W2
-        '''
-        if fix_seed:
-            np.random.seed(0)
-
-        self.A = A
-        self.S = S
-
-        self.n_row = A.shape[0]
-        self.n_col = A.shape[1]
-
-        self.n_topic = n_topic
-        self.max_iter = max_iter
-        self.alpha = alpha
-        self.beta = beta
-        self.B = np.ones([self.n_topic, 1])
-        self.max_err = max_err
-
-        if rand_init:
-            self.nmf_init_rand()
-        else:
-            self.nmf_init(IW1, IW2, IH)
-        self.nmf_iter()
-
-    def nmf_init_rand(self):
-        self.W1 = np.random.random((self.n_row, self.n_topic))
-        self.W2 = np.random.random((self.n_row, self.n_topic))
-        self.H = np.random.random((self.n_col, self.n_topic))
-
-        for k in range(self.n_topic):
-            self.W1[:, k] /= norm(self.W1[:, k])
-            self.W2[:, k] /= norm(self.W2[:, k])
-
-    def nmf_init(self, IW1, IW2, IH):
-        self.W1 = IW1
-        self.W2 = IW2
-        self.H = IH
-
-        for k in range(self.n_topic):
-            self.W1[:, k] /= norm(self.W1[:, k])
-            self.W2[:, k] /= norm(self.W2[:, k])
-
-    def nmf_iter(self):
-        loss_old = 1e20
-        print('loop begin')
-        start_time = time.time()
-        for i in range(self.max_iter):
-            self.nmf_solver()
-            loss = self.nmf_loss()
-            if loss_old - loss < self.max_err:
-                break
-            loss_old = loss
-            end_time = time.time()
-            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
-
-    def nmf_solver(self):
-        '''
-        using BCD framework
-        '''
-        epss = 1e-20
-        # Update W1
-        AH = np.dot(self.A, self.H)
-        SW2 = np.dot(self.S, self.W2)
-        HtH = np.dot(self.H.T, self.H)
-        W2tW2 = np.dot(self.W2.T, self.W2)
-        W11 = self.W1.dot(self.B)
-
-        for k in range(self.n_topic):
-            num0 = HtH[k, k] * self.W1[:, k] + self.alpha * W2tW2[k, k] * self.W1[:, k]
-            num1 = AH[:, k] + self.alpha * SW2[:, k]
-            num2 = np.dot(self.W1, HtH[:, k]) + self.alpha * np.dot(self.W1, W2tW2[:, k]) + self.beta * W11[0]
-            self.W1[:, k] = num0 + num1 - num2
-            self.W1[:, k] = np.maximum(self.W1[:, k], epss)  # project > 0
-            self.W1[:, k] /= norm(self.W1[:, k]) + epss  # normalize
-        # Update W2
-        W1tW1 = self.W1.T.dot(self.W1)
-        StW1 = np.dot(self.S, self.W1)
-        for k in range(self.n_topic):
-            self.W2[:, k] = self.W2[:, k] + StW1[:, k] - np.dot(self.W2, W1tW1[:, k])
-            self.W2[:, k] = np.maximum(self.W2[:, k], epss)
-        # Update H
-        AtW1 = np.dot(self.A.T, self.W1)
-        for k in range(self.n_topic):
-            self.H[:, k] = self.H[:, k] + AtW1[:, k] - np.dot(self.H, W1tW1[:, k])
-            self.H[:, k] = np.maximum(self.H[:, k], epss)
-
-    def nmf_loss(self):
-        '''
-        Calculate loss
-        '''
-        loss = norm(self.A - np.dot(self.W1, np.transpose(self.H)), 'fro') ** 2 / 2.0
-        if self.alpha > 0:
-            loss += self.alpha * norm(np.dot(self.W1, np.transpose(self.W2)) - self.S, 'fro') ** 2 / 2.0
-        if self.beta > 0:
-            loss += self.beta * norm(self.W1, 1) ** 2 / 2.0
-
-        return loss
-
-    def get_lowrank_matrix(self):
-        return self.W1, self.W2, self.H
-
-    def get_decomposition_matrix(self):
-        return self.W1, self.W2, self.H
-
-    def save_format(self, W1file='W.txt', W2file='Wc.txt', Hfile='H.txt'):
-        np.savetxt(W1file, self.W1)
-        np.savetxt(W2file, self.W2)
-        np.savetxt(Hfile, self.H)
-
-
-'''
-Topic Modeling via NMF
-'''
-
-
-class NMF(object):
-    def __init__(
-            self,
-            A, IW=[], IH=[],
-            n_topic=10, max_iter=100, max_err=1e-3,
-            rand_init=True):
-        '''
-        A = WH^T
-        '''
-        self.A = A
-        self.n_row = A.shape[0]
-        self.n_col = A.shape[1]
-
-        self.n_topic = n_topic
-        self.max_iter = max_iter
-        self.max_err = max_err
-
-        self.obj = []
-        if rand_init:
-            self.nmf_init_rand()
-        else:
-            self.nmf_init(IW, IH)
-        self.nmf_iter()
-
-    def nmf_init_rand(self):
-        self.W = np.random.random((self.n_row, self.n_topic))
-        self.H = np.random.random((self.n_col, self.n_topic))
-
-        for k in range(self.n_topic):
-            self.W[:, k] /= norm(self.W[:, k])
-
-    def nmf_init(self, IW, IH):
-        self.W = IW
-        self.H = IH
-
-        for k in range(self.n_topic):
-            self.W[:, k] /= norm(self.W[:, k])
-
-    def nmf_iter(self):
-        loss_old = 1e20
-        print('loop begin')
-        start_time = time.time()
-        for i in range(self.max_iter):
-            self.nmf_solver()
-            loss = self.nmf_loss()
-            self.obj.append(loss)
-
-            if loss_old - loss < self.max_err:
-                break
-            loss_old = loss
-            end_time = time.time()
-            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
-        print('loop end')
-
-    def nmf_solver(self):
-        '''
-        regular NMF without constraint.
-        Block Coordinate Decent
-        '''
-        epss = 1e-20
-
-        HtH = self.H.T.dot(self.H)
-        AH = self.A.dot(self.H)
-        for k in range(self.n_topic):
-            tmpW = self.W[:, k] * HtH[k, k] + AH[:, k] - np.dot(self.W, HtH[:, k])
-            self.W[:, k] = np.maximum(tmpW, epss)
-            self.W[:, k] /= norm(self.W[:, k]) + epss
-
-        WtW = self.W.T.dot(self.W)
-        AtW = self.A.T.dot(self.W)
-        for k in range(self.n_topic):
-            self.H[:, k] = self.H[:, k] * WtW[k, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
-            self.H[:, k] = np.maximum(self.H[:, k], epss)
-
-    def nmf_loss(self):
-        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
-        return loss
-
-    def get_loss(self):
-        return np.array(self.obj)
-
-    def get_lowrank_matrix(self):
-        return self.W, self.H
-
-    def get_decomposition_matrix(self):
-        return self.W, self.H
-
-    def save_format(self, Wfile='W.txt', Hfile='H.txt'):
-        np.savetxt(Wfile, self.W)
-        np.savetxt(Hfile, self.H)
\ No newline at end of file
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index 56a0994..1553fff 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -5,9 +5,8 @@
 def data_preparation(text, vocab_min_count=1, vocab_max_size=10000):
     """
     This function expects a list of documents (sentences) and returns the needed data to
-    make topic modeling for short text using NMF.
+    make topic modeling for short text using NMF model.
     :return:
-
     """
 
     vocab = {}
@@ -37,11 +36,9 @@ def data_preparation(text, vocab_min_count=1, vocab_max_size=10000):
     return encoded_text_id, vocab_list, vocab_arr
 
 
-def train_model(model,n_docs, n_terms, docs, n_topics= 20, max_iter= 20, max_err=0.1, alpha = 0, beta=0):
+def train_model(model, encoded_text_id, vocab_list, n_topics= 20, max_iter= 20, max_err=0.1, alpha = 0, beta=0):
     """
     :param model:
-    :param n_docs:
-    :param n_terms:
     :param docs:
     :param n_topics:
     :param max_iter:
@@ -50,10 +47,14 @@ def train_model(model,n_docs, n_terms, docs, n_topics= 20, max_iter= 20, max_err
     :param beta:
     :return:
     """
+
+    n_docs = len(encoded_text_id)
+    n_terms = len(vocab_list)
+
     if model == 'nmf':
         dt_mat = np.zeros([n_terms, n_docs])
         for k in range(n_docs):
-            for j in docs[k]:
+            for j in encoded_text_id[k]:
                 dt_mat[j, k] += 1.0
         model = NMF(
             dt_mat,
@@ -63,6 +64,81 @@ def train_model(model,n_docs, n_terms, docs, n_topics= 20, max_iter= 20, max_err
 
     return model
 
+
+def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
+    """
+    Computes the PMi score for each topic and the topKeywords describing each of them.
+    :param model:
+    :param encoded_text_id:
+    :param vocab_list:
+    :return: topics = dictionnary with the topic number and its topkeywords
+             pmi_score = dictionnary with the topic number and its PMI score
+    """
+
+    dt_mat = __build_cooccurence_matrix(n_terms=len(vocab_list), encoded_text_id=encoded_text_id)
+    W,_ = model.get_decomposition_matrix()
+    n_topic = W.shape[1]
+    PMI_arr = []
+    for k in range(n_topic):
+        top_keywords_index = W[:, k].argsort()[::-1][:n_topKeyword]
+        PMI_arr.append(__calculate_PMI(dt_mat, top_keywords_index))
+
+    index = np.argsort(PMI_arr)
+    topics = {}
+    pmi_score = {}
+    for k in index:
+        words = []
+        for w in np.argsort(W[:, k])[::-1][:n_topKeyword]:
+            words.append(vocab_list[w])
+        # Complete the topic and the score dicts. Format {Topic_number: words or score}
+        topics[k] = words
+        pmi_score[k] = PMI_arr[k]
+
+    return topics, pmi_score
+
+def get_assigned_topics(model):
+    """
+    Assign the topic number to the sentences used when training the model
+    :param model: trained model
+    :return topics_list: list having the same length as the training text containing topics assigned to each sentence.
+    """
+    _, H = model.get_decomposition_matrix()
+    H_probs = H / H.sum(axis=1, keepdims=True)
+    topics_list = list(np.argmax(H_probs, axis=1) + 1)
+    return topics_list
+
+
+def __build_cooccurence_matrix(n_terms, encoded_text_id):
+    dt_mat = np.zeros([n_terms, n_terms])
+    for itm in encoded_text_id:
+        for kk in itm:
+            for jj in itm:
+                if kk != jj:
+                    dt_mat[int(kk), int(jj)] += 1.0
+    return dt_mat
+
+def __calculate_PMI(AA, topKeywordsIndex):
+    '''
+    Method to compute PMi score
+    Reference:
+    Short and Sparse Text Topic Modeling via Self-Aggregation
+    '''
+    D1 = np.sum(AA)
+    n_tp = len(topKeywordsIndex)
+    PMI = []
+    for index1 in topKeywordsIndex:
+        for index2 in topKeywordsIndex:
+            if index2 < index1:
+                if AA[index1, index2] == 0:
+                    PMI.append(0.0)
+                else:
+                    C1 = np.sum(AA[index1])
+                    C2 = np.sum(AA[index2])
+                    PMI.append(np.log(AA[index1,index2]*D1/C1/C2))
+    avg_PMI = 2.0*np.sum(PMI)/float(n_tp)/(float(n_tp)-1.0)
+
+    return avg_PMI
+
 ## TEST
 
 text = ['abs souscription ird abs collaborateur message bloquant finaliser merci selectionner un personne physique comme assurer principal',
@@ -71,14 +147,15 @@ def train_model(model,n_docs, n_terms, docs, n_topics= 20, max_iter= 20, max_err
 'abs souscription ird souscription un affaire nouveau abs collab arriver pas finaliser son contrat']
 encoded_text_id, vocab_list, vocab_arr = data_preparation(text)
 
-print(encoded_text_id)
+#print(encoded_text_id)
+#print(vocab_arr)
+
+x = train_model('nmf', encoded_text_id, vocab_list, n_topics= 3)
+topics, pmi_score = show_dominant_topic(x, encoded_text_id, vocab_list, n_topKeyword =10)
 
-print(vocab_arr)
+print(topics)
 
-n_docs = len(encoded_text_id)
-n_terms = len(vocab_list)
-docs = encoded_text_id
-vocab = vocab_list
+print(pmi_score)
 
-x = train_model('nmf', n_docs, n_terms, docs)
-W, H = x.get_decomposition_matrix()
+list_of_topics= get_assigned_topics(x)
+print(list_of_topics)

From 7b036bb2c75ff2b4ada197c8ad32657f70840113 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Tue, 27 Aug 2019 17:51:29 +0200
Subject: [PATCH 229/496] add pyldavis viz for Short text

---
 tests/test_topic_modeling_shot_text.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 tests/test_topic_modeling_shot_text.py

diff --git a/tests/test_topic_modeling_shot_text.py b/tests/test_topic_modeling_shot_text.py
new file mode 100644
index 0000000..e69de29

From 16f4f1ae4992484256a3cba9b6bad3644058c2c0 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Tue, 27 Aug 2019 17:53:46 +0200
Subject: [PATCH 230/496] add pyldavis viz for Short text

---
 .../models/topic_modeling_short_text.py       | 118 +++++++++++-------
 1 file changed, 72 insertions(+), 46 deletions(-)

diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index 1553fff..38fc507 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -1,12 +1,15 @@
 import re
-from nmf_seanmf_models import *
+from nautilus_nlp.models.nmf_model import *
 import numpy as np
+import pyLDAvis
 
-def data_preparation(text, vocab_min_count=1, vocab_max_size=10000):
+def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
     """
     This function expects a list of documents (sentences) and returns the needed data to
     make topic modeling for short text using NMF model.
-    :return:
+    :return:  encoded_text_id: list of encoded sentences using vocab IDs
+              vocab_list: list of vocabulary
+              vocab_arr: array with vocab frequency counts
     """
 
     vocab = {}
@@ -36,31 +39,30 @@ def data_preparation(text, vocab_min_count=1, vocab_max_size=10000):
     return encoded_text_id, vocab_list, vocab_arr
 
 
-def train_model(model, encoded_text_id, vocab_list, n_topics= 20, max_iter= 20, max_err=0.1, alpha = 0, beta=0):
+def train_nmf_model(encoded_text_id, vocab_list, n_topics= 20, max_iter= 20, max_err=0.1, alpha = 0, beta=0):
     """
-    :param model:
-    :param docs:
-    :param n_topics:
-    :param max_iter:
-    :param max_err:
-    :param alpha:
-    :param beta:
-    :return:
+    :param encoded_text_id: list of encoded sentences
+    :param vocab_list: list of vocabulary
+    :param n_topics: number of topics
+    :param max_iter: maximum number of iterations while training
+    :param max_err: training error
+    :param alpha: regularization param for the NMF model
+    :param beta: regularization param for the NMF model
+    :return: Trained NMF model
     """
 
     n_docs = len(encoded_text_id)
     n_terms = len(vocab_list)
 
-    if model == 'nmf':
-        dt_mat = np.zeros([n_terms, n_docs])
-        for k in range(n_docs):
-            for j in encoded_text_id[k]:
-                dt_mat[j, k] += 1.0
-        model = NMF(
-            dt_mat,
-            n_topic=n_topics,
-            max_iter=max_iter,
-            max_err=max_err)
+    dt_mat = np.zeros([n_terms, n_docs])
+    for k in range(n_docs):
+        for j in encoded_text_id[k]:
+            dt_mat[j, k] += 1.0
+    model = NMF(
+        dt_mat,
+        n_topic=n_topics,
+        max_iter=max_iter,
+        max_err=max_err)
 
     return model
 
@@ -68,9 +70,9 @@ def train_model(model, encoded_text_id, vocab_list, n_topics= 20, max_iter= 20,
 def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
     """
     Computes the PMi score for each topic and the topKeywords describing each of them.
-    :param model:
-    :param encoded_text_id:
-    :param vocab_list:
+    :param model: trained NMF model
+    :param encoded_text_id: list of encoded sentences
+    :param vocab_list: list of vocabulary
     :return: topics = dictionnary with the topic number and its topkeywords
              pmi_score = dictionnary with the topic number and its PMI score
     """
@@ -99,7 +101,7 @@ def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
 def get_assigned_topics(model):
     """
     Assign the topic number to the sentences used when training the model
-    :param model: trained model
+    :param model: trained model for short text
     :return topics_list: list having the same length as the training text containing topics assigned to each sentence.
     """
     _, H = model.get_decomposition_matrix()
@@ -108,6 +110,49 @@ def get_assigned_topics(model):
     return topics_list
 
 
+def show_pyldavis(model, encoded_text_id, vocab_arr):
+    """
+    :param model: trained model
+    :param encoded_text_id: encoded_text_id: list of encoded sentences
+    :param vocab_arr: array of vocabulary frequency
+    :return: pyldavis topics plot
+    """
+    data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
+    vis_data = pyLDAvis.prepare(**data)
+    return pyLDAvis.display(vis_data)
+
+def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
+    """
+    Transform the model decomposed matrix to create topic term and document topics matrices
+    and prepare data to feed pyldavis.
+    link : http://jeriwieringa.com/2018/07/17/pyLDAviz-and-Mallet/
+    :return dict of data needed by pyldavis
+    """
+    # 1 List of documents lengths
+    doc_length_values=[]
+    for doc in encoded_text_id:
+        doc_length_values.append(len(doc))
+    # 2 List of vocab
+    list_vocab = list(map(lambda x: x[0], vocab_arr))
+    # 3 List of vocab. Frequency
+    freq_vocab = list(map(lambda x: x[1], vocab_arr))
+    W, H = model.get_decomposition_matrix()
+    # Normlize the decomposition to get probabilities
+    W_probs = W / W.sum(axis=1, keepdims=True)
+    # 4 topic term matrix phi
+    phi = W_probs.T
+    # 5 document term matrix theta
+    theta = H / H.sum(axis=1, keepdims=True)
+
+    data = {'topic_term_dists': phi,
+            'doc_topic_dists': theta,
+            'doc_lengths': doc_length_values,
+            'vocab': list_vocab,
+            'term_frequency': freq_vocab
+            }
+
+    return data
+
 def __build_cooccurence_matrix(n_terms, encoded_text_id):
     dt_mat = np.zeros([n_terms, n_terms])
     for itm in encoded_text_id:
@@ -115,6 +160,7 @@ def __build_cooccurence_matrix(n_terms, encoded_text_id):
             for jj in itm:
                 if kk != jj:
                     dt_mat[int(kk), int(jj)] += 1.0
+
     return dt_mat
 
 def __calculate_PMI(AA, topKeywordsIndex):
@@ -139,23 +185,3 @@ def __calculate_PMI(AA, topKeywordsIndex):
 
     return avg_PMI
 
-## TEST
-
-text = ['abs souscription ird abs collaborateur message bloquant finaliser merci selectionner un personne physique comme assurer principal',
-'ref contrat af752001033 date heure creation avener non technique abs abs souscription ird collaborateur souhaiter repasser contrat mensuel selectionner bon rib car rib present sur contrat être errone celer cause impaye sur contrat',
-'demande refair devis faire depuis im non traduire dans ab avec declanchement visa correction un message erreur',
-'abs souscription ird souscription un affaire nouveau abs collab arriver pas finaliser son contrat']
-encoded_text_id, vocab_list, vocab_arr = data_preparation(text)
-
-#print(encoded_text_id)
-#print(vocab_arr)
-
-x = train_model('nmf', encoded_text_id, vocab_list, n_topics= 3)
-topics, pmi_score = show_dominant_topic(x, encoded_text_id, vocab_list, n_topKeyword =10)
-
-print(topics)
-
-print(pmi_score)
-
-list_of_topics= get_assigned_topics(x)
-print(list_of_topics)

From 0057b5c9349dcec910953238b8da8433b53f97f3 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Wed, 28 Aug 2019 10:46:38 +0200
Subject: [PATCH 231/496] Add notebook short text modeling

---
 nautilus_nlp/models/nmf_model.py              |   3 -
 .../models/topic_modeling_short_text.py       |  10 +-
 .../6. Topic Modeling - short text.ipynb      | 312 ++++++++++++++++++
 ...t.py => test_topic_modeling_short_text.py} |   0
 4 files changed, 320 insertions(+), 5 deletions(-)
 create mode 100644 notebooks/6. Topic Modeling - short text.ipynb
 rename tests/{test_topic_modeling_shot_text.py => test_topic_modeling_short_text.py} (100%)

diff --git a/nautilus_nlp/models/nmf_model.py b/nautilus_nlp/models/nmf_model.py
index 252b612..93a6715 100644
--- a/nautilus_nlp/models/nmf_model.py
+++ b/nautilus_nlp/models/nmf_model.py
@@ -88,9 +88,6 @@ def nmf_loss(self):
     def get_loss(self):
         return np.array(self.obj)
 
-    def get_lowrank_matrix(self):
-        return self.W, self.H
-
     def get_decomposition_matrix(self):
         return self.W, self.H
 
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index 38fc507..9b1bb8f 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -36,6 +36,7 @@ def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
         sentence = re.split('\s', sentence)
         sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
         encoded_text_id.append(sentence)
+
     return encoded_text_id, vocab_list, vocab_arr
 
 
@@ -104,9 +105,11 @@ def get_assigned_topics(model):
     :param model: trained model for short text
     :return topics_list: list having the same length as the training text containing topics assigned to each sentence.
     """
+
     _, H = model.get_decomposition_matrix()
     H_probs = H / H.sum(axis=1, keepdims=True)
     topics_list = list(np.argmax(H_probs, axis=1) + 1)
+
     return topics_list
 
 
@@ -117,8 +120,10 @@ def show_pyldavis(model, encoded_text_id, vocab_arr):
     :param vocab_arr: array of vocabulary frequency
     :return: pyldavis topics plot
     """
+
     data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
     vis_data = pyLDAvis.prepare(**data)
+
     return pyLDAvis.display(vis_data)
 
 def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
@@ -128,6 +133,7 @@ def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
     link : http://jeriwieringa.com/2018/07/17/pyLDAviz-and-Mallet/
     :return dict of data needed by pyldavis
     """
+
     # 1 List of documents lengths
     doc_length_values=[]
     for doc in encoded_text_id:
@@ -169,6 +175,7 @@ def __calculate_PMI(AA, topKeywordsIndex):
     Reference:
     Short and Sparse Text Topic Modeling via Self-Aggregation
     '''
+
     D1 = np.sum(AA)
     n_tp = len(topKeywordsIndex)
     PMI = []
@@ -183,5 +190,4 @@ def __calculate_PMI(AA, topKeywordsIndex):
                     PMI.append(np.log(AA[index1,index2]*D1/C1/C2))
     avg_PMI = 2.0*np.sum(PMI)/float(n_tp)/(float(n_tp)-1.0)
 
-    return avg_PMI
-
+    return avg_PMI
\ No newline at end of file
diff --git a/notebooks/6. Topic Modeling - short text.ipynb b/notebooks/6. Topic Modeling - short text.ipynb
new file mode 100644
index 0000000..e6b7417
--- /dev/null
+++ b/notebooks/6. Topic Modeling - short text.ipynb	
@@ -0,0 +1,312 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Topic modeling for short text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nautilus_nlp.models.topic_modeling_short_text import *\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Preparation "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is possible to use Nautilus functions to clean and preprocess raw text. The ouput of the cleaning process should be a list of sentences (strings). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = ['Cola 1.5L Carrefour',\n",
+    " 'Pepsi Cola Light 1.5L',\n",
+    " 'Pepsi Cola Twist Light',\n",
+    " 'Cola 1.5L CRF DISC',\n",
+    " 'Coca-Cola Light 1.5L',\n",
+    " 'Coca-Cola Light 4x0.5L',\n",
+    " 'Coca-Cola Light 6x0.3L',\n",
+    " 'Panzani 200g x 4 bio',\n",
+    " 'Rustichella 150g bio',\n",
+    " 'De Cecco - Fusilli bio',\n",
+    " 'Gerblé sans Gluten50g',\n",
+    " 'Penne de riz 100g sans gluten',\n",
+    " 'Spaghetti de maïs 50g sans Glute']\n",
+    "\n",
+    "encoded_text_id, vocab_list, vocab_arr = prepare_data(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['1.5L', 4],\n",
+       " ['Coca-Cola', 3],\n",
+       " ['Cola', 4],\n",
+       " ['Light', 5],\n",
+       " ['Pepsi', 2],\n",
+       " ['bio', 3],\n",
+       " ['de', 2],\n",
+       " ['sans', 3]]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vocab_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2, 0], [4, 2, 3, 0], [4, 2, 3], [2, 0], [1, 3, 0], [1, 3], [1, 3], [5], [5], [5], [7], [6, 7], [6, 7]]\n",
+      "[[2, 0], [4, 2, 3, 0], [4, 2, 3], [2, 0], [1, 3, 0], [1, 3], [1, 3], [5], [5], [5], [7], [6, 7], [6, 7]]\n",
+      "[['1.5L', 4], ['Coca-Cola', 3], ['Cola', 4], ['Light', 5], ['Pepsi', 2], ['bio', 3], ['de', 2], ['sans', 3]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(encoded_text_id)\n",
+    "print(encoded_text_id)\n",
+    "print(vocab_arr)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train the NMF model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loop begin\n",
+      "Step=0, Loss=5.91164999713721, Time=0.0003788471221923828s\n",
+      "Step=1, Loss=4.985359112354993, Time=0.0023560523986816406s\n",
+      "Step=2, Loss=4.323260756623319, Time=0.0044097900390625s\n",
+      "Step=3, Loss=4.021169618891115, Time=0.005792856216430664s\n",
+      "Step=4, Loss=3.9201702703506403, Time=0.006429910659790039s\n",
+      "loop end\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = train_nmf_model(encoded_text_id, vocab_list, n_topics= 3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get keywords description of the topics\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "topic 2 : Pmi: 0.22768662780017118 ['1.5L', 'Cola', 'Pepsi', 'Light', 'bio']\n",
+      "topic 0 : Pmi: 0.42152248372930645 ['Light', 'Pepsi', 'sans', 'Cola', 'de']\n",
+      "topic 1 : Pmi: 0.4373829867469703 ['Coca-Cola', 'Light', '1.5L', 'sans', 'de']\n"
+     ]
+    }
+   ],
+   "source": [
+    "topics, pmi_score = show_dominant_topic(x, encoded_text_id, vocab_list, n_topKeyword =5)\n",
+    "\n",
+    "\n",
+    "for t in topics.keys():\n",
+    "    print('topic', t,': Pmi:', pmi_score[t], topics[t])\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assign topics to sentences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3, 3, 1, 3, 2, 2, 2, 3, 3, 3, 1, 1, 1]\n",
+      "topic  3 ---> Cola 1.5L Carrefour\n",
+      "topic  3 ---> Pepsi Cola Light 1.5L\n",
+      "topic  1 ---> Pepsi Cola Twist Light\n",
+      "topic  3 ---> Cola 1.5L CRF DISC\n",
+      "topic  2 ---> Coca-Cola Light 1.5L\n",
+      "topic  2 ---> Coca-Cola Light 4x0.5L\n",
+      "topic  2 ---> Coca-Cola Light 6x0.3L\n",
+      "topic  3 ---> Panzani 200g x 4 bio\n",
+      "topic  3 ---> Rustichella 150g bio\n",
+      "topic  3 ---> De Cecco - Fusilli bio\n",
+      "topic  1 ---> Gerblé sans Gluten50g\n",
+      "topic  1 ---> Penne de riz 100g sans gluten\n",
+      "topic  1 ---> Spaghetti de maïs 50g sans Glute\n"
+     ]
+    }
+   ],
+   "source": [
+    "list_of_topics= get_assigned_topics(x)\n",
+    "print(list_of_topics)\n",
+    "\n",
+    "for i in range(len(list_of_topics)) : \n",
+    "    print('topic ', list_of_topics[i],'--->',text[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot topics with pyldavis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/kaislaribi/anaconda3/envs/DataScience/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el973044658186304304441590\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "\n",
+       "var ldavis_el973044658186304304441590_data = {\"mdsDat\": {\"x\": [-0.07207828005578075, -0.27085879991560496, 0.3429370799713856], \"y\": [-0.28854072014416055, 0.19509552731956145, 0.09344519282459897], \"topics\": [1, 2, 3], \"cluster\": [1, 1, 1], \"Freq\": [40.10719756781703, 35.155302123790236, 24.73750030839274]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\"], \"Freq\": [9.0, 9.0, 10.0, 6.0, 9.0, 9.0, 10.0, 7.0, 10.427871367632429, 8.486483566091643, 7.272729095695264, 1.2006516108182672, 0.41204790158952415, 6.421558091596925e-20, 5.013797446678124e-20, 2.4883131435107527e-20, 9.14037855218546, 9.14037855218546, 8.087967197531219, 4.1120406489053, 2.76558788795799, 1.253310853243789e-18, 3.2243865698780584e-20, 2.5201191342509088e-20, 6.431750080182113, 3.2841126876287086, 1.1974180259819893, 5.98670235584042e-19, 3.97476403756747e-20, 3.103399751032683e-20, 1.6274775761634947e-20, 1.153801355593048e-20], \"Term\": [\"de\", \"sans\", \"bio\", \"Coca-Cola\", \"Pepsi\", \"1.5L\", \"Cola\", \"Light\", \"bio\", \"1.5L\", \"Cola\", \"Pepsi\", \"Light\", \"de\", \"sans\", \"Coca-Cola\", \"sans\", \"de\", \"Pepsi\", \"Light\", \"Cola\", \"bio\", \"Coca-Cola\", \"1.5L\", \"Coca-Cola\", \"Light\", \"1.5L\", \"bio\", \"de\", \"sans\", \"Pepsi\", \"Cola\"], \"Total\": [9.0, 9.0, 10.0, 6.0, 9.0, 9.0, 10.0, 7.0, 10.427871367632429, 9.683901592073632, 10.038316983653253, 9.288618808349486, 7.808201238123532, 9.14037855218546, 9.14037855218546, 6.431750080182113, 9.14037855218546, 9.14037855218546, 9.288618808349486, 7.808201238123532, 10.038316983653253, 10.427871367632429, 6.431750080182113, 9.683901592073632, 6.431750080182113, 7.808201238123532, 9.683901592073632, 10.427871367632429, 9.14037855218546, 9.14037855218546, 9.288618808349486, 10.038316983653253], \"loglift\": [8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 1.9316, 1.7996, 1.6093, -0.1143, -1.0102, -44.4731, -44.7206, -45.0697, 2.0634, 2.0634, 1.925, 1.4221, 0.7742, -41.5018, -44.6788, -45.3345, 2.4149, 1.5488, 0.3246, -41.8892, -44.4696, -44.717, -45.3786, -45.8002], \"logprob\": [8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0, -0.206, -0.3604, -2.1616, -3.2311, -46.5365, -46.784, -47.4846, 0.0, 0.0, -0.1223, -0.7988, -1.1954, -43.4334, -47.0937, -47.3401, 0.0, -0.6722, -1.6811, -43.8208, -46.533, -46.7805, -47.4259, -47.7699]}, \"token.table\": {\"Topic\": [1, 3, 3, 1, 2, 2, 3, 1, 2, 1, 2, 2], \"Freq\": [0.8261133102124952, 0.1032641637765619, 0.9328720682862902, 0.6973280492535796, 0.2988548782515341, 0.5122818787597335, 0.3842114090698001, 0.10765863263772928, 0.8612690611018342, 0.9589684843101806, 0.984641932346238, 0.984641932346238], \"Term\": [\"1.5L\", \"1.5L\", \"Coca-Cola\", \"Cola\", \"Cola\", \"Light\", \"Light\", \"Pepsi\", \"Pepsi\", \"bio\", \"de\", \"sans\"]}, \"R\": 8, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 2]};\n",
+       "\n",
+       "function LDAvis_load_lib(url, callback){\n",
+       "  var s = document.createElement('script');\n",
+       "  s.src = url;\n",
+       "  s.async = true;\n",
+       "  s.onreadystatechange = s.onload = callback;\n",
+       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "}\n",
+       "\n",
+       "if(typeof(LDAvis) !== \"undefined\"){\n",
+       "   // already loaded: just create the visualization\n",
+       "   !function(LDAvis){\n",
+       "       new LDAvis(\"#\" + \"ldavis_el973044658186304304441590\", ldavis_el973044658186304304441590_data);\n",
+       "   }(LDAvis);\n",
+       "}else if(typeof define === \"function\" && define.amd){\n",
+       "   // require.js is available: use it to load d3/LDAvis\n",
+       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el973044658186304304441590\", ldavis_el973044658186304304441590_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el973044658186304304441590\", ldavis_el973044658186304304441590_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "show_pyldavis(x, encoded_text_id, vocab_arr)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/test_topic_modeling_shot_text.py b/tests/test_topic_modeling_short_text.py
similarity index 100%
rename from tests/test_topic_modeling_shot_text.py
rename to tests/test_topic_modeling_short_text.py

From 4d44f0f9836aefbe1f762e7274fb6f43d7b0f793 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Wed, 28 Aug 2019 11:25:32 +0200
Subject: [PATCH 232/496] tests for short text modeling

---
 .../models/topic_modeling_short_text.py       |  2 +-
 tests/test_topic_modeling_short_text.py       | 27 +++++++++++++++++++
 2 files changed, 28 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index 9b1bb8f..930c305 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -108,7 +108,7 @@ def get_assigned_topics(model):
 
     _, H = model.get_decomposition_matrix()
     H_probs = H / H.sum(axis=1, keepdims=True)
-    topics_list = list(np.argmax(H_probs, axis=1) + 1)
+    topics_list = list(np.argmax(H_probs, axis=1))
 
     return topics_list
 
diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index e69de29..0b3867c 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -0,0 +1,27 @@
+import pytest
+from nautilus_nlp.models.topic_modeling_short_text import prepare_data
+
+
+def test_prepare_data():
+
+    text = ['Cola 1.5L Carrefour',
+            'Pepsi Cola Light 1.5L',
+            'Pepsi Cola Twist Light',
+            'Cola 1.5L CRF DISC',
+            'Coca-Cola Light 1.5L',
+            'Coca-Cola Light 4x0.5L',
+            'Coca-Cola Light 6x0.3L',
+            'Panzani 200g x 4 bio',
+            'Rustichella 150g bio',
+            'De Cecco - Fusilli bio',
+            'Gerblé sans Gluten50g',
+            'Penne de riz 100g sans gluten',
+            'Spaghetti de maïs 50g sans Glute']
+
+    expected1 = [['1.5L', 4],['Coca-Cola', 3],['Cola', 4],['Light', 5],['Pepsi', 2],['bio', 3],['de', 2],['sans', 3]]
+    expected2 = [['1.5L', 4], ['Coca-Cola', 3], ['Cola', 4], ['Light', 5], ['bio', 3], ['sans', 3]]
+    _,_,output1 = prepare_data(text)
+    _,_,output2 = prepare_data(text, vocab_min_count=2)
+
+    assert output1 == expected1
+    assert output2 == expected2

From 3edfe12b36b9da59f2b629e857abbb71cd96bf3d Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 28 Aug 2019 19:14:56 +0200
Subject: [PATCH 233/496] Change the travis script for fasttext install

---
 .travis.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 6c093cd..93381c6 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,8 +6,7 @@ services:
   - docker
 
 before_script:
-  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && cd ..
-  - git clone https://github.com/facebookresearch/fastText.git && cd  fastText && pip install . && cd .. && ls 
+  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
   - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 
   - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip

From 1cb200f37821845fc97a2b8442b4b2d56cd18fd6 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Thu, 29 Aug 2019 15:33:16 +0200
Subject: [PATCH 234/496] Fix requirement to solve security issue with NLTK
 package

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 4dfae63..59d0f2e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -21,7 +21,7 @@ matplotlib>=3.0.3
 mosestokenizer
 numpy>1.15.4
 stop_words==2018.7.23
-nltk==3.4
+nltk>=3.4.5
 textblob==0.15.3
 textblob_fr==0.2.0
 pandas>=0.23.4

From 5e105375231b03122be336c07e8bbe96bee1ca8a Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Thu, 5 Sep 2019 14:34:26 +0200
Subject: [PATCH 235/496] improve doc string

---
 nautilus_nlp/models/nmf_model.py              | 63 ++++++++++---------
 .../models/topic_modeling_short_text.py       |  9 +--
 2 files changed, 39 insertions(+), 33 deletions(-)

diff --git a/nautilus_nlp/models/nmf_model.py b/nautilus_nlp/models/nmf_model.py
index 93a6715..0a18a6b 100644
--- a/nautilus_nlp/models/nmf_model.py
+++ b/nautilus_nlp/models/nmf_model.py
@@ -1,7 +1,7 @@
-
 import time
 import numpy as np
 from numpy.linalg import norm
+from tqdm import tqdm
 
 '''
 Topic Modeling via NMF
@@ -13,9 +13,19 @@ def __init__(
             A, IW=[], IH=[],
             n_topic=10, max_iter=100, max_err=1e-3,
             rand_init=True):
-        '''
-        A = WH^T
-        '''
+        """
+        The objective of the NMF model is to approximate the term-document matrix A by two lower-rank matrices W and H.
+        The process is iterative and we denote IW and IH the the matrix W and H that are updated at each step.
+
+        :param A: The term-document matrix
+        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+        and its elements are the weights of the corresponding keywords.
+        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
+        :param n_topic: Number of selected topics
+        :param max_iter: Maximum number of iterations to update W and H
+        :param max_err: maximum error under which we consider that the loop converged
+        :param rand_init: random init boolean
+        """
         self.A = A
         self.n_row = A.shape[0]
         self.n_col = A.shape[1]
@@ -24,42 +34,41 @@ def __init__(
         self.max_iter = max_iter
         self.max_err = max_err
 
-        self.obj = []
-        if rand_init:
-            self.nmf_init_rand()
-        else:
-            self.nmf_init(IW, IH)
+        self.loss_hist = []
+        self.nmf_mat_init(rand_init)
         self.nmf_iter()
 
-    def nmf_init_rand(self):
-        self.W = np.random.random((self.n_row, self.n_topic))
-        self.H = np.random.random((self.n_col, self.n_topic))
-
-        for k in range(self.n_topic):
-            self.W[:, k] /= norm(self.W[:, k])
-
-    def nmf_init(self, IW, IH):
-        self.W = IW
-        self.H = IH
-
+    def nmf_mat_init(self, rand_init):
+        """
+        Init Matrices W and H initially either randomly or using existing IW, IH matrices taken when iterating.
+        :param rand_init: Boolean indicating initial random init
+        """
+        if rand_init:
+            self.W = np.random.random((self.n_row, self.n_topic))
+            self.H = np.random.random((self.n_col, self.n_topic))
+        else:
+            self.W = IW
+            self.H = IH
         for k in range(self.n_topic):
             self.W[:, k] /= norm(self.W[:, k])
 
     def nmf_iter(self):
+        """
+        Main iterative loop for matrix decomposition
+        """
         loss_old = 1e20
-        print('loop begin')
         start_time = time.time()
-        for i in range(self.max_iter):
+        for i in tqdm(range(self.max_iter)):
             self.nmf_solver()
             loss = self.nmf_loss()
-            self.obj.append(loss)
+            self.loss_hist.append(loss)
 
             if loss_old - loss < self.max_err:
+                print('Matrix decomposition loop converged!')
                 break
             loss_old = loss
             end_time = time.time()
             print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
-        print('loop end')
 
     def nmf_solver(self):
         '''
@@ -86,11 +95,7 @@ def nmf_loss(self):
         return loss
 
     def get_loss(self):
-        return np.array(self.obj)
+        return np.array(self.loss_hist)
 
     def get_decomposition_matrix(self):
         return self.W, self.H
-
-    def save_format(self, Wfile='W.txt', Hfile='H.txt'):
-        np.savetxt(Wfile, self.W)
-        np.savetxt(Hfile, self.H)
\ No newline at end of file
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index 930c305..bc1c82b 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -5,13 +5,13 @@
 
 def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
     """
-    This function expects a list of documents (sentences) and returns the needed data to
-    make topic modeling for short text using NMF model.
+    :param text: list of str on which the topic modeling will be performed
+    :param vocab_min_count: minimum number of occurrences of a word to be considered in the vocabulary
+    :param vocab_max_size: maximum number of word in the vocabulary
     :return:  encoded_text_id: list of encoded sentences using vocab IDs
               vocab_list: list of vocabulary
               vocab_arr: array with vocab frequency counts
     """
-
     vocab = {}
     for sentence in text:
         sentence = re.split('\s', sentence)
@@ -190,4 +190,5 @@ def __calculate_PMI(AA, topKeywordsIndex):
                     PMI.append(np.log(AA[index1,index2]*D1/C1/C2))
     avg_PMI = 2.0*np.sum(PMI)/float(n_tp)/(float(n_tp)-1.0)
 
-    return avg_PMI
\ No newline at end of file
+    return avg_PMI
+

From 25fd5c94ee12107a1a196627e626c34faedbc100 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Fri, 6 Sep 2019 13:00:20 +0200
Subject: [PATCH 236/496] improve code cooccurence matrix

---
 .../models/topic_modeling_short_text.py       | 42 +++++++++++++------
 1 file changed, 29 insertions(+), 13 deletions(-)

diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index bc1c82b..c0b366a 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -1,4 +1,6 @@
 import re
+from collections import Counter
+from itertools import permutations
 from nautilus_nlp.models.nmf_model import *
 import numpy as np
 import pyLDAvis
@@ -13,13 +15,14 @@ def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
               vocab_arr: array with vocab frequency counts
     """
     vocab = {}
+
+    # Tokens_list is a list of sub-lists where each sub-list contains a sentences' tokens.
+    tokens_list=[]
     for sentence in text:
         sentence = re.split('\s', sentence)
-        for wd in sentence:
-                try:
-                    vocab[wd] += 1
-                except:
-                    vocab[wd] = 1
+        tokens_list.append(sentence)
+    vocab = dict(Counter(x for xs in tokens_list for x in xs))
+
     # Create Vocab array ( list of sorted vocab + counts )
     vocab_arr = [[wd, vocab[wd]] for wd in vocab if vocab[wd] > vocab_min_count]
     vocab_arr = sorted(vocab_arr, key=lambda k: k[1])[::-1]
@@ -99,6 +102,7 @@ def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
 
     return topics, pmi_score
 
+
 def get_assigned_topics(model):
     """
     Assign the topic number to the sentences used when training the model
@@ -126,6 +130,7 @@ def show_pyldavis(model, encoded_text_id, vocab_arr):
 
     return pyLDAvis.display(vis_data)
 
+
 def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
     """
     Transform the model decomposed matrix to create topic term and document topics matrices
@@ -159,15 +164,26 @@ def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
 
     return data
 
+
+
 def __build_cooccurence_matrix(n_terms, encoded_text_id):
-    dt_mat = np.zeros([n_terms, n_terms])
-    for itm in encoded_text_id:
-        for kk in itm:
-            for jj in itm:
-                if kk != jj:
-                    dt_mat[int(kk), int(jj)] += 1.0
-
-    return dt_mat
+    """
+    The cooccurence matrix represents the number of times each word
+    appeared in the same context as another word from the vocabulary.
+    The matrix has the size of vocab. columns and rows denote the vocab.
+    Cell values represent the number of times words occured together in the same sentence
+
+    :param :encoded_text_id : list of encoded sentences
+    :return: res: the co-occurence matrix
+
+    """
+    res = np.zeros([n_terms, n_terms])
+    for row in encoded_text_id:
+        counts = Counter(row)
+        for key_from, key_to in permutations(counts, 2):
+            res[key_from, key_to] += counts[key_from] * counts[key_to]
+    return res
+
 
 def __calculate_PMI(AA, topKeywordsIndex):
     '''

From e022d931d5e23ba843983eaafce4c693b7b5d663 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Mon, 9 Sep 2019 15:32:15 +0200
Subject: [PATCH 237/496] test cases

---
 tests/test_topic_modeling_short_text.py | 108 ++++++++++++++++++++----
 1 file changed, 92 insertions(+), 16 deletions(-)

diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index 0b3867c..bd1149d 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -1,22 +1,24 @@
-import pytest
-from nautilus_nlp.models.topic_modeling_short_text import prepare_data
+from nautilus_nlp.models.topic_modeling_short_text import prepare_data, \
+    __build_cooccurence_matrix, train_nmf_model, prepare_data_pyldavis, \
+    show_dominant_topic, get_assigned_topics
+import numpy as np
 
+text = ['Cola 1.5L Carrefour',
+        'Pepsi Cola Light 1.5L',
+        'Pepsi Cola Twist Light',
+        'Cola 1.5L CRF DISC',
+        'Coca-Cola Light 1.5L',
+        'Coca-Cola Light 4x0.5L',
+        'Coca-Cola Light 6x0.3L',
+        'Panzani 200g x 4 bio',
+        'Rustichella 150g bio',
+        'De Cecco - Fusilli bio',
+        'Gerblé sans Gluten50g',
+        'Penne de riz 100g sans gluten',
+        'Spaghetti de maïs 50g sans Glute']
 
-def test_prepare_data():
 
-    text = ['Cola 1.5L Carrefour',
-            'Pepsi Cola Light 1.5L',
-            'Pepsi Cola Twist Light',
-            'Cola 1.5L CRF DISC',
-            'Coca-Cola Light 1.5L',
-            'Coca-Cola Light 4x0.5L',
-            'Coca-Cola Light 6x0.3L',
-            'Panzani 200g x 4 bio',
-            'Rustichella 150g bio',
-            'De Cecco - Fusilli bio',
-            'Gerblé sans Gluten50g',
-            'Penne de riz 100g sans gluten',
-            'Spaghetti de maïs 50g sans Glute']
+def test_prepare_data():
 
     expected1 = [['1.5L', 4],['Coca-Cola', 3],['Cola', 4],['Light', 5],['Pepsi', 2],['bio', 3],['de', 2],['sans', 3]]
     expected2 = [['1.5L', 4], ['Coca-Cola', 3], ['Cola', 4], ['Light', 5], ['bio', 3], ['sans', 3]]
@@ -25,3 +27,77 @@ def test_prepare_data():
 
     assert output1 == expected1
     assert output2 == expected2
+
+
+def test_build_cooccurence_matrix():
+    # The co-occurence matrix is an array with the list of vocab in rows and in columns
+    # The weights denote the occurrences of a word i with a word j in same sentence for i != j and 0 elsewhere
+
+    case_1 = [[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]]
+    case_2 = [[0, 1, 2, 3, 4], [5, 6], [1]]
+
+    mat_1 = __build_cooccurence_matrix(4, case_1)
+    mat_2 = __build_cooccurence_matrix(7, case_2)
+
+    expected_1 =np.array(
+      [[0., 1., 2., 4.],
+       [1., 0., 2., 3.],
+       [2., 2., 0., 4.],
+       [4., 3., 4., 0.]])
+
+
+    expected_2 = np.array(
+      [[0., 1., 1., 1., 1., 0., 0.],
+       [1., 0., 1., 1., 1., 0., 0.],
+       [1., 1., 0., 1., 1., 0., 0.],
+       [1., 1., 1., 0., 1., 0., 0.],
+       [1., 1., 1., 1., 0., 0., 0.],
+       [0., 0., 0., 0., 0., 0., 1.],
+       [0., 0., 0., 0., 0., 1., 0.]])
+
+    assert np.array_equal(mat_1, expected_1)
+    assert np.array_equal(mat_2, expected_2)
+
+
+def test_show_dominant_topic():
+    encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
+    n_topics = 3
+    n_topKeyword = 2
+    model = train_nmf_model(encoded_text_id, vocab_list, n_topics=n_topics)
+    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword = n_topKeyword)
+
+    assert len(pmi_score) == n_topics
+    assert len(topics) == n_topics
+    for i in topics.values():
+        assert len(i) == n_topKeyword
+
+
+def test_get_assigned_topics():
+    encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
+    n_topics = 3
+    model = train_nmf_model(encoded_text_id, vocab_list, n_topics=n_topics)
+    topics_list = get_assigned_topics(model)
+
+    assert len(topics_list) == len(text)
+    for topic_num in topics_list:
+        assert topic_num<n_topics
+
+
+def test_prepare_data_pyldavis():
+
+    encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
+    n_topics = 3
+    model = train_nmf_model(encoded_text_id, vocab_list, n_topics=n_topics)
+    data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
+    phi= data['topic_term_dists']
+    theta= data['doc_topic_dists']
+    doc_length_values = data['doc_lengths']
+    list_vocab=data['vocab']
+    freq_vocab = data['term_frequency']
+
+    assert phi.shape == (n_topics, len(list_vocab))
+    assert theta.shape == (len(text), n_topics)
+    assert len(doc_length_values) == len(text)
+    assert len(list_vocab) == len(freq_vocab)
+
+

From 25b1a32931cc4300f83136af8e6aaf1e7a7dc8ac Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Thu, 12 Sep 2019 15:37:09 +0200
Subject: [PATCH 238/496] add seanmf model

---
 nautilus_nlp/models/seanmf_model.py           | 116 ++++++++++++++++++
 .../models/topic_modeling_short_text.py       |  83 ++++++++++---
 2 files changed, 184 insertions(+), 15 deletions(-)
 create mode 100644 nautilus_nlp/models/seanmf_model.py

diff --git a/nautilus_nlp/models/seanmf_model.py b/nautilus_nlp/models/seanmf_model.py
new file mode 100644
index 0000000..6fbe37f
--- /dev/null
+++ b/nautilus_nlp/models/seanmf_model.py
@@ -0,0 +1,116 @@
+
+import time
+import numpy as np
+from numpy.linalg import norm
+from tqdm import tqdm
+
+
+class SeaNMF(object):
+    def __init__(
+            self,
+            A, S,
+            IW=[], IWc=[], IH=[],
+            alpha=1.0, beta=0.1, n_topic=10, max_iter=100, max_err=1e-3,
+            rand_init=True, fix_seed=False):
+        '''
+        0.5*||A-WH^T||_F^2+0.5*alpha*||S-WW_c^T||_F^2+0.5*beta*||W||_1^2
+        '''
+        if fix_seed:
+            np.random.seed(0)
+
+        self.A = A
+        self.S = S
+
+        self.n_row = A.shape[0]
+        self.n_col = A.shape[1]
+
+        self.n_topic = n_topic
+        self.max_iter = max_iter
+        self.alpha = alpha
+        self.beta = beta
+        self.B = np.ones([self.n_topic, 1])
+        self.max_err = max_err
+        self.snmf_mat_init(rand_init, IW,  IWc, IH)
+        self.snmf_iter()
+
+    def snmf_mat_init(self, rand_init, IW=[], IWc=[], IH=[]):
+        """
+        Init Matrices W,Wc and H initially either randomly or using existing IW,IWc IH matrices taken when iterating.
+        :param rand_init: Boolean indicating initial random init
+        """
+        if rand_init:
+            self.W = np.random.random((self.n_row, self.n_topic))
+            self.Wc = np.random.random((self.n_row, self.n_topic))
+            self.H = np.random.random((self.n_col, self.n_topic))
+        else:
+            self.W = IW
+            self.Wc = IWc
+            self.H = IH
+        for k in range(self.n_topic):
+            self.W[:, k] /= norm(self.W[:, k])
+            self.Wc[:, k] /= norm(self.Wc[:, k])
+
+    def snmf_iter(self):
+        """
+        Main iterative loop for matrix decomposition
+        """
+        loss_old = 1e20
+        start_time = time.time()
+        for i in tqdm(range(self.max_iter)):
+            self.snmf_solver()
+            loss = self.snmf_loss()
+            if loss_old - loss < self.max_err:
+                print('Matrix decomposition loop converged!')
+                break
+            loss_old = loss
+            end_time = time.time()
+            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
+
+    def snmf_solver(self):
+        '''
+        using BCD framework
+        Alogorithm 1: Equations to update W, wc, H are described in the paper
+        http://dmkd.cs.vt.edu/papers/WWW18.pdf
+        '''
+
+        epss = 1e-20
+        # Update W
+        AH = np.dot(self.A, self.H)
+        SWc = np.dot(self.S, self.Wc)
+        HtH = np.dot(self.H.T, self.H)
+        WctWc = np.dot(self.Wc.T, self.Wc)
+        W1 = self.W.dot(self.B)
+
+        for k in range(self.n_topic):
+            num0 = HtH[k, k] * self.W[:, k] + self.alpha * WctWc[k, k] * self.W[:, k]
+            num1 = AH[:, k] + self.alpha * SWc[:, k]
+            num2 = np.dot(self.W, HtH[:, k]) + self.alpha * np.dot(self.W, WctWc[:, k]) + self.beta * W1[0]
+            self.W[:, k] = num0 + num1 - num2
+            self.W[:, k] = np.maximum(self.W[:, k], epss)  # project > 0
+            self.W[:, k] /= norm(self.W[:, k]) + epss  # normalize
+        # Update Wc
+        WtW = self.W.T.dot(self.W)
+        StW = np.dot(self.S, self.W)
+        for k in range(self.n_topic):
+            self.Wc[:, k] = self.Wc[:, k] + StW[:, k] - np.dot(self.Wc, WtW[:, k])
+            self.Wc[:, k] = np.maximum(self.Wc[:, k], epss)
+        # Update H
+        AtW = np.dot(self.A.T, self.W)
+        for k in range(self.n_topic):
+            self.H[:, k] = self.H[:, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
+            self.H[:, k] = np.maximum(self.H[:, k], epss)
+
+    def snmf_loss(self):
+        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        if self.alpha > 0:
+            loss += self.alpha * norm(np.dot(self.W, np.transpose(self.Wc)) - self.S, 'fro') ** 2 / 2.0
+        if self.beta > 0:
+            loss += self.beta * norm(self.W, 1) ** 2 / 2.0
+
+        return loss
+
+    def get_decomposition_matrix(self):
+        # Wc was not considered to keep same structure as NMF
+        return self.W, self.H
+
+
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index c0b366a..4f72169 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -1,10 +1,12 @@
 import re
 from collections import Counter
-from itertools import permutations
+from itertools import product
 from nautilus_nlp.models.nmf_model import *
+from nautilus_nlp.models.seanmf_model import *
 import numpy as np
 import pyLDAvis
 
+
 def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
     """
     :param text: list of str on which the topic modeling will be performed
@@ -43,8 +45,9 @@ def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
     return encoded_text_id, vocab_list, vocab_arr
 
 
-def train_nmf_model(encoded_text_id, vocab_list, n_topics= 20, max_iter= 20, max_err=0.1, alpha = 0, beta=0):
+def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20, max_iter=20, max_err=0.1, alpha=0, beta=0):
     """
+    :param model_name: string = 'nmf' or 'seanmf'
     :param encoded_text_id: list of encoded sentences
     :param vocab_list: list of vocabulary
     :param n_topics: number of topics
@@ -58,17 +61,43 @@ def train_nmf_model(encoded_text_id, vocab_list, n_topics= 20, max_iter= 20, max
     n_docs = len(encoded_text_id)
     n_terms = len(vocab_list)
 
+    if model_name == 'nmf':
+        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
+        model = NMF(
+            dt_mat,
+            n_topic=n_topics,
+            max_iter=max_iter,
+            max_err=max_err)
+
+    elif model_name == 'seanmf':
+        # Calculate co-occurence matrix
+        cm = __build_cooccurence_matrix(n_terms, encoded_text_id)
+        # Calculate PPMI
+        SS = __calulate_PPMI(cm, n_terms)
+        # Build doc-term matrix
+        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
+        model = SeaNMF(
+            dt_mat, SS,
+            alpha=alpha,
+            beta=beta,
+            n_topic=n_topics,
+            max_iter=max_iter,
+            max_err=max_err,
+            fix_seed=1024)
+
+    else:
+        model = None
+        print('Invalid model name: Use nmf or seanmf')
+
+    return model
+
+
+def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
     dt_mat = np.zeros([n_terms, n_docs])
     for k in range(n_docs):
         for j in encoded_text_id[k]:
             dt_mat[j, k] += 1.0
-    model = NMF(
-        dt_mat,
-        n_topic=n_topics,
-        max_iter=max_iter,
-        max_err=max_err)
-
-    return model
+    return dt_mat
 
 
 def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
@@ -82,6 +111,7 @@ def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
     """
 
     dt_mat = __build_cooccurence_matrix(n_terms=len(vocab_list), encoded_text_id=encoded_text_id)
+    np.fill_diagonal(dt_mat, 0)
     W,_ = model.get_decomposition_matrix()
     n_topic = W.shape[1]
     PMI_arr = []
@@ -111,6 +141,7 @@ def get_assigned_topics(model):
     """
 
     _, H = model.get_decomposition_matrix()
+    # The weights of the H matrix are converted into probabilities
     H_probs = H / H.sum(axis=1, keepdims=True)
     topics_list = list(np.argmax(H_probs, axis=1))
 
@@ -165,14 +196,12 @@ def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
     return data
 
 
-
 def __build_cooccurence_matrix(n_terms, encoded_text_id):
     """
     The cooccurence matrix represents the number of times each word
     appeared in the same context as another word from the vocabulary.
-    The matrix has the size of vocab. columns and rows denote the vocab.
-    Cell values represent the number of times words occured together in the same sentence
-
+    The matrix has n_terms x n_terms size, columns and rows denote the vocab.
+    Cell values represent the number of times words occured together in the same sentence.
     :param :encoded_text_id : list of encoded sentences
     :return: res: the co-occurence matrix
 
@@ -180,11 +209,36 @@ def __build_cooccurence_matrix(n_terms, encoded_text_id):
     res = np.zeros([n_terms, n_terms])
     for row in encoded_text_id:
         counts = Counter(row)
-        for key_from, key_to in permutations(counts, 2):
+        for key_from, key_to in product(counts, repeat=2):
             res[key_from, key_to] += counts[key_from] * counts[key_to]
     return res
 
 
+def __calulate_PPMI(cm, n_terms):
+    D1 = np.sum(cm)
+    print('D1= ', D1)
+    SS = D1 * cm
+    print('SS= ',SS)
+    for k in range(n_terms):
+        SS[k] /= np.sum(cm[k])
+    for k in range(n_terms):
+        SS[:, k] /= np.sum(cm[:, k])
+    print('SS = ', SS )
+    cm = []  # release memory
+    SS[SS == 0] = 1.0
+    SS = np.log(SS)
+    SS[SS < 0.0] = 0.0
+    return SS
+
+
+def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
+    dt_mat = np.zeros([n_terms, n_docs])
+    for k in range(n_docs):
+        for j in encoded_text_id[k]:
+            dt_mat[j, k] += 1.0
+    return dt_mat
+
+
 def __calculate_PMI(AA, topKeywordsIndex):
     '''
     Method to compute PMi score
@@ -207,4 +261,3 @@ def __calculate_PMI(AA, topKeywordsIndex):
     avg_PMI = 2.0*np.sum(PMI)/float(n_tp)/(float(n_tp)-1.0)
 
     return avg_PMI
-

From 9431bf0f9b5976d29e2155d16a7e3e916f841c25 Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Mon, 16 Sep 2019 13:24:48 +0200
Subject: [PATCH 239/496] imporve tests

---
 tests/test_topic_modeling_short_text.py | 111 +++++++++++++-----------
 1 file changed, 59 insertions(+), 52 deletions(-)

diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index bd1149d..e52b40e 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -1,7 +1,8 @@
 from nautilus_nlp.models.topic_modeling_short_text import prepare_data, \
-    __build_cooccurence_matrix, train_nmf_model, prepare_data_pyldavis, \
+    __build_cooccurence_matrix, train_shorttext_model, prepare_data_pyldavis, \
     show_dominant_topic, get_assigned_topics
 import numpy as np
+import pytest
 
 text = ['Cola 1.5L Carrefour',
         'Pepsi Cola Light 1.5L',
@@ -18,53 +19,27 @@
         'Spaghetti de maïs 50g sans Glute']
 
 
-def test_prepare_data():
+@pytest.mark.parametrize(
+    "input_text,  expected_output",
+    [
+        (text, [['1.5L', 4], ['Coca-Cola', 3], ['Cola', 4], ['Light', 5], ['Pepsi', 2], ['bio', 3], ['de', 2], ['sans', 3]]),
+        ([],[]),
+        (['',''],[]),
+    ],
+)
+def test_prepare_data(input_text,  expected_output):
+    assert prepare_data(input_text), expected_output
 
-    expected1 = [['1.5L', 4],['Coca-Cola', 3],['Cola', 4],['Light', 5],['Pepsi', 2],['bio', 3],['de', 2],['sans', 3]]
-    expected2 = [['1.5L', 4], ['Coca-Cola', 3], ['Cola', 4], ['Light', 5], ['bio', 3], ['sans', 3]]
-    _,_,output1 = prepare_data(text)
-    _,_,output2 = prepare_data(text, vocab_min_count=2)
 
-    assert output1 == expected1
-    assert output2 == expected2
+@pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
+@pytest.mark.parametrize("n_topics", [3,0])
+@pytest.mark.parametrize("n_topKeyword", [2,0])
+def test_show_dominant_topic(model_name,  n_topics, n_topKeyword):
 
-
-def test_build_cooccurence_matrix():
-    # The co-occurence matrix is an array with the list of vocab in rows and in columns
-    # The weights denote the occurrences of a word i with a word j in same sentence for i != j and 0 elsewhere
-
-    case_1 = [[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]]
-    case_2 = [[0, 1, 2, 3, 4], [5, 6], [1]]
-
-    mat_1 = __build_cooccurence_matrix(4, case_1)
-    mat_2 = __build_cooccurence_matrix(7, case_2)
-
-    expected_1 =np.array(
-      [[0., 1., 2., 4.],
-       [1., 0., 2., 3.],
-       [2., 2., 0., 4.],
-       [4., 3., 4., 0.]])
-
-
-    expected_2 = np.array(
-      [[0., 1., 1., 1., 1., 0., 0.],
-       [1., 0., 1., 1., 1., 0., 0.],
-       [1., 1., 0., 1., 1., 0., 0.],
-       [1., 1., 1., 0., 1., 0., 0.],
-       [1., 1., 1., 1., 0., 0., 0.],
-       [0., 0., 0., 0., 0., 0., 1.],
-       [0., 0., 0., 0., 0., 1., 0.]])
-
-    assert np.array_equal(mat_1, expected_1)
-    assert np.array_equal(mat_2, expected_2)
-
-
-def test_show_dominant_topic():
     encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
-    n_topics = 3
-    n_topKeyword = 2
-    model = train_nmf_model(encoded_text_id, vocab_list, n_topics=n_topics)
-    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword = n_topKeyword)
+
+    model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
+    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword=n_topKeyword)
 
     assert len(pmi_score) == n_topics
     assert len(topics) == n_topics
@@ -72,22 +47,25 @@ def test_show_dominant_topic():
         assert len(i) == n_topKeyword
 
 
-def test_get_assigned_topics():
+@pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
+@pytest.mark.parametrize("n_topics", [3])
+@pytest.mark.parametrize("n_topKeyword", [2])
+def test_get_assigned_topics(model_name,n_topics,n_topKeyword):
     encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
-    n_topics = 3
-    model = train_nmf_model(encoded_text_id, vocab_list, n_topics=n_topics)
+    model = train_shorttext_model(model_name,encoded_text_id, vocab_list, n_topics=n_topics)
     topics_list = get_assigned_topics(model)
 
     assert len(topics_list) == len(text)
     for topic_num in topics_list:
-        assert topic_num<n_topics
-
+        assert topic_num < n_topics
 
-def test_prepare_data_pyldavis():
 
+@pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
+@pytest.mark.parametrize("n_topics", [0,3])
+def test_prepare_data_pyldavis(model_name, n_topics):
     encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
-    n_topics = 3
-    model = train_nmf_model(encoded_text_id, vocab_list, n_topics=n_topics)
+    model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
+
     data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
     phi= data['topic_term_dists']
     theta= data['doc_topic_dists']
@@ -101,3 +79,32 @@ def test_prepare_data_pyldavis():
     assert len(list_vocab) == len(freq_vocab)
 
 
+@pytest.mark.parametrize(
+    "input_coded,  expected_output",
+    [
+     ([[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]],
+      [[5., 1., 2., 4.],
+       [1., 2., 2., 3.],
+       [2., 2., 4., 4.],
+       [4., 3., 4., 6.]]),
+
+     ([[0, 1, 2, 3, 4], [5, 6], [1]],
+         [[1., 1., 1., 1., 1., 0., 0.],
+          [1., 2., 1., 1., 1., 0., 0.],
+          [1., 1., 1., 1., 1., 0., 0.],
+          [1., 1., 1., 1., 1., 0., 0.],
+          [1., 1., 1., 1., 1., 0., 0.],
+          [0., 0., 0., 0., 0., 1., 1.],
+          [0., 0., 0., 0., 0., 1., 1.]]
+         )
+    ],
+)
+def test_build_cooccurence_matrix(input_coded, expected_output):
+    # The co-occurence matrix is an array with the list of vocab in rows and in columns
+    # The weights denote the occurrences of a word i with a word j in same sentence.
+
+    flat_list = [item for sublist in input_coded for item in sublist]
+    nb_vocab = len(set(flat_list)) # number of distinct words
+    mat = __build_cooccurence_matrix(nb_vocab, input_coded)
+
+    assert np.array_equal(mat, np.array(expected_output))

From b5af7eee3e4ecef2d7ea3a1f0952cd4b13fae99d Mon Sep 17 00:00:00 2001
From: Kais LARIBI <kais.laribi@artefact.com>
Date: Mon, 16 Sep 2019 13:38:12 +0200
Subject: [PATCH 240/496] doc string for seanmf

---
 nautilus_nlp/models/seanmf_model.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/models/seanmf_model.py b/nautilus_nlp/models/seanmf_model.py
index 6fbe37f..639110b 100644
--- a/nautilus_nlp/models/seanmf_model.py
+++ b/nautilus_nlp/models/seanmf_model.py
@@ -12,9 +12,24 @@ def __init__(
             IW=[], IWc=[], IH=[],
             alpha=1.0, beta=0.1, n_topic=10, max_iter=100, max_err=1e-3,
             rand_init=True, fix_seed=False):
-        '''
-        0.5*||A-WH^T||_F^2+0.5*alpha*||S-WW_c^T||_F^2+0.5*beta*||W||_1^2
-        '''
+        """
+        Seanmf is a topic modeling algorithm, paper:  http://dmkd.cs.vt.edu/papers/WWW18.pdf.
+        It finds an approximation to the term-document matrix A by two lower-rank matrices W and H,
+        at each iteration a context matrix Wc are computed and used to update W.
+        :param A: document term matrix
+        :param S: Word-context (semantic) correlation matrix
+        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+        and its elements are the weights of the corresponding keywords.
+        :param IWc: Latent factor matrix of contexts.
+        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
+        :param alpha: Seanmf algorithm parameter
+        :param beta: Seanmf algorithm parameter
+        :param n_topic: Number of selected topics
+        :param max_iter: Maximum number of iterations to update W and H
+        :param max_err: maximum error under which we consider that the loop converged
+        :param rand_init: random init boolean
+        :param fix_seed: int number to fix random seed.
+        """
         if fix_seed:
             np.random.seed(0)
 

From eea07133d495092973d78d58587cbbdc29fb637c Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Wed, 2 Oct 2019 19:04:13 +0200
Subject: [PATCH 241/496] change lda fit function on unseen doc

---
 nautilus_nlp/models/Spacy_model.py    | 61 ---------------------------
 nautilus_nlp/models/topic_modeling.py |  2 +-
 2 files changed, 1 insertion(+), 62 deletions(-)
 delete mode 100644 nautilus_nlp/models/Spacy_model.py

diff --git a/nautilus_nlp/models/Spacy_model.py b/nautilus_nlp/models/Spacy_model.py
deleted file mode 100644
index c7eca79..0000000
--- a/nautilus_nlp/models/Spacy_model.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import spacy
-
-class spacy_model:
-
-    def __init__(self, lang: str):
-        try:
-            self.model = spacy.load(lang)
-        except Exception as e:
-            print(e)
-            print(f"Cannot load lang {lang}, loading default")
-            self.model = spacy.load("xx")
-
-    def get_spacy_doc(self, text):
-
-        return self.model(text)
-
-    @staticmethod
-    def get_tokens_from_document(spacydoc: spacy.tokens.doc.Doc):
-
-        return [tokens for tokens in spacydoc]
-
-    def get_tokens_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [tokens for tokens in spacydoc]
-
-    @staticmethod
-    def get_sentence_from_document(spacydoc: spacy.tokens.doc.Doc):
-        return [sent for sent in spacydoc.sents]
-
-    def get_sentence_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [sent for sent in spacydoc.sents]
-
-    @staticmethod
-    def get_tokenized_sentence_from_document(spacydoc: spacy.tokens.doc.Doc):
-        return [[(token) for token in sent] for sent in spacydoc.sents]
-
-    def get_tokenized_sentence_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [[(token) for token in sent] for sent in spacydoc.sents]
-
-    @staticmethod
-    def get_entities_from_document(spacydoc):
-        return [(ent, ent.label_) for ent in spacydoc.ents]
-
-    @staticmethod
-    def get_entities_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [(ent, ent.label_) for ent in spacydoc.ents]
-
-    @staticmethod
-    def get_lemma_from_document(spacydoc: spacy.tokens.doc.Doc) -> list:
-        return [token.lemma_ for token in spacydoc]
-
-    def get_lemma_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [token.lemma_ for token in spacydoc]
-
-    def get_pos_from_str(self, text: str):
-        spacydoc = self.model(text)
-        return [token.pos_ for token in spacydoc]
\ No newline at end of file
diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 518d8c0..9ffd52f 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -217,7 +217,7 @@ def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
 
 def fit_data(model, bow):
     """Test the model on new, unseen documents"""
-    return model[bow]
+    return model.get_document_topics(bow, minimum_probability=0.000000000000001)
 
 
 # Visualization

From b2fde909a07b65dc618102abaf861755aab1c4cd Mon Sep 17 00:00:00 2001
From: Thomas Griseau <thomas.griseau@artefact.com>
Date: Tue, 15 Oct 2019 13:59:11 +0200
Subject: [PATCH 242/496] Compare string with == instead of is statement

---
 nautilus_nlp/preprocessing/tokenizer.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/preprocessing/tokenizer.py b/nautilus_nlp/preprocessing/tokenizer.py
index 1fddaa9..7885dc9 100644
--- a/nautilus_nlp/preprocessing/tokenizer.py
+++ b/nautilus_nlp/preprocessing/tokenizer.py
@@ -39,15 +39,15 @@ def tokenize(text: str, lang_module: str = 'en_spacy')-> list:
     list
         Outputs a list of tokens.
     """
-    if lang_module is 'en_nltk':
+    if lang_module == 'en_nltk':
         return nltk.word_tokenize(text)
-    elif lang_module is 'en_spacy':
+    elif lang_module == 'en_spacy':
         spacydoc = english_spacy(text)
         return [tokens.text for tokens in spacydoc]
-    elif lang_module is 'fr_spacy':
+    elif lang_module == 'fr_spacy':
         spacydoc = french_spacy(text)
         return [tokens.text for tokens in spacydoc]
-    elif lang_module is 'fr_moses':
+    elif lang_module == 'fr_moses':
         t = MosesTokenizer(lang='fr')
         return t.tokenize(text, escape=False)
 

From fa74ae8d06399970d48b1bf00c88c0bb741e08ae Mon Sep 17 00:00:00 2001
From: William Jaubert <jaubert.william@gmail.com>
Date: Wed, 16 Oct 2019 09:47:06 +0200
Subject: [PATCH 243/496] change minimum probability threshold to 0

---
 nautilus_nlp/models/topic_modeling.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index 9ffd52f..fdee8df 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -217,7 +217,7 @@ def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
 
 def fit_data(model, bow):
     """Test the model on new, unseen documents"""
-    return model.get_document_topics(bow, minimum_probability=0.000000000000001)
+    return model.get_document_topics(bow, minimum_probability=0)
 
 
 # Visualization

From 3002884ca76020dd0af5a4b6f64705e0927938d8 Mon Sep 17 00:00:00 2001
From: Robin <rdoume@gmail.com>
Date: Wed, 12 Feb 2020 10:41:25 +0100
Subject: [PATCH 244/496] Change licensing to LGPLv3

---
 .travis.yml                                   |  17 ++
 LICENSE                                       | 165 +++++++++++++++++-
 data/external/get_language_dataset.sh         |  17 ++
 data/external/get_stanfordtweets.sh           |  17 ++
 docker/build_docker.sh                        |  17 ++
 docs/conf.py                                  |  17 ++
 nautilus_nlp/__init__.py                      |  17 ++
 nautilus_nlp/config/__init__.py               |  17 ++
 nautilus_nlp/config/config.py                 |  17 ++
 nautilus_nlp/data/__init__.py                 |  17 ++
 nautilus_nlp/data/make_dataset.py             |  17 ++
 nautilus_nlp/doc.py                           |  17 ++
 nautilus_nlp/models/__init__.py               |  17 ++
 nautilus_nlp/models/biterm_model.py           |  17 ++
 nautilus_nlp/models/fasttext_classifier.py    |  17 ++
 nautilus_nlp/models/fasttext_embedding.py     |  17 ++
 nautilus_nlp/models/language_detector.py      |  17 ++
 nautilus_nlp/models/nmf_model.py              |  17 ++
 nautilus_nlp/models/seanmf_model.py           |  17 ++
 nautilus_nlp/models/sentiment_detector.py     |  17 ++
 nautilus_nlp/models/sk_vectorizer.py          |  17 ++
 nautilus_nlp/models/topic_modeling.py         |  17 ++
 .../models/topic_modeling_short_text.py       |  17 ++
 nautilus_nlp/preprocessing/__init__.py        |  17 ++
 .../preprocessing/keyword_extractor.py        |  17 ++
 nautilus_nlp/preprocessing/lemmatization.py   |  17 ++
 nautilus_nlp/preprocessing/preprocess.py      |  17 ++
 nautilus_nlp/preprocessing/stemming.py        |  17 ++
 .../preprocessing/text_vectorizers.py         |  17 ++
 nautilus_nlp/preprocessing/tokenizer.py       |  17 ++
 .../scripts/download_ft_langdetect.sh         |  17 ++
 nautilus_nlp/scripts/download_spacy_models.sh |  17 ++
 nautilus_nlp/scripts/init_langmodel.sh        |  17 ++
 nautilus_nlp/scripts/install_fasttext.sh      |  17 ++
 nautilus_nlp/scripts/install_java.sh          |  17 ++
 nautilus_nlp/scripts/install_mallet.sh        |  17 ++
 nautilus_nlp/utils/__init__.py                |  17 ++
 nautilus_nlp/utils/compat.py                  |  17 ++
 nautilus_nlp/utils/constants.py               |  17 ++
 nautilus_nlp/utils/emoji.py                   |  17 ++
 nautilus_nlp/utils/file_loader.py             |  17 ++
 nautilus_nlp/utils/keyword_extractor.py       |  17 ++
 nautilus_nlp/utils/ngrams_analysis.py         |  17 ++
 nautilus_nlp/utils/phone_number.py            |  17 ++
 nautilus_nlp/utils/text_summary.py            |  17 ++
 nautilus_nlp/utils/tokenizer.py               |  17 ++
 nautilus_nlp/utils/vector_similarity.py       |  17 ++
 nautilus_nlp/utils/visualize.py               |  17 ++
 setup.py                                      |  17 ++
 test_environment.py                           |  17 ++
 tests/test_biterm.py                          |  17 ++
 tests/test_doc.py                             |  17 ++
 tests/test_document_loader.py                 |  17 ++
 tests/test_extraction.py                      |  17 ++
 tests/test_fix_bad_encoding.py                |  17 ++
 tests/test_language_detection.py              |  17 ++
 tests/test_lemmatization.py                   |  17 ++
 tests/test_ngrams_analysis.py                 |  17 ++
 tests/test_phone_number.py                    |  17 ++
 tests/test_preprocessor.py                    |  17 ++
 tests/test_stemming.py                        |  17 ++
 tests/test_text_summary.py                    |  17 ++
 tests/test_tokenizer.py                       |  17 ++
 tests/test_topic_modeling_short_text.py       |  17 ++
 64 files changed, 1231 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 93381c6..0304bca 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 language: python
 
 os:
diff --git a/LICENSE b/LICENSE
index 617fc8b..153d416 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,10 +1,165 @@
+                   GNU LESSER GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
 
-The MIT License (MIT)
-Copyright (c) 2019, Robin Doumerc
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
 
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+  This version of the GNU Lesser General Public License incorporates
+the terms and conditions of version 3 of the GNU General Public
+License, supplemented by the additional permissions listed below.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+  0. Additional Definitions.
 
+  As used herein, "this License" refers to version 3 of the GNU Lesser
+General Public License, and the "GNU GPL" refers to version 3 of the GNU
+General Public License.
+
+  "The Library" refers to a covered work governed by this License,
+other than an Application or a Combined Work as defined below.
+
+  An "Application" is any work that makes use of an interface provided
+by the Library, but which is not otherwise based on the Library.
+Defining a subclass of a class defined by the Library is deemed a mode
+of using an interface provided by the Library.
+
+  A "Combined Work" is a work produced by combining or linking an
+Application with the Library.  The particular version of the Library
+with which the Combined Work was made is also called the "Linked
+Version".
+
+  The "Minimal Corresponding Source" for a Combined Work means the
+Corresponding Source for the Combined Work, excluding any source code
+for portions of the Combined Work that, considered in isolation, are
+based on the Application, and not on the Linked Version.
+
+  The "Corresponding Application Code" for a Combined Work means the
+object code and/or source code for the Application, including any data
+and utility programs needed for reproducing the Combined Work from the
+Application, but excluding the System Libraries of the Combined Work.
+
+  1. Exception to Section 3 of the GNU GPL.
+
+  You may convey a covered work under sections 3 and 4 of this License
+without being bound by section 3 of the GNU GPL.
+
+  2. Conveying Modified Versions.
+
+  If you modify a copy of the Library, and, in your modifications, a
+facility refers to a function or data to be supplied by an Application
+that uses the facility (other than as an argument passed when the
+facility is invoked), then you may convey a copy of the modified
+version:
+
+   a) under this License, provided that you make a good faith effort to
+   ensure that, in the event an Application does not supply the
+   function or data, the facility still operates, and performs
+   whatever part of its purpose remains meaningful, or
+
+   b) under the GNU GPL, with none of the additional permissions of
+   this License applicable to that copy.
+
+  3. Object Code Incorporating Material from Library Header Files.
+
+  The object code form of an Application may incorporate material from
+a header file that is part of the Library.  You may convey such object
+code under terms of your choice, provided that, if the incorporated
+material is not limited to numerical parameters, data structure
+layouts and accessors, or small macros, inline functions and templates
+(ten or fewer lines in length), you do both of the following:
+
+   a) Give prominent notice with each copy of the object code that the
+   Library is used in it and that the Library and its use are
+   covered by this License.
+
+   b) Accompany the object code with a copy of the GNU GPL and this license
+   document.
+
+  4. Combined Works.
+
+  You may convey a Combined Work under terms of your choice that,
+taken together, effectively do not restrict modification of the
+portions of the Library contained in the Combined Work and reverse
+engineering for debugging such modifications, if you also do each of
+the following:
+
+   a) Give prominent notice with each copy of the Combined Work that
+   the Library is used in it and that the Library and its use are
+   covered by this License.
+
+   b) Accompany the Combined Work with a copy of the GNU GPL and this license
+   document.
+
+   c) For a Combined Work that displays copyright notices during
+   execution, include the copyright notice for the Library among
+   these notices, as well as a reference directing the user to the
+   copies of the GNU GPL and this license document.
+
+   d) Do one of the following:
+
+       0) Convey the Minimal Corresponding Source under the terms of this
+       License, and the Corresponding Application Code in a form
+       suitable for, and under terms that permit, the user to
+       recombine or relink the Application with a modified version of
+       the Linked Version to produce a modified Combined Work, in the
+       manner specified by section 6 of the GNU GPL for conveying
+       Corresponding Source.
+
+       1) Use a suitable shared library mechanism for linking with the
+       Library.  A suitable mechanism is one that (a) uses at run time
+       a copy of the Library already present on the user's computer
+       system, and (b) will operate properly with a modified version
+       of the Library that is interface-compatible with the Linked
+       Version.
+
+   e) Provide Installation Information, but only if you would otherwise
+   be required to provide such information under section 6 of the
+   GNU GPL, and only to the extent that such information is
+   necessary to install and execute a modified version of the
+   Combined Work produced by recombining or relinking the
+   Application with a modified version of the Linked Version. (If
+   you use option 4d0, the Installation Information must accompany
+   the Minimal Corresponding Source and Corresponding Application
+   Code. If you use option 4d1, you must provide the Installation
+   Information in the manner specified by section 6 of the GNU GPL
+   for conveying Corresponding Source.)
+
+  5. Combined Libraries.
+
+  You may place library facilities that are a work based on the
+Library side by side in a single library together with other library
+facilities that are not Applications and are not covered by this
+License, and convey such a combined library under terms of your
+choice, if you do both of the following:
+
+   a) Accompany the combined library with a copy of the same work based
+   on the Library, uncombined with any other library facilities,
+   conveyed under the terms of this License.
+
+   b) Give prominent notice with the combined library that part of it
+   is a work based on the Library, and explaining where to find the
+   accompanying uncombined form of the same work.
+
+  6. Revised Versions of the GNU Lesser General Public License.
+
+  The Free Software Foundation may publish revised and/or new versions
+of the GNU Lesser General Public License from time to time. Such new
+versions will be similar in spirit to the present version, but may
+differ in detail to address new problems or concerns.
+
+  Each version is given a distinguishing version number. If the
+Library as you received it specifies that a certain numbered version
+of the GNU Lesser General Public License "or any later version"
+applies to it, you have the option of following the terms and
+conditions either of that published version or of any later version
+published by the Free Software Foundation. If the Library as you
+received it does not specify a version number of the GNU Lesser
+General Public License, you may choose any version of the GNU Lesser
+General Public License ever published by the Free Software Foundation.
+
+  If the Library as you received it specifies that a proxy can decide
+whether future versions of the GNU Lesser General Public License shall
+apply, that proxy's public statement of acceptance of any version is
+permanent authorization for you to choose that version for the
+Library.
\ No newline at end of file
diff --git a/data/external/get_language_dataset.sh b/data/external/get_language_dataset.sh
index 9fddef8..4e87a8b 100644
--- a/data/external/get_language_dataset.sh
+++ b/data/external/get_language_dataset.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/bin/bash
 wget -O wili.zip https://zenodo.org/record/841984/files/wili-2018.zip?download=1
 mkdir -p wili && cp wili.zip wili && cd wili && unzip wili.zip && cd ..
diff --git a/data/external/get_stanfordtweets.sh b/data/external/get_stanfordtweets.sh
index 4423bcf..ef7ae1e 100644
--- a/data/external/get_stanfordtweets.sh
+++ b/data/external/get_stanfordtweets.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/bin/bash 
 wget -O trainingandtestdata.zip http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip trainingandtestdata.zip
 mkdir -p  tweets_sentiment && cp trainingandtestdata.zip tweets_sentiment && cd tweets_sentiment && unzip trainingandtestdata.zip
diff --git a/docker/build_docker.sh b/docker/build_docker.sh
index bfd80cc..40d9933 100644
--- a/docker/build_docker.sh
+++ b/docker/build_docker.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/bin/bash
 cp docker/Dockerfile .
 docker build -t nautilus_nlp:latest .
diff --git a/docs/conf.py b/docs/conf.py
index 43b0a98..02f1785 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 #
 # Configuration file for the Sphinx documentation builder.
diff --git a/nautilus_nlp/__init__.py b/nautilus_nlp/__init__.py
index e69de29..d46139b 100644
--- a/nautilus_nlp/__init__.py
+++ b/nautilus_nlp/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/config/__init__.py b/nautilus_nlp/config/__init__.py
index e69de29..d46139b 100644
--- a/nautilus_nlp/config/__init__.py
+++ b/nautilus_nlp/config/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/config/config.py b/nautilus_nlp/config/config.py
index ed87b43..4f880ff 100644
--- a/nautilus_nlp/config/config.py
+++ b/nautilus_nlp/config/config.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/usr/local/bin/python3
 import os 
 
diff --git a/nautilus_nlp/data/__init__.py b/nautilus_nlp/data/__init__.py
index e69de29..d46139b 100644
--- a/nautilus_nlp/data/__init__.py
+++ b/nautilus_nlp/data/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/data/make_dataset.py b/nautilus_nlp/data/make_dataset.py
index 1bb5467..186fb78 100644
--- a/nautilus_nlp/data/make_dataset.py
+++ b/nautilus_nlp/data/make_dataset.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 import click
 import logging
diff --git a/nautilus_nlp/doc.py b/nautilus_nlp/doc.py
index e5f69f0..420b2a7 100644
--- a/nautilus_nlp/doc.py
+++ b/nautilus_nlp/doc.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import functools
 import pkg_resources
 import nautilus_nlp
diff --git a/nautilus_nlp/models/__init__.py b/nautilus_nlp/models/__init__.py
index e69de29..d46139b 100644
--- a/nautilus_nlp/models/__init__.py
+++ b/nautilus_nlp/models/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
index a30d80f..8aa1ab4 100644
--- a/nautilus_nlp/models/biterm_model.py
+++ b/nautilus_nlp/models/biterm_model.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import numpy as np
 from biterm.btm import oBTM
 from biterm.utility import vec_to_biterms, topic_summuary
diff --git a/nautilus_nlp/models/fasttext_classifier.py b/nautilus_nlp/models/fasttext_classifier.py
index 9d0fee4..39e0b08 100644
--- a/nautilus_nlp/models/fasttext_classifier.py
+++ b/nautilus_nlp/models/fasttext_classifier.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from sklearn.base import BaseEstimator, ClassifierMixin
 import fastText
 import multiprocessing
diff --git a/nautilus_nlp/models/fasttext_embedding.py b/nautilus_nlp/models/fasttext_embedding.py
index 79f7d8a..f3432b7 100644
--- a/nautilus_nlp/models/fasttext_embedding.py
+++ b/nautilus_nlp/models/fasttext_embedding.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import fastText
 import multiprocessing
 
diff --git a/nautilus_nlp/models/language_detector.py b/nautilus_nlp/models/language_detector.py
index d124871..74eb3fc 100644
--- a/nautilus_nlp/models/language_detector.py
+++ b/nautilus_nlp/models/language_detector.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.models.fasttext_classifier import Fasttext_clf as langdetect
 from nautilus_nlp.preprocessing.preprocess import remove_EOL_characters
 import pkg_resources
diff --git a/nautilus_nlp/models/nmf_model.py b/nautilus_nlp/models/nmf_model.py
index 0a18a6b..005c09b 100644
--- a/nautilus_nlp/models/nmf_model.py
+++ b/nautilus_nlp/models/nmf_model.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import time
 import numpy as np
 from numpy.linalg import norm
diff --git a/nautilus_nlp/models/seanmf_model.py b/nautilus_nlp/models/seanmf_model.py
index 639110b..7248148 100644
--- a/nautilus_nlp/models/seanmf_model.py
+++ b/nautilus_nlp/models/seanmf_model.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
 import time
 import numpy as np
diff --git a/nautilus_nlp/models/sentiment_detector.py b/nautilus_nlp/models/sentiment_detector.py
index 439050b..146eeac 100644
--- a/nautilus_nlp/models/sentiment_detector.py
+++ b/nautilus_nlp/models/sentiment_detector.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.utils.tokenizer import _convert_tokens_to_string, _convert_string_to_tokens
 
 import textblob
diff --git a/nautilus_nlp/models/sk_vectorizer.py b/nautilus_nlp/models/sk_vectorizer.py
index fd407d6..926735a 100644
--- a/nautilus_nlp/models/sk_vectorizer.py
+++ b/nautilus_nlp/models/sk_vectorizer.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.base import BaseEstimator, ClassifierMixin
 
diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
index fdee8df..686c824 100644
--- a/nautilus_nlp/models/topic_modeling.py
+++ b/nautilus_nlp/models/topic_modeling.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import gensim
 import logging
 import os
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
index 4f72169..73fad26 100644
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ b/nautilus_nlp/models/topic_modeling_short_text.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import re
 from collections import Counter
 from itertools import product
diff --git a/nautilus_nlp/preprocessing/__init__.py b/nautilus_nlp/preprocessing/__init__.py
index e69de29..d46139b 100644
--- a/nautilus_nlp/preprocessing/__init__.py
+++ b/nautilus_nlp/preprocessing/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/preprocessing/keyword_extractor.py b/nautilus_nlp/preprocessing/keyword_extractor.py
index 11933bc..304fe02 100644
--- a/nautilus_nlp/preprocessing/keyword_extractor.py
+++ b/nautilus_nlp/preprocessing/keyword_extractor.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from flashtext import KeywordProcessor
 
 def extract_keywords(text:str, keyword, case_sensitive=True) -> list:
diff --git a/nautilus_nlp/preprocessing/lemmatization.py b/nautilus_nlp/preprocessing/lemmatization.py
index 2b54ae2..2ee94e7 100644
--- a/nautilus_nlp/preprocessing/lemmatization.py
+++ b/nautilus_nlp/preprocessing/lemmatization.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import spacy
 import nltk
 nltk.download('wordnet')
diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 4467cf0..8dfb218 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 
 from __future__ import absolute_import, division, print_function, unicode_literals
diff --git a/nautilus_nlp/preprocessing/stemming.py b/nautilus_nlp/preprocessing/stemming.py
index a4dfc0e..92028ec 100644
--- a/nautilus_nlp/preprocessing/stemming.py
+++ b/nautilus_nlp/preprocessing/stemming.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nltk.stem.snowball import *
 
 
diff --git a/nautilus_nlp/preprocessing/text_vectorizers.py b/nautilus_nlp/preprocessing/text_vectorizers.py
index 68fa172..dec03dc 100644
--- a/nautilus_nlp/preprocessing/text_vectorizers.py
+++ b/nautilus_nlp/preprocessing/text_vectorizers.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import spacy
 from gensim.corpora import Dictionary
 from gensim.models.tfidfmodel import TfidfModel
diff --git a/nautilus_nlp/preprocessing/tokenizer.py b/nautilus_nlp/preprocessing/tokenizer.py
index 7885dc9..4236f23 100644
--- a/nautilus_nlp/preprocessing/tokenizer.py
+++ b/nautilus_nlp/preprocessing/tokenizer.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import nltk
 from sacremoses import MosesTokenizer, MosesDetokenizer
 import spacy
diff --git a/nautilus_nlp/scripts/download_ft_langdetect.sh b/nautilus_nlp/scripts/download_ft_langdetect.sh
index 92e628a..300dc9a 100644
--- a/nautilus_nlp/scripts/download_ft_langdetect.sh
+++ b/nautilus_nlp/scripts/download_ft_langdetect.sh
@@ -1,2 +1,19 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 wget https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz 
 cp lid.176.ftz data/
\ No newline at end of file
diff --git a/nautilus_nlp/scripts/download_spacy_models.sh b/nautilus_nlp/scripts/download_spacy_models.sh
index 0a3a374..cd84284 100644
--- a/nautilus_nlp/scripts/download_spacy_models.sh
+++ b/nautilus_nlp/scripts/download_spacy_models.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 python -m spacy download en_core_web_sm
 python -m spacy download de_core_news_sm 
 python -m spacy download fr_core_news_sm 
diff --git a/nautilus_nlp/scripts/init_langmodel.sh b/nautilus_nlp/scripts/init_langmodel.sh
index 75ad255..2bf0b48 100644
--- a/nautilus_nlp/scripts/init_langmodel.sh
+++ b/nautilus_nlp/scripts/init_langmodel.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/bin/bash
 
 echo 'Which Language do you want to use: en/fr?'
diff --git a/nautilus_nlp/scripts/install_fasttext.sh b/nautilus_nlp/scripts/install_fasttext.sh
index b53f600..c5dc339 100644
--- a/nautilus_nlp/scripts/install_fasttext.sh
+++ b/nautilus_nlp/scripts/install_fasttext.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/bin/bash
 wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip && unzip v0.2.0.zip && cd fastText-0.2.0 && make
 git clone https://github.com/facebookresearch/fastText.git && cd fastText && pip install .
\ No newline at end of file
diff --git a/nautilus_nlp/scripts/install_java.sh b/nautilus_nlp/scripts/install_java.sh
index 648abf3..8c894d2 100644
--- a/nautilus_nlp/scripts/install_java.sh
+++ b/nautilus_nlp/scripts/install_java.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 sudo add-apt-repository ppa:openjdk-r/ppa  # only Ubuntu 17.4 and earlier
 sudo apt update
 apt search openjdk
diff --git a/nautilus_nlp/scripts/install_mallet.sh b/nautilus_nlp/scripts/install_mallet.sh
index 99d2af5..51ff9f7 100644
--- a/nautilus_nlp/scripts/install_mallet.sh
+++ b/nautilus_nlp/scripts/install_mallet.sh
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/bin/bash
 wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip 
 unzip mallet-2.0.8.zip
diff --git a/nautilus_nlp/utils/__init__.py b/nautilus_nlp/utils/__init__.py
index e69de29..d46139b 100644
--- a/nautilus_nlp/utils/__init__.py
+++ b/nautilus_nlp/utils/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/utils/compat.py b/nautilus_nlp/utils/compat.py
index 31d04d6..644ac2b 100644
--- a/nautilus_nlp/utils/compat.py
+++ b/nautilus_nlp/utils/compat.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from __future__ import print_function
 
 import sys
diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index 5f5418a..e98cce9 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 """
 Collection of regular expressions and other (small, generally useful) constants.
diff --git a/nautilus_nlp/utils/emoji.py b/nautilus_nlp/utils/emoji.py
index b7f9534..6ee23f1 100644
--- a/nautilus_nlp/utils/emoji.py
+++ b/nautilus_nlp/utils/emoji.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import csv
 
 def rebuilt_emoji_dictionaries(filename):
diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 08a816e..73ef8e3 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import io
 import chardet
 import glob
diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/utils/keyword_extractor.py
index 1956983..28545c2 100644
--- a/nautilus_nlp/utils/keyword_extractor.py
+++ b/nautilus_nlp/utils/keyword_extractor.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from flashtext import KeywordProcessor
 
 def extract_keywords(text, keyword, case_sensitive=True):
diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/utils/ngrams_analysis.py
index 268fe3c..3716ee1 100644
--- a/nautilus_nlp/utils/ngrams_analysis.py
+++ b/nautilus_nlp/utils/ngrams_analysis.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 """
 Functions to calculate words or ngrams frequencies.
diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index b922484..8f76205 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import re
 import phonenumbers as _phonenumbers
 
diff --git a/nautilus_nlp/utils/text_summary.py b/nautilus_nlp/utils/text_summary.py
index db498bf..9fe6eb3 100644
--- a/nautilus_nlp/utils/text_summary.py
+++ b/nautilus_nlp/utils/text_summary.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from summa.summarizer import summarize
 
 
diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index a550bf7..49fac6b 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import nltk
 from sacremoses import MosesTokenizer, MosesDetokenizer
 import spacy
diff --git a/nautilus_nlp/utils/vector_similarity.py b/nautilus_nlp/utils/vector_similarity.py
index 1971c39..174d1a2 100644
--- a/nautilus_nlp/utils/vector_similarity.py
+++ b/nautilus_nlp/utils/vector_similarity.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import numpy as np
 import math
 
diff --git a/nautilus_nlp/utils/visualize.py b/nautilus_nlp/utils/visualize.py
index bb9cfb1..c7e58b4 100644
--- a/nautilus_nlp/utils/visualize.py
+++ b/nautilus_nlp/utils/visualize.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 '''
 import matplotlib
 import numpy as np
diff --git a/setup.py b/setup.py
index 5b02f87..b9b77e0 100644
--- a/setup.py
+++ b/setup.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from setuptools import find_packages, setup
 import setuptools
 import setuptools.command.install
diff --git a/test_environment.py b/test_environment.py
index d0ac4a7..341e9cb 100644
--- a/test_environment.py
+++ b/test_environment.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import sys
 
 REQUIRED_PYTHON = "python3"
diff --git a/tests/test_biterm.py b/tests/test_biterm.py
index 04bd067..975d42c 100644
--- a/tests/test_biterm.py
+++ b/tests/test_biterm.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.models.biterm_model import BitermModel
 import pandas as pd
 import pytest
diff --git a/tests/test_doc.py b/tests/test_doc.py
index 1354f89..75e9dcc 100644
--- a/tests/test_doc.py
+++ b/tests/test_doc.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 """
 Testing for textpipe doc.py
 """
diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index f445f26..0be9eb7 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 
 import os 
diff --git a/tests/test_extraction.py b/tests/test_extraction.py
index 461e801..f2a1a74 100644
--- a/tests/test_extraction.py
+++ b/tests/test_extraction.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.preprocessing.keyword_extractor import extract_keywords
 
 str_="""Les moteurs de recherche tels Google, Exalead ou Yahoo! sont
diff --git a/tests/test_fix_bad_encoding.py b/tests/test_fix_bad_encoding.py
index a2a14ea..097a2e2 100644
--- a/tests/test_fix_bad_encoding.py
+++ b/tests/test_fix_bad_encoding.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
 import pytest
 import numpy as np
diff --git a/tests/test_language_detection.py b/tests/test_language_detection.py
index d2b4260..b6b8d87 100644
--- a/tests/test_language_detection.py
+++ b/tests/test_language_detection.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.models import language_detector
 import pytest
 import numpy as np
diff --git a/tests/test_lemmatization.py b/tests/test_lemmatization.py
index ce205f4..c23667f 100644
--- a/tests/test_lemmatization.py
+++ b/tests/test_lemmatization.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 from nautilus_nlp.preprocessing.lemmatization import lemmatize_french_tokens, lemmatize_english_tokens
 
diff --git a/tests/test_ngrams_analysis.py b/tests/test_ngrams_analysis.py
index 5e42781..f20ff5d 100644
--- a/tests/test_ngrams_analysis.py
+++ b/tests/test_ngrams_analysis.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.utils.ngrams_analysis import frequent_words
 import pytest
 
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index 61fe9a6..0043734 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import nautilus_nlp.utils.phone_number as phone
 
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index d06ca80..bd6b6c9 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
 from nautilus_nlp.preprocessing.preprocess import (
diff --git a/tests/test_stemming.py b/tests/test_stemming.py
index faf4b0d..731bc14 100644
--- a/tests/test_stemming.py
+++ b/tests/test_stemming.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 from nautilus_nlp.preprocessing.stemming import stem_tokens
 
diff --git a/tests/test_text_summary.py b/tests/test_text_summary.py
index e233812..954fef6 100644
--- a/tests/test_text_summary.py
+++ b/tests/test_text_summary.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.utils.text_summary import summarize_text
 
 case1 = """Automatic summarization is the process of reducing a text document with a \
diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py
index 744de9d..a156529 100644
--- a/tests/test_tokenizer.py
+++ b/tests/test_tokenizer.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize
 
diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index e52b40e..405fc6b 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.models.topic_modeling_short_text import prepare_data, \
     __build_cooccurence_matrix, train_shorttext_model, prepare_data_pyldavis, \
     show_dominant_topic, get_assigned_topics

From 5578f1da198b006deee4c7d5458f187581028160 Mon Sep 17 00:00:00 2001
From: BrianLz <45944551+BrianLz@users.noreply.github.com>
Date: Thu, 19 Mar 2020 15:57:08 +0100
Subject: [PATCH 245/496] Updated README.md

Updated installation command according to new 2FA system (from https to SSH)
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 279cd7e..bed36ab 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@ Beware, this package has been tested on Python **3.6** & **3.7**, and will proba
 To install this library you should first clone the repository:
 
 ```
-git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp
+git clone git@github.com:artefactory/nautilus-nlp.git && cd nautilus_nlp/
 ```
 
 **If you don't use the docker container, we strongly advise you to do these steps in a virtual environnement**
@@ -211,4 +211,4 @@ You can now open the file index.html located in the build folder.
     ├── wiki               <- Where the Markdown for the Wiki lives
     ├── setup.py           <- makes project pip installable (pip install -e .) so nautilus_nlp can be imported
     ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
-                              generated with `pip freeze > requirements.txt`    
\ No newline at end of file
+                              generated with `pip freeze > requirements.txt`    

From c42a470a7fc132bac32588d7ef4b29e8cef739bd Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 23 Mar 2020 11:34:27 +0100
Subject: [PATCH 246/496] adding trendspotter cleaning functions

---
 nautilus_nlp/preprocessing/preprocess.py | 138 ++++++++++++++++++++++-
 tests/test_preprocessor.py               | 104 ++++++++++++++++-
 2 files changed, 240 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/preprocess.py
index 8dfb218..9aee2f0 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/preprocess.py
@@ -25,6 +25,7 @@
 import unicodedata
 
 import emoji as _emoji
+import regex
 from ftfy import fix_text as _fix_text
 from stop_words import get_stop_words as _get_stop_words
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
@@ -284,6 +285,22 @@ def unpack_english_contractions(text:str) -> str:
     return text
 
 
+def filter_non_latin_characters(text:str) -> str:
+    """
+    Function that filters non latin characters of a text
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string
+    """
+    text = regex.sub(r'[^\p{Latin}1-9]', ' ', text).strip()
+    return re.sub(' +', ' ', text)
+
+
 def replace_urls(text:str, replace_with:str="*URL*") -> str:
     """
     Replace all URLs in ``text`` str with ``replace_with`` str.
@@ -320,7 +337,6 @@ def replace_emails(text, replace_with="*EMAIL*") -> str:
     return constants.EMAIL_REGEX.sub(replace_with, text)
 
 
-
 def replace_phone_numbers(text, replace_with:str="*PHONE*",
                                 method:str="regex",
                                 country_format_to_detect:list=[None,'FR','US','GB']) -> str:
@@ -475,6 +491,72 @@ def remove_accents(text:str, method:str="unicode") -> str:
         raise ValueError(msg)
 
 
+def remove_mentions(text:str) -> str:
+    """
+    Function that removes words preceded with a '@'
+
+    Parameters
+    ----------
+    text : str
+    
+    Returns
+    -------
+    string
+    """
+    return normalize_whitespace(re.sub(r'@\w*', '', text))
+
+
+def extract_mentions(text:str) -> str:
+    """
+    Function that extracts words preceded with a '@'
+    eg. "I take care of my skin with @thisproduct" --> ["@thisproduct"]
+
+    Parameters
+    ----------
+    text : str
+    
+    Returns
+    -------
+    string
+    """
+    return re.findall(r'[@][^\s@]+', text)
+
+
+def remove_html_tags(text:str) -> str:
+    """
+    Function that removes words between < and >
+
+    Parameters
+    ----------
+    text : str
+    
+    Returns
+    -------
+    string
+    """
+    return normalize_whitespace(re.sub(r'<.*?>', '', text))
+
+
+def remove_smallwords(tokens_list:list, smallwords_threshold:int) -> list:
+    """
+    Function that removes words which length is below a threshold
+    ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
+
+    Parameters
+    ----------
+    text : list
+        list of strings
+    smallwords_threshold: int
+        threshold of small word
+
+    Returns
+    -------
+    list
+    """
+    result = [word for word in tokens_list if len(word) > smallwords_threshold]
+    return result
+
+
 def remove_emoji(text:str) -> str:
     """
     Remove emoji from any str by stripping any unicode in the range of Emoji unicode
@@ -514,6 +596,60 @@ def convert_emoji_to_text(text:str, code_delimiters=(':', ':')) -> str:
     return _emoji.demojize(text, delimiters=code_delimiters)
 
 
+def extract_emojis(text:str) -> list:
+    """
+    Function that extracts emojis from a text and translates them into words
+    eg. "I take care of my skin 😀 :(" --> [":grinning_face:"]
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    list
+        list of all emojis converted with their unicode conventions
+    """
+    emoji_pattern = _emoji.get_emoji_regexp()
+    emojis_in_text = re.findall(emoji_pattern, text)
+    emojis_converted = [convert_emoji_to_text(emoji_text) for emoji_text in emojis_in_text]
+    return emojis_converted
+
+
+def extract_hashtags(text) -> list:
+    """
+    Function that extracts words preceded with a '#'
+    eg. "I take care of my skin #selfcare#selfestim" --> ["skincare", "selfestim"]
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    list
+        list of all hashtags
+    """
+    return re.findall(r'[#][^\s#]+', text)
+
+
+def remove_hashtag(text) -> str:
+    """
+    Function that removes words preceded with a '#'
+    eg. "I take care of my skin #selfcare#selfestim" --> "I take care of my skin"
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    str
+        text of a post without hashtags
+    """
+    return normalize_whitespace(re.sub(r'#\w*', '', text))
+
+
 def preprocess_text(
     text,
     remove_eol_char=True,
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index bd6b6c9..03ebba8 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -35,10 +35,112 @@
     replace_currency_symbols,
     remove_punct,
     remove_emoji,
-    convert_emoji_to_text
+    convert_emoji_to_text,
+    extract_emojis,
+    remove_mentions,
+    extract_mentions,
+    remove_html_tags,
+    remove_smallwords,
+    extract_hashtags,
+    remove_hashtag,
+    filter_non_latin_characters
 )
 import nautilus_nlp.utils.phone_number as phone
 
+@pytest.mark.parametrize("text, expected_result",
+                         [("ACV water + cinnamon + turmeric + cucumber + lemon. 👍🏻",
+                           [":thumbs_up_light_skin_tone:"]),
+                          ("This is a text without emojis",
+                           [])])
+def test_extract_emojis(text, expected_result):
+    result = extract_emojis(text)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize("text, expected_result",
+                         [("I take care of my skin with @hellobody",
+                           "I take care of my skin with"),
+                          ("This is a text without mentions",
+                           "This is a text without mentions")])
+def test_remove_mentions(text, expected_result):
+    result = remove_mentions(text)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize("text, expected_result",
+                         [("I take care of my skin with @hellobody",
+                           ["@hellobody"]),
+                          ("This is a text without mentions",
+                           [])])
+def test_extract_mentions(text, expected_result):
+    result = extract_mentions(text)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize("text, expected_result",
+                         [("This is a text with <html> content of html tag </html>",
+                           "This is a text with content of html tag"),
+                          ("This is a text without html tags",
+                           "This is a text without html tags")])
+def test_remove_html_tags(text, expected_result):
+    result = remove_html_tags(text)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize("tokens_list, smallwords_threshold, expected_result",
+                         [(["I", "take", "care", "of", "my", "skin"],
+                           2,
+                           ["take", "care", "skin"]),
+                          (["This", "text", "contains", "only", "long", "words"],
+                           2,
+                           ["This", "text", "contains", "only", "long", "words"])])
+def test_remove_smallwords(tokens_list, smallwords_threshold, expected_result):
+    result = remove_smallwords(tokens_list, smallwords_threshold)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize("text, expected_result",
+                         [("this is a #hashtag in the middle of the text",
+                           ["#hashtag"]),
+                          ("#this is a hashtag in the beginning of the text",
+                           ["#this"]),
+                          ("this is a hashtag in the end of the #text",
+                           ["#text"]),
+                          ("this is a text with no hashtag",
+                           []),
+                          ("this is a text with #many #hashtags",
+                           ["#many", "#hashtags"])]
+                         )
+def test_extract_hashtags(text, expected_result):
+    result = extract_hashtags(text)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize("text, expected_result",
+                         [("this is a #hashtag in the middle of the text",
+                           "this is a in the middle of the text"),
+                          ("#this is a hashtag in the beginning of the text",
+                           "is a hashtag in the beginning of the text"),
+                          ("this is a hashtag in the end of the #text",
+                           "this is a hashtag in the end of the"),
+                          ("this is a text with no hashtag",
+                           "this is a text with no hashtag"),
+                          ("this is a text with #many #hashtags",
+                           "this is a text with")]
+                         )
+def test_remove_hashtag(text, expected_result):
+    result = remove_hashtag(text)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize("text, expected_filtered_text",
+                         [("كلمات Learn 3 Arabic كلمات words EASILY- Vocabulary #1 تعلم ٣ جديدة",
+                           "Learn 3 Arabic words EASILY Vocabulary 1")])
+def test_filter_non_latin_characters(text, expected_filtered_text):
+    filtered_text = filter_non_latin_characters(text)
+    assert expected_filtered_text == filtered_text
+
+
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [

From 04a70a38d2e28205ddd8b8edf8d5b8d1422df164 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 23 Mar 2020 11:34:39 +0100
Subject: [PATCH 247/496] adding regex reqirement

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 59d0f2e..256e891 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -35,6 +35,7 @@ vaderSentiment==3.2.1
 google-compute-engine==2.8.13
 flashtext==2.7
 phonenumbers==8.10.12
+regex==2019.8.19p
 emoji>=0.5.2
 summa==1.2.0
 biterm==0.1.5

From 9dfa8adc994cd5845ef850e8881bda0b8e7cc1e1 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 23 Mar 2020 12:01:35 +0100
Subject: [PATCH 248/496] typo in requirements

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 256e891..8b4b58d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -35,7 +35,7 @@ vaderSentiment==3.2.1
 google-compute-engine==2.8.13
 flashtext==2.7
 phonenumbers==8.10.12
-regex==2019.8.19p
+regex==2019.8.19
 emoji>=0.5.2
 summa==1.2.0
 biterm==0.1.5

From bb305ac30a3320939e27c3df7a82d1875fb9b21e Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Wed, 25 Mar 2020 11:28:12 +0100
Subject: [PATCH 249/496] refacto preprocessing and utils folders

---
 .../{utils => analysis}/keyword_extractor.py  |   0
 .../{utils => analysis}/ngrams_analysis.py    |   0
 .../{utils => analysis}/text_summary.py       |   0
 nautilus_nlp/{utils => analysis}/visualize.py |   0
 .../text_vectorizers.py                       |   0
 .../preprocessing/additional_preprocess.py    | 117 ++++
 .../preprocessing/keyword_extractor.py        |  47 --
 nautilus_nlp/preprocessing/lemmatization.py   | 129 -----
 .../{preprocess.py => main_preprocess.py}     | 502 ++++--------------
 .../preprocessing/social_preprocess.py        | 164 ++++++
 nautilus_nlp/preprocessing/stemming.py        |  53 --
 nautilus_nlp/preprocessing/tokenizer.py       | 150 ------
 nautilus_nlp/utils/stopwords.py               |  87 +++
 13 files changed, 472 insertions(+), 777 deletions(-)
 rename nautilus_nlp/{utils => analysis}/keyword_extractor.py (100%)
 rename nautilus_nlp/{utils => analysis}/ngrams_analysis.py (100%)
 rename nautilus_nlp/{utils => analysis}/text_summary.py (100%)
 rename nautilus_nlp/{utils => analysis}/visualize.py (100%)
 rename nautilus_nlp/{preprocessing => models}/text_vectorizers.py (100%)
 create mode 100644 nautilus_nlp/preprocessing/additional_preprocess.py
 delete mode 100644 nautilus_nlp/preprocessing/keyword_extractor.py
 delete mode 100644 nautilus_nlp/preprocessing/lemmatization.py
 rename nautilus_nlp/preprocessing/{preprocess.py => main_preprocess.py} (65%)
 create mode 100644 nautilus_nlp/preprocessing/social_preprocess.py
 delete mode 100644 nautilus_nlp/preprocessing/stemming.py
 delete mode 100644 nautilus_nlp/preprocessing/tokenizer.py
 create mode 100644 nautilus_nlp/utils/stopwords.py

diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/analysis/keyword_extractor.py
similarity index 100%
rename from nautilus_nlp/utils/keyword_extractor.py
rename to nautilus_nlp/analysis/keyword_extractor.py
diff --git a/nautilus_nlp/utils/ngrams_analysis.py b/nautilus_nlp/analysis/ngrams_analysis.py
similarity index 100%
rename from nautilus_nlp/utils/ngrams_analysis.py
rename to nautilus_nlp/analysis/ngrams_analysis.py
diff --git a/nautilus_nlp/utils/text_summary.py b/nautilus_nlp/analysis/text_summary.py
similarity index 100%
rename from nautilus_nlp/utils/text_summary.py
rename to nautilus_nlp/analysis/text_summary.py
diff --git a/nautilus_nlp/utils/visualize.py b/nautilus_nlp/analysis/visualize.py
similarity index 100%
rename from nautilus_nlp/utils/visualize.py
rename to nautilus_nlp/analysis/visualize.py
diff --git a/nautilus_nlp/preprocessing/text_vectorizers.py b/nautilus_nlp/models/text_vectorizers.py
similarity index 100%
rename from nautilus_nlp/preprocessing/text_vectorizers.py
rename to nautilus_nlp/models/text_vectorizers.py
diff --git a/nautilus_nlp/preprocessing/additional_preprocess.py b/nautilus_nlp/preprocessing/additional_preprocess.py
new file mode 100644
index 0000000..5ec89d5
--- /dev/null
+++ b/nautilus_nlp/preprocessing/additional_preprocess.py
@@ -0,0 +1,117 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import re
+import regex
+
+def remove_multiple_spaces_and_strip_text(text: str) -> str:
+    """
+    Remove multiple spaces, strip text, and remove '-', '*' characters.
+
+    Parameters
+    ----------
+    text : str
+        the text to be processed
+
+    Returns
+    -------
+    string
+        the text with removed multiple spaces and strip text
+    """
+    regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
+    for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
+        text = re.sub(regex_remove_multiple_spaces, " ", text)
+        text = text.strip()
+    return text
+
+
+def remove_tokens_with_nonletters(tokens: list) -> list:
+    """
+    Inputs a list of tokens, outputs a list of tokens without tokens that
+    includes numbers of special caracters.
+    ['foo','bar','124','34euros'] -> ['foo','bar']
+
+    Parameters
+    ----------
+    tokens : list
+        list of tokens to be cleaned
+
+    Returns
+    -------
+    list
+        list of tokens without tokens with numbers
+    """
+    return [word for word in tokens if re.search("[^a-zA-Z]", word) is None]
+
+
+def remove_special_caracters_from_tokenslist(tokens: list) -> list:
+    """ 
+    Remove tokens that doesn't contains any number or letter. 
+    eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
+
+    Parameters
+    ----------
+    tokens : list
+        list of tokens to be cleaned
+
+    Returns
+    -------
+    list
+        list of tokens without tokens that contains only special caracters
+    
+    """
+    return [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
+
+
+def filter_non_latin_characters(text:str) -> str:
+    """
+    Function that filters non latin characters of a text
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string
+    """
+    text = regex.sub(r'[^\p{Latin}1-9]', ' ', text).strip()
+    return re.sub(' +', ' ', text)
+
+
+def remove_smallwords(tokens_list:list, smallwords_threshold:int) -> list:
+    """
+    Function that removes words which length is below a threshold
+    ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
+
+    Parameters
+    ----------
+    text : list
+        list of strings
+    smallwords_threshold: int
+        threshold of small word
+
+    Returns
+    -------
+    list
+    """
+    result = [word for word in tokens_list if len(word) > smallwords_threshold]
+    return result
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/keyword_extractor.py b/nautilus_nlp/preprocessing/keyword_extractor.py
deleted file mode 100644
index 304fe02..0000000
--- a/nautilus_nlp/preprocessing/keyword_extractor.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from flashtext import KeywordProcessor
-
-def extract_keywords(text:str, keyword, case_sensitive=True) -> list:
-    """
-    Extract Keywords from a document.
-
-    Parameters
-    ----------
-    text : str
-        Text to extract keywords from
-    keyword : str or list 
-        Single keyword (str) or list of keywords (list)
-    case_sensitive : bool
-        If True, will be case-sensitive.
-
-    Returns
-    -------
-    string
-        return list of extracted keyworkds
-    """
-    processor=KeywordProcessor(case_sensitive=case_sensitive)
-    if isinstance(keyword,list):
-        processor.add_keywords_from_list(keyword)
-    elif isinstance(keyword,str):
-        processor.add_keyword(keyword)
-    elif isinstance(keyword,dict):
-        processor.add_keywords_from_dict(keyword)
-
-    return processor.extract_keywords(text)
-    
diff --git a/nautilus_nlp/preprocessing/lemmatization.py b/nautilus_nlp/preprocessing/lemmatization.py
deleted file mode 100644
index 2ee94e7..0000000
--- a/nautilus_nlp/preprocessing/lemmatization.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import spacy
-import nltk
-nltk.download('wordnet')
-nltk.download('averaged_perceptron_tagger')
-from nltk.stem import WordNetLemmatizer 
-from nltk.corpus import wordnet
-
-try:
-    french_spacy = spacy.load('fr_core_news_sm')
-except:
-    raise OSError("""You must install French langage to use SpaCy. 
-                    python -m spacy download fr
-                    See https://spacy.io/usage/ for details
-                """)
-try:
-    english_spacy = spacy.load('en_core_web_sm')
-except:
-    raise OSError("""You must install english langage to use SpaCy. 
-                    python -m spacy download en
-                    See https://spacy.io/usage/ for details
-                """)             
-
-
-def lemmatize_french_tokens(tokens:list, module:str='spacy')->list:
-    """
-    Wrappers of SpaCy french lemmatizers (based on FrenchLeffLemmatizer but 
-    faster and more accurate.) 
-
-    Parameters
-    ----------
-    tokens : list
-        List of tokens
-    module : ({'spacy'})
-        Only spaCy is available. We removed FrenchLeffLemmatizer for performance
-        considerations.
-
-    Returns
-    -------
-    list
-        list of lemmatized tokens
-    """
-
-    tokens = _make_sure_input_is_list_of_tokens(tokens)
-
-    if module == 'spacy':
-        # Doc : https://spacy.io/api/token#attributes
-        text = ' '.join(tokens)
-        doc = french_spacy(text)
-        return [token.lemma_ for token in doc]
-
-    else:
-        raise ValueError("must pass a valid module name!")
-
-
-def lemmatize_english_tokens(tokens:list, module:str='spacy')->list:
-    """
-    Wrapper of SpaCy english lemmatizer and NLTK WordNet.
-
-    Parameters
-    ----------
-    tokens : list
-        List of tokens
-    module : ({'spacy'},{'nltk'})
-        Tokenizer module. Default: 'spacy'
-
-    Returns
-    -------
-    list
-        list of lemmatized tokens
-    """
-    tokens = _make_sure_input_is_list_of_tokens(tokens)
-
-    if module == 'nltk':
-        # Doc : https://github.com/ClaudeCoulombe/FrenchLefffLemmatizer
-        lemmatizer = WordNetLemmatizer()
-        return [lemmatizer.lemmatize(word, _get_wordnet_pos(word)) for word in tokens]
-
-    elif module == 'spacy':
-        # Doc : https://spacy.io/api/token#attributes
-        text = ' '.join(tokens)
-        doc = english_spacy(text)
-        return [token.lemma_ for token in doc]
-
-    else:
-        raise ValueError("must pass a valid module name!")
-
-
-def _get_wordnet_pos(word):
-    """
-    Map POS tag to first character lemmatize() accepts
-    """
-    tag = nltk.pos_tag([word])[0][1][0].upper()
-    tag_dict = {"J": wordnet.ADJ,
-                "N": wordnet.NOUN,
-                "V": wordnet.VERB,
-                "R": wordnet.ADV}
-
-    return tag_dict.get(tag, wordnet.NOUN)        
-
-
-def _make_sure_input_is_list_of_tokens(tokens):
-    """
-    Raises an error if input is not a list, and convert "None" to blank list
-    to handle dataset with no text.
-    """    
-    if type(tokens) is list:
-        return tokens
-    elif tokens is None:
-        return []
-    elif type(tokens) is str:
-        raise ValueError("must pass a list of tokens, not text!")    
-
diff --git a/nautilus_nlp/preprocessing/preprocess.py b/nautilus_nlp/preprocessing/main_preprocess.py
similarity index 65%
rename from nautilus_nlp/preprocessing/preprocess.py
rename to nautilus_nlp/preprocessing/main_preprocess.py
index 9aee2f0..d91c4a2 100644
--- a/nautilus_nlp/preprocessing/preprocess.py
+++ b/nautilus_nlp/preprocessing/main_preprocess.py
@@ -19,43 +19,127 @@
 
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-import os
-import json
 import re
 import unicodedata
-
-import emoji as _emoji
-import regex
 from ftfy import fix_text as _fix_text
-from stop_words import get_stop_words as _get_stop_words
-from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 
+from nautilus_nlp.utils.stopwords_utils import get_stopwords
 from nautilus_nlp.utils.phone_number import extract_phone_numbers as _extract_phone_numbers
 from nautilus_nlp.utils import constants
-from nautilus_nlp.config.config import ROOT_FOLDER
-from nautilus_nlp.utils.file_loader import documents_loader
-
-STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
 
 
-def remove_multiple_spaces_and_strip_text(text: str) -> str:
+def preprocess_text(
+    text,
+    remove_eol_char=True,
+    fix_unicode=False,
+    lowercase=False,
+    no_urls=False,
+    no_emails=False,
+    no_phone_numbers=False,
+    no_numbers=False,
+    no_currency_symbols=False,
+    no_punct=False,
+    no_contractions=False,
+    no_accents=False,
+    replace_with=' ', 
+    no_stopwords=None,
+    phone_countries_format=[None,'US','FR'],
+    phone_method='regex'
+) -> str:
     """
-    Remove multiple spaces, strip text, and remove '-', '*' characters.
+    Normalize various aspects of a raw text doc. A convenience function for
+    applying all other preprocessing functions in one go.
 
     Parameters
     ----------
     text : str
-        the text to be processed
-
+        raw text to preprocess
+    fix_unicode : bool
+        if True, fix "broken" unicode such as mojibake and garbled HTML entities
+    remove_eol_char : bool 
+        if True, will remove the end-of-line characters \\n
+    lowercase : bool
+        if True, all text is lower-cased
+    no_urls : bool
+        if True, replace all URL strings with '*URL*' or with "replace_with" if 
+        specified.
+    no_emails : bool
+        if True, replace all email strings with '*EMAIL*' or with "replace_with" if 
+        specified.
+    no_phone_numbers : bool
+        if True, replace all phone number strings with '*PHONE*' or with "replace_with" if 
+        specified.
+    no_numbers : bool
+        if True, replace all number-like strings with '*NUMBER*' or with "replace_with" if 
+        specified.
+    no_currency_symbols : bool
+        if True, if True, replace all currency symbols with their standard 
+        3-letter abbreviations.
+    no_punct : bool
+        if True, remove all punctuation (replace with empty string)
+    no_contractions : bool
+        if True, if True, replace *English* contractions with their unshortened forms
+    no_accents : bool
+        if True, replace all accented characters with unaccented versions
+    replace_with : string
+        The string you want the entities to be replaced with.
+    no_stopwords : 2-letter country code
+        If specified, will remove the stopwords of the given language. 
+        Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
+         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
+         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
+         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
+          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
+          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
+           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']    
+    phone_countries_format : list
+        formats of the phone numbers to be removed. Full list is available at
+    utils.SUPPORTED_COUNTRY
+    phone_method : ['regex','detection']
+        regex is faster but will omit a lot of numbers, while detection will 
+        catch every numbers, but takes a while.
     Returns
     -------
     string
-        the text with removed multiple spaces and strip text
+        input ``text`` processed according to function args        
+
+    Warning
+    -------
+    These changes may negatively affect subsequent NLP analysis performed
+        on the text, so choose carefully, and preprocess at your own risk!        
     """
-    regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
-    for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
-        text = re.sub(regex_remove_multiple_spaces, " ", text)
-        text = text.strip()
+    assert isinstance(text, str), "The text to preprocess must be a string"
+
+    if fix_unicode is True:
+        text = fix_bad_unicode(text, normalization="NFC")
+    if remove_eol_char is True:
+        text = remove_EOL_characters(text)
+    if no_urls is True:
+        text = replace_urls(text, replace_with=replace_with)
+    if no_emails is True:
+        text = replace_emails(text, replace_with=replace_with)
+    if no_phone_numbers is True:
+        text = replace_phone_numbers(text,  replace_with=replace_with,
+                                            method=phone_method,
+                                            country_format_to_detect=phone_countries_format)
+    if no_numbers is True:
+        text = replace_numbers(text, replace_with=replace_with)
+    if no_currency_symbols is True:
+        text = replace_currency_symbols(text, replace_with=replace_with)
+    if no_contractions is True:
+        text = unpack_english_contractions(text)
+    if no_accents is True:
+        text = remove_accents(text, method="unicode")
+    if no_punct is True:
+        text = remove_punct(text)
+    if lowercase is True:
+        text = text.lower()
+    if no_stopwords is not None:
+        stopwords = get_stopwords(no_stopwords)
+        text = ' '.join(remove_stopwords(text, stopwords))
+    # always normalize whitespace; treat linebreaks separately from spacing
+    text = normalize_whitespace(text)
+    
     return text
 
 
@@ -74,102 +158,6 @@ def remove_EOL_characters(text: str) -> str:
     return text.replace("\n", " ")
 
 
-def remove_tokens_with_nonletters(tokens: list) -> list:
-    """
-    Inputs a list of tokens, outputs a list of tokens without tokens that
-    includes numbers of special caracters.
-    ['foo','bar','124','34euros'] -> ['foo','bar']
-
-    Parameters
-    ----------
-    tokens : list
-        list of tokens to be cleaned
-
-    Returns
-    -------
-    list
-        list of tokens without tokens with numbers
-    """
-    return [word for word in tokens if re.search("[^a-zA-Z]", word) is None]
-
-
-def remove_special_caracters_from_tokenslist(tokens: list) -> list:
-    """ 
-    Remove tokens that doesn't contains any number or letter. 
-    eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
-
-    Parameters
-    ----------
-    tokens : list
-        list of tokens to be cleaned
-
-    Returns
-    -------
-    list
-        list of tokens without tokens that contains only special caracters
-    
-    """
-    return [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
-
-
-def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
-    stopwords = documents_loader(filepath)
-    stopwords = json.loads(stopwords)
-    return stopwords
-
-
-def get_stopwords(lang: str = "en") -> list:
-    """
-    Inputs a language code, returns a list of stopwords for the specified language
-
-    Parameters
-    ----------
-    lang : str
-        Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
-         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
-         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
-         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
-          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
-          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
-           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
-
-    Returns
-    -------
-    list
-        list of stopwords for a given language
-
-    Raises
-    ------
-    ValueError
-        When language is not available yet or incorrect country code
-    """
-    if type(lang) == str and len(lang) == 2:
-        lang = lang.lower()
-
-        custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
-        stopwords = []
-
-        supported_lang_lib = list(_LANGUAGE_MAPPING.keys())
-        supported_lang_custom = list(custom_stopwords.keys())
-        supported_lang = supported_lang_lib + supported_lang_custom
-        if lang in supported_lang:
-            if lang in supported_lang_lib:
-                stopwords += _get_stop_words(lang)
-            if lang in supported_lang_custom:
-                stopwords += custom_stopwords[lang]
-        else:
-            raise ValueError(
-                "Language not available yet or incorrect country code. Supported languages: {}".format(
-                    supported_lang
-                )
-            )
-    else:
-        raise ValueError(
-            'Please input a valid country code, in 2 letters. Eg. "us" for USA. '
-        )
-    return list(set(stopwords))
-
-
 def remove_stopwords(text_or_tokens, stopwords: list) -> list:
     """ 
     Remove stopwords from a list of tokens or a text.
@@ -285,20 +273,7 @@ def unpack_english_contractions(text:str) -> str:
     return text
 
 
-def filter_non_latin_characters(text:str) -> str:
-    """
-    Function that filters non latin characters of a text
-
-    Parameters
-    ----------
-    text : string
 
-    Returns
-    -------
-    string
-    """
-    text = regex.sub(r'[^\p{Latin}1-9]', ' ', text).strip()
-    return re.sub(' +', ' ', text)
 
 
 def replace_urls(text:str, replace_with:str="*URL*") -> str:
@@ -491,280 +466,11 @@ def remove_accents(text:str, method:str="unicode") -> str:
         raise ValueError(msg)
 
 
-def remove_mentions(text:str) -> str:
-    """
-    Function that removes words preceded with a '@'
 
-    Parameters
-    ----------
-    text : str
-    
-    Returns
-    -------
-    string
-    """
-    return normalize_whitespace(re.sub(r'@\w*', '', text))
 
 
-def extract_mentions(text:str) -> str:
-    """
-    Function that extracts words preceded with a '@'
-    eg. "I take care of my skin with @thisproduct" --> ["@thisproduct"]
 
-    Parameters
-    ----------
-    text : str
-    
-    Returns
-    -------
-    string
-    """
-    return re.findall(r'[@][^\s@]+', text)
 
 
-def remove_html_tags(text:str) -> str:
-    """
-    Function that removes words between < and >
 
-    Parameters
-    ----------
-    text : str
-    
-    Returns
-    -------
-    string
-    """
-    return normalize_whitespace(re.sub(r'<.*?>', '', text))
-
-
-def remove_smallwords(tokens_list:list, smallwords_threshold:int) -> list:
-    """
-    Function that removes words which length is below a threshold
-    ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
-
-    Parameters
-    ----------
-    text : list
-        list of strings
-    smallwords_threshold: int
-        threshold of small word
-
-    Returns
-    -------
-    list
-    """
-    result = [word for word in tokens_list if len(word) > smallwords_threshold]
-    return result
-
-
-def remove_emoji(text:str) -> str:
-    """
-    Remove emoji from any str by stripping any unicode in the range of Emoji unicode
-    as defined in the unicode convention: 
-    http://www.unicode.org/emoji/charts/full-emoji-list.html
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    str
-    """
-    emoji_pattern = _emoji.get_emoji_regexp()
-    word = emoji_pattern.sub("", text)
-    return word
-
-
-def convert_emoji_to_text(text:str, code_delimiters=(':', ':')) -> str:
-    """
-    Convert emoji to their CLDR Short Name, according to the unicode convention
-    http://www.unicode.org/emoji/charts/full-emoji-list.html
-    eg. 😀 --> :grinning_face:
-
-    Parameters
-    ----------
-    text : str
-        code_delimiters : tuple of symbols around the emoji code. 
-        eg: (':',':') --> :grinning_face:
-
-    Returns
-    -------
-    str
-        string 
-    """
-    return _emoji.demojize(text, delimiters=code_delimiters)
-
-
-def extract_emojis(text:str) -> list:
-    """
-    Function that extracts emojis from a text and translates them into words
-    eg. "I take care of my skin 😀 :(" --> [":grinning_face:"]
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    list
-        list of all emojis converted with their unicode conventions
-    """
-    emoji_pattern = _emoji.get_emoji_regexp()
-    emojis_in_text = re.findall(emoji_pattern, text)
-    emojis_converted = [convert_emoji_to_text(emoji_text) for emoji_text in emojis_in_text]
-    return emojis_converted
-
-
-def extract_hashtags(text) -> list:
-    """
-    Function that extracts words preceded with a '#'
-    eg. "I take care of my skin #selfcare#selfestim" --> ["skincare", "selfestim"]
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    list
-        list of all hashtags
-    """
-    return re.findall(r'[#][^\s#]+', text)
-
-
-def remove_hashtag(text) -> str:
-    """
-    Function that removes words preceded with a '#'
-    eg. "I take care of my skin #selfcare#selfestim" --> "I take care of my skin"
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    str
-        text of a post without hashtags
-    """
-    return normalize_whitespace(re.sub(r'#\w*', '', text))
-
-
-def preprocess_text(
-    text,
-    remove_eol_char=True,
-    fix_unicode=False,
-    lowercase=False,
-    no_urls=False,
-    no_emails=False,
-    no_phone_numbers=False,
-    no_numbers=False,
-    no_currency_symbols=False,
-    no_punct=False,
-    no_contractions=False,
-    no_accents=False,
-    no_emoji=False,
-    replace_with=' ', 
-    no_stopwords=None,
-    phone_countries_format=[None,'US','FR'],
-    phone_method='regex'
-) -> str:
-    """
-    Normalize various aspects of a raw text doc. A convenience function for
-    applying all other preprocessing functions in one go.
-
-    Parameters
-    ----------
-    text : str
-        raw text to preprocess
-    fix_unicode : bool
-        if True, fix "broken" unicode such as mojibake and garbled HTML entities
-    remove_eol_char : bool 
-        if True, will remove the end-of-line characters \\n
-    lowercase : bool
-        if True, all text is lower-cased
-    no_urls : bool
-        if True, replace all URL strings with '*URL*' or with "replace_with" if 
-        specified.
-    no_emails : bool
-        if True, replace all email strings with '*EMAIL*' or with "replace_with" if 
-        specified.
-    no_phone_numbers : bool
-        if True, replace all phone number strings with '*PHONE*' or with "replace_with" if 
-        specified.
-    no_numbers : bool
-        if True, replace all number-like strings with '*NUMBER*' or with "replace_with" if 
-        specified.
-    no_currency_symbols : bool
-        if True, if True, replace all currency symbols with their standard 
-        3-letter abbreviations.
-    no_punct : bool
-        if True, remove all punctuation (replace with empty string)
-    no_contractions : bool
-        if True, if True, replace *English* contractions with their unshortened forms
-    no_accents : bool
-        if True, replace all accented characters with unaccented versions
-    no_emoji : bool
-        if True, remove all emojis from text
-    replace_with : string
-        The string you want the entities to be replaced with.
-    no_stopwords : 2-letter country code
-        If specified, will remove the stopwords of the given language. 
-        Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
-         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
-         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
-         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
-          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
-          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
-           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']    
-    phone_countries_format : list
-        formats of the phone numbers to be removed. Full list is available at
-    utils.SUPPORTED_COUNTRY
-    phone_method : ['regex','detection']
-        regex is faster but will omit a lot of numbers, while detection will 
-        catch every numbers, but takes a while.
-    Returns
-    -------
-    string
-        input ``text`` processed according to function args        
-
-    Warning
-    -------
-    These changes may negatively affect subsequent NLP analysis performed
-        on the text, so choose carefully, and preprocess at your own risk!        
-    """
-    assert isinstance(text, str), "The text to preprocess must be a string"
 
-    if fix_unicode is True:
-        text = fix_bad_unicode(text, normalization="NFC")
-    if remove_eol_char is True:
-        text = remove_EOL_characters(text)
-    if no_urls is True:
-        text = replace_urls(text, replace_with=replace_with)
-    if no_emails is True:
-        text = replace_emails(text, replace_with=replace_with)
-    if no_phone_numbers is True:
-        text = replace_phone_numbers(text,  replace_with=replace_with,
-                                            method=phone_method,
-                                            country_format_to_detect=phone_countries_format)
-    if no_numbers is True:
-        text = replace_numbers(text, replace_with=replace_with)
-    if no_currency_symbols is True:
-        text = replace_currency_symbols(text, replace_with=replace_with)
-    if no_emoji is True:
-        text = remove_emoji(text)
-    if no_contractions is True:
-        text = unpack_english_contractions(text)
-    if no_accents is True:
-        text = remove_accents(text, method="unicode")
-    if no_punct is True:
-        text = remove_punct(text)
-    if lowercase is True:
-        text = text.lower()
-    if no_stopwords is not None:
-        stopwords = get_stopwords(no_stopwords)
-        text = ' '.join(remove_stopwords(text, stopwords))
-    # always normalize whitespace; treat linebreaks separately from spacing
-    text = normalize_whitespace(text)
-    
-    return text
diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
new file mode 100644
index 0000000..a88fad2
--- /dev/null
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -0,0 +1,164 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import re
+import emoji as _emoji
+
+from nautilus_nlp.utils.preprocess import normalize_whitespace
+
+
+def remove_mentions(text:str) -> str:
+    """
+    Function that removes words preceded with a '@'
+
+    Parameters
+    ----------
+    text : str
+    
+    Returns
+    -------
+    string
+    """
+    return normalize_whitespace(re.sub(r'@\w*', '', text))
+
+
+def extract_mentions(text:str) -> str:
+    """
+    Function that extracts words preceded with a '@'
+    eg. "I take care of my skin with @thisproduct" --> ["@thisproduct"]
+
+    Parameters
+    ----------
+    text : str
+    
+    Returns
+    -------
+    string
+    """
+    return re.findall(r'[@][^\s@]+', text)
+
+
+def remove_html_tags(text:str) -> str:
+    """
+    Function that removes words between < and >
+
+    Parameters
+    ----------
+    text : str
+    
+    Returns
+    -------
+    string
+    """
+    return normalize_whitespace(re.sub(r'<.*?>', '', text))
+
+
+def remove_emoji(text:str) -> str:
+    """
+    Remove emoji from any str by stripping any unicode in the range of Emoji unicode
+    as defined in the unicode convention: 
+    http://www.unicode.org/emoji/charts/full-emoji-list.html
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    str
+    """
+    emoji_pattern = _emoji.get_emoji_regexp()
+    word = emoji_pattern.sub("", text)
+    return word
+
+
+def convert_emoji_to_text(text:str, code_delimiters=(':', ':')) -> str:
+    """
+    Convert emoji to their CLDR Short Name, according to the unicode convention
+    http://www.unicode.org/emoji/charts/full-emoji-list.html
+    eg. 😀 --> :grinning_face:
+
+    Parameters
+    ----------
+    text : str
+        code_delimiters : tuple of symbols around the emoji code. 
+        eg: (':',':') --> :grinning_face:
+
+    Returns
+    -------
+    str
+        string 
+    """
+    return _emoji.demojize(text, delimiters=code_delimiters)
+
+
+def extract_emojis(text:str) -> list:
+    """
+    Function that extracts emojis from a text and translates them into words
+    eg. "I take care of my skin 😀 :(" --> [":grinning_face:"]
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    list
+        list of all emojis converted with their unicode conventions
+    """
+    emoji_pattern = _emoji.get_emoji_regexp()
+    emojis_in_text = re.findall(emoji_pattern, text)
+    emojis_converted = [convert_emoji_to_text(emoji_text) for emoji_text in emojis_in_text]
+    return emojis_converted
+
+
+def extract_hashtags(text) -> list:
+    """
+    Function that extracts words preceded with a '#'
+    eg. "I take care of my skin #selfcare#selfestim" --> ["skincare", "selfestim"]
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    list
+        list of all hashtags
+    """
+    return re.findall(r'[#][^\s#]+', text)
+
+
+def remove_hashtag(text) -> str:
+    """
+    Function that removes words preceded with a '#'
+    eg. "I take care of my skin #selfcare#selfestim" --> "I take care of my skin"
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    str
+        text of a post without hashtags
+    """
+    return normalize_whitespace(re.sub(r'#\w*', '', text))
diff --git a/nautilus_nlp/preprocessing/stemming.py b/nautilus_nlp/preprocessing/stemming.py
deleted file mode 100644
index 92028ec..0000000
--- a/nautilus_nlp/preprocessing/stemming.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nltk.stem.snowball import *
-
-
-def stem_tokens(tokens: list, lang: str ='english')-> list:
-    """
-    Wrapper of NLTK's Snowball stemmers : http://www.nltk.org/howto/stem.html
-
-    Parameters
-    ----------
-    tokens : list
-        List of tokens
-    lang : string
-        Supported languages: ({'arabic', 'danish', 'dutch', 'english', 'finnish', 'french',
-        'german', 'hungarian', 'italian', 'norwegian', 'porter', 'portuguese', 
-        'romanian', 'russian', 'spanish', 'swedish'}): 
-
-    Returns
-    -------
-    list
-        list of stemmed tokens
-    """
-
-    supported_lang = [lang for lang in SnowballStemmer.languages]
-    
-    if lang in supported_lang:
-        stemmer = eval(lang.capitalize()+'Stemmer()')
-    else:
-        raise ValueError("Langage not supported of mispelled")
-    
-    # Make sure tokens are actually a list, and handle NaN. 
-    if type(tokens) is list:
-        return [stemmer.stem(token) for token in tokens]
-    elif tokens is None:
-        return []
-    elif type(tokens) is str:
-        raise ValueError("must pass a list of tokens, not text!")
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/tokenizer.py b/nautilus_nlp/preprocessing/tokenizer.py
deleted file mode 100644
index 4236f23..0000000
--- a/nautilus_nlp/preprocessing/tokenizer.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import nltk
-from sacremoses import MosesTokenizer, MosesDetokenizer
-import spacy
-from spacy.lang.fr import French
-from spacy.lang.en import English
-import spacy.lang as spacylang
-nltk.download('punkt')
-
-try:
-    french_spacy = spacylang.fr.French()
-except:
-    raise OSError("""You must install French langage to use SpaCy. 
-                    python -m spacy download fr
-                    See https://spacy.io/usage/ for details
-                """)
-try:
-    english_spacy = spacylang.en.English()
-except:
-    raise OSError("""You must install english langage to use SpaCy. 
-                    python -m spacy download en
-                    See https://spacy.io/usage/ for details
-                """)             
-
-
-def tokenize(text: str, lang_module: str = 'en_spacy')-> list:
-    """
-    Split text into a list of tokens. 
-
-    Parameters
-    ----------
-    lang_module
-        ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
-        the tokenization module according to the langage and the implementation.
-        Recommanded: Spacy (faster, better results). To process other langages
-        import models.Spacy_models
-
-    Returns
-    -------
-    list
-        Outputs a list of tokens.
-    """
-    if lang_module == 'en_nltk':
-        return nltk.word_tokenize(text)
-    elif lang_module == 'en_spacy':
-        spacydoc = english_spacy(text)
-        return [tokens.text for tokens in spacydoc]
-    elif lang_module == 'fr_spacy':
-        spacydoc = french_spacy(text)
-        return [tokens.text for tokens in spacydoc]
-    elif lang_module == 'fr_moses':
-        t = MosesTokenizer(lang='fr')
-        return t.tokenize(text, escape=False)
-
-
-def untokenize(tokens:list, lang:str='fr')->str:
-    """
-    Inputs a list of tokens, output the text joined.
-    Wrapper of https://github.com/alvations/sacremoses
-
-    Parameters
-    ----------
-    lang
-        Supported languages: ({'fr', 'en', 'cs','it','ga'})
-
-    Returns
-    -------
-    string
-    """
-    d = MosesDetokenizer(lang=lang)
-    text = d.detokenize(tokens, unescape=False)
-    return text    
-
-
-def _convert_tokens_to_string(tokens_or_str, lang:str='en')->str:
-    """
-    Test if the input is tokens or string, and convert tokens to string
-    using untokenize(). If the input is null, it will return an empty string. 
-
-    Parameters
-    ----------
-    lang
-        Supported languages: ({'fr', 'en', 'cs','it','ga'}).
-
-    Returns
-    -------
-    string
-
-    Raises
-    -------
-    ValueError
-        If the input is not string, list or Nonetype.     
-    """
-    if type(tokens_or_str) is str:
-        return tokens_or_str
-    elif type(tokens_or_str) is list:
-        return untokenize(tokens_or_str, lang=lang)
-    elif type(tokens_or_str) is None:
-        return ''
-    else:
-        raise ValueError('Please input string or tokens')
-
-
-def _convert_string_to_tokens(tokens_or_str, lang_module:str='en_spacy')->str:
-    """
-    Test if the input is tokens or string, and convert string to tokens
-    using tokenize(). If the input is null, it will return an empty list. 
-
-    Parameters
-    ----------
-    lang_module
-        ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'}): choose
-        the tokenization module according to the langage and the implementation.
-        Recommanded: Spacy (faster, better results). To process other langages
-        import models.Spacy_models
-
-    Returns
-    -------
-    list
-        list of tokens
-
-    Raises
-    -------
-    ValueError
-        If the input is not string, list or Nonetype.     
-    """
-    if type(tokens_or_str) is str:
-        return tokenize(tokens_or_str, lang_module=lang_module)
-    elif type(tokens_or_str) is list:
-        return tokens_or_str
-    elif type(tokens_or_str) is None:
-        return []
-    else:
-        raise ValueError('Please input string or tokens')
\ No newline at end of file
diff --git a/nautilus_nlp/utils/stopwords.py b/nautilus_nlp/utils/stopwords.py
new file mode 100644
index 0000000..0ea81a2
--- /dev/null
+++ b/nautilus_nlp/utils/stopwords.py
@@ -0,0 +1,87 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import os
+from stop_words import get_stop_words as _get_stop_words
+from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
+from nautilus_nlp.config.config import ROOT_FOLDER
+from nautilus_nlp.utils.file_loader import documents_loader
+
+STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
+
+
+def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
+    stopwords = documents_loader(filepath)
+    stopwords = json.loads(stopwords)
+    return stopwords
+
+
+def get_stopwords(lang: str = "en") -> list:
+    """
+    Inputs a language code, returns a list of stopwords for the specified language
+
+    Parameters
+    ----------
+    lang : str
+        Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
+         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
+         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
+         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
+          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
+          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
+           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
+
+    Returns
+    -------
+    list
+        list of stopwords for a given language
+
+    Raises
+    ------
+    ValueError
+        When language is not available yet or incorrect country code
+    """
+    if type(lang) == str and len(lang) == 2:
+        lang = lang.lower()
+
+        custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
+        stopwords = []
+
+        supported_lang_lib = list(_LANGUAGE_MAPPING.keys())
+        supported_lang_custom = list(custom_stopwords.keys())
+        supported_lang = supported_lang_lib + supported_lang_custom
+        if lang in supported_lang:
+            if lang in supported_lang_lib:
+                stopwords += _get_stop_words(lang)
+            if lang in supported_lang_custom:
+                stopwords += custom_stopwords[lang]
+        else:
+            raise ValueError(
+                "Language not available yet or incorrect country code. Supported languages: {}".format(
+                    supported_lang
+                )
+            )
+    else:
+        raise ValueError(
+            'Please input a valid country code, in 2 letters. Eg. "us" for USA. '
+        )
+    return list(set(stopwords))
\ No newline at end of file

From 46b229ad015c7d261000f01c1d8b4769693c225e Mon Sep 17 00:00:00 2001
From: Bruce DELATTRE <bruce.delattre@FRART0231M.local>
Date: Wed, 25 Mar 2020 13:07:08 +0100
Subject: [PATCH 250/496] Cleaning repository and updating tests

---
 .travis.yml                                   |   7 -
 models/.gitkeep                               |   0
 nautilus_nlp/doc.py                           | 344 ------------------
 .../scripts/download_ft_langdetect.sh         |  19 -
 nautilus_nlp/scripts/download_spacy_models.sh |  22 --
 nautilus_nlp/scripts/init_langmodel.sh        |  27 --
 nautilus_nlp/scripts/install_fasttext.sh      |  20 -
 nautilus_nlp/scripts/install_java.sh          |  21 --
 nautilus_nlp/scripts/install_mallet.sh        |  21 --
 reports/.gitkeep                              |   0
 reports/figures/.gitkeep                      |   0
 tests/test_doc.py                             | 161 --------
 tests/test_extraction.py                      |  33 --
 tests/test_fix_bad_encoding.py                |   2 +-
 tests/test_lemmatization.py                   |  39 --
 tests/test_ngrams_analysis.py                 |  38 --
 tests/test_preprocessor.py                    |  30 +-
 tests/test_stemming.py                        |  33 --
 tests/test_text_summary.py                    |  44 ---
 tests/test_tokenizer.py                       |  63 ----
 20 files changed, 18 insertions(+), 906 deletions(-)
 delete mode 100644 models/.gitkeep
 delete mode 100644 nautilus_nlp/doc.py
 delete mode 100644 nautilus_nlp/scripts/download_ft_langdetect.sh
 delete mode 100644 nautilus_nlp/scripts/download_spacy_models.sh
 delete mode 100644 nautilus_nlp/scripts/init_langmodel.sh
 delete mode 100644 nautilus_nlp/scripts/install_fasttext.sh
 delete mode 100644 nautilus_nlp/scripts/install_java.sh
 delete mode 100644 nautilus_nlp/scripts/install_mallet.sh
 delete mode 100644 reports/.gitkeep
 delete mode 100644 reports/figures/.gitkeep
 delete mode 100644 tests/test_doc.py
 delete mode 100644 tests/test_extraction.py
 delete mode 100644 tests/test_lemmatization.py
 delete mode 100644 tests/test_ngrams_analysis.py
 delete mode 100644 tests/test_stemming.py
 delete mode 100644 tests/test_text_summary.py
 delete mode 100644 tests/test_tokenizer.py

diff --git a/.travis.yml b/.travis.yml
index 0304bca..907e302 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -21,13 +21,6 @@ os:
   - linux
 services:
   - docker
-
-before_script:
-  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
-  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
-
-  - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
-  - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
 install:
   - pip install -r requirements.txt
diff --git a/models/.gitkeep b/models/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/doc.py b/nautilus_nlp/doc.py
deleted file mode 100644
index 420b2a7..0000000
--- a/nautilus_nlp/doc.py
+++ /dev/null
@@ -1,344 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import functools
-import pkg_resources
-import nautilus_nlp
-import spacy
-import textacy
-from collections import Counter
-import re
-import unicodedata
-import spacy
-import spacy.matcher
-import textacy
-import textacy.keyterms
-import textacy.text_utils
-
-class NautilusMissingModelException(Exception):
-    """Raised when the requested model is missing"""
-
-    pass
-
-
-class Doc:
-    """
-    Create a doc instance of text, obtain cleaned, readable text and
-    metadata from this doc.
-
-    Properties:
-    raw: incoming, unedited text
-    language: 2-letter code for the language of the text
-    is_detected_language: is the language detected or specified beforehand
-    is_reliable_language: is the language specified or was it reliably detected
-    _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
-    """
-
-    def __init__(
-        self,
-        raw,
-        language=None,
-        spacy_nlps=None,
-        langdetect=None,
-        sentiment_detect=None,
-    ):
-        self.raw = raw
-        self._spacy_nlps = spacy_nlps or dict()
-        self._language = language
-        self._is_reliable_language = 1 if language else None
-        self._language_detector = langdetect
-        self._sentiment_detector = sentiment_detect
-        self._text_stats = {}
-
-    @property
-    def language(self):
-        """
-        Provided or detected language of a text
-        
-        >>> from nautilus_nlp.doc import Doc
-        >>> Doc('Test sentence for testing text').language
-        'en'
-        >>> Doc('Test sentence for testing text', language='en').language
-        'en'
-        >>> Doc('Test', hint_language='nl').language
-        'nl'
-        """
-
-        if not self._language:
-            if self._language_detector:
-                self._language,self._is_reliable_language = self._language_detector.detect_language(
-                    self.clean
-                )
-            else:
-                raise NautilusMissingModelException(
-                    "You must either provide a language for the document or an instance of LangDetector"
-                )
-
-        return self._language
-
-    @property
-    def _spacy_doc(self):
-        """
-        Loads the default spacy doc or creates one if necessary
-
-        >>> doc = Doc('Test sentence for testing text')
-        >>> type(doc._spacy_doc)
-        <class 'spacy.tokens.doc.Doc'>
-        """
-        lang = self.language
-
-        return self._load_spacy_doc(lang)
-
-    def _load_spacy_doc(self, lang, model_name=None):
-        """
-        Loads a spacy doc or creates one if necessary
-        """
-        # Load default spacy model if necessary, if not loaded already
-        if lang not in self._spacy_nlps or (
-            model_name is None and model_name not in self._spacy_nlps[lang]
-        ):
-            if lang not in self._spacy_nlps:
-                self._spacy_nlps[lang] = {}
-            self._spacy_nlps[lang][None] = self._get_default_nlp(lang)
-        if model_name not in self._spacy_nlps[lang] and model_name is not None:
-            raise NautilusMissingModelException(
-                f"Custom model {model_name} " f"is missing."
-            )
-        nlp = self._spacy_nlps[lang][model_name]
-        doc = nlp(self.clean_text())
-        return doc
-
-    @staticmethod
-    @functools.lru_cache()
-    def _get_default_nlp(lang):
-        """
-        Loads the spacy default language module for the Doc's language
-        """
-        try:
-            if lang!='un':
-                return spacy.load(
-                    "{}_core_{}_sm".format(lang, "web" if lang == "en" else "news")
-                )
-            else:
-                return spacy.load('xx_ent_wiki_sm')
-        except IOError:
-            raise NautilusMissingModelException(
-                f'Default model for language "{lang}" is not available. You should try to run python -m spacy download {lang}_core_news_sm'
-            
-            )
-
-    @property
-    def clean(self):
-        """
-        Cleaned text with sensible defaults.
-        >>> doc = Doc('“Please clean this piece… of text</b>„')
-        >>> doc.clean
-        '"Please clean this piece... of text"'
-        
-        Right now this is done here by a simple regex. Next step is to use the preprocessing functions
-        """
-
-        return self.clean_text()
-
-    @functools.lru_cache()
-    def clean_text(self, clean_dots=True, clean_quotes=True, clean_whitespace=True):
-        """
-        Clean text and normalise punctuation.
-        >>> doc = Doc('“Please clean this piece… of text„')
-        >>> doc.clean_text(False, False, False, False) == doc.raw
-        True
-        """
-        text = self.raw
-        text.replace('\n',' ')
-        if clean_dots:
-            text = re.sub(r"…", "...", text)
-        if clean_quotes:
-            text = re.sub(r"[`‘’‛⸂⸃⸌⸍⸜⸝]", "'", text)
-            text = re.sub(r"[„“]|(\'\')|(,,)", '"', text)
-        if clean_whitespace:
-            text = re.sub(r"\s+", " ", text).strip()
-
-        return text
-
-    @property
-    def lemma(self):
-
-        return self.get_lemma()
-    
-    @functools.lru_cache()
-    def get_lemma(self,model_name=None):
-        
-        return [token.lemma_ for token in  self._load_spacy_doc(self.language, model_name)]
-
-    @property
-    def entities(self):
-        """
-        A list of the named entities with sensible defaults.
-
-        >>> doc = Doc('Sentence for testing Google text')
-        >>> doc.entities
-        [('Google', 'ORG')]
-        """
-        return self.find_entities()
-
-    @functools.lru_cache()
-    def find_entities(self, model_name=None):
-        """
-        Extract a list of the named entities in text, with the possibility of using a custom model.
-
-        >>> doc = Doc('Sentence for testing Google text')
-        >>> doc.find_entities()
-        [('Google', 'ORG')]
-        """
-
-        return list(
-            {
-                (ent.text, ent.label_)
-                for ent in self._load_spacy_doc(self.language, model_name).ents
-            }
-        )
-
-    @property
-    def n_sentences(self):
-        """
-        Extract the number of sentences from text
-
-        >>> doc = Doc('Test sentence for testing text. And another sentence for testing!')
-        >>> doc.n_sentences
-        2
-        """
-        return len(list(self._spacy_doc.sents))
-
-    @property
-    def sentences(self):
-        """
-        Extract the text and character offset (begin) of sentences from text
-
-        >>> doc = Doc('Test sentence for testing text. And another one with, some, punctuation! And stuff.')
-        >>> doc.sentences
-        [('Test sentence for testing text.', 0), ('And another one with, some, punctuation!', 32), ('And stuff.', 73)]
-        """
-
-        return [(span.text, span.start_char) for span in self._spacy_doc.sents]
-
-    @property
-    def n_words(self):
-        """
-        Extract the number of words from text
-
-        >>> doc = Doc('Test sentence for testing text')
-        >>> doc.n_words
-        5
-        """
-        return len(self.words)
-
-    @property
-    def words(self):
-        """
-        Extract the text and character offset (begin) of words from text
-
-        >>> doc = Doc('Test sentence for testing text.')
-        >>> doc.words
-        [('Test', 0), ('sentence', 5), ('for', 14), ('testing', 18), ('text', 26), ('.', 30)]
-        """
-
-        return [(token.text, token.idx) for token in self._spacy_doc]
-
-    @property
-    def word_counts(self):
-        """
-        Extract words with their counts
-
-        >>> doc = Doc('Test sentence for testing vectorisation of a sentence.')
-        >>> doc.word_counts
-        {'Test': 1, 'sentence': 2, 'for': 1, 'testing': 1, 'vectorisation': 1, 'of': 1, 'a': 1, '.': 1}
-        """
-
-        return dict(Counter(word for word, _ in self.words))
-
-    @property
-    def complexity(self):
-        """
-        Determine the complexity of text using the Flesch
-        reading ease test ranging from 0.0 - 100.0 with 0.0
-        being the most difficult to read.
-
-        >>> doc = Doc('Test sentence for testing text')
-        >>> doc.complexity
-        83.32000000000004
-        """
-        if not self._text_stats:
-            self._text_stats = textacy.TextStats(self._spacy_doc)
-        if self._text_stats.n_syllables == 0:
-            return 100
-        return self._text_stats.flesch_reading_ease
-
-    @property
-    def sentiment(self):
-        """
-        Returns polarity score (-1 to 1) and a subjectivity score (0 to 1)
-
-        >>> doc = Doc('C'est trop cool !.')
-        >>> doc.sentiment
-        (0.8, 0.9666666666666667)
-        """
-
-        raise NautilusMissingModelException(f"No sentiment model for {self.language}")
-
-    @functools.lru_cache()
-    def extract_keyterms(self, ranker="textrank", n_terms=10, **kwargs):
-        """
-        Extract and rank key terms in the document by proxying to
-        `textacy.keyterms`. Returns a list of (term, score) tuples. Depending
-        on the ranking algorithm used, terms can consist of multiple words.
-
-        Available rankers are TextRank (textrank), SingleRank (singlerank) and
-        SGRank ('sgrank').
-
-        >>> doc = Doc('Amsterdam is the awesome capital of the Netherlands.')
-        >>> doc.extract_keyterms(n_terms=3)
-        [('awesome', 0.32456160227748454), ('capital', 0.32456160227748454), ('Amsterdam', 0.17543839772251532)]
-        >>> doc.extract_keyterms(ranker='sgrank')
-        [('awesome capital', 0.5638711013322963), ('Netherlands', 0.22636566128805719), ('Amsterdam', 0.20976323737964653)]
-        >>> doc.extract_keyterms(ranker='sgrank', ngrams=(1))
-        [('Netherlands', 0.4020557546031188), ('capital', 0.29395103364295216), ('awesome', 0.18105611227666252), ('Amsterdam', 0.12293709947726655)]
-        """
-        if self.n_words < 1:
-            return []
-        rankers = ["textrank", "sgrank", "singlerank"]
-        if ranker not in rankers:
-            raise ValueError(
-                f'ranker "{ranker}" not available; use one ' f"of {rankers}"
-            )
-        ranking_fn = getattr(textacy.keyterms, ranker)
-        return ranking_fn(self._spacy_doc, n_keyterms=n_terms, **kwargs)
-
-    @property
-    def keyterms(self):
-        """
-        Return textranked keyterms for the document.
-
-        >>> doc = Doc('Amsterdam is the awesome capital of the Netherlands.')
-        >>> doc.extract_keyterms(n_terms=3)
-        [('awesome', 0.32456160227748454), ('capital', 0.32456160227748454), ('Amsterdam', 0.17543839772251532)]
-        """
-        return self.extract_keyterms()
-   
-    @functools.lru_cache()
-    def extract_keywords(self,keyword_list:list):
-        from flashtext import KeywordProcessor
-        return KeywordProcessor().add_keywords_from_list(keyword_list).extract_keywords(self.raw)
diff --git a/nautilus_nlp/scripts/download_ft_langdetect.sh b/nautilus_nlp/scripts/download_ft_langdetect.sh
deleted file mode 100644
index 300dc9a..0000000
--- a/nautilus_nlp/scripts/download_ft_langdetect.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-wget https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz 
-cp lid.176.ftz data/
\ No newline at end of file
diff --git a/nautilus_nlp/scripts/download_spacy_models.sh b/nautilus_nlp/scripts/download_spacy_models.sh
deleted file mode 100644
index cd84284..0000000
--- a/nautilus_nlp/scripts/download_spacy_models.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-python -m spacy download en_core_web_sm
-python -m spacy download de_core_news_sm 
-python -m spacy download fr_core_news_sm 
-python -m spacy download es_core_news_sm 
-python -m spacy download nl_core_news_sm
diff --git a/nautilus_nlp/scripts/init_langmodel.sh b/nautilus_nlp/scripts/init_langmodel.sh
deleted file mode 100644
index 2bf0b48..0000000
--- a/nautilus_nlp/scripts/init_langmodel.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-#!/bin/bash
-
-echo 'Which Language do you want to use: en/fr?'
-read lang
-echo "Now downloading spacy models for language $lang"
-python -m spacy download $lang
-python -m spacy download xx
-
-wget https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.$lang.300.bin.gz 
-cp cc.$lang.300.bin.gz data/  && gunzip data/cc.$lang.300.bin.gz
diff --git a/nautilus_nlp/scripts/install_fasttext.sh b/nautilus_nlp/scripts/install_fasttext.sh
deleted file mode 100644
index c5dc339..0000000
--- a/nautilus_nlp/scripts/install_fasttext.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-#!/bin/bash
-wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip && unzip v0.2.0.zip && cd fastText-0.2.0 && make
-git clone https://github.com/facebookresearch/fastText.git && cd fastText && pip install .
\ No newline at end of file
diff --git a/nautilus_nlp/scripts/install_java.sh b/nautilus_nlp/scripts/install_java.sh
deleted file mode 100644
index 8c894d2..0000000
--- a/nautilus_nlp/scripts/install_java.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-sudo add-apt-repository ppa:openjdk-r/ppa  # only Ubuntu 17.4 and earlier
-sudo apt update
-apt search openjdk
-sudo apt install openjdk-8-jdk
\ No newline at end of file
diff --git a/nautilus_nlp/scripts/install_mallet.sh b/nautilus_nlp/scripts/install_mallet.sh
deleted file mode 100644
index 51ff9f7..0000000
--- a/nautilus_nlp/scripts/install_mallet.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-#!/bin/bash
-wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip 
-unzip mallet-2.0.8.zip
-rm mallet-2.0.8.zip
\ No newline at end of file
diff --git a/reports/.gitkeep b/reports/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/reports/figures/.gitkeep b/reports/figures/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/test_doc.py b/tests/test_doc.py
deleted file mode 100644
index 75e9dcc..0000000
--- a/tests/test_doc.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-"""
-Testing for textpipe doc.py
-"""
-import pytest
-import random
-import spacy
-from nautilus_nlp.models.language_detector import LangDetector
-from nautilus_nlp.doc import Doc, NautilusMissingModelException
-
-TEXT_1 = """
-Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock. They incorporated Google as a privately held company on September 4, 1998. An initial public offering (IPO) took place on August 19, 2004, and Google moved to its headquarters in Mountain View, California, nicknamed the Googleplex. In August 2015, Google announced plans to reorganize its various interests as a conglomerate called Alphabet Inc. Google is Alphabet's leading subsidiary and will continue to be the umbrella company for Alphabet's Internet interests. Sundar Pichai was appointed CEO of Google, replacing Larry Page who became the CEO of Alphabet.
-"""
-
-TEXT_2 = """Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données.
-            Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer,
-            mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres.
-            L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique  
-"""
-
-TEXT_3 = ""
-
-TEXT_4 = """this is a paragraph
-this is a paragraph
-"""
-
-TEXT_5 = """Mark Zuckerberg is sinds de oprichting van Facebook de directeur van het bedrijf."""
-
-TEXT_6 = """
-မြန်မာဘာသာစကားသည် တိဘက်-ဗမာနွယ် ဘာသာစကားများ အုပ်စုတွင် ပါဝင်သည်။
-တိဘက်-ဗမာနွယ် ဘာသာစကားများ အုပ်စုသည် တရုတ်-တိဗက်နွယ် ဘာသာစကားများ
-မိသားစု ထဲတွင် ပါသည်။ မြန်မာဘာသာသည် တက်ကျသံရှိသော
-၊နိမ့်မြင့်အမှတ်အသားရှိ ဖြစ်သော၊ ဧကဝဏ္ဏစကားလုံး အလွန်များသော ဘာသာစကား
-ဖြစ်သည်။ ကတ္တား-ကံ-တြိယာ စကားလုံးအစီအစဉ်ဖြင့် ရေးသော သရုပ်ခွဲဘာသာစကား
-လည်းဖြစ်သည်။ မြန်မာအက္ခရာများသည် ဗြာဟ္မီအက္ခရာ သို့မဟုတ် ဗြာဟ္မီအက္ခရာမှ
-ဆက်ခံထားသောမွန်အက္ခရာတို့မှ ဆင်းသက်လာသည်။
-"""
-
-TEXT_7 = """\nHi <<First Name>>\nthis is filler text \xa325 more filler.\nadditilnal 
-filler.\nyet more\xa0still more\xa0filler.\n\xa0\nmore\nfiller.\x03\n\t\t\t\t\t\t    
-almost there \n\\n\nthe end\n"""
-
-ents_model = spacy.blank("nl")
-custom_spacy_nlps = {"nl": {"ents": ents_model}}
-detector = LangDetector()
-
-DOC_1 = Doc(TEXT_1, language="en")
-DOC_2 = Doc(TEXT_2, language="fr")
-DOC_4 = Doc(TEXT_4, "en")
-DOC_5 = Doc(TEXT_5, language="nl", spacy_nlps=custom_spacy_nlps)
-DOC_6 = Doc(TEXT_6, langdetect=detector)
-DOC_7 = Doc(TEXT_7, "en")
-
-
-def test_load_custom_model():
-    """
-    The custom spacy language modules should be correctly loaded into the doc.
-    """
-    model_mapping = {"nl": "ents"}
-    lang = DOC_5.language
-    assert lang == "nl"
-    assert sorted(DOC_5.find_entities()) == sorted(
-        [("Mark Zuckerberg", "PER"), ("Facebook", "PER")]
-    )
-    assert DOC_5.find_entities(model_mapping[lang]) == []
-
-
-def test_nwords_nsents():
-    assert DOC_1.n_words == 145
-    assert DOC_2.n_words == 83
-    assert DOC_1.n_sentences == 7
-    assert DOC_2.n_sentences == 3
-
-
-def test_entities():
-    assert sorted(DOC_1.entities) == sorted(
-        [
-            ("1998", "DATE"),
-            ("56 percent", "PERCENT"),
-            ("Alphabet", "GPE"),
-            ("Alphabet", "ORG"),
-            ("Alphabet Inc. Google", "ORG"),
-            ("August 19, 2004", "DATE"),
-            ("August 2015", "DATE"),
-            ("California", "GPE"),
-            ("Google", "ORG"),
-            ("Googleplex", "ORG"),
-            ("IPO", "ORG"),
-            ("Larry Page", "PERSON"),
-            ("Mountain View", "GPE"),
-            ("Ph.D.", "PERSON"),
-            ("September 4, 1998", "DATE"),
-            ("Sergey Brin", "PERSON"),
-            ("Stanford University", "ORG"),
-            ("Sundar Pichai", "PERSON"),
-            ("about 14 percent", "PERCENT"),
-        ]
-    )
-    assert sorted(DOC_2.entities) == sorted(
-        [
-            ("Exalead", "ORG"),
-            ("Google", "ORG"),
-            ("HITS", "MISC"),
-            ("PageRank", "MISC"),
-            ("Yahoo!", "ORG"),
-        ]
-    )
-
-
-def test_complexity():
-    assert DOC_1.complexity == 48.06961538461539
-    assert DOC_2.complexity == 78.634_035_087_719_3
-
-
-def test_clean():
-    assert len(TEXT_1) >= len(DOC_1.clean)
-    assert len(TEXT_2) >= len(DOC_2.clean)
-
-
-def test_clean_newlines():
-    assert " ".join(TEXT_4.split()) == DOC_4.clean
-
-
-def test_extract_keyterms():
-    non_ranker = "bulthaup"
-    rankers = ["textrank", "sgrank", "singlerank"]
-    with pytest.raises(
-        ValueError,
-        message=f'algorithm "{non_ranker}" not ' f"available; use one of {rankers}",
-    ):
-        DOC_1.extract_keyterms(ranker=non_ranker)
-    assert len(DOC_1.extract_keyterms()) == 10
-    # limits number of keyterms
-    assert len(DOC_1.extract_keyterms(n_terms=2)) == 2
-    # works with other rankers
-    assert isinstance(DOC_2.extract_keyterms(ranker=random.choice(rankers)), list)
-
-
-def test_missing_language_model():
-    with pytest.raises(NautilusMissingModelException):
-        DOC_6.n_words
-
-
-def test_non_utf_chars():
-    assert DOC_7.language == "en"
diff --git a/tests/test_extraction.py b/tests/test_extraction.py
deleted file mode 100644
index f2a1a74..0000000
--- a/tests/test_extraction.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.preprocessing.keyword_extractor import extract_keywords
-
-str_="""Les moteurs de recherche tels Google, Exalead ou Yahoo! sont
- des applications très connues de fouille de textes sur de grandes masses de données. 
- Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon 
- dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS 
- dans le milieu académique"""
-
-dict_extract={"US_Companies":['Yahoo','Google'],
- "French_Companies":['Exalead']
-}
-def test_keyword_extraction():
-    assert extract_keywords(str_,['Google'])==['Google','Google']
-    assert extract_keywords(str_,'Google')==['Google','Google']
-    assert extract_keywords(str_,['Google','Yahoo'])==['Google','Yahoo','Google']
-    assert extract_keywords(str_,dict_extract) == ['US_Companies', 'French_Companies', 'US_Companies', 'US_Companies']
diff --git a/tests/test_fix_bad_encoding.py b/tests/test_fix_bad_encoding.py
index 097a2e2..ad44230 100644
--- a/tests/test_fix_bad_encoding.py
+++ b/tests/test_fix_bad_encoding.py
@@ -18,7 +18,7 @@
 
 import pytest
 import numpy as np
-from nautilus_nlp.preprocessing.preprocess import fix_bad_unicode
+from nautilus_nlp.preprocessing.main_preprocess import fix_bad_unicode
 
 
 
diff --git a/tests/test_lemmatization.py b/tests/test_lemmatization.py
deleted file mode 100644
index c23667f..0000000
--- a/tests/test_lemmatization.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import pytest
-from nautilus_nlp.preprocessing.lemmatization import lemmatize_french_tokens, lemmatize_english_tokens
-
-def test_lemmatize_french_tokens_spacy():
-    input_tokens = ['Ceci', 'est', 'un', 'texte', 'français', ',', "j'", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']
-    expected = ['ceci', 'être', 'un', 'texte', 'français', ',', 'j', "'", 'adorer', 'ton', 'frit', 'bien', 'gras', 'yolo', '!']
-    res = lemmatize_french_tokens(input_tokens, module='spacy')
-    assert res == expected
-
-
-def test_lemmatize_english_tokens_spacy():
-    input_tokens = ['The', 'strip', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
-    expected = ['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']
-    res = lemmatize_english_tokens(input_tokens, module='spacy')
-    assert res == expected
-
-
-def test_lemmatize_english_tokens_nltk():
-    input_tokens = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']
-    expected = ['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']
-    res = lemmatize_english_tokens(input_tokens, module='nltk')
-    assert res == expected
\ No newline at end of file
diff --git a/tests/test_ngrams_analysis.py b/tests/test_ngrams_analysis.py
deleted file mode 100644
index f20ff5d..0000000
--- a/tests/test_ngrams_analysis.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.utils.ngrams_analysis import frequent_words
-import pytest
-
-
-def test_frequent_words():
-    list_words = ['Hello', 'world', 'this', 'is', 'an', 'example', 'of', 'ngrams', 'count', 'an', 'example',
-                  'to', 'test', 'this', 'is', 'an', 'example', 'function', 'hello', 'world']
-
-    res_1 = frequent_words(list_words, ngrams_number=1, number_top_words=10)
-    res_2 = frequent_words(list_words, ngrams_number=2, number_top_words=3)
-    res_3 = frequent_words(list_words, ngrams_number=3, number_top_words=5)
-
-    exp_res_1 = [('an', 3), ('example', 3), ('world', 2), ('this', 2), ('is', 2),
-                 ('Hello', 1), ('of', 1), ('ngrams', 1), ('count', 1), ('to', 1)]
-    exp_res_2 = [('an example', 3), ('this is', 2), ('is an', 2)]
-    exp_res_3 = [('this is an', 2), ('is an example', 2), ('Hello world this', 1),
-                 ('world this is', 1), ('an example of', 1)]
-
-    assert res_1 == exp_res_1
-    assert res_2 == exp_res_2
-    assert res_3 == exp_res_3
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 03ebba8..aa1ddea 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,13 +17,27 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from nautilus_nlp.preprocessing.preprocess import (
+from nautilus_nlp.preprocessing.additional_preprocess import (
     remove_multiple_spaces_and_strip_text,
+    remove_tokens_with_nonletters,
+    remove_special_caracters_from_tokenslist,
+    filter_non_latin_characters,
+    remove_smallwords
+)
+from nautilus_nlp.preprocessing.social_preprocess import (
+    remove_emoji,
+    convert_emoji_to_text,
+    extract_emojis,
+    remove_mentions,
+    extract_mentions,
+    remove_html_tags,
+    extract_hashtags,
+    remove_hashtag
+)
+from nautilus_nlp.preprocessing.main_preprocess import (
     remove_accents,
     fix_bad_unicode,
     remove_EOL_characters,
-    remove_tokens_with_nonletters,
-    remove_special_caracters_from_tokenslist,
     get_stopwords,
     remove_stopwords,
     normalize_whitespace,
@@ -34,16 +48,6 @@
     replace_numbers,
     replace_currency_symbols,
     remove_punct,
-    remove_emoji,
-    convert_emoji_to_text,
-    extract_emojis,
-    remove_mentions,
-    extract_mentions,
-    remove_html_tags,
-    remove_smallwords,
-    extract_hashtags,
-    remove_hashtag,
-    filter_non_latin_characters
 )
 import nautilus_nlp.utils.phone_number as phone
 
diff --git a/tests/test_stemming.py b/tests/test_stemming.py
deleted file mode 100644
index 731bc14..0000000
--- a/tests/test_stemming.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import pytest
-from nautilus_nlp.preprocessing.stemming import stem_tokens
-
-def test_stem_en():
-    input_tokens = ['I','survived','these', 'dogs']
-    expected = ['i', 'surviv', 'these', 'dog']
-    res = stem_tokens(input_tokens, lang='english')
-    assert res == expected
-
-def test_stem_fr():
-    input_tokens = ['je', 'mangerai', 'dans', 'les', 'cuisines', 'du', 'château']
-    expected = ['je', 'mang', 'dan', 'le', 'cuisin', 'du', 'château']
-    res = stem_tokens(input_tokens, lang='french')
-    assert res == expected
-
-    
\ No newline at end of file
diff --git a/tests/test_text_summary.py b/tests/test_text_summary.py
deleted file mode 100644
index 954fef6..0000000
--- a/tests/test_text_summary.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.utils.text_summary import summarize_text
-
-case1 = """Automatic summarization is the process of reducing a text document with a \
-computer program in order to create a summary that retains the most important points \
-of the original document. As the problem of information overload has grown, and as \
-the quantity of data has increased, so has interest in automatic summarization. \
-Technologies that can make a coherent summary take into account variables such as \
-length, writing style and syntax. An example of the use of summarization technology \
-is search engines such as Google. Document summarization is another."""
-
-case2 = ["Automatic summarization is the process of reducing a text document with a " \
-"computer program in order to create a summary that retains the most important points " \
-"of the original document. ", "As the problem of information overload has grown, and as" \
-"the quantity of data has increased, so has interest in automatic summarization. "\
-"Technologies that can make a coherent summary take into account variables such as "\
-"length, writing style and syntax." ,"An example of the use of summarization technology "\
-"is search engines such as Google.", "Document summarization is another."]
-
-result = """Automatic summarization is the process of reducing a text document with a computer \
-program in order to create a summary that retains the most important points of the original document."""
-
-
-def test_summarize_text():
-    # Single string containing all text
-    assert summarize_text(case1) == result
-    # Text split in many sentences (list of strings)
-    assert summarize_text(case2) == result
diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py
deleted file mode 100644
index a156529..0000000
--- a/tests/test_tokenizer.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import pytest
-from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize
-
-
-def test_tokenize_fr_spacy():
-    input_str = """Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique"""
-    expected = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!', 'sont', 'des', 'applications', 'très', 'connues', 'de', 'fouille', 'de', 'textes', 'sur', 'de', 'grandes', 'masses', 'de', 'données', '.', 'Cependant', ',', 'les', 'moteurs', 'de', 'recherche', 'ne', 'se', 'basent', 'pas', 'uniquement', 'sur', 'le', 'texte', 'pour', "l'", 'indexer', ',', 'mais', 'également', 'sur', 'la', 'façon', 'dont', 'les', 'pages', 'sont', 'mises', 'en', 'valeur', 'les', 'unes', 'par', 'rapport', 'aux', 'autres', '.', "L'", 'algorithme', 'utilisé', 'par', 'Google', 'est', 'PageRank', ',', 'et', 'il', 'est', 'courant', 'de', 'voir', 'HITS', 'dans', 'le', 'milieu', 'académique']
-    res = tokenize(input_str, lang_module="fr_spacy")
-    assert res == expected
-
-
-def test_tokenize_fr_moses():
-    input_str = """Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique"""
-    expected = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!', 'sont', 'des', 'applications', 'très', 'connues', 'de', 'fouille', 'de', 'textes', 'sur', 'de', 'grandes', 'masses', 'de', 'données', '.', 'Cependant', ',', 'les', 'moteurs', 'de', 'recherche', 'ne', 'se', 'basent', 'pas', 'uniquement', 'sur', 'le', 'texte', 'pour', "l'", 'indexer', ',', 'mais', 'également', 'sur', 'la', 'façon', 'dont', 'les', 'pages', 'sont', 'mises', 'en', 'valeur', 'les', 'unes', 'par', 'rapport', 'aux', 'autres', '.', "L'", 'algorithme', 'utilisé', 'par', 'Google', 'est', 'PageRank', ',', 'et', 'il', 'est', 'courant', 'de', 'voir', 'HITS', 'dans', 'le', 'milieu', 'académique']
-    res = tokenize(input_str, lang_module="fr_moses")
-    assert res == expected    
-
-def test_tokenize_null_input():
-    input_str = ''
-    expected = []
-    res = tokenize(input_str, lang_module="en_spacy")
-    assert res == expected
-
-def test_tokenize_en_spacy():
-    input_str = "Let's play together!"
-    expected = ['Let', "'s", 'play', 'together', '!']
-    res = tokenize(input_str, lang_module="en_spacy")
-    assert res == expected
-    
-def test_tokenize_en_nltk():
-    input_str = "Let's play together!"
-    expected = ['Let', "'s", 'play', 'together', '!']
-    res = tokenize(input_str, lang_module="en_nltk")
-    assert res == expected    
-
-def test_untokenize_en():
-    input_str = ['Let', "'s", 'play', 'together', '!']
-    expected = "Let's play together!"
-    res = untokenize(input_str,lang='en')
-    assert res == expected
-
-def test_untokenize_fr():
-    input_str = ['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo', '!']
-    expected = "Les moteurs de recherche tels Google, Exalead ou Yahoo !"
-    res = untokenize(input_str,lang='fr')
-    assert res == expected
\ No newline at end of file

From ad7e0afcce2178e97b88cedd1f5be5993f392249 Mon Sep 17 00:00:00 2001
From: Bruce DELATTRE <bruce.delattre@FRART0231M.local>
Date: Wed, 25 Mar 2020 13:15:12 +0100
Subject: [PATCH 251/496] Fix/ Trying to fix tests

---
 .travis.yml                                     | 7 +++++++
 nautilus_nlp/preprocessing/main_preprocess.py   | 2 +-
 nautilus_nlp/preprocessing/social_preprocess.py | 2 +-
 3 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 907e302..0304bca 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -21,6 +21,13 @@ os:
   - linux
 services:
   - docker
+
+before_script:
+  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
+  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
+
+  - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
+  - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
 install:
   - pip install -r requirements.txt
diff --git a/nautilus_nlp/preprocessing/main_preprocess.py b/nautilus_nlp/preprocessing/main_preprocess.py
index d91c4a2..60d0303 100644
--- a/nautilus_nlp/preprocessing/main_preprocess.py
+++ b/nautilus_nlp/preprocessing/main_preprocess.py
@@ -23,7 +23,7 @@
 import unicodedata
 from ftfy import fix_text as _fix_text
 
-from nautilus_nlp.utils.stopwords_utils import get_stopwords
+from nautilus_nlp.utils.stopwords import get_stopwords
 from nautilus_nlp.utils.phone_number import extract_phone_numbers as _extract_phone_numbers
 from nautilus_nlp.utils import constants
 
diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index a88fad2..4984e1b 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -22,7 +22,7 @@
 import re
 import emoji as _emoji
 
-from nautilus_nlp.utils.preprocess import normalize_whitespace
+from nautilus_nlp.preprocessing.main_preprocess import normalize_whitespace
 
 
 def remove_mentions(text:str) -> str:

From 5289b2554ec04d7596708f899393d041e0298cd8 Mon Sep 17 00:00:00 2001
From: Bruce DELATTRE <bruce.delattre@FRART0231M.local>
Date: Wed, 25 Mar 2020 13:21:58 +0100
Subject: [PATCH 252/496] Updating import in language detection

---
 nautilus_nlp/models/language_detector.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/models/language_detector.py b/nautilus_nlp/models/language_detector.py
index 74eb3fc..255347b 100644
--- a/nautilus_nlp/models/language_detector.py
+++ b/nautilus_nlp/models/language_detector.py
@@ -16,7 +16,7 @@
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.models.fasttext_classifier import Fasttext_clf as langdetect
-from nautilus_nlp.preprocessing.preprocess import remove_EOL_characters
+from nautilus_nlp.preprocessing.main_preprocess import remove_EOL_characters
 import pkg_resources
 
 lang_path = pkg_resources.resource_filename(

From ad51b1eb50711b844a180af891f317e6c9cd54ab Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 7 May 2020 12:51:44 +0200
Subject: [PATCH 253/496] compiling latin regex outside

---
 nautilus_nlp/preprocessing/additional_preprocess.py | 11 +++++++----
 nautilus_nlp/utils/constants.py                     |  3 ++-
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/nautilus_nlp/preprocessing/additional_preprocess.py b/nautilus_nlp/preprocessing/additional_preprocess.py
index 5ec89d5..98d12eb 100644
--- a/nautilus_nlp/preprocessing/additional_preprocess.py
+++ b/nautilus_nlp/preprocessing/additional_preprocess.py
@@ -20,7 +20,9 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import re
-import regex
+from nautilus_nlp.utils import constants
+from nautilus_nlp.preprocessing.main_preprocess import normalize_whitespace
+
 
 def remove_multiple_spaces_and_strip_text(text: str) -> str:
     """
@@ -93,8 +95,9 @@ def filter_non_latin_characters(text:str) -> str:
     -------
     string
     """
-    text = regex.sub(r'[^\p{Latin}1-9]', ' ', text).strip()
-    return re.sub(' +', ' ', text)
+    text = constants.LATIN_CHARACTERS_RE.sub(' ', text)
+    #text = regex.sub(r'[^\p{Latin}1-9]', ' ', text).strip()
+    return normalize_whitespace(text)
 
 
 def remove_smallwords(tokens_list:list, smallwords_threshold:int) -> list:
@@ -114,4 +117,4 @@ def remove_smallwords(tokens_list:list, smallwords_threshold:int) -> list:
     list
     """
     result = [word for word in tokens_list if len(word) > smallwords_threshold]
-    return result
\ No newline at end of file
+    return result
diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index e98cce9..80e4cfa 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -23,6 +23,7 @@
 
 import os
 import re
+import regex
 import sys
 import unicodedata
 
@@ -33,7 +34,6 @@
 
 
 
-
 NUMERIC_NE_TYPES = {
                     "ORDINAL",
                     "CARDINAL",
@@ -213,3 +213,4 @@
 NEG_DIGIT_TERM_RE = re.compile(r"(-) (\d)", flags=re.UNICODE)
 WEIRD_HYPHEN_SPACE_TERM_RE = re.compile(r"(?<=[^\W\d]) (-[^\W\d])", flags=re.UNICODE)
 WEIRD_APOSTR_SPACE_TERM_RE = re.compile(r"([^\W\d]+) ('[a-z]{1,2}\b)", flags=re.UNICODE)
+LATIN_CHARACTERS_RE = regex.compile(r'[^\p{Latin}1-9]')

From a4c955696890679ee5b9b3f88c2a421cb6aea4be Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 7 May 2020 13:32:24 +0200
Subject: [PATCH 254/496] removing useless comment

---
 nautilus_nlp/preprocessing/additional_preprocess.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/additional_preprocess.py b/nautilus_nlp/preprocessing/additional_preprocess.py
index 98d12eb..2d4b403 100644
--- a/nautilus_nlp/preprocessing/additional_preprocess.py
+++ b/nautilus_nlp/preprocessing/additional_preprocess.py
@@ -96,7 +96,6 @@ def filter_non_latin_characters(text:str) -> str:
     string
     """
     text = constants.LATIN_CHARACTERS_RE.sub(' ', text)
-    #text = regex.sub(r'[^\p{Latin}1-9]', ' ', text).strip()
     return normalize_whitespace(text)
 
 

From d70817b6c6da4bacdc5fdd060a967c0c58c2873b Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 7 May 2020 13:38:17 +0200
Subject: [PATCH 255/496] compiling all contractions regex in constants file

---
 nautilus_nlp/preprocessing/main_preprocess.py | 26 +++++++------------
 nautilus_nlp/utils/constants.py               | 13 ++++++++++
 2 files changed, 23 insertions(+), 16 deletions(-)

diff --git a/nautilus_nlp/preprocessing/main_preprocess.py b/nautilus_nlp/preprocessing/main_preprocess.py
index 60d0303..ddf4a21 100644
--- a/nautilus_nlp/preprocessing/main_preprocess.py
+++ b/nautilus_nlp/preprocessing/main_preprocess.py
@@ -245,31 +245,25 @@ def unpack_english_contractions(text:str) -> str:
     -------
     string    
     """
-
-    # standard
-    text = re.sub(
-        r"(\b)([Aa]re|[Cc]ould|[Dd]id|[Dd]oes|[Dd]o|[Hh]ad|[Hh]as|[Hh]ave|[Ii]s|[Mm]ight|[Mm]ust|[Ss]hould|[Ww]ere|[Ww]ould)n't",
+    text = constants.CONTRACTION_NT_NOT.sub(
         r"\1\2 not",
         text,
     )
-    text = re.sub(
-        r"(\b)([Hh]e|[Ii]|[Ss]he|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'ll",
+    text = constants.CONTRACTION_LL_WILL.sub(
         r"\1\2 will",
         text,
     )
-    text = re.sub(r"(\b)([Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Yy]ou)'re", r"\1\2 are", text)
-    text = re.sub(
-        r"(\b)([Ii]|[Ss]hould|[Tt]hey|[Ww]e|[Ww]hat|[Ww]ho|[Ww]ould|[Yy]ou)'ve",
+    text = constants.CONTRACTION_RE_ARE.sub(r"\1\2 are", text)
+    text = constants.CONTRACTION_VE_HAVE.sub(
         r"\1\2 have",
         text,
     )
-    # non-standard
-    text = re.sub(r"(\b)([Cc]a)n't", r"\1\2n not", text)
-    text = re.sub(r"(\b)([Ii])'m", r"\1\2 am", text)
-    text = re.sub(r"(\b)([Ll]et)'s", r"\1\2 us", text)
-    text = re.sub(r"(\b)([Ww])on't", r"\1\2ill not", text)
-    text = re.sub(r"(\b)([Ss])han't", r"\1\2hall not", text)
-    text = re.sub(r"(\b)([Yy])(?:'all|a'll)", r"\1\2ou all", text)
+    text = constants.CONTRACTION_CANT_CANNOT.sub(r"\1\2n not", text)
+    text = constants.CONTRACTION_M_AM.sub(r"\1\2 am", text)
+    text = constants.CONTRACTION_LET_LETUS.sub(r"\1\2 us", text)
+    text = constants.CONTRACTION_WONT_WILLNOT.sub(r"\1\2ill not", text)
+    text = constants.CONTRACTION_SHANT_SHALLNOT.sub(r"\1\2hall not", text)
+    text = constants.CONTRACTION_YALL_YOUALL.sub(r"\1\2ou all", text)
     return text
 
 
diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index 80e4cfa..0ab14e8 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -214,3 +214,16 @@
 WEIRD_HYPHEN_SPACE_TERM_RE = re.compile(r"(?<=[^\W\d]) (-[^\W\d])", flags=re.UNICODE)
 WEIRD_APOSTR_SPACE_TERM_RE = re.compile(r"([^\W\d]+) ('[a-z]{1,2}\b)", flags=re.UNICODE)
 LATIN_CHARACTERS_RE = regex.compile(r'[^\p{Latin}1-9]')
+
+# ENGLISH CONTRACTIONS
+CONTRACTION_NT_NOT = re.compile(
+    r"(\b)(are|could|did|does|do|had|has|have|is|might|must|should|were|would)n't", re.IGNORECASE)
+CONTRACTION_LL_WILL = re.compile(r"(\b)(he|i|she|they|we|what|who|you)'ll", re.IGNORECASE)
+CONTRACTION_RE_ARE = re.compile(r"(\b)(they|we|what|who|you)'re", re.IGNORECASE)
+CONTRACTION_VE_HAVE = re.compile(r"(\b)(i|should|they|we|what|who|would|you)'ve", re.IGNORECASE)
+CONTRACTION_CANT_CANNOT = re.compile(r"(\b)(ca)n't", re.IGNORECASE)
+CONTRACTION_M_AM = re.compile(r"(\b)(i)'m", re.IGNORECASE)
+CONTRACTION_LET_LETUS = re.compile(r"(\b)(let)'s", re.IGNORECASE)
+CONTRACTION_WONT_WILLNOT = re.compile(r"(\b)(w)on't", re.IGNORECASE)
+CONTRACTION_SHANT_SHALLNOT = re.compile(r"(\b)(s)han't", re.IGNORECASE)
+CONTRACTION_YALL_YOUALL = re.compile(r"(\b)(y)(?:'all|a'll)", re.IGNORECASE)

From 731eeacaf04777d1647da80d0f1c870940c42fe3 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 7 May 2020 13:45:17 +0200
Subject: [PATCH 256/496] compiling all social regex in constants file

---
 nautilus_nlp/preprocessing/social_preprocess.py | 17 ++++++++---------
 nautilus_nlp/utils/constants.py                 |  8 ++++++++
 2 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index 4984e1b..ffa1fc8 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -23,6 +23,7 @@
 import emoji as _emoji
 
 from nautilus_nlp.preprocessing.main_preprocess import normalize_whitespace
+from nautilus_nlp.utils import constants
 
 
 def remove_mentions(text:str) -> str:
@@ -37,7 +38,7 @@ def remove_mentions(text:str) -> str:
     -------
     string
     """
-    return normalize_whitespace(re.sub(r'@\w*', '', text))
+    return normalize_whitespace(constants.AT_PATTERN.sub('', text))
 
 
 def extract_mentions(text:str) -> str:
@@ -53,7 +54,7 @@ def extract_mentions(text:str) -> str:
     -------
     string
     """
-    return re.findall(r'[@][^\s@]+', text)
+    return constants.AT_PATTERN.findall(text)
 
 
 def remove_html_tags(text:str) -> str:
@@ -68,7 +69,7 @@ def remove_html_tags(text:str) -> str:
     -------
     string
     """
-    return normalize_whitespace(re.sub(r'<.*?>', '', text))
+    return normalize_whitespace(constants.HTML_TAG_PATTERN.sub('', text))
 
 
 def remove_emoji(text:str) -> str:
@@ -85,8 +86,7 @@ def remove_emoji(text:str) -> str:
     -------
     str
     """
-    emoji_pattern = _emoji.get_emoji_regexp()
-    word = emoji_pattern.sub("", text)
+    word = constants.EMOJI_PATTERN.sub("", text)
     return word
 
 
@@ -124,8 +124,7 @@ def extract_emojis(text:str) -> list:
     list
         list of all emojis converted with their unicode conventions
     """
-    emoji_pattern = _emoji.get_emoji_regexp()
-    emojis_in_text = re.findall(emoji_pattern, text)
+    emojis_in_text = constants.EMOJI_PATTERN.findall(text)
     emojis_converted = [convert_emoji_to_text(emoji_text) for emoji_text in emojis_in_text]
     return emojis_converted
 
@@ -144,7 +143,7 @@ def extract_hashtags(text) -> list:
     list
         list of all hashtags
     """
-    return re.findall(r'[#][^\s#]+', text)
+    return constants.HASHTAG_PATTERN.findall(text)
 
 
 def remove_hashtag(text) -> str:
@@ -161,4 +160,4 @@ def remove_hashtag(text) -> str:
     str
         text of a post without hashtags
     """
-    return normalize_whitespace(re.sub(r'#\w*', '', text))
+    return normalize_whitespace(constants.HASHTAG_PATTERN.sub('', text))
diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index 0ab14e8..4595660 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -26,6 +26,8 @@
 import regex
 import sys
 import unicodedata
+import emoji as _emoji
+
 
 from . import  compat
 from . import file_loader as util
@@ -227,3 +229,9 @@
 CONTRACTION_WONT_WILLNOT = re.compile(r"(\b)(w)on't", re.IGNORECASE)
 CONTRACTION_SHANT_SHALLNOT = re.compile(r"(\b)(s)han't", re.IGNORECASE)
 CONTRACTION_YALL_YOUALL = re.compile(r"(\b)(y)(?:'all|a'll)", re.IGNORECASE)
+
+# SOCIAL DATA
+EMOJI_PATTERN = _emoji.get_emoji_regexp()
+HASHTAG_PATTERN = re.compile(r'#\w*')
+AT_PATTERN = re.compile(r'@\w*')
+HTML_TAG_PATTERN = re.compile(r'<.*?>')

From 7c7179b294459ea1524cc43667e59d10004d5cd2 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 7 May 2020 13:50:13 +0200
Subject: [PATCH 257/496] enabling only tokenizing part of spacy to avoid
 additional computing (such as POS tagging for example) and casting return
 type to string

---
 nautilus_nlp/utils/tokenizer.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 49fac6b..5eae255 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -24,14 +24,14 @@
 #nltk.download('punkt')
 
 try:
-    french_spacy = spacylang.fr.French()
+    french_spacy = spacylang.fr.French().tokenizer
 except OSError:
     raise OSError("""You must install French langage to use SpaCy. 
                     python -m spacy download fr
                     See https://spacy.io/usage/ for details
                 """)
 try:
-    english_spacy = spacylang.en.English()
+    english_spacy = spacylang.en.English().tokenizer
 except OSError:
     raise OSError("""You must install english langage to use SpaCy. 
                     python -m spacy download en
@@ -59,10 +59,10 @@ def tokenize(text: str, lang_module: str = 'en_spacy'):
         return nltk.word_tokenize(text)
     elif lang_module is 'en_spacy':
         spacydoc = english_spacy(text)
-        return list(spacydoc)
+        return [spacy_token.text for spacy_token in spacydoc]
     elif lang_module is 'fr_spacy':
         spacydoc = french_spacy(text)
-        return list(spacydoc)
+        return [spacy_token.text for spacy_token in spacydoc]
     elif lang_module is 'fr_moses':
         t = MosesTokenizer(lang='fr')
         return t.tokenize(text, escape=False)

From 879bb406f73a8e1958a2dfced70d16a21282ac25 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 1 Jun 2020 16:17:06 +0200
Subject: [PATCH 258/496] refacto preprocessing into classes

---
 nautilus_nlp/models/language_detector.py      |   5 +-
 .../preprocessing/additional_preprocess.py    | 119 -----
 .../preprocessing/data_augmentation.py        | 107 ++++
 nautilus_nlp/preprocessing/main_preprocess.py | 470 ------------------
 .../preprocessing/social_preprocess.py        | 294 ++++++-----
 nautilus_nlp/preprocessing/text_preprocess.py | 417 ++++++++++++++++
 .../preprocessing/token_preprocess.py         | 109 ++++
 tests/test_fix_bad_encoding.py                |   5 +-
 tests/test_preprocessor.py                    | 140 +++---
 9 files changed, 871 insertions(+), 795 deletions(-)
 delete mode 100644 nautilus_nlp/preprocessing/additional_preprocess.py
 create mode 100644 nautilus_nlp/preprocessing/data_augmentation.py
 delete mode 100644 nautilus_nlp/preprocessing/main_preprocess.py
 create mode 100644 nautilus_nlp/preprocessing/text_preprocess.py
 create mode 100644 nautilus_nlp/preprocessing/token_preprocess.py

diff --git a/nautilus_nlp/models/language_detector.py b/nautilus_nlp/models/language_detector.py
index 255347b..cc3a0a9 100644
--- a/nautilus_nlp/models/language_detector.py
+++ b/nautilus_nlp/models/language_detector.py
@@ -16,7 +16,7 @@
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from nautilus_nlp.models.fasttext_classifier import Fasttext_clf as langdetect
-from nautilus_nlp.preprocessing.main_preprocess import remove_EOL_characters
+from nautilus_nlp.preprocessing.text_preprocess import TextPreprocessor
 import pkg_resources
 
 lang_path = pkg_resources.resource_filename(
@@ -49,5 +49,6 @@ def detect_language(self, text_to_detect=None):
         language: 
             2-letter code for the language of the text
         """
-        best_guesses = self.model.predict(remove_EOL_characters(text_to_detect))
+        preprocessor = TextPreprocessor(text_to_detect)
+        best_guesses = self.model.predict(preprocessor.remove_EOL_characters())
         return best_guesses[0][0].replace("__label__", ""), best_guesses[1][0]
diff --git a/nautilus_nlp/preprocessing/additional_preprocess.py b/nautilus_nlp/preprocessing/additional_preprocess.py
deleted file mode 100644
index 2d4b403..0000000
--- a/nautilus_nlp/preprocessing/additional_preprocess.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import re
-from nautilus_nlp.utils import constants
-from nautilus_nlp.preprocessing.main_preprocess import normalize_whitespace
-
-
-def remove_multiple_spaces_and_strip_text(text: str) -> str:
-    """
-    Remove multiple spaces, strip text, and remove '-', '*' characters.
-
-    Parameters
-    ----------
-    text : str
-        the text to be processed
-
-    Returns
-    -------
-    string
-        the text with removed multiple spaces and strip text
-    """
-    regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
-    for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
-        text = re.sub(regex_remove_multiple_spaces, " ", text)
-        text = text.strip()
-    return text
-
-
-def remove_tokens_with_nonletters(tokens: list) -> list:
-    """
-    Inputs a list of tokens, outputs a list of tokens without tokens that
-    includes numbers of special caracters.
-    ['foo','bar','124','34euros'] -> ['foo','bar']
-
-    Parameters
-    ----------
-    tokens : list
-        list of tokens to be cleaned
-
-    Returns
-    -------
-    list
-        list of tokens without tokens with numbers
-    """
-    return [word for word in tokens if re.search("[^a-zA-Z]", word) is None]
-
-
-def remove_special_caracters_from_tokenslist(tokens: list) -> list:
-    """ 
-    Remove tokens that doesn't contains any number or letter. 
-    eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
-
-    Parameters
-    ----------
-    tokens : list
-        list of tokens to be cleaned
-
-    Returns
-    -------
-    list
-        list of tokens without tokens that contains only special caracters
-    
-    """
-    return [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
-
-
-def filter_non_latin_characters(text:str) -> str:
-    """
-    Function that filters non latin characters of a text
-
-    Parameters
-    ----------
-    text : string
-
-    Returns
-    -------
-    string
-    """
-    text = constants.LATIN_CHARACTERS_RE.sub(' ', text)
-    return normalize_whitespace(text)
-
-
-def remove_smallwords(tokens_list:list, smallwords_threshold:int) -> list:
-    """
-    Function that removes words which length is below a threshold
-    ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
-
-    Parameters
-    ----------
-    text : list
-        list of strings
-    smallwords_threshold: int
-        threshold of small word
-
-    Returns
-    -------
-    list
-    """
-    result = [word for word in tokens_list if len(word) > smallwords_threshold]
-    return result
diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
new file mode 100644
index 0000000..69b84e1
--- /dev/null
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -0,0 +1,107 @@
+import copy
+import logging
+import nlpaug.augmenter.word as naw
+import re
+
+def augment_utterance(text, method, stopwords, intent=None, entities=None):
+    """
+    Given ``text`` str, create a new similar utterance by modifying some words
+    in the initial sentence, modifications depend on the chosen method 
+    (substitution with synonym, addition, deletion). If intent and/or entities 
+    are given as input, they will remain unchanged.
+
+    Parameters
+    ----------
+    text : string
+    method : string
+        augmenter to use ('wordnet_synonym' or 'aug_sub_bert')
+    stopwords : list
+        list of words to freeze throughout the augmentation
+    intent : string
+        intent associated to text if any
+    entities : list
+        entities associated to text if any, must be in the following format:
+        [
+            {
+                'entity': str,
+                'startCharIndex': int,
+                'endCharIndex': int
+            },
+            {
+                ...
+            }
+        ]
+
+    Returns
+    -------
+    dictionary with augmented text and optional keys depending on input   
+    """
+    new_utt = {}
+    if entities:
+        formatted_entities = [(text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),entities[i]['entity']) for i in range(len(entities))]
+    augmenter = select_augmenter(method,stopwords)
+    new_utt['text'] = augmenter.augment(text)
+    if intent:
+        new_utt['intent'] = intent
+    if entities:
+        if are_entities_in_augmented_text(entities,new_utt['text']):        
+            new_utt['entities'] = get_augmented_entities(new_utt['text'],formatted_entities)
+            return clean_sentence_entities(new_utt)
+        else:
+            logging.info('Text was not correctly augmented so not added')
+    else:
+        return new_utt
+
+def are_entities_in_augmented_text(entities,augmented_text):
+    check = True
+    for ent in entities:
+        if ent['word'] not in augmented_text:
+            check = False
+    return check
+
+def select_augmenter(method,stopwords,use_stopwords=True):
+    if use_stopwords:
+        stopwords = stopwords
+    else: 
+        stopwords = []
+    if method == 'wordnet_synonym':
+        augmenter = naw.SynonymAug(aug_src='wordnet',stopwords=stopwords)
+    elif method == 'aug_sub_bert':
+        augmenter = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute",stopwords=stopwords)  
+    return(augmenter) 
+
+def get_augmented_entities(sentence_augmented,entities):
+    entities_augmented = []
+    for entity in entities :
+        regex = r'(?:^|\W)' + re.escape(entity[0].strip()) + '(?:$|\W)'
+        if (re.search(re.compile(regex), sentence_augmented)):
+            start_index = re.search(regex,sentence_augmented).start()+1
+            end_index = re.search(regex,sentence_augmented).end()-1
+            new_entity = {'entity': entity[1],'word': sentence_augmented[start_index:end_index],'startCharIndex': start_index,'endCharIndex': end_index}
+            entities_augmented.append(new_entity) 
+    return entities_augmented
+
+def clean_sentence_entities(sentence_input):
+    sentence = copy.copy(sentence_input)
+    for element1 in sentence['entities']: 
+        for element2 in sentence['entities'] :
+            result = check_interval_included(element1,element2)
+            if result:
+                try :                                                                       
+                    sentence[1]['entities'].remove(result[0])
+                except : 
+                    logging.info("Cant remove entity : {} \n entities are now :{} \n for sentence : {} ".format(result,sentence['entities'],sentence['text']))
+                    continue
+    return(sentence)       
+
+def check_interval_included(element1,element2):
+    if ((element1 != element2) and (element1['startCharIndex'] >= element2['startCharIndex']) and (element1['endCharIndex'] <= element2['endCharIndex'])):
+        return((element1,element2))
+    elif ((element1 != element2) and (element2['startCharIndex'] >= element1['startCharIndex']) and (element2['endCharIndex'] <= element1['endCharIndex'])):
+        return((element2,element1))
+    elif ((element1 != element2) and (element1['startCharIndex'] >= element2['startCharIndex']) and (element1['endCharIndex'] >= element2['endCharIndex']) and (element1['startCharIndex'] <= element2['endCharIndex']-1)):
+        return((element1,element2))
+    elif ((element1 != element2) and (element2['startCharIndex'] >= element1['startCharIndex']) and (element2['endCharIndex'] >= element1['endCharIndex']) and (element2['startCharIndex'] < element1['endCharIndex']-1)):
+        return((element2,element1))
+    else :
+        return(False)
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/main_preprocess.py b/nautilus_nlp/preprocessing/main_preprocess.py
deleted file mode 100644
index ddf4a21..0000000
--- a/nautilus_nlp/preprocessing/main_preprocess.py
+++ /dev/null
@@ -1,470 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import re
-import unicodedata
-from ftfy import fix_text as _fix_text
-
-from nautilus_nlp.utils.stopwords import get_stopwords
-from nautilus_nlp.utils.phone_number import extract_phone_numbers as _extract_phone_numbers
-from nautilus_nlp.utils import constants
-
-
-def preprocess_text(
-    text,
-    remove_eol_char=True,
-    fix_unicode=False,
-    lowercase=False,
-    no_urls=False,
-    no_emails=False,
-    no_phone_numbers=False,
-    no_numbers=False,
-    no_currency_symbols=False,
-    no_punct=False,
-    no_contractions=False,
-    no_accents=False,
-    replace_with=' ', 
-    no_stopwords=None,
-    phone_countries_format=[None,'US','FR'],
-    phone_method='regex'
-) -> str:
-    """
-    Normalize various aspects of a raw text doc. A convenience function for
-    applying all other preprocessing functions in one go.
-
-    Parameters
-    ----------
-    text : str
-        raw text to preprocess
-    fix_unicode : bool
-        if True, fix "broken" unicode such as mojibake and garbled HTML entities
-    remove_eol_char : bool 
-        if True, will remove the end-of-line characters \\n
-    lowercase : bool
-        if True, all text is lower-cased
-    no_urls : bool
-        if True, replace all URL strings with '*URL*' or with "replace_with" if 
-        specified.
-    no_emails : bool
-        if True, replace all email strings with '*EMAIL*' or with "replace_with" if 
-        specified.
-    no_phone_numbers : bool
-        if True, replace all phone number strings with '*PHONE*' or with "replace_with" if 
-        specified.
-    no_numbers : bool
-        if True, replace all number-like strings with '*NUMBER*' or with "replace_with" if 
-        specified.
-    no_currency_symbols : bool
-        if True, if True, replace all currency symbols with their standard 
-        3-letter abbreviations.
-    no_punct : bool
-        if True, remove all punctuation (replace with empty string)
-    no_contractions : bool
-        if True, if True, replace *English* contractions with their unshortened forms
-    no_accents : bool
-        if True, replace all accented characters with unaccented versions
-    replace_with : string
-        The string you want the entities to be replaced with.
-    no_stopwords : 2-letter country code
-        If specified, will remove the stopwords of the given language. 
-        Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
-         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
-         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
-         'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
-          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
-          'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
-           'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']    
-    phone_countries_format : list
-        formats of the phone numbers to be removed. Full list is available at
-    utils.SUPPORTED_COUNTRY
-    phone_method : ['regex','detection']
-        regex is faster but will omit a lot of numbers, while detection will 
-        catch every numbers, but takes a while.
-    Returns
-    -------
-    string
-        input ``text`` processed according to function args        
-
-    Warning
-    -------
-    These changes may negatively affect subsequent NLP analysis performed
-        on the text, so choose carefully, and preprocess at your own risk!        
-    """
-    assert isinstance(text, str), "The text to preprocess must be a string"
-
-    if fix_unicode is True:
-        text = fix_bad_unicode(text, normalization="NFC")
-    if remove_eol_char is True:
-        text = remove_EOL_characters(text)
-    if no_urls is True:
-        text = replace_urls(text, replace_with=replace_with)
-    if no_emails is True:
-        text = replace_emails(text, replace_with=replace_with)
-    if no_phone_numbers is True:
-        text = replace_phone_numbers(text,  replace_with=replace_with,
-                                            method=phone_method,
-                                            country_format_to_detect=phone_countries_format)
-    if no_numbers is True:
-        text = replace_numbers(text, replace_with=replace_with)
-    if no_currency_symbols is True:
-        text = replace_currency_symbols(text, replace_with=replace_with)
-    if no_contractions is True:
-        text = unpack_english_contractions(text)
-    if no_accents is True:
-        text = remove_accents(text, method="unicode")
-    if no_punct is True:
-        text = remove_punct(text)
-    if lowercase is True:
-        text = text.lower()
-    if no_stopwords is not None:
-        stopwords = get_stopwords(no_stopwords)
-        text = ' '.join(remove_stopwords(text, stopwords))
-    # always normalize whitespace; treat linebreaks separately from spacing
-    text = normalize_whitespace(text)
-    
-    return text
-
-
-def remove_EOL_characters(text: str) -> str:
-    """
-    Remove end of line (\n) char.
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    str
-    """
-    return text.replace("\n", " ")
-
-
-def remove_stopwords(text_or_tokens, stopwords: list) -> list:
-    """ 
-    Remove stopwords from a list of tokens or a text.
-    eg. ['I','like','when','you','move','your','body','!'] -> ['I', 'move', 'body', '!']
-
-    Parameters
-    ----------
-    text_or_tokens : list or string
-        list of tokens to be cleaned
-
-    Returns
-    -------
-    list
-        list of tokens without stopwords
-
-    Raises
-    ------
-    ValueError
-        When inputs is not a string or a list
-    """
-    if type(text_or_tokens) is str:
-        return [word for word in text_or_tokens.split() if word not in stopwords]
-    elif type(text_or_tokens) is list:
-        return [word for word in text_or_tokens if word not in stopwords]
-    else:
-        raise ValueError("must input string or list of tokens")
-
-
-def fix_bad_unicode(text: str, normalization: str = "NFC") -> str:
-    """
-    Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
-    this includes mojibake, HTML entities and other code cruft,
-    and non-standard forms for display purposes.
-
-    Parameters
-    ----------
-    text : string
-
-    normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): 
-        if 'NFC', combines characters and diacritics written using separate code points,
-        e.g. converting "e" plus an acute accent modifier into "é"; unicode
-        can be converted to NFC form without any change in its meaning!
-        if 'NFKC', additional normalizations are applied that can change
-        the meanings of characters, e.g. ellipsis characters will be replaced
-        with three periods
-    Returns
-    -------
-    string
-    """
-    return _fix_text(text, normalization=normalization)
-
-
-def normalize_whitespace(text:str) -> str:
-    """
-    Given ``text`` str, replace one or more spacings with a single space, and one
-    or more linebreaks with a single newline. Also strip leading/trailing whitespace.
-    eg. "   foo  bar  " -> "foo bar"
-
-    Parameters
-    ----------
-    text : string
-
-    Returns
-    -------
-    string    
-    """
-    return constants.NONBREAKING_SPACE_REGEX.sub(
-        " ", constants.LINEBREAK_REGEX.sub(r"\n", text)
-    ).strip()
-
-
-
-def unpack_english_contractions(text:str) -> str:
-    """
-    Replace *English* contractions in ``text`` str with their unshortened forms.
-    N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
-    so are left as-is.
-    eg. "You're fired. She's nice." -> "You are fired. She's nice."
-
-    Parameters
-    ----------
-    text : string
-
-    Returns
-    -------
-    string    
-    """
-    text = constants.CONTRACTION_NT_NOT.sub(
-        r"\1\2 not",
-        text,
-    )
-    text = constants.CONTRACTION_LL_WILL.sub(
-        r"\1\2 will",
-        text,
-    )
-    text = constants.CONTRACTION_RE_ARE.sub(r"\1\2 are", text)
-    text = constants.CONTRACTION_VE_HAVE.sub(
-        r"\1\2 have",
-        text,
-    )
-    text = constants.CONTRACTION_CANT_CANNOT.sub(r"\1\2n not", text)
-    text = constants.CONTRACTION_M_AM.sub(r"\1\2 am", text)
-    text = constants.CONTRACTION_LET_LETUS.sub(r"\1\2 us", text)
-    text = constants.CONTRACTION_WONT_WILLNOT.sub(r"\1\2ill not", text)
-    text = constants.CONTRACTION_SHANT_SHALLNOT.sub(r"\1\2hall not", text)
-    text = constants.CONTRACTION_YALL_YOUALL.sub(r"\1\2ou all", text)
-    return text
-
-
-
-
-
-def replace_urls(text:str, replace_with:str="*URL*") -> str:
-    """
-    Replace all URLs in ``text`` str with ``replace_with`` str.
-
-    Parameters
-    ----------
-    text : string
-    replace_with : string
-        the string you want the URL to be replaced with.
-
-    Returns
-    -------
-    string
-    """    
-    return constants.URL_REGEX.sub(
-        replace_with, constants.SHORT_URL_REGEX.sub(replace_with, text)
-    )
-
-
-def replace_emails(text, replace_with="*EMAIL*") -> str:
-    """
-    Replace all emails in ``text`` str with ``replace_with`` str
-
-    Parameters
-    ----------
-    text : string
-    replace_with : string
-        the string you want the email address to be replaced with.
-
-    Returns
-    -------
-    string
-    """
-    return constants.EMAIL_REGEX.sub(replace_with, text)
-
-
-def replace_phone_numbers(text, replace_with:str="*PHONE*",
-                                method:str="regex",
-                                country_format_to_detect:list=[None,'FR','US','GB']) -> str:
-    """
-    Replace all phone numbers in ``text`` str with ``replace_with`` str
-
-    Parameters
-    ----------
-    text : string
-    replace_with : string
-        the string you want the phone number to be replaced with.
-    method : ['regex','detection']
-        regex is faster but will omit a lot of numbers, while detection will 
-        catch every numbers, but takes a while.
-    country_format_to_detect : list 
-        If a list of country code is specified, will catch every number formatted.
-        Only when method = 'detection'.
-    Returns
-    -------
-    string
-    """
-    if method == 'regex':
-        return constants.PHONE_REGEX.sub(replace_with, text)
-        
-    elif method == 'detection':
-        found_nums = _extract_phone_numbers(text, countrylist=country_format_to_detect)
-
-        # order by lenght to avoid truncated numbers to be removed first.
-        found_nums.sort(key=len,reverse=True) 
-        for phone_number in found_nums:
-            text = text.replace(phone_number, replace_with)
-        
-        return text
-    else:
-        raise ValueError('Please input a valid method between "regex" or "detection"')
-
-
-def replace_numbers(text, replace_with="*NUMBER*") -> str:
-    """
-    Replace all numbers in ``text`` str with ``replace_with`` str.
-
-    Parameters
-    ----------
-    text : string
-    replace_with : string
-        the string you want the number to be replaced with.
-
-    Returns
-    -------
-    string
-    """        
-    return constants.NUMBERS_REGEX.sub(replace_with, text)
-
-
-def replace_currency_symbols(text, replace_with=None) -> str:
-    """
-    Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
-
-    Parameters
-    ----------
-    text : str
-        raw text
-    replace_with : None or string
-        if None (default), replace symbols with
-            their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
-            otherwise, pass in a string with which to replace all symbols
-            (e.g. "*CURRENCY*")
-
-    Returns
-    -------
-    string
-    """          
-    if replace_with is None:
-        for k, v in constants.CURRENCIES.items():
-            text = text.replace(k, v)
-        return text
-    else:
-        return constants.CURRENCY_REGEX.sub(replace_with, text)
-
-
-def remove_punct(text, marks=None) -> str:
-    """
-    Remove punctuation from ``text`` by replacing all instances of ``marks``
-    with whitespace.
-
-    Parameters
-    ----------
-    text : str
-        raw text
-    
-    marks : str or None
-        If specified, remove only the characters in this string,
-        e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
-        Otherwise, all punctuation marks are removed.
-
-    Returns
-    -------
-    string
-
-    Note
-    -------
-    When ``marks=None``, Python's built-in :meth:`str.translate()` is
-    used to remove punctuation; otherwise, a regular expression is used
-    instead. The former's performance is about 5-10x faster.    
-    """       
-    if marks:
-        return re.sub("[{}]+".format(re.escape(marks)), " ", text, flags=re.UNICODE)
-    else:
-        return text.translate(constants.PUNCT_TRANSLATE_UNICODE)
-
-
-def remove_accents(text:str, method:str="unicode") -> str:
-    """
-    Remove accents from any accented unicode characters in ``text`` str, either by
-    transforming them into ascii equivalents or removing them entirely.
-
-    Parameters
-    ----------
-    text : str
-        raw text
-    
-    method : ({'unicode', 'ascii'})
-        if 'unicode', remove accented
-        char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
-        remove accented char for any unicode symbol
-
-        NB: the 'ascii' method is notably faster than 'unicode', but less good
-
-    Returns
-    -------
-    string
-
-    Raises
-    -------
-    ValueError
-        if ``method`` is not in {'unicode', 'ascii'}   
-    """
-    if method == "unicode":
-        return "".join(
-            c
-            for c in unicodedata.normalize("NFKD", text)
-            if not unicodedata.combining(c)
-        )
-    elif method == "ascii":
-        return (
-            unicodedata.normalize("NFKD", text)
-            .encode("ascii", errors="ignore")
-            .decode("ascii")
-        )
-    else:
-        msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
-        raise ValueError(msg)
-
-
-
-
-
-
-
-
-
-
diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index ffa1fc8..cde003e 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -21,143 +21,163 @@
 
 import re
 import emoji as _emoji
-
-from nautilus_nlp.preprocessing.main_preprocess import normalize_whitespace
 from nautilus_nlp.utils import constants
 
 
-def remove_mentions(text:str) -> str:
-    """
-    Function that removes words preceded with a '@'
-
-    Parameters
-    ----------
-    text : str
-    
-    Returns
-    -------
-    string
-    """
-    return normalize_whitespace(constants.AT_PATTERN.sub('', text))
-
-
-def extract_mentions(text:str) -> str:
-    """
-    Function that extracts words preceded with a '@'
-    eg. "I take care of my skin with @thisproduct" --> ["@thisproduct"]
-
-    Parameters
-    ----------
-    text : str
-    
-    Returns
-    -------
-    string
-    """
-    return constants.AT_PATTERN.findall(text)
-
-
-def remove_html_tags(text:str) -> str:
-    """
-    Function that removes words between < and >
-
-    Parameters
-    ----------
-    text : str
-    
-    Returns
-    -------
-    string
-    """
-    return normalize_whitespace(constants.HTML_TAG_PATTERN.sub('', text))
-
-
-def remove_emoji(text:str) -> str:
-    """
-    Remove emoji from any str by stripping any unicode in the range of Emoji unicode
-    as defined in the unicode convention: 
-    http://www.unicode.org/emoji/charts/full-emoji-list.html
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    str
-    """
-    word = constants.EMOJI_PATTERN.sub("", text)
-    return word
-
-
-def convert_emoji_to_text(text:str, code_delimiters=(':', ':')) -> str:
-    """
-    Convert emoji to their CLDR Short Name, according to the unicode convention
-    http://www.unicode.org/emoji/charts/full-emoji-list.html
-    eg. 😀 --> :grinning_face:
-
-    Parameters
-    ----------
-    text : str
-        code_delimiters : tuple of symbols around the emoji code. 
-        eg: (':',':') --> :grinning_face:
-
-    Returns
-    -------
-    str
-        string 
-    """
-    return _emoji.demojize(text, delimiters=code_delimiters)
-
-
-def extract_emojis(text:str) -> list:
-    """
-    Function that extracts emojis from a text and translates them into words
-    eg. "I take care of my skin 😀 :(" --> [":grinning_face:"]
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    list
-        list of all emojis converted with their unicode conventions
-    """
-    emojis_in_text = constants.EMOJI_PATTERN.findall(text)
-    emojis_converted = [convert_emoji_to_text(emoji_text) for emoji_text in emojis_in_text]
-    return emojis_converted
-
-
-def extract_hashtags(text) -> list:
-    """
-    Function that extracts words preceded with a '#'
-    eg. "I take care of my skin #selfcare#selfestim" --> ["skincare", "selfestim"]
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    list
-        list of all hashtags
-    """
-    return constants.HASHTAG_PATTERN.findall(text)
-
-
-def remove_hashtag(text) -> str:
-    """
-    Function that removes words preceded with a '#'
-    eg. "I take care of my skin #selfcare#selfestim" --> "I take care of my skin"
-
-    Parameters
-    ----------
-    text : str
-
-    Returns
-    -------
-    str
-        text of a post without hashtags
-    """
-    return normalize_whitespace(constants.HASHTAG_PATTERN.sub('', text))
+class SocialPreprocessor():
+
+    def __init__(self,text):
+        self.text = text
+
+    def remove_mentions(self) -> str:
+        """
+        Function that removes words preceded with a '@'
+
+        Parameters
+        ----------
+        text : str
+        
+        Returns
+        -------
+        string
+        """
+        self.text = self.normalize_whitespace(constants.AT_PATTERN.sub('', self.text))
+        return self.text
+
+    def extract_mentions(self) -> list:
+        """
+        Function that extracts words preceded with a '@'
+        eg. "I take care of my skin with @thisproduct" --> ["@thisproduct"]
+
+        Parameters
+        ----------
+        text : str
+        
+        Returns
+        -------
+        string
+        """
+        return constants.AT_PATTERN.findall(self.text)
+
+    def remove_html_tags(self) -> str:
+        """
+        Function that removes words between < and >
+
+        Parameters
+        ----------
+        text : str
+        
+        Returns
+        -------
+        string
+        """
+        self.text = self.normalize_whitespace(constants.HTML_TAG_PATTERN.sub('', self.text))
+        return self.text
+
+    def remove_emoji(self) -> str:
+        """
+        Remove emoji from any str by stripping any unicode in the range of Emoji unicode
+        as defined in the unicode convention: 
+        http://www.unicode.org/emoji/charts/full-emoji-list.html
+
+        Parameters
+        ----------
+        text : str
+
+        Returns
+        -------
+        str
+        """
+        self.text = constants.EMOJI_PATTERN.sub("", self.text)
+        return self.text
+
+    def convert_emoji_to_text(self, code_delimiters=(':', ':'), input_str=None) -> str:
+        """
+        Convert emoji to their CLDR Short Name, according to the unicode convention
+        http://www.unicode.org/emoji/charts/full-emoji-list.html
+        eg. 😀 --> :grinning_face:
+
+        Parameters
+        ----------
+        text : str
+            code_delimiters : tuple of symbols around the emoji code. 
+            eg: (':',':') --> :grinning_face:
+
+        Returns
+        -------
+        str
+            string 
+        """
+        if input_str:
+            return _emoji.demojize(input_str, delimiters=code_delimiters)
+        else:
+            return _emoji.demojize(self.text, delimiters=code_delimiters)
+
+    def extract_emojis(self) -> list:
+        """
+        Function that extracts emojis from a text and translates them into words
+        eg. "I take care of my skin 😀 :(" --> [":grinning_face:"]
+
+        Parameters
+        ----------
+        text : str
+
+        Returns
+        -------
+        list
+            list of all emojis converted with their unicode conventions
+        """
+        emojis_in_text = constants.EMOJI_PATTERN.findall(self.text)
+        emojis_converted = [self.convert_emoji_to_text(input_str=emoji_text) for emoji_text in emojis_in_text]
+        return emojis_converted
+
+    def extract_hashtags(self) -> list:
+        """
+        Function that extracts words preceded with a '#'
+        eg. "I take care of my skin #selfcare#selfestim" --> ["skincare", "selfestim"]
+
+        Parameters
+        ----------
+        text : str
+
+        Returns
+        -------
+        list
+            list of all hashtags
+        """
+        return constants.HASHTAG_PATTERN.findall(self.text)
+
+    def remove_hashtag(self) -> str:
+        """
+        Function that removes words preceded with a '#'
+        eg. "I take care of my skin #selfcare#selfestim" --> "I take care of my skin"
+
+        Parameters
+        ----------
+        text : str
+
+        Returns
+        -------
+        str
+            text of a post without hashtags
+        """
+        self.text = self.normalize_whitespace(constants.HASHTAG_PATTERN.sub('', self.text))
+        return self.text
+
+    def normalize_whitespace(self, text) -> str:
+        """
+        Given ``text`` str, replace one or more spacings with a single space, and one
+        or more linebreaks with a single newline. Also strip leading/trailing whitespace.
+        eg. "   foo  bar  " -> "foo bar"
+
+        Parameters
+        ----------
+        text : string
+
+        Returns
+        -------
+        string    
+        """
+        return constants.NONBREAKING_SPACE_REGEX.sub(
+            " ", constants.LINEBREAK_REGEX.sub(r"\n", text)
+        ).strip()
diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
new file mode 100644
index 0000000..718b061
--- /dev/null
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -0,0 +1,417 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import re
+import regex
+import unicodedata
+from ftfy import fix_text as _fix_text
+
+from nautilus_nlp.utils.stopwords import get_stopwords
+from nautilus_nlp.utils.phone_number import extract_phone_numbers as _extract_phone_numbers
+from nautilus_nlp.utils import constants
+
+class TextPreprocessor():
+
+    def __init__(self,text):
+        if isinstance(text,str):
+            self.text = text
+        else:
+            raise ValueError("Input must be a string")
+
+    def clean_text(self,lang='en') -> str:
+        stopwords = get_stopwords(lang)
+        self.text = self.fix_bad_unicode(normalization="NFC")
+        self.text = self.remove_EOL_characters()
+        self.text = self.remove_accents(method="unicode")
+        self.text = self.remove_punct()
+        self.text = self.text.lower()
+        self.text = self.remove_stopwords(stopwords=stopwords)
+        return self.normalize_whitespace()
+
+    def remove_EOL_characters(self) -> str:
+        """
+        Remove end of line (\n) char.
+
+        Parameters
+        ----------
+        text : str
+
+        Returns
+        -------
+        str
+        """
+        self.text = self.text.replace("\n", " ")
+        return self.text
+
+    def remove_stopwords(self, stopwords: list) -> str:
+        """ 
+        Remove stopwords from a text.
+        eg. 'I like when you move your body !' -> 'I move body !'
+
+        Parameters
+        ----------
+        stopwords : list of stopwords to remove
+
+        Returns
+        -------
+        str
+            text without stopwords
+
+        Raises
+        ------
+        ValueError
+            When inputs is not a string
+        """
+        self.text =  ' '.join([word.strip() for word in self.text.split() if word not in stopwords])
+        
+        return self.text
+
+    def fix_bad_unicode(self, normalization: str = "NFC") -> str:
+        """
+        Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
+        this includes mojibake, HTML entities and other code cruft,
+        and non-standard forms for display purposes.
+
+        Parameters
+        ----------
+        text : string
+
+        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): 
+            if 'NFC', combines characters and diacritics written using separate code points,
+            e.g. converting "e" plus an acute accent modifier into "é"; unicode
+            can be converted to NFC form without any change in its meaning!
+            if 'NFKC', additional normalizations are applied that can change
+            the meanings of characters, e.g. ellipsis characters will be replaced
+            with three periods
+        Returns
+        -------
+        string
+        """
+        self.text = _fix_text(self.text, normalization=normalization)
+        return self.text
+
+    def normalize_whitespace(self) -> str:
+        """
+        Given ``text`` str, replace one or more spacings with a single space, and one
+        or more linebreaks with a single newline. Also strip leading/trailing whitespace.
+        eg. "   foo  bar  " -> "foo bar"
+
+        Parameters
+        ----------
+        text : string
+
+        Returns
+        -------
+        string    
+        """
+        self.text = constants.NONBREAKING_SPACE_REGEX.sub(
+            " ", constants.LINEBREAK_REGEX.sub(r"\n", self.text)
+        ).strip()
+        return self.text
+
+    def unpack_english_contractions(self) -> str:
+        """
+        Replace *English* contractions in ``text`` str with their unshortened forms.
+        N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
+        so are left as-is.
+        eg. "You're fired. She's nice." -> "You are fired. She's nice."
+
+        Parameters
+        ----------
+        text : string
+
+        Returns
+        -------
+        string    
+        """
+
+        # standard
+        self.text = constants.CONTRACTION_NT_NOT.sub(
+            r"\1\2 not",
+            self.text,
+        )
+        self.text = constants.CONTRACTION_LL_WILL.sub(
+            r"\1\2 will",
+            self.text,
+        )
+        self.text = constants.CONTRACTION_RE_ARE.sub(r"\1\2 are", self.text)
+        self.text = constants.CONTRACTION_VE_HAVE.sub(
+            r"\1\2 have",
+            self.text,
+        )
+        self.text = constants.CONTRACTION_CANT_CANNOT.sub(r"\1\2n not", self.text)
+        self.text = constants.CONTRACTION_M_AM.sub(r"\1\2 am", self.text)
+        self.text = constants.CONTRACTION_LET_LETUS.sub(r"\1\2 us", self.text)
+        self.text = constants.CONTRACTION_WONT_WILLNOT.sub(r"\1\2ill not", self.text)
+        self.text = constants.CONTRACTION_SHANT_SHALLNOT.sub(r"\1\2hall not", self.text)
+        self.text = constants.CONTRACTION_YALL_YOUALL.sub(r"\1\2ou all", self.text)
+        return self.text
+
+    def replace_urls(self, replace_with:str="*URL*") -> str:
+        """
+        Replace all URLs in ``text`` str with ``replace_with`` str.
+
+        Parameters
+        ----------
+        text : string
+        replace_with : string
+            the string you want the URL to be replaced with.
+
+        Returns
+        -------
+        string
+        """    
+        self.text = constants.URL_REGEX.sub(
+            replace_with, constants.SHORT_URL_REGEX.sub(replace_with, self.text)
+        )
+        return self.text
+
+    def replace_emails(self, replace_with="*EMAIL*") -> str:
+        """
+        Replace all emails in ``text`` str with ``replace_with`` str
+
+        Parameters
+        ----------
+        text : string
+        replace_with : string
+            the string you want the email address to be replaced with.
+
+        Returns
+        -------
+        string
+        """
+        self.text = constants.EMAIL_REGEX.sub(replace_with, self.text)
+        return self.text
+
+    def replace_phone_numbers(self, replace_with:str="*PHONE*",
+                                    method:str="regex",
+                                    country_format_to_detect:list=[None,'FR','US','GB'],
+                                    return_text: bool = False) -> str:
+        """
+        Replace all phone numbers in ``text`` str with ``replace_with`` str
+
+        Parameters
+        ----------
+        text : string
+        replace_with : string
+            the string you want the phone number to be replaced with.
+        method : ['regex','detection']
+            regex is faster but will omit a lot of numbers, while detection will 
+            catch every numbers, but takes a while.
+        country_format_to_detect : list 
+            If a list of country code is specified, will catch every number formatted.
+            Only when method = 'detection'.
+        Returns
+        -------
+        string
+        """
+        if method == 'regex':
+            self.text = constants.PHONE_REGEX.sub(replace_with, self.text)
+        elif method == 'detection':
+            found_nums = _extract_phone_numbers(self.text, countrylist=country_format_to_detect)
+
+            # order by lenght to avoid truncated numbers to be removed first.
+            found_nums.sort(key=len,reverse=True) 
+            for phone_number in found_nums:
+                self.text = self.text.replace(phone_number, replace_with)
+        else:
+            raise ValueError('Please input a valid method between "regex" or "detection"')
+        return self.text
+
+    def replace_numbers(self, replace_with="*NUMBER*") -> str:
+        """
+        Replace all numbers in ``text`` str with ``replace_with`` str.
+
+        Parameters
+        ----------
+        text : string
+        replace_with : string
+            the string you want the number to be replaced with.
+
+        Returns
+        -------
+        string
+        """        
+        self.text = constants.NUMBERS_REGEX.sub(replace_with, self.text)
+        return self.text
+
+    def replace_currency_symbols(self, replace_with=None) -> str:
+        """
+        Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
+
+        Parameters
+        ----------
+        text : str
+            raw text
+        replace_with : None or string
+            if None (default), replace symbols with
+                their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
+                otherwise, pass in a string with which to replace all symbols
+                (e.g. "*CURRENCY*")
+
+        Returns
+        -------
+        string
+        """          
+        if replace_with is None:
+            for k, v in constants.CURRENCIES.items():
+                self.text = self.text.replace(k, v)                
+        else:
+            self.text = constants.CURRENCY_REGEX.sub(replace_with, self.text)
+        return self.text
+
+    def remove_punct(self, marks=None) -> str:
+        """
+        Remove punctuation from ``text`` by replacing all instances of ``marks``
+        with whitespace.
+
+        Parameters
+        ----------
+        text : str
+            raw text
+        
+        marks : str or None
+            If specified, remove only the characters in this string,
+            e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
+            Otherwise, all punctuation marks are removed.
+
+        Returns
+        -------
+        string
+
+        Note
+        -------
+        When ``marks=None``, Python's built-in :meth:`str.translate()` is
+        used to remove punctuation; otherwise, a regular expression is used
+        instead. The former's performance is about 5-10x faster.    
+        """       
+        if marks:
+            self.text = re.sub("[{}]+".format(re.escape(marks)), " ", self.text, flags=re.UNICODE)
+        else:
+            self.text = self.text.translate(constants.PUNCT_TRANSLATE_UNICODE)
+        return self.text
+
+    def remove_accents(self, method:str="unicode") -> str:
+        """
+        Remove accents from any accented unicode characters in ``text`` str, either by
+        transforming them into ascii equivalents or removing them entirely.
+
+        Parameters
+        ----------
+        text : str
+            raw text
+        
+        method : ({'unicode', 'ascii'})
+            if 'unicode', remove accented
+            char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
+            remove accented char for any unicode symbol
+
+            NB: the 'ascii' method is notably faster than 'unicode', but less good
+
+        Returns
+        -------
+        string
+
+        Raises
+        -------
+        ValueError
+            if ``method`` is not in {'unicode', 'ascii'}   
+        """
+        if method == "unicode":
+            self.text = "".join(
+                c
+                for c in unicodedata.normalize("NFKD", self.text)
+                if not unicodedata.combining(c)
+            )
+        elif method == "ascii":
+            self.text = (
+                unicodedata.normalize("NFKD", self.text)
+                .encode("ascii", errors="ignore")
+                .decode("ascii")
+            )
+        else:
+            msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
+            raise ValueError(msg)
+        return self.text
+
+    def remove_multiple_spaces_and_strip_text(self) -> str:
+        """
+        Remove multiple spaces, strip text, and remove '-', '*' characters.
+
+        Parameters
+        ----------
+        text : str
+            the text to be processed
+
+        Returns
+        -------
+        string
+            the text with removed multiple spaces and strip text
+        """
+        regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
+        for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
+            self.text = re.sub(regex_remove_multiple_spaces, " ", self.text)
+            self.text = self.text.strip()
+        return self.text
+
+    def filter_non_latin_characters(self) -> str:
+        """
+        Function that filters non latin characters of a text
+
+        Parameters
+        ----------
+        text : string
+
+        Returns
+        -------
+        string
+        """
+        self.text = constants.LATIN_CHARACTERS_RE.sub(' ', self.text)
+        self.text = self.normalize_whitespace()
+        return self.text
+
+    def remove_smallwords(self, smallwords_threshold:int) -> list:
+        """
+        Function that removes words which length is below a threshold
+        'Hello my name is John Doe' --> 'Hello name John Doe'
+
+        Parameters
+        ----------
+        text : str
+        smallwords_threshold: int
+            threshold of small word
+
+        Returns
+        -------
+        str
+        """
+        self.text = ' '.join([word for word in self.text.split() if len(word) > smallwords_threshold])
+        return self.text
+
+
+
+
+
+
+
+
+
+
diff --git a/nautilus_nlp/preprocessing/token_preprocess.py b/nautilus_nlp/preprocessing/token_preprocess.py
new file mode 100644
index 0000000..5e3f3b6
--- /dev/null
+++ b/nautilus_nlp/preprocessing/token_preprocess.py
@@ -0,0 +1,109 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import re
+
+class TokenPreprocessor():
+
+    def __init__(self,tokens):
+        if isinstance(tokens,list):
+            self.tokens = tokens
+        else:
+            raise ValueError("Input must be a list")
+
+    def remove_stopwords(self, stopwords: list) -> str:
+        """ 
+        Remove stopwords from a text.
+        eg. 'I like when you move your body !' -> 'I move body !'
+
+        Parameters
+        ----------
+        stopwords : list of stopwords to remove
+
+        Returns
+        -------
+        list
+            tokens without stopwords
+
+        Raises
+        ------
+        ValueError
+            When inputs is not a list
+        """
+        self.tokens = [word for word in self.tokens if word not in stopwords]
+        return self.tokens
+
+    def remove_tokens_with_nonletters(self) -> list:
+        """
+        Inputs a list of tokens, outputs a list of tokens without tokens that
+        includes numbers of special caracters.
+        ['foo','bar','124','34euros'] -> ['foo','bar']
+
+        Parameters
+        ----------
+        tokens : list
+            list of tokens to be cleaned
+
+        Returns
+        -------
+        list
+            list of tokens without tokens with numbers
+        """
+        self.tokens = [word for word in self.tokens if re.search("[^a-zA-Z]", word) is None]
+        return self.tokens
+
+    def remove_special_caracters_from_tokenslist(self) -> list:
+        """ 
+        Remove tokens that doesn't contains any number or letter. 
+        eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
+
+        Parameters
+        ----------
+        tokens : list
+            list of tokens to be cleaned
+
+        Returns
+        -------
+        list
+            list of tokens without tokens that contains only special caracters
+        
+        """
+        self.tokens = [word for word in self.tokens if re.search("[a-zA-Z0-9]", word)]
+        return self.tokens
+
+    def remove_smallwords(self, smallwords_threshold:int) -> list:
+        """
+        Function that removes words which length is below a threshold
+        ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
+
+        Parameters
+        ----------
+        text : list
+            list of strings
+        smallwords_threshold: int
+            threshold of small word
+
+        Returns
+        -------
+        list
+        """
+        self.tokens = [word for word in self.tokens if len(word) > smallwords_threshold]
+        return self.tokens
\ No newline at end of file
diff --git a/tests/test_fix_bad_encoding.py b/tests/test_fix_bad_encoding.py
index ad44230..50d4ca3 100644
--- a/tests/test_fix_bad_encoding.py
+++ b/tests/test_fix_bad_encoding.py
@@ -18,7 +18,7 @@
 
 import pytest
 import numpy as np
-from nautilus_nlp.preprocessing.main_preprocess import fix_bad_unicode
+from nautilus_nlp.preprocessing.text_preprocess import TextPreprocessor
 
 
 
@@ -45,5 +45,6 @@
     ],
 )
 def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
-    result = fix_bad_unicode(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.fix_bad_unicode()
     np.testing.assert_string_equal(result, expected_str)
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index aa1ddea..d93635b 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,39 +17,13 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from nautilus_nlp.preprocessing.additional_preprocess import (
-    remove_multiple_spaces_and_strip_text,
-    remove_tokens_with_nonletters,
-    remove_special_caracters_from_tokenslist,
-    filter_non_latin_characters,
-    remove_smallwords
-)
-from nautilus_nlp.preprocessing.social_preprocess import (
-    remove_emoji,
-    convert_emoji_to_text,
-    extract_emojis,
-    remove_mentions,
-    extract_mentions,
-    remove_html_tags,
-    extract_hashtags,
-    remove_hashtag
-)
-from nautilus_nlp.preprocessing.main_preprocess import (
-    remove_accents,
-    fix_bad_unicode,
-    remove_EOL_characters,
-    get_stopwords,
-    remove_stopwords,
-    normalize_whitespace,
-    unpack_english_contractions,
-    replace_urls,
-    replace_emails,
-    replace_phone_numbers,
-    replace_numbers,
-    replace_currency_symbols,
-    remove_punct,
-)
+from nautilus_nlp.preprocessing.text_preprocess import TextPreprocessor
+from nautilus_nlp.preprocessing.social_preprocess import SocialPreprocessor
+from nautilus_nlp.preprocessing.token_preprocess import TokenPreprocessor
 import nautilus_nlp.utils.phone_number as phone
+from nautilus_nlp.utils.stopwords import get_stopwords
+
+
 
 @pytest.mark.parametrize("text, expected_result",
                          [("ACV water + cinnamon + turmeric + cucumber + lemon. 👍🏻",
@@ -57,7 +31,8 @@
                           ("This is a text without emojis",
                            [])])
 def test_extract_emojis(text, expected_result):
-    result = extract_emojis(text)
+    preprocessor = SocialPreprocessor(text)
+    result = preprocessor.extract_emojis()
     assert expected_result == result
 
 
@@ -67,7 +42,8 @@ def test_extract_emojis(text, expected_result):
                           ("This is a text without mentions",
                            "This is a text without mentions")])
 def test_remove_mentions(text, expected_result):
-    result = remove_mentions(text)
+    preprocessor = SocialPreprocessor(text)
+    result = preprocessor.remove_mentions()
     assert expected_result == result
 
 
@@ -77,7 +53,8 @@ def test_remove_mentions(text, expected_result):
                           ("This is a text without mentions",
                            [])])
 def test_extract_mentions(text, expected_result):
-    result = extract_mentions(text)
+    preprocessor = SocialPreprocessor(text)
+    result = preprocessor.extract_mentions()
     assert expected_result == result
 
 
@@ -87,7 +64,8 @@ def test_extract_mentions(text, expected_result):
                           ("This is a text without html tags",
                            "This is a text without html tags")])
 def test_remove_html_tags(text, expected_result):
-    result = remove_html_tags(text)
+    preprocessor = SocialPreprocessor(text)
+    result = preprocessor.remove_html_tags()
     assert expected_result == result
 
 
@@ -99,7 +77,8 @@ def test_remove_html_tags(text, expected_result):
                            2,
                            ["This", "text", "contains", "only", "long", "words"])])
 def test_remove_smallwords(tokens_list, smallwords_threshold, expected_result):
-    result = remove_smallwords(tokens_list, smallwords_threshold)
+    preprocessor = TokenPreprocessor(tokens_list)
+    result = preprocessor.remove_smallwords(smallwords_threshold)
     assert expected_result == result
 
 
@@ -116,7 +95,8 @@ def test_remove_smallwords(tokens_list, smallwords_threshold, expected_result):
                            ["#many", "#hashtags"])]
                          )
 def test_extract_hashtags(text, expected_result):
-    result = extract_hashtags(text)
+    preprocessor = SocialPreprocessor(text)
+    result = preprocessor.extract_hashtags()
     assert expected_result == result
 
 
@@ -133,7 +113,8 @@ def test_extract_hashtags(text, expected_result):
                            "this is a text with")]
                          )
 def test_remove_hashtag(text, expected_result):
-    result = remove_hashtag(text)
+    preprocessor = SocialPreprocessor(text)
+    result = preprocessor.remove_hashtag()
     assert expected_result == result
 
 
@@ -141,8 +122,9 @@ def test_remove_hashtag(text, expected_result):
                          [("كلمات Learn 3 Arabic كلمات words EASILY- Vocabulary #1 تعلم ٣ جديدة",
                            "Learn 3 Arabic words EASILY Vocabulary 1")])
 def test_filter_non_latin_characters(text, expected_filtered_text):
-    filtered_text = filter_non_latin_characters(text)
-    assert expected_filtered_text == filtered_text
+    preprocessor = TextPreprocessor(text)
+    result = preprocessor.filter_non_latin_characters()
+    assert expected_filtered_text == result
 
 
 @pytest.mark.parametrize(
@@ -156,7 +138,8 @@ def test_filter_non_latin_characters(text, expected_filtered_text):
     ],
 )
 def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
-    result = remove_multiple_spaces_and_strip_text(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.remove_multiple_spaces_and_strip_text()
     np.testing.assert_string_equal(result, expected_str)
 
 @pytest.mark.parametrize(
@@ -168,21 +151,24 @@ def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
     ],
 )
 def test_remove_EOL_characters(input_str, expected_str):
-    result = remove_EOL_characters(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.remove_EOL_characters()
     np.testing.assert_string_equal(result, expected_str)    
 
 
 def test_remove_tokens_with_nonletters():
     input_tokens = ['foo','bar','124','34euros']
     expected_output = ['foo','bar']
-    result = remove_tokens_with_nonletters(input_tokens)
+    preprocessor = TokenPreprocessor(input_tokens)
+    result = preprocessor.remove_tokens_with_nonletters()
     np.testing.assert_array_equal(result,expected_output)
 
 
 def test_remove_special_caracters_from_tokenslist():
     input_tokens = ['foo','bar','---',"'s",'#']
     expected_output = ['foo','bar',"'s"]
-    result = remove_special_caracters_from_tokenslist(input_tokens)
+    preprocessor = TokenPreprocessor(input_tokens)
+    result = preprocessor.remove_special_caracters_from_tokenslist()
     np.testing.assert_array_equal(result, expected_output)
 
 
@@ -196,21 +182,33 @@ def test_get_stopwords():
 @pytest.mark.parametrize(
     "input_tokens, expected_output",
     [
-        (['I','like','when','you','move','your','body','!'], ['I', 'move', 'body', '!']),
-        ('I like when you move your body !', ['I', 'move', 'body', '!']),
+        (['I','like','when','you','move','your','body','!'], ['I', 'move', 'body', '!'])
     ],
 )    
-def test_remove_stopwords(input_tokens, expected_output):
+def test_remove_stopwords_tokens(input_tokens, expected_output):
+    stopwords = get_stopwords('en')
+    preprocessor = TokenPreprocessor(input_tokens)
+    result = preprocessor.remove_stopwords(stopwords)
+    np.testing.assert_array_equal(result, expected_output)
+
+@pytest.mark.parametrize(
+    "input_str, expected_output",
+    [
+        ('I like when you move your body !', 'I move body !'),
+    ],
+)  
+def test_remove_stopwords_text(input_str, expected_output):
     stopwords = get_stopwords('en')
-    result = remove_stopwords(input_tokens, stopwords)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.remove_stopwords(stopwords)
     np.testing.assert_array_equal(result, expected_output)
 
 
 def test_remove_accents():
     input_str = "éèëêàù"
     expected_str = "eeeeau"
-
-    result = remove_accents(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.remove_accents()
     np.testing.assert_string_equal(result, expected_str)
 
 
@@ -237,7 +235,8 @@ def test_remove_accents():
     ],
 )
 def test_fix_bad_unicode(input_str, expected_str):
-    result = fix_bad_unicode(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.fix_bad_unicode()
     np.testing.assert_string_equal(result, expected_str)
 
 
@@ -249,7 +248,8 @@ def test_fix_bad_unicode(input_str, expected_str):
     ],
 )
 def test_normalize_whitespace(input_str, expected_str):
-    result = normalize_whitespace(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.normalize_whitespace()
     np.testing.assert_equal(result, expected_str)
 
 @pytest.mark.parametrize(
@@ -264,7 +264,8 @@ def test_normalize_whitespace(input_str, expected_str):
     ]
     )
 def test_unpack_english_contractions(input_str, expected_str):
-    result = unpack_english_contractions(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.unpack_english_contractions()
     np.testing.assert_equal(result, expected_str)
 
 @pytest.mark.parametrize(
@@ -280,7 +281,8 @@ def test_unpack_english_contractions(input_str, expected_str):
     ]
     )
 def test_replace_urls(input_str, expected_str):
-    result = replace_urls(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.replace_urls()
     np.testing.assert_equal(result, expected_str)
 
 
@@ -294,7 +296,8 @@ def test_replace_urls(input_str, expected_str):
     ]
     )
 def test_replace_emails(input_str, expected_str):
-    result = replace_emails(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.replace_emails()
     np.testing.assert_equal(result, expected_str)
 
 
@@ -315,10 +318,12 @@ def test_replace_emails(input_str, expected_str):
     ]
     )
 def test_replace_phone_numbers(input_str, expected_str):
-    result = replace_phone_numbers(input_str,   replace_with="*PHONE*", 
-                                                method="detection",
-                                                country_format_to_detect=phone.SUPPORTED_COUNTRY
-                                                )
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.replace_phone_numbers(
+        replace_with="*PHONE*",
+        method="detection",
+        country_format_to_detect=phone.SUPPORTED_COUNTRY
+        )
     np.testing.assert_equal(result, expected_str)
 
 
@@ -332,7 +337,8 @@ def test_replace_phone_numbers(input_str, expected_str):
     ]
     )
 def test_replace_numbers(input_str, expected_str):
-    result = replace_numbers(input_str)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.replace_numbers()
     np.testing.assert_equal(result, expected_str)
 
 
@@ -347,7 +353,8 @@ def test_replace_numbers(input_str, expected_str):
     ]
     )
 def test_replace_currency_symbols(input_str, param, expected_str):
-    result = replace_currency_symbols(input_str, replace_with=param)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.replace_currency_symbols(replace_with=param)
     np.testing.assert_equal(result, expected_str)
 
 @pytest.mark.parametrize(
@@ -372,7 +379,8 @@ def test_replace_currency_symbols(input_str, param, expected_str):
     ]
     )
 def test_remove_punct(input_str, param, expected_str):
-    result = remove_punct(input_str, marks=param)
+    preprocessor = TextPreprocessor(input_str)
+    result = preprocessor.remove_punct(marks=param)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -390,7 +398,8 @@ def test_remove_punct(input_str, param, expected_str):
     ]
     )
 def test_remove_emoji(input_str, expected_str):
-    result = remove_emoji(input_str)
+    preprocessor = SocialPreprocessor(input_str)
+    result = preprocessor.remove_emoji()
     np.testing.assert_equal(result, expected_str)
 
 
@@ -404,5 +413,6 @@ def test_remove_emoji(input_str, expected_str):
     ]
     )
 def test_convert_emoji_to_text(input_str, expected_str):
-    result = convert_emoji_to_text(input_str)
+    preprocessor = SocialPreprocessor(input_str)
+    result = preprocessor.convert_emoji_to_text()
     np.testing.assert_equal(result, expected_str)    
\ No newline at end of file

From 697f53118548d3f38b8df0350d0a37195ea7cf93 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:42:03 +0200
Subject: [PATCH 259/496] removing models/ folder

---
 .../external/get_language_dataset.sh          |   5 +-
 .../external/get_stanfordtweets.sh            |   3 +
 docker/Dockerfile                             |  48 ---
 nautilus_nlp/models/.gitkeep                  |   0
 nautilus_nlp/models/biterm_model.py           | 162 ---------
 nautilus_nlp/models/fasttext_classifier.py    | 155 ---------
 nautilus_nlp/models/fasttext_embedding.py     | 118 -------
 nautilus_nlp/models/language_detector.py      |  54 ---
 nautilus_nlp/models/nmf_model.py              | 118 -------
 nautilus_nlp/models/seanmf_model.py           | 148 --------
 nautilus_nlp/models/sentiment_detector.py     |  65 ----
 nautilus_nlp/models/sk_vectorizer.py          |  34 --
 nautilus_nlp/models/text_vectorizers.py       | 295 ----------------
 nautilus_nlp/models/topic_modeling.py         | 315 ------------------
 .../models/topic_modeling_short_text.py       | 280 ----------------
 15 files changed, 6 insertions(+), 1794 deletions(-)
 rename docker/build_docker.sh => datasets/external/get_language_dataset.sh (84%)
 rename nautilus_nlp/models/__init__.py => datasets/external/get_stanfordtweets.sh (76%)
 delete mode 100644 docker/Dockerfile
 delete mode 100644 nautilus_nlp/models/.gitkeep
 delete mode 100644 nautilus_nlp/models/biterm_model.py
 delete mode 100644 nautilus_nlp/models/fasttext_classifier.py
 delete mode 100644 nautilus_nlp/models/fasttext_embedding.py
 delete mode 100644 nautilus_nlp/models/language_detector.py
 delete mode 100644 nautilus_nlp/models/nmf_model.py
 delete mode 100644 nautilus_nlp/models/seanmf_model.py
 delete mode 100644 nautilus_nlp/models/sentiment_detector.py
 delete mode 100644 nautilus_nlp/models/sk_vectorizer.py
 delete mode 100644 nautilus_nlp/models/text_vectorizers.py
 delete mode 100644 nautilus_nlp/models/topic_modeling.py
 delete mode 100644 nautilus_nlp/models/topic_modeling_short_text.py

diff --git a/docker/build_docker.sh b/datasets/external/get_language_dataset.sh
similarity index 84%
rename from docker/build_docker.sh
rename to datasets/external/get_language_dataset.sh
index 40d9933..4e87a8b 100644
--- a/docker/build_docker.sh
+++ b/datasets/external/get_language_dataset.sh
@@ -16,5 +16,6 @@
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/bin/bash
-cp docker/Dockerfile .
-docker build -t nautilus_nlp:latest .
+wget -O wili.zip https://zenodo.org/record/841984/files/wili-2018.zip?download=1
+mkdir -p wili && cp wili.zip wili && cd wili && unzip wili.zip && cd ..
+
diff --git a/nautilus_nlp/models/__init__.py b/datasets/external/get_stanfordtweets.sh
similarity index 76%
rename from nautilus_nlp/models/__init__.py
rename to datasets/external/get_stanfordtweets.sh
index d46139b..ef7ae1e 100644
--- a/nautilus_nlp/models/__init__.py
+++ b/datasets/external/get_stanfordtweets.sh
@@ -15,3 +15,6 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+#!/bin/bash 
+wget -O trainingandtestdata.zip http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip trainingandtestdata.zip
+mkdir -p  tweets_sentiment && cp trainingandtestdata.zip tweets_sentiment && cd tweets_sentiment && unzip trainingandtestdata.zip
diff --git a/docker/Dockerfile b/docker/Dockerfile
deleted file mode 100644
index 8530975..0000000
--- a/docker/Dockerfile
+++ /dev/null
@@ -1,48 +0,0 @@
-FROM ubuntu:latest
-
-SHELL ["/bin/bash", "-c"]
-
-RUN apt-get update --fix-missing && \
-    apt-get install -y wget bzip2 ca-certificates curl git && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.5.11-Linux-x86_64.sh -O ~/miniconda.sh && \
-    /bin/bash ~/miniconda.sh -b -p /opt/conda && \
-    rm ~/miniconda.sh && \
-    /opt/conda/bin/conda clean -tipsy && \
-    ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
-    echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
-    echo "conda activate base" >> ~/.bashrc
-
-ENV TINI_VERSION v0.16.1
-ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /usr/bin/tini
-RUN chmod +x /usr/bin/tini
-
-
-RUN apt-get update && apt-get install -y build-essential unzip 
-RUN apt-get install -y g++-5 && apt-get install -y gcc-5
-RUN apt-get install -y python3-pip && alias pip=pip3 && pip3 install --upgrade pip
-RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 10 && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 20 && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 10 && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 20 && update-alternatives --install /usr/bin/cc cc /usr/bin/gcc 30 && update-alternatives --set cc /usr/bin/gcc && update-alternatives --install /usr/bin/c++ c++ /usr/bin/g++ 30 && update-alternatives --set c++ /usr/bin/g++
-
-RUN wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip && unzip v0.2.0.zip && cd fastText-0.2.0 && make
-RUN git clone https://github.com/facebookresearch/fastText.git 
-RUN cd fastText && pip3 install .
-RUN  pip3 install pandas schedule nltk pendulum bounter spacy==2.1 jupyterlab confluent-kafka xxhash scikit-learn
-RUN python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
-RUN python3 -m nltk.downloader stopwords 
-
-RUN wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip 
-    unzip mallet-2.0.8.zip
-    rm mallet-2.0.8.zip
-
-RUN sudo add-apt-repository ppa:openjdk-r/ppa  # only Ubuntu 17.4 and earlier
-    sudo apt update
-    apt search openjdk
-    sudo apt install openjdk-8-jdk
-
-RUN echo "alias python='python3'" >> .bash_aliases
-RUN echo "alias pip='pip3' ">> ~/.bash_aliases
-RUN source ~/.bashrc
-ENTRYPOINT [ "/usr/bin/tini", "--" ]
-CMD [ "/bin/bash" ]
diff --git a/nautilus_nlp/models/.gitkeep b/nautilus_nlp/models/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/models/biterm_model.py b/nautilus_nlp/models/biterm_model.py
deleted file mode 100644
index 8aa1ab4..0000000
--- a/nautilus_nlp/models/biterm_model.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import numpy as np
-from biterm.btm import oBTM
-from biterm.utility import vec_to_biterms, topic_summuary
-from sklearn.feature_extraction.text import CountVectorizer
-import pyLDAvis
-
-
-class BitermModel:
-
-    def __init__(self, data, nb_topics, nb_iteration, lang):
-        """
-        Model for topic modelling. Particularly useful for short texts.
-
-        Parameters
-        ----------
-        data : list
-            a list of string, each string can be a document
-        nb_topics : positive int
-
-        nb_iteration : positive int
-
-        lang : str
-            _language to remove the stop words, can be setup to None
-
-        Returns
-        -------
-        string
-            the text with removed multiple spaces and strip text
-        """        
-        self.is_int_positive(nb_topics)
-        self.is_int_positive(nb_iteration)
-        self.is_list_of_string(data)
-
-        self.data = data
-        self.nb_topics = nb_topics
-        self.nb_iteration = nb_iteration
-        self.lang = lang
-        self._topics = None
-        self._btm = None
-        self._vectorize_text = None
-        self._vocabulary = None
-
-    @staticmethod
-    def is_int_positive(number):
-        """
-        Function to check if the input parameter is a integer and positive 
-        otherwise raise an error
-
-        Parameters
-        ----------
-        number : str
-            
-        Returns
-        -------
-        str:
-            the text with removed multiple spaces and strip text
-            
-        """  
-        if not isinstance(number, int):
-            raise ValueError("Parameter {} has to be an integer".format(number))
-        if number < 1:
-            raise ValueError("Parameter {} has to be positive".format(number))
-
-    @staticmethod
-    def is_list_of_string(data):
-        """
-        Function to check if the input parameter is a list of strings otherwise raise an error
-        
-        Parameters
-        ----------
-        data
-        
-        Returns
-        -------
-        """
-        if not isinstance(data, list):
-            raise ValueError("{} has to be a list".format(data))
-        if len(data) == 0:
-            raise ValueError("{} is empty".format(data))
-        for document in data:
-            if not isinstance(document, str):
-                raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
-
-    def compute_topics(self, nb_word_per_cluster):
-        """
-        Main function computing the topic modeling, topics
-        
-        Parameters
-        ----------
-        nb_word_per_cluster : positive integer
-        
-        Returns
-        -------
-        dict :    
-            a dictionary containing the the different topics with the top words 
-            and coherence associated
-        """
-        vec = CountVectorizer(stop_words=self.lang)
-        self._vectorize_text = vec.fit_transform(self.data).toarray()
-        self._vocabulary = np.array(vec.get_feature_names())
-
-        biterms = vec_to_biterms(self._vectorize_text)
-        self._btm = oBTM(num_topics=self.nb_topics, V=self._vocabulary)
-        self._topics = self._btm.fit_transform(biterms, iterations=self.nb_iteration)
-
-        results = topic_summuary(self._btm.phi_wz.T, self._vectorize_text, self._vocabulary, nb_word_per_cluster, verbose=False)
-
-        return results
-
-    def get_document_topic(self, index):
-        """
-        Get the cluster associated to the specified document
-                
-        Parameters
-        ----------
-        index : positive integer
-            the document index
-
-        Returns
-        -------
-        the cluster index
-        """
-        if self._topics is None:
-            raise ValueError("Model needs to be trained first")
-
-        return self._topics[index].argmax()
-
-    def save_pyLDAvis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.html'):
-        """
-        Function saving the pyLDAvis plot associated with the compute_topics function
-                
-        Parameters
-        ----------
-        path_to_output : str
-            path to save the plut, must be a html file
-
-        Returns
-        -------
-        """        
-        if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
-            raise ValueError("Model needs to be trained first")
-
-        vis = pyLDAvis.prepare(self._btm.phi_wz.T, self._topics, np.count_nonzero(self._vectorize_text, axis=1), self._vocabulary,
-                               np.sum(self._vectorize_text, axis=0))
-        pyLDAvis.save_html(vis, path_to_output)
diff --git a/nautilus_nlp/models/fasttext_classifier.py b/nautilus_nlp/models/fasttext_classifier.py
deleted file mode 100644
index 39e0b08..0000000
--- a/nautilus_nlp/models/fasttext_classifier.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from sklearn.base import BaseEstimator, ClassifierMixin
-import fastText
-import multiprocessing
-
-
-class Fasttext_clf(BaseEstimator, ClassifierMixin):
-    def __init__(self, path=None):
-        try:
-            self.model = fastText.load_model(path)
-        except Exception as e:
-            print(e)
-            self.model = None
-
-    def fit(self, X, y):
-        # Todo Build SKlearn compatible API for training
-        return self
-
-    def train(
-        self,
-        train_data,
-        lr=0.1,
-        dim=100,
-        ws=5,
-        epoch=5,
-        minCount=1,
-        minCountLabel=0,
-        minn=0,
-        maxn=0,
-        neg=5,
-        wordNgrams=1,
-        loss="softmax",
-        bucket=2000000,
-        thread=multiprocessing.cpu_count() - 1,
-        lrUpdateRate=100,
-        t=1e-4,
-        label="__label__",
-        verbose=2,
-        pretrainedVectors="",
-    ):
-        """
-    Train a supervised model and return a model object.
-    input must be a filepath. The input text does not need to be tokenized
-    as per the tokenize function, but it must be preprocessed and encoded
-    as UTF-8. You might want to consult standard preprocessing scripts such
-    as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html
-    The input file must must contain at least one label per line. For an
-    example consult the example datasets which are part of the fastText
-    repository such as the dataset pulled by classification-example.sh.
-    """
-        self.model = fastText.train_supervised(
-            input=train_data,
-            lr=lr,
-            dim=dim,
-            ws=ws,
-            epoch=epoch,
-            minCount=minCount,
-            minCountLabel=minCountLabel,
-            minn=minn,
-            maxn=maxn,
-            neg=neg,
-            wordNgrams=wordNgrams,
-            loss=loss,
-            bucket=bucket,
-            thread=thread,
-            lrUpdateRate=lrUpdateRate,
-            t=t,
-            label=label,
-            verbose=verbose,
-            pretrainedVectors=pretrainedVectors,
-        )
-
-    def predict(self, text, k=1, threshold=0.0, on_unicode_error="strict"):
-        """
-        Given a string, get a list of labels and a list of
-        corresponding probabilities. k controls the number
-        of returned labels. A choice of 5, will return the 5
-        most probable labels. By default this returns only
-        the most likely label and probability. threshold filters
-        the returned labels by a threshold on probability. A
-        choice of 0.5 will return labels with at least 0.5
-        probability. k and threshold will be applied together to
-        determine the returned labels.
-        This function assumes to be given
-        a single line of text. We split words on whitespace (space,
-        newline, tab, vertical tab) and the control characters carriage
-        return, formfeed and the null character.
-        If the model is not supervised, this function will throw a ValueError.
-        If given a list of strings, it will return a list of results as usually
-        received for a single line of text.
-        """
-        r = self.model.predict(text)
-        return r
-
-    def predict_proba(self, text):
-        result_predict = self.model.predict(text)
-        if result_predict and len(result_predict) >= 2:
-            probas = result_predict[1]
-            if probas:
-                return probas[0]
-        return False
-
-    def score(self, X, y=None):
-        return sum(self.predict(X))
-
-    def quantize(
-        self,
-        input=None,
-        qout=False,
-        cutoff=0,
-        retrain=False,
-        epoch=None,
-        lr=None,
-        thread=None,
-        verbose=None,
-        dsub=2,
-        qnorm=False,
-    ):
-        self.model.quantize(
-            input, qout, cutoff, retrain, epoch, lr, thread, verbose, dsub, qnorm
-        )
-
-    def test(self, path, k=1):
-        """Evaluate supervised model using file given by path"""
-        return self.model.test(path, k)
-
-    def test_label(self, path, k=1, threshold=0.0):
-        """
-        Return the precision and recall score for each label.
-        The returned value is a dictionary, where the key is the label.
-        For example:
-        f.test_label(...)
-        {'__label__italian-cuisine' : {'precision' : 0.7, 'recall' : 0.74}}
-        """
-        return self.model.test_label(path, k, threshold)
-
-    def save_model(self, path):
-        """Save the model to the given path"""
-        self.model.save_model(path)
diff --git a/nautilus_nlp/models/fasttext_embedding.py b/nautilus_nlp/models/fasttext_embedding.py
deleted file mode 100644
index f3432b7..0000000
--- a/nautilus_nlp/models/fasttext_embedding.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import fastText
-import multiprocessing
-
-
-class FasttextEmbedding(object):
-    def __init__(self, path=None):
-        try:
-            self.model = fastText.load_model(path)
-        except Exception as e:
-            print(e)
-            self.model = None
-
-    def get_word_vector(self, word: str):
-        """
-        Return the wordvector of a word according to pretrained model
-
-        Parameters
-        ----------
-        word : string
-            the input document
-
-        Returns
-        -------
-        array
-        """
-
-        return self.model.get_word_vector(word)
-
-    def get_document_vector(self, document: str):
-        """
-        Return the wordvector of a full document according to pretrained model
-        To build the vector,  each word-wordvector is divided by its norm, then the array of vector is averaged
-        The document must be cleaned beforehand (no EOL)
-
-        Parameters
-        ----------
-        Input document : string
-            the input document
-
-        Returns
-        -------
-        array
-        """
-        return self.model.get_sentence_vector(document)
-
-    def train(
-        self,
-        input,
-        model="skipgram",
-        lr=0.05,
-        dim=100,
-        ws=5,
-        epoch=5,
-        minCount=5,
-        minCountLabel=0,
-        minn=3,
-        maxn=6,
-        neg=5,
-        wordNgrams=1,
-        loss="ns",
-        bucket=2000000,
-        thread=multiprocessing.cpu_count() - 1,
-        lrUpdateRate=100,
-        t=1e-4,
-        label="__label__",
-        verbose=2,
-        pretrainedVectors="",
-    ):
-        """
-        Train an unsupervised model and return a model object.
-        input must be a filepath. The input text does not need to be tokenized
-        as per the tokenize function, but it must be preprocessed and encoded
-        as UTF-8. You might want to consult standard preprocessing scripts such
-        as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html
-        The input field must not contain any labels or use the specified label prefix
-        unless it is ok for those words to be ignored. For an example consult the
-        dataset pulled by the example script word-vector-example.sh, which is
-        part of the fastText repository.
-        """
-        self.model.train_unsupervised(
-            input=input,
-            model=model,
-            lr=lr,
-            dim=dim,
-            ws=ws,
-            epoch=epoch,
-            minCount=minCount,
-            minCountLabel=minCountLabel,
-            minn=minn,
-            maxn=maxn,
-            neg=neg,
-            wordNgrams=wordNgrams,
-            loss=loss,
-            bucket=bucket,
-            thread=thread,
-            lrUpdateRate=lrUpdateRate,
-            t=t,
-            label=label,
-            verbose=verbose,
-            pretrainedVectors=pretrainedVectors,
-        )
diff --git a/nautilus_nlp/models/language_detector.py b/nautilus_nlp/models/language_detector.py
deleted file mode 100644
index cc3a0a9..0000000
--- a/nautilus_nlp/models/language_detector.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.models.fasttext_classifier import Fasttext_clf as langdetect
-from nautilus_nlp.preprocessing.text_preprocess import TextPreprocessor
-import pkg_resources
-
-lang_path = pkg_resources.resource_filename(
-    "nautilus_nlp.data", "lang_identification.ftz"
-)
-
-
-class LangDetector:
-    """ 
-    This class is to instantiante a language detector.
-    """
-
-    def __init__(self, path=None):
-        self.path = path if path is not None else lang_path
-        self.model = langdetect(self.path)
-
-    def detect_language(self, text_to_detect=None):
-        """
-        Detected the language of a text
-
-        Parameters
-        ----------
-        hint_language : string
-            language you expect your text to be
-
-        Returns
-        -------
-        is_reliable : 
-            is the top language is much better than 2nd best language?
-        language: 
-            2-letter code for the language of the text
-        """
-        preprocessor = TextPreprocessor(text_to_detect)
-        best_guesses = self.model.predict(preprocessor.remove_EOL_characters())
-        return best_guesses[0][0].replace("__label__", ""), best_guesses[1][0]
diff --git a/nautilus_nlp/models/nmf_model.py b/nautilus_nlp/models/nmf_model.py
deleted file mode 100644
index 005c09b..0000000
--- a/nautilus_nlp/models/nmf_model.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import time
-import numpy as np
-from numpy.linalg import norm
-from tqdm import tqdm
-
-'''
-Topic Modeling via NMF
-'''
-
-class NMF(object):
-    def __init__(
-            self,
-            A, IW=[], IH=[],
-            n_topic=10, max_iter=100, max_err=1e-3,
-            rand_init=True):
-        """
-        The objective of the NMF model is to approximate the term-document matrix A by two lower-rank matrices W and H.
-        The process is iterative and we denote IW and IH the the matrix W and H that are updated at each step.
-
-        :param A: The term-document matrix
-        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
-        and its elements are the weights of the corresponding keywords.
-        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
-        :param n_topic: Number of selected topics
-        :param max_iter: Maximum number of iterations to update W and H
-        :param max_err: maximum error under which we consider that the loop converged
-        :param rand_init: random init boolean
-        """
-        self.A = A
-        self.n_row = A.shape[0]
-        self.n_col = A.shape[1]
-
-        self.n_topic = n_topic
-        self.max_iter = max_iter
-        self.max_err = max_err
-
-        self.loss_hist = []
-        self.nmf_mat_init(rand_init)
-        self.nmf_iter()
-
-    def nmf_mat_init(self, rand_init):
-        """
-        Init Matrices W and H initially either randomly or using existing IW, IH matrices taken when iterating.
-        :param rand_init: Boolean indicating initial random init
-        """
-        if rand_init:
-            self.W = np.random.random((self.n_row, self.n_topic))
-            self.H = np.random.random((self.n_col, self.n_topic))
-        else:
-            self.W = IW
-            self.H = IH
-        for k in range(self.n_topic):
-            self.W[:, k] /= norm(self.W[:, k])
-
-    def nmf_iter(self):
-        """
-        Main iterative loop for matrix decomposition
-        """
-        loss_old = 1e20
-        start_time = time.time()
-        for i in tqdm(range(self.max_iter)):
-            self.nmf_solver()
-            loss = self.nmf_loss()
-            self.loss_hist.append(loss)
-
-            if loss_old - loss < self.max_err:
-                print('Matrix decomposition loop converged!')
-                break
-            loss_old = loss
-            end_time = time.time()
-            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
-
-    def nmf_solver(self):
-        '''
-        regular NMF without constraint.
-        Block Coordinate Decent
-        '''
-        epss = 1e-20
-
-        HtH = self.H.T.dot(self.H)
-        AH = self.A.dot(self.H)
-        for k in range(self.n_topic):
-            tmpW = self.W[:, k] * HtH[k, k] + AH[:, k] - np.dot(self.W, HtH[:, k])
-            self.W[:, k] = np.maximum(tmpW, epss)
-            self.W[:, k] /= norm(self.W[:, k]) + epss
-
-        WtW = self.W.T.dot(self.W)
-        AtW = self.A.T.dot(self.W)
-        for k in range(self.n_topic):
-            self.H[:, k] = self.H[:, k] * WtW[k, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
-            self.H[:, k] = np.maximum(self.H[:, k], epss)
-
-    def nmf_loss(self):
-        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
-        return loss
-
-    def get_loss(self):
-        return np.array(self.loss_hist)
-
-    def get_decomposition_matrix(self):
-        return self.W, self.H
diff --git a/nautilus_nlp/models/seanmf_model.py b/nautilus_nlp/models/seanmf_model.py
deleted file mode 100644
index 7248148..0000000
--- a/nautilus_nlp/models/seanmf_model.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-
-import time
-import numpy as np
-from numpy.linalg import norm
-from tqdm import tqdm
-
-
-class SeaNMF(object):
-    def __init__(
-            self,
-            A, S,
-            IW=[], IWc=[], IH=[],
-            alpha=1.0, beta=0.1, n_topic=10, max_iter=100, max_err=1e-3,
-            rand_init=True, fix_seed=False):
-        """
-        Seanmf is a topic modeling algorithm, paper:  http://dmkd.cs.vt.edu/papers/WWW18.pdf.
-        It finds an approximation to the term-document matrix A by two lower-rank matrices W and H,
-        at each iteration a context matrix Wc are computed and used to update W.
-        :param A: document term matrix
-        :param S: Word-context (semantic) correlation matrix
-        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
-        and its elements are the weights of the corresponding keywords.
-        :param IWc: Latent factor matrix of contexts.
-        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
-        :param alpha: Seanmf algorithm parameter
-        :param beta: Seanmf algorithm parameter
-        :param n_topic: Number of selected topics
-        :param max_iter: Maximum number of iterations to update W and H
-        :param max_err: maximum error under which we consider that the loop converged
-        :param rand_init: random init boolean
-        :param fix_seed: int number to fix random seed.
-        """
-        if fix_seed:
-            np.random.seed(0)
-
-        self.A = A
-        self.S = S
-
-        self.n_row = A.shape[0]
-        self.n_col = A.shape[1]
-
-        self.n_topic = n_topic
-        self.max_iter = max_iter
-        self.alpha = alpha
-        self.beta = beta
-        self.B = np.ones([self.n_topic, 1])
-        self.max_err = max_err
-        self.snmf_mat_init(rand_init, IW,  IWc, IH)
-        self.snmf_iter()
-
-    def snmf_mat_init(self, rand_init, IW=[], IWc=[], IH=[]):
-        """
-        Init Matrices W,Wc and H initially either randomly or using existing IW,IWc IH matrices taken when iterating.
-        :param rand_init: Boolean indicating initial random init
-        """
-        if rand_init:
-            self.W = np.random.random((self.n_row, self.n_topic))
-            self.Wc = np.random.random((self.n_row, self.n_topic))
-            self.H = np.random.random((self.n_col, self.n_topic))
-        else:
-            self.W = IW
-            self.Wc = IWc
-            self.H = IH
-        for k in range(self.n_topic):
-            self.W[:, k] /= norm(self.W[:, k])
-            self.Wc[:, k] /= norm(self.Wc[:, k])
-
-    def snmf_iter(self):
-        """
-        Main iterative loop for matrix decomposition
-        """
-        loss_old = 1e20
-        start_time = time.time()
-        for i in tqdm(range(self.max_iter)):
-            self.snmf_solver()
-            loss = self.snmf_loss()
-            if loss_old - loss < self.max_err:
-                print('Matrix decomposition loop converged!')
-                break
-            loss_old = loss
-            end_time = time.time()
-            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
-
-    def snmf_solver(self):
-        '''
-        using BCD framework
-        Alogorithm 1: Equations to update W, wc, H are described in the paper
-        http://dmkd.cs.vt.edu/papers/WWW18.pdf
-        '''
-
-        epss = 1e-20
-        # Update W
-        AH = np.dot(self.A, self.H)
-        SWc = np.dot(self.S, self.Wc)
-        HtH = np.dot(self.H.T, self.H)
-        WctWc = np.dot(self.Wc.T, self.Wc)
-        W1 = self.W.dot(self.B)
-
-        for k in range(self.n_topic):
-            num0 = HtH[k, k] * self.W[:, k] + self.alpha * WctWc[k, k] * self.W[:, k]
-            num1 = AH[:, k] + self.alpha * SWc[:, k]
-            num2 = np.dot(self.W, HtH[:, k]) + self.alpha * np.dot(self.W, WctWc[:, k]) + self.beta * W1[0]
-            self.W[:, k] = num0 + num1 - num2
-            self.W[:, k] = np.maximum(self.W[:, k], epss)  # project > 0
-            self.W[:, k] /= norm(self.W[:, k]) + epss  # normalize
-        # Update Wc
-        WtW = self.W.T.dot(self.W)
-        StW = np.dot(self.S, self.W)
-        for k in range(self.n_topic):
-            self.Wc[:, k] = self.Wc[:, k] + StW[:, k] - np.dot(self.Wc, WtW[:, k])
-            self.Wc[:, k] = np.maximum(self.Wc[:, k], epss)
-        # Update H
-        AtW = np.dot(self.A.T, self.W)
-        for k in range(self.n_topic):
-            self.H[:, k] = self.H[:, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
-            self.H[:, k] = np.maximum(self.H[:, k], epss)
-
-    def snmf_loss(self):
-        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
-        if self.alpha > 0:
-            loss += self.alpha * norm(np.dot(self.W, np.transpose(self.Wc)) - self.S, 'fro') ** 2 / 2.0
-        if self.beta > 0:
-            loss += self.beta * norm(self.W, 1) ** 2 / 2.0
-
-        return loss
-
-    def get_decomposition_matrix(self):
-        # Wc was not considered to keep same structure as NMF
-        return self.W, self.H
-
-
diff --git a/nautilus_nlp/models/sentiment_detector.py b/nautilus_nlp/models/sentiment_detector.py
deleted file mode 100644
index 146eeac..0000000
--- a/nautilus_nlp/models/sentiment_detector.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.utils.tokenizer import _convert_tokens_to_string, _convert_string_to_tokens
-
-import textblob
-from textblob import Blobber
-from textblob import TextBlob
-from textblob_fr import PatternTagger, PatternAnalyzer
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-
-
-def compute_sentiment_score(tokens_or_txt, lang_module:str='en_textblob')->float:
-    """
-    Compute a sentiment score using pre-trained lexicons: TextBlob or Vader.
-    Output a score from -1 to 1, depending if the text is negative neutral
-    of positive.
-
-    Parameters
-    ----------
-    tokens_or_txt
-        the text to be processed
-
-    lang_module
-        ('fr_textblob','en_textblob','en_vader')
-
-    Returns
-    -------
-    float 
-        Polarity score from -1 to 1
-    """
-    text = _convert_tokens_to_string(tokens_or_txt)
-    output = ''
-    if lang_module is 'fr_textblob':
-        tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())
-        blob = tb(text)
-        output = blob.sentiment[0]
-
-    elif lang_module is 'en_textblob':
-        blob = TextBlob(text)
-        output = blob.sentiment.polarity
-
-    elif lang_module is 'en_vader':
-        analyser = SentimentIntensityAnalyzer()
-        snt = analyser.polarity_scores(text)
-        output = snt['compound']
-    else:
-        raise ValueError('Please enter a valid module name!')
-
-    assert output != ''
-    return output
\ No newline at end of file
diff --git a/nautilus_nlp/models/sk_vectorizer.py b/nautilus_nlp/models/sk_vectorizer.py
deleted file mode 100644
index 926735a..0000000
--- a/nautilus_nlp/models/sk_vectorizer.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.base import BaseEstimator, ClassifierMixin
-
-
-class sk_vectorizer(BaseEstimator, ClassifierMixin):
-    def __init__(self):
-        self.model = CountVectorizer()
-
-    def fit(self, train_data):
-        self.model.fit(train_data)
-
-    def fit_transform(self, train_data):
-        self.model.fit_transform(train_data)
-        return train_data
-
-    def transform(self, data):
-        return self.model.transform(data)
diff --git a/nautilus_nlp/models/text_vectorizers.py b/nautilus_nlp/models/text_vectorizers.py
deleted file mode 100644
index dec03dc..0000000
--- a/nautilus_nlp/models/text_vectorizers.py
+++ /dev/null
@@ -1,295 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import spacy
-from gensim.corpora import Dictionary
-from gensim.models.tfidfmodel import TfidfModel
-from gensim import corpora, models, similarities
-from gensim.matutils import sparse2full
-import numpy as np
-import math
-
-from sklearn.feature_extraction.text import TfidfVectorizer as _TfidfVectorizer
-
-
-class TfidfTextVectorizer(object):
-    """
-    Convert a collection of raw documents to a matrix of TF-IDF features.
-
-    Inputs a list of string. 
-    and the list of feature name.
-    Wrapper of https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
-
-    Parameters
-    ----------
-    input=’content’, encoding=’utf-8’, decode_error=’strict’, strip_accents=None,
-    lowercase=True, preprocessor=None, tokenizer=None, analyzer=’word’,
-    stop_words=None, token_pattern=’(?u)\b\w\w+\b’, ngram_range=(1, 1),
-    max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False,
-    dtype=<class ‘numpy.float64’>, norm=’l2’, use_idf=True, smooth_idf=True, sublinear_tf=False
-    """
-    
-    def __init__(self, **kwargs):
-        self.tfidf_vectorizer = _TfidfVectorizer(**kwargs)
-
-
-    def _get_features_name(self):
-        '''
-        Array mapping from feature integer indices to feature name
-        '''
-        self.feature_names = self.tfidf_vectorizer.get_feature_names()
-        return self.feature_names
-
-
-    def compute_tfidf(self, raw_documents):
-        '''
-        Learn vocabulary and idf, return term-document matrix.
-
-        Input a list of documents (string)
-        Output the wordcount vector matrix.
-
-        params
-        ------
-        raw_documents : iterable
-            an iterable which yields either str, unicode or file objects
-
-        returns
-        -------
-        X : sparse matrix, [n_samples, n_features]
-            Tf-idf-weighted document-term matrix.
-        '''
-        self.word_count_vector = self.tfidf_vectorizer.fit_transform(raw_documents)
-        self._get_features_name()
-        return self.word_count_vector    
-
-
-    def apply_tfidf_to_documents(self, raw_document):
-        '''
-        Apply the tf-idf weights to a document or a list of documents.
-        Equivalent to .transform() method in sci-kit learn.
-        
-        Uses the vocabulary and document frequencies (df) learned by fit 
-        (or fit_transform), and convert documents to document-term matrix.
-
-        parameters
-        ---------
-        raw_documents : iterable or document
-            an iterable which yields either str, unicode or file objects, or 
-        a string.
-
-        Returns
-        -------
-        X : sparse matrix, [n_samples, n_features]
-            Tf-idf-weighted document-term matrix.
-        '''
-        if type(raw_document) == str:
-            raw_document = [raw_document]
-        return self.tfidf_vectorizer.transform(raw_document)
-    
-    
-    def _sort_coo(self, coo_matrix):
-        '''sort the tf-idf vectors by descending order of scores'''
-        tuples = zip(coo_matrix.col, coo_matrix.data)
-        return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
-    
-    
-    def _extract_topn_from_vector(self, feature_names, sorted_items, topn=10):
-        """get the feature names and tf-idf score of top n items"""
-
-        #use only topn items from vector
-        sorted_items = sorted_items[:topn]
-
-        score_vals = []
-        feature_vals = []
-
-        # word index and corresponding tf-idf score
-        for idx, score in sorted_items:
-
-            #keep track of feature name and its corresponding score
-            score_vals.append(round(score, 3))
-            feature_vals.append(feature_names[idx])
-
-        #create a tuples of feature,score
-        #results = zip(feature_vals,score_vals)
-        results= {}
-        for idx in range(len(feature_vals)):
-            results[feature_vals[idx]]=score_vals[idx]
-
-        return results
-
-
-    def get_top_tfidf_per_doc(self, text:str, n:int=10)->list:
-        '''
-        compute TF-IDF for a given doc, and returns a list of the top N weighted words
-
-        parameters
-        ---------
-        text : sparse matrix, [n_samples, n_features]
-            If specified, will return the top weighted words for the given matrix,
-            otherwise will give the result of the tf-idf matrix
-        a string.
-
-        n : number of terms to display
-
-        Returns
-        -------
-        top-n weighted terms : dict
-            List of top terms for the given document
-        '''
-        tf_idf_vector= self.apply_tfidf_to_documents([text])
-        sorted_items=self._sort_coo(tf_idf_vector.tocoo())
-        return list(self._extract_topn_from_vector(self.feature_names, sorted_items, n).keys())
-    
-
-    def get_top_tfidf(self, tfidf_matrix=None, n:int=10)->list:
-        '''
-        Input a tf-idf matrix, and returns a dict of the top N weighted words,
-        with their weight.
-
-        parameters
-        ---------
-        tfidf_matrix : sparse matrix, [n_samples, n_features]
-            If specified, will return the top weighted words for the given matrix,
-            otherwise will give the result of the tf-idf matrix
-        a string.
-
-        Returns
-        -------
-        top-n weighted terms : dict
-            Dict of top terms and their associated weighted.
-        '''
-        if tfidf_matrix is None:
-            tfidf_matrix = self.word_count_vector
-        return self._extract_topn_from_vector(self.feature_names, self._sort_coo(tfidf_matrix.tocoo()), topn=n)
-
-
-class GensimTextVectorizer(object):
-    '''
-    Gensim's implementation of TF-IDF, BOW models etc. 
-    '''
-    def __init__(self, doc_list):
-        # Initialize
-        self.doc_list = doc_list
-        self.nlp, self.docs, self.docs_dict = self._preprocess(self.doc_list)
-
-    # Functions to lemmatise docs
-    def _keep_token(self, t):
-        return t.is_alpha and not (t.is_space or t.is_punct or t.is_stop or t.like_num)
-
-    def _lemmatize_doc(self, doc):
-        return [t.lemma_ for t in doc if self._keep_token(t)]
-
-    # Gensim to create a dictionary and filter out stop and infrequent words (lemmas).
-    def _get_docs_dict(self, docs):
-        docs_dict = Dictionary(docs)
-        # CAREFUL: For small corpus please carefully modify the parameters for filter_extremes, or simply comment it out.
-        docs_dict.filter_extremes(no_below=5, no_above=0.2)
-        docs_dict.compactify()
-        return docs_dict
-
-    # Preprocess docs
-    def _preprocess(self, doc_list):
-        # Load spacy model
-        nlp = spacy.load("en")
-        # lemmatise docs
-        docs = [self._lemmatize_doc(nlp(doc)) for doc in doc_list]
-        # Get docs dictionary
-        docs_dict = self._get_docs_dict(docs)
-        return nlp, docs, docs_dict
-
-
-    def _get_tfidf(self, docs, docs_dict):
-        '''
-        Gensim can again be used to create a bag-of-words representation of each document,
-        Build the TF-IDF model, and compute the TF-IDF vector for each document.
-        '''
-        docs_corpus = [docs_dict.doc2bow(doc) for doc in docs]
-        model_tfidf = TfidfModel(docs_corpus, id2word=docs_dict)
-        docs_tfidf = model_tfidf[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(docs_dict)) for c in docs_tfidf])
-        return docs_vecs
-
-    def _document_vector(self, doc, docs_dict, nlp):
-        """
-        Get avg w2v for one document. Remove out-of-vocabulary words.
-        """
-        
-        doc_vector = [nlp(word).vector for word in doc if word in docs_dict.token2id]
-        return np.mean(doc_vector, axis=0)
-
-
-    def avg_wv(self):
-        '''
-        Get average vector for document list
-        '''
-        docs_vecs = np.vstack(
-            [self._document_vector(doc, self.docs_dict, self.nlp) for doc in self.docs]
-        )
-        return docs_vecs
-
-
-    def get_tfidf(self):
-        '''
-        Get TF-IDF vector for document list
-        '''
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_tfidf = TfidfModel(docs_corpus, id2word=self.docs_dict)
-        docs_tfidf = model_tfidf[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_tfidf])
-        return docs_vecs
-
-    
-    def get_lsi(self, num_topics=300):
-        '''
-        Get Latent Semantic Indexing(LSI) vector for document list
-        '''
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_lsi = models.LsiModel(docs_corpus, num_topics, id2word=self.docs_dict)
-        docs_lsi = model_lsi[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lsi])
-        return docs_vecs
-
-    
-    def get_rp(self):
-        '''
-        Get Random Projections(RP) vector for document list
-        '''
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_rp = models.RpModel(docs_corpus, id2word=self.docs_dict)
-        docs_rp = model_rp[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_rp])
-        return docs_vecs
-
-    def get_lda(self, num_topics=100):
-        '''
-        Get Latent Dirichlet Allocation(LDA) vector for document list
-        '''
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_lda = models.LdaModel(docs_corpus, num_topics, id2word=self.docs_dict)
-        docs_lda = model_lda[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_lda])
-        return docs_vecs
-
-    def get_hdp(self):
-        '''
-        Get Hierarchical Dirichlet Process(HDP) vector for document list
-        '''
-        docs_corpus = [self.docs_dict.doc2bow(doc) for doc in self.docs]
-        model_hdp = models.HdpModel(docs_corpus, id2word=self.docs_dict)
-        docs_hdp = model_hdp[docs_corpus]
-        docs_vecs = np.vstack([sparse2full(c, len(self.docs_dict)) for c in docs_hdp])
-        return docs_vecs
diff --git a/nautilus_nlp/models/topic_modeling.py b/nautilus_nlp/models/topic_modeling.py
deleted file mode 100644
index 686c824..0000000
--- a/nautilus_nlp/models/topic_modeling.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import gensim
-import logging
-import os
-import pyLDAvis
-import pyLDAvis.gensim 
-from gensim.models import CoherenceModel
-from gensim.models.wrappers import LdaMallet
-import matplotlib.pyplot as plt
-
-from IPython.display import HTML
-
-logging.getLogger("gensim").setLevel(logging.WARNING)
-
-
-def create_dictionary(data):
-    """ 
-    Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
-    
-    Parameters
-    ----------
-    data : list of list of tokens
-       
-    Returns
-    -------
-    list of list of tuples
-    """
-    return gensim.corpora.Dictionary(data)
-
-def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
-    """ 
-    Remove very rare and very common words
-
-    Parameters
-    ----------
-    dictionary: dictionary containing the number of times a word appears in the dataset set
-    no_below : int, optional
-    Keep tokens which are contained in at least `no_below` documents.
-    no_above : float, optional
-    Keep tokens which are contained in no more than `no_above` documents
-    (fraction of total corpus size, not an absolute number).
-
-    (Add to docstring) + other func
-    """
-    return dictionary.filter_extremes(no_below=no_below, no_above=no_above, **kwargs)
-
-
-def create_bow_corpus(data, dictionary):
-    
-    """ 
-    Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
-    The produced corpus is a mapping of (token_id, token_count).
-
-    Parameters
-    ----------
-    data : list of list of tokens
-       
-    Returns
-    -------
-    list of list of tuples
-    """
-    texts = data
-    corpus = [dictionary.doc2bow(text) for text in texts]
-    return corpus
-
-### Find Number of topics
-
-def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, step=4):
-    """
-    Compute c_v coherence for various number of topics
-
-    /!\ It takes a really long time.
-
-    Parameters:
-    ----------
-    dictionary : Gensim dictionary
-    bow_corpus : Gensim bow corpus
-    texts : List of input texts
-    limit : Max num of topics
-
-    Returns:
-    -------
-    model_list : List of LDA topic models
-    coherence_values : Coherence values corresponding to the LDA model with respective number of topics
-    """
-    coherence_values = []
-    model_list = []
-    for num_topics in range(start, limit, step):
-        model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus,
-                                           id2word=dictionary,
-                                          num_topics=num_topics, 
-                                          random_state=0,
-                                          update_every=5,
-                                          chunksize=1000,
-                                          passes=10)
-        model_list.append(model)
-        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')
-        coherence_values.append(coherencemodel.get_coherence())
-
-    return model_list, coherence_values
-
-def plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4):
-    """
-    Plot the coherence scores per number of topics
-
-    Parameters:
-    ----------
-    coherence_values : list of coherence scores for various number of topics
-    start : int. Min num of topics
-    limit : int. Max num of topics
-    step: int
-
-    Output:
-    -------
-    Lineplot
-    """
-    x = range(start, limit, step)
-    plt.plot(x, coherence_values)
-    plt.xlabel("Num Topics")
-    plt.ylabel("Coherence score")
-    plt.legend(("coherence_values"), loc='best')
-    return plt.show()
-
-def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
-    """
-    Print the coherences scores for the ldamodels that had been tested with different number of topics
-    """
-    x = range(start, limit, step)
-    for m, cv in zip(x, coherence_values):
-        print("Num Topics =", m, " has Coherence Value of", round(cv, 4))
-
-
-### LdaModel: Gensim & Mallet
-
-def train_lda_model(bow_corpus, dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
-    """ Train the lda model on the corpus
-      
-    Parameters
-    ----------
-    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).
-    dictionary: corpora.Dictionary. Mapping from word IDs to words
-    num_topics: int
-    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
-    mallet_path: str, optionnal if model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file 
-    
-    Returns
-    -------
-    gensim.ldamodel
-    """
-    if model == 'gensim':
-        model = train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs)
-    elif model == 'mallet':
-        if mallet_path is None:
-            raise ValueError('You must precise the path to the mallet-2.0.8 file that has been downloaded before')
-        else:
-            model = train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs)
-    else:
-        raise ValueError('Please enter a valid model name: gensim or mallet')
-    return model
-
-def train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs):
-
-    model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0, **kwargs)
-    return model
-
-def train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs):
-    
-    os.environ['MALLET_PATH'] = mallet_path
-    mallet = '$MALLET_PATH/mallet-2.0.8/bin/mallet'
-    model = gensim.models.wrappers.LdaMallet(mallet, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='composant', random_seed=0, **kwargs)
-    return model
-
-
-def save_model(model, model_name):
-    """ 
-    Save the model that has been trained. The model will be saved on your current emplacement.
-        
-    Parameters
-    ----------
-    model: ldamodel
-    model_name: str. 
-        Name the model that will be saved
-    """
-    return model.save(os.path.join(model_name))
-
-
-def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
-    """
-    Detected the language of a text
-
-    Parameters
-    ----------
-    model_path: str
-        path where the model has been saved
-    model_name: str
-        name of the saved model
-    model : str
-        Precise the topic modeling model wanted, must be "gensim" or "mallet"
-    model_prefix : str
-        By default, 'composant' default prefix used while saving the mallet model with train_lda_model function.         
-    
-    Returns
-    -------
-    is_reliable : 
-        is the top language is much better than 2nd best language?
-    language: 
-        2-letter code for the language of the text
-    """
-    if model =='gensim':
-        ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
-    elif model =='mallet':
-        ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
-        if model_prefix is not None:
-            ldamodel.prefix = model_path+'/'+ model_prefix
-    else:
-        raise ValueError('Please enter a valid model name: gensim or mallet')
-    return ldamodel
-
-def fit_data(model, bow):
-    """Test the model on new, unseen documents"""
-    return model.get_document_topics(bow, minimum_probability=0)
-
-
-# Visualization
-
-
-def visualize_topics(model, bow_corpus, dictionary, model_type=None):
-    """ 
-    Visualize the topics-keywords with the pyLDAvis interactive chart.
-        (Work well in notebook)
-        
-    Parameters
-    ----------
-    model: LDA model: gensim or mallet
-    bow_corpus : iterable of list of tokens. 
-    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
-    model : str. Precise the topic modeling model used, must be "gensim" or "mallet"
-    
-    Returns:
-    ----------
-    3D interactive chart
-    """
-    if model_type == 'mallet':
-        model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
-    elif model_type == 'gensim':
-        model_vis = model
-    elif model_type is None:
-        raise ValueError('You forgot to precise your model type, it must be: gensim or mallet')
-    else:
-        raise ValueError('Please enter a valid model name: gensim or mallet') 
-    return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
-
-def save_pyldavis(pyldavis, vis_path, vis_name):
-    """ 
-    Save the pyldavis interactive chart
-    
-    Parameters
-    ----------
-    pyldavis: pyLDAvis._prepare.PreparedData
-    vis_path: str
-    vis_name: str
-    """ 
-    return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name + '{}'.format('.html')))
-
-
-
-def show_pyldavis(vis_path, vis_name):
-    """ 
-    Display the HTML of the saved pyldavis interactive chart
-
-    Parameters
-    ----------
-    vis_path: str
-    vis_name: str
-    """
-    return HTML(filename=os.path.join(vis_path, vis_name + '{}'.format('.html')))
-
-def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
-    """ Print the dominant topics in the document, its score and the topics' top keywords.
-    
-    Quick way to interpret the topics
-
-    Parameters
-    ----------
-    gensim.ldamodel
-    model: ldamodel
-    bow_corpus: iterable of list of tokens.
-    topic_number: int. Pick the number of topics displayed
-    topn: int. Number of topics' top keyword displayed
-
-    """
-    i = 0
-    for index, score in sorted(model[bow_corpus], key=lambda tup: -1*tup[1]): 
-        weight = model.show_topic(index, topn=topn)
-        keywords = [i[0] for i in weight]
-        print("Score: {}\t Topic: {}".format(score, keywords))
-        i +=1
-        if i == topic_number:
-            break
diff --git a/nautilus_nlp/models/topic_modeling_short_text.py b/nautilus_nlp/models/topic_modeling_short_text.py
deleted file mode 100644
index 73fad26..0000000
--- a/nautilus_nlp/models/topic_modeling_short_text.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import re
-from collections import Counter
-from itertools import product
-from nautilus_nlp.models.nmf_model import *
-from nautilus_nlp.models.seanmf_model import *
-import numpy as np
-import pyLDAvis
-
-
-def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
-    """
-    :param text: list of str on which the topic modeling will be performed
-    :param vocab_min_count: minimum number of occurrences of a word to be considered in the vocabulary
-    :param vocab_max_size: maximum number of word in the vocabulary
-    :return:  encoded_text_id: list of encoded sentences using vocab IDs
-              vocab_list: list of vocabulary
-              vocab_arr: array with vocab frequency counts
-    """
-    vocab = {}
-
-    # Tokens_list is a list of sub-lists where each sub-list contains a sentences' tokens.
-    tokens_list=[]
-    for sentence in text:
-        sentence = re.split('\s', sentence)
-        tokens_list.append(sentence)
-    vocab = dict(Counter(x for xs in tokens_list for x in xs))
-
-    # Create Vocab array ( list of sorted vocab + counts )
-    vocab_arr = [[wd, vocab[wd]] for wd in vocab if vocab[wd] > vocab_min_count]
-    vocab_arr = sorted(vocab_arr, key=lambda k: k[1])[::-1]
-    vocab_arr = vocab_arr[:vocab_max_size]
-    vocab_arr = sorted(vocab_arr)
-
-    vocab_list = list(map(lambda x:x[0], vocab_arr))
-    # Create Vocab to ID dictionnary
-    vocab2id = {itm[1][0]: itm[0] for itm in enumerate(vocab_arr)}
-
-    # Create ID representation of text (ie: each sentence is a list of vocabId )
-    encoded_text_id = []
-    for sentence in text:
-        sentence = re.split('\s', sentence)
-        sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
-        encoded_text_id.append(sentence)
-
-    return encoded_text_id, vocab_list, vocab_arr
-
-
-def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20, max_iter=20, max_err=0.1, alpha=0, beta=0):
-    """
-    :param model_name: string = 'nmf' or 'seanmf'
-    :param encoded_text_id: list of encoded sentences
-    :param vocab_list: list of vocabulary
-    :param n_topics: number of topics
-    :param max_iter: maximum number of iterations while training
-    :param max_err: training error
-    :param alpha: regularization param for the NMF model
-    :param beta: regularization param for the NMF model
-    :return: Trained NMF model
-    """
-
-    n_docs = len(encoded_text_id)
-    n_terms = len(vocab_list)
-
-    if model_name == 'nmf':
-        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
-        model = NMF(
-            dt_mat,
-            n_topic=n_topics,
-            max_iter=max_iter,
-            max_err=max_err)
-
-    elif model_name == 'seanmf':
-        # Calculate co-occurence matrix
-        cm = __build_cooccurence_matrix(n_terms, encoded_text_id)
-        # Calculate PPMI
-        SS = __calulate_PPMI(cm, n_terms)
-        # Build doc-term matrix
-        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
-        model = SeaNMF(
-            dt_mat, SS,
-            alpha=alpha,
-            beta=beta,
-            n_topic=n_topics,
-            max_iter=max_iter,
-            max_err=max_err,
-            fix_seed=1024)
-
-    else:
-        model = None
-        print('Invalid model name: Use nmf or seanmf')
-
-    return model
-
-
-def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
-    dt_mat = np.zeros([n_terms, n_docs])
-    for k in range(n_docs):
-        for j in encoded_text_id[k]:
-            dt_mat[j, k] += 1.0
-    return dt_mat
-
-
-def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
-    """
-    Computes the PMi score for each topic and the topKeywords describing each of them.
-    :param model: trained NMF model
-    :param encoded_text_id: list of encoded sentences
-    :param vocab_list: list of vocabulary
-    :return: topics = dictionnary with the topic number and its topkeywords
-             pmi_score = dictionnary with the topic number and its PMI score
-    """
-
-    dt_mat = __build_cooccurence_matrix(n_terms=len(vocab_list), encoded_text_id=encoded_text_id)
-    np.fill_diagonal(dt_mat, 0)
-    W,_ = model.get_decomposition_matrix()
-    n_topic = W.shape[1]
-    PMI_arr = []
-    for k in range(n_topic):
-        top_keywords_index = W[:, k].argsort()[::-1][:n_topKeyword]
-        PMI_arr.append(__calculate_PMI(dt_mat, top_keywords_index))
-
-    index = np.argsort(PMI_arr)
-    topics = {}
-    pmi_score = {}
-    for k in index:
-        words = []
-        for w in np.argsort(W[:, k])[::-1][:n_topKeyword]:
-            words.append(vocab_list[w])
-        # Complete the topic and the score dicts. Format {Topic_number: words or score}
-        topics[k] = words
-        pmi_score[k] = PMI_arr[k]
-
-    return topics, pmi_score
-
-
-def get_assigned_topics(model):
-    """
-    Assign the topic number to the sentences used when training the model
-    :param model: trained model for short text
-    :return topics_list: list having the same length as the training text containing topics assigned to each sentence.
-    """
-
-    _, H = model.get_decomposition_matrix()
-    # The weights of the H matrix are converted into probabilities
-    H_probs = H / H.sum(axis=1, keepdims=True)
-    topics_list = list(np.argmax(H_probs, axis=1))
-
-    return topics_list
-
-
-def show_pyldavis(model, encoded_text_id, vocab_arr):
-    """
-    :param model: trained model
-    :param encoded_text_id: encoded_text_id: list of encoded sentences
-    :param vocab_arr: array of vocabulary frequency
-    :return: pyldavis topics plot
-    """
-
-    data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
-    vis_data = pyLDAvis.prepare(**data)
-
-    return pyLDAvis.display(vis_data)
-
-
-def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
-    """
-    Transform the model decomposed matrix to create topic term and document topics matrices
-    and prepare data to feed pyldavis.
-    link : http://jeriwieringa.com/2018/07/17/pyLDAviz-and-Mallet/
-    :return dict of data needed by pyldavis
-    """
-
-    # 1 List of documents lengths
-    doc_length_values=[]
-    for doc in encoded_text_id:
-        doc_length_values.append(len(doc))
-    # 2 List of vocab
-    list_vocab = list(map(lambda x: x[0], vocab_arr))
-    # 3 List of vocab. Frequency
-    freq_vocab = list(map(lambda x: x[1], vocab_arr))
-    W, H = model.get_decomposition_matrix()
-    # Normlize the decomposition to get probabilities
-    W_probs = W / W.sum(axis=1, keepdims=True)
-    # 4 topic term matrix phi
-    phi = W_probs.T
-    # 5 document term matrix theta
-    theta = H / H.sum(axis=1, keepdims=True)
-
-    data = {'topic_term_dists': phi,
-            'doc_topic_dists': theta,
-            'doc_lengths': doc_length_values,
-            'vocab': list_vocab,
-            'term_frequency': freq_vocab
-            }
-
-    return data
-
-
-def __build_cooccurence_matrix(n_terms, encoded_text_id):
-    """
-    The cooccurence matrix represents the number of times each word
-    appeared in the same context as another word from the vocabulary.
-    The matrix has n_terms x n_terms size, columns and rows denote the vocab.
-    Cell values represent the number of times words occured together in the same sentence.
-    :param :encoded_text_id : list of encoded sentences
-    :return: res: the co-occurence matrix
-
-    """
-    res = np.zeros([n_terms, n_terms])
-    for row in encoded_text_id:
-        counts = Counter(row)
-        for key_from, key_to in product(counts, repeat=2):
-            res[key_from, key_to] += counts[key_from] * counts[key_to]
-    return res
-
-
-def __calulate_PPMI(cm, n_terms):
-    D1 = np.sum(cm)
-    print('D1= ', D1)
-    SS = D1 * cm
-    print('SS= ',SS)
-    for k in range(n_terms):
-        SS[k] /= np.sum(cm[k])
-    for k in range(n_terms):
-        SS[:, k] /= np.sum(cm[:, k])
-    print('SS = ', SS )
-    cm = []  # release memory
-    SS[SS == 0] = 1.0
-    SS = np.log(SS)
-    SS[SS < 0.0] = 0.0
-    return SS
-
-
-def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
-    dt_mat = np.zeros([n_terms, n_docs])
-    for k in range(n_docs):
-        for j in encoded_text_id[k]:
-            dt_mat[j, k] += 1.0
-    return dt_mat
-
-
-def __calculate_PMI(AA, topKeywordsIndex):
-    '''
-    Method to compute PMi score
-    Reference:
-    Short and Sparse Text Topic Modeling via Self-Aggregation
-    '''
-
-    D1 = np.sum(AA)
-    n_tp = len(topKeywordsIndex)
-    PMI = []
-    for index1 in topKeywordsIndex:
-        for index2 in topKeywordsIndex:
-            if index2 < index1:
-                if AA[index1, index2] == 0:
-                    PMI.append(0.0)
-                else:
-                    C1 = np.sum(AA[index1])
-                    C2 = np.sum(AA[index2])
-                    PMI.append(np.log(AA[index1,index2]*D1/C1/C2))
-    avg_PMI = 2.0*np.sum(PMI)/float(n_tp)/(float(n_tp)-1.0)
-
-    return avg_PMI

From 11c26e45137595e2f46dae034b04d93f7f9144ae Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:42:26 +0200
Subject: [PATCH 260/496] moved all topic modeling in specific folder

---
 nautilus_nlp/topic_modeling/biterm_model.py   | 162 +++++++++
 nautilus_nlp/topic_modeling/lda.py            | 315 ++++++++++++++++++
 nautilus_nlp/topic_modeling/nmf_model.py      | 118 +++++++
 nautilus_nlp/topic_modeling/seanmf_model.py   | 148 ++++++++
 .../topic_modeling_short_text.py              | 280 ++++++++++++++++
 5 files changed, 1023 insertions(+)
 create mode 100644 nautilus_nlp/topic_modeling/biterm_model.py
 create mode 100644 nautilus_nlp/topic_modeling/lda.py
 create mode 100644 nautilus_nlp/topic_modeling/nmf_model.py
 create mode 100644 nautilus_nlp/topic_modeling/seanmf_model.py
 create mode 100644 nautilus_nlp/topic_modeling/topic_modeling_short_text.py

diff --git a/nautilus_nlp/topic_modeling/biterm_model.py b/nautilus_nlp/topic_modeling/biterm_model.py
new file mode 100644
index 0000000..8aa1ab4
--- /dev/null
+++ b/nautilus_nlp/topic_modeling/biterm_model.py
@@ -0,0 +1,162 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+import numpy as np
+from biterm.btm import oBTM
+from biterm.utility import vec_to_biterms, topic_summuary
+from sklearn.feature_extraction.text import CountVectorizer
+import pyLDAvis
+
+
+class BitermModel:
+
+    def __init__(self, data, nb_topics, nb_iteration, lang):
+        """
+        Model for topic modelling. Particularly useful for short texts.
+
+        Parameters
+        ----------
+        data : list
+            a list of string, each string can be a document
+        nb_topics : positive int
+
+        nb_iteration : positive int
+
+        lang : str
+            _language to remove the stop words, can be setup to None
+
+        Returns
+        -------
+        string
+            the text with removed multiple spaces and strip text
+        """        
+        self.is_int_positive(nb_topics)
+        self.is_int_positive(nb_iteration)
+        self.is_list_of_string(data)
+
+        self.data = data
+        self.nb_topics = nb_topics
+        self.nb_iteration = nb_iteration
+        self.lang = lang
+        self._topics = None
+        self._btm = None
+        self._vectorize_text = None
+        self._vocabulary = None
+
+    @staticmethod
+    def is_int_positive(number):
+        """
+        Function to check if the input parameter is a integer and positive 
+        otherwise raise an error
+
+        Parameters
+        ----------
+        number : str
+            
+        Returns
+        -------
+        str:
+            the text with removed multiple spaces and strip text
+            
+        """  
+        if not isinstance(number, int):
+            raise ValueError("Parameter {} has to be an integer".format(number))
+        if number < 1:
+            raise ValueError("Parameter {} has to be positive".format(number))
+
+    @staticmethod
+    def is_list_of_string(data):
+        """
+        Function to check if the input parameter is a list of strings otherwise raise an error
+        
+        Parameters
+        ----------
+        data
+        
+        Returns
+        -------
+        """
+        if not isinstance(data, list):
+            raise ValueError("{} has to be a list".format(data))
+        if len(data) == 0:
+            raise ValueError("{} is empty".format(data))
+        for document in data:
+            if not isinstance(document, str):
+                raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
+
+    def compute_topics(self, nb_word_per_cluster):
+        """
+        Main function computing the topic modeling, topics
+        
+        Parameters
+        ----------
+        nb_word_per_cluster : positive integer
+        
+        Returns
+        -------
+        dict :    
+            a dictionary containing the the different topics with the top words 
+            and coherence associated
+        """
+        vec = CountVectorizer(stop_words=self.lang)
+        self._vectorize_text = vec.fit_transform(self.data).toarray()
+        self._vocabulary = np.array(vec.get_feature_names())
+
+        biterms = vec_to_biterms(self._vectorize_text)
+        self._btm = oBTM(num_topics=self.nb_topics, V=self._vocabulary)
+        self._topics = self._btm.fit_transform(biterms, iterations=self.nb_iteration)
+
+        results = topic_summuary(self._btm.phi_wz.T, self._vectorize_text, self._vocabulary, nb_word_per_cluster, verbose=False)
+
+        return results
+
+    def get_document_topic(self, index):
+        """
+        Get the cluster associated to the specified document
+                
+        Parameters
+        ----------
+        index : positive integer
+            the document index
+
+        Returns
+        -------
+        the cluster index
+        """
+        if self._topics is None:
+            raise ValueError("Model needs to be trained first")
+
+        return self._topics[index].argmax()
+
+    def save_pyLDAvis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.html'):
+        """
+        Function saving the pyLDAvis plot associated with the compute_topics function
+                
+        Parameters
+        ----------
+        path_to_output : str
+            path to save the plut, must be a html file
+
+        Returns
+        -------
+        """        
+        if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
+            raise ValueError("Model needs to be trained first")
+
+        vis = pyLDAvis.prepare(self._btm.phi_wz.T, self._topics, np.count_nonzero(self._vectorize_text, axis=1), self._vocabulary,
+                               np.sum(self._vectorize_text, axis=0))
+        pyLDAvis.save_html(vis, path_to_output)
diff --git a/nautilus_nlp/topic_modeling/lda.py b/nautilus_nlp/topic_modeling/lda.py
new file mode 100644
index 0000000..686c824
--- /dev/null
+++ b/nautilus_nlp/topic_modeling/lda.py
@@ -0,0 +1,315 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+import gensim
+import logging
+import os
+import pyLDAvis
+import pyLDAvis.gensim 
+from gensim.models import CoherenceModel
+from gensim.models.wrappers import LdaMallet
+import matplotlib.pyplot as plt
+
+from IPython.display import HTML
+
+logging.getLogger("gensim").setLevel(logging.WARNING)
+
+
+def create_dictionary(data):
+    """ 
+    Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
+    
+    Parameters
+    ----------
+    data : list of list of tokens
+       
+    Returns
+    -------
+    list of list of tuples
+    """
+    return gensim.corpora.Dictionary(data)
+
+def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
+    """ 
+    Remove very rare and very common words
+
+    Parameters
+    ----------
+    dictionary: dictionary containing the number of times a word appears in the dataset set
+    no_below : int, optional
+    Keep tokens which are contained in at least `no_below` documents.
+    no_above : float, optional
+    Keep tokens which are contained in no more than `no_above` documents
+    (fraction of total corpus size, not an absolute number).
+
+    (Add to docstring) + other func
+    """
+    return dictionary.filter_extremes(no_below=no_below, no_above=no_above, **kwargs)
+
+
+def create_bow_corpus(data, dictionary):
+    
+    """ 
+    Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
+    The produced corpus is a mapping of (token_id, token_count).
+
+    Parameters
+    ----------
+    data : list of list of tokens
+       
+    Returns
+    -------
+    list of list of tuples
+    """
+    texts = data
+    corpus = [dictionary.doc2bow(text) for text in texts]
+    return corpus
+
+### Find Number of topics
+
+def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, step=4):
+    """
+    Compute c_v coherence for various number of topics
+
+    /!\ It takes a really long time.
+
+    Parameters:
+    ----------
+    dictionary : Gensim dictionary
+    bow_corpus : Gensim bow corpus
+    texts : List of input texts
+    limit : Max num of topics
+
+    Returns:
+    -------
+    model_list : List of LDA topic models
+    coherence_values : Coherence values corresponding to the LDA model with respective number of topics
+    """
+    coherence_values = []
+    model_list = []
+    for num_topics in range(start, limit, step):
+        model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus,
+                                           id2word=dictionary,
+                                          num_topics=num_topics, 
+                                          random_state=0,
+                                          update_every=5,
+                                          chunksize=1000,
+                                          passes=10)
+        model_list.append(model)
+        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')
+        coherence_values.append(coherencemodel.get_coherence())
+
+    return model_list, coherence_values
+
+def plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4):
+    """
+    Plot the coherence scores per number of topics
+
+    Parameters:
+    ----------
+    coherence_values : list of coherence scores for various number of topics
+    start : int. Min num of topics
+    limit : int. Max num of topics
+    step: int
+
+    Output:
+    -------
+    Lineplot
+    """
+    x = range(start, limit, step)
+    plt.plot(x, coherence_values)
+    plt.xlabel("Num Topics")
+    plt.ylabel("Coherence score")
+    plt.legend(("coherence_values"), loc='best')
+    return plt.show()
+
+def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
+    """
+    Print the coherences scores for the ldamodels that had been tested with different number of topics
+    """
+    x = range(start, limit, step)
+    for m, cv in zip(x, coherence_values):
+        print("Num Topics =", m, " has Coherence Value of", round(cv, 4))
+
+
+### LdaModel: Gensim & Mallet
+
+def train_lda_model(bow_corpus, dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
+    """ Train the lda model on the corpus
+      
+    Parameters
+    ----------
+    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).
+    dictionary: corpora.Dictionary. Mapping from word IDs to words
+    num_topics: int
+    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    mallet_path: str, optionnal if model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file 
+    
+    Returns
+    -------
+    gensim.ldamodel
+    """
+    if model == 'gensim':
+        model = train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs)
+    elif model == 'mallet':
+        if mallet_path is None:
+            raise ValueError('You must precise the path to the mallet-2.0.8 file that has been downloaded before')
+        else:
+            model = train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs)
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet')
+    return model
+
+def train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs):
+
+    model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0, **kwargs)
+    return model
+
+def train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs):
+    
+    os.environ['MALLET_PATH'] = mallet_path
+    mallet = '$MALLET_PATH/mallet-2.0.8/bin/mallet'
+    model = gensim.models.wrappers.LdaMallet(mallet, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='composant', random_seed=0, **kwargs)
+    return model
+
+
+def save_model(model, model_name):
+    """ 
+    Save the model that has been trained. The model will be saved on your current emplacement.
+        
+    Parameters
+    ----------
+    model: ldamodel
+    model_name: str. 
+        Name the model that will be saved
+    """
+    return model.save(os.path.join(model_name))
+
+
+def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
+    """
+    Detected the language of a text
+
+    Parameters
+    ----------
+    model_path: str
+        path where the model has been saved
+    model_name: str
+        name of the saved model
+    model : str
+        Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    model_prefix : str
+        By default, 'composant' default prefix used while saving the mallet model with train_lda_model function.         
+    
+    Returns
+    -------
+    is_reliable : 
+        is the top language is much better than 2nd best language?
+    language: 
+        2-letter code for the language of the text
+    """
+    if model =='gensim':
+        ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
+    elif model =='mallet':
+        ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
+        if model_prefix is not None:
+            ldamodel.prefix = model_path+'/'+ model_prefix
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet')
+    return ldamodel
+
+def fit_data(model, bow):
+    """Test the model on new, unseen documents"""
+    return model.get_document_topics(bow, minimum_probability=0)
+
+
+# Visualization
+
+
+def visualize_topics(model, bow_corpus, dictionary, model_type=None):
+    """ 
+    Visualize the topics-keywords with the pyLDAvis interactive chart.
+        (Work well in notebook)
+        
+    Parameters
+    ----------
+    model: LDA model: gensim or mallet
+    bow_corpus : iterable of list of tokens. 
+    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
+    model : str. Precise the topic modeling model used, must be "gensim" or "mallet"
+    
+    Returns:
+    ----------
+    3D interactive chart
+    """
+    if model_type == 'mallet':
+        model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
+    elif model_type == 'gensim':
+        model_vis = model
+    elif model_type is None:
+        raise ValueError('You forgot to precise your model type, it must be: gensim or mallet')
+    else:
+        raise ValueError('Please enter a valid model name: gensim or mallet') 
+    return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
+
+def save_pyldavis(pyldavis, vis_path, vis_name):
+    """ 
+    Save the pyldavis interactive chart
+    
+    Parameters
+    ----------
+    pyldavis: pyLDAvis._prepare.PreparedData
+    vis_path: str
+    vis_name: str
+    """ 
+    return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name + '{}'.format('.html')))
+
+
+
+def show_pyldavis(vis_path, vis_name):
+    """ 
+    Display the HTML of the saved pyldavis interactive chart
+
+    Parameters
+    ----------
+    vis_path: str
+    vis_name: str
+    """
+    return HTML(filename=os.path.join(vis_path, vis_name + '{}'.format('.html')))
+
+def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
+    """ Print the dominant topics in the document, its score and the topics' top keywords.
+    
+    Quick way to interpret the topics
+
+    Parameters
+    ----------
+    gensim.ldamodel
+    model: ldamodel
+    bow_corpus: iterable of list of tokens.
+    topic_number: int. Pick the number of topics displayed
+    topn: int. Number of topics' top keyword displayed
+
+    """
+    i = 0
+    for index, score in sorted(model[bow_corpus], key=lambda tup: -1*tup[1]): 
+        weight = model.show_topic(index, topn=topn)
+        keywords = [i[0] for i in weight]
+        print("Score: {}\t Topic: {}".format(score, keywords))
+        i +=1
+        if i == topic_number:
+            break
diff --git a/nautilus_nlp/topic_modeling/nmf_model.py b/nautilus_nlp/topic_modeling/nmf_model.py
new file mode 100644
index 0000000..005c09b
--- /dev/null
+++ b/nautilus_nlp/topic_modeling/nmf_model.py
@@ -0,0 +1,118 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+import time
+import numpy as np
+from numpy.linalg import norm
+from tqdm import tqdm
+
+'''
+Topic Modeling via NMF
+'''
+
+class NMF(object):
+    def __init__(
+            self,
+            A, IW=[], IH=[],
+            n_topic=10, max_iter=100, max_err=1e-3,
+            rand_init=True):
+        """
+        The objective of the NMF model is to approximate the term-document matrix A by two lower-rank matrices W and H.
+        The process is iterative and we denote IW and IH the the matrix W and H that are updated at each step.
+
+        :param A: The term-document matrix
+        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+        and its elements are the weights of the corresponding keywords.
+        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
+        :param n_topic: Number of selected topics
+        :param max_iter: Maximum number of iterations to update W and H
+        :param max_err: maximum error under which we consider that the loop converged
+        :param rand_init: random init boolean
+        """
+        self.A = A
+        self.n_row = A.shape[0]
+        self.n_col = A.shape[1]
+
+        self.n_topic = n_topic
+        self.max_iter = max_iter
+        self.max_err = max_err
+
+        self.loss_hist = []
+        self.nmf_mat_init(rand_init)
+        self.nmf_iter()
+
+    def nmf_mat_init(self, rand_init):
+        """
+        Init Matrices W and H initially either randomly or using existing IW, IH matrices taken when iterating.
+        :param rand_init: Boolean indicating initial random init
+        """
+        if rand_init:
+            self.W = np.random.random((self.n_row, self.n_topic))
+            self.H = np.random.random((self.n_col, self.n_topic))
+        else:
+            self.W = IW
+            self.H = IH
+        for k in range(self.n_topic):
+            self.W[:, k] /= norm(self.W[:, k])
+
+    def nmf_iter(self):
+        """
+        Main iterative loop for matrix decomposition
+        """
+        loss_old = 1e20
+        start_time = time.time()
+        for i in tqdm(range(self.max_iter)):
+            self.nmf_solver()
+            loss = self.nmf_loss()
+            self.loss_hist.append(loss)
+
+            if loss_old - loss < self.max_err:
+                print('Matrix decomposition loop converged!')
+                break
+            loss_old = loss
+            end_time = time.time()
+            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
+
+    def nmf_solver(self):
+        '''
+        regular NMF without constraint.
+        Block Coordinate Decent
+        '''
+        epss = 1e-20
+
+        HtH = self.H.T.dot(self.H)
+        AH = self.A.dot(self.H)
+        for k in range(self.n_topic):
+            tmpW = self.W[:, k] * HtH[k, k] + AH[:, k] - np.dot(self.W, HtH[:, k])
+            self.W[:, k] = np.maximum(tmpW, epss)
+            self.W[:, k] /= norm(self.W[:, k]) + epss
+
+        WtW = self.W.T.dot(self.W)
+        AtW = self.A.T.dot(self.W)
+        for k in range(self.n_topic):
+            self.H[:, k] = self.H[:, k] * WtW[k, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
+            self.H[:, k] = np.maximum(self.H[:, k], epss)
+
+    def nmf_loss(self):
+        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        return loss
+
+    def get_loss(self):
+        return np.array(self.loss_hist)
+
+    def get_decomposition_matrix(self):
+        return self.W, self.H
diff --git a/nautilus_nlp/topic_modeling/seanmf_model.py b/nautilus_nlp/topic_modeling/seanmf_model.py
new file mode 100644
index 0000000..7248148
--- /dev/null
+++ b/nautilus_nlp/topic_modeling/seanmf_model.py
@@ -0,0 +1,148 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+import time
+import numpy as np
+from numpy.linalg import norm
+from tqdm import tqdm
+
+
+class SeaNMF(object):
+    def __init__(
+            self,
+            A, S,
+            IW=[], IWc=[], IH=[],
+            alpha=1.0, beta=0.1, n_topic=10, max_iter=100, max_err=1e-3,
+            rand_init=True, fix_seed=False):
+        """
+        Seanmf is a topic modeling algorithm, paper:  http://dmkd.cs.vt.edu/papers/WWW18.pdf.
+        It finds an approximation to the term-document matrix A by two lower-rank matrices W and H,
+        at each iteration a context matrix Wc are computed and used to update W.
+        :param A: document term matrix
+        :param S: Word-context (semantic) correlation matrix
+        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+        and its elements are the weights of the corresponding keywords.
+        :param IWc: Latent factor matrix of contexts.
+        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
+        :param alpha: Seanmf algorithm parameter
+        :param beta: Seanmf algorithm parameter
+        :param n_topic: Number of selected topics
+        :param max_iter: Maximum number of iterations to update W and H
+        :param max_err: maximum error under which we consider that the loop converged
+        :param rand_init: random init boolean
+        :param fix_seed: int number to fix random seed.
+        """
+        if fix_seed:
+            np.random.seed(0)
+
+        self.A = A
+        self.S = S
+
+        self.n_row = A.shape[0]
+        self.n_col = A.shape[1]
+
+        self.n_topic = n_topic
+        self.max_iter = max_iter
+        self.alpha = alpha
+        self.beta = beta
+        self.B = np.ones([self.n_topic, 1])
+        self.max_err = max_err
+        self.snmf_mat_init(rand_init, IW,  IWc, IH)
+        self.snmf_iter()
+
+    def snmf_mat_init(self, rand_init, IW=[], IWc=[], IH=[]):
+        """
+        Init Matrices W,Wc and H initially either randomly or using existing IW,IWc IH matrices taken when iterating.
+        :param rand_init: Boolean indicating initial random init
+        """
+        if rand_init:
+            self.W = np.random.random((self.n_row, self.n_topic))
+            self.Wc = np.random.random((self.n_row, self.n_topic))
+            self.H = np.random.random((self.n_col, self.n_topic))
+        else:
+            self.W = IW
+            self.Wc = IWc
+            self.H = IH
+        for k in range(self.n_topic):
+            self.W[:, k] /= norm(self.W[:, k])
+            self.Wc[:, k] /= norm(self.Wc[:, k])
+
+    def snmf_iter(self):
+        """
+        Main iterative loop for matrix decomposition
+        """
+        loss_old = 1e20
+        start_time = time.time()
+        for i in tqdm(range(self.max_iter)):
+            self.snmf_solver()
+            loss = self.snmf_loss()
+            if loss_old - loss < self.max_err:
+                print('Matrix decomposition loop converged!')
+                break
+            loss_old = loss
+            end_time = time.time()
+            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
+
+    def snmf_solver(self):
+        '''
+        using BCD framework
+        Alogorithm 1: Equations to update W, wc, H are described in the paper
+        http://dmkd.cs.vt.edu/papers/WWW18.pdf
+        '''
+
+        epss = 1e-20
+        # Update W
+        AH = np.dot(self.A, self.H)
+        SWc = np.dot(self.S, self.Wc)
+        HtH = np.dot(self.H.T, self.H)
+        WctWc = np.dot(self.Wc.T, self.Wc)
+        W1 = self.W.dot(self.B)
+
+        for k in range(self.n_topic):
+            num0 = HtH[k, k] * self.W[:, k] + self.alpha * WctWc[k, k] * self.W[:, k]
+            num1 = AH[:, k] + self.alpha * SWc[:, k]
+            num2 = np.dot(self.W, HtH[:, k]) + self.alpha * np.dot(self.W, WctWc[:, k]) + self.beta * W1[0]
+            self.W[:, k] = num0 + num1 - num2
+            self.W[:, k] = np.maximum(self.W[:, k], epss)  # project > 0
+            self.W[:, k] /= norm(self.W[:, k]) + epss  # normalize
+        # Update Wc
+        WtW = self.W.T.dot(self.W)
+        StW = np.dot(self.S, self.W)
+        for k in range(self.n_topic):
+            self.Wc[:, k] = self.Wc[:, k] + StW[:, k] - np.dot(self.Wc, WtW[:, k])
+            self.Wc[:, k] = np.maximum(self.Wc[:, k], epss)
+        # Update H
+        AtW = np.dot(self.A.T, self.W)
+        for k in range(self.n_topic):
+            self.H[:, k] = self.H[:, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
+            self.H[:, k] = np.maximum(self.H[:, k], epss)
+
+    def snmf_loss(self):
+        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        if self.alpha > 0:
+            loss += self.alpha * norm(np.dot(self.W, np.transpose(self.Wc)) - self.S, 'fro') ** 2 / 2.0
+        if self.beta > 0:
+            loss += self.beta * norm(self.W, 1) ** 2 / 2.0
+
+        return loss
+
+    def get_decomposition_matrix(self):
+        # Wc was not considered to keep same structure as NMF
+        return self.W, self.H
+
+
diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
new file mode 100644
index 0000000..73fad26
--- /dev/null
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -0,0 +1,280 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+import re
+from collections import Counter
+from itertools import product
+from nautilus_nlp.models.nmf_model import *
+from nautilus_nlp.models.seanmf_model import *
+import numpy as np
+import pyLDAvis
+
+
+def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
+    """
+    :param text: list of str on which the topic modeling will be performed
+    :param vocab_min_count: minimum number of occurrences of a word to be considered in the vocabulary
+    :param vocab_max_size: maximum number of word in the vocabulary
+    :return:  encoded_text_id: list of encoded sentences using vocab IDs
+              vocab_list: list of vocabulary
+              vocab_arr: array with vocab frequency counts
+    """
+    vocab = {}
+
+    # Tokens_list is a list of sub-lists where each sub-list contains a sentences' tokens.
+    tokens_list=[]
+    for sentence in text:
+        sentence = re.split('\s', sentence)
+        tokens_list.append(sentence)
+    vocab = dict(Counter(x for xs in tokens_list for x in xs))
+
+    # Create Vocab array ( list of sorted vocab + counts )
+    vocab_arr = [[wd, vocab[wd]] for wd in vocab if vocab[wd] > vocab_min_count]
+    vocab_arr = sorted(vocab_arr, key=lambda k: k[1])[::-1]
+    vocab_arr = vocab_arr[:vocab_max_size]
+    vocab_arr = sorted(vocab_arr)
+
+    vocab_list = list(map(lambda x:x[0], vocab_arr))
+    # Create Vocab to ID dictionnary
+    vocab2id = {itm[1][0]: itm[0] for itm in enumerate(vocab_arr)}
+
+    # Create ID representation of text (ie: each sentence is a list of vocabId )
+    encoded_text_id = []
+    for sentence in text:
+        sentence = re.split('\s', sentence)
+        sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
+        encoded_text_id.append(sentence)
+
+    return encoded_text_id, vocab_list, vocab_arr
+
+
+def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20, max_iter=20, max_err=0.1, alpha=0, beta=0):
+    """
+    :param model_name: string = 'nmf' or 'seanmf'
+    :param encoded_text_id: list of encoded sentences
+    :param vocab_list: list of vocabulary
+    :param n_topics: number of topics
+    :param max_iter: maximum number of iterations while training
+    :param max_err: training error
+    :param alpha: regularization param for the NMF model
+    :param beta: regularization param for the NMF model
+    :return: Trained NMF model
+    """
+
+    n_docs = len(encoded_text_id)
+    n_terms = len(vocab_list)
+
+    if model_name == 'nmf':
+        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
+        model = NMF(
+            dt_mat,
+            n_topic=n_topics,
+            max_iter=max_iter,
+            max_err=max_err)
+
+    elif model_name == 'seanmf':
+        # Calculate co-occurence matrix
+        cm = __build_cooccurence_matrix(n_terms, encoded_text_id)
+        # Calculate PPMI
+        SS = __calulate_PPMI(cm, n_terms)
+        # Build doc-term matrix
+        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
+        model = SeaNMF(
+            dt_mat, SS,
+            alpha=alpha,
+            beta=beta,
+            n_topic=n_topics,
+            max_iter=max_iter,
+            max_err=max_err,
+            fix_seed=1024)
+
+    else:
+        model = None
+        print('Invalid model name: Use nmf or seanmf')
+
+    return model
+
+
+def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
+    dt_mat = np.zeros([n_terms, n_docs])
+    for k in range(n_docs):
+        for j in encoded_text_id[k]:
+            dt_mat[j, k] += 1.0
+    return dt_mat
+
+
+def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
+    """
+    Computes the PMi score for each topic and the topKeywords describing each of them.
+    :param model: trained NMF model
+    :param encoded_text_id: list of encoded sentences
+    :param vocab_list: list of vocabulary
+    :return: topics = dictionnary with the topic number and its topkeywords
+             pmi_score = dictionnary with the topic number and its PMI score
+    """
+
+    dt_mat = __build_cooccurence_matrix(n_terms=len(vocab_list), encoded_text_id=encoded_text_id)
+    np.fill_diagonal(dt_mat, 0)
+    W,_ = model.get_decomposition_matrix()
+    n_topic = W.shape[1]
+    PMI_arr = []
+    for k in range(n_topic):
+        top_keywords_index = W[:, k].argsort()[::-1][:n_topKeyword]
+        PMI_arr.append(__calculate_PMI(dt_mat, top_keywords_index))
+
+    index = np.argsort(PMI_arr)
+    topics = {}
+    pmi_score = {}
+    for k in index:
+        words = []
+        for w in np.argsort(W[:, k])[::-1][:n_topKeyword]:
+            words.append(vocab_list[w])
+        # Complete the topic and the score dicts. Format {Topic_number: words or score}
+        topics[k] = words
+        pmi_score[k] = PMI_arr[k]
+
+    return topics, pmi_score
+
+
+def get_assigned_topics(model):
+    """
+    Assign the topic number to the sentences used when training the model
+    :param model: trained model for short text
+    :return topics_list: list having the same length as the training text containing topics assigned to each sentence.
+    """
+
+    _, H = model.get_decomposition_matrix()
+    # The weights of the H matrix are converted into probabilities
+    H_probs = H / H.sum(axis=1, keepdims=True)
+    topics_list = list(np.argmax(H_probs, axis=1))
+
+    return topics_list
+
+
+def show_pyldavis(model, encoded_text_id, vocab_arr):
+    """
+    :param model: trained model
+    :param encoded_text_id: encoded_text_id: list of encoded sentences
+    :param vocab_arr: array of vocabulary frequency
+    :return: pyldavis topics plot
+    """
+
+    data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
+    vis_data = pyLDAvis.prepare(**data)
+
+    return pyLDAvis.display(vis_data)
+
+
+def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
+    """
+    Transform the model decomposed matrix to create topic term and document topics matrices
+    and prepare data to feed pyldavis.
+    link : http://jeriwieringa.com/2018/07/17/pyLDAviz-and-Mallet/
+    :return dict of data needed by pyldavis
+    """
+
+    # 1 List of documents lengths
+    doc_length_values=[]
+    for doc in encoded_text_id:
+        doc_length_values.append(len(doc))
+    # 2 List of vocab
+    list_vocab = list(map(lambda x: x[0], vocab_arr))
+    # 3 List of vocab. Frequency
+    freq_vocab = list(map(lambda x: x[1], vocab_arr))
+    W, H = model.get_decomposition_matrix()
+    # Normlize the decomposition to get probabilities
+    W_probs = W / W.sum(axis=1, keepdims=True)
+    # 4 topic term matrix phi
+    phi = W_probs.T
+    # 5 document term matrix theta
+    theta = H / H.sum(axis=1, keepdims=True)
+
+    data = {'topic_term_dists': phi,
+            'doc_topic_dists': theta,
+            'doc_lengths': doc_length_values,
+            'vocab': list_vocab,
+            'term_frequency': freq_vocab
+            }
+
+    return data
+
+
+def __build_cooccurence_matrix(n_terms, encoded_text_id):
+    """
+    The cooccurence matrix represents the number of times each word
+    appeared in the same context as another word from the vocabulary.
+    The matrix has n_terms x n_terms size, columns and rows denote the vocab.
+    Cell values represent the number of times words occured together in the same sentence.
+    :param :encoded_text_id : list of encoded sentences
+    :return: res: the co-occurence matrix
+
+    """
+    res = np.zeros([n_terms, n_terms])
+    for row in encoded_text_id:
+        counts = Counter(row)
+        for key_from, key_to in product(counts, repeat=2):
+            res[key_from, key_to] += counts[key_from] * counts[key_to]
+    return res
+
+
+def __calulate_PPMI(cm, n_terms):
+    D1 = np.sum(cm)
+    print('D1= ', D1)
+    SS = D1 * cm
+    print('SS= ',SS)
+    for k in range(n_terms):
+        SS[k] /= np.sum(cm[k])
+    for k in range(n_terms):
+        SS[:, k] /= np.sum(cm[:, k])
+    print('SS = ', SS )
+    cm = []  # release memory
+    SS[SS == 0] = 1.0
+    SS = np.log(SS)
+    SS[SS < 0.0] = 0.0
+    return SS
+
+
+def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
+    dt_mat = np.zeros([n_terms, n_docs])
+    for k in range(n_docs):
+        for j in encoded_text_id[k]:
+            dt_mat[j, k] += 1.0
+    return dt_mat
+
+
+def __calculate_PMI(AA, topKeywordsIndex):
+    '''
+    Method to compute PMi score
+    Reference:
+    Short and Sparse Text Topic Modeling via Self-Aggregation
+    '''
+
+    D1 = np.sum(AA)
+    n_tp = len(topKeywordsIndex)
+    PMI = []
+    for index1 in topKeywordsIndex:
+        for index2 in topKeywordsIndex:
+            if index2 < index1:
+                if AA[index1, index2] == 0:
+                    PMI.append(0.0)
+                else:
+                    C1 = np.sum(AA[index1])
+                    C2 = np.sum(AA[index2])
+                    PMI.append(np.log(AA[index1,index2]*D1/C1/C2))
+    avg_PMI = 2.0*np.sum(PMI)/float(n_tp)/(float(n_tp)-1.0)
+
+    return avg_PMI

From ca6023f5bd3f1c923697bb8fb9d182b0fa4df05f Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:42:49 +0200
Subject: [PATCH 261/496] renamed author

---
 docs/conf.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 02f1785..fffe634 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -37,8 +37,8 @@
 # -- Project information -----------------------------------------------------
 
 project = 'Nautilus_nlp'
-copyright = '2019, Robin Doumerc'
-author = 'Robin Doumerc'
+copyright = '2020, Artefact'
+author = 'Artefact'
 
 # The short X.Y version
 version = '0.1.0'

From 2c614dc76cc9c57db72a73c3353e933a7a8a63e5 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:43:07 +0200
Subject: [PATCH 262/496] renaming data to datasets

---
 data/external/get_language_dataset.sh | 21 ---------------------
 data/external/get_stanfordtweets.sh   | 20 --------------------
 2 files changed, 41 deletions(-)
 delete mode 100644 data/external/get_language_dataset.sh
 delete mode 100644 data/external/get_stanfordtweets.sh

diff --git a/data/external/get_language_dataset.sh b/data/external/get_language_dataset.sh
deleted file mode 100644
index 4e87a8b..0000000
--- a/data/external/get_language_dataset.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-#!/bin/bash
-wget -O wili.zip https://zenodo.org/record/841984/files/wili-2018.zip?download=1
-mkdir -p wili && cp wili.zip wili && cd wili && unzip wili.zip && cd ..
-
diff --git a/data/external/get_stanfordtweets.sh b/data/external/get_stanfordtweets.sh
deleted file mode 100644
index ef7ae1e..0000000
--- a/data/external/get_stanfordtweets.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-#!/bin/bash 
-wget -O trainingandtestdata.zip http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip trainingandtestdata.zip
-mkdir -p  tweets_sentiment && cp trainingandtestdata.zip tweets_sentiment && cd tweets_sentiment && unzip trainingandtestdata.zip

From b3a44cf4019d54a2a4d9f31bfaf22851cfa07249 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:43:35 +0200
Subject: [PATCH 263/496] deleting wiki folder

---
 wiki/Entities_extraction.md |  5 -----
 wiki/README.md              | 22 ----------------------
 wiki/Speech_to_text.md      |  6 ------
 wiki/Text_classification.md |  7 -------
 wiki/Text_generation.md     |  1 -
 wiki/Text_processing.md     | 37 -------------------------------------
 wiki/Topic_Modeling.md      |  7 -------
 7 files changed, 85 deletions(-)
 delete mode 100644 wiki/Entities_extraction.md
 delete mode 100644 wiki/README.md
 delete mode 100644 wiki/Speech_to_text.md
 delete mode 100644 wiki/Text_classification.md
 delete mode 100644 wiki/Text_generation.md
 delete mode 100644 wiki/Text_processing.md
 delete mode 100644 wiki/Topic_Modeling.md

diff --git a/wiki/Entities_extraction.md b/wiki/Entities_extraction.md
deleted file mode 100644
index 7fbbf2c..0000000
--- a/wiki/Entities_extraction.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Entities Extraction
-
-## Part of Speech Tagging
-
-## Entity detections
\ No newline at end of file
diff --git a/wiki/README.md b/wiki/README.md
deleted file mode 100644
index b98f83e..0000000
--- a/wiki/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# Nautilus NLP SUMMARY
-
-This Mardown is used as the summary for the following parts:
-
-## **Text Based NLP**
-
-### [*Topic Modeling*](Topic_Modeling.md)
-
-### [*Text Classification*](Text_classification.md)
-
-### [*Entities Extraction*](Entities_extraction.md)
-
-### [*Text Processing*](Text_processing.md)
-
-### [*Text Generation*](Text_generation.md)
-
-
-## **Audio-Based NLP**
-
-### [*Speech to Text*](Speech_to_text.md)
-
-### [*Voice Classification*](Voice_Classification.md)
\ No newline at end of file
diff --git a/wiki/Speech_to_text.md b/wiki/Speech_to_text.md
deleted file mode 100644
index 0f6eb92..0000000
--- a/wiki/Speech_to_text.md
+++ /dev/null
@@ -1,6 +0,0 @@
-# Speech to Text
-
-
-## Corpus
-
-## State of the art
diff --git a/wiki/Text_classification.md b/wiki/Text_classification.md
deleted file mode 100644
index b0d8c19..0000000
--- a/wiki/Text_classification.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Text Classification (Applied to Sentiment Analysis)
-
-## Machine-learning based
-
-## Word-vector based
-
-## Deep-learning based
\ No newline at end of file
diff --git a/wiki/Text_generation.md b/wiki/Text_generation.md
deleted file mode 100644
index 1ae1192..0000000
--- a/wiki/Text_generation.md
+++ /dev/null
@@ -1 +0,0 @@
-# Text Generation
\ No newline at end of file
diff --git a/wiki/Text_processing.md b/wiki/Text_processing.md
deleted file mode 100644
index a9c7740..0000000
--- a/wiki/Text_processing.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Text Processing
-
-## Introduction & Best practice
-
-## Encoding / Decoding
-
-In our python programming lives,  pretty much everyone working with text data encountered one day the terrible:
-
-    `UnicodeDecodeError: 'machine' codec can't decode character 'somethingsomething' in position x: ordinal not in range(z)`
-And you probably spend the next hour trying to figure out to get out of this mess.
-
-Usually, most of programming language automatically infer the encoding of the text. However, Python does not, and this can lead to a ton of problem.
-
-Encoding is the table of representation between the binary code understood by the computer and the letters as we see them. Therefore, everytime you see text on a screen, it is encoded in a way.
-The problem is, pretty much every country in the world had his own way of encoding and these encodings still persist: The encoding module of python can understand around 100 different encoding.
-
-So, how do we get out of this mess?
-
-
-### **The Absolute Rule:**
-Use 'UTF-8' as default encoding for your processes.
-
-### If you are still in Python2.7
-*And you want to use it until its end of life(December 2019)*
-
-
-
-### If you are in Python 3
-
-
-## Stemmatization / Lemmatization
-
-## Vector Representation
-
-### Bag of Word & TF-IDF
-
-### Word Embeddings
diff --git a/wiki/Topic_Modeling.md b/wiki/Topic_Modeling.md
deleted file mode 100644
index 9a04ef4..0000000
--- a/wiki/Topic_Modeling.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Topic Modelling
-
-## LDA
-
-## LSA 
-
-## Topic clustering/propagation?
\ No newline at end of file

From 604071871962a6d6918f19f994045b76f20e31d2 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:44:32 +0200
Subject: [PATCH 264/496] removing tox

---
 tox.ini | 3 ---
 1 file changed, 3 deletions(-)
 delete mode 100644 tox.ini

diff --git a/tox.ini b/tox.ini
deleted file mode 100644
index c32fbd8..0000000
--- a/tox.ini
+++ /dev/null
@@ -1,3 +0,0 @@
-[flake8]
-max-line-length = 79
-max-complexity = 10

From 977d37654e4279463ee292a51e86f42defffa6fb Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:45:41 +0200
Subject: [PATCH 265/496] renaming author

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index b9b77e0..39d4c4c 100644
--- a/setup.py
+++ b/setup.py
@@ -42,7 +42,7 @@ def run(self):
     packages=find_packages(),
     version=version,
     description='All the goto functions you need to handle NLP use-cases',
-    author='Robin Doumerc',
+    author='Artefact',
     license='MIT',
     url='https://github.com/artefactory/nautilus-nlp',
     classifiers=[

From 9eb7d58df829c7b06e38c1e2baa93e4f9a71ce57 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 15:46:03 +0200
Subject: [PATCH 266/496] removing test_environnement.py

---
 test_environment.py | 42 ------------------------------------------
 1 file changed, 42 deletions(-)
 delete mode 100644 test_environment.py

diff --git a/test_environment.py b/test_environment.py
deleted file mode 100644
index 341e9cb..0000000
--- a/test_environment.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import sys
-
-REQUIRED_PYTHON = "python3"
-
-
-def main():
-    system_major = sys.version_info.major
-    if REQUIRED_PYTHON == "python":
-        required_major = 2
-    elif REQUIRED_PYTHON == "python3":
-        required_major = 3
-    else:
-        raise ValueError("Unrecognized python interpreter: {}".format(
-            REQUIRED_PYTHON))
-
-    if system_major != required_major:
-        raise TypeError(
-            "This project requires Python {}. Found: Python {}".format(
-                required_major, sys.version))
-    else:
-        print(">>> Development environment passes all tests!")
-
-
-if __name__ == '__main__':
-    main()

From d7e8f0fdffa446dd556ffe751f7612f7d16daeb8 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 16:04:34 +0200
Subject: [PATCH 267/496] added frequent words in keyword_extractor

---
 nautilus_nlp/analysis/keyword_extractor.py | 27 ++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/nautilus_nlp/analysis/keyword_extractor.py b/nautilus_nlp/analysis/keyword_extractor.py
index 28545c2..acdb963 100644
--- a/nautilus_nlp/analysis/keyword_extractor.py
+++ b/nautilus_nlp/analysis/keyword_extractor.py
@@ -45,4 +45,31 @@ def extract_keywords(text, keyword, case_sensitive=True):
         processor.add_keywords_from_dict(keyword)
 
     return processor.extract_keywords(text)
+
+
+def frequent_words(list_words, ngrams_number=1, number_top_words=10):
+    """
+    Create n-grams for list of tokens
+
+    Parameters
+    ----------    
+    ngrams_number : int
     
+    number_top_words : int 
+        output dataframe length
+
+    Returns
+    -------
+    DataFrame
+        Dataframe with the entities and their frequencies.
+    """             
+    frequent = []
+    if ngrams_number == 1:
+        pass
+    elif ngrams_number >= 2:
+        list_words = create_ngrams(list_words, ngrams_number)
+    else:
+        raise ValueError("number of n-grams should be >= 1")
+    x = Counter(list_words)
+    frequent = x.most_common(number_top_words)
+    return frequent

From 96ec9fc1a9f286bcc389b8c455bd8d7160064a5f Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 16:04:43 +0200
Subject: [PATCH 268/496] returning a generator instead of list

---
 nautilus_nlp/analysis/ngrams.py | 42 +++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 nautilus_nlp/analysis/ngrams.py

diff --git a/nautilus_nlp/analysis/ngrams.py b/nautilus_nlp/analysis/ngrams.py
new file mode 100644
index 0000000..cc892c7
--- /dev/null
+++ b/nautilus_nlp/analysis/ngrams.py
@@ -0,0 +1,42 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+# -*- coding: utf-8 -*-
+"""
+Functions to calculate words or ngrams frequencies.
+"""
+from collections import Counter
+
+
+def create_ngrams(token_list, n):
+    """
+    Create n-grams for list of tokens
+
+    Parameters
+    ----------    
+    token_list : list
+        list of strings
+    n :
+        number of elements in the n-gram
+
+    Returns
+    -------
+    Generator
+        generator of all n-grams
+    """
+    ngrams = zip(*[token_list[i:] for i in range(n)])
+    return (" ".join(ngram) for ngram in ngrams)

From 33f1f542c4c3058635aabbd2bc28c7c06b36e8b3 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 16:04:56 +0200
Subject: [PATCH 269/496] making language non optional

---
 nautilus_nlp/analysis/text_summary.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/nautilus_nlp/analysis/text_summary.py b/nautilus_nlp/analysis/text_summary.py
index 9fe6eb3..b109146 100644
--- a/nautilus_nlp/analysis/text_summary.py
+++ b/nautilus_nlp/analysis/text_summary.py
@@ -33,7 +33,7 @@ def is_list_of_strings(lst):
     return bool(lst) and isinstance(lst, list) and all(isinstance(elem, str) for elem in lst)
 
 
-def summarize_text(txt, ratio=0.2, words=None, language="english"):
+def summarize_text(txt, ratio=0.2, language, words=None):
     """
     Parameters
     ----------    
@@ -41,10 +41,10 @@ def summarize_text(txt, ratio=0.2, words=None, language="english"):
         Sting or list of strings containing text to summarize
     ratio : float
         Percentage giving the output text length in reference to the input length.
-    words : 
-        number of words of the output text
     language :
         text language. eg. "english"
+    words : 
+        number of words of the output text or None
 
     Returns
     -------
@@ -54,9 +54,6 @@ def summarize_text(txt, ratio=0.2, words=None, language="english"):
 
     if is_list_of_strings(txt):
         txt = ' '.join(txt)
-    elif isinstance(txt, str):
-        pass
-    else:
-        raise ValueError("Text parameter must be a Unicode object (str) or list of str!")
+    elif not isinstance(txt, str):
+        raise TypeError("Text parameter must be a Unicode object (str) or list of str!")
     return summarize(txt, ratio, words, language)
-

From a815c6afc56c70e2c05a06aabce8cbd5fa8bf556 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 16:05:21 +0200
Subject: [PATCH 270/496] removing useless commented lines

---
 nautilus_nlp/analysis/visualize.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/nautilus_nlp/analysis/visualize.py b/nautilus_nlp/analysis/visualize.py
index c7e58b4..6f8d36b 100644
--- a/nautilus_nlp/analysis/visualize.py
+++ b/nautilus_nlp/analysis/visualize.py
@@ -15,11 +15,6 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-'''
-import matplotlib
-import numpy as np
-
-'''
 import matplotlib.pyplot as plt
 import wordcloud
 plt.rcParams["figure.figsize"] = [16,9]

From 99fd7e195324aa29a272e1f57615e3d19e61d529 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 8 Sep 2020 16:05:52 +0200
Subject: [PATCH 271/496] renaming ngrams_analysis.py to ngrams.py

---
 nautilus_nlp/analysis/ngrams_analysis.py | 70 ------------------------
 1 file changed, 70 deletions(-)
 delete mode 100644 nautilus_nlp/analysis/ngrams_analysis.py

diff --git a/nautilus_nlp/analysis/ngrams_analysis.py b/nautilus_nlp/analysis/ngrams_analysis.py
deleted file mode 100644
index 3716ee1..0000000
--- a/nautilus_nlp/analysis/ngrams_analysis.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
-"""
-Functions to calculate words or ngrams frequencies.
-"""
-from collections import Counter
-
-
-def create_ngrams(token, n):
-    """
-    Create n-grams for list of tokens
-
-    Parameters
-    ----------    
-    token : list
-        list of strings
-    n :
-        number of elements in the n-gram
-
-    Returns
-    -------
-    list
-        list of n-grams
-    """    
-    ngrams = zip(*[token[i:] for i in range(n)])
-    return [" ".join(ngram) for ngram in ngrams]
-
-
-def frequent_words(list_words, ngrams_number=1, number_top_words=10):
-    """
-    Create n-grams for list of tokens
-
-    Parameters
-    ----------    
-    ngrams_number : int
-    
-    number_top_words : int 
-        output dataframe length
-
-    Returns
-    -------
-    DataFrame
-        Dataframe with the entities and their frequencies.
-    """             
-    frequent = []
-    if ngrams_number == 1:
-        pass
-    elif ngrams_number >= 2:
-        list_words = create_ngrams(list_words, ngrams_number)
-    else:
-        raise ValueError("number of n-grams should be >= 1")
-    x = Counter(list_words)
-    frequent = x.most_common(number_top_words)
-    return frequent

From 64b99da04da2307f3bce48fe3f1ed0313ed00dda Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 16:48:51 +0200
Subject: [PATCH 272/496] cleaning requirements

---
 requirements.txt | 65 ++++++++++++++++--------------------------------
 1 file changed, 21 insertions(+), 44 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 8b4b58d..88a97f3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,46 +1,23 @@
-# local package
-#-e .
-
-# external requirements
-Sphinx
-sphinx_rtd_theme
-coverage
-python-dotenv>=0.5.1
-pillow
-pytest
-
-#library requirements
-pyLDAvis==2.1.2
-gensim==3.7.1
-sacremoses==0.0.13
-stop-words==2018.7.23
-spacy==2.1.3
-ftfy<5.0.0,>=4.2.0
-wordcloud>=1.5.0
-matplotlib>=3.0.3
-mosestokenizer
-numpy>1.15.4
-stop_words==2018.7.23
-nltk>=3.4.5
-textblob==0.15.3
-textblob_fr==0.2.0
-pandas>=0.23.4
+numpy==1.18.1
 chardet==3.0.4
-setuptools==40.8.0
-textacy==0.6.3
-#fastText==0.8.3
-gensim==3.7.1
-scikit_learn==0.20.3
-vaderSentiment==3.2.1
-google-compute-engine==2.8.13
-flashtext==2.7
-phonenumbers==8.10.12
-regex==2019.8.19
-emoji>=0.5.2
-summa==1.2.0
+pandas==0.25.1
+spacy==2.1.8
+click==7.1.1
+matplotlib==3.1.1
+tqdm==4.45.0
 biterm==0.1.5
-
-
-
-
-
+flashtext==2.7
+ftfy==5.8
+gensim==3.8.3
+ipython==7.18.1
+nlpaug==0.0.20
+nltk==3.5
+phonenumbers==8.12.9
+pyLDAvis==2.1.2
+pytest==6.0.2
+python-dotenv==0.14.0
+regex==2020.7.14
+sacremoses==0.0.43
+scikit_learn==0.23.2
+stop_words==2018.7.23
+wordcloud==1.8.0

From 078ba268bb026c65bd6173b9c68fe6ad1e994b8e Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 16:49:35 +0200
Subject: [PATCH 273/496] adding summa requirement

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 88a97f3..5b30424 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -21,3 +21,4 @@ sacremoses==0.0.43
 scikit_learn==0.23.2
 stop_words==2018.7.23
 wordcloud==1.8.0
+summa==1.2.0

From c143d61dfa719cdd61f492748f9c96cca3f94a8a Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 16:53:06 +0200
Subject: [PATCH 274/496] changing path in test biterm

---
 tests/test_biterm.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_biterm.py b/tests/test_biterm.py
index 975d42c..5f9c3a7 100644
--- a/tests/test_biterm.py
+++ b/tests/test_biterm.py
@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.models.biterm_model import BitermModel
+from nautilus_nlp.topic_modeling.biterm_model import BitermModel
 import pandas as pd
 import pytest
 

From aa89f2fabcc11cb71f54f0b9d3dbd3aab68872b1 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 16:56:21 +0200
Subject: [PATCH 275/496] adding comment TODO

---
 nautilus_nlp/preprocessing/text_preprocess.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
index 718b061..1b4353a 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -37,6 +37,7 @@ def __init__(self,text):
             raise ValueError("Input must be a string")
 
     def clean_text(self,lang='en') -> str:
+        #TODO : check how to pipe operations
         stopwords = get_stopwords(lang)
         self.text = self.fix_bad_unicode(normalization="NFC")
         self.text = self.remove_EOL_characters()

From eb7ab373b365980fb9129f223f03b3324f1a49e0 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 16:57:01 +0200
Subject: [PATCH 276/496] updating path for files topic modeling

---
 nautilus_nlp/topic_modeling/topic_modeling_short_text.py | 4 ++--
 tests/test_topic_modeling_short_text.py                  | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
index 73fad26..951f5fb 100644
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -18,8 +18,8 @@
 import re
 from collections import Counter
 from itertools import product
-from nautilus_nlp.models.nmf_model import *
-from nautilus_nlp.models.seanmf_model import *
+from nautilus_nlp.topic_modeling.nmf_model import *
+from nautilus_nlp.topic_modeling.seanmf_model import *
 import numpy as np
 import pyLDAvis
 
diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index 405fc6b..9a8caac 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.models.topic_modeling_short_text import prepare_data, \
+from nautilus_nlp.topic_modeling.topic_modeling_short_text import prepare_data, \
     __build_cooccurence_matrix, train_shorttext_model, prepare_data_pyldavis, \
     show_dominant_topic, get_assigned_topics
 import numpy as np

From 69fea2651793d0dd80a6dbc2996c90db3c71040b Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 16:57:13 +0200
Subject: [PATCH 277/496] removing test for language detection

---
 tests/test_language_detection.py | 48 --------------------------------
 1 file changed, 48 deletions(-)
 delete mode 100644 tests/test_language_detection.py

diff --git a/tests/test_language_detection.py b/tests/test_language_detection.py
deleted file mode 100644
index b6b8d87..0000000
--- a/tests/test_language_detection.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.models import language_detector
-import pytest
-import numpy as np
-
-model = language_detector.LangDetector()
-
-TEXT_1 = """Кипрская война (итал. Guerra di Cipro; тур. Kıbrıs Savaşı) — одна из нескольких войн между Османской империей и Венецианской республикой за господство в Восточном Средиземноморье. Сначала Венеция воевала с османами одна. Затем, когда сформировалась Священная лига (в которую помимо Венеции входили Испания с Неаполем и Сицилией, Республика Генуя, Герцогство Савойское, госпитальеры, Великое герцогство Тосканское и другие итальянские государства), Османская империя воевала уже против Лиги."""
-TEXT_2 = """
-Wiborada († 1. Mai 926 in St. Gallen) war eine Einsied­lerin, geweihte Jung­frau und Märtyrin der katho­lischen Kirche. Sie lebte als Inklusin in St. Gallen und wurde wäh­rend eines Ungarn­einfalls getötet. Ihre letzte Ruhe­stätte, deren genaue Lage bei der Kirche St. Mangen heute nicht mehr be­kannt ist, war über Jahr­hunderte hinweg Ziel vieler Wall­fahrer. Sie wurde im Jahr 1047 von Papst Cle­mens II. heilig­gespro­chen und gilt als Schutz­patronin der Pfarr­haus­hälte­rinnen, Köchin­nen, Biblio­theken und Bücher­freunde. In der Ikono­grafie wird Wiborada im Habit darge­stellt; als ikono­grafische Heiligen­attri­bute sind ihr eine Helle­barde als Ver­weis auf das Marty­rium und ein Buch beige­geben."""
-TEXT_3 = """De geschiedenis van het werelddeel Europa valt ruwweg chronologisch in te delen in de prehistorie, de klassieke oudheid, de middeleeuwen, de nieuwe tijd, de moderne tijd en de eigentijdse tijd."""
-TEXT_4 = """
-The absolute difference between At and Ft is divided by half the sum of absolute values of the actual value At and the forecast value Ft. The value of this calculation is summed for every fitted point t and divided again by the number of fitted points n.
-"""
-TEXT_5 = """Joseph Athanase Doumerc dit Paul Doumer est un homme d'État français né le 22 mars 1857 à Aurillac (Cantal) et mort assassiné le 7 mai 1932 à Paris. Il a été président de la République du 13 juin 1931 à sa mort."""
-TEXT_6 = """1997年加延地震是于5月10日协调世界时7時57分发生在伊朗北部呼罗珊的一起重大地震,是该地区自1990年以来最大的一次地震,矩震级为7.3,中心位于马什哈德以南约270公里的一个名叫阿德库尔的乡村。这也是该国1997年所发生的第三场地震,造成严重的灾情,比尔詹德-加延地区变得满目疮痍,有1,567人丧生,超过2,300人受伤,5万人无家可归,超过15,000幢房屋遭到破坏或损毁,美国地质调查局形容这是1997年最致命的一场地震。其后还发生了约155次余震造成进一步的破坏并迫使幸存者离开。最终估计此次灾害的损失约为1亿美元。围绕地震震中的地区几乎全部被毁,这与乡村地区建筑质量不佳有很大关系,联合国建议调整相应建筑法规。按死亡人数计算,自进入20世纪以来,伊朗平均每3,000人就有1人在地震相关事件中遇难,一位美国地球物理学家建议为了解决持续的公共安全隐患,需要有一个全国范围的重建方案。"""
-
-
-@pytest.mark.parametrize(
-    "text, lang",
-    [
-        (TEXT_1, "ru"),
-        (TEXT_2, "de"),
-        (TEXT_3, "nl"),
-        (TEXT_4, "en"),
-        (TEXT_5, "fr"),
-        (TEXT_6, "zh"),
-    ],
-)
-def test_detect_language(text, lang):
-    result = model.detect_language(text)[0]
-    np.testing.assert_string_equal(result, lang)

From 7e572b7d4f397fa2edc0b5ca1b8432376cedfa1d Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:02:41 +0200
Subject: [PATCH 278/496] adding pylint to requirements

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 5b30424..14c8197 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,3 +22,4 @@ scikit_learn==0.23.2
 stop_words==2018.7.23
 wordcloud==1.8.0
 summa==1.2.0
+pylint==2.4.4

From 535406763518fde037e01489337bbf49d14dc335 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:02:58 +0200
Subject: [PATCH 279/496] fixing lint issues on keyword_extractor.py

---
 nautilus_nlp/analysis/keyword_extractor.py | 24 +++++++++++++---------
 1 file changed, 14 insertions(+), 10 deletions(-)

diff --git a/nautilus_nlp/analysis/keyword_extractor.py b/nautilus_nlp/analysis/keyword_extractor.py
index acdb963..4ef2b49 100644
--- a/nautilus_nlp/analysis/keyword_extractor.py
+++ b/nautilus_nlp/analysis/keyword_extractor.py
@@ -15,14 +15,18 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from collections import Counter
+
 from flashtext import KeywordProcessor
+from nautilus_nlp.analysis.ngrams import create_ngrams
+
 
 def extract_keywords(text, keyword, case_sensitive=True):
     """
     Extract Keywords from a document.
 
     Parameters
-    ----------    
+    ----------
     text : str
         Text to extract keywords from
     keyword :
@@ -35,13 +39,13 @@ def extract_keywords(text, keyword, case_sensitive=True):
     list
         Return list of extracted keyworkds
     """
-    
-    processor=KeywordProcessor(case_sensitive=case_sensitive)
-    if isinstance(keyword,list):
+
+    processor = KeywordProcessor(case_sensitive=case_sensitive)
+    if isinstance(keyword, list):
         processor.add_keywords_from_list(keyword)
-    elif isinstance(keyword,str):
+    elif isinstance(keyword, str):
         processor.add_keyword(keyword)
-    elif isinstance(keyword,dict):
+    elif isinstance(keyword, dict):
         processor.add_keywords_from_dict(keyword)
 
     return processor.extract_keywords(text)
@@ -52,17 +56,17 @@ def frequent_words(list_words, ngrams_number=1, number_top_words=10):
     Create n-grams for list of tokens
 
     Parameters
-    ----------    
+    ----------
     ngrams_number : int
-    
-    number_top_words : int 
+
+    number_top_words : int
         output dataframe length
 
     Returns
     -------
     DataFrame
         Dataframe with the entities and their frequencies.
-    """             
+    """
     frequent = []
     if ngrams_number == 1:
         pass

From d43eef4a17fd68471575e7eda7e5b679d5e3dec7 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:05:07 +0200
Subject: [PATCH 280/496] fixing lint issues on ngrams.py

---
 nautilus_nlp/analysis/ngrams.py | 14 ++++----------
 1 file changed, 4 insertions(+), 10 deletions(-)

diff --git a/nautilus_nlp/analysis/ngrams.py b/nautilus_nlp/analysis/ngrams.py
index cc892c7..7369b93 100644
--- a/nautilus_nlp/analysis/ngrams.py
+++ b/nautilus_nlp/analysis/ngrams.py
@@ -16,21 +16,15 @@
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
-"""
-Functions to calculate words or ngrams frequencies.
-"""
-from collections import Counter
-
-
-def create_ngrams(token_list, n):
+def create_ngrams(token_list, nb_elements):
     """
     Create n-grams for list of tokens
 
     Parameters
-    ----------    
+    ----------
     token_list : list
         list of strings
-    n :
+    nb_elements :
         number of elements in the n-gram
 
     Returns
@@ -38,5 +32,5 @@ def create_ngrams(token_list, n):
     Generator
         generator of all n-grams
     """
-    ngrams = zip(*[token_list[i:] for i in range(n)])
+    ngrams = zip(*[token_list[index_token:] for index_token in range(nb_elements)])
     return (" ".join(ngram) for ngram in ngrams)

From 13415e62fb997ba5c4c1eef25ff6af87354a679d Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:07:01 +0200
Subject: [PATCH 281/496] fixing lint issues on text_summary.py

---
 nautilus_nlp/analysis/text_summary.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/nautilus_nlp/analysis/text_summary.py b/nautilus_nlp/analysis/text_summary.py
index b109146..02457c6 100644
--- a/nautilus_nlp/analysis/text_summary.py
+++ b/nautilus_nlp/analysis/text_summary.py
@@ -21,36 +21,35 @@
 def is_list_of_strings(lst):
     """
     Parameters
-    ----------    
+    ----------
     lst : list
 
     Returns
     -------
     book
         boolean indicator
-    """      
-    
+    """
     return bool(lst) and isinstance(lst, list) and all(isinstance(elem, str) for elem in lst)
 
 
-def summarize_text(txt, ratio=0.2, language, words=None):
+def summarize_text(txt, ratio=0.2, language="english", words=None):
     """
     Parameters
-    ----------    
+    ----------
     txt : str
         Sting or list of strings containing text to summarize
     ratio : float
         Percentage giving the output text length in reference to the input length.
     language :
         text language. eg. "english"
-    words : 
+    words :
         number of words of the output text or None
 
     Returns
     -------
     string
         string containing the summarized text
-    """      
+    """
 
     if is_list_of_strings(txt):
         txt = ' '.join(txt)

From 8d90917a354fa667931b4f080353ac01b508ddb8 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:15:57 +0200
Subject: [PATCH 282/496] fix lint issues on nautilus_nlp/analysis/visualize.py

---
 nautilus_nlp/analysis/visualize.py | 38 +++++++++++++++---------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/nautilus_nlp/analysis/visualize.py b/nautilus_nlp/analysis/visualize.py
index 6f8d36b..dd16cfb 100644
--- a/nautilus_nlp/analysis/visualize.py
+++ b/nautilus_nlp/analysis/visualize.py
@@ -15,52 +15,52 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from collections import Counter
+
 import matplotlib.pyplot as plt
 import wordcloud
-plt.rcParams["figure.figsize"] = [16,9]
 
+plt.rcParams["figure.figsize"] = [16, 9]
 
-def make_word_cloud(text_or_counter, stop_words=[]):
 
-    if type(text_or_counter) is str:
-        myWordcloud = wordcloud.WordCloud(stopwords=stop_words).generate(text_or_counter)
+def make_word_cloud(text_or_counter, stop_words=None):
+    if isinstance(text_or_counter, str):
+        word_cloud = wordcloud.WordCloud(stopwords=stop_words).generate(text_or_counter)
     else:
-        for w in stop_words:
-            del text_or_counter[w]
-        myWordcloud = wordcloud.WordCloud(stopwords=stop_words).generate_from_frequencies(text_or_counter)
-    plt.imshow(myWordcloud)
+        if stop_words is not None:
+            text_or_counter = Counter(word for word in text_or_counter if word not in stop_words)
+        word_cloud = wordcloud.WordCloud(stopwords=stop_words).generate_from_frequencies(text_or_counter)
+    plt.imshow(word_cloud)
     plt.axis("off")
     plt.show()
 
 
 def print_concordance(tokens, query_word, width=110, n_results=None):
     '''
-    Inputs a list of token and a query word, outputs all the sentences that 
-    contains the query word, display in a nice way. 
+    Inputs a list of token and a query word, outputs all the sentences that
+    contains the query word, display in a nice way.
     width = Integer. Number of caracters to display per text chunk
     n_results = Integer. If not null, filters the number of results displayed.
-    This function is an adaptation of NLTK's print_concordance function. 
+    This function is an adaptation of NLTK's print_concordance function.
     Source: http://www.nltk.org/_modules/nltk/text.html
     '''
     half_width = (width - len(query_word) - 2) // 2
     context = width // 4  # approx number of words of context
-    
+
     results = [i for i, j in enumerate(tokens) if j == query_word]
     if len(results) > 0:
-        if n_results == None:
+        if n_results is None:
             n_results = len(results)
-        print('{} matches for "{}":'.format(len(results),query_word)) 
+        print('{} matches for "{}":'.format(len(results), query_word))
         for i in results[:n_results]:
-            
             # Find the context of query word.
-            left_context = tokens[max(0, i - context) : i]
-            right_context = tokens[i + 1 : i + context]
+            left_context = tokens[max(0, i - context): i]
+            right_context = tokens[i + 1: i + context]
             # Create the pretty lines with the query_word in the middle.
             left_print = ' '.join(left_context)[-half_width:]
             right_print = ' '.join(right_context)[:half_width]
             # The WYSIWYG line of the concordance.
             line_print = ' '.join([left_print, query_word, right_print])
-            before = tokens[:]
             print(line_print)
     else:
-        print('No match for "{}"'.format(query_word))
\ No newline at end of file
+        print('No match for "{}"'.format(query_word))

From dae686bda227a5e01753d92580fe4f3aa804b135 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:17:31 +0200
Subject: [PATCH 283/496] linting config.py

---
 nautilus_nlp/config/config.py | 57 ++++++++++++++++++++++++++++++++---
 1 file changed, 53 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/config/config.py b/nautilus_nlp/config/config.py
index 4f880ff..35681cf 100644
--- a/nautilus_nlp/config/config.py
+++ b/nautilus_nlp/config/config.py
@@ -16,11 +16,60 @@
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/usr/local/bin/python3
-import os 
+import os
 
-path_data = "data/"
-langmodel_name = "lid.176.ftz"
 
 ROOT_FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
 
-COUNTRY_MAPPING_ISO = {'af': 'Afghanistan',  'ax': 'Åland Islands',  'al': 'Albania',  'dz': 'Algeria',  'as': 'American Samoa',  'ad': 'Andorra',  'ao': 'Angola',  'ai': 'Anguilla',  'aq': 'Antarctica',  'ag': 'Antigua and Barbuda',  'ar': 'Argentina',  'am': 'Armenia',  'aw': 'Aruba',  'au': 'Australia',  'at': 'Austria',  'az': 'Azerbaijan',  'bs': 'Bahamas',  'bh': 'Bahrain',  'bd': 'Bangladesh',  'bb': 'Barbados',  'by': 'Belarus',  'be': 'Belgium',  'bz': 'Belize',  'bj': 'Benin',  'bm': 'Bermuda',  'bt': 'Bhutan',  'bo': 'Bolivia (Plurinational State of)',  'bq': 'Bonaire, Sint Eustatius and Saba',  'ba': 'Bosnia and Herzegovina',  'bw': 'Botswana',  'bv': 'Bouvet Island',  'br': 'Brazil',  'io': 'British Indian Ocean Territory',  'bn': 'Brunei Darussalam',  'bg': 'Bulgaria',  'bf': 'Burkina Faso',  'bi': 'Burundi',  'cv': 'Cabo Verde',  'kh': 'Cambodia',  'cm': 'Cameroon',  'ca': 'Canada',  'ky': 'Cayman Islands',  'cf': 'Central African Republic',  'td': 'Chad',  'cl': 'Chile',  'cn': 'China',  'cx': 'Christmas Island',  'cc': 'Cocos (Keeling) Islands',  'co': 'Colombia',  'km': 'Comoros',  'cg': 'Congo',  'cd': 'Congo, Democratic Republic of the',  'ck': 'Cook Islands',  'cr': 'Costa Rica',  'ci': "Côte d'Ivoire",  'hr': 'Croatia',  'cu': 'Cuba',  'cw': 'Curaçao',  'cy': 'Cyprus',  'cz': 'Czechia',  'dk': 'Denmark',  'dj': 'Djibouti',  'dm': 'Dominica',  'do': 'Dominican Republic',  'ec': 'Ecuador',  'eg': 'Egypt',  'sv': 'El Salvador',  'gq': 'Equatorial Guinea',  'er': 'Eritrea',  'ee': 'Estonia',  'sz': 'Eswatini',  'et': 'Ethiopia',  'fk': 'Falkland Islands (Malvinas)',  'fo': 'Faroe Islands',  'fj': 'Fiji',  'fi': 'Finland',  'fr': 'France',  'gf': 'French Guiana',  'pf': 'French Polynesia',  'tf': 'French Southern Territories',  'ga': 'Gabon',  'gm': 'Gambia',  'ge': 'Georgia',  'de': 'Germany',  'gh': 'Ghana',  'gi': 'Gibraltar',  'gr': 'Greece',  'gl': 'Greenland',  'gd': 'Grenada',  'gp': 'Guadeloupe',  'gu': 'Guam',  'gt': 'Guatemala',  'gg': 'Guernsey',  'gn': 'Guinea',  'gw': 'Guinea-Bissau',  'gy': 'Guyana',  'ht': 'Haiti',  'hm': 'Heard Island and McDonald Islands',  'va': 'Holy See',  'hn': 'Honduras',  'hk': 'Hong Kong',  'hu': 'Hungary',  'is': 'Iceland',  'in': 'India',  'id': 'Indonesia',  'ir': 'Iran (Islamic Republic of)',  'iq': 'Iraq',  'ie': 'Ireland',  'im': 'Isle of Man',  'il': 'Israel',  'it': 'Italy',  'jm': 'Jamaica',  'jp': 'Japan',  'je': 'Jersey',  'jo': 'Jordan',  'kz': 'Kazakhstan',  'ke': 'Kenya',  'ki': 'Kiribati',  'kp': "Korea (Democratic People's Republic of)",  'kr': 'Korea, Republic of',  'kw': 'Kuwait',  'kg': 'Kyrgyzstan',  'la': "Lao People's Democratic Republic",  'lv': 'Latvia',  'lb': 'Lebanon',  'ls': 'Lesotho',  'lr': 'Liberia',  'ly': 'Libya',  'li': 'Liechtenstein',  'lt': 'Lithuania',  'lu': 'Luxembourg',  'mo': 'Macao',  'mg': 'Madagascar',  'mw': 'Malawi',  'my': 'Malaysia',  'mv': 'Maldives',  'ml': 'Mali',  'mt': 'Malta',  'mh': 'Marshall Islands',  'mq': 'Martinique',  'mr': 'Mauritania',  'mu': 'Mauritius',  'yt': 'Mayotte',  'mx': 'Mexico',  'fm': 'Micronesia (Federated States of)',  'md': 'Moldova, Republic of',  'mc': 'Monaco',  'mn': 'Mongolia',  'me': 'Montenegro',  'ms': 'Montserrat',  'ma': 'Morocco',  'mz': 'Mozambique',  'mm': 'Myanmar',  'na': 'Namibia',  'nr': 'Nauru',  'np': 'Nepal',  'nl': 'Netherlands',  'nc': 'New Caledonia',  'nz': 'New Zealand',  'ni': 'Nicaragua',  'ne': 'Niger',  'ng': 'Nigeria',  'nu': 'Niue',  'nf': 'Norfolk Island',  'mk': 'North Macedonia',  'mp': 'Northern Mariana Islands',  'no': 'Norway',  'om': 'Oman',  'pk': 'Pakistan',  'pw': 'Palau',  'ps': 'Palestine, State of',  'pa': 'Panama',  'pg': 'Papua New Guinea',  'py': 'Paraguay',  'pe': 'Peru',  'ph': 'Philippines',  'pn': 'Pitcairn',  'pl': 'Poland',  'pt': 'Portugal',  'pr': 'Puerto Rico',  'qa': 'Qatar',  're': 'Réunion',  'ro': 'Romania',  'ru': 'Russian Federation',  'rw': 'Rwanda',  'bl': 'Saint Barthélemy',  'sh': 'Saint Helena, Ascension and Tristan da Cunha',  'kn': 'Saint Kitts and Nevis',  'lc': 'Saint Lucia',  'mf': 'Saint Martin (French part)',  'pm': 'Saint Pierre and Miquelon',  'vc': 'Saint Vincent and the Grenadines',  'ws': 'Samoa',  'sm': 'San Marino',  'st': 'Sao Tome and Principe',  'sa': 'Saudi Arabia',  'sn': 'Senegal',  'rs': 'Serbia',  'sc': 'Seychelles',  'sl': 'Sierra Leone',  'sg': 'Singapore',  'sx': 'Sint Maarten (Dutch part)',  'sk': 'Slovakia',  'si': 'Slovenia',  'sb': 'Solomon Islands',  'so': 'Somalia',  'za': 'South Africa',  'gs': 'South Georgia and the South Sandwich Islands',  'ss': 'South Sudan',  'es': 'Spain',  'lk': 'Sri Lanka',  'sd': 'Sudan',  'sr': 'Suriname',  'sj': 'Svalbard and Jan Mayen',  'se': 'Sweden',  'ch': 'Switzerland',  'sy': 'Syrian Arab Republic',  'tw': 'Taiwan, Province of China',  'tj': 'Tajikistan',  'tz': 'Tanzania, United Republic of',  'th': 'Thailand',  'tl': 'Timor-Leste',  'tg': 'Togo',  'tk': 'Tokelau',  'to': 'Tonga',  'tt': 'Trinidad and Tobago',  'tn': 'Tunisia',  'tr': 'Turkey',  'tm': 'Turkmenistan',  'tc': 'Turks and Caicos Islands',  'tv': 'Tuvalu',  'ug': 'Uganda',  'ua': 'Ukraine',  'ae': 'United Arab Emirates',  'gb': 'United Kingdom of Great Britain and Northern Ireland',  'us': 'United States of America',  'um': 'United States Minor Outlying Islands',  'uy': 'Uruguay',  'uz': 'Uzbekistan',  'vu': 'Vanuatu',  've': 'Venezuela (Bolivarian Republic of)',  'vn': 'Viet Nam',  'vg': 'Virgin Islands (British)',  'vi': 'Virgin Islands (U.S.)',  'wf': 'Wallis and Futuna',  'eh': 'Western Sahara',  'ye': 'Yemen',  'zm': 'Zambia',  'zw': 'Zimbabwe'}
\ No newline at end of file
+COUNTRY_MAPPING_ISO = {
+    'af': 'Afghanistan', 'ax': 'Åland Islands', 'al': 'Albania', 'dz': 'Algeria', 'as': 'American Samoa', 'ad':
+    'Andorra', 'ao': 'Angola', 'ai': 'Anguilla', 'aq': 'Antarctica', 'ag': 'Antigua and Barbuda', 'ar': 'Argentina',
+    'am': 'Armenia', 'aw': 'Aruba', 'au': 'Australia', 'at': 'Austria', 'az': 'Azerbaijan', 'bs': 'Bahamas',
+    'bh': 'Bahrain', 'bd': 'Bangladesh', 'bb': 'Barbados', 'by': 'Belarus', 'be': 'Belgium', 'bz': 'Belize',
+    'bj': 'Benin', 'bm': 'Bermuda', 'bt': 'Bhutan', 'bo': 'Bolivia (Plurinational State of)',
+    'bq': 'Bonaire, Sint Eustatius and Saba', 'ba': 'Bosnia and Herzegovina', 'bw': 'Botswana', 'bv': 'Bouvet Island',
+    'br': 'Brazil', 'io': 'British Indian Ocean Territory', 'bn': 'Brunei Darussalam', 'bg': 'Bulgaria',
+    'bf': 'Burkina Faso', 'bi': 'Burundi', 'cv': 'Cabo Verde', 'kh': 'Cambodia', 'cm': 'Cameroon', 'ca': 'Canada',
+    'ky': 'Cayman Islands', 'cf': 'Central African Republic', 'td': 'Chad', 'cl': 'Chile', 'cn': 'China',
+    'cx': 'Christmas Island', 'cc': 'Cocos (Keeling) Islands', 'co': 'Colombia', 'km': 'Comoros', 'cg': 'Congo',
+    'cd': 'Congo, Democratic Republic of the', 'ck': 'Cook Islands', 'cr': 'Costa Rica', 'ci': "Côte d'Ivoire",
+    'hr': 'Croatia', 'cu': 'Cuba', 'cw': 'Curaçao', 'cy': 'Cyprus', 'cz': 'Czechia', 'dk': 'Denmark', 'dj': 'Djibouti',
+    'dm': 'Dominica', 'do': 'Dominican Republic', 'ec': 'Ecuador', 'eg': 'Egypt', 'sv': 'El Salvador',
+    'gq': 'Equatorial Guinea', 'er': 'Eritrea', 'ee': 'Estonia', 'sz': 'Eswatini', 'et': 'Ethiopia',
+    'fk': 'Falkland Islands (Malvinas)', 'fo': 'Faroe Islands', 'fj': 'Fiji', 'fi': 'Finland', 'fr': 'France',
+    'gf': 'French Guiana', 'pf': 'French Polynesia', 'tf': 'French Southern Territories', 'ga': 'Gabon', 'gm': 'Gambia',
+    'ge': 'Georgia', 'de': 'Germany', 'gh': 'Ghana', 'gi': 'Gibraltar', 'gr': 'Greece', 'gl': 'Greenland',
+    'gd': 'Grenada', 'gp': 'Guadeloupe', 'gu': 'Guam', 'gt': 'Guatemala', 'gg': 'Guernsey', 'gn': 'Guinea',
+    'gw': 'Guinea-Bissau', 'gy': 'Guyana', 'ht': 'Haiti', 'hm': 'Heard Island and McDonald Islands', 'va': 'Holy See',
+    'hn': 'Honduras', 'hk': 'Hong Kong', 'hu': 'Hungary', 'is': 'Iceland', 'in': 'India', 'id': 'Indonesia',
+    'ir': 'Iran (Islamic Republic of)', 'iq': 'Iraq', 'ie': 'Ireland', 'im': 'Isle of Man', 'il': 'Israel', 'it':
+    'Italy', 'jm': 'Jamaica', 'jp': 'Japan', 'je': 'Jersey', 'jo': 'Jordan', 'kz': 'Kazakhstan', 'ke': 'Kenya',
+    'ki': 'Kiribati', 'kp': "Korea (Democratic People's Republic of)", 'kr': 'Korea, Republic of', 'kw': 'Kuwait',
+    'kg': 'Kyrgyzstan', 'la': "Lao People's Democratic Republic", 'lv': 'Latvia', 'lb': 'Lebanon', 'ls': 'Lesotho',
+    'lr': 'Liberia', 'ly': 'Libya', 'li': 'Liechtenstein', 'lt': 'Lithuania', 'lu': 'Luxembourg', 'mo': 'Macao',
+    'mg': 'Madagascar', 'mw': 'Malawi', 'my': 'Malaysia', 'mv': 'Maldives', 'ml': 'Mali', 'mt': 'Malta',
+    'mh': 'Marshall Islands', 'mq': 'Martinique', 'mr': 'Mauritania', 'mu': 'Mauritius', 'yt': 'Mayotte',
+    'mx': 'Mexico', 'fm': 'Micronesia (Federated States of)', 'md': 'Moldova, Republic of', 'mc': 'Monaco',
+    'mn': 'Mongolia', 'me': 'Montenegro', 'ms': 'Montserrat', 'ma': 'Morocco', 'mz': 'Mozambique', 'mm': 'Myanmar',
+    'na': 'Namibia', 'nr': 'Nauru', 'np': 'Nepal', 'nl': 'Netherlands', 'nc': 'New Caledonia', 'nz': 'New Zealand',
+    'ni': 'Nicaragua', 'ne': 'Niger', 'ng': 'Nigeria', 'nu': 'Niue', 'nf': 'Norfolk Island', 'mk': 'North Macedonia',
+    'mp': 'Northern Mariana Islands', 'no': 'Norway', 'om': 'Oman', 'pk': 'Pakistan', 'pw': 'Palau',
+    'ps': 'Palestine, State of', 'pa': 'Panama', 'pg': 'Papua New Guinea', 'py': 'Paraguay', 'pe': 'Peru',
+    'ph': 'Philippines', 'pn': 'Pitcairn', 'pl': 'Poland', 'pt': 'Portugal', 'pr': 'Puerto Rico', 'qa': 'Qatar',
+    're': 'Réunion', 'ro': 'Romania', 'ru': 'Russian Federation', 'rw': 'Rwanda', 'bl': 'Saint Barthélemy',
+    'sh': 'Saint Helena, Ascension and Tristan da Cunha', 'kn': 'Saint Kitts and Nevis', 'lc': 'Saint Lucia',
+    'mf': 'Saint Martin (French part)', 'pm': 'Saint Pierre and Miquelon', 'vc': 'Saint Vincent and the Grenadines',
+    'ws': 'Samoa', 'sm': 'San Marino', 'st': 'Sao Tome and Principe', 'sa': 'Saudi Arabia', 'sn': 'Senegal',
+    'rs': 'Serbia', 'sc': 'Seychelles', 'sl': 'Sierra Leone', 'sg': 'Singapore', 'sx': 'Sint Maarten (Dutch part)',
+    'sk': 'Slovakia', 'si': 'Slovenia', 'sb': 'Solomon Islands', 'so': 'Somalia', 'za': 'South Africa',
+    'gs': 'South Georgia and the South Sandwich Islands', 'ss': 'South Sudan', 'es': 'Spain', 'lk': 'Sri Lanka',
+    'sd': 'Sudan', 'sr': 'Suriname', 'sj': 'Svalbard and Jan Mayen', 'se': 'Sweden', 'ch': 'Switzerland',
+    'sy': 'Syrian Arab Republic', 'tw': 'Taiwan, Province of China', 'tj': 'Tajikistan',
+    'tz': 'Tanzania, United Republic of', 'th': 'Thailand', 'tl': 'Timor-Leste', 'tg': 'Togo', 'tk': 'Tokelau',
+    'to': 'Tonga', 'tt': 'Trinidad and Tobago', 'tn': 'Tunisia', 'tr': 'Turkey', 'tm': 'Turkmenistan',
+    'tc': 'Turks and Caicos Islands', 'tv': 'Tuvalu', 'ug': 'Uganda', 'ua': 'Ukraine', 'ae': 'United Arab Emirates',
+    'gb': 'United Kingdom of Great Britain and Northern Ireland', 'us': 'United States of America',
+    'um': 'United States Minor Outlying Islands', 'uy': 'Uruguay', 'uz': 'Uzbekistan', 'vu': 'Vanuatu',
+    've': 'Venezuela (Bolivarian Republic of)', 'vn': 'Viet Nam', 'vg': 'Virgin Islands (British)',
+    'vi': 'Virgin Islands (U.S.)', 'wf': 'Wallis and Futuna', 'eh': 'Western Sahara', 'ye': 'Yemen', 'zm': 'Zambia',
+    'zw': 'Zimbabwe'}

From c467246a87523cf3ae3e631bf2fc92cedbef623b Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:50:12 +0200
Subject: [PATCH 284/496] not downloading fasttext in CI

---
 .travis.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 0304bca..ff06902 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -23,7 +23,6 @@ services:
   - docker
 
 before_script:
-  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
   - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
 
   - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip

From 22374e829c6071c2f098398b92780d08ee4f0ca9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 17:59:54 +0200
Subject: [PATCH 285/496] downloading spacy from requirements

---
 .travis.yml      | 2 --
 requirements.txt | 6 ++++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index ff06902..964c33b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -23,8 +23,6 @@ services:
   - docker
 
 before_script:
-  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
-
   - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
   - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
diff --git a/requirements.txt b/requirements.txt
index 14c8197..3c52e2d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,14 +1,16 @@
 numpy==1.18.1
 chardet==3.0.4
 pandas==0.25.1
-spacy==2.1.8
+spacy==2.2.4
+https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz
+https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.2.5/fr_core_news_sm-2.2.5.tar.gz
 click==7.1.1
 matplotlib==3.1.1
 tqdm==4.45.0
 biterm==0.1.5
 flashtext==2.7
 ftfy==5.8
-gensim==3.8.3
+gensim==3.7.1
 ipython==7.18.1
 nlpaug==0.0.20
 nltk==3.5

From 5f3fea6e397c90e2de50fb2232fa8e9cdbf201f8 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 18:03:59 +0200
Subject: [PATCH 286/496] installing in CI with pip3

---
 .travis.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 964c33b..8eb1c9e 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -27,7 +27,7 @@ before_script:
   - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
 install:
-  - pip install -r requirements.txt
-  - pip install -e .
+  - pip3 install -r requirements.txt
+  - pip3 install -e .
 script:
   - pytest tests/*

From 66f2e6649d062bca04eaf50e9243c6cf0756c80a Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 18:08:04 +0200
Subject: [PATCH 287/496] adding external requirements to fix CI

---
 requirements.txt | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index 3c52e2d..0b561bb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,11 @@
+# external requirements
+Sphinx
+sphinx_rtd_theme
+coverage
+python-dotenv>=0.5.1
+pillow
+pytest==6.0.2
+
 numpy==1.18.1
 chardet==3.0.4
 pandas==0.25.1

From b98b2afb31c0f8cc1df4e35104e003bb99f1e748 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 18:10:42 +0200
Subject: [PATCH 288/496] adding external requirements to fix CI

---
 requirements.txt | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 0b561bb..c7a93b1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
 # external requirements
-Sphinx
-sphinx_rtd_theme
-coverage
+Sphinx==3.2.1
+sphinx_rtd_theme==0.5.0
+coverage==5.3
 python-dotenv>=0.5.1
 pillow
 pytest==6.0.2
@@ -25,7 +25,6 @@ nltk==3.5
 phonenumbers==8.12.9
 pyLDAvis==2.1.2
 pytest==6.0.2
-python-dotenv==0.14.0
 regex==2020.7.14
 sacremoses==0.0.43
 scikit_learn==0.23.2

From b4b4c3597dcc6bea227a66d71694c007e6e9d473 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 18:13:41 +0200
Subject: [PATCH 289/496] removing ipython to fix CI

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c7a93b1..928dad5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -19,7 +19,6 @@ biterm==0.1.5
 flashtext==2.7
 ftfy==5.8
 gensim==3.7.1
-ipython==7.18.1
 nlpaug==0.0.20
 nltk==3.5
 phonenumbers==8.12.9

From eda21ec8f2ef592e063b4b155a86edfa94b67bc8 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 18:17:46 +0200
Subject: [PATCH 290/496] removing pip3

---
 .travis.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 8eb1c9e..964c33b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -27,7 +27,7 @@ before_script:
   - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
 install:
-  - pip3 install -r requirements.txt
-  - pip3 install -e .
+  - pip install -r requirements.txt
+  - pip install -e .
 script:
   - pytest tests/*

From 5b9d72c89e2884928b486423e3013175c121869d Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 18:25:20 +0200
Subject: [PATCH 291/496] rollback to old versions packages

---
 requirements.txt | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 928dad5..bbe857c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,7 +6,9 @@ python-dotenv>=0.5.1
 pillow
 pytest==6.0.2
 
-numpy==1.18.1
+pyLDAvis==2.1.2
+gensim==3.7.1
+numpy==1.15.4
 chardet==3.0.4
 pandas==0.25.1
 spacy==2.2.4
@@ -17,17 +19,16 @@ matplotlib==3.1.1
 tqdm==4.45.0
 biterm==0.1.5
 flashtext==2.7
-ftfy==5.8
-gensim==3.7.1
+ftfy<5.0.0,>=4.2.0
 nlpaug==0.0.20
-nltk==3.5
-phonenumbers==8.12.9
-pyLDAvis==2.1.2
+nltk==3.4.5
+phonenumbers==8.10.12
 pytest==6.0.2
-regex==2020.7.14
+regex==2019.8.19
 sacremoses==0.0.43
-scikit_learn==0.23.2
+scikit_learn==0.20.3
 stop_words==2018.7.23
 wordcloud==1.8.0
+emoji>=0.5.2
 summa==1.2.0
 pylint==2.4.4

From 32e2bedfab36628babd40ace62bdfa5128819c33 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 24 Sep 2020 18:29:16 +0200
Subject: [PATCH 292/496] rolling back to old requirements and CI

---
 .travis.yml      |  5 ++++-
 requirements.txt | 51 +++++++++++++++++++++++++++---------------------
 2 files changed, 33 insertions(+), 23 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 964c33b..d2220c0 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -23,6 +23,9 @@ services:
   - docker
 
 before_script:
+  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
+  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
+
   - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
   - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
   
@@ -30,4 +33,4 @@ install:
   - pip install -r requirements.txt
   - pip install -e .
 script:
-  - pytest tests/*
+  - pytest tests/*
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index bbe857c..36d1946 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,34 +1,41 @@
+# local package
+#-e .
+
 # external requirements
-Sphinx==3.2.1
-sphinx_rtd_theme==0.5.0
-coverage==5.3
+Sphinx
+sphinx_rtd_theme
+coverage
 python-dotenv>=0.5.1
 pillow
-pytest==6.0.2
+pytest
 
+#library requirements
 pyLDAvis==2.1.2
 gensim==3.7.1
-numpy==1.15.4
+sacremoses==0.0.13
+stop-words==2018.7.23
+spacy==2.1.3
+ftfy<5.0.0,>=4.2.0
+wordcloud>=1.5.0
+matplotlib>=3.0.3
+mosestokenizer
+numpy>1.15.4
+stop_words==2018.7.23
+nltk>=3.4.5
+textblob==0.15.3
+textblob_fr==0.2.0
+pandas>=0.23.4
 chardet==3.0.4
-pandas==0.25.1
-spacy==2.2.4
-https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz
-https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.2.5/fr_core_news_sm-2.2.5.tar.gz
-click==7.1.1
-matplotlib==3.1.1
-tqdm==4.45.0
-biterm==0.1.5
+setuptools==40.8.0
+textacy==0.6.3
+#fastText==0.8.3
+gensim==3.7.1
+scikit_learn==0.20.3
+vaderSentiment==3.2.1
+google-compute-engine==2.8.13
 flashtext==2.7
-ftfy<5.0.0,>=4.2.0
-nlpaug==0.0.20
-nltk==3.4.5
 phonenumbers==8.10.12
-pytest==6.0.2
 regex==2019.8.19
-sacremoses==0.0.43
-scikit_learn==0.20.3
-stop_words==2018.7.23
-wordcloud==1.8.0
 emoji>=0.5.2
 summa==1.2.0
-pylint==2.4.4
+biterm==0.1.5

From b309e35c0c63bfae0bb8e96e2f4ee4110b5ad295 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 10:41:49 +0200
Subject: [PATCH 293/496] removing make_dataset.py file

---
 nautilus_nlp/data/make_dataset.py | 47 -------------------------------
 1 file changed, 47 deletions(-)
 delete mode 100644 nautilus_nlp/data/make_dataset.py

diff --git a/nautilus_nlp/data/make_dataset.py b/nautilus_nlp/data/make_dataset.py
deleted file mode 100644
index 186fb78..0000000
--- a/nautilus_nlp/data/make_dataset.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
-import click
-import logging
-from pathlib import Path
-from dotenv import find_dotenv, load_dotenv
-
-
-@click.command()
-@click.argument("input_filepath", type=click.Path(exists=True))
-@click.argument("output_filepath", type=click.Path())
-def main(input_filepath, output_filepath):
-    """ Runs data processing scripts to turn raw data from (../raw) into
-        cleaned data ready to be analyzed (saved in ../processed).
-    """
-    logger = logging.getLogger(__name__)
-    logger.info("making final data set from raw data")
-
-
-if __name__ == "__main__":
-    log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-    logging.basicConfig(level=logging.INFO, format=log_fmt)
-
-    # not used in this stub but often useful for finding various files
-    project_dir = Path(__file__).resolve().parents[2]
-
-    # find .env automagically by walking up directories until it's found, then
-    # load up the .env entries as environment variables
-    load_dotenv(find_dotenv())
-
-    main()

From 4eb3d37c3356d724fb849258d3752b0a15860123 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:14:50 +0200
Subject: [PATCH 294/496] linting data_augmentation.py

---
 .../preprocessing/data_augmentation.py        | 118 ++++++++++--------
 requirements.txt                              |   1 +
 2 files changed, 69 insertions(+), 50 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 69b84e1..1263260 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -1,13 +1,19 @@
 import copy
 import logging
-import nlpaug.augmenter.word as naw
 import re
 
+import nlpaug.augmenter.word as naw
+
+
+class CouldNotAugment(ValueError):
+    pass
+
+
 def augment_utterance(text, method, stopwords, intent=None, entities=None):
     """
     Given ``text`` str, create a new similar utterance by modifying some words
-    in the initial sentence, modifications depend on the chosen method 
-    (substitution with synonym, addition, deletion). If intent and/or entities 
+    in the initial sentence, modifications depend on the chosen method
+    (substitution with synonym, addition, deletion). If intent and/or entities
     are given as input, they will remain unchanged.
 
     Parameters
@@ -34,74 +40,86 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
 
     Returns
     -------
-    dictionary with augmented text and optional keys depending on input   
+    dictionary with augmented text and optional keys depending on input
     """
     new_utt = {}
-    if entities:
-        formatted_entities = [(text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),entities[i]['entity']) for i in range(len(entities))]
-    augmenter = select_augmenter(method,stopwords)
+    augmenter = select_augmenter(method, stopwords)
     new_utt['text'] = augmenter.augment(text)
-    if intent:
+    if intent is not None:
         new_utt['intent'] = intent
-    if entities:
-        if are_entities_in_augmented_text(entities,new_utt['text']):        
-            new_utt['entities'] = get_augmented_entities(new_utt['text'],formatted_entities)
+    if entities is not None:
+        formatted_entities = [(text[entities[i]['startCharIndex']:entities[i]['endCharIndex']
+                                    ].strip(), entities[i]['entity']) for i in range(len(entities))]
+        if are_entities_in_augmented_text(entities, new_utt['text']):
+            new_utt['entities'] = get_augmented_entities(new_utt['text'], formatted_entities)
             return clean_sentence_entities(new_utt)
-        else:
-            logging.info('Text was not correctly augmented so not added')
-    else:
-        return new_utt
+        raise CouldNotAugment('Text was not correctly augmented so not added')
+    return new_utt
 
-def are_entities_in_augmented_text(entities,augmented_text):
+
+def are_entities_in_augmented_text(entities, augmented_text):
     check = True
     for ent in entities:
         if ent['word'] not in augmented_text:
             check = False
     return check
 
-def select_augmenter(method,stopwords,use_stopwords=True):
-    if use_stopwords:
-        stopwords = stopwords
-    else: 
+
+def select_augmenter(method, stopwords, use_stopwords=True):
+    if not use_stopwords:
         stopwords = []
     if method == 'wordnet_synonym':
-        augmenter = naw.SynonymAug(aug_src='wordnet',stopwords=stopwords)
+        augmenter = naw.SynonymAug(aug_src='wordnet', stopwords=stopwords)
     elif method == 'aug_sub_bert':
-        augmenter = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute",stopwords=stopwords)  
-    return(augmenter) 
+        augmenter = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute", stopwords=stopwords)
+    return(augmenter)
+
 
-def get_augmented_entities(sentence_augmented,entities):
+def get_augmented_entities(sentence_augmented, entities):
     entities_augmented = []
-    for entity in entities :
-        regex = r'(?:^|\W)' + re.escape(entity[0].strip()) + '(?:$|\W)'
+    for entity in entities:
+        regex = r'(?:^|\W)' + re.escape(entity[0].strip()) + r'(?:$|\W)'
         if (re.search(re.compile(regex), sentence_augmented)):
-            start_index = re.search(regex,sentence_augmented).start()+1
-            end_index = re.search(regex,sentence_augmented).end()-1
-            new_entity = {'entity': entity[1],'word': sentence_augmented[start_index:end_index],'startCharIndex': start_index,'endCharIndex': end_index}
-            entities_augmented.append(new_entity) 
+            start_index = re.search(regex, sentence_augmented).start()+1
+            end_index = re.search(regex, sentence_augmented).end()-1
+            new_entity = {
+                'entity': entity[1],
+                'word': sentence_augmented[start_index: end_index],
+                'startCharIndex': start_index, 'endCharIndex': end_index}
+            entities_augmented.append(new_entity)
     return entities_augmented
 
+
 def clean_sentence_entities(sentence_input):
     sentence = copy.copy(sentence_input)
-    for element1 in sentence['entities']: 
-        for element2 in sentence['entities'] :
-            result = check_interval_included(element1,element2)
-            if result:
-                try :                                                                       
+    for element1 in sentence['entities']:
+        for element2 in sentence['entities']:
+            result = check_interval_included(element1, element2)
+            if result is not None:
+                try:
                     sentence[1]['entities'].remove(result[0])
-                except : 
-                    logging.info("Cant remove entity : {} \n entities are now :{} \n for sentence : {} ".format(result,sentence['entities'],sentence['text']))
+                except IndexError:
+                    logging.warning(
+                        "Cant remove entity : {} \n entities are now :{} \n for sentence : {} ".format(
+                            result, sentence['entities'],
+                            sentence['text']))
                     continue
-    return(sentence)       
-
-def check_interval_included(element1,element2):
-    if ((element1 != element2) and (element1['startCharIndex'] >= element2['startCharIndex']) and (element1['endCharIndex'] <= element2['endCharIndex'])):
-        return((element1,element2))
-    elif ((element1 != element2) and (element2['startCharIndex'] >= element1['startCharIndex']) and (element2['endCharIndex'] <= element1['endCharIndex'])):
-        return((element2,element1))
-    elif ((element1 != element2) and (element1['startCharIndex'] >= element2['startCharIndex']) and (element1['endCharIndex'] >= element2['endCharIndex']) and (element1['startCharIndex'] <= element2['endCharIndex']-1)):
-        return((element1,element2))
-    elif ((element1 != element2) and (element2['startCharIndex'] >= element1['startCharIndex']) and (element2['endCharIndex'] >= element1['endCharIndex']) and (element2['startCharIndex'] < element1['endCharIndex']-1)):
-        return((element2,element1))
-    else :
-        return(False)
\ No newline at end of file
+    return sentence
+
+
+def check_interval_included(element1, element2):
+    if ((element1 != element2) and (element1['startCharIndex'] >= element2['startCharIndex']) and
+            (element1['endCharIndex'] <= element2['endCharIndex'])):
+        return element1, element2
+    if ((element1 != element2) and (element2['startCharIndex'] >= element1['startCharIndex']) and
+            (element2['endCharIndex'] <= element1['endCharIndex'])):
+        return element2, element1
+    if ((element1 != element2) and (element1['startCharIndex'] >= element2['startCharIndex']) and
+            (element1['endCharIndex'] >= element2['endCharIndex']) and
+            (element1['startCharIndex'] <= element2['endCharIndex']-1)):
+        return element1, element2
+    if ((element1 != element2) and (element2['startCharIndex'] >= element1['startCharIndex']) and
+            (element2['endCharIndex'] >= element1['endCharIndex']) and
+            (element2['startCharIndex'] < element1['endCharIndex']-1)):
+        return element2, element1
+    return None
diff --git a/requirements.txt b/requirements.txt
index 36d1946..87479ee 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -39,3 +39,4 @@ regex==2019.8.19
 emoji>=0.5.2
 summa==1.2.0
 biterm==0.1.5
+nlpaug==1.0.1

From 100067b4ca4296e3108601ebb3a78882712966b3 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:15:01 +0200
Subject: [PATCH 295/496] adding pylintrc

---
 pylintrc | 382 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 382 insertions(+)
 create mode 100644 pylintrc

diff --git a/pylintrc b/pylintrc
new file mode 100644
index 0000000..f16004a
--- /dev/null
+++ b/pylintrc
@@ -0,0 +1,382 @@
+[MASTER]
+
+# Specify a configuration file.
+#rcfile=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=CVS
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Use multiple processes to speed up Pylint.
+jobs=4
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code
+extension-pkg-whitelist=
+
+# Allow optimization of some AST trees. This will activate a peephole AST
+# optimizer, which will apply various small optimizations. For instance, it can
+# be used to obtain the result of joining multiple strings with the addition
+# operator. Joining a lot of strings can lead to a maximum recursion error in
+# Pylint and this flag can prevent that. It has one side effect, the resulting
+# AST will be different than the one from reality.
+optimize-ast=no
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
+confidence=
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+#enable=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once).You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use"--disable=all --enable=classes
+# --disable=W"
+disable=unpacking-in-except,unicode-builtin,getslice-method,intern-builtin,long-suffix,reload-builtin,oct-method,indexing-exception,file-builtin,basestring-builtin,coerce-method,old-ne-operator,old-raise-syntax,import-star-module-level,range-builtin-not-iterating,map-builtin-not-iterating,backtick,dict-iter-method,hex-method,nonzero-method,buffer-builtin,setslice-method,xrange-builtin,no-absolute-import,unichr-builtin,long-builtin,old-division,using-cmp-argument,parameter-unpacking,reduce-builtin,old-octal-literal,filter-builtin-not-iterating,cmp-builtin,raising-string,useless-suppression,print-statement,round-builtin,input-builtin,raw_input-builtin,coerce-builtin,next-method-called,standarderror-builtin,suppressed-message,metaclass-assignment,apply-builtin,delslice-method,cmp-method,zip-builtin-not-iterating,execfile-builtin,dict-view-method,missing-docstring,too-few-public-methods,superfluous-parens,logging-format-interpolation,too-many-arguments
+
+
+[REPORTS]
+
+# Set the output format. Available formats are text, parseable, colorized, msvs
+# (visual studio) and html. You can also give a reporter class, eg
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Put messages in a separate file for each module / package specified on the
+# command line instead of printing them on stdout. Reports (if any) will be
+# written in a file name "pylint_global.[txt|html]".
+files-output=no
+
+# Tells whether to display a full report or only the messages
+reports=yes
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details
+#msg-template=
+
+
+[VARIABLES]
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# A regular expression matching the name of dummy variables (i.e. expectedly
+# not used).
+dummy-variables-rgx=_$|dummy
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid to define new builtins when possible.
+additional-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,_cb
+
+# Enable pylint to get numpy and pandas dependencies
+generated-members=pandas.*,numpy.*
+
+
+[SIMILARITIES]
+
+# Minimum lines number of a similarity.
+min-similarity-lines=15
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+
+[SPELLING]
+
+# Spelling dictionary name. Available dictionaries: none. To make it working
+# install python-enchant package.
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[LOGGING]
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format
+logging-modules=logging
+
+
+[FORMAT]
+
+# Maximum number of characters on a single line.
+max-line-length=120
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+# List of optional constructs for which whitespace checking is disabled. `dict-
+# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
+# `trailing-comma` allows a space between comma and closing bracket: (a, ).
+# `empty-line` allows space-only lines.
+no-space-check=trailing-comma,dict-separator
+
+# Maximum number of lines in a module
+max-module-lines=1000
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Number of spaces of indent required inside a hanging  or continued line.
+indent-after-paren=4
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+
+[BASIC]
+
+# List of builtins function names that should not be used, separated by a comma
+bad-functions=map,filter
+
+# Good variable names which should always be accepted, separated by a comma
+good-names=i,j,k,s,ex,Run,_,X,standardized_X,df,logger,y,X_train,X_test,X_predict,X_validate
+
+# Bad variable names which should always be refused, separated by a comma
+bad-names=foo,bar,baz,toto,tutu,tata
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Include a hint for the correct naming format with invalid-name
+include-naming-hint=no
+
+# Regular expression matching correct constant names
+const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Naming hint for constant names
+const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
+
+# Regular expression matching correct class names
+class-rgx=[A-Z_][a-zA-Z0-9]+$
+
+# Naming hint for class names
+class-name-hint=[A-Z_][a-zA-Z0-9]+$
+
+# Regular expression matching correct attribute names
+attr-rgx=[a-z_][a-zX0-9_]{2,30}$
+
+# Naming hint for attribute names
+attr-name-hint=[a-z_][a-zX0-9_]{2,30}$
+
+# Regular expression matching correct class attribute names
+class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Naming hint for class attribute names
+class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
+
+# Regular expression matching correct function names
+function-rgx=[a-z_]([a-z0-9_]{2,30})*$
+
+# Naming hint for function names
+function-name-hint=[a-z_]([a-z0-9_]{2,30})*$
+
+# Regular expression matching correct inline iteration names
+inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
+
+# Naming hint for inline iteration names
+inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
+
+# Regular expression matching correct argument names
+argument-rgx=[a-z_][a-z0-9_]{2,30}$
+
+# Naming hint for argument names
+argument-name-hint=[a-z_][a-z0-9_]{2,30}$
+
+# Regular expression matching correct method names
+method-rgx=[a-z_]([a-z0-9_]{2,30})*$
+
+# Naming hint for method names
+method-name-hint=[a-z_]([a-z0-9_]{2,30})*$
+
+# Regular expression matching correct variable names
+variable-rgx=[a-z_]([a-z0-9_]{2,30})*$
+
+# Naming hint for variable names
+variable-name-hint=[a-z_]([a-z0-9_]{2,30})*$
+
+# Regular expression matching correct module names
+module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Naming hint for module names
+module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^_
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+
+[ELIF]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,XXX
+
+
+[TYPECHECK]
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis. It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# List of classes names for which member attributes should not be checked
+# (useful for classes with attributes dynamically set). This supports can work
+# with qualified names.
+ignored-classes=DatetimeIndex
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=
+
+
+[IMPORTS]
+
+# Deprecated modules which should not be used, separated by a comma
+deprecated-modules=optparse
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled)
+import-graph=
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled)
+ext-import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled)
+int-import-graph=
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method
+max-args=6
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore
+ignored-argument-names=_.*
+
+# Maximum number of locals for function / method body
+max-locals=20
+
+# Maximum number of return / yield for function / method body
+max-returns=6
+
+# Maximum number of branch for function / method body
+max-branches=12
+
+# Maximum number of statements in function / method body
+max-statements=51
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of boolean expressions in a if statement
+max-bool-expr=5
+
+
+[CLASSES]
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,__new__,setUp
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=mcs
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,_fields,_replace,_source,_make
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception"
+overgeneral-exceptions=Exception

From 269628003006114a07f2e7f56b99329859e6fc33 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:17:54 +0200
Subject: [PATCH 296/496] linting social_preprocess.py

---
 .../preprocessing/social_preprocess.py        | 25 +++++++++----------
 1 file changed, 12 insertions(+), 13 deletions(-)

diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index cde003e..8bab161 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -19,14 +19,13 @@
 
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-import re
 import emoji as _emoji
 from nautilus_nlp.utils import constants
 
 
 class SocialPreprocessor():
 
-    def __init__(self,text):
+    def __init__(self, text):
         self.text = text
 
     def remove_mentions(self) -> str:
@@ -36,7 +35,7 @@ def remove_mentions(self) -> str:
         Parameters
         ----------
         text : str
-        
+
         Returns
         -------
         string
@@ -52,7 +51,7 @@ def extract_mentions(self) -> list:
         Parameters
         ----------
         text : str
-        
+
         Returns
         -------
         string
@@ -66,7 +65,7 @@ def remove_html_tags(self) -> str:
         Parameters
         ----------
         text : str
-        
+
         Returns
         -------
         string
@@ -77,7 +76,7 @@ def remove_html_tags(self) -> str:
     def remove_emoji(self) -> str:
         """
         Remove emoji from any str by stripping any unicode in the range of Emoji unicode
-        as defined in the unicode convention: 
+        as defined in the unicode convention:
         http://www.unicode.org/emoji/charts/full-emoji-list.html
 
         Parameters
@@ -100,18 +99,17 @@ def convert_emoji_to_text(self, code_delimiters=(':', ':'), input_str=None) -> s
         Parameters
         ----------
         text : str
-            code_delimiters : tuple of symbols around the emoji code. 
+            code_delimiters : tuple of symbols around the emoji code.
             eg: (':',':') --> :grinning_face:
 
         Returns
         -------
         str
-            string 
+            string
         """
-        if input_str:
+        if input_str is not None:
             return _emoji.demojize(input_str, delimiters=code_delimiters)
-        else:
-            return _emoji.demojize(self.text, delimiters=code_delimiters)
+        return _emoji.demojize(self.text, delimiters=code_delimiters)
 
     def extract_emojis(self) -> list:
         """
@@ -164,7 +162,8 @@ def remove_hashtag(self) -> str:
         self.text = self.normalize_whitespace(constants.HASHTAG_PATTERN.sub('', self.text))
         return self.text
 
-    def normalize_whitespace(self, text) -> str:
+    @staticmethod
+    def normalize_whitespace(text) -> str:
         """
         Given ``text`` str, replace one or more spacings with a single space, and one
         or more linebreaks with a single newline. Also strip leading/trailing whitespace.
@@ -176,7 +175,7 @@ def normalize_whitespace(self, text) -> str:
 
         Returns
         -------
-        string    
+        string
         """
         return constants.NONBREAKING_SPACE_REGEX.sub(
             " ", constants.LINEBREAK_REGEX.sub(r"\n", text)

From 9b147a86ae29d70e54ef5d2bdf34d6df6c8ff79c Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:30:55 +0200
Subject: [PATCH 297/496] linting text_preprocess.py

---
 nautilus_nlp/preprocessing/text_preprocess.py | 80 ++++++++-----------
 nautilus_nlp/utils/phone_number.py            |  2 +-
 tests/test_preprocessor.py                    |  4 +-
 3 files changed, 38 insertions(+), 48 deletions(-)

diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
index 1b4353a..5c112cb 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -17,37 +17,39 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 
-from __future__ import absolute_import, division, print_function, unicode_literals
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
 
 import re
-import regex
 import unicodedata
-from ftfy import fix_text as _fix_text
 
-from nautilus_nlp.utils.stopwords import get_stopwords
-from nautilus_nlp.utils.phone_number import extract_phone_numbers as _extract_phone_numbers
+from ftfy import fix_text as _fix_text
 from nautilus_nlp.utils import constants
+from nautilus_nlp.utils.phone_number import \
+    extract_phone_numbers as _extract_phone_numbers
+from nautilus_nlp.utils.stopwords import get_stopwords
+
 
 class TextPreprocessor():
 
-    def __init__(self,text):
-        if isinstance(text,str):
+    def __init__(self, text):
+        if isinstance(text, str):
             self.text = text
         else:
             raise ValueError("Input must be a string")
 
-    def clean_text(self,lang='en') -> str:
+    def clean_text(self, lang='en') -> str:
         #TODO : check how to pipe operations
         stopwords = get_stopwords(lang)
         self.text = self.fix_bad_unicode(normalization="NFC")
-        self.text = self.remove_EOL_characters()
+        self.text = self.remove_eol_characters()
         self.text = self.remove_accents(method="unicode")
         self.text = self.remove_punct()
         self.text = self.text.lower()
         self.text = self.remove_stopwords(stopwords=stopwords)
         return self.normalize_whitespace()
 
-    def remove_EOL_characters(self) -> str:
+    def remove_eol_characters(self) -> str:
         """
         Remove end of line (\n) char.
 
@@ -63,7 +65,7 @@ def remove_EOL_characters(self) -> str:
         return self.text
 
     def remove_stopwords(self, stopwords: list) -> str:
-        """ 
+        """
         Remove stopwords from a text.
         eg. 'I like when you move your body !' -> 'I move body !'
 
@@ -81,8 +83,7 @@ def remove_stopwords(self, stopwords: list) -> str:
         ValueError
             When inputs is not a string
         """
-        self.text =  ' '.join([word.strip() for word in self.text.split() if word not in stopwords])
-        
+        self.text = ' '.join([word.strip() for word in self.text.split() if word not in stopwords])
         return self.text
 
     def fix_bad_unicode(self, normalization: str = "NFC") -> str:
@@ -95,7 +96,7 @@ def fix_bad_unicode(self, normalization: str = "NFC") -> str:
         ----------
         text : string
 
-        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}): 
+        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}):
             if 'NFC', combines characters and diacritics written using separate code points,
             e.g. converting "e" plus an acute accent modifier into "é"; unicode
             can be converted to NFC form without any change in its meaning!
@@ -121,7 +122,7 @@ def normalize_whitespace(self) -> str:
 
         Returns
         -------
-        string    
+        string
         """
         self.text = constants.NONBREAKING_SPACE_REGEX.sub(
             " ", constants.LINEBREAK_REGEX.sub(r"\n", self.text)
@@ -141,7 +142,7 @@ def unpack_english_contractions(self) -> str:
 
         Returns
         -------
-        string    
+        string
         """
 
         # standard
@@ -166,7 +167,7 @@ def unpack_english_contractions(self) -> str:
         self.text = constants.CONTRACTION_YALL_YOUALL.sub(r"\1\2ou all", self.text)
         return self.text
 
-    def replace_urls(self, replace_with:str="*URL*") -> str:
+    def replace_urls(self, replace_with: str = "*URL*") -> str:
         """
         Replace all URLs in ``text`` str with ``replace_with`` str.
 
@@ -179,7 +180,7 @@ def replace_urls(self, replace_with:str="*URL*") -> str:
         Returns
         -------
         string
-        """    
+        """
         self.text = constants.URL_REGEX.sub(
             replace_with, constants.SHORT_URL_REGEX.sub(replace_with, self.text)
         )
@@ -202,10 +203,9 @@ def replace_emails(self, replace_with="*EMAIL*") -> str:
         self.text = constants.EMAIL_REGEX.sub(replace_with, self.text)
         return self.text
 
-    def replace_phone_numbers(self, replace_with:str="*PHONE*",
-                                    method:str="regex",
-                                    country_format_to_detect:list=[None,'FR','US','GB'],
-                                    return_text: bool = False) -> str:
+    def replace_phone_numbers(self, country_format_to_detect: list,
+                              replace_with: str = "*PHONE*",
+                              method: str = "regex") -> str:
         """
         Replace all phone numbers in ``text`` str with ``replace_with`` str
 
@@ -215,9 +215,9 @@ def replace_phone_numbers(self, replace_with:str="*PHONE*",
         replace_with : string
             the string you want the phone number to be replaced with.
         method : ['regex','detection']
-            regex is faster but will omit a lot of numbers, while detection will 
+            regex is faster but will omit a lot of numbers, while detection will
             catch every numbers, but takes a while.
-        country_format_to_detect : list 
+        country_format_to_detect : list
             If a list of country code is specified, will catch every number formatted.
             Only when method = 'detection'.
         Returns
@@ -230,7 +230,7 @@ def replace_phone_numbers(self, replace_with:str="*PHONE*",
             found_nums = _extract_phone_numbers(self.text, countrylist=country_format_to_detect)
 
             # order by lenght to avoid truncated numbers to be removed first.
-            found_nums.sort(key=len,reverse=True) 
+            found_nums.sort(key=len, reverse=True)
             for phone_number in found_nums:
                 self.text = self.text.replace(phone_number, replace_with)
         else:
@@ -250,7 +250,7 @@ def replace_numbers(self, replace_with="*NUMBER*") -> str:
         Returns
         -------
         string
-        """        
+        """
         self.text = constants.NUMBERS_REGEX.sub(replace_with, self.text)
         return self.text
 
@@ -271,10 +271,10 @@ def replace_currency_symbols(self, replace_with=None) -> str:
         Returns
         -------
         string
-        """          
+        """
         if replace_with is None:
             for k, v in constants.CURRENCIES.items():
-                self.text = self.text.replace(k, v)                
+                self.text = self.text.replace(k, v)
         else:
             self.text = constants.CURRENCY_REGEX.sub(replace_with, self.text)
         return self.text
@@ -288,7 +288,7 @@ def remove_punct(self, marks=None) -> str:
         ----------
         text : str
             raw text
-        
+
         marks : str or None
             If specified, remove only the characters in this string,
             e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
@@ -302,15 +302,15 @@ def remove_punct(self, marks=None) -> str:
         -------
         When ``marks=None``, Python's built-in :meth:`str.translate()` is
         used to remove punctuation; otherwise, a regular expression is used
-        instead. The former's performance is about 5-10x faster.    
-        """       
+        instead. The former's performance is about 5-10x faster.
+        """
         if marks:
             self.text = re.sub("[{}]+".format(re.escape(marks)), " ", self.text, flags=re.UNICODE)
         else:
             self.text = self.text.translate(constants.PUNCT_TRANSLATE_UNICODE)
         return self.text
 
-    def remove_accents(self, method:str="unicode") -> str:
+    def remove_accents(self, method: str = "unicode") -> str:
         """
         Remove accents from any accented unicode characters in ``text`` str, either by
         transforming them into ascii equivalents or removing them entirely.
@@ -319,7 +319,7 @@ def remove_accents(self, method:str="unicode") -> str:
         ----------
         text : str
             raw text
-        
+
         method : ({'unicode', 'ascii'})
             if 'unicode', remove accented
             char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
@@ -334,7 +334,7 @@ def remove_accents(self, method:str="unicode") -> str:
         Raises
         -------
         ValueError
-            if ``method`` is not in {'unicode', 'ascii'}   
+            if ``method`` is not in {'unicode', 'ascii'}
         """
         if method == "unicode":
             self.text = "".join(
@@ -389,7 +389,7 @@ def filter_non_latin_characters(self) -> str:
         self.text = self.normalize_whitespace()
         return self.text
 
-    def remove_smallwords(self, smallwords_threshold:int) -> list:
+    def remove_smallwords(self, smallwords_threshold: int) -> list:
         """
         Function that removes words which length is below a threshold
         'Hello my name is John Doe' --> 'Hello name John Doe'
@@ -406,13 +406,3 @@ def remove_smallwords(self, smallwords_threshold:int) -> list:
         """
         self.text = ' '.join([word for word in self.text.split() if len(word) > smallwords_threshold])
         return self.text
-
-
-
-
-
-
-
-
-
-
diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 8f76205..9819879 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -66,7 +66,7 @@ def find_phone_numbers(string, region_code=None):
     return [match.raw_string for match in _phonenumbers.PhoneNumberMatcher(string, region_code)]
 
 
-def extract_phone_numbers(string:str, countrylist:list=[None,'FR','US','GB'])->list:
+def extract_phone_numbers(string:str, countrylist: list)->list:
     '''
     Find phone numbers in a string, returns a list of phone numbers. 
 
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index d93635b..eaf8edb 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -150,9 +150,9 @@ def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
         ("hello world\n", "hello world ")
     ],
 )
-def test_remove_EOL_characters(input_str, expected_str):
+def test_remove_eol_characters(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.remove_EOL_characters()
+    result = preprocessor.remove_eol_characters()
     np.testing.assert_string_equal(result, expected_str)    
 
 

From 01d6cd9ef4eefab8dcd4ee4e91ba6d34320132eb Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:32:20 +0200
Subject: [PATCH 298/496] linting stopwords.py

---
 nautilus_nlp/utils/stopwords.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/nautilus_nlp/utils/stopwords.py b/nautilus_nlp/utils/stopwords.py
index 0ea81a2..c94e812 100644
--- a/nautilus_nlp/utils/stopwords.py
+++ b/nautilus_nlp/utils/stopwords.py
@@ -43,10 +43,10 @@ def get_stopwords(lang: str = "en") -> list:
     ----------
     lang : str
         Supported languages: ['ar', 'bg', 'ca', 'cz', 'da', 'nl', 'en',
-         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru', 
-         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo', 
+         'fi', 'fr', 'de', 'hi', 'hu', 'id', 'it', 'nb', 'pl', 'pt', 'ro', 'ru',
+         'sk', 'es', 'sv', 'tr', 'uk', 'vi', 'af', 'ha', 'so', 'st', 'sw', 'yo',
          'zu', 'da', 'de', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'ko', 'nl',
-          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl', 
+          'no', 'pl', 'pt', 'ru', 'sv', 'tr', 'zh', 'eo', 'he', 'la', 'sk', 'sl',
           'br', 'ca', 'cs', 'el', 'eu', 'ga', 'gl', 'hy', 'id', 'ja', 'lv', 'th',
            'ar', 'bg', 'bn', 'fa', 'hi', 'mr', 'ro', 'en']
 
@@ -60,9 +60,8 @@ def get_stopwords(lang: str = "en") -> list:
     ValueError
         When language is not available yet or incorrect country code
     """
-    if type(lang) == str and len(lang) == 2:
+    if isinstance(lang, str) and len(lang) == 2:
         lang = lang.lower()
-
         custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
         stopwords = []
 
@@ -84,4 +83,4 @@ def get_stopwords(lang: str = "en") -> list:
         raise ValueError(
             'Please input a valid country code, in 2 letters. Eg. "us" for USA. '
         )
-    return list(set(stopwords))
\ No newline at end of file
+    return list(set(stopwords))

From f164f86571016961a10ca07f6f79af5195105a24 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:35:02 +0200
Subject: [PATCH 299/496] linting token_preprocess.py

---
 nautilus_nlp/preprocessing/token_preprocess.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/nautilus_nlp/preprocessing/token_preprocess.py b/nautilus_nlp/preprocessing/token_preprocess.py
index 5e3f3b6..b753564 100644
--- a/nautilus_nlp/preprocessing/token_preprocess.py
+++ b/nautilus_nlp/preprocessing/token_preprocess.py
@@ -23,14 +23,14 @@
 
 class TokenPreprocessor():
 
-    def __init__(self,tokens):
-        if isinstance(tokens,list):
+    def __init__(self, tokens):
+        if isinstance(tokens, list):
             self.tokens = tokens
         else:
-            raise ValueError("Input must be a list")
+            raise TypeError("Input must be a list")
 
     def remove_stopwords(self, stopwords: list) -> str:
-        """ 
+        """
         Remove stopwords from a text.
         eg. 'I like when you move your body !' -> 'I move body !'
 
@@ -71,8 +71,8 @@ def remove_tokens_with_nonletters(self) -> list:
         return self.tokens
 
     def remove_special_caracters_from_tokenslist(self) -> list:
-        """ 
-        Remove tokens that doesn't contains any number or letter. 
+        """
+        Remove tokens that doesn't contains any number or letter.
         eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
 
         Parameters
@@ -84,12 +84,12 @@ def remove_special_caracters_from_tokenslist(self) -> list:
         -------
         list
             list of tokens without tokens that contains only special caracters
-        
+
         """
         self.tokens = [word for word in self.tokens if re.search("[a-zA-Z0-9]", word)]
         return self.tokens
 
-    def remove_smallwords(self, smallwords_threshold:int) -> list:
+    def remove_smallwords(self, smallwords_threshold: int) -> list:
         """
         Function that removes words which length is below a threshold
         ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
@@ -106,4 +106,4 @@ def remove_smallwords(self, smallwords_threshold:int) -> list:
         list
         """
         self.tokens = [word for word in self.tokens if len(word) > smallwords_threshold]
-        return self.tokens
\ No newline at end of file
+        return self.tokens

From c11ff9a8b5f4a6d924f5e8c6298f844297d79b6a Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:48:41 +0200
Subject: [PATCH 300/496] fixing phone_number tests

---
 nautilus_nlp/utils/phone_number.py | 12 ++++++------
 tests/test_phone_number.py         |  6 +++---
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 9819879..ac4ece8 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -66,9 +66,9 @@ def find_phone_numbers(string, region_code=None):
     return [match.raw_string for match in _phonenumbers.PhoneNumberMatcher(string, region_code)]
 
 
-def extract_phone_numbers(string:str, countrylist: list)->list:
+def extract_phone_numbers(text:str, countrylist: list)->list:
     '''
-    Find phone numbers in a string, returns a list of phone numbers. 
+    Find phone numbers in a text, returns a list of phone numbers. 
 
     Parameters
     ----------
@@ -76,11 +76,11 @@ def extract_phone_numbers(string:str, countrylist: list)->list:
         Look for phone numbers formatted according to the specified countlist. 
         supported value: look SUPPORTED_COUNTRY variable.
     '''
-    res = []
+    all_phone_numbers = []
     for country in countrylist:
-        new_numbers_founds = find_phone_numbers(string, region_code=country)
-        res += new_numbers_founds
-    return list(set(res))
+        new_numbers_founds = find_phone_numbers(text, region_code=country)
+        all_phone_numbers.extend(new_numbers_founds)
+    return list(set(all_phone_numbers))
 
 
 class phoneParser(object):
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index 0043734..96d16f3 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -28,18 +28,18 @@ def test_extract_phone_number_us():
     input_str = '(541) 754-3010 is a US. Phone'
     expected = ['(541) 754-3010']
     res = phone.extract_phone_numbers(input_str, countrylist=['US'])
-    assert res == expected    
+    assert res == expected
 
 def test_extract_phone_number_fr():
     input_str = '06.25.09.32.56 is a FR Phone'
     expected = ['06.25.09.32.56']
-    res = phone.extract_phone_numbers(input_str)
+    res = phone.extract_phone_numbers(input_str, countrylist=['FR'])
     assert res == expected
 
 def test_extract_phone_number_international():
     input_str = '+33625093423 is an international Phone number'
     expected = ['+33625093423']
-    res = phone.extract_phone_numbers(input_str)
+    res = phone.extract_phone_numbers(input_str, countrylist=['US', 'GB', 'FR', None])
     assert res == expected
 
 def test_phoneParser_us():

From 1db6e33bce630ac291846938e021999c8a324ef9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 11:55:07 +0200
Subject: [PATCH 301/496] fixing pylint phone_number

---
 nautilus_nlp/utils/phone_number.py | 77 +++++++++++++++---------------
 tests/test_phone_number.py         | 15 +++---
 2 files changed, 45 insertions(+), 47 deletions(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index ac4ece8..a1583e9 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -15,30 +15,29 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import re
 import phonenumbers as _phonenumbers
 
 SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM', 
-                    'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT', 
-                    'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES', 
-                    'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE', 
-                    'NO', 'SJ', 'PL', 'DE', 'PE', 'MX', 'CU', 'AR', 'BR', 'CL', 'CO', 'VE', 
-                    'MY', 'AU', 'CC', 'CX', 'ID', 'PH', 'NZ', 'SG', 'TH', 'JP', 'KR', 'VN', 
-                    'CN', 'TR', 'IN', 'PK', 'AF', 'LK', 'MM', 'IR', 'SS', 'MA', 'EH', 'DZ', 
-                    'TN', 'LY', 'GM', 'SN', 'MR', 'ML', 'GN', 'CI', 'BF', 'NE', 'TG', 'BJ', 
-                    'MU', 'LR', 'SL', 'GH', 'NG', 'TD', 'CF', 'CM', 'CV', 'ST', 'GQ', 'GA', 
-                    'CG', 'CD', 'AO', 'GW', 'IO', 'AC', 'SC', 'SD', 'RW', 'ET', 'SO', 'DJ', 
-                    'KE', 'TZ', 'UG', 'BI', 'MZ', 'ZM', 'MG', 'RE', 'YT', 'ZW', 'NA', 'MW', 
-                    'LS', 'BW', 'SZ', 'KM', 'SH', 'TA', 'ER', 'AW', 'FO', 'GL', 'GI', 'PT', 
-                    'LU', 'IE', 'IS', 'AL', 'MT', 'CY', 'FI', 'AX', 'BG', 'LT', 'LV', 'EE', 
-                    'MD', 'AM', 'BY', 'AD', 'MC', 'SM', 'UA', 'RS', 'ME', 'XK', 'HR', 'SI', 
-                    'BA', 'MK', 'CZ', 'SK', 'LI', 'FK', 'BZ', 'GT', 'SV', 'HN', 'NI', 'CR', 
-                    'PA', 'PM', 'HT', 'GP', 'BL', 'MF', 'BO', 'GY', 'EC', 'GF', 'PY', 'MQ',
-                    'SR', 'UY', 'CW', 'BQ', 'TL', 'NF', 'BN', 'NR', 'PG', 'TO', 'SB', 'VU', 
-                    'FJ', 'PW', 'WF', 'CK', 'NU', 'WS', 'KI', 'NC', 'TV', 'PF', 'TK', 'FM', 
-                    'MH', 'KP', 'HK', 'MO', 'KH', 'LA', 'BD', 'TW', 'MV', 'LB', 'JO', 'SY',
-                    'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN', 
-                    'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ','DO']
+                     'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT',
+                     'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES',
+                     'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE',
+                     'NO', 'SJ', 'PL', 'DE', 'PE', 'MX', 'CU', 'AR', 'BR', 'CL', 'CO', 'VE',
+                     'MY', 'AU', 'CC', 'CX', 'ID', 'PH', 'NZ', 'SG', 'TH', 'JP', 'KR', 'VN',
+                     'CN', 'TR', 'IN', 'PK', 'AF', 'LK', 'MM', 'IR', 'SS', 'MA', 'EH', 'DZ',
+                     'TN', 'LY', 'GM', 'SN', 'MR', 'ML', 'GN', 'CI', 'BF', 'NE', 'TG', 'BJ',
+                     'MU', 'LR', 'SL', 'GH', 'NG', 'TD', 'CF', 'CM', 'CV', 'ST', 'GQ', 'GA',
+                     'CG', 'CD', 'AO', 'GW', 'IO', 'AC', 'SC', 'SD', 'RW', 'ET', 'SO', 'DJ',
+                     'KE', 'TZ', 'UG', 'BI', 'MZ', 'ZM', 'MG', 'RE', 'YT', 'ZW', 'NA', 'MW',
+                     'LS', 'BW', 'SZ', 'KM', 'SH', 'TA', 'ER', 'AW', 'FO', 'GL', 'GI', 'PT',
+                     'LU', 'IE', 'IS', 'AL', 'MT', 'CY', 'FI', 'AX', 'BG', 'LT', 'LV', 'EE',
+                     'MD', 'AM', 'BY', 'AD', 'MC', 'SM', 'UA', 'RS', 'ME', 'XK', 'HR', 'SI',
+                     'BA', 'MK', 'CZ', 'SK', 'LI', 'FK', 'BZ', 'GT', 'SV', 'HN', 'NI', 'CR',
+                     'PA', 'PM', 'HT', 'GP', 'BL', 'MF', 'BO', 'GY', 'EC', 'GF', 'PY', 'MQ',
+                     'SR', 'UY', 'CW', 'BQ', 'TL', 'NF', 'BN', 'NR', 'PG', 'TO', 'SB', 'VU',
+                     'FJ', 'PW', 'WF', 'CK', 'NU', 'WS', 'KI', 'NC', 'TV', 'PF', 'TK', 'FM',
+                     'MH', 'KP', 'HK', 'MO', 'KH', 'LA', 'BD', 'TW', 'MV', 'LB', 'JO', 'SY',
+                     'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN',
+                     'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ', 'DO']
 
 
 def find_phone_numbers(string, region_code=None):
@@ -49,12 +48,12 @@ def find_phone_numbers(string, region_code=None):
     Parameters
     ----------
     region_code
-        If specified, will find the number of the specified country. 
+        If specified, will find the number of the specified country.
     eg. 06.25.09.32.67 if "FR" is specified.
 
     If not specified, only works for international-formatted phone numbers.
-    - ie. phone number with +counttry code specified 
-    eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.        
+    - ie. phone number with +counttry code specified
+    eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.
 
     region_code
         supported value: look SUPPORTED_COUNTRY variable.
@@ -66,14 +65,14 @@ def find_phone_numbers(string, region_code=None):
     return [match.raw_string for match in _phonenumbers.PhoneNumberMatcher(string, region_code)]
 
 
-def extract_phone_numbers(text:str, countrylist: list)->list:
+def extract_phone_numbers(text: str, countrylist: list)->list:
     '''
-    Find phone numbers in a text, returns a list of phone numbers. 
+    Find phone numbers in a text, returns a list of phone numbers.
 
     Parameters
     ----------
     countrylist: list
-        Look for phone numbers formatted according to the specified countlist. 
+        Look for phone numbers formatted according to the specified countlist.
         supported value: look SUPPORTED_COUNTRY variable.
     '''
     all_phone_numbers = []
@@ -83,28 +82,28 @@ def extract_phone_numbers(text:str, countrylist: list)->list:
     return list(set(all_phone_numbers))
 
 
-class phoneParser(object):
+class phone_parser(object):
     """
     Python port of Google's libphonenumber.
-    https://github.com/daviddrysdale/python-phonenumbers 
+    https://github.com/daviddrysdale/python-phonenumbers
     """
 
     def __init__(self):
         self.region_code = None
-        self.string = None
+        self.text = None
         self.parsed_num = None
 
-    def parse_number(self, string:str, region_code=None):
+    def parse_number(self, text: str, region_code=None):
         '''
         Parameters
         ----------
         region_code
-            If specified, will find the number of the specified country. 
+            If specified, will find the number of the specified country.
         eg. 06.25.09.32.67 if "FR" is specified.
 
         If not specified, only works for international-formatted phone numbers.
-        - ie. phone number with +counttry code specified 
-        eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.        
+        - ie. phone number with +counttry code specified
+        eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.
 
         region_code
             supported value: look SUPPORTED_COUNTRY variable.
@@ -112,17 +111,17 @@ def parse_number(self, string:str, region_code=None):
         Raises
         ------
         NumberParseException
-            If the string doesn't contains phone number of is the parser fails.             
+            If the string doesn't contains phone number of is the parser fails.
         '''
         self.region_code = region_code
-        self.string = string        
-        self.parsed_num = _phonenumbers.parse(self.string, self.region_code)
+        self.text = text
+        self.parsed_num = _phonenumbers.parse(self.text, self.region_code)
         return self.parsed_num
 
     def format_number(self, num_format):
         '''
         ['E164','INTERNATIONAL','NATIONAL','RFC3966']
         '''
+        # TODO: why exec ???
         standard_format = exec('_phonenumbers.PhoneNumberFormat.'+num_format)
-        
-        return _phonenumbers.format_number(self.parsed_num, standard_format)
\ No newline at end of file
+        return _phonenumbers.format_number(self.parsed_num, standard_format)
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index 96d16f3..724a4b9 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -15,7 +15,6 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import pytest
 import nautilus_nlp.utils.phone_number as phone
 
 def test_extract_phone_number():
@@ -42,18 +41,18 @@ def test_extract_phone_number_international():
     res = phone.extract_phone_numbers(input_str, countrylist=['US', 'GB', 'FR', None])
     assert res == expected
 
-def test_phoneParser_us():
+def test_phone_parser_us():
     input_str = '(541) 754-3010'
     expected = '541-754-3010'
-    p = phone.phoneParser()
-    p.parse_number(input_str,region_code='US')
+    p = phone.phone_parser()
+    p.parse_number(input_str, region_code='US')
     res = p.format_number('INTERNATIONAL')
     assert res == expected
 
-def test_phoneParser_fr():
+def test_phone_parser_fr():
     input_str = '0625093267'
     expected = '6 25 09 32 67'
-    p = phone.phoneParser()
-    p.parse_number(input_str,region_code='FR')
+    p = phone.phone_parser()
+    p.parse_number(input_str, region_code='FR')
     res = p.format_number('E164')
-    assert res == expected    
\ No newline at end of file
+    assert res == expected

From 6ce83b480f9126c6991de69aebda4f08ee98d3af Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 12:12:08 +0200
Subject: [PATCH 302/496] linting tokenizer.py

---
 nautilus_nlp/utils/tokenizer.py | 111 +++++++++++++++++++-------------
 1 file changed, 65 insertions(+), 46 deletions(-)

diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 5eae255..f06def1 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -18,34 +18,55 @@
 import nltk
 from sacremoses import MosesTokenizer, MosesDetokenizer
 import spacy
-from spacy.lang.fr import French
-from spacy.lang.en import English
-import spacy.lang as spacylang
-#nltk.download('punkt')
-
-try:
-    french_spacy = spacylang.fr.French().tokenizer
-except OSError:
-    raise OSError("""You must install French langage to use SpaCy. 
-                    python -m spacy download fr
-                    See https://spacy.io/usage/ for details
-                """)
-try:
-    english_spacy = spacylang.en.English().tokenizer
-except OSError:
-    raise OSError("""You must install english langage to use SpaCy. 
-                    python -m spacy download en
-                    See https://spacy.io/usage/ for details
-                """)             
+
+class LanguageNotHandled(Exception):
+    pass
+
+class SpacyModel:
+    class SingletonSpacyModel:
+        def __init__(self, lang):
+            self.lang = lang
+            if lang == 'en':
+                self.model = spacy.load('en_core_web_sm')
+            elif lang == 'fr':
+                self.model = spacy.load('fr_core_news_sm')
+            elif lang == 'ko':
+                self.model = spacy.blank('ko')
+            elif lang == 'ja':
+                self.model = spacy.blank('ja')
+            else:
+                raise(LanguageNotHandled('This spacy model is not available'))
+
+    model = None
+
+    def __init__(self, lang):
+        if not SpacyModel.model:
+            SpacyModel.model = SpacyModel.SingletonSpacyModel(lang).model
+
+    def get_lang_model(self):
+        return self.model.lang
+
+
+def _get_spacy_tokenizer(lang):
+    """
+    Function that gets the right tokenizer given the language
+    :param str lang: language in which text is written
+                     Languages handled : ["en", "fr", "ko", "ja"]
+
+    :return: spacy tokenizer
+    :rtype: spacy.tokenizer.Tokenizer
+    """
+    model = SpacyModel(lang).model
+    return model.tokenizer
 
 
 def tokenize(text: str, lang_module: str = 'en_spacy'):
     """
-    Convert text to a list of tokens. 
+    Convert text to a list of tokens.
 
     Parameters
-    ----------    
-    lang_module : ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses'})
+    ----------
+    lang_module : ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses', 'ko_spacy', 'ja_spacy'})
         choose the tokenization module according to the langage and the implementation.
         Recommanded: Spacy (faster, better results). To process other langages
         import models.Spacy_models
@@ -54,18 +75,18 @@ def tokenize(text: str, lang_module: str = 'en_spacy'):
     -------
     list
         list of string
-    """      
-    if lang_module is 'en_nltk':
-        return nltk.word_tokenize(text)
-    elif lang_module is 'en_spacy':
-        spacydoc = english_spacy(text)
-        return [spacy_token.text for spacy_token in spacydoc]
-    elif lang_module is 'fr_spacy':
-        spacydoc = french_spacy(text)
+    """
+    if "spacy" in lang_module:
+        lang = lang_module.split()[0]
+        spacymodel = _get_spacy_tokenizer(lang)
+        spacydoc = spacymodel(text)
         return [spacy_token.text for spacy_token in spacydoc]
-    elif lang_module is 'fr_moses':
-        t = MosesTokenizer(lang='fr')
-        return t.tokenize(text, escape=False)
+    if lang_module == 'en_nltk':
+        return nltk.word_tokenize(text)
+    if lang_module == 'fr_moses':
+        return MosesTokenizer(lang='fr').tokenize(text, escape=False)
+    raise ValueError("Please pass a lang_module in list of values "\
+                     "{'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses', 'ko_spacy', 'ja_spacy'}")
 
 
 def untokenize(tokens, lang='fr'):
@@ -74,32 +95,30 @@ def untokenize(tokens, lang='fr'):
     ["J'", 'ai'] >>> "J' ai"
 
     Parameters
-    ----------    
+    ----------
     lang : string
-        language code 
+        language code
     '''
     d = MosesDetokenizer(lang=lang)
     text = d.detokenize(tokens, unescape=False)
-    return text    
+    return text
 
 
 def _convert_tokens_to_string(tokens_or_str):
-    if type(tokens_or_str) is str:
+    if isinstance(tokens_or_str, str):
         return tokens_or_str
-    elif type(tokens_or_str) is list:
+    if isinstance(tokens_or_str, list):
         return untokenize(tokens_or_str)
-    elif type(tokens_or_str) is None:
+    if tokens_or_str is None:
         return ''
-    else:
-        raise ValueError('Please input string or tokens')
+    raise TypeError('Please input string or tokens')
 
 
 def _convert_string_to_tokens(tokens_or_str, lang_module='en_spacy'):
-    if type(tokens_or_str) is str:
+    if isinstance(tokens_or_str, str):
         return tokenize(tokens_or_str, lang_module=lang_module)
-    elif type(tokens_or_str) is list:
+    if isinstance(tokens_or_str, list):
         return tokens_or_str
-    elif type(tokens_or_str) is None:
+    if tokens_or_str is None:
         return []
-    else:
-        raise ValueError('Please input string or tokens')
\ No newline at end of file
+    raise TypeError('Please input string or tokens')

From db3f176b9755607355aa18a72856f6aff1921e98 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 12:17:19 +0200
Subject: [PATCH 303/496] removing compat.py

---
 nautilus_nlp/utils/compat.py | 45 ------------------------------------
 1 file changed, 45 deletions(-)
 delete mode 100644 nautilus_nlp/utils/compat.py

diff --git a/nautilus_nlp/utils/compat.py b/nautilus_nlp/utils/compat.py
deleted file mode 100644
index 644ac2b..0000000
--- a/nautilus_nlp/utils/compat.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from __future__ import print_function
-
-import sys
-
-is_python2 = int(sys.version[0]) == 2
-is_windows = sys.platform.startswith("win")
-is_linux = sys.platform.startswith("linux")
-is_osx = sys.platform == "darwin"
-
-
-import csv
-import pickle
-from builtins import zip as zip_
-from urllib.parse import urljoin
-
-range_ = range
-
-bytes_ = bytes
-unicode_ = str
-string_types = (bytes, str)
-int_types = (int,)
-chr_ = chr
-
-def unicode_to_bytes(s, encoding="utf8", errors="strict"):
-    return s.encode(encoding=encoding, errors=errors)
-
-def bytes_to_unicode(b, encoding="utf8", errors="strict"):
-    return b.decode(encoding=encoding, errors=errors)

From 3abc58eba86287694c86054f74ce2da75cc325a8 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 12:20:26 +0200
Subject: [PATCH 304/496] linting constants.py

---
 nautilus_nlp/utils/constants.py | 36 ++++++++++++++-------------------
 1 file changed, 15 insertions(+), 21 deletions(-)

diff --git a/nautilus_nlp/utils/constants.py b/nautilus_nlp/utils/constants.py
index 4595660..401811b 100644
--- a/nautilus_nlp/utils/constants.py
+++ b/nautilus_nlp/utils/constants.py
@@ -21,30 +21,23 @@
 """
 from __future__ import unicode_literals
 
-import os
 import re
-import regex
 import sys
 import unicodedata
-import emoji as _emoji
-
-
-from . import  compat
-from . import file_loader as util
-
-
 
+import regex
 
+import emoji as _emoji
 
 NUMERIC_NE_TYPES = {
-                    "ORDINAL",
-                    "CARDINAL",
-                    "MONEY",
-                    "QUANTITY",
-                    "PERCENT",
-                    "TIME",
-                    "DATE",
-                    }
+    "ORDINAL",
+    "CARDINAL",
+    "MONEY",
+    "QUANTITY",
+    "PERCENT",
+    "TIME",
+    "DATE",
+}
 SUBJ_DEPS = {"agent", "csubj", "csubjpass", "expl", "nsubj", "nsubjpass"}
 OBJ_DEPS = {"attr", "dobj", "dative", "oprd"}
 AUX_DEPS = {"aux", "auxpass", "neg"}
@@ -136,8 +129,8 @@
 PUNCT_TRANSLATE_UNICODE = dict.fromkeys(
     (
         i
-        for i in compat.range_(sys.maxunicode)
-        if unicodedata.category(compat.chr_(i)).startswith("P")
+        for i in range(sys.maxunicode)
+        if unicodedata.category(chr(i)).startswith("P")
     ),
     " ",
 )
@@ -155,10 +148,11 @@
     r"(?:^|(?<=[^\w)]))(\+?1[ .-]?)?(\(?\d{3}\)?[ .-]?)?(\d{3}[ .-]?\d{4})(\s?(?:ext\.?|[#x-])\s?\d{2,6})?(?:$|(?=\W))"
 )
 NUMBERS_REGEX = re.compile(
-    r"(?:^|(?<=[^\w,.]))[+–-]?(([1-9]\d{0,2}(,\d{3})+(\.\d*)?)|([1-9]\d{0,2}([ .]\d{3})+(,\d*)?)|(\d*?[.,]\d+)|\d+)(?:|(?=\b))"
+    r"(?:^|(?<=[^\w,.]))[+–-]?(([1-9]\d{0,2}(,\d{3})+(\.\d*)?)|([1-9]\d{0,2}([ .]\d{3})+(,\d*)?)|"
+    r"(\d*?[.,]\d+)|\d+)(?:|(?=\b))"
 )
 CURRENCY_REGEX = re.compile(
-    "({})+".format("|".join(re.escape(c) for c in CURRENCIES.keys()))
+    "({})+".format("|".join(re.escape(c) for c in CURRENCIES))
 )
 LINEBREAK_REGEX = re.compile(r"((\r\n)|[\n\v])+")
 NONBREAKING_SPACE_REGEX = re.compile(r"(?!\n)\s+")

From 3c139ed55929cfa1342360053f99be39daa361c6 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 17:05:34 +0200
Subject: [PATCH 305/496] removing unused utils emoji

---
 nautilus_nlp/utils/emoji.py | 1998 -----------------------------------
 1 file changed, 1998 deletions(-)
 delete mode 100644 nautilus_nlp/utils/emoji.py

diff --git a/nautilus_nlp/utils/emoji.py b/nautilus_nlp/utils/emoji.py
deleted file mode 100644
index 6ee23f1..0000000
--- a/nautilus_nlp/utils/emoji.py
+++ /dev/null
@@ -1,1998 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import csv
-
-def rebuilt_emoji_dictionaries(filename):
-    """ 
-    This function recreate the dictionnaries with emoji2unicode_name, emoji2sentiment  that are hardcoded below.
-    
-    Parameters
-    ----------
-    filename : string
-        csv filename
-       
-    Returns
-    -------
-    dict
-
-    This dataset is based on and can be found:
-    Kralj Novak, Petra; Smailović, Jasmina; Sluban, Borut and Mozetič, Igor, 2015,
-    Emoji Sentiment Ranking 1.0, Slovenian language resource repository CLARIN.SI,
-    http://hdl.handle.net/11356/1048.
-
-    """
-
-    emoji2unicode_name, emoji2sentiment = dict(),dict()
-    with open(filename) as csvin:
-        for emoji in csv.DictReader(csvin):
-            for key, value in emoji.items():
-                if key in ('Occurrences', 'Positive', 'Neutral', 'Negative'):
-                    emoji[key] = int(value)
-                elif key in ('Position',):
-                    emoji[key] = float(value)
-
-            emoji['Sentiment'] = (emoji['Positive'] - emoji['Negative']) / \
-                                max(100, (emoji['Positive'] + emoji['Neutral'] + emoji['Negative']))
-            emoji2unicode_name[emoji['Emoji']] = emoji['Unicode name']
-            emoji2sentiment[emoji['Emoji']] = emoji['Sentiment']
-
-    return emoji2unicode_name, emoji2sentiment
-
-emoji2unicode_name = {
-    '😂': 'FACE WITH TEARS OF JOY',
-    '❤': 'HEAVY BLACK HEART',
-    '♥': 'BLACK HEART SUIT',
-    '😍': 'SMILING FACE WITH HEART-SHAPED EYES',
-    '😭': 'LOUDLY CRYING FACE',
-    '😘': 'FACE THROWING A KISS',
-    '😊': 'SMILING FACE WITH SMILING EYES',
-    '👌': 'OK HAND SIGN',
-    '💕': 'TWO HEARTS',
-    '👏': 'CLAPPING HANDS SIGN',
-    '😁': 'GRINNING FACE WITH SMILING EYES',
-    '☺': 'WHITE SMILING FACE',
-    '♡': 'WHITE HEART SUIT',
-    '👍': 'THUMBS UP SIGN',
-    '😩': 'WEARY FACE',
-    '🙏': 'PERSON WITH FOLDED HANDS',
-    '✌': 'VICTORY HAND',
-    '😏': 'SMIRKING FACE',
-    '😉': 'WINKING FACE',
-    '🙌': 'PERSON RAISING BOTH HANDS IN CELEBRATION',
-    '🙈': 'SEE-NO-EVIL MONKEY',
-    '💪': 'FLEXED BICEPS',
-    '😄': 'SMILING FACE WITH OPEN MOUTH AND SMILING EYES',
-    '😒': 'UNAMUSED FACE',
-    '💃': 'DANCER',
-    '💖': 'SPARKLING HEART',
-    '😃': 'SMILING FACE WITH OPEN MOUTH',
-    '😔': 'PENSIVE FACE',
-    '😱': 'FACE SCREAMING IN FEAR',
-    '🎉': 'PARTY POPPER',
-    '😜': 'FACE WITH STUCK-OUT TONGUE AND WINKING EYE',
-    '☯': 'YIN YANG',
-    '🌸': 'CHERRY BLOSSOM',
-    '💜': 'PURPLE HEART',
-    '💙': 'BLUE HEART',
-    '✨': 'SPARKLES',
-    '😳': 'FLUSHED FACE',
-    '💗': 'GROWING HEART',
-    '★': 'BLACK STAR',
-    '█': 'FULL BLOCK',
-    '☀': 'BLACK SUN WITH RAYS',
-    '😡': 'POUTING FACE',
-    '😎': 'SMILING FACE WITH SUNGLASSES',
-    '😢': 'CRYING FACE',
-    '💋': 'KISS MARK',
-    '😋': 'FACE SAVOURING DELICIOUS FOOD',
-    '🙊': 'SPEAK-NO-EVIL MONKEY',
-    '😴': 'SLEEPING FACE',
-    '🎶': 'MULTIPLE MUSICAL NOTES',
-    '💞': 'REVOLVING HEARTS',
-    '😌': 'RELIEVED FACE',
-    '🔥': 'FIRE',
-    '💯': 'HUNDRED POINTS SYMBOL',
-    '🔫': 'PISTOL',
-    '💛': 'YELLOW HEART',
-    '💁': 'INFORMATION DESK PERSON',
-    '💚': 'GREEN HEART',
-    '♫': 'BEAMED EIGHTH NOTES',
-    '😞': 'DISAPPOINTED FACE',
-    '😆': 'SMILING FACE WITH OPEN MOUTH AND TIGHTLY-CLOSED EYES',
-    '😝': 'FACE WITH STUCK-OUT TONGUE AND TIGHTLY-CLOSED EYES',
-    '😪': 'SLEEPY FACE',
-    '�': 'REPLACEMENT CHARACTER',
-    '😫': 'TIRED FACE',
-    '😅': 'SMILING FACE WITH OPEN MOUTH AND COLD SWEAT',
-    '👊': 'FISTED HAND SIGN',
-    '💀': 'SKULL',
-    '😀': 'GRINNING FACE',
-    '😚': 'KISSING FACE WITH CLOSED EYES',
-    '😻': 'SMILING CAT FACE WITH HEART-SHAPED EYES',
-    '©': 'COPYRIGHT SIGN',
-    '👀': 'EYES',
-    '💘': 'HEART WITH ARROW',
-    '🐓': 'ROOSTER',
-    '☕': 'HOT BEVERAGE',
-    '👋': 'WAVING HAND SIGN',
-    '✋': 'RAISED HAND',
-    '🎊': 'CONFETTI BALL',
-    '🍕': 'SLICE OF PIZZA',
-    '❄': 'SNOWFLAKE',
-    '😥': 'DISAPPOINTED BUT RELIEVED FACE',
-    '😕': 'CONFUSED FACE',
-    '💥': 'COLLISION SYMBOL',
-    '💔': 'BROKEN HEART',
-    '😤': 'FACE WITH LOOK OF TRIUMPH',
-    '😈': 'SMILING FACE WITH HORNS',
-    '►': 'BLACK RIGHT-POINTING POINTER',
-    '✈': 'AIRPLANE',
-    '🔝': 'TOP WITH UPWARDS ARROW ABOVE',
-    '😰': 'FACE WITH OPEN MOUTH AND COLD SWEAT',
-    '⚽': 'SOCCER BALL',
-    '😑': 'EXPRESSIONLESS FACE',
-    '👑': 'CROWN',
-    '😹': 'CAT FACE WITH TEARS OF JOY',
-    '👉': 'WHITE RIGHT POINTING BACKHAND INDEX',
-    '🍃': 'LEAF FLUTTERING IN WIND',
-    '🎁': 'WRAPPED PRESENT',
-    '😠': 'ANGRY FACE',
-    '🐧': 'PENGUIN',
-    '☆': 'WHITE STAR',
-    '🍀': 'FOUR LEAF CLOVER',
-    '🎈': 'BALLOON',
-    '🎅': 'FATHER CHRISTMAS',
-    '😓': 'FACE WITH COLD SWEAT',
-    '😣': 'PERSEVERING FACE',
-    '😐': 'NEUTRAL FACE',
-    '✊': 'RAISED FIST',
-    '😨': 'FEARFUL FACE',
-    '😖': 'CONFOUNDED FACE',
-    '💤': 'SLEEPING SYMBOL',
-    '💓': 'BEATING HEART',
-    '👎': 'THUMBS DOWN SIGN',
-    '💦': 'SPLASHING SWEAT SYMBOL',
-    '✔': 'HEAVY CHECK MARK',
-    '😷': 'FACE WITH MEDICAL MASK',
-    '⚡': 'HIGH VOLTAGE SIGN',
-    '🙋': 'HAPPY PERSON RAISING ONE HAND',
-    '🎄': 'CHRISTMAS TREE',
-    '💩': 'PILE OF POO',
-    '🎵': 'MUSICAL NOTE',
-    '➡': 'BLACK RIGHTWARDS ARROW',
-    '😛': 'FACE WITH STUCK-OUT TONGUE',
-    '😬': 'GRIMACING FACE',
-    '👯': 'WOMAN WITH BUNNY EARS',
-    '💎': 'GEM STONE',
-    '🌿': 'HERB',
-    '🎂': 'BIRTHDAY CAKE',
-    '🌟': 'GLOWING STAR',
-    '🔮': 'CRYSTAL BALL',
-    '❗': 'HEAVY EXCLAMATION MARK SYMBOL',
-    '👫': 'MAN AND WOMAN HOLDING HANDS',
-    '🏆': 'TROPHY',
-    '✖': 'HEAVY MULTIPLICATION X',
-    '☝': 'WHITE UP POINTING INDEX',
-    '😙': 'KISSING FACE WITH SMILING EYES',
-    '⛄': 'SNOWMAN WITHOUT SNOW',
-    '👅': 'TONGUE',
-    '♪': 'EIGHTH NOTE',
-    '🍂': 'FALLEN LEAF',
-    '💏': 'KISS',
-    '🔪': 'HOCHO',
-    '🌴': 'PALM TREE',
-    '👈': 'WHITE LEFT POINTING BACKHAND INDEX',
-    '🌹': 'ROSE',
-    '🙆': 'FACE WITH OK GESTURE',
-    '➜': 'HEAVY ROUND-TIPPED RIGHTWARDS ARROW',
-    '👻': 'GHOST',
-    '💰': 'MONEY BAG',
-    '🍻': 'CLINKING BEER MUGS',
-    '🙅': 'FACE WITH NO GOOD GESTURE',
-    '🌞': 'SUN WITH FACE',
-    '🍁': 'MAPLE LEAF',
-    '⭐': 'WHITE MEDIUM STAR',
-    '▪': 'BLACK SMALL SQUARE',
-    '🎀': 'RIBBON',
-    '━': 'BOX DRAWINGS HEAVY HORIZONTAL',
-    '☷': 'TRIGRAM FOR EARTH',
-    '🐷': 'PIG FACE',
-    '🙉': 'HEAR-NO-EVIL MONKEY',
-    '🌺': 'HIBISCUS',
-    '💅': 'NAIL POLISH',
-    '🐶': 'DOG FACE',
-    '🌚': 'NEW MOON WITH FACE',
-    '👽': 'EXTRATERRESTRIAL ALIEN',
-    '🎤': 'MICROPHONE',
-    '👭': 'TWO WOMEN HOLDING HANDS',
-    '🎧': 'HEADPHONE',
-    '👆': 'WHITE UP POINTING BACKHAND INDEX',
-    '🍸': 'COCKTAIL GLASS',
-    '🍷': 'WINE GLASS',
-    '®': 'REGISTERED SIGN',
-    '🍉': 'WATERMELON',
-    '😇': 'SMILING FACE WITH HALO',
-    '☑': 'BALLOT BOX WITH CHECK',
-    '🏃': 'RUNNER',
-    '😿': 'CRYING CAT FACE',
-    '│': 'BOX DRAWINGS LIGHT VERTICAL',
-    '💣': 'BOMB',
-    '🍺': 'BEER MUG',
-    '▶': 'BLACK RIGHT-POINTING TRIANGLE',
-    '😲': 'ASTONISHED FACE',
-    '🎸': 'GUITAR',
-    '🍹': 'TROPICAL DRINK',
-    '💫': 'DIZZY SYMBOL',
-    '📚': 'BOOKS',
-    '😶': 'FACE WITHOUT MOUTH',
-    '🌷': 'TULIP',
-    '💝': 'HEART WITH RIBBON',
-    '💨': 'DASH SYMBOL',
-    '🏈': 'AMERICAN FOOTBALL',
-    '💍': 'RING',
-    '☔': 'UMBRELLA WITH RAIN DROPS',
-    '👸': 'PRINCESS',
-    '🇪': 'REGIONAL INDICATOR SYMBOL LETTER E',
-    '░': 'LIGHT SHADE',
-    '🍩': 'DOUGHNUT',
-    '👾': 'ALIEN MONSTER',
-    '☁': 'CLOUD',
-    '🌻': 'SUNFLOWER',
-    '😵': 'DIZZY FACE',
-    '📒': 'LEDGER',
-    '↿': 'UPWARDS HARPOON WITH BARB LEFTWARDS',
-    '🐯': 'TIGER FACE',
-    '👼': 'BABY ANGEL',
-    '🍔': 'HAMBURGER',
-    '😸': 'GRINNING CAT FACE WITH SMILING EYES',
-    '👶': 'BABY',
-    '↾': 'UPWARDS HARPOON WITH BARB RIGHTWARDS',
-    '💐': 'BOUQUET',
-    '🌊': 'WATER WAVE',
-    '🍦': 'SOFT ICE CREAM',
-    '🍓': 'STRAWBERRY',
-    '👇': 'WHITE DOWN POINTING BACKHAND INDEX',
-    '💆': 'FACE MASSAGE',
-    '🍴': 'FORK AND KNIFE',
-    '😧': 'ANGUISHED FACE',
-    '🇸': 'REGIONAL INDICATOR SYMBOL LETTER S',
-    '😮': 'FACE WITH OPEN MOUTH',
-    '▓': 'DARK SHADE',
-    '🚫': 'NO ENTRY SIGN',
-    '😽': 'KISSING CAT FACE WITH CLOSED EYES',
-    '🌈': 'RAINBOW',
-    '🙀': 'WEARY CAT FACE',
-    '⚠': 'WARNING SIGN',
-    '🎮': 'VIDEO GAME',
-    '╯': 'BOX DRAWINGS LIGHT ARC UP AND LEFT',
-    '🍆': 'AUBERGINE',
-    '🍰': 'SHORTCAKE',
-    '✓': 'CHECK MARK',
-    '👐': 'OPEN HANDS SIGN',
-    '🙇': 'PERSON BOWING DEEPLY',
-    '🍟': 'FRENCH FRIES',
-    '🍌': 'BANANA',
-    '💑': 'COUPLE WITH HEART',
-    '👬': 'TWO MEN HOLDING HANDS',
-    '🐣': 'HATCHING CHICK',
-    '🎃': 'JACK-O-LANTERN',
-    '▬': 'BLACK RECTANGLE',
-    '': 'OBJECT REPLACEMENT CHARACTER',
-    '😟': 'WORRIED FACE',
-    '🐾': 'PAW PRINTS',
-    '🎓': 'GRADUATION CAP',
-    '🏊': 'SWIMMER',
-    '🍫': 'CHOCOLATE BAR',
-    '📷': 'CAMERA',
-    '👄': 'MOUTH',
-    '🌼': 'BLOSSOM',
-    '🚶': 'PEDESTRIAN',
-    '🐱': 'CAT FACE',
-    '║': 'BOX DRAWINGS DOUBLE VERTICAL',
-    '🐸': 'FROG FACE',
-    '🇺': 'REGIONAL INDICATOR SYMBOL LETTER U',
-    '👿': 'IMP',
-    '🚬': 'SMOKING SYMBOL',
-    '✿': 'BLACK FLORETTE',
-    '📖': 'OPEN BOOK',
-    '🐒': 'MONKEY',
-    '🌍': 'EARTH GLOBE EUROPE-AFRICA',
-    '┊': 'BOX DRAWINGS LIGHT QUADRUPLE DASH VERTICAL',
-    '🐥': 'FRONT-FACING BABY CHICK',
-    '🌀': 'CYCLONE',
-    '🐼': 'PANDA FACE',
-    '🎥': 'MOVIE CAMERA',
-    '💄': 'LIPSTICK',
-    '💸': 'MONEY WITH WINGS',
-    '⛔': 'NO ENTRY',
-    '●': 'BLACK CIRCLE',
-    '🏀': 'BASKETBALL AND HOOP',
-    '💉': 'SYRINGE',
-    '💟': 'HEART DECORATION',
-    '🚗': 'AUTOMOBILE',
-    '😯': 'HUSHED FACE',
-    '📝': 'MEMO',
-    '═': 'BOX DRAWINGS DOUBLE HORIZONTAL',
-    '♦': 'BLACK DIAMOND SUIT',
-    '💭': 'THOUGHT BALLOON',
-    '🌙': 'CRESCENT MOON',
-    '🐟': 'FISH',
-    '👣': 'FOOTPRINTS',
-    '☞': 'WHITE RIGHT POINTING INDEX',
-    '✂': 'BLACK SCISSORS',
-    '🗿': 'MOYAI',
-    '🍝': 'SPAGHETTI',
-    '👪': 'FAMILY',
-    '🍭': 'LOLLIPOP',
-    '🌃': 'NIGHT WITH STARS',
-    '❌': 'CROSS MARK',
-    '🐰': 'RABBIT FACE',
-    '💊': 'PILL',
-    '🚨': 'POLICE CARS REVOLVING LIGHT',
-    '😦': 'FROWNING FACE WITH OPEN MOUTH',
-    '🍪': 'COOKIE',
-    '🍣': 'SUSHI',
-    '╭': 'BOX DRAWINGS LIGHT ARC DOWN AND RIGHT',
-    '✧': 'WHITE FOUR POINTED STAR',
-    '🎆': 'FIREWORKS',
-    '╮': 'BOX DRAWINGS LIGHT ARC DOWN AND LEFT',
-    '🎎': 'JAPANESE DOLLS',
-    '🇩': 'REGIONAL INDICATOR SYMBOL LETTER D',
-    '✅': 'WHITE HEAVY CHECK MARK',
-    '👹': 'JAPANESE OGRE',
-    '📱': 'MOBILE PHONE',
-    '🙍': 'PERSON FROWNING',
-    '🍑': 'PEACH',
-    '🎼': 'MUSICAL SCORE',
-    '🔊': 'SPEAKER WITH THREE SOUND WAVES',
-    '🌌': 'MILKY WAY',
-    '🍎': 'RED APPLE',
-    '🐻': 'BEAR FACE',
-    '─': 'BOX DRAWINGS LIGHT HORIZONTAL',
-    '╰': 'BOX DRAWINGS LIGHT ARC UP AND RIGHT',
-    '💇': 'HAIRCUT',
-    '♬': 'BEAMED SIXTEENTH NOTES',
-    '♚': 'BLACK CHESS KING',
-    '🔴': 'LARGE RED CIRCLE',
-    '🍱': 'BENTO BOX',
-    '🍊': 'TANGERINE',
-    '🍒': 'CHERRIES',
-    '🐭': 'MOUSE FACE',
-    '👟': 'ATHLETIC SHOE',
-    '🌎': 'EARTH GLOBE AMERICAS',
-    '🍍': 'PINEAPPLE',
-    '🐮': 'COW FACE',
-    '📲': 'MOBILE PHONE WITH RIGHTWARDS ARROW AT LEFT',
-    '☼': 'WHITE SUN WITH RAYS',
-    '🌅': 'SUNRISE',
-    '🇷': 'REGIONAL INDICATOR SYMBOL LETTER R',
-    '👠': 'HIGH-HEELED SHOE',
-    '🌽': 'EAR OF MAIZE',
-    '💧': 'DROPLET',
-    '❓': 'BLACK QUESTION MARK ORNAMENT',
-    '🍬': 'CANDY',
-    '😺': 'SMILING CAT FACE WITH OPEN MOUTH',
-    '🐴': 'HORSE FACE',
-    '🚀': 'ROCKET',
-    '¦': 'BROKEN BAR',
-    '💢': 'ANGER SYMBOL',
-    '🎬': 'CLAPPER BOARD',
-    '🍧': 'SHAVED ICE',
-    '🍜': 'STEAMING BOWL',
-    '🐏': 'RAM',
-    '🐘': 'ELEPHANT',
-    '👧': 'GIRL',
-    '⠀': 'BRAILLE PATTERN BLANK',
-    '🏄': 'SURFER',
-    '➤': 'BLACK RIGHTWARDS ARROWHEAD',
-    '⬆': 'UPWARDS BLACK ARROW',
-    '🍋': 'LEMON',
-    '🆗': 'SQUARED OK',
-    '⚪': 'MEDIUM WHITE CIRCLE',
-    '📺': 'TELEVISION',
-    '🍅': 'TOMATO',
-    '⛅': 'SUN BEHIND CLOUD',
-    '🐢': 'TURTLE',
-    '👙': 'BIKINI',
-    '🏡': 'HOUSE WITH GARDEN',
-    '🌾': 'EAR OF RICE',
-    '◉': 'FISHEYE',
-    '✏': 'PENCIL',
-    '🐬': 'DOLPHIN',
-    '🍤': 'FRIED SHRIMP',
-    '🇹': 'REGIONAL INDICATOR SYMBOL LETTER T',
-    '♣': 'BLACK CLUB SUIT',
-    '🐝': 'HONEYBEE',
-    '🌝': 'FULL MOON WITH FACE',
-    '🇮': 'REGIONAL INDICATOR SYMBOL LETTER I',
-    '🔋': 'BATTERY',
-    '🐍': 'SNAKE',
-    '♔': 'WHITE CHESS KING',
-    '🍳': 'COOKING',
-    '🔵': 'LARGE BLUE CIRCLE',
-    '😾': 'POUTING CAT FACE',
-    '🌕': 'FULL MOON SYMBOL',
-    '🐨': 'KOALA',
-    '🔐': 'CLOSED LOCK WITH KEY',
-    '💿': 'OPTICAL DISC',
-    '❁': 'EIGHT PETALLED OUTLINED BLACK FLORETTE',
-    '🌳': 'DECIDUOUS TREE',
-    '👰': 'BRIDE WITH VEIL',
-    '❀': 'WHITE FLORETTE',
-    '⚓': 'ANCHOR',
-    '🚴': 'BICYCLIST',
-    '▀': 'UPPER HALF BLOCK',
-    '👗': 'DRESS',
-    '➕': 'HEAVY PLUS SIGN',
-    '💬': 'SPEECH BALLOON',
-    '▒': 'MEDIUM SHADE',
-    '🔜': 'SOON WITH RIGHTWARDS ARROW ABOVE',
-    '🍨': 'ICE CREAM',
-    '💲': 'HEAVY DOLLAR SIGN',
-    '⛽': 'FUEL PUMP',
-    '🍙': 'RICE BALL',
-    '🍗': 'POULTRY LEG',
-    '🍲': 'POT OF FOOD',
-    '🍥': 'FISH CAKE WITH SWIRL DESIGN',
-    '▸': 'BLACK RIGHT-POINTING SMALL TRIANGLE',
-    '♛': 'BLACK CHESS QUEEN',
-    '😼': 'CAT FACE WITH WRY SMILE',
-    '🐙': 'OCTOPUS',
-    '👨': 'MAN',
-    '🍚': 'COOKED RICE',
-    '🍖': 'MEAT ON BONE',
-    '♨': 'HOT SPRINGS',
-    '🎹': 'MUSICAL KEYBOARD',
-    '♕': 'WHITE CHESS QUEEN',
-    '▃': 'LOWER THREE EIGHTHS BLOCK',
-    '🚘': 'ONCOMING AUTOMOBILE',
-    '🍏': 'GREEN APPLE',
-    '👩': 'WOMAN',
-    '👦': 'BOY',
-    '🇬': 'REGIONAL INDICATOR SYMBOL LETTER G',
-    '🇧': 'REGIONAL INDICATOR SYMBOL LETTER B',
-    '☠': 'SKULL AND CROSSBONES',
-    '🐠': 'TROPICAL FISH',
-    '🚹': 'MENS SYMBOL',
-    '💵': 'BANKNOTE WITH DOLLAR SIGN',
-    '✰': 'SHADOWED WHITE STAR',
-    '╠': 'BOX DRAWINGS DOUBLE VERTICAL AND RIGHT',
-    '👛': 'PURSE',
-    '🚙': 'RECREATIONAL VEHICLE',
-    '🌱': 'SEEDLING',
-    '💻': 'PERSONAL COMPUTER',
-    '🌏': 'EARTH GLOBE ASIA-AUSTRALIA',
-    '▄': 'LOWER HALF BLOCK',
-    '👓': 'EYEGLASSES',
-    '◄': 'BLACK LEFT-POINTING POINTER',
-    '⚾': 'BASEBALL',
-    '🌲': 'EVERGREEN TREE',
-    '👴': 'OLDER MAN',
-    '🏠': 'HOUSE BUILDING',
-    '🍇': 'GRAPES',
-    '🍘': 'RICE CRACKER',
-    '🍛': 'CURRY AND RICE',
-    '🐇': 'RABBIT',
-    '🔞': 'NO ONE UNDER EIGHTEEN SYMBOL',
-    '👵': 'OLDER WOMAN',
-    '◀': 'BLACK LEFT-POINTING TRIANGLE',
-    '🔙': 'BACK WITH LEFTWARDS ARROW ABOVE',
-    '🌵': 'CACTUS',
-    '🐽': 'PIG NOSE',
-    '🍮': 'CUSTARD',
-    '🎇': 'FIREWORK SPARKLER',
-    '🐎': 'HORSE',
-    '➔': 'HEAVY WIDE-HEADED RIGHTWARDS ARROW',
-    '💶': 'BANKNOTE WITH EURO SIGN',
-    '🐤': 'BABY CHICK',
-    '╩': 'BOX DRAWINGS DOUBLE UP AND HORIZONTAL',
-    '🛀': 'BATH',
-    '🌑': 'NEW MOON SYMBOL',
-    '🚲': 'BICYCLE',
-    '🐑': 'SHEEP',
-    '🏁': 'CHEQUERED FLAG',
-    '🍞': 'BREAD',
-    '🎾': 'TENNIS RACQUET AND BALL',
-    '╚': 'BOX DRAWINGS DOUBLE UP AND RIGHT',
-    '🈹': 'SQUARED CJK UNIFIED IDEOGRAPH-5272',
-    '🐳': 'SPOUTING WHALE',
-    '👮': 'POLICE OFFICER',
-    '☹': 'WHITE FROWNING FACE',
-    '🐵': 'MONKEY FACE',
-    '✪': 'CIRCLED WHITE STAR',
-    '◕': 'CIRCLE WITH ALL BUT UPPER LEFT QUADRANT BLACK',
-    '🗼': 'TOKYO TOWER',
-    '▐': 'RIGHT HALF BLOCK',
-    '♠': 'BLACK SPADE SUIT',
-    '┳': 'BOX DRAWINGS HEAVY DOWN AND HORIZONTAL',
-    '👺': 'JAPANESE GOBLIN',
-    '🐚': 'SPIRAL SHELL',
-    '👂': 'EAR',
-    '🗽': 'STATUE OF LIBERTY',
-    '🍵': 'TEACUP WITHOUT HANDLE',
-    '🆒': 'SQUARED COOL',
-    '🍯': 'HONEY POT',
-    '🐺': 'WOLF FACE',
-    '⇨': 'RIGHTWARDS WHITE ARROW',
-    '➨': 'HEAVY CONCAVE-POINTED BLACK RIGHTWARDS ARROW',
-    '🌓': 'FIRST QUARTER MOON SYMBOL',
-    '🔒': 'LOCK',
-    '╬': 'BOX DRAWINGS DOUBLE VERTICAL AND HORIZONTAL',
-    '👳': 'MAN WITH TURBAN',
-    '🌂': 'CLOSED UMBRELLA',
-    '🚌': 'BUS',
-    '♩': 'QUARTER NOTE',
-    '🍡': 'DANGO',
-    '❥': 'ROTATED HEAVY BLACK HEART BULLET',
-    '🎡': 'FERRIS WHEEL',
-    '💌': 'LOVE LETTER',
-    '🐩': 'POODLE',
-    '🌜': 'LAST QUARTER MOON WITH FACE',
-    '⌚': 'WATCH',
-    '🚿': 'SHOWER',
-    '🐖': 'PIG',
-    '🔆': 'HIGH BRIGHTNESS SYMBOL',
-    '🌛': 'FIRST QUARTER MOON WITH FACE',
-    '💂': 'GUARDSMAN',
-    '🐔': 'CHICKEN',
-    '🙎': 'PERSON WITH POUTING FACE',
-    '🏩': 'LOVE HOTEL',
-    '🇫': 'REGIONAL INDICATOR SYMBOL LETTER F',
-    '🔨': 'HAMMER',
-    '📢': 'PUBLIC ADDRESS LOUDSPEAKER',
-    '🐦': 'BIRD',
-    '🐲': 'DRAGON FACE',
-    '♻': 'BLACK UNIVERSAL RECYCLING SYMBOL',
-    '🌘': 'WANING CRESCENT MOON SYMBOL',
-    '🍐': 'PEAR',
-    '🌔': 'WAXING GIBBOUS MOON SYMBOL',
-    '╥': 'BOX DRAWINGS DOWN DOUBLE AND HORIZONTAL SINGLE',
-    '❊': 'EIGHT TEARDROP-SPOKED PROPELLER ASTERISK',
-    '👖': 'JEANS',
-    '🚺': 'WOMENS SYMBOL',
-    '😗': 'KISSING FACE',
-    '🎭': 'PERFORMING ARTS',
-    '🐄': 'COW',
-    '◟': 'LOWER LEFT QUADRANT CIRCULAR ARC',
-    '🍢': 'ODEN',
-    '🎨': 'ARTIST PALETTE',
-    '⬇': 'DOWNWARDS BLACK ARROW',
-    '🚼': 'BABY SYMBOL',
-    '⛲': 'FOUNTAIN',
-    '▁': 'LOWER ONE EIGHTH BLOCK',
-    '🇴': 'REGIONAL INDICATOR SYMBOL LETTER O',
-    '🌗': 'LAST QUARTER MOON SYMBOL',
-    '🌖': 'WANING GIBBOUS MOON SYMBOL',
-    '🔅': 'LOW BRIGHTNESS SYMBOL',
-    '👜': 'HANDBAG',
-    '🐌': 'SNAIL',
-    '💼': 'BRIEFCASE',
-    '🚕': 'TAXI',
-    '🐹': 'HAMSTER FACE',
-    '🌠': 'SHOOTING STAR',
-    '🐈': 'CAT',
-    '⇧': 'UPWARDS WHITE ARROW',
-    '☎': 'BLACK TELEPHONE',
-    '🌁': 'FOGGY',
-    '⚫': 'MEDIUM BLACK CIRCLE',
-    '♧': 'WHITE CLUB SUIT',
-    '🏰': 'EUROPEAN CASTLE',
-    '🚵': 'MOUNTAIN BICYCLIST',
-    '🎢': 'ROLLER COASTER',
-    '🎷': 'SAXOPHONE',
-    '🎐': 'WIND CHIME',
-    '┈': 'BOX DRAWINGS LIGHT QUADRUPLE DASH HORIZONTAL',
-    '╗': 'BOX DRAWINGS DOUBLE DOWN AND LEFT',
-    '╱': 'BOX DRAWINGS LIGHT DIAGONAL UPPER RIGHT TO LOWER LEFT',
-    '🌇': 'SUNSET OVER BUILDINGS',
-    '⏰': 'ALARM CLOCK',
-    '⇩': 'DOWNWARDS WHITE ARROW',
-    '🚂': 'STEAM LOCOMOTIVE',
-    '◠': 'UPPER HALF CIRCLE',
-    '🎿': 'SKI AND SKI BOOT',
-    '✦': 'BLACK FOUR POINTED STAR',
-    '🆔': 'SQUARED ID',
-    '⛪': 'CHURCH',
-    '🌒': 'WAXING CRESCENT MOON SYMBOL',
-    '🐪': 'DROMEDARY CAMEL',
-    '╔': 'BOX DRAWINGS DOUBLE DOWN AND RIGHT',
-    '╝': 'BOX DRAWINGS DOUBLE UP AND LEFT',
-    '👔': 'NECKTIE',
-    '🔱': 'TRIDENT EMBLEM',
-    '🆓': 'SQUARED FREE',
-    '🐋': 'WHALE',
-    '▽': 'WHITE DOWN-POINTING TRIANGLE',
-    '▂': 'LOWER ONE QUARTER BLOCK',
-    '🐛': 'BUG',
-    '👕': 'T-SHIRT',
-    '🚋': 'TRAM CAR',
-    '💳': 'CREDIT CARD',
-    '🌆': 'CITYSCAPE AT DUSK',
-    '🏧': 'AUTOMATED TELLER MACHINE',
-    '💡': 'ELECTRIC LIGHT BULB',
-    '🔹': 'SMALL BLUE DIAMOND',
-    '⬅': 'LEFTWARDS BLACK ARROW',
-    '🍠': 'ROASTED SWEET POTATO',
-    '🐫': 'BACTRIAN CAMEL',
-    '🏪': 'CONVENIENCE STORE',
-    '۩': 'ARABIC PLACE OF SAJDAH',
-    '🇱': 'REGIONAL INDICATOR SYMBOL LETTER L',
-    '📹': 'VIDEO CAMERA',
-    '👞': 'MANS SHOE',
-    '🚑': 'AMBULANCE',
-    '🆘': 'SQUARED SOS',
-    '👚': 'WOMANS CLOTHES',
-    '🚍': 'ONCOMING BUS',
-    '□': 'WHITE SQUARE',
-    '🐂': 'OX',
-    '🚣': 'ROWBOAT',
-    '✳': 'EIGHT SPOKED ASTERISK',
-    '🏉': 'RUGBY FOOTBALL',
-    '🗻': 'MOUNT FUJI',
-    '🐀': 'RAT',
-    '╦': 'BOX DRAWINGS DOUBLE DOWN AND HORIZONTAL',
-    '⛺': 'TENT',
-    '🐕': 'DOG',
-    '🏂': 'SNOWBOARDER',
-    '👡': 'WOMANS SANDAL',
-    '📻': 'RADIO',
-    '✒': 'BLACK NIB',
-    '🌰': 'CHESTNUT',
-    '🏢': 'OFFICE BUILDING',
-    '🎒': 'SCHOOL SATCHEL',
-    '⌒': 'ARC',
-    '🏫': 'SCHOOL',
-    '📴': 'MOBILE PHONE OFF',
-    '🚢': 'SHIP',
-    '🚚': 'DELIVERY TRUCK',
-    '🐉': 'DRAGON',
-    '❒': 'UPPER RIGHT SHADOWED WHITE SQUARE',
-    '🐊': 'CROCODILE',
-    '🔔': 'BELL',
-    '◢': 'BLACK LOWER RIGHT TRIANGLE',
-    '🏥': 'HOSPITAL',
-    '❔': 'WHITE QUESTION MARK ORNAMENT',
-    '🚖': 'ONCOMING TAXI',
-    '🃏': 'PLAYING CARD BLACK JOKER',
-    '▼': 'BLACK DOWN-POINTING TRIANGLE',
-    '▌': 'LEFT HALF BLOCK',
-    '☛': 'BLACK RIGHT POINTING INDEX',
-    '✩': 'STRESS OUTLINED WHITE STAR',
-    '💒': 'WEDDING',
-    '🚤': 'SPEEDBOAT',
-    '🐐': 'GOAT',
-    '■': 'BLACK SQUARE',
-    '🔚': 'END WITH LEFTWARDS ARROW ABOVE',
-    '🎻': 'VIOLIN',
-    '🔷': 'LARGE BLUE DIAMOND',
-    '🚦': 'VERTICAL TRAFFIC LIGHT',
-    '🔓': 'OPEN LOCK',
-    '🎽': 'RUNNING SHIRT WITH SASH',
-    '📅': 'CALENDAR',
-    '🎺': 'TRUMPET',
-    '✯': 'PINWHEEL STAR',
-    '🍈': 'MELON',
-    '✉': 'ENVELOPE',
-    '╣': 'BOX DRAWINGS DOUBLE VERTICAL AND LEFT',
-    '◤': 'BLACK UPPER LEFT TRIANGLE',
-    '○': 'WHITE CIRCLE',
-    '🍼': 'BABY BOTTLE',
-    '📀': 'DVD',
-    '🚛': 'ARTICULATED LORRY',
-    '📓': 'NOTEBOOK',
-    '☉': 'SUN',
-    '💴': 'BANKNOTE WITH YEN SIGN',
-    '┼': 'BOX DRAWINGS LIGHT VERTICAL AND HORIZONTAL',
-    '🐃': 'WATER BUFFALO',
-    '➰': 'CURLY LOOP',
-    '🔌': 'ELECTRIC PLUG',
-    '🍄': 'MUSHROOM',
-    '📕': 'CLOSED BOOK',
-    '📣': 'CHEERING MEGAPHONE',
-    '🚓': 'POLICE CAR',
-    '🐗': 'BOAR',
-    '↪': 'RIGHTWARDS ARROW WITH HOOK',
-    '⛳': 'FLAG IN HOLE',
-    '┻': 'BOX DRAWINGS HEAVY UP AND HORIZONTAL',
-    '┛': 'BOX DRAWINGS HEAVY UP AND LEFT',
-    '┃': 'BOX DRAWINGS HEAVY VERTICAL',
-    '👱': 'PERSON WITH BLOND HAIR',
-    '⏳': 'HOURGLASS WITH FLOWING SAND',
-    '💺': 'SEAT',
-    '🏇': 'HORSE RACING',
-    '☻': 'BLACK SMILING FACE',
-    '📞': 'TELEPHONE RECEIVER',
-    'Ⓐ': 'CIRCLED LATIN CAPITAL LETTER A',
-    '🌉': 'BRIDGE AT NIGHT',
-    '🚩': 'TRIANGULAR FLAG ON POST',
-    '✎': 'LOWER RIGHT PENCIL',
-    '📃': 'PAGE WITH CURL',
-    '🏨': 'HOTEL',
-    '📌': 'PUSHPIN',
-    '♎': 'LIBRA',
-    '💷': 'BANKNOTE WITH POUND SIGN',
-    '🚄': 'HIGH-SPEED TRAIN',
-    '▲': 'BLACK UP-POINTING TRIANGLE',
-    '⛵': 'SAILBOAT',
-    '🔸': 'SMALL ORANGE DIAMOND',
-    '⌛': 'HOURGLASS',
-    '🚜': 'TRACTOR',
-    '🐆': 'LEOPARD',
-    '👒': 'WOMANS HAT',
-    '❕': 'WHITE EXCLAMATION MARK ORNAMENT',
-    '🔛': 'ON WITH EXCLAMATION MARK WITH LEFT RIGHT ARROW ABOVE',
-    '♢': 'WHITE DIAMOND SUIT',
-    '🇲': 'REGIONAL INDICATOR SYMBOL LETTER M',
-    '❅': 'TIGHT TRIFOLIATE SNOWFLAKE',
-    '👝': 'POUCH',
-    '✞': 'SHADOWED WHITE LATIN CROSS',
-    '◡': 'LOWER HALF CIRCLE',
-    '🎋': 'TANABATA TREE',
-    '👥': 'BUSTS IN SILHOUETTE',
-    '📵': 'NO MOBILE PHONES',
-    '🐡': 'BLOWFISH',
-    '◆': 'BLACK DIAMOND',
-    '🏯': 'JAPANESE CASTLE',
-    '☂': 'UMBRELLA',
-    '🔭': 'TELESCOPE',
-    '🎪': 'CIRCUS TENT',
-    '🐜': 'ANT',
-    '♌': 'LEO',
-    '☐': 'BALLOT BOX',
-    '👷': 'CONSTRUCTION WORKER',
-    '↳': 'DOWNWARDS ARROW WITH TIP RIGHTWARDS',
-    '🔈': 'SPEAKER',
-    '📄': 'PAGE FACING UP',
-    '📍': 'ROUND PUSHPIN',
-    '🚐': 'MINIBUS',
-    '🚔': 'ONCOMING POLICE CAR',
-    '🌋': 'VOLCANO',
-    '📡': 'SATELLITE ANTENNA',
-    '⏩': 'BLACK RIGHT-POINTING DOUBLE TRIANGLE',
-    '🚳': 'NO BICYCLES',
-    '✘': 'HEAVY BALLOT X',
-    '۞': 'ARABIC START OF RUB EL HIZB',
-    '☾': 'LAST QUARTER MOON',
-    '🅰': 'NEGATIVE SQUARED LATIN CAPITAL LETTER A',
-    '📥': 'INBOX TRAY',
-    '🇼': 'REGIONAL INDICATOR SYMBOL LETTER W',
-    '┓': 'BOX DRAWINGS HEAVY DOWN AND LEFT',
-    '┣': 'BOX DRAWINGS HEAVY VERTICAL AND RIGHT',
-    'Ⓛ': 'CIRCLED LATIN CAPITAL LETTER L',
-    'Ⓔ': 'CIRCLED LATIN CAPITAL LETTER E',
-    '🔦': 'ELECTRIC TORCH',
-    '👤': 'BUST IN SILHOUETTE',
-    '🚁': 'HELICOPTER',
-    '🎠': 'CAROUSEL HORSE',
-    '🐁': 'MOUSE',
-    '📗': 'GREEN BOOK',
-    '┐': 'BOX DRAWINGS LIGHT DOWN AND LEFT',
-    '☮': 'PEACE SYMBOL',
-    '♂': 'MALE SIGN',
-    '◞': 'LOWER RIGHT QUADRANT CIRCULAR ARC',
-    '📯': 'POSTAL HORN',
-    '🔩': 'NUT AND BOLT',
-    '👢': 'WOMANS BOOTS',
-    '◂': 'BLACK LEFT-POINTING SMALL TRIANGLE',
-    '📰': 'NEWSPAPER',
-    '📶': 'ANTENNA WITH BARS',
-    '🚥': 'HORIZONTAL TRAFFIC LIGHT',
-    '🌄': 'SUNRISE OVER MOUNTAINS',
-    '🗾': 'SILHOUETTE OF JAPAN',
-    '🔶': 'LARGE ORANGE DIAMOND',
-    '🏤': 'EUROPEAN POST OFFICE',
-    '🎩': 'TOP HAT',
-    'Ⓜ': 'CIRCLED LATIN CAPITAL LETTER M',
-    '🔧': 'WRENCH',
-    '🐅': 'TIGER',
-    '♮': 'MUSIC NATURAL SIGN',
-    '🅾': 'NEGATIVE SQUARED LATIN CAPITAL LETTER O',
-    '🔄': 'ANTICLOCKWISE DOWNWARDS AND UPWARDS OPEN CIRCLE ARROWS',
-    '☄': 'COMET',
-    '☨': 'CROSS OF LORRAINE',
-    '📦': 'PACKAGE',
-    '🚊': 'TRAM',
-    '🔲': 'BLACK SQUARE BUTTON',
-    '🔁': 'CLOCKWISE RIGHTWARDS AND LEFTWARDS OPEN CIRCLE ARROWS',
-    '△': 'WHITE UP-POINTING TRIANGLE',
-    '📆': 'TEAR-OFF CALENDAR',
-    '❛': 'HEAVY SINGLE TURNED COMMA QUOTATION MARK ORNAMENT',
-    '📉': 'CHART WITH DOWNWARDS TREND',
-    '▵': 'WHITE UP-POINTING SMALL TRIANGLE',
-    '🔎': 'RIGHT-POINTING MAGNIFYING GLASS',
-    '☜': 'WHITE LEFT POINTING INDEX',
-    '🇯': 'REGIONAL INDICATOR SYMBOL LETTER J',
-    '🇵': 'REGIONAL INDICATOR SYMBOL LETTER P',
-    '📘': 'BLUE BOOK',
-    '✡': 'STAR OF DAVID',
-    'ⓔ': 'CIRCLED LATIN SMALL LETTER E',
-    '🔑': 'KEY',
-    '🔃': 'CLOCKWISE DOWNWARDS AND UPWARDS OPEN CIRCLE ARROWS',
-    '👃': 'NOSE',
-    '⭕': 'HEAVY LARGE CIRCLE',
-    '🔘': 'RADIO BUTTON',
-    'ⓒ': 'CIRCLED LATIN SMALL LETTER C',
-    '🚭': 'NO SMOKING SYMBOL',
-    '🚉': 'STATION',
-    '🚪': 'DOOR',
-    '➳': 'WHITE-FEATHERED RIGHTWARDS ARROW',
-    '🚃': 'RAILWAY CAR',
-    '┯': 'BOX DRAWINGS DOWN LIGHT AND HORIZONTAL HEAVY',
-    '🏬': 'DEPARTMENT STORE',
-    '☽': 'FIRST QUARTER MOON',
-    '🆙': 'SQUARED UP WITH EXCLAMATION MARK',
-    '🆖': 'SQUARED NG',
-    '☪': 'STAR AND CRESCENT',
-    '┗': 'BOX DRAWINGS HEAVY UP AND RIGHT',
-    '🚮': 'PUT LITTER IN ITS PLACE SYMBOL',
-    '┫': 'BOX DRAWINGS HEAVY VERTICAL AND LEFT',
-    'Ⓞ': 'CIRCLED LATIN CAPITAL LETTER O',
-    '❇': 'SPARKLE',
-    '✴': 'EIGHT POINTED BLACK STAR',
-    '┌': 'BOX DRAWINGS LIGHT DOWN AND RIGHT',
-    '☊': 'ASCENDING NODE',
-    '🔕': 'BELL WITH CANCELLATION STROKE',
-    '⬛': 'BLACK LARGE SQUARE',
-    '❝': 'HEAVY DOUBLE TURNED COMMA QUOTATION MARK ORNAMENT',
-    '❞': 'HEAVY DOUBLE COMMA QUOTATION MARK ORNAMENT',
-    '🚞': 'MOUNTAIN RAILWAY',
-    '🍶': 'SAKE BOTTLE AND CUP',
-    '🌐': 'GLOBE WITH MERIDIANS',
-    '♀': 'FEMALE SIGN',
-    '🚅': 'HIGH-SPEED TRAIN WITH BULLET NOSE',
-    '🚒': 'FIRE ENGINE',
-    '➣': 'THREE-D BOTTOM-LIGHTED RIGHTWARDS ARROWHEAD',
-    '♋': 'CANCER',
-    '♍': 'VIRGO',
-    '🕝': 'CLOCK FACE TWO-THIRTY',
-    'ⓐ': 'CIRCLED LATIN SMALL LETTER A',
-    '✗': 'BALLOT X',
-    '📙': 'ORANGE BOOK',
-    'Ⓢ': 'CIRCLED LATIN CAPITAL LETTER S',
-    '📋': 'CLIPBOARD',
-    '⇢': 'RIGHTWARDS DASHED ARROW',
-    '🎱': 'BILLIARDS',
-    '🐞': 'LADY BEETLE',
-    '🔺': 'UP-POINTING RED TRIANGLE',
-    'ⓡ': 'CIRCLED LATIN SMALL LETTER R',
-    '🎍': 'PINE DECORATION',
-    '♤': 'WHITE SPADE SUIT',
-    '🎲': 'GAME DIE',
-    '🎯': 'DIRECT HIT',
-    '〠': 'POSTAL MARK FACE',
-    '🔉': 'SPEAKER WITH ONE SOUND WAVE',
-    '↩': 'LEFTWARDS ARROW WITH HOOK',
-    '🚾': 'WATER CLOSET',
-    '🎣': 'FISHING POLE AND FISH',
-    '🔣': 'INPUT SYMBOL FOR SYMBOLS',
-    '❎': 'NEGATIVE SQUARED CROSS MARK',
-    '➥': 'HEAVY BLACK CURVED DOWNWARDS AND RIGHTWARDS ARROW',
-    '🅱': 'NEGATIVE SQUARED LATIN CAPITAL LETTER B',
-    '🎌': 'CROSSED FLAGS',
-    '◣': 'BLACK LOWER LEFT TRIANGLE',
-    '⏬': 'BLACK DOWN-POINTING DOUBLE TRIANGLE',
-    '♭': 'MUSIC FLAT SIGN',
-    '💠': 'DIAMOND SHAPE WITH A DOT INSIDE',
-    'ⓞ': 'CIRCLED LATIN SMALL LETTER O',
-    '🔳': 'WHITE SQUARE BUTTON',
-    '🏭': 'FACTORY',
-    '🔰': 'JAPANESE SYMBOL FOR BEGINNER',
-    '🎳': 'BOWLING',
-    '☚': 'BLACK LEFT POINTING INDEX',
-    '➽': 'HEAVY WEDGE-TAILED RIGHTWARDS ARROW',
-    '➫': 'BACK-TILTED SHADOWED WHITE RIGHTWARDS ARROW',
-    '➖': 'HEAVY MINUS SIGN',
-    '🏮': 'IZAKAYA LANTERN',
-    '📛': 'NAME BADGE',
-    '꒰': 'YI RADICAL SHY',
-    '꒱': 'YI RADICAL VEP',
-    '◝': 'UPPER RIGHT QUADRANT CIRCULAR ARC',
-    '📑': 'BOOKMARK TABS',
-    '🎦': 'CINEMA',
-    'ⓧ': 'CIRCLED LATIN SMALL LETTER X',
-    '🇨': 'REGIONAL INDICATOR SYMBOL LETTER C',
-    '🇳': 'REGIONAL INDICATOR SYMBOL LETTER N',
-    '🔟': 'KEYCAP TEN',
-    '〓': 'GETA MARK',
-    'ⓜ': 'CIRCLED LATIN SMALL LETTER M',
-    '➠': 'HEAVY DASHED TRIANGLE-HEADED RIGHTWARDS ARROW',
-    '🚆': 'TRAIN',
-    '🚠': 'MOUNTAIN CABLEWAY',
-    '℅': 'CARE OF',
-    '☃': 'SNOWMAN',
-    '🚽': 'TOILET',
-    '📐': 'TRIANGULAR RULER',
-    'ⓝ': 'CIRCLED LATIN SMALL LETTER N',
-    '✮': 'HEAVY OUTLINED BLACK STAR',
-    '⇦': 'LEFTWARDS WHITE ARROW',
-    '👲': 'MAN WITH GUA PI MAO',
-    '🚡': 'AERIAL TRAMWAY',
-    '🎑': 'MOON VIEWING CEREMONY',
-    '🔬': 'MICROSCOPE',
-    '➗': 'HEAVY DIVISION SIGN',
-    '📈': 'CHART WITH UPWARDS TREND',
-    '⌘': 'PLACE OF INTEREST SIGN',
-    '⏪': 'BLACK LEFT-POINTING DOUBLE TRIANGLE',
-    '╹': 'BOX DRAWINGS HEAVY UP',
-    '◎': 'BULLSEYE',
-    '🔼': 'UP-POINTING SMALL RED TRIANGLE',
-    '꒦': 'YI RADICAL GGUO',
-    '📎': 'PAPERCLIP',
-    '⑅': 'OCR BOW TIE',
-    '⍝': 'APL FUNCTIONAL SYMBOL UP SHOE JOT',
-    '📁': 'FILE FOLDER',
-    '✭': 'OUTLINED BLACK STAR',
-    '➲': 'CIRCLED HEAVY WHITE RIGHTWARDS ARROW',
-    '♓': 'PISCES',
-    '┏': 'BOX DRAWINGS HEAVY DOWN AND RIGHT',
-    '☇': 'LIGHTNING',
-    '♺': 'RECYCLING SYMBOL FOR GENERIC MATERIALS',
-    '♞': 'BLACK CHESS KNIGHT',
-    '࿎': 'TIBETAN SIGN RDEL NAG RDEL DKAR',
-    '📠': 'FAX MACHINE',
-    '👘': 'KIMONO',
-    '↙': 'SOUTH WEST ARROW',
-    'Ⓕ': 'CIRCLED LATIN CAPITAL LETTER F',
-    'Ⓦ': 'CIRCLED LATIN CAPITAL LETTER W',
-    'Ⓟ': 'CIRCLED LATIN CAPITAL LETTER P',
-    '🕑': 'CLOCK FACE TWO OCLOCK',
-    '💽': 'MINIDISC',
-    '🕛': 'CLOCK FACE TWELVE OCLOCK',
-    '🎫': 'TICKET',
-    '♈': 'ARIES',
-    '📟': 'PAGER',
-    '℃': 'DEGREE CELSIUS',
-    '↬': 'RIGHTWARDS ARROW WITH LOOP',
-    '🕒': 'CLOCK FACE THREE OCLOCK',
-    '🇰': 'REGIONAL INDICATOR SYMBOL LETTER K',
-    '↱': 'UPWARDS ARROW WITH TIP RIGHTWARDS',
-    '✍': 'WRITING HAND',
-    '⇐': 'LEFTWARDS DOUBLE ARROW',
-    '🏦': 'BANK',
-    '🔻': 'DOWN-POINTING RED TRIANGLE',
-    'ⓟ': 'CIRCLED LATIN SMALL LETTER P',
-    'ⓕ': 'CIRCLED LATIN SMALL LETTER F',
-    'ⓘ': 'CIRCLED LATIN SMALL LETTER I',
-    '♿': 'WHEELCHAIR SYMBOL',
-    '⇗': 'NORTH EAST DOUBLE ARROW',
-    '⇘': 'SOUTH EAST DOUBLE ARROW',
-    'ⓨ': 'CIRCLED LATIN SMALL LETTER Y',
-    'ⓙ': 'CIRCLED LATIN SMALL LETTER J',
-    '▫': 'WHITE SMALL SQUARE',
-    '🔇': 'SPEAKER WITH CANCELLATION STROKE',
-    '⌃': 'UP ARROWHEAD',
-    '🔖': 'BOOKMARK',
-    '📜': 'SCROLL',
-    '♏': 'SCORPIUS',
-    '🚝': 'MONORAIL',
-    '☢': 'RADIOACTIVE SIGN',
-    '🎏': 'CARP STREAMER',
-    '┘': 'BOX DRAWINGS LIGHT UP AND LEFT',
-    '✝': 'LATIN CROSS',
-    '❖': 'BLACK DIAMOND MINUS WHITE X',
-    '⍣': 'APL FUNCTIONAL SYMBOL STAR DIAERESIS',
-    '📮': 'POSTBOX',
-    '🕕': 'CLOCK FACE SIX OCLOCK',
-    '🇭': 'REGIONAL INDICATOR SYMBOL LETTER H',
-    '◜': 'UPPER LEFT QUADRANT CIRCULAR ARC',
-    '🔯': 'SIX POINTED STAR WITH MIDDLE DOT',
-    '➸': 'HEAVY BLACK-FEATHERED RIGHTWARDS ARROW',
-    '꒵': 'YI RADICAL JJY',
-    '🕥': 'CLOCK FACE TEN-THIRTY',
-    '♙': 'WHITE CHESS PAWN',
-    '▿': 'WHITE DOWN-POINTING SMALL TRIANGLE',
-    '⚃': 'DIE FACE-4',
-    '✽': 'HEAVY TEARDROP-SPOKED ASTERISK',
-    '📼': 'VIDEOCASSETTE',
-    '🕐': 'CLOCK FACE ONE OCLOCK',
-    '🀄': 'MAHJONG TILE RED DRAGON',
-    '✾': 'SIX PETALLED BLACK AND WHITE FLORETTE',
-    '✬': 'BLACK CENTRE WHITE STAR',
-    '🆑': 'SQUARED CL',
-    '✫': 'OPEN CENTRE BLACK STAR',
-    '🕔': 'CLOCK FACE FIVE OCLOCK',
-    '❣': 'HEAVY HEART EXCLAMATION MARK ORNAMENT',
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-    '➢': 'THREE-D TOP-LIGHTED RIGHTWARDS ARROWHEAD',
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-    '📫': 'CLOSED MAILBOX WITH RAISED FLAG',
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-    '♊': 'GEMINI',
-    '🈂': 'SQUARED KATAKANA SA',
-    '🎰': 'SLOT MACHINE',
-    '҂': 'CYRILLIC THOUSANDS SIGN',
-    '╤': 'BOX DRAWINGS DOWN SINGLE AND HORIZONTAL DOUBLE',
-    '➛': 'DRAFTING POINT RIGHTWARDS ARROW',
-    '♝': 'BLACK CHESS BISHOP',
-    '❋': 'HEAVY EIGHT TEARDROP-SPOKED PROPELLER ASTERISK',
-    '✆': 'TELEPHONE LOCATION SIGN',
-    '📔': 'NOTEBOOK WITH DECORATIVE COVER'
-}
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-    '❣': 0.01,
-    '➱': 0.0,
-    '🆕': 0.0,
-    '➢': 0.0,
-    '↕': 0.0,
-    '📫': 0.01,
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-    '🎰': -0.01,
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-    '╤': -0.01,
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-    '♝': 0.0,
-    '❋': 0.0,
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-    '📔': 0.01
-}
\ No newline at end of file

From 194738c2ab3bef99acd3f2bc6379cdba68a6d41e Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 17:34:09 +0200
Subject: [PATCH 306/496] fix pylint file_loader

---
 nautilus_nlp/utils/file_loader.py | 132 +++++++++++++++---------------
 1 file changed, 64 insertions(+), 68 deletions(-)

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 73ef8e3..876b54f 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -15,16 +15,16 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import io
-import chardet
+import codecs
 import glob
-import re
-import os 
-from os import listdir
-from os.path import isfile, isdir, join
+import io
 import json
 import logging
+import os
+import re
+import shutil
 
+import chardet
 
 logging.basicConfig(level=logging.INFO)
 
@@ -36,38 +36,39 @@ def open_textfile(filepath, encoding='utf-8'):
 
 
 def detect_encoding(file_path_or_string, n_lines=100):
-    """ 
+    """
     Predict a file's encoding using chardet
-    
+
     Parameters
     ----------
     file_path_or_string : string
         if filepath, will open the file. Otherwise will predict from the string
     n_lines : int
-        number of line to predict from 
-       
+        number of line to predict from
+
     Returns
     -------
     the code of the detected encoding
     """
-    if isfile(file_path_or_string):
+    if os.path.isfile(file_path_or_string):
         with open(file_path_or_string, 'rb') as f:                      # Open the file as binary data
-            rawdata = b''.join([f.readline() for _ in range(n_lines)])  # Join binary lines for specified number of lines
-    elif type(file_path_or_string) is bytes:
+            # Join binary lines for specified number of lines
+            rawdata = b''.join([f.readline() for _ in range(n_lines)])
+    elif isinstance(file_path_or_string, bytes):
         rawdata = file_path_or_string
     return chardet.detect(rawdata)
 
 
 def text_loader(filepath, encoding=None, detectencoding=True):
     '''
-    This util loads a file. If the encoding is specified, will use the specified 
-    encoding to load the text file. 
-    If not specified, this function tries to open the doc as UTF-U, and if 
-    it fails it will try to detect the encoding using **detect_encoding** 
+    This util loads a file. If the encoding is specified, will use the specified
+    encoding to load the text file.
+    If not specified, this function tries to open the doc as UTF-U, and if
+    it fails it will try to detect the encoding using **detect_encoding**
 
     Parameters
     ----------
-    filepath : str 
+    filepath : str
     encoding : str
         If the encoding is specified, will use the specified encoding to load the text file.
     detect_encoding : bool
@@ -79,25 +80,24 @@ def text_loader(filepath, encoding=None, detectencoding=True):
     '''
     if encoding is not None:
         return open_textfile(filepath, encoding=encoding)
-    else:
-        try:
-            return open_textfile(filepath, encoding='utf-8')
-        except UnicodeDecodeError:
-            logging.warning('Encoding for {} is not UTF-8.'.format(filepath))
-            if detectencoding is True:
-                logging.warning('Trying to detect encoding for {}'.format(filepath))
-                detected_encoding = detect_encoding(filepath)
-                logging.info('{filepath}: detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(filepath=filepath, encod=detected_encoding['encoding'],
-                                                                                                        conf_rate=detected_encoding['confidence']))
-                return open_textfile(filepath, encoding=detected_encoding['encoding'])
-            else:
-                raise UnicodeDecodeError('Cannot load document using utf-8. Try to detect encoding using detectencoding=True')
+    try:
+        return open_textfile(filepath, encoding='utf-8')
+    except UnicodeDecodeError:
+        logging.warning('Encoding for {} is not UTF-8.'.format(filepath))
+        if detectencoding is True:
+            logging.warning('Trying to detect encoding for {}'.format(filepath))
+            detected_encoding = detect_encoding(filepath)
+            logging.info('{filepath}: detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(
+                filepath=filepath, encod=detected_encoding['encoding'], conf_rate=detected_encoding['confidence']))
+            return open_textfile(filepath, encoding=detected_encoding['encoding'])
+        raise UnicodeDecodeError(
+            'Cannot load document using utf-8. Try to detect encoding using detectencoding=True')
 
 
 def get_subfolders_path(folder):
     if not folder.endswith("/"):
         folder = folder + "/"
-    return [folder + f+'/' for f in listdir(folder)if isdir(join(folder, f)) and f != ".DS_Store"]
+    return [folder + f+'/' for f in os.listdir(folder)if os.path.isdir(os.path.join(folder, f)) and f != ".DS_Store"]
 
 
 def list_files_in_subdir(filepath):
@@ -105,29 +105,29 @@ def list_files_in_subdir(filepath):
     Get a list of all the filepath of files in directory and subdirectory.
     '''
     res = []
-    for path, subdirs, files in os.walk(filepath):
+    for path, _, files in os.walk(filepath):
         for name in files:
             res.append(os.path.join(path, name))
     return res
 
 
-def list_files(filepath:str):
-    """  
-    inputs a filepath. 
-    Outputs a list of filepath. 
+def list_files(filepath: str):
+    """
+    inputs a filepath.
+    Outputs a list of filepath.
     Supports regex
     """
 
-    if isdir(filepath) and len(re.findall(r"[\w.]$",filepath)):
-        filepath=filepath+'/*'
+    if os.path.isdir(filepath) and len(re.findall(r"[\w.]$", filepath)) > 0:
+        filepath = filepath+'/*'
     if filepath.endswith('/'):
-        filepath=filepath+'*'
-    return[file for file in glob.glob(filepath) if isfile(file)]            
+        filepath = filepath+'*'
+    return[file for file in glob.glob(filepath) if os.path.isdir(file)]
 
 
-def documents_loader(filepath:str, encoding=None, detectencoding=True, output_as='dict'):
+def documents_loader(filepath: str, encoding=None, detectencoding=True, output_as='dict'):
     '''
-    Input a filepath, a filepath with wildcard (eg. *.txt), 
+    Input a filepath, a filepath with wildcard (eg. *.txt),
     or a list of filepaths.
     Output a string, or a dict of strings.
 
@@ -137,41 +137,41 @@ def documents_loader(filepath:str, encoding=None, detectencoding=True, output_as
         A filepath with wildcard (eg. *.txt), or a list of filepaths.
     output_as: list or dict.
         If dict, key will be the filename.
-    encoding: 
-        if not specified, will try to detect encoding except if detectencoding is false. 
-    detectencoding : bool 
-        if True and if encoding is not specified, will try to detect encoding using chardet. 
-    
+    encoding:
+        if not specified, will try to detect encoding except if detectencoding is false.
+    detectencoding : bool
+        if True and if encoding is not specified, will try to detect encoding using chardet.
+
     Returns
     -------
     string
     the document loaded
     '''
-   
-    if type(filepath) is str:
+    if isinstance(filepath, str):
         documents = list_files(filepath)
         nb_of_documents = len(documents)
-    elif type(filepath) is list:
+    elif isinstance(filepath, list):
         nb_of_documents = len(filepath)
         documents = filepath
     else:
         raise IOError('Please enter a valid filepath or a valid list of filepath')
-    
+
     if nb_of_documents == 1:
-        return text_loader(documents[0],encoding=encoding, detectencoding=detectencoding)
-    elif nb_of_documents > 1:
+        return text_loader(
+            documents[0], encoding=encoding, detectencoding=detectencoding)
+    if nb_of_documents > 1:
         if output_as == 'list':
-            return [text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents]
-        elif output_as == 'dict':
-            return { document : text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents}
-        else:
-            raise ValueError('Enter a valid output format between list or dict')
-    else:
-        raise IOError('No files detected in {}'.format(filepath))        
+            return [text_loader(
+                document, encoding=encoding, detectencoding=detectencoding) for document in documents]
+        if output_as == 'dict':
+            return {document: text_loader(
+                document, encoding=encoding, detectencoding=detectencoding) for document in documents}
+        raise ValueError('Enter a valid output format between list or dict')
+    raise IOError('No files detected in {}'.format(filepath))
 
 
 #################
-## CSV Loader
+# CSV Loader
 
 def encode_columns(df, columns_to_encode):
     '''
@@ -187,21 +187,17 @@ def decode_columns(df, columns_to_encode):
     '''
     for col in columns_to_encode:
         df[col] = df[col].apply(json.loads)
-  
+
 
 #################
-## Encoding functions
+# Encoding functions
 
 
 def convert_encoding(file_path, input_encoding, output_encoding):
     '''
     Encode a file according to a specified encoding
     '''
-    import codecs
-    import shutil
-
     with codecs.open(file_path, encoding=input_encoding) as input_file:
         with codecs.open(
                 'encoded_'+file_path, "w", encoding=output_encoding) as output_file:
             shutil.copyfileobj(input_file, output_file)
-

From 5529c6e9087679f4c37c1307e56c7a44d2f32f03 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 18:13:27 +0200
Subject: [PATCH 307/496] fixing pylint on phone_number.py

---
 nautilus_nlp/utils/phone_number.py | 16 ++++++++++++----
 tests/test_phone_number.py         |  8 ++++----
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index a1583e9..f3af336 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -17,7 +17,7 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import phonenumbers as _phonenumbers
 
-SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM', 
+SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM',
                      'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT',
                      'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES',
                      'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE',
@@ -39,6 +39,12 @@
                      'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN',
                      'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ', 'DO']
 
+FORMAT_NUMBERS = {
+    "E164": _phonenumbers.PhoneNumberFormat.E164,
+    "INTERNATIONAL": _phonenumbers.PhoneNumberFormat.INTERNATIONAL,
+    "NATIONAL": _phonenumbers.PhoneNumberFormat.NATIONAL,
+    "RFC3966": _phonenumbers.PhoneNumberFormat.RFC3966
+}
 
 def find_phone_numbers(string, region_code=None):
     """
@@ -82,7 +88,7 @@ def extract_phone_numbers(text: str, countrylist: list)->list:
     return list(set(all_phone_numbers))
 
 
-class phone_parser(object):
+class PhoneParser:
     """
     Python port of Google's libphonenumber.
     https://github.com/daviddrysdale/python-phonenumbers
@@ -118,10 +124,12 @@ def parse_number(self, text: str, region_code=None):
         self.parsed_num = _phonenumbers.parse(self.text, self.region_code)
         return self.parsed_num
 
+
     def format_number(self, num_format):
         '''
         ['E164','INTERNATIONAL','NATIONAL','RFC3966']
         '''
-        # TODO: why exec ???
-        standard_format = exec('_phonenumbers.PhoneNumberFormat.'+num_format)
+        standard_format = FORMAT_NUMBERS.get(num_format)
+        if standard_format is None:
+            raise ValueError(f"Please choose a num_format in {list(FORMAT_NUMBERS.keys())}")
         return _phonenumbers.format_number(self.parsed_num, standard_format)
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index 724a4b9..e416f09 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -43,16 +43,16 @@ def test_extract_phone_number_international():
 
 def test_phone_parser_us():
     input_str = '(541) 754-3010'
-    expected = '541-754-3010'
-    p = phone.phone_parser()
+    expected = '+1 541-754-3010'
+    p = phone.PhoneParser()
     p.parse_number(input_str, region_code='US')
     res = p.format_number('INTERNATIONAL')
     assert res == expected
 
 def test_phone_parser_fr():
     input_str = '0625093267'
-    expected = '6 25 09 32 67'
-    p = phone.phone_parser()
+    expected = '+33625093267'
+    p = phone.PhoneParser()
     p.parse_number(input_str, region_code='FR')
     res = p.format_number('E164')
     assert res == expected

From 9fa3b4c219886f70029b141bb841eb9af95906b9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 18:15:25 +0200
Subject: [PATCH 308/496] removing uncalled utils vector_similarity

---
 nautilus_nlp/utils/vector_similarity.py | 60 -------------------------
 1 file changed, 60 deletions(-)
 delete mode 100644 nautilus_nlp/utils/vector_similarity.py

diff --git a/nautilus_nlp/utils/vector_similarity.py b/nautilus_nlp/utils/vector_similarity.py
deleted file mode 100644
index 174d1a2..0000000
--- a/nautilus_nlp/utils/vector_similarity.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import numpy as np
-import math
-
-
-def _VectorSize(vec):
-    return math.sqrt(sum(math.pow(v, 2) for v in vec))
-
-
-def _InnerProduct(vec1, vec2):
-    return sum(v1 * v2 for v1, v2 in zip(vec1, vec2))
-
-
-def _Theta(vec1, vec2):
-    return math.acos(Cosine(vec1, vec2)) + 10
-
-
-def _Magnitude_Difference(vec1, vec2):
-    return abs(_VectorSize(vec1) - _VectorSize(vec2))
-
-
-def Euclidean(vec1, vec2):
-    return math.sqrt(sum(math.pow((v1 - v2), 2) for v1, v2 in zip(vec1, vec2)))
-
-
-def Cosine(vec1, vec2):
-    result = _InnerProduct(vec1, vec2) / (_VectorSize(vec1) * _VectorSize(vec2))
-    return result
-
-
-def Triangle(vec1, vec2):
-    theta = math.radians(_Theta(vec1, vec2))
-    return (_VectorSize(vec1) * _VectorSize(vec2) * math.sin(theta)) / 2
-
-
-def Sector(vec1, vec2):
-    ED = Euclidean(vec1, vec2)
-    MD = _Magnitude_Difference(vec1, vec2)
-    theta = _Theta(vec1, vec2)
-    return math.pi * math.pow((ED + MD), 2) * theta / 360
-
-
-def TS_SS(vec1, vec2):
-    return Triangle(vec1, vec2) * Sector(vec1, vec2)

From 78b48a048362421e74394fb5c648a7d323677957 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 18:23:56 +0200
Subject: [PATCH 309/496] linting test_biterm.py

---
 tests/test_biterm.py | 62 ++++++++++++++++++++++----------------------
 1 file changed, 31 insertions(+), 31 deletions(-)

diff --git a/tests/test_biterm.py b/tests/test_biterm.py
index 5f9c3a7..21e7320 100644
--- a/tests/test_biterm.py
+++ b/tests/test_biterm.py
@@ -15,11 +15,11 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.topic_modeling.biterm_model import BitermModel
 import pandas as pd
 import pytest
+from nautilus_nlp.topic_modeling.biterm_model import BitermModel
 
-text = ['Cola 1.5L Carrefour',
+TEXT = ['Cola 1.5L Carrefour',
         'Pepsi Cola Light 1.5L',
         'Pepsi Cola Twist Light',
         'Cola 1.5L CRF DISC',
@@ -33,30 +33,30 @@
         'Penne de riz 100g sans gluten',
         'Spaghetti de maïs 50g sans Glute']
 
-nb_topics = 5
-nb_word_per_cluster = 5
-nb_iteration = 100
-language = 'english'
+NB_TOPICS = 5
+NB_WORD_PER_CLUSTER = 5
+NB_ITERATION = 100
+LANGUAGE = 'english'
 
 
 @pytest.mark.parametrize(
     "input_text, input_nb_topic , input_nb_iteration , input_language",
     [
-        (text, -1, nb_iteration, language),
-        (text, "Panzani", nb_iteration, language),
-        (text, 3.4, nb_iteration, language),
-        (text, (3, 5), nb_iteration, language),
-        (text, [3, 5], nb_iteration, language),
-        (text, nb_topics, (2, 4), language),
-        (text, nb_topics, [1, 3], language),
-        (text, nb_topics, -1, language),
-        (text, nb_topics, "Panzani", language),
-        (text, nb_topics, 2.4, language),
-        (3, nb_topics, nb_iteration, language),
-        ("Panzani", nb_topics, nb_iteration, language),
-        (("Panzani", "Rustichella"), nb_topics, nb_iteration, language),
-        ([], nb_topics, nb_iteration, language),
-        (["Panzani", "Rustichella", 3], nb_topics, nb_iteration, language)
+        (TEXT, -1, NB_ITERATION, LANGUAGE),
+        (TEXT, "Panzani", NB_ITERATION, LANGUAGE),
+        (TEXT, 3.4, NB_ITERATION, LANGUAGE),
+        (TEXT, (3, 5), NB_ITERATION, LANGUAGE),
+        (TEXT, [3, 5], NB_ITERATION, LANGUAGE),
+        (TEXT, NB_TOPICS, (2, 4), LANGUAGE),
+        (TEXT, NB_TOPICS, [1, 3], LANGUAGE),
+        (TEXT, NB_TOPICS, -1, LANGUAGE),
+        (TEXT, NB_TOPICS, "Panzani", LANGUAGE),
+        (TEXT, NB_TOPICS, 2.4, LANGUAGE),
+        (3, NB_TOPICS, NB_ITERATION, LANGUAGE),
+        ("Panzani", NB_TOPICS, NB_ITERATION, LANGUAGE),
+        (("Panzani", "Rustichella"), NB_TOPICS, NB_ITERATION, LANGUAGE),
+        ([], NB_TOPICS, NB_ITERATION, LANGUAGE),
+        (["Panzani", "Rustichella", 3], NB_TOPICS, NB_ITERATION, LANGUAGE)
     ]
 )
 def text_input_parameter_error_handling(input_text
@@ -68,18 +68,18 @@ def text_input_parameter_error_handling(input_text
 
 
 def test_number_topic_correct():
-    biterm_model = BitermModel(data=text
-                               , nb_topics=nb_topics
-                               , nb_iteration=nb_iteration
-                               , lang=language)
-    clusters = biterm_model.compute_topics(nb_word_per_cluster=nb_word_per_cluster)
-    assert len(pd.DataFrame(clusters)) == nb_topics
+    biterm_model = BitermModel(data=TEXT
+                               , nb_topics=NB_TOPICS
+                               , nb_iteration=NB_ITERATION
+                               , lang=LANGUAGE)
+    clusters = biterm_model.compute_topics(nb_word_per_cluster=NB_WORD_PER_CLUSTER)
+    assert len(pd.DataFrame(clusters)) == NB_TOPICS
 
 
 def test_no_initialisation():
     with pytest.raises(ValueError):
-        biter_model = BitermModel(data=text
-                                  , nb_topics=nb_topics
-                                  , nb_iteration=nb_iteration,
-                                  lang=language)
+        biter_model = BitermModel(data=TEXT,
+                                  nb_topics=NB_TOPICS,
+                                  nb_iteration=NB_ITERATION,
+                                  lang=LANGUAGE)
         biter_model.get_document_topic(2)

From 6e9613e914cd080cc51785e253745ceb98be165e Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 18:47:34 +0200
Subject: [PATCH 310/496] fixing lint file_loader

---
 nautilus_nlp/utils/file_loader.py | 33 ++++++++++++-------------------
 1 file changed, 13 insertions(+), 20 deletions(-)

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 876b54f..cda3997 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -15,13 +15,13 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import codecs
 import glob
 import io
 import json
 import logging
 import os
 import re
+import codecs
 import shutil
 
 import chardet
@@ -51,8 +51,7 @@ def detect_encoding(file_path_or_string, n_lines=100):
     the code of the detected encoding
     """
     if os.path.isfile(file_path_or_string):
-        with open(file_path_or_string, 'rb') as f:                      # Open the file as binary data
-            # Join binary lines for specified number of lines
+        with open(file_path_or_string, 'rb') as f:
             rawdata = b''.join([f.readline() for _ in range(n_lines)])
     elif isinstance(file_path_or_string, bytes):
         rawdata = file_path_or_string
@@ -65,7 +64,6 @@ def text_loader(filepath, encoding=None, detectencoding=True):
     encoding to load the text file.
     If not specified, this function tries to open the doc as UTF-U, and if
     it fails it will try to detect the encoding using **detect_encoding**
-
     Parameters
     ----------
     filepath : str
@@ -73,7 +71,6 @@ def text_loader(filepath, encoding=None, detectencoding=True):
         If the encoding is specified, will use the specified encoding to load the text file.
     detect_encoding : bool
     If file is not encoded into UTF-8, try to detect encoding using the chardet library.
-
     Returns
     -------
     string
@@ -90,8 +87,8 @@ def text_loader(filepath, encoding=None, detectencoding=True):
             logging.info('{filepath}: detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(
                 filepath=filepath, encod=detected_encoding['encoding'], conf_rate=detected_encoding['confidence']))
             return open_textfile(filepath, encoding=detected_encoding['encoding'])
-        raise UnicodeDecodeError(
-            'Cannot load document using utf-8. Try to detect encoding using detectencoding=True')
+        raise UnicodeDecodeError('Cannot load document using utf-8. '\
+                                    'Try to detect encoding using detectencoding=True')
 
 
 def get_subfolders_path(folder):
@@ -122,7 +119,7 @@ def list_files(filepath: str):
         filepath = filepath+'/*'
     if filepath.endswith('/'):
         filepath = filepath+'*'
-    return[file for file in glob.glob(filepath) if os.path.isdir(file)]
+    return [file for file in glob.glob(filepath) if os.path.isfile(file)]
 
 
 def documents_loader(filepath: str, encoding=None, detectencoding=True, output_as='dict'):
@@ -130,7 +127,6 @@ def documents_loader(filepath: str, encoding=None, detectencoding=True, output_a
     Input a filepath, a filepath with wildcard (eg. *.txt),
     or a list of filepaths.
     Output a string, or a dict of strings.
-
     Parameters
     ----------
     filepath: filepath
@@ -147,6 +143,7 @@ def documents_loader(filepath: str, encoding=None, detectencoding=True, output_a
     string
     the document loaded
     '''
+
     if isinstance(filepath, str):
         documents = list_files(filepath)
         nb_of_documents = len(documents)
@@ -154,24 +151,22 @@ def documents_loader(filepath: str, encoding=None, detectencoding=True, output_a
         nb_of_documents = len(filepath)
         documents = filepath
     else:
-        raise IOError('Please enter a valid filepath or a valid list of filepath')
+        raise TypeError('Please enter a valid filepath or a valid list of filepath')
 
     if nb_of_documents == 1:
-        return text_loader(
-            documents[0], encoding=encoding, detectencoding=detectencoding)
+        return text_loader(documents[0], encoding=encoding, detectencoding=detectencoding)
     if nb_of_documents > 1:
         if output_as == 'list':
-            return [text_loader(
-                document, encoding=encoding, detectencoding=detectencoding) for document in documents]
+            return [text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents]
         if output_as == 'dict':
-            return {document: text_loader(
+            return {document : text_loader(
                 document, encoding=encoding, detectencoding=detectencoding) for document in documents}
-        raise ValueError('Enter a valid output format between list or dict')
+        raise TypeError('Enter a valid output format between list or dict')
     raise IOError('No files detected in {}'.format(filepath))
 
 
 #################
-# CSV Loader
+## CSV Loader
 
 def encode_columns(df, columns_to_encode):
     '''
@@ -190,9 +185,7 @@ def decode_columns(df, columns_to_encode):
 
 
 #################
-# Encoding functions
-
-
+## Encoding functions
 def convert_encoding(file_path, input_encoding, output_encoding):
     '''
     Encode a file according to a specified encoding

From 853f66ddc3f3b293a6602aaf9f63ab5a1f5f6192 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 18:51:26 +0200
Subject: [PATCH 311/496] linting test_document_loader.py

---
 tests/test_document_loader.py | 37 ++++++++++++++---------------------
 1 file changed, 15 insertions(+), 22 deletions(-)

diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index 0be9eb7..a757619 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -17,21 +17,21 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 
-import os 
-import pytest
+import os
+
 import numpy as np
-from nautilus_nlp.utils.file_loader import documents_loader, list_files, detect_encoding
+from nautilus_nlp.utils.file_loader import (detect_encoding, documents_loader)
 
-testdoc_latin1 = "J'aime les frites bien grasse étalon châpeau!"
-testdoc_utf8 = "Un deuxième exemple de texte en utf-8 cette fois!"
+TESTDOC_LATIN1 = "J'aime les frites bien grasse étalon châpeau!"
+TESTDOC_UTF8 = "Un deuxième exemple de texte en utf-8 cette fois!"
 
 def create_files():
-    encoded_s = testdoc_latin1.encode('latin-1')
+    encoded_s = TESTDOC_LATIN1.encode('latin-1')
     with open('testdoc_latin1.txt', 'wb') as f:
         f.write(encoded_s)
 
 
-    encoded_s = testdoc_utf8.encode('utf-8')
+    encoded_s = TESTDOC_UTF8.encode('utf-8')
     with open('testdoc_utf8.txt', 'wb') as f:
         f.write(encoded_s)
     return True
@@ -40,7 +40,7 @@ def create_files():
 def test_openfile_with_encoding():
     create_files()
     input_str = "testdoc_latin1.txt"
-    expected_str = testdoc_latin1
+    expected_str = TESTDOC_LATIN1
     result = documents_loader(input_str, encoding='latin-1')
     np.testing.assert_string_equal(result, expected_str)
     remove_files()
@@ -49,7 +49,7 @@ def test_openfile_with_encoding():
 def test_openfile_utf8():
     create_files()
     input_str = "testdoc_utf8.txt"
-    expected_str = testdoc_utf8
+    expected_str = TESTDOC_UTF8
     result = documents_loader(input_str)
     np.testing.assert_string_equal(result, expected_str)
     remove_files()
@@ -57,9 +57,9 @@ def test_openfile_utf8():
 def test_encoding_detection():
     create_files()
     input_str = "testdoc_latin1.txt"
-    expected_str = testdoc_latin1
+    expected_str = TESTDOC_LATIN1
     result = documents_loader(input_str)
-    np.testing.assert_string_equal(result, expected_str)    
+    np.testing.assert_string_equal(result, expected_str)
     remove_files()
 
 def test_load_several_docs_wildcard():
@@ -67,14 +67,14 @@ def test_load_several_docs_wildcard():
     expected = {'testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
                 'testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
     result = documents_loader('test*.txt', output_as='dict')
-    np.testing.assert_equal(result, expected)   
+    np.testing.assert_equal(result, expected)
     remove_files()
 
 def test_load_several_docs_list():
     create_files()
     expected = {'testdoc_latin1.txt': "J'aime les frites bien grasse étalon châpeau!",
                 'testdoc_utf8.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}
-    result = documents_loader(['testdoc_latin1.txt','testdoc_utf8.txt'], output_as='dict')
+    result = documents_loader(['testdoc_latin1.txt', 'testdoc_utf8.txt'], output_as='dict')
     np.testing.assert_equal(result, expected)
     remove_files()
 
@@ -83,18 +83,11 @@ def test_load_several_docs_output_list():
     create_files()
     expected = ["J'aime les frites bien grasse étalon châpeau!",
                 'Un deuxième exemple de texte en utf-8 cette fois!']
-    result = documents_loader(['testdoc_latin1.txt','testdoc_utf8.txt'], output_as='list')
+    result = documents_loader(['testdoc_latin1.txt', 'testdoc_utf8.txt'], output_as='list')
     remove_files()
     return len(expected) == len(result) and sorted(expected) == sorted(result)
 
 
-#@pytest.mark.parametrize("input_filepath", ['*.txt','','testfolder_fileloader'])
-#def test_list_files(input_filepath):
-#    expected = ['testdoc_latin1.txt','testdoc_utf8.txt']
-#    result = list_files(input_filepath)
-#   return len(expected) == len(result) and sorted(expected) == sorted(result)
-
-
 def test_detect_encoding():
     create_files()
     expected = {'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}
@@ -104,4 +97,4 @@ def test_detect_encoding():
 
 def remove_files():
     os.remove('testdoc_latin1.txt')
-    os.remove('testdoc_utf8.txt')
\ No newline at end of file
+    os.remove('testdoc_utf8.txt')

From 3a64275915f25b0e6fede09d83b9ad0803a21a88 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 18:53:16 +0200
Subject: [PATCH 312/496] linting tests/test_fix_bad_encoding

---
 tests/test_fix_bad_encoding.py | 36 ++++++++++++++--------------------
 1 file changed, 15 insertions(+), 21 deletions(-)

diff --git a/tests/test_fix_bad_encoding.py b/tests/test_fix_bad_encoding.py
index 50d4ca3..cfc21ba 100644
--- a/tests/test_fix_bad_encoding.py
+++ b/tests/test_fix_bad_encoding.py
@@ -21,29 +21,23 @@
 from nautilus_nlp.preprocessing.text_preprocess import TextPreprocessor
 
 
-
 @pytest.mark.parametrize(
     "input_str, expected_str",
-    [
-    ('Les augmentations de rémunérations',
-  'Les augmentations de rémunérations'),
- ("rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social",
-  "rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social"),
- ('Limitations de vitesse et sécurité routière',
-  'Limitations de vitesse et sécurité routière'),
- ('Pour un nouveau contrat citoyen', 'Pour un nouveau contrat citoyen'),
- ('Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets',
-  'Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets'),
- ('proportienelle', 'proportienelle'),
- ('Pour plus de démocratie participative',
-  'Pour plus de démocratie participative'),
- ('Transparence de la vie public', 'Transparence de la vie public'),
- ('18 mois de trop....ca suffit macron',
-  '18 mois de trop....ca suffit macron'),
- ('Egalité devant les infractions routières',
-  'Egalité devant les infractions routières')
-    ],
-)
+    [('Les augmentations de rémunérations', 'Les augmentations de rémunérations'),
+     ("rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social",
+      "rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social"),
+     ('Limitations de vitesse et sécurité routière', 'Limitations de vitesse et sécurité routière'),
+     ('Pour un nouveau contrat citoyen', 'Pour un nouveau contrat citoyen'),
+     (
+         'Développer les démarches de budget participatif dans les collectivités et associer les citoyens '\
+           'dans la réalisation des projets',
+         'Développer les démarches de budget participatif dans les collectivités et associer les citoyens '\
+           'dans la réalisation des projets'),
+     ('proportienelle', 'proportienelle'),
+     ('Pour plus de démocratie participative', 'Pour plus de démocratie participative'),
+     ('Transparence de la vie public', 'Transparence de la vie public'),
+     ('18 mois de trop....ca suffit macron', '18 mois de trop....ca suffit macron'),
+     ('Egalité devant les infractions routières', 'Egalité devant les infractions routières')],)
 def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.fix_bad_unicode()

From 75914c40cce234cb1222931c03ccedc9fbff26b9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 19:01:04 +0200
Subject: [PATCH 313/496] linting tests/test_preprocessor.py

---
 tests/test_preprocessor.py | 204 ++++++++++++++++++-------------------
 1 file changed, 101 insertions(+), 103 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index eaf8edb..37524f8 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -24,7 +24,6 @@
 from nautilus_nlp.utils.stopwords import get_stopwords
 
 
-
 @pytest.mark.parametrize("text, expected_result",
                          [("ACV water + cinnamon + turmeric + cucumber + lemon. 👍🏻",
                            [":thumbs_up_light_skin_tone:"]),
@@ -142,6 +141,7 @@ def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
     result = preprocessor.remove_multiple_spaces_and_strip_text()
     np.testing.assert_string_equal(result, expected_str)
 
+
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
@@ -153,50 +153,51 @@ def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
 def test_remove_eol_characters(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.remove_eol_characters()
-    np.testing.assert_string_equal(result, expected_str)    
+    np.testing.assert_string_equal(result, expected_str)
 
 
 def test_remove_tokens_with_nonletters():
-    input_tokens = ['foo','bar','124','34euros']
-    expected_output = ['foo','bar']
+    input_tokens = ['foo', 'bar', '124', '34euros']
+    expected_output = ['foo', 'bar']
     preprocessor = TokenPreprocessor(input_tokens)
     result = preprocessor.remove_tokens_with_nonletters()
-    np.testing.assert_array_equal(result,expected_output)
+    np.testing.assert_array_equal(result, expected_output)
 
 
 def test_remove_special_caracters_from_tokenslist():
-    input_tokens = ['foo','bar','---',"'s",'#']
-    expected_output = ['foo','bar',"'s"]
+    input_tokens = ['foo', 'bar', '---', "'s", '#']
+    expected_output = ['foo', 'bar', "'s"]
     preprocessor = TokenPreprocessor(input_tokens)
     result = preprocessor.remove_special_caracters_from_tokenslist()
     np.testing.assert_array_equal(result, expected_output)
 
 
 def test_get_stopwords():
-    languages_to_test = ['fr','en','ga','zh']
+    languages_to_test = ['fr', 'en', 'ga', 'zh']
     for lang in languages_to_test:
         result = get_stopwords(lang)
-        assert len(result) > 0 and type(result) == list
+        assert len(result) > 0 and isinstance(result, list)
 
 
 @pytest.mark.parametrize(
     "input_tokens, expected_output",
     [
-        (['I','like','when','you','move','your','body','!'], ['I', 'move', 'body', '!'])
+        (['I', 'like', 'when', 'you', 'move', 'your', 'body', '!'], ['I', 'move', 'body', '!'])
     ],
-)    
+)
 def test_remove_stopwords_tokens(input_tokens, expected_output):
     stopwords = get_stopwords('en')
     preprocessor = TokenPreprocessor(input_tokens)
     result = preprocessor.remove_stopwords(stopwords)
     np.testing.assert_array_equal(result, expected_output)
 
+
 @pytest.mark.parametrize(
     "input_str, expected_output",
     [
         ('I like when you move your body !', 'I move body !'),
     ],
-)  
+)
 def test_remove_stopwords_text(input_str, expected_output):
     stopwords = get_stopwords('en')
     preprocessor = TextPreprocessor(input_str)
@@ -214,26 +215,21 @@ def test_remove_accents():
 
 @pytest.mark.parametrize(
     "input_str, expected_str",
-    [
-    ('Les augmentations de rémunérations',
-  'Les augmentations de rémunérations'),
- ("rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social",
-  "rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social"),
- ('Limitations de vitesse et sécurité routière',
-  'Limitations de vitesse et sécurité routière'),
- ('Pour un nouveau contrat citoyen', 'Pour un nouveau contrat citoyen'),
- ('Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets',
-  'Développer les démarches de budget participatif dans les collectivités et associer les citoyens dans la réalisation des projets'),
- ('proportienelle', 'proportienelle'),
- ('Pour plus de démocratie participative',
-  'Pour plus de démocratie participative'),
- ('Transparence de la vie public', 'Transparence de la vie public'),
- ('18 mois de trop....ca suffit macron',
-  '18 mois de trop....ca suffit macron'),
- ('Egalité devant les infractions routières',
-  'Egalité devant les infractions routières')
-    ],
-)
+    [('Les augmentations de rémunérations', 'Les augmentations de rémunérations'),
+     ("rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social",
+      "rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social"),
+     ('Limitations de vitesse et sécurité routière', 'Limitations de vitesse et sécurité routière'),
+     ('Pour un nouveau contrat citoyen', 'Pour un nouveau contrat citoyen'),
+     (
+         'Développer les démarches de budget participatif dans les collectivités et associer les citoyens'\
+             ' dans la réalisation des projets',
+         'Développer les démarches de budget participatif dans les collectivités et associer les citoyens'\
+             ' dans la réalisation des projets'),
+     ('proportienelle', 'proportienelle'),
+     ('Pour plus de démocratie participative', 'Pour plus de démocratie participative'),
+     ('Transparence de la vie public', 'Transparence de la vie public'),
+     ('18 mois de trop....ca suffit macron', '18 mois de trop....ca suffit macron'),
+     ('Egalité devant les infractions routières', 'Egalité devant les infractions routières')],)
 def test_fix_bad_unicode(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.fix_bad_unicode()
@@ -252,34 +248,35 @@ def test_normalize_whitespace(input_str, expected_str):
     result = preprocessor.normalize_whitespace()
     np.testing.assert_equal(result, expected_str)
 
+
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
         ("I can't tell how we've done.", 'I can not tell how we have done.'),
         ("You're fired. She's nice.", "You are fired. She's nice."),
-        ("Let's go!",'Let us go!'),
-        ("You've been missing",'You have been missing'),
-        ("I'm sure you're leaving",'I am sure you are leaving'),
-        ("We'll survive.","We will survive.")
+        ("Let's go!", 'Let us go!'),
+        ("You've been missing", 'You have been missing'),
+        ("I'm sure you're leaving", 'I am sure you are leaving'),
+        ("We'll survive.", "We will survive.")
     ]
-    )
+)
 def test_unpack_english_contractions(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.unpack_english_contractions()
     np.testing.assert_equal(result, expected_str)
 
+
 @pytest.mark.parametrize(
     "input_str, expected_str",
-    [
-        ("Wan't to contribute to Nautilus? read https://github.com/artefactory/nautilus-nlp/blob/docs/CONTRIBUTING.md first",
-         "Wan't to contribute to Nautilus? read *URL* first"),
-        ("The ip address of my VM is http://34.76.182.5:8888", "The ip address of my VM is *URL*"),
-        ("If you go to http://internet.org, you will find a website hosted by FB.",
-         "If you go to *URL*, you will find a website hosted by FB."),
-        ("Ishttps://waaaou.com/ available?",'Is*URL* available?'),
-        ("mailto:hugo.vasselin@artefact.com",'*URL*')
-    ]
-    )
+    [(
+        "Wan't to contribute to Nautilus? read https://github.com/artefactory/nautilus-nlp/blob/docs/CONTRIBUTING.md"\
+            " first",
+        "Wan't to contribute to Nautilus? read *URL* first"),
+     ("The ip address of my VM is http://34.76.182.5:8888", "The ip address of my VM is *URL*"),
+     ("If you go to http://internet.org, you will find a website hosted by FB.",
+      "If you go to *URL*, you will find a website hosted by FB."),
+     ("Ishttps://waaaou.com/ available?", 'Is*URL* available?'),
+     ("mailto:hugo.vasselin@artefact.com", '*URL*')])
 def test_replace_urls(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.replace_urls()
@@ -289,12 +286,12 @@ def test_replace_urls(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("my email:hugo.vasselin@artefact.com","my email:*EMAIL*"),
+        ("my email:hugo.vasselin@artefact.com", "my email:*EMAIL*"),
         ("v543143@nwytg.net is a temporary email", "*EMAIL* is a temporary email"),
-        ("our emails used to be name.surname@artefact.is","our emails used to be *EMAIL*"),
-        ("chaudasse_du_13@hotmail.fr,C ton email bb?",'*EMAIL*,C ton email bb?')
+        ("our emails used to be name.surname@artefact.is", "our emails used to be *EMAIL*"),
+        ("chaudasse_du_13@hotmail.fr,C ton email bb?", '*EMAIL*,C ton email bb?')
     ]
-    )
+)
 def test_replace_emails(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.replace_emails()
@@ -304,38 +301,38 @@ def test_replace_emails(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("mon 06 bb: 0625093267","mon 06 bb: *PHONE*"),
-        ("mon 06 bb: 06.25.09.32.67","mon 06 bb: *PHONE*"),
-        ("call me at +33625093267","call me at *PHONE*"),
-        ("call me at +33 6 25 09 32 67","call me at *PHONE*"),
-        ("call me at +33 625 093 267","call me at *PHONE*"),
+        ("mon 06 bb: 0625093267", "mon 06 bb: *PHONE*"),
+        ("mon 06 bb: 06.25.09.32.67", "mon 06 bb: *PHONE*"),
+        ("call me at +33625093267", "call me at *PHONE*"),
+        ("call me at +33 6 25 09 32 67", "call me at *PHONE*"),
+        ("call me at +33 625 093 267", "call me at *PHONE*"),
         ("if this unit test doesn't work, call 3615 and says 'ROBIN'",
          "if this unit test doesn't work, call *PHONE* and says 'ROBIN'"),
-        ('(541) 754-3010 is a US. Phone','*PHONE* is a US. Phone'),
-        ('+1-541-754-3010 is an international Phone','*PHONE* is an international Phone'),
-        ('+1-541-754-3010 Dialed in the US','*PHONE* Dialed in the US'),
-        ('+1-541-754-3010 Dialed from Germany','*PHONE* Dialed from Germany')
+        ('(541) 754-3010 is a US. Phone', '*PHONE* is a US. Phone'),
+        ('+1-541-754-3010 is an international Phone', '*PHONE* is an international Phone'),
+        ('+1-541-754-3010 Dialed in the US', '*PHONE* Dialed in the US'),
+        ('+1-541-754-3010 Dialed from Germany', '*PHONE* Dialed from Germany')
     ]
-    )
+)
 def test_replace_phone_numbers(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.replace_phone_numbers(
         replace_with="*PHONE*",
         method="detection",
         country_format_to_detect=phone.SUPPORTED_COUNTRY
-        )
+    )
     np.testing.assert_equal(result, expected_str)
 
 
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("123, 3 petits chats","*NUMBER*, *NUMBER* petits chats"),
-        ("l0ve 2 twa <3","l0ve *NUMBER* twa <*NUMBER*"),
-        ("Give me 45bucks!","Give me *NUMBER*bucks!"),
-        ("call me at +33625093267","call me at *NUMBER*")
+        ("123, 3 petits chats", "*NUMBER*, *NUMBER* petits chats"),
+        ("l0ve 2 twa <3", "l0ve *NUMBER* twa <*NUMBER*"),
+        ("Give me 45bucks!", "Give me *NUMBER*bucks!"),
+        ("call me at +33625093267", "call me at *NUMBER*")
     ]
-    )
+)
 def test_replace_numbers(input_str, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.replace_numbers()
@@ -345,39 +342,40 @@ def test_replace_numbers(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, param, expected_str",
     [
-        ("Give me 23$",None,"Give me 23USD"),
-        ("Give me 23£",None,"Give me 23GBP"),
-        ("Give me 23 £",None,"Give me 23 GBP"),
-        ("Give me 23 €",None,"Give me 23 EUR"),
-        ("¥ is both japanese yen and Chinese Renminbi","*CUR*","*CUR* is both japanese yen and Chinese Renminbi")
+        ("Give me 23$", None, "Give me 23USD"),
+        ("Give me 23£", None, "Give me 23GBP"),
+        ("Give me 23 £", None, "Give me 23 GBP"),
+        ("Give me 23 €", None, "Give me 23 EUR"),
+        ("¥ is both japanese yen and Chinese Renminbi", "*CUR*", "*CUR* is both japanese yen and Chinese Renminbi")
     ]
-    )
+)
 def test_replace_currency_symbols(input_str, param, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.replace_currency_symbols(replace_with=param)
     np.testing.assert_equal(result, expected_str)
 
+
 @pytest.mark.parametrize(
     "input_str, param, expected_str",
     [
-        ("Seriously...",None,"Seriously   "),
-        ("Seriously?",None,"Seriously "),
-        ("Seriously ?",None,"Seriously  "),
-        ("Seriously???",None,"Seriously   "),
-        ("Seriously?!",None,"Seriously  "),
-        ('"Seriously"',None," Seriously "),
-        ('Seriously:',None,"Seriously "),
-        ('Seriously;',None,"Seriously "),
-        ("'Seriously'",None," Seriously "),
-        ("'Seriously'",'.,;',"'Seriously'"),
-        ("Seriously.,.",'.,;',"Seriously "),
-        ("Seriously...",'.,;',"Seriously "),
-        ("Seriously.!.",'.,;',"Seriously ! "),
-        ("hugo.vasselin@artefact.com",'.,;',"hugo vasselin@artefact com"),
-        ("hugo.vasselin@artefact.com",None,"hugo vasselin artefact com"),
-        ("hugo-vasselin@artefact.com",None,"hugo vasselin artefact com")
+        ("Seriously...", None, "Seriously   "),
+        ("Seriously?", None, "Seriously "),
+        ("Seriously ?", None, "Seriously  "),
+        ("Seriously???", None, "Seriously   "),
+        ("Seriously?!", None, "Seriously  "),
+        ('"Seriously"', None, " Seriously "),
+        ('Seriously:', None, "Seriously "),
+        ('Seriously;', None, "Seriously "),
+        ("'Seriously'", None, " Seriously "),
+        ("'Seriously'", '.,;', "'Seriously'"),
+        ("Seriously.,.", '.,;', "Seriously "),
+        ("Seriously...", '.,;', "Seriously "),
+        ("Seriously.!.", '.,;', "Seriously ! "),
+        ("hugo.vasselin@artefact.com", '.,;', "hugo vasselin@artefact com"),
+        ("hugo.vasselin@artefact.com", None, "hugo vasselin artefact com"),
+        ("hugo-vasselin@artefact.com", None, "hugo vasselin artefact com")
     ]
-    )
+)
 def test_remove_punct(input_str, param, expected_str):
     preprocessor = TextPreprocessor(input_str)
     result = preprocessor.remove_punct(marks=param)
@@ -387,16 +385,16 @@ def test_remove_punct(input_str, param, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("👉👌",""),
-        ("🎅🏿⌚",""),
-        ("🥖✊💦",""),
-        ("✊",""),
+        ("👉👌", ""),
+        ("🎅🏿⌚", ""),
+        ("🥖✊💦", ""),
+        ("✊", ""),
         ("J'espère que les 🚓 vont pas lire ce test",
-        "J'espère que les  vont pas lire ce test"),
+         "J'espère que les  vont pas lire ce test"),
         ("J'espère que les vont pas lire ce test🚓",
-        "J'espère que les vont pas lire ce test")
+         "J'espère que les vont pas lire ce test")
     ]
-    )
+)
 def test_remove_emoji(input_str, expected_str):
     preprocessor = SocialPreprocessor(input_str)
     result = preprocessor.remove_emoji()
@@ -406,13 +404,13 @@ def test_remove_emoji(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("👉👌",":backhand_index_pointing_right::OK_hand:"),
-        ("🎅🏿⌚",":Santa_Claus_dark_skin_tone::watch:"),
-        ("🥖✊💦",":baguette_bread::raised_fist::sweat_droplets:"),
-        ("✊",":raised_fist:")
+        ("👉👌", ":backhand_index_pointing_right::OK_hand:"),
+        ("🎅🏿⌚", ":Santa_Claus_dark_skin_tone::watch:"),
+        ("🥖✊💦", ":baguette_bread::raised_fist::sweat_droplets:"),
+        ("✊", ":raised_fist:")
     ]
-    )
+)
 def test_convert_emoji_to_text(input_str, expected_str):
     preprocessor = SocialPreprocessor(input_str)
     result = preprocessor.convert_emoji_to_text()
-    np.testing.assert_equal(result, expected_str)    
\ No newline at end of file
+    np.testing.assert_equal(result, expected_str)

From c895d52df6c49f2d8b48e21f09c82562021b529c Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 1 Oct 2020 19:01:16 +0200
Subject: [PATCH 314/496] linting tests/test_topic_modeling_short_text.py

---
 tests/test_topic_modeling_short_text.py | 81 +++++++++++++------------
 1 file changed, 43 insertions(+), 38 deletions(-)

diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index 9a8caac..e4570b4 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -15,13 +15,13 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.topic_modeling.topic_modeling_short_text import prepare_data, \
-    __build_cooccurence_matrix, train_shorttext_model, prepare_data_pyldavis, \
-    show_dominant_topic, get_assigned_topics
 import numpy as np
 import pytest
+from nautilus_nlp.topic_modeling.topic_modeling_short_text import (
+    __build_cooccurence_matrix, get_assigned_topics, prepare_data,
+    prepare_data_pyldavis, show_dominant_topic, train_shorttext_model)
 
-text = ['Cola 1.5L Carrefour',
+TEXT = ['Cola 1.5L Carrefour',
         'Pepsi Cola Light 1.5L',
         'Pepsi Cola Twist Light',
         'Cola 1.5L CRF DISC',
@@ -36,76 +36,81 @@
         'Spaghetti de maïs 50g sans Glute']
 
 
-@pytest.mark.parametrize(
-    "input_text,  expected_output",
-    [
-        (text, [['1.5L', 4], ['Coca-Cola', 3], ['Cola', 4], ['Light', 5], ['Pepsi', 2], ['bio', 3], ['de', 2], ['sans', 3]]),
-        ([],[]),
-        (['',''],[]),
-    ],
-)
-def test_prepare_data(input_text,  expected_output):
+@pytest.mark.parametrize("input_text,  expected_output",
+                         [(TEXT,
+                           [['1.5L', 4],
+                            ['Coca-Cola', 3],
+                            ['Cola', 4],
+                            ['Light', 5],
+                            ['Pepsi', 2],
+                            ['bio', 3],
+                            ['de', 2],
+                            ['sans', 3]]),
+                          ([],
+                           []),
+                          (['', ''],
+                           []), ],)
+def test_prepare_data(input_text, expected_output):
     assert prepare_data(input_text), expected_output
 
 
 @pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
-@pytest.mark.parametrize("n_topics", [3,0])
-@pytest.mark.parametrize("n_topKeyword", [2,0])
-def test_show_dominant_topic(model_name,  n_topics, n_topKeyword):
+@pytest.mark.parametrize("n_topics", [3, 0])
+@pytest.mark.parametrize("n_keywords", [2, 0])
+def test_show_dominant_topic(model_name, n_topics, n_keywords):
 
-    encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
+    encoded_text_id, vocab_list, _ = prepare_data(TEXT)
 
     model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
-    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword=n_topKeyword)
+    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword=n_keywords)
 
     assert len(pmi_score) == n_topics
     assert len(topics) == n_topics
     for i in topics.values():
-        assert len(i) == n_topKeyword
+        assert len(i) == n_keywords
 
 
 @pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
 @pytest.mark.parametrize("n_topics", [3])
-@pytest.mark.parametrize("n_topKeyword", [2])
-def test_get_assigned_topics(model_name,n_topics,n_topKeyword):
-    encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
-    model = train_shorttext_model(model_name,encoded_text_id, vocab_list, n_topics=n_topics)
+def test_get_assigned_topics(model_name, n_topics):
+    encoded_text_id, vocab_list, _ = prepare_data(TEXT)
+    model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
     topics_list = get_assigned_topics(model)
 
-    assert len(topics_list) == len(text)
+    assert len(topics_list) == len(TEXT)
     for topic_num in topics_list:
         assert topic_num < n_topics
 
 
 @pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
-@pytest.mark.parametrize("n_topics", [0,3])
+@pytest.mark.parametrize("n_topics", [0, 3])
 def test_prepare_data_pyldavis(model_name, n_topics):
-    encoded_text_id, vocab_list, vocab_arr = prepare_data(text)
+    encoded_text_id, vocab_list, vocab_arr = prepare_data(TEXT)
     model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
 
     data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
-    phi= data['topic_term_dists']
-    theta= data['doc_topic_dists']
+    phi = data['topic_term_dists']
+    theta = data['doc_topic_dists']
     doc_length_values = data['doc_lengths']
-    list_vocab=data['vocab']
+    list_vocab = data['vocab']
     freq_vocab = data['term_frequency']
 
     assert phi.shape == (n_topics, len(list_vocab))
-    assert theta.shape == (len(text), n_topics)
-    assert len(doc_length_values) == len(text)
+    assert theta.shape == (len(TEXT), n_topics)
+    assert len(doc_length_values) == len(TEXT)
     assert len(list_vocab) == len(freq_vocab)
 
 
 @pytest.mark.parametrize(
     "input_coded,  expected_output",
     [
-     ([[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]],
-      [[5., 1., 2., 4.],
-       [1., 2., 2., 3.],
-       [2., 2., 4., 4.],
-       [4., 3., 4., 6.]]),
+        ([[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]],
+         [[5., 1., 2., 4.],
+          [1., 2., 2., 3.],
+          [2., 2., 4., 4.],
+          [4., 3., 4., 6.]]),
 
-     ([[0, 1, 2, 3, 4], [5, 6], [1]],
+        ([[0, 1, 2, 3, 4], [5, 6], [1]],
          [[1., 1., 1., 1., 1., 0., 0.],
           [1., 2., 1., 1., 1., 0., 0.],
           [1., 1., 1., 1., 1., 0., 0.],
@@ -121,7 +126,7 @@ def test_build_cooccurence_matrix(input_coded, expected_output):
     # The weights denote the occurrences of a word i with a word j in same sentence.
 
     flat_list = [item for sublist in input_coded for item in sublist]
-    nb_vocab = len(set(flat_list)) # number of distinct words
+    nb_vocab = len(set(flat_list))  # number of distinct words
     mat = __build_cooccurence_matrix(nb_vocab, input_coded)
 
     assert np.array_equal(mat, np.array(expected_output))

From 6b9a5e23dab13ad8e51ca9de9aff3592470395f0 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 14:01:51 +0200
Subject: [PATCH 315/496] linting biterm_model.py

---
 nautilus_nlp/topic_modeling/biterm_model.py | 48 ++++++++++++---------
 1 file changed, 28 insertions(+), 20 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/biterm_model.py b/nautilus_nlp/topic_modeling/biterm_model.py
index 8aa1ab4..22146ac 100644
--- a/nautilus_nlp/topic_modeling/biterm_model.py
+++ b/nautilus_nlp/topic_modeling/biterm_model.py
@@ -16,14 +16,16 @@
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import numpy as np
+import pyLDAvis
 from biterm.btm import oBTM
-from biterm.utility import vec_to_biterms, topic_summuary
+from biterm.utility import topic_summuary, vec_to_biterms
 from sklearn.feature_extraction.text import CountVectorizer
-import pyLDAvis
 
 
 class BitermModel:
 
+    # pylint: disable=too-many-instance-attributes
+
     def __init__(self, data, nb_topics, nb_iteration, lang):
         """
         Model for topic modelling. Particularly useful for short texts.
@@ -43,7 +45,7 @@ def __init__(self, data, nb_topics, nb_iteration, lang):
         -------
         string
             the text with removed multiple spaces and strip text
-        """        
+        """
         self.is_int_positive(nb_topics)
         self.is_int_positive(nb_iteration)
         self.is_list_of_string(data)
@@ -60,19 +62,19 @@ def __init__(self, data, nb_topics, nb_iteration, lang):
     @staticmethod
     def is_int_positive(number):
         """
-        Function to check if the input parameter is a integer and positive 
+        Function to check if the input parameter is a integer and positive
         otherwise raise an error
 
         Parameters
         ----------
         number : str
-            
+
         Returns
         -------
         str:
             the text with removed multiple spaces and strip text
-            
-        """  
+
+        """
         if not isinstance(number, int):
             raise ValueError("Parameter {} has to be an integer".format(number))
         if number < 1:
@@ -82,11 +84,11 @@ def is_int_positive(number):
     def is_list_of_string(data):
         """
         Function to check if the input parameter is a list of strings otherwise raise an error
-        
+
         Parameters
         ----------
         data
-        
+
         Returns
         -------
         """
@@ -101,15 +103,15 @@ def is_list_of_string(data):
     def compute_topics(self, nb_word_per_cluster):
         """
         Main function computing the topic modeling, topics
-        
+
         Parameters
         ----------
         nb_word_per_cluster : positive integer
-        
+
         Returns
         -------
-        dict :    
-            a dictionary containing the the different topics with the top words 
+        dict :
+            a dictionary containing the the different topics with the top words
             and coherence associated
         """
         vec = CountVectorizer(stop_words=self.lang)
@@ -120,14 +122,17 @@ def compute_topics(self, nb_word_per_cluster):
         self._btm = oBTM(num_topics=self.nb_topics, V=self._vocabulary)
         self._topics = self._btm.fit_transform(biterms, iterations=self.nb_iteration)
 
-        results = topic_summuary(self._btm.phi_wz.T, self._vectorize_text, self._vocabulary, nb_word_per_cluster, verbose=False)
+        results = topic_summuary(
+            self._btm.phi_wz.T, self._vectorize_text,
+            self._vocabulary, nb_word_per_cluster, verbose=False
+        )
 
         return results
 
     def get_document_topic(self, index):
         """
         Get the cluster associated to the specified document
-                
+
         Parameters
         ----------
         index : positive integer
@@ -142,10 +147,10 @@ def get_document_topic(self, index):
 
         return self._topics[index].argmax()
 
-    def save_pyLDAvis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.html'):
+    def save_pyldavis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.html'):
         """
         Function saving the pyLDAvis plot associated with the compute_topics function
-                
+
         Parameters
         ----------
         path_to_output : str
@@ -153,10 +158,13 @@ def save_pyLDAvis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.htm
 
         Returns
         -------
-        """        
+        """
         if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
             raise ValueError("Model needs to be trained first")
 
-        vis = pyLDAvis.prepare(self._btm.phi_wz.T, self._topics, np.count_nonzero(self._vectorize_text, axis=1), self._vocabulary,
-                               np.sum(self._vectorize_text, axis=0))
+        vis = pyLDAvis.prepare(
+            self._btm.phi_wz.T, self._topics,
+            np.count_nonzero(self._vectorize_text, axis=1), self._vocabulary,
+            np.sum(self._vectorize_text, axis=0)
+        )
         pyLDAvis.save_html(vis, path_to_output)

From a3ab9a912c1a61555a5b3af3ffb4db7a44060cd7 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 14:13:11 +0200
Subject: [PATCH 316/496] linting lda.py

---
 nautilus_nlp/topic_modeling/lda.py | 118 +++++++++++++++--------------
 requirements.txt                   |   1 +
 2 files changed, 63 insertions(+), 56 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/lda.py b/nautilus_nlp/topic_modeling/lda.py
index 686c824..3363805 100644
--- a/nautilus_nlp/topic_modeling/lda.py
+++ b/nautilus_nlp/topic_modeling/lda.py
@@ -15,36 +15,36 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import gensim
 import logging
 import os
+
+import gensim
+import matplotlib.pyplot as plt
 import pyLDAvis
-import pyLDAvis.gensim 
+import pyLDAvis.gensim
 from gensim.models import CoherenceModel
 from gensim.models.wrappers import LdaMallet
-import matplotlib.pyplot as plt
-
 from IPython.display import HTML
 
 logging.getLogger("gensim").setLevel(logging.WARNING)
 
 
 def create_dictionary(data):
-    """ 
+    """
     Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
-    
+
     Parameters
     ----------
     data : list of list of tokens
-       
+
     Returns
     -------
     list of list of tuples
     """
     return gensim.corpora.Dictionary(data)
 
-def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
-    """ 
+def filter_extremes(dictionary, no_below=15, no_above=0.3, **kwargs):
+    """
     Remove very rare and very common words
 
     Parameters
@@ -62,15 +62,15 @@ def filter_extremes(dictionary, no_below=15, no_above=0.3 , **kwargs) :
 
 
 def create_bow_corpus(data, dictionary):
-    
-    """ 
+
+    """
     Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
     The produced corpus is a mapping of (token_id, token_count).
 
     Parameters
     ----------
     data : list of list of tokens
-       
+
     Returns
     -------
     list of list of tuples
@@ -85,7 +85,7 @@ def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, s
     """
     Compute c_v coherence for various number of topics
 
-    /!\ It takes a really long time.
+    WARNING: It takes a really long time.
 
     Parameters:
     ----------
@@ -102,13 +102,15 @@ def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, s
     coherence_values = []
     model_list = []
     for num_topics in range(start, limit, step):
-        model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus,
-                                           id2word=dictionary,
-                                          num_topics=num_topics, 
-                                          random_state=0,
-                                          update_every=5,
-                                          chunksize=1000,
-                                          passes=10)
+        model = gensim.models.ldamodel.LdaModel(
+            corpus=bow_corpus,
+            id2word=dictionary,
+            num_topics=num_topics,
+            random_state=0,
+            update_every=5,
+            chunksize=1000,
+            passes=10
+        )
         model_list.append(model)
         coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')
         coherence_values.append(coherencemodel.get_coherence())
@@ -142,23 +144,24 @@ def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
     Print the coherences scores for the ldamodels that had been tested with different number of topics
     """
     x = range(start, limit, step)
-    for m, cv in zip(x, coherence_values):
-        print("Num Topics =", m, " has Coherence Value of", round(cv, 4))
+    for m, c_value in zip(x, coherence_values):
+        print("Num Topics =", m, " has Coherence Value of", round(c_value, 4))
 
 
 ### LdaModel: Gensim & Mallet
 
 def train_lda_model(bow_corpus, dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
     """ Train the lda model on the corpus
-      
+
     Parameters
     ----------
-    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape (num_terms, num_documents).
+    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape \
+    (num_terms, num_documents).
     dictionary: corpora.Dictionary. Mapping from word IDs to words
     num_topics: int
     model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
-    mallet_path: str, optionnal if model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file 
-    
+    mallet_path: str, optionnal if model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file
+
     Returns
     -------
     gensim.ldamodel
@@ -168,39 +171,42 @@ def train_lda_model(bow_corpus, dictionary, num_topics, model='gensim', mallet_p
     elif model == 'mallet':
         if mallet_path is None:
             raise ValueError('You must precise the path to the mallet-2.0.8 file that has been downloaded before')
-        else:
-            model = train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs)
+        model = train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs)
     else:
         raise ValueError('Please enter a valid model name: gensim or mallet')
     return model
 
 def train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs):
-
-    model = gensim.models.ldamodel.LdaModel(corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, passes=10, minimum_probability=0.001, random_state=0, **kwargs)
+    model = gensim.models.ldamodel.LdaModel(
+        corpus=bow_corpus, id2word=dictionary, num_topics=num_topics,
+        passes=10, minimum_probability=0.001, random_state=0, **kwargs
+    )
     return model
 
 def train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs):
-    
     os.environ['MALLET_PATH'] = mallet_path
     mallet = '$MALLET_PATH/mallet-2.0.8/bin/mallet'
-    model = gensim.models.wrappers.LdaMallet(mallet, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics, prefix='composant', random_seed=0, **kwargs)
+    model = gensim.models.wrappers.LdaMallet(
+        mallet, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics,
+        prefix='composant', random_seed=0, **kwargs
+    )
     return model
 
 
 def save_model(model, model_name):
-    """ 
+    """
     Save the model that has been trained. The model will be saved on your current emplacement.
-        
+
     Parameters
     ----------
     model: ldamodel
-    model_name: str. 
+    model_name: str.
         Name the model that will be saved
     """
     return model.save(os.path.join(model_name))
 
 
-def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
+def load_model(model_path, model_name, model='gensim', model_prefix='composant'):
     """
     Detected the language of a text
 
@@ -213,19 +219,19 @@ def load_model(model_path,model_name, model='gensim', model_prefix='composant'):
     model : str
         Precise the topic modeling model wanted, must be "gensim" or "mallet"
     model_prefix : str
-        By default, 'composant' default prefix used while saving the mallet model with train_lda_model function.         
-    
+        By default, 'composant' default prefix used while saving the mallet model with train_lda_model function.
+
     Returns
     -------
-    is_reliable : 
+    is_reliable :
         is the top language is much better than 2nd best language?
-    language: 
+    language:
         2-letter code for the language of the text
     """
-    if model =='gensim':
-        ldamodel = gensim.models.LdaModel.load(os.path.join(model_path,model_name))
-    elif model =='mallet':
-        ldamodel = LdaMallet.load(os.path.join(model_path,model_name))
+    if model == 'gensim':
+        ldamodel = gensim.models.LdaModel.load(os.path.join(model_path, model_name))
+    elif model == 'mallet':
+        ldamodel = LdaMallet.load(os.path.join(model_path, model_name))
         if model_prefix is not None:
             ldamodel.prefix = model_path+'/'+ model_prefix
     else:
@@ -241,17 +247,17 @@ def fit_data(model, bow):
 
 
 def visualize_topics(model, bow_corpus, dictionary, model_type=None):
-    """ 
+    """
     Visualize the topics-keywords with the pyLDAvis interactive chart.
         (Work well in notebook)
-        
+
     Parameters
     ----------
     model: LDA model: gensim or mallet
-    bow_corpus : iterable of list of tokens. 
+    bow_corpus : iterable of list of tokens.
     dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
     model : str. Precise the topic modeling model used, must be "gensim" or "mallet"
-    
+
     Returns:
     ----------
     3D interactive chart
@@ -263,25 +269,25 @@ def visualize_topics(model, bow_corpus, dictionary, model_type=None):
     elif model_type is None:
         raise ValueError('You forgot to precise your model type, it must be: gensim or mallet')
     else:
-        raise ValueError('Please enter a valid model name: gensim or mallet') 
+        raise ValueError('Please enter a valid model name: gensim or mallet')
     return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
 
 def save_pyldavis(pyldavis, vis_path, vis_name):
-    """ 
+    """
     Save the pyldavis interactive chart
-    
+
     Parameters
     ----------
     pyldavis: pyLDAvis._prepare.PreparedData
     vis_path: str
     vis_name: str
-    """ 
+    """
     return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name + '{}'.format('.html')))
 
 
 
 def show_pyldavis(vis_path, vis_name):
-    """ 
+    """
     Display the HTML of the saved pyldavis interactive chart
 
     Parameters
@@ -293,7 +299,7 @@ def show_pyldavis(vis_path, vis_name):
 
 def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
     """ Print the dominant topics in the document, its score and the topics' top keywords.
-    
+
     Quick way to interpret the topics
 
     Parameters
@@ -306,10 +312,10 @@ def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
 
     """
     i = 0
-    for index, score in sorted(model[bow_corpus], key=lambda tup: -1*tup[1]): 
+    for index, score in sorted(model[bow_corpus], key=lambda tup: -1*tup[1]):
         weight = model.show_topic(index, topn=topn)
         keywords = [i[0] for i in weight]
         print("Score: {}\t Topic: {}".format(score, keywords))
-        i +=1
+        i += 1
         if i == topic_number:
             break
diff --git a/requirements.txt b/requirements.txt
index 87479ee..8de1a43 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -40,3 +40,4 @@ emoji>=0.5.2
 summa==1.2.0
 biterm==0.1.5
 nlpaug==1.0.1
+IPython==7.18.1

From 812b49e36e41e21cdd0f52da796330ce7fe581ab Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 14:17:05 +0200
Subject: [PATCH 317/496] fix ipython

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 8de1a43..d71d229 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -40,4 +40,4 @@ emoji>=0.5.2
 summa==1.2.0
 biterm==0.1.5
 nlpaug==1.0.1
-IPython==7.18.1
+ipython==7.18.1

From aaa4065b9af59f74ef607256b88fd6ed9be59d43 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 14:19:49 +0200
Subject: [PATCH 318/496] fix ipython version

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d71d229..c485909 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -40,4 +40,4 @@ emoji>=0.5.2
 summa==1.2.0
 biterm==0.1.5
 nlpaug==1.0.1
-ipython==7.18.1
+ipython==7.16.1

From ad49727018b8318ff86e1564cbc02277c1317a0d Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 14:37:31 +0200
Subject: [PATCH 319/496] linting nmf_model.py

---
 nautilus_nlp/topic_modeling/nmf_model.py | 56 ++++++++++++------------
 pylintrc                                 |  2 +-
 2 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/nmf_model.py b/nautilus_nlp/topic_modeling/nmf_model.py
index 005c09b..8d2650c 100644
--- a/nautilus_nlp/topic_modeling/nmf_model.py
+++ b/nautilus_nlp/topic_modeling/nmf_model.py
@@ -20,21 +20,19 @@
 from numpy.linalg import norm
 from tqdm import tqdm
 
-'''
-Topic Modeling via NMF
-'''
 
-class NMF(object):
+class NMF:
+
+    # pylint: disable=too-many-instance-attributes
+
     def __init__(
-            self,
-            A, IW=[], IH=[],
-            n_topic=10, max_iter=100, max_err=1e-3,
-            rand_init=True):
+            self, mat_a, mat_iw, mat_ih, n_topic=10,
+            max_iter=100, max_err=1e-3, rand_init=True):
         """
         The objective of the NMF model is to approximate the term-document matrix A by two lower-rank matrices W and H.
         The process is iterative and we denote IW and IH the the matrix W and H that are updated at each step.
 
-        :param A: The term-document matrix
+        :param a: The term-document matrix
         :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
         and its elements are the weights of the corresponding keywords.
         :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
@@ -43,9 +41,11 @@ def __init__(
         :param max_err: maximum error under which we consider that the loop converged
         :param rand_init: random init boolean
         """
-        self.A = A
-        self.n_row = A.shape[0]
-        self.n_col = A.shape[1]
+        self.mat_a = mat_a
+        self.mat_iw = mat_iw
+        self.mat_ih = mat_ih
+        self.n_row = mat_a.shape[0]
+        self.n_col = mat_a.shape[1]
 
         self.n_topic = n_topic
         self.max_iter = max_iter
@@ -61,13 +61,13 @@ def nmf_mat_init(self, rand_init):
         :param rand_init: Boolean indicating initial random init
         """
         if rand_init:
-            self.W = np.random.random((self.n_row, self.n_topic))
-            self.H = np.random.random((self.n_col, self.n_topic))
+            self.mat_w = np.random.random((self.n_row, self.n_topic))
+            self.mat_h = np.random.random((self.n_col, self.n_topic))
         else:
-            self.W = IW
-            self.H = IH
+            self.mat_w = self.mat_iw
+            self.mat_h = self.mat_ih
         for k in range(self.n_topic):
-            self.W[:, k] /= norm(self.W[:, k])
+            self.mat_w[:, k] /= norm(self.mat_w[:, k])
 
     def nmf_iter(self):
         """
@@ -94,25 +94,25 @@ def nmf_solver(self):
         '''
         epss = 1e-20
 
-        HtH = self.H.T.dot(self.H)
-        AH = self.A.dot(self.H)
+        mat_hth = self.mat_h.T.dot(self.mat_h)
+        mat_ah = self.mat_a.dot(self.mat_h)
         for k in range(self.n_topic):
-            tmpW = self.W[:, k] * HtH[k, k] + AH[:, k] - np.dot(self.W, HtH[:, k])
-            self.W[:, k] = np.maximum(tmpW, epss)
-            self.W[:, k] /= norm(self.W[:, k]) + epss
+            tmp_w = self.mat_w[:, k] * mat_hth[k, k] + mat_ah[:, k] - np.dot(self.mat_w, mat_hth[:, k])
+            self.mat_w[:, k] = np.maximum(tmp_w, epss)
+            self.mat_w[:, k] /= norm(self.mat_w[:, k]) + epss
 
-        WtW = self.W.T.dot(self.W)
-        AtW = self.A.T.dot(self.W)
+        mat_wtw = self.mat_w.T.dot(self.mat_w)
+        mat_atw = self.mat_a.T.dot(self.mat_w)
         for k in range(self.n_topic):
-            self.H[:, k] = self.H[:, k] * WtW[k, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
-            self.H[:, k] = np.maximum(self.H[:, k], epss)
+            self.mat_h[:, k] = self.mat_h[:, k] * mat_wtw[k, k] + mat_atw[:, k] - np.dot(self.mat_h, mat_wtw[:, k])
+            self.mat_h[:, k] = np.maximum(self.mat_h[:, k], epss)
 
     def nmf_loss(self):
-        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        loss = norm(self.mat_a - np.dot(self.mat_w, np.transpose(self.mat_h)), 'fro') ** 2 / 2.0
         return loss
 
     def get_loss(self):
         return np.array(self.loss_hist)
 
     def get_decomposition_matrix(self):
-        return self.W, self.H
+        return self.mat_w, self.mat_h
diff --git a/pylintrc b/pylintrc
index f16004a..54d4c85 100644
--- a/pylintrc
+++ b/pylintrc
@@ -291,7 +291,7 @@ ignore-mixin-members=yes
 # (useful for modules/projects where namespaces are manipulated during runtime
 # and thus existing member attributes cannot be deduced by static analysis. It
 # supports qualified module names, as well as Unix pattern matching.
-ignored-modules=
+ignored-modules=numpy
 
 # List of classes names for which member attributes should not be checked
 # (useful for classes with attributes dynamically set). This supports can work

From ba6505e2ab6712a82023b2d050642a5cecb74a7f Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 14:41:44 +0200
Subject: [PATCH 320/496] update docstring

---
 nautilus_nlp/topic_modeling/nmf_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/nmf_model.py b/nautilus_nlp/topic_modeling/nmf_model.py
index 8d2650c..1a9f38d 100644
--- a/nautilus_nlp/topic_modeling/nmf_model.py
+++ b/nautilus_nlp/topic_modeling/nmf_model.py
@@ -32,10 +32,10 @@ def __init__(
         The objective of the NMF model is to approximate the term-document matrix A by two lower-rank matrices W and H.
         The process is iterative and we denote IW and IH the the matrix W and H that are updated at each step.
 
-        :param a: The term-document matrix
-        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+        :param mat_a: The term-document matrix
+        :param mat_iw: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
         and its elements are the weights of the corresponding keywords.
-        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
+        :param mat_ih: The row vector H(j,:) is the latent representation for document j in terms of K topics
         :param n_topic: Number of selected topics
         :param max_iter: Maximum number of iterations to update W and H
         :param max_err: maximum error under which we consider that the loop converged

From 3a0afe28247b90ceab82169c4534a4f70152e228 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 15:02:53 +0200
Subject: [PATCH 321/496] linting seanmf_model.py and fix tests

---
 nautilus_nlp/topic_modeling/seanmf_model.py   | 101 +++++++++---------
 .../topic_modeling_short_text.py              |   5 +
 2 files changed, 55 insertions(+), 51 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/seanmf_model.py b/nautilus_nlp/topic_modeling/seanmf_model.py
index 7248148..9513435 100644
--- a/nautilus_nlp/topic_modeling/seanmf_model.py
+++ b/nautilus_nlp/topic_modeling/seanmf_model.py
@@ -22,23 +22,23 @@
 from tqdm import tqdm
 
 
-class SeaNMF(object):
+class SeaNMF:
+
+    # pylint: disable=too-many-instance-attributes
+
     def __init__(
-            self,
-            A, S,
-            IW=[], IWc=[], IH=[],
-            alpha=1.0, beta=0.1, n_topic=10, max_iter=100, max_err=1e-3,
-            rand_init=True, fix_seed=False):
+            self, mat_a, mat_s, mat_iw, mat_iwc, mat_ih, alpha=1.0, beta=0.1, n_topic=10,
+            max_iter=100, max_err=1e-3, rand_init=True, fix_seed=False):
         """
         Seanmf is a topic modeling algorithm, paper:  http://dmkd.cs.vt.edu/papers/WWW18.pdf.
         It finds an approximation to the term-document matrix A by two lower-rank matrices W and H,
         at each iteration a context matrix Wc are computed and used to update W.
-        :param A: document term matrix
-        :param S: Word-context (semantic) correlation matrix
-        :param IW: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+        :param mat_a: document term matrix
+        :param mat_s: Word-context (semantic) correlation matrix
+        :param mat_iw: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
         and its elements are the weights of the corresponding keywords.
-        :param IWc: Latent factor matrix of contexts.
-        :param IH: The row vector H(j,:) is the latent representation for document j in terms of K topics
+        :param mat_iwc: Latent factor matrix of contexts.
+        :param mat_ih: The row vector H(j,:) is the latent representation for document j in terms of K topics
         :param alpha: Seanmf algorithm parameter
         :param beta: Seanmf algorithm parameter
         :param n_topic: Number of selected topics
@@ -50,37 +50,37 @@ def __init__(
         if fix_seed:
             np.random.seed(0)
 
-        self.A = A
-        self.S = S
+        self.mat_a = mat_a
+        self.mat_s = mat_s
 
-        self.n_row = A.shape[0]
-        self.n_col = A.shape[1]
+        self.n_row = mat_a.shape[0]
+        self.n_col = mat_a.shape[1]
 
         self.n_topic = n_topic
         self.max_iter = max_iter
         self.alpha = alpha
         self.beta = beta
-        self.B = np.ones([self.n_topic, 1])
+        self.mat_b = np.ones([self.n_topic, 1])
         self.max_err = max_err
-        self.snmf_mat_init(rand_init, IW,  IWc, IH)
+        self.snmf_mat_init(rand_init, mat_iw, mat_iwc, mat_ih)
         self.snmf_iter()
 
-    def snmf_mat_init(self, rand_init, IW=[], IWc=[], IH=[]):
+    def snmf_mat_init(self, rand_init, mat_iw, mat_iwc, mat_ih):
         """
         Init Matrices W,Wc and H initially either randomly or using existing IW,IWc IH matrices taken when iterating.
         :param rand_init: Boolean indicating initial random init
         """
         if rand_init:
-            self.W = np.random.random((self.n_row, self.n_topic))
-            self.Wc = np.random.random((self.n_row, self.n_topic))
-            self.H = np.random.random((self.n_col, self.n_topic))
+            self.mat_w = np.random.random((self.n_row, self.n_topic))
+            self.mat_wc = np.random.random((self.n_row, self.n_topic))
+            self.mat_h = np.random.random((self.n_col, self.n_topic))
         else:
-            self.W = IW
-            self.Wc = IWc
-            self.H = IH
+            self.mat_w = mat_iw
+            self.mat_wc = mat_iwc
+            self.mat_h = mat_ih
         for k in range(self.n_topic):
-            self.W[:, k] /= norm(self.W[:, k])
-            self.Wc[:, k] /= norm(self.Wc[:, k])
+            self.mat_w[:, k] /= norm(self.mat_w[:, k])
+            self.mat_wc[:, k] /= norm(self.mat_wc[:, k])
 
     def snmf_iter(self):
         """
@@ -101,48 +101,47 @@ def snmf_iter(self):
     def snmf_solver(self):
         '''
         using BCD framework
-        Alogorithm 1: Equations to update W, wc, H are described in the paper
+        Alogorithm 1: Equations to update W, wc, H matrices are described in the paper
         http://dmkd.cs.vt.edu/papers/WWW18.pdf
         '''
 
         epss = 1e-20
         # Update W
-        AH = np.dot(self.A, self.H)
-        SWc = np.dot(self.S, self.Wc)
-        HtH = np.dot(self.H.T, self.H)
-        WctWc = np.dot(self.Wc.T, self.Wc)
-        W1 = self.W.dot(self.B)
+        mat_ah = np.dot(self.mat_a, self.mat_h)
+        mat_swc = np.dot(self.mat_s, self.mat_wc)
+        mat_hth = np.dot(self.mat_h.T, self.mat_h)
+        mat_wctwc = np.dot(self.mat_wc.T, self.mat_wc)
+        mat_w1 = self.mat_w.dot(self.mat_b)
 
         for k in range(self.n_topic):
-            num0 = HtH[k, k] * self.W[:, k] + self.alpha * WctWc[k, k] * self.W[:, k]
-            num1 = AH[:, k] + self.alpha * SWc[:, k]
-            num2 = np.dot(self.W, HtH[:, k]) + self.alpha * np.dot(self.W, WctWc[:, k]) + self.beta * W1[0]
-            self.W[:, k] = num0 + num1 - num2
-            self.W[:, k] = np.maximum(self.W[:, k], epss)  # project > 0
-            self.W[:, k] /= norm(self.W[:, k]) + epss  # normalize
+            num0 = mat_hth[k, k] * self.mat_w[:, k] + self.alpha * mat_wctwc[k, k] * self.mat_w[:, k]
+            num1 = mat_ah[:, k] + self.alpha * mat_swc[:, k]
+            num2 = np.dot(self.mat_w, mat_hth[:, k]) + self.alpha * np.dot(
+                self.mat_w, mat_wctwc[:, k]) + self.beta * mat_w1[0]
+            self.mat_w[:, k] = num0 + num1 - num2
+            self.mat_w[:, k] = np.maximum(self.mat_w[:, k], epss)  # project > 0
+            self.mat_w[:, k] /= norm(self.mat_w[:, k]) + epss  # normalize
         # Update Wc
-        WtW = self.W.T.dot(self.W)
-        StW = np.dot(self.S, self.W)
+        mat_wtw = self.mat_w.T.dot(self.mat_w)
+        mat_stw = np.dot(self.mat_s, self.mat_w)
         for k in range(self.n_topic):
-            self.Wc[:, k] = self.Wc[:, k] + StW[:, k] - np.dot(self.Wc, WtW[:, k])
-            self.Wc[:, k] = np.maximum(self.Wc[:, k], epss)
+            self.mat_wc[:, k] = self.mat_wc[:, k] + mat_stw[:, k] - np.dot(self.mat_wc, mat_wtw[:, k])
+            self.mat_wc[:, k] = np.maximum(self.mat_wc[:, k], epss)
         # Update H
-        AtW = np.dot(self.A.T, self.W)
+        mat_atw = np.dot(self.mat_a.T, self.mat_w)
         for k in range(self.n_topic):
-            self.H[:, k] = self.H[:, k] + AtW[:, k] - np.dot(self.H, WtW[:, k])
-            self.H[:, k] = np.maximum(self.H[:, k], epss)
+            self.mat_h[:, k] = self.mat_h[:, k] + mat_atw[:, k] - np.dot(self.mat_h, mat_wtw[:, k])
+            self.mat_h[:, k] = np.maximum(self.mat_h[:, k], epss)
 
     def snmf_loss(self):
-        loss = norm(self.A - np.dot(self.W, np.transpose(self.H)), 'fro') ** 2 / 2.0
+        loss = norm(self.mat_a - np.dot(self.mat_w, np.transpose(self.mat_h)), 'fro') ** 2 / 2.0
         if self.alpha > 0:
-            loss += self.alpha * norm(np.dot(self.W, np.transpose(self.Wc)) - self.S, 'fro') ** 2 / 2.0
+            loss += self.alpha * norm(np.dot(self.mat_w, np.transpose(self.mat_wc)) - self.mat_s, 'fro') ** 2 / 2.0
         if self.beta > 0:
-            loss += self.beta * norm(self.W, 1) ** 2 / 2.0
+            loss += self.beta * norm(self.mat_w, 1) ** 2 / 2.0
 
         return loss
 
     def get_decomposition_matrix(self):
         # Wc was not considered to keep same structure as NMF
-        return self.W, self.H
-
-
+        return self.mat_w, self.mat_h
diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
index 951f5fb..630a91a 100644
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -82,6 +82,8 @@ def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20,
         dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
         model = NMF(
             dt_mat,
+            mat_iw=[],
+            mat_ih=[],
             n_topic=n_topics,
             max_iter=max_iter,
             max_err=max_err)
@@ -95,6 +97,9 @@ def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20,
         dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
         model = SeaNMF(
             dt_mat, SS,
+            mat_iw=[],
+            mat_iwc=[],
+            mat_ih=[],
             alpha=alpha,
             beta=beta,
             n_topic=n_topics,

From 3cd41be4c365b47721095a1265a51bba2bd0ed83 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 5 Oct 2020 15:21:55 +0200
Subject: [PATCH 322/496] linting topic_modeling_short_text.py

---
 .../topic_modeling_short_text.py              | 117 +++++++++---------
 tests/test_topic_modeling_short_text.py       |   2 +-
 2 files changed, 57 insertions(+), 62 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
index 630a91a..a31a772 100644
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -18,10 +18,11 @@
 import re
 from collections import Counter
 from itertools import product
-from nautilus_nlp.topic_modeling.nmf_model import *
-from nautilus_nlp.topic_modeling.seanmf_model import *
+
 import numpy as np
 import pyLDAvis
+from nautilus_nlp.topic_modeling.nmf_model import NMF
+from nautilus_nlp.topic_modeling.seanmf_model import SeaNMF
 
 
 def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
@@ -36,9 +37,9 @@ def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
     vocab = {}
 
     # Tokens_list is a list of sub-lists where each sub-list contains a sentences' tokens.
-    tokens_list=[]
+    tokens_list = []
     for sentence in text:
-        sentence = re.split('\s', sentence)
+        sentence = re.split(r'\s', sentence)
         tokens_list.append(sentence)
     vocab = dict(Counter(x for xs in tokens_list for x in xs))
 
@@ -48,21 +49,23 @@ def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
     vocab_arr = vocab_arr[:vocab_max_size]
     vocab_arr = sorted(vocab_arr)
 
-    vocab_list = list(map(lambda x:x[0], vocab_arr))
+    vocab_list = list(map(lambda x: x[0], vocab_arr))
     # Create Vocab to ID dictionnary
     vocab2id = {itm[1][0]: itm[0] for itm in enumerate(vocab_arr)}
 
     # Create ID representation of text (ie: each sentence is a list of vocabId )
     encoded_text_id = []
     for sentence in text:
-        sentence = re.split('\s', sentence)
+        sentence = re.split(r'\s', sentence)
         sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
         encoded_text_id.append(sentence)
 
     return encoded_text_id, vocab_list, vocab_arr
 
 
-def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20, max_iter=20, max_err=0.1, alpha=0, beta=0):
+def train_shorttext_model(
+    model_name, encoded_text_id, vocab_list, n_topics=20,
+    max_iter=20, max_err=0.1, alpha=0, beta=0):
     """
     :param model_name: string = 'nmf' or 'seanmf'
     :param encoded_text_id: list of encoded sentences
@@ -90,13 +93,13 @@ def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20,
 
     elif model_name == 'seanmf':
         # Calculate co-occurence matrix
-        cm = __build_cooccurence_matrix(n_terms, encoded_text_id)
+        cooc_mat = __build_cooccurence_matrix(n_terms, encoded_text_id)
         # Calculate PPMI
-        SS = __calulate_PPMI(cm, n_terms)
+        mat_ss = __calculate_ppmi(cooc_mat, n_terms)
         # Build doc-term matrix
         dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
         model = SeaNMF(
-            dt_mat, SS,
+            dt_mat, mat_ss,
             mat_iw=[],
             mat_iwc=[],
             mat_ih=[],
@@ -114,15 +117,7 @@ def train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=20,
     return model
 
 
-def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
-    dt_mat = np.zeros([n_terms, n_docs])
-    for k in range(n_docs):
-        for j in encoded_text_id[k]:
-            dt_mat[j, k] += 1.0
-    return dt_mat
-
-
-def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
+def show_dominant_topic(model, encoded_text_id, vocab_list, n_top_keyword=10):
     """
     Computes the PMi score for each topic and the topKeywords describing each of them.
     :param model: trained NMF model
@@ -134,23 +129,23 @@ def show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword =10):
 
     dt_mat = __build_cooccurence_matrix(n_terms=len(vocab_list), encoded_text_id=encoded_text_id)
     np.fill_diagonal(dt_mat, 0)
-    W,_ = model.get_decomposition_matrix()
-    n_topic = W.shape[1]
-    PMI_arr = []
+    mat_w, _ = model.get_decomposition_matrix()
+    n_topic = mat_w.shape[1]
+    pmi_arr = []
     for k in range(n_topic):
-        top_keywords_index = W[:, k].argsort()[::-1][:n_topKeyword]
-        PMI_arr.append(__calculate_PMI(dt_mat, top_keywords_index))
+        top_keywords_index = mat_w[:, k].argsort()[::-1][:n_top_keyword]
+        pmi_arr.append(__calculate_pmi(dt_mat, top_keywords_index))
 
-    index = np.argsort(PMI_arr)
+    index = np.argsort(pmi_arr)
     topics = {}
     pmi_score = {}
     for k in index:
         words = []
-        for w in np.argsort(W[:, k])[::-1][:n_topKeyword]:
+        for w in np.argsort(mat_w[:, k])[::-1][:n_top_keyword]:
             words.append(vocab_list[w])
         # Complete the topic and the score dicts. Format {Topic_number: words or score}
         topics[k] = words
-        pmi_score[k] = PMI_arr[k]
+        pmi_score[k] = pmi_arr[k]
 
     return topics, pmi_score
 
@@ -162,10 +157,10 @@ def get_assigned_topics(model):
     :return topics_list: list having the same length as the training text containing topics assigned to each sentence.
     """
 
-    _, H = model.get_decomposition_matrix()
+    _, mat_h = model.get_decomposition_matrix()
     # The weights of the H matrix are converted into probabilities
-    H_probs = H / H.sum(axis=1, keepdims=True)
-    topics_list = list(np.argmax(H_probs, axis=1))
+    h_probs = mat_h / mat_h.sum(axis=1, keepdims=True)
+    topics_list = list(np.argmax(h_probs, axis=1))
 
     return topics_list
 
@@ -193,20 +188,20 @@ def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
     """
 
     # 1 List of documents lengths
-    doc_length_values=[]
+    doc_length_values = []
     for doc in encoded_text_id:
         doc_length_values.append(len(doc))
     # 2 List of vocab
     list_vocab = list(map(lambda x: x[0], vocab_arr))
     # 3 List of vocab. Frequency
     freq_vocab = list(map(lambda x: x[1], vocab_arr))
-    W, H = model.get_decomposition_matrix()
+    mat_w, mat_h = model.get_decomposition_matrix()
     # Normlize the decomposition to get probabilities
-    W_probs = W / W.sum(axis=1, keepdims=True)
+    w_probs = mat_w / mat_w.sum(axis=1, keepdims=True)
     # 4 topic term matrix phi
-    phi = W_probs.T
+    phi = w_probs.T
     # 5 document term matrix theta
-    theta = H / H.sum(axis=1, keepdims=True)
+    theta = mat_h / mat_h.sum(axis=1, keepdims=True)
 
     data = {'topic_term_dists': phi,
             'doc_topic_dists': theta,
@@ -236,21 +231,21 @@ def __build_cooccurence_matrix(n_terms, encoded_text_id):
     return res
 
 
-def __calulate_PPMI(cm, n_terms):
-    D1 = np.sum(cm)
-    print('D1= ', D1)
-    SS = D1 * cm
-    print('SS= ',SS)
+def __calculate_ppmi(cooc_mat, n_terms):
+    mat_d1 = np.sum(cooc_mat)
+    print('D1= ', mat_d1)
+    mat_ss = mat_d1 * cooc_mat
+    print('SS= ', mat_ss)
     for k in range(n_terms):
-        SS[k] /= np.sum(cm[k])
+        mat_ss[k] /= np.sum(cooc_mat[k])
     for k in range(n_terms):
-        SS[:, k] /= np.sum(cm[:, k])
-    print('SS = ', SS )
-    cm = []  # release memory
-    SS[SS == 0] = 1.0
-    SS = np.log(SS)
-    SS[SS < 0.0] = 0.0
-    return SS
+        mat_ss[:, k] /= np.sum(cooc_mat[:, k])
+    print('SS = ', mat_ss)
+    cooc_mat = []  # release memory
+    mat_ss[mat_ss == 0] = 1.0
+    mat_ss = np.log(mat_ss)
+    mat_ss[mat_ss < 0.0] = 0.0
+    return mat_ss
 
 
 def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
@@ -261,25 +256,25 @@ def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
     return dt_mat
 
 
-def __calculate_PMI(AA, topKeywordsIndex):
+def __calculate_pmi(mat_aa, top_keywords_index):
     '''
     Method to compute PMi score
     Reference:
     Short and Sparse Text Topic Modeling via Self-Aggregation
     '''
 
-    D1 = np.sum(AA)
-    n_tp = len(topKeywordsIndex)
-    PMI = []
-    for index1 in topKeywordsIndex:
-        for index2 in topKeywordsIndex:
+    mat_d1 = np.sum(mat_aa)
+    n_tp = len(top_keywords_index)
+    mat_pmi = []
+    for index1 in top_keywords_index:
+        for index2 in top_keywords_index:
             if index2 < index1:
-                if AA[index1, index2] == 0:
-                    PMI.append(0.0)
+                if mat_aa[index1, index2] == 0:
+                    mat_pmi.append(0.0)
                 else:
-                    C1 = np.sum(AA[index1])
-                    C2 = np.sum(AA[index2])
-                    PMI.append(np.log(AA[index1,index2]*D1/C1/C2))
-    avg_PMI = 2.0*np.sum(PMI)/float(n_tp)/(float(n_tp)-1.0)
+                    mat_c1 = np.sum(mat_aa[index1])
+                    mat_c2 = np.sum(mat_aa[index2])
+                    mat_pmi.append(np.log(mat_aa[index1,index2]*mat_d1/mat_c1/mat_c2))
+    avg_pmi = 2.0*np.sum(mat_pmi)/float(n_tp)/(float(n_tp)-1.0)
 
-    return avg_PMI
+    return avg_pmi
diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index e4570b4..2265738 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -62,7 +62,7 @@ def test_show_dominant_topic(model_name, n_topics, n_keywords):
     encoded_text_id, vocab_list, _ = prepare_data(TEXT)
 
     model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
-    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_topKeyword=n_keywords)
+    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_top_keyword=n_keywords)
 
     assert len(pmi_score) == n_topics
     assert len(topics) == n_topics

From 5357ac91bbd3073a208a71edd84c438f9048f552 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 5 Oct 2020 15:43:16 +0200
Subject: [PATCH 323/496] adding linting to CI

---
 .travis.yml       | 4 +++-
 tests/__init__.py | 0
 2 files changed, 3 insertions(+), 1 deletion(-)
 create mode 100644 tests/__init__.py

diff --git a/.travis.yml b/.travis.yml
index d2220c0..e99a71f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -33,4 +33,6 @@ install:
   - pip install -r requirements.txt
   - pip install -e .
 script:
-  - pytest tests/*
\ No newline at end of file
+  - pylint nautilus_nlp/
+  - pylint tests/
+  - pytest tests/*
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29

From babcb3c2162ea763471fb2c728b13a0a9be0742d Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 5 Oct 2020 17:59:33 +0200
Subject: [PATCH 324/496] adding pylint to requirements

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index c485909..00dc863 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -41,3 +41,4 @@ summa==1.2.0
 biterm==0.1.5
 nlpaug==1.0.1
 ipython==7.16.1
+pylint==2.4.4

From 91577321dd71b4f6296c24c3e81ade7785a529a9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 7 Oct 2020 10:03:27 +0200
Subject: [PATCH 325/496] replacing x variable with counter

---
 nautilus_nlp/analysis/keyword_extractor.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/analysis/keyword_extractor.py b/nautilus_nlp/analysis/keyword_extractor.py
index 4ef2b49..ab86a7d 100644
--- a/nautilus_nlp/analysis/keyword_extractor.py
+++ b/nautilus_nlp/analysis/keyword_extractor.py
@@ -74,6 +74,6 @@ def frequent_words(list_words, ngrams_number=1, number_top_words=10):
         list_words = create_ngrams(list_words, ngrams_number)
     else:
         raise ValueError("number of n-grams should be >= 1")
-    x = Counter(list_words)
-    frequent = x.most_common(number_top_words)
+    counter = Counter(list_words)
+    frequent = counter.most_common(number_top_words)
     return frequent

From d2762e786d1797a55c5f175cbcb96a594df2553c Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 7 Oct 2020 10:04:02 +0200
Subject: [PATCH 326/496]  replacing bad return to lign

---
 nautilus_nlp/analysis/keyword_extractor.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nautilus_nlp/analysis/keyword_extractor.py b/nautilus_nlp/analysis/keyword_extractor.py
index ab86a7d..94154e5 100644
--- a/nautilus_nlp/analysis/keyword_extractor.py
+++ b/nautilus_nlp/analysis/keyword_extractor.py
@@ -58,7 +58,6 @@ def frequent_words(list_words, ngrams_number=1, number_top_words=10):
     Parameters
     ----------
     ngrams_number : int
-
     number_top_words : int
         output dataframe length
 

From e52e490573c6bddac5d058b85c04d5efcf9c5d20 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 7 Oct 2020 10:10:00 +0200
Subject: [PATCH 327/496] renaming variable words to nb_words

---
 nautilus_nlp/analysis/text_summary.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/analysis/text_summary.py b/nautilus_nlp/analysis/text_summary.py
index 02457c6..6469729 100644
--- a/nautilus_nlp/analysis/text_summary.py
+++ b/nautilus_nlp/analysis/text_summary.py
@@ -32,7 +32,7 @@ def is_list_of_strings(lst):
     return bool(lst) and isinstance(lst, list) and all(isinstance(elem, str) for elem in lst)
 
 
-def summarize_text(txt, ratio=0.2, language="english", words=None):
+def summarize_text(txt, ratio=0.2, language="english", nb_words=None):
     """
     Parameters
     ----------
@@ -42,7 +42,7 @@ def summarize_text(txt, ratio=0.2, language="english", words=None):
         Percentage giving the output text length in reference to the input length.
     language :
         text language. eg. "english"
-    words :
+    nb_words :
         number of words of the output text or None
 
     Returns
@@ -55,4 +55,4 @@ def summarize_text(txt, ratio=0.2, language="english", words=None):
         txt = ' '.join(txt)
     elif not isinstance(txt, str):
         raise TypeError("Text parameter must be a Unicode object (str) or list of str!")
-    return summarize(txt, ratio, words, language)
+    return summarize(txt, ratio, nb_words, language)

From b7ce7f72650215f34a5d244dfec6f54beb530adf Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 7 Oct 2020 10:10:54 +0200
Subject: [PATCH 328/496] adding example of variable countrylist in docstring

---
 nautilus_nlp/utils/phone_number.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index f3af336..3647908 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -77,7 +77,7 @@ def extract_phone_numbers(text: str, countrylist: list)->list:
 
     Parameters
     ----------
-    countrylist: list
+    countrylist: list (eg. [None,'FR','US','GB'])
         Look for phone numbers formatted according to the specified countlist.
         supported value: look SUPPORTED_COUNTRY variable.
     '''

From fe33739c420aa1a60ccb9afcc47a82b4a64d051c Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 7 Oct 2020 10:11:58 +0200
Subject: [PATCH 329/496] typo counttry > country

---
 nautilus_nlp/utils/phone_number.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 3647908..7cde267 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -58,7 +58,7 @@ def find_phone_numbers(string, region_code=None):
     eg. 06.25.09.32.67 if "FR" is specified.
 
     If not specified, only works for international-formatted phone numbers.
-    - ie. phone number with +counttry code specified
+    - ie. phone number with +country code specified
     eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.
 
     region_code
@@ -108,7 +108,7 @@ def parse_number(self, text: str, region_code=None):
         eg. 06.25.09.32.67 if "FR" is specified.
 
         If not specified, only works for international-formatted phone numbers.
-        - ie. phone number with +counttry code specified
+        - ie. phone number with +country code specified
         eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.
 
         region_code

From 97266d72cf3c417d788888fccd83eb78b1b3d778 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 7 Oct 2020 12:26:23 +0200
Subject: [PATCH 330/496] putting anonymous phone number in tests

---
 tests/test_phone_number.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index e416f09..4aff394 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -30,14 +30,14 @@ def test_extract_phone_number_us():
     assert res == expected
 
 def test_extract_phone_number_fr():
-    input_str = '06.25.09.32.56 is a FR Phone'
-    expected = ['06.25.09.32.56']
+    input_str = '06.00.00.00.00 is a FR Phone'
+    expected = ['06.00.00.00.00']
     res = phone.extract_phone_numbers(input_str, countrylist=['FR'])
     assert res == expected
 
 def test_extract_phone_number_international():
-    input_str = '+33625093423 is an international Phone number'
-    expected = ['+33625093423']
+    input_str = '+33600000000 is an international Phone number'
+    expected = ['+33600000000']
     res = phone.extract_phone_numbers(input_str, countrylist=['US', 'GB', 'FR', None])
     assert res == expected
 
@@ -50,8 +50,8 @@ def test_phone_parser_us():
     assert res == expected
 
 def test_phone_parser_fr():
-    input_str = '0625093267'
-    expected = '+33625093267'
+    input_str = '0600000000'
+    expected = '+33600000000'
     p = phone.PhoneParser()
     p.parse_number(input_str, region_code='FR')
     res = p.format_number('E164')

From 6683a73c68f7e7f6dff891e0f268319f4b335f43 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 7 Oct 2020 12:28:42 +0200
Subject: [PATCH 331/496] putting anonymous phone number in tests

---
 nautilus_nlp/utils/phone_number.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 7cde267..66571ca 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -55,11 +55,11 @@ def find_phone_numbers(string, region_code=None):
     ----------
     region_code
         If specified, will find the number of the specified country.
-    eg. 06.25.09.32.67 if "FR" is specified.
+    eg. 06.00.00.00.00 if "FR" is specified.
 
     If not specified, only works for international-formatted phone numbers.
     - ie. phone number with +country code specified
-    eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.
+    eg. 06.00.00.00.00 will return an error but +33 6 00 00 00 00 will work.
 
     region_code
         supported value: look SUPPORTED_COUNTRY variable.
@@ -105,11 +105,11 @@ def parse_number(self, text: str, region_code=None):
         ----------
         region_code
             If specified, will find the number of the specified country.
-        eg. 06.25.09.32.67 if "FR" is specified.
+        eg. 06.00.00.00.00 if "FR" is specified.
 
         If not specified, only works for international-formatted phone numbers.
         - ie. phone number with +country code specified
-        eg. 06.25.09.32.67 will return an error but +33 6 25 09 32 67 will work.
+        eg. 06.00.00.00.00 will return an error but +33 6 00 00 00 00 will work.
 
         region_code
             supported value: look SUPPORTED_COUNTRY variable.

From 59145ab3ade6c4c15bdbcb1b671f9c260c294724 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 8 Oct 2020 11:00:28 +0200
Subject: [PATCH 332/496] replacing logging.warning with warnings.warn

---
 nautilus_nlp/utils/file_loader.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index cda3997..5bbcc80 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -15,19 +15,17 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+import codecs
 import glob
 import io
 import json
-import logging
 import os
 import re
-import codecs
 import shutil
+import warnings
 
 import chardet
 
-logging.basicConfig(level=logging.INFO)
-
 
 def open_textfile(filepath, encoding='utf-8'):
     with io.open(filepath, 'r', encoding=encoding) as f:
@@ -80,12 +78,11 @@ def text_loader(filepath, encoding=None, detectencoding=True):
     try:
         return open_textfile(filepath, encoding='utf-8')
     except UnicodeDecodeError:
-        logging.warning('Encoding for {} is not UTF-8.'.format(filepath))
+        warnings.warn(f'Encoding for {filepath} is not UTF-8.')
         if detectencoding is True:
-            logging.warning('Trying to detect encoding for {}'.format(filepath))
             detected_encoding = detect_encoding(filepath)
-            logging.info('{filepath}: detected encoding is {encod}, with a confidence rate of {conf_rate}'.format(
-                filepath=filepath, encod=detected_encoding['encoding'], conf_rate=detected_encoding['confidence']))
+            warnings.warn(f'{filepath}: detected encoding is {detected_encoding["encoding"]},\
+                            with a confidence rate of {detected_encoding["confidence"]}')
             return open_textfile(filepath, encoding=detected_encoding['encoding'])
         raise UnicodeDecodeError('Cannot load document using utf-8. '\
                                     'Try to detect encoding using detectencoding=True')

From 7de3eccd1e015984245916f0f2194d012f9082c0 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Wed, 14 Oct 2020 18:28:05 +0200
Subject: [PATCH 333/496] refacto data augmentation functions

---
 .../preprocessing/data_augmentation.py        | 128 ++++++++++++++++--
 1 file changed, 117 insertions(+), 11 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 1263260..a0df7b9 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -8,6 +8,8 @@
 class CouldNotAugment(ValueError):
     pass
 
+class UnavailableAugmenter(ValueError):
+    pass
 
 def augment_utterance(text, method, stopwords, intent=None, entities=None):
     """
@@ -30,6 +32,7 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
         [
             {
                 'entity': str,
+                'word': str,
                 'startCharIndex': int,
                 'endCharIndex': int
             },
@@ -43,21 +46,51 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
     dictionary with augmented text and optional keys depending on input
     """
     new_utt = {}
-    augmenter = select_augmenter(method, stopwords)
+    augmenter = get_augmenter(method, stopwords)
     new_utt['text'] = augmenter.augment(text)
     if intent is not None:
         new_utt['intent'] = intent
     if entities is not None:
-        formatted_entities = [(text[entities[i]['startCharIndex']:entities[i]['endCharIndex']
-                                    ].strip(), entities[i]['entity']) for i in range(len(entities))]
+        formatted_entities = [(
+            text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),
+            entities[i]['entity']) for i in range(len(entities)
+        )]
         if are_entities_in_augmented_text(entities, new_utt['text']):
-            new_utt['entities'] = get_augmented_entities(new_utt['text'], formatted_entities)
+            new_utt['entities'] = get_augmented_entities(
+                new_utt['text'],
+                formatted_entities
+            )
             return clean_sentence_entities(new_utt)
         raise CouldNotAugment('Text was not correctly augmented so not added')
     return new_utt
 
 
 def are_entities_in_augmented_text(entities, augmented_text):
+    """
+    Given a list of entities, check if all the words associated to each entity
+    are still present in augmented text.
+
+    Parameters
+    ----------
+    entities : list
+        entities associated to initial text, must be in the following format:
+        [
+            {
+                'entity': str,
+                'word': str,
+                'startCharIndex': int,
+                'endCharIndex': int
+            },
+            {
+                ...
+            }
+        ]
+    augmented_text : str
+
+    Returns
+    -------
+    True if all entities are present in augmented text, False otherwise
+    """
     check = True
     for ent in entities:
         if ent['word'] not in augmented_text:
@@ -65,17 +98,53 @@ def are_entities_in_augmented_text(entities, augmented_text):
     return check
 
 
-def select_augmenter(method, stopwords, use_stopwords=True):
-    if not use_stopwords:
-        stopwords = []
+def get_augmenter(method, stopwords=None):
+    """
+    Initialize an augmenter depending on the given method.
+
+    Parameters
+    ----------
+    method : str (supported methods: wordnet_synonym and aug_sub_bert)
+    stopwords : list
+        list of words to freeze throughout the augmentation
+
+    Returns
+    -------
+    Initialized nlpaug augmenter
+    """
     if method == 'wordnet_synonym':
-        augmenter = naw.SynonymAug(aug_src='wordnet', stopwords=stopwords)
-    elif method == 'aug_sub_bert':
-        augmenter = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute", stopwords=stopwords)
-    return(augmenter)
+        return naw.SynonymAug(aug_src='wordnet', stopwords=stopwords)
+    if method == 'aug_sub_bert':
+        return naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute", stopwords=stopwords)
+    raise UnavailableAugmenter('The given augmenter is not supported yet')
 
 
 def get_augmented_entities(sentence_augmented, entities):
+    """
+    Get entities with updated positions (start and end) in augmented text
+
+    Parameters
+    ----------
+    sentence_augmented : str
+        augmented text
+    entities : list
+        entities associated to initial text, must be in the following format:
+        [
+            {
+                'entity': str,
+                'word': str,
+                'startCharIndex': int,
+                'endCharIndex': int
+            },
+            {
+                ...
+            }
+        ]
+
+    Returns
+    -------
+    Entities with updated positions related to augmented text
+    """
     entities_augmented = []
     for entity in entities:
         regex = r'(?:^|\W)' + re.escape(entity[0].strip()) + r'(?:$|\W)'
@@ -91,6 +160,23 @@ def get_augmented_entities(sentence_augmented, entities):
 
 
 def clean_sentence_entities(sentence_input):
+    """
+    Paired entities check to remove nested entities, the longest entity is kept
+
+    Parameters
+    ----------
+    sentence_input : dict
+        dictionary in the following format
+        {
+            'text' : str,
+            'entities' : list of dict,
+            'intent' : str (optional)
+        }
+
+    Returns
+    -------
+    Sentence input with cleaned entities
+    """
     sentence = copy.copy(sentence_input)
     for element1 in sentence['entities']:
         for element2 in sentence['entities']:
@@ -108,6 +194,26 @@ def clean_sentence_entities(sentence_input):
 
 
 def check_interval_included(element1, element2):
+    """
+    Comparison of two entities on start and end positions to find if they are nested
+
+    Parameters
+    ----------
+    element1 : dict
+    element2 : dict
+        both of them in the following format
+        {
+            'entity': str,
+            'word': str,
+            'startCharIndex': int,
+            'endCharIndex': int
+        }
+
+    Returns
+    -------
+    If there is an entity to remove among the two returns a tuple (element to remove, element to keep)
+    If not, returns None
+    """
     if ((element1 != element2) and (element1['startCharIndex'] >= element2['startCharIndex']) and
             (element1['endCharIndex'] <= element2['endCharIndex'])):
         return element1, element2

From e598d95ae0eb90397c89ba0091545c3383b820fe Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Wed, 14 Oct 2020 18:43:05 +0200
Subject: [PATCH 334/496] fix linting

---
 nautilus_nlp/preprocessing/data_augmentation.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index a0df7b9..8ac1de7 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -53,8 +53,7 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
     if entities is not None:
         formatted_entities = [(
             text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),
-            entities[i]['entity']) for i in range(len(entities)
-        )]
+            entities[i]['entity']) for i in range(len(entities))]
         if are_entities_in_augmented_text(entities, new_utt['text']):
             new_utt['entities'] = get_augmented_entities(
                 new_utt['text'],

From 0aa06a83f7b7b6aca8be2122a8c96bf49bcd73f0 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Wed, 14 Oct 2020 18:58:08 +0200
Subject: [PATCH 335/496] refacto data aug functions, remove utterance notion

---
 .../preprocessing/data_augmentation.py        | 61 ++++++++++---------
 1 file changed, 32 insertions(+), 29 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 8ac1de7..b0177af 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -11,7 +11,7 @@ class CouldNotAugment(ValueError):
 class UnavailableAugmenter(ValueError):
     pass
 
-def augment_utterance(text, method, stopwords, intent=None, entities=None):
+def augment_utterance(text, method, stopwords, entities=None):
     """
     Given ``text`` str, create a new similar utterance by modifying some words
     in the initial sentence, modifications depend on the chosen method
@@ -25,8 +25,6 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
         augmenter to use ('wordnet_synonym' or 'aug_sub_bert')
     stopwords : list
         list of words to freeze throughout the augmentation
-    intent : string
-        intent associated to text if any
     entities : list
         entities associated to text if any, must be in the following format:
         [
@@ -43,25 +41,22 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
 
     Returns
     -------
-    dictionary with augmented text and optional keys depending on input
+    Augmented text and optional augmented entities
     """
-    new_utt = {}
     augmenter = get_augmenter(method, stopwords)
-    new_utt['text'] = augmenter.augment(text)
-    if intent is not None:
-        new_utt['intent'] = intent
+    augmented_text = augmenter.augment(text)
     if entities is not None:
         formatted_entities = [(
             text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),
             entities[i]['entity']) for i in range(len(entities))]
-        if are_entities_in_augmented_text(entities, new_utt['text']):
-            new_utt['entities'] = get_augmented_entities(
-                new_utt['text'],
+        if are_entities_in_augmented_text(entities, augmented_text):
+            augmented_entities = get_augmented_entities(
+                augmented_text,
                 formatted_entities
             )
-            return clean_sentence_entities(new_utt)
+            return clean_sentence_entities(text, augmented_entities)
         raise CouldNotAugment('Text was not correctly augmented so not added')
-    return new_utt
+    return augmented_text
 
 
 def are_entities_in_augmented_text(entities, augmented_text):
@@ -158,38 +153,46 @@ def get_augmented_entities(sentence_augmented, entities):
     return entities_augmented
 
 
-def clean_sentence_entities(sentence_input):
+def clean_sentence_entities(text, entities):
     """
     Paired entities check to remove nested entities, the longest entity is kept
 
     Parameters
     ----------
-    sentence_input : dict
-        dictionary in the following format
-        {
-            'text' : str,
-            'entities' : list of dict,
-            'intent' : str (optional)
-        }
+    text : str
+        augmented text
+    entities : list
+        entities associated to augmented text, must be in the following format:
+        [
+            {
+                'entity': str,
+                'word': str,
+                'startCharIndex': int,
+                'endCharIndex': int
+            },
+            {
+                ...
+            }
+        ]
 
     Returns
     -------
-    Sentence input with cleaned entities
+    Augmented text and cleaned entities
     """
-    sentence = copy.copy(sentence_input)
-    for element1 in sentence['entities']:
-        for element2 in sentence['entities']:
+    entities_to_clean = copy.copy(entities)
+    for element1 in entities_to_clean:
+        for element2 in entities_to_clean:
             result = check_interval_included(element1, element2)
             if result is not None:
                 try:
-                    sentence[1]['entities'].remove(result[0])
+                    entities_to_clean.remove(result[0])
                 except IndexError:
                     logging.warning(
                         "Cant remove entity : {} \n entities are now :{} \n for sentence : {} ".format(
-                            result, sentence['entities'],
-                            sentence['text']))
+                            result, entities_to_clean,
+                            text))
                     continue
-    return sentence
+    return text, entities_to_clean
 
 
 def check_interval_included(element1, element2):

From 9cadf3aad26b2000a2649aaa51c65535509ca362 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 15 Oct 2020 18:48:11 +0200
Subject: [PATCH 336/496] rename function and clarify custom errors message

---
 nautilus_nlp/preprocessing/data_augmentation.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index b0177af..1e47928 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -11,12 +11,13 @@ class CouldNotAugment(ValueError):
 class UnavailableAugmenter(ValueError):
     pass
 
-def augment_utterance(text, method, stopwords, entities=None):
+def augment_text(text, method, stopwords=None, entities=None):
     """
-    Given ``text`` str, create a new similar utterance by modifying some words
-    in the initial sentence, modifications depend on the chosen method
-    (substitution with synonym, addition, deletion). If intent and/or entities
-    are given as input, they will remain unchanged.
+    Given a text with or without associated entities, generate a new text by
+    modifying some words in the initial one, modifications depend on the chosen
+    method (substitution with synonym, addition, deletion). If entities are
+    given as input, they will remain unchanged. If you want some words other
+    than entities to remain unchanged, specify it within the stopwords argument.
 
     Parameters
     ----------
@@ -110,7 +111,8 @@ def get_augmenter(method, stopwords=None):
         return naw.SynonymAug(aug_src='wordnet', stopwords=stopwords)
     if method == 'aug_sub_bert':
         return naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute", stopwords=stopwords)
-    raise UnavailableAugmenter('The given augmenter is not supported yet')
+    raise UnavailableAugmenter('The given augmenter is not supported. You must choose one \
+        of the following: wordnet_synonym or aug_sub_bert')
 
 
 def get_augmented_entities(sentence_augmented, entities):

From 6e2571a9d342cdbd212dd0e4a6cb1d0e14fd7102 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 18:30:20 +0200
Subject: [PATCH 337/496] feat: add unit test

---
 nautilus_nlp/analysis/keyword_extractor.py | 38 ++++++++++++++--------
 1 file changed, 24 insertions(+), 14 deletions(-)

diff --git a/nautilus_nlp/analysis/keyword_extractor.py b/nautilus_nlp/analysis/keyword_extractor.py
index 94154e5..a615f45 100644
--- a/nautilus_nlp/analysis/keyword_extractor.py
+++ b/nautilus_nlp/analysis/keyword_extractor.py
@@ -15,29 +15,36 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from typing import Union, List, Tuple, Dict
 from collections import Counter
 
 from flashtext import KeywordProcessor
 from nautilus_nlp.analysis.ngrams import create_ngrams
 
 
-def extract_keywords(text, keyword, case_sensitive=True):
+def extract_keywords(
+        text: str, keyword: Union[str, List[str], Dict[str, List[str]]], case_sensitive: bool = True
+    ) -> List[str]:
     """
-    Extract Keywords from a document.
+    Extract Keywords from a document, using FlashText
 
     Parameters
     ----------
     text : str
-        Text to extract keywords from
-    keyword :
-        Single keyword (str) or list of keywords (list)
-    case_sensitive :
+        Text to extract keywords from. 
+    keyword : Union[str, list[str], dict[str, list[str]]]
+        Single keyword (str), list of keywords (list), or keyword dict.
+        Example of keyword dict: keyword_dict = {
+                    "java": ["java_2e", "java programing"],
+                    "product management": ["PM", "product manager"]
+                }
+    case_sensitive : bool
         If False, will be case insensitive.
 
     Returns
     -------
     list
-        Return list of extracted keyworkds
+        Return list of extracted keywords
     """
 
     processor = KeywordProcessor(case_sensitive=case_sensitive)
@@ -51,28 +58,31 @@ def extract_keywords(text, keyword, case_sensitive=True):
     return processor.extract_keywords(text)
 
 
-def frequent_words(list_words, ngrams_number=1, number_top_words=10):
+def get_frequent_words(
+        tokens: List[str], ngrams_number: int = 1, number_top_words: int = 10) -> List[Tuple[str, int]]:
     """
-    Create n-grams for list of tokens
+    Create n-grams for a list of tokens
 
     Parameters
     ----------
+    tokens : list
+        list of tokens
     ngrams_number : int
     number_top_words : int
-        output dataframe length
+        number of top keywords to return
 
     Returns
     -------
-    DataFrame
-        Dataframe with the entities and their frequencies.
+    list[tuple[str, int]]
+        Returns a list of the top n words ().
     """
     frequent = []
     if ngrams_number == 1:
         pass
     elif ngrams_number >= 2:
-        list_words = create_ngrams(list_words, ngrams_number)
+        tokens = create_ngrams(tokens, ngrams_number)
     else:
         raise ValueError("number of n-grams should be >= 1")
-    counter = Counter(list_words)
+    counter = Counter(tokens)
     frequent = counter.most_common(number_top_words)
     return frequent

From 90d87f1bc4cefe8ad0e850e240a23aae97526cc9 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 18:30:30 +0200
Subject: [PATCH 338/496] feat: add unit test

---
 tests/test_keyword_extractor.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 tests/test_keyword_extractor.py

diff --git a/tests/test_keyword_extractor.py b/tests/test_keyword_extractor.py
new file mode 100644
index 0000000..c4e83b3
--- /dev/null
+++ b/tests/test_keyword_extractor.py
@@ -0,0 +1,20 @@
+import pytest
+from nautilus_nlp.analysis.keyword_extractor import get_frequent_words
+
+
+@pytest.mark.parametrize(
+    "input_tokens, expected, ngrams_number, number_top_words",
+    [
+        (['I', 'eat', 'orange', 'oranges', 'at', 'orange'], [('orange', 2)], 1, 1), 
+        (['Un', 'chat', 'rose', 'reste', 'un', 'chat', 'rose'], [('chat rose', 2)], 2, 1), 
+    ]
+    )
+def test_get_frequent_words(input_tokens, expected, ngrams_number, number_top_words):
+    result = get_frequent_words(
+        input_tokens, ngrams_number=ngrams_number, number_top_words=number_top_words)
+    assert result == expected
+
+
+def test_get_frequent_words_output_lenght():
+    input_tokens = ['one', 'two', 'three', 'four', 'five', 'six', 'seven']
+    assert len(get_frequent_words(input_tokens, number_top_words=5)) == 5

From 35954e2bf45642ebf8cd5745dfa3a56e99bb7ccd Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 18:38:11 +0200
Subject: [PATCH 339/496] feat: add test for kw extractor

---
 tests/test_keyword_extractor.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/tests/test_keyword_extractor.py b/tests/test_keyword_extractor.py
index c4e83b3..66261d5 100644
--- a/tests/test_keyword_extractor.py
+++ b/tests/test_keyword_extractor.py
@@ -1,5 +1,5 @@
 import pytest
-from nautilus_nlp.analysis.keyword_extractor import get_frequent_words
+from nautilus_nlp.analysis.keyword_extractor import get_frequent_words, extract_keywords
 
 
 @pytest.mark.parametrize(
@@ -18,3 +18,17 @@ def test_get_frequent_words(input_tokens, expected, ngrams_number, number_top_wo
 def test_get_frequent_words_output_lenght():
     input_tokens = ['one', 'two', 'three', 'four', 'five', 'six', 'seven']
     assert len(get_frequent_words(input_tokens, number_top_words=5)) == 5
+
+
+@pytest.mark.parametrize(
+    "input_text, keyword, case_sensitive, expected",
+    [
+        ("I eat an orange oranges at Orange", 'orange', False, ['orange', 'orange']),
+        ("I eat an orange oranges at Orange", 'orange', True, ['orange']),
+        ("I eat an orange oranges at Orange", {'orange': ['orange', 'oranges']}, False, ['orange', 'orange', 'orange']),
+        ("I eat an orange oranges at Orange", {'orange': ['orange', 'oranges']}, True, ['orange', 'orange']),
+        ("I eat an orange oranges at Orange", {'fruit': ['orange', 'oranges']}, True, ['fruit', 'fruit']),
+    ]
+    )
+def test_extract_keywords(input_text, keyword, case_sensitive, expected):
+    assert extract_keywords(input_text, keyword=keyword, case_sensitive=case_sensitive) == expected
\ No newline at end of file

From 20bf151d43c2e7d710c8dba48d148804aa4e6337 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 18:42:30 +0200
Subject: [PATCH 340/496] refacto: add typing

---
 nautilus_nlp/analysis/ngrams.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/analysis/ngrams.py b/nautilus_nlp/analysis/ngrams.py
index 7369b93..89cbb13 100644
--- a/nautilus_nlp/analysis/ngrams.py
+++ b/nautilus_nlp/analysis/ngrams.py
@@ -16,15 +16,18 @@
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
-def create_ngrams(token_list, nb_elements):
+from typing import List, Generator
+
+
+def create_ngrams(tokens: List[str], nb_elements: int) -> Generator:
     """
     Create n-grams for list of tokens
 
     Parameters
     ----------
-    token_list : list
+    tokens : list
         list of strings
-    nb_elements :
+    nb_elements : int
         number of elements in the n-gram
 
     Returns
@@ -32,5 +35,5 @@ def create_ngrams(token_list, nb_elements):
     Generator
         generator of all n-grams
     """
-    ngrams = zip(*[token_list[index_token:] for index_token in range(nb_elements)])
+    ngrams = zip(*[tokens[index_token:] for index_token in range(nb_elements)])
     return (" ".join(ngram) for ngram in ngrams)

From 5f8d8f0875deee9bcf832db34125492258d23818 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 18:47:02 +0200
Subject: [PATCH 341/496] feat: add typing and docstring

---
 nautilus_nlp/analysis/text_summary.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/nautilus_nlp/analysis/text_summary.py b/nautilus_nlp/analysis/text_summary.py
index 6469729..d4b227d 100644
--- a/nautilus_nlp/analysis/text_summary.py
+++ b/nautilus_nlp/analysis/text_summary.py
@@ -15,10 +15,11 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from typing import Optional, Any
 from summa.summarizer import summarize
 
 
-def is_list_of_strings(lst):
+def is_list_of_strings(lst: Any) -> bool:
     """
     Parameters
     ----------
@@ -26,23 +27,26 @@ def is_list_of_strings(lst):
 
     Returns
     -------
-    book
-        boolean indicator
+    book : bool
+        True if is a list of strings, else returns False
     """
     return bool(lst) and isinstance(lst, list) and all(isinstance(elem, str) for elem in lst)
 
 
-def summarize_text(txt, ratio=0.2, language="english", nb_words=None):
+def summarize_text(
+    txt: str, ratio: float = 0.2, language: str = "english", nb_words: Optional[int] = None) -> str:
     """
+    This function uses the summa library to summazize text
+
     Parameters
     ----------
     txt : str
         Sting or list of strings containing text to summarize
     ratio : float
         Percentage giving the output text length in reference to the input length.
-    language :
+    language : str
         text language. eg. "english"
-    nb_words :
+    nb_words : int 
         number of words of the output text or None
 
     Returns
@@ -50,7 +54,6 @@ def summarize_text(txt, ratio=0.2, language="english", nb_words=None):
     string
         string containing the summarized text
     """
-
     if is_list_of_strings(txt):
         txt = ' '.join(txt)
     elif not isinstance(txt, str):

From 68a9824950e37b399f57d52b8d195c13d76bf88e Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 18:59:04 +0200
Subject: [PATCH 342/496] feat: add docstring and typing

---
 nautilus_nlp/analysis/visualize.py | 40 +++++++++++++++++++++++-------
 1 file changed, 31 insertions(+), 9 deletions(-)

diff --git a/nautilus_nlp/analysis/visualize.py b/nautilus_nlp/analysis/visualize.py
index dd16cfb..f933a59 100644
--- a/nautilus_nlp/analysis/visualize.py
+++ b/nautilus_nlp/analysis/visualize.py
@@ -15,15 +15,28 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from typing import List, Optional, Union
 from collections import Counter
 
 import matplotlib.pyplot as plt
 import wordcloud
+import warnings
 
 plt.rcParams["figure.figsize"] = [16, 9]
 
 
-def make_word_cloud(text_or_counter, stop_words=None):
+def make_word_cloud(text_or_counter: Union[str, list], stop_words: Optional[List[str]] = None):
+    '''
+    Prints a word cloud from a text, or a word count. 
+
+    Parameters
+    ----------
+    text_or_counter : Union[str, list]
+        The text or the Counter to be ploted as wordcloud. 
+        Example of counter: [('cat', 2), ('dog', 1)]
+    stop_words: Optional[List[str]]
+        List of words to be ignored
+    '''    
     if isinstance(text_or_counter, str):
         word_cloud = wordcloud.WordCloud(stopwords=stop_words).generate(text_or_counter)
     else:
@@ -35,14 +48,23 @@ def make_word_cloud(text_or_counter, stop_words=None):
     plt.show()
 
 
-def print_concordance(tokens, query_word, width=110, n_results=None):
+def print_concordance(
+        tokens: List[str], query_word: str, width: int = 110, n_results: Optional[int] = None):
     '''
-    Inputs a list of token and a query word, outputs all the sentences that
-    contains the query word, display in a nice way.
-    width = Integer. Number of caracters to display per text chunk
-    n_results = Integer. If not null, filters the number of results displayed.
-    This function is an adaptation of NLTK's print_concordance function.
-    Source: http://www.nltk.org/_modules/nltk/text.html
+    Inputs a list of token and a query word, and print all the sentences that contains the query\
+    word, display in a nice way. This function is an adaptation of NLTK's print_concordance\
+    function. Source: http://www.nltk.org/_modules/nltk/text.html
+
+    Parameters
+    ----------
+    tokens : list
+        list of words
+    query_word : str
+        the word to be searched for in the list of tokens
+    width : int
+        Number of caracters to be display per text chunk
+    n_results : Optional[int]
+        If specified, will print only the N results
     '''
     half_width = (width - len(query_word) - 2) // 2
     context = width // 4  # approx number of words of context
@@ -63,4 +85,4 @@ def print_concordance(tokens, query_word, width=110, n_results=None):
             line_print = ' '.join([left_print, query_word, right_print])
             print(line_print)
     else:
-        print('No match for "{}"'.format(query_word))
+        warnings.warn('No match for "{}"'.format(query_word))

From d15d3802021b71e936e6a66e3b93ba0d6e718e80 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 19:06:14 +0200
Subject: [PATCH 343/496] feat: add typing and docstr

---
 nautilus_nlp/preprocessing/data_augmentation.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 1263260..e05da45 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -1,3 +1,4 @@
+from typing import List, Optional
 import copy
 import logging
 import re
@@ -9,7 +10,8 @@ class CouldNotAugment(ValueError):
     pass
 
 
-def augment_utterance(text, method, stopwords, intent=None, entities=None):
+def augment_utterance(
+        text: str, method: str, stopwords: list[str], intent: Optional[str] = None, entities: Optional[list]=None) -> dict:
     """
     Given ``text`` str, create a new similar utterance by modifying some words
     in the initial sentence, modifications depend on the chosen method
@@ -19,7 +21,7 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
     Parameters
     ----------
     text : string
-    method : string
+    method : string {'wordnet_synonym', 'aug_sub_bert'}
         augmenter to use ('wordnet_synonym' or 'aug_sub_bert')
     stopwords : list
         list of words to freeze throughout the augmentation
@@ -40,24 +42,25 @@ def augment_utterance(text, method, stopwords, intent=None, entities=None):
 
     Returns
     -------
-    dictionary with augmented text and optional keys depending on input
+    dict
+        dictionary with augmented text and optional keys depending on input
     """
     new_utt = {}
-    augmenter = select_augmenter(method, stopwords)
+    augmenter = _select_augmenter(method, stopwords)
     new_utt['text'] = augmenter.augment(text)
     if intent is not None:
         new_utt['intent'] = intent
     if entities is not None:
         formatted_entities = [(text[entities[i]['startCharIndex']:entities[i]['endCharIndex']
                                     ].strip(), entities[i]['entity']) for i in range(len(entities))]
-        if are_entities_in_augmented_text(entities, new_utt['text']):
+        if _are_entities_in_augmented_text(entities, new_utt['text']):
             new_utt['entities'] = get_augmented_entities(new_utt['text'], formatted_entities)
             return clean_sentence_entities(new_utt)
         raise CouldNotAugment('Text was not correctly augmented so not added')
     return new_utt
 
 
-def are_entities_in_augmented_text(entities, augmented_text):
+def _are_entities_in_augmented_text(entities: list, augmented_text: str) -> bool:
     check = True
     for ent in entities:
         if ent['word'] not in augmented_text:
@@ -65,7 +68,7 @@ def are_entities_in_augmented_text(entities, augmented_text):
     return check
 
 
-def select_augmenter(method, stopwords, use_stopwords=True):
+def _select_augmenter(method, stopwords, use_stopwords=True):
     if not use_stopwords:
         stopwords = []
     if method == 'wordnet_synonym':

From e866ca3fb261aab8f91e518cc9f8739499385dca Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 19:17:08 +0200
Subject: [PATCH 344/496] feat: harmonize docstring + tying

---
 .../preprocessing/social_preprocess.py        | 16 +++++--
 nautilus_nlp/preprocessing/text_preprocess.py | 44 ++++++++++---------
 2 files changed, 35 insertions(+), 25 deletions(-)

diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index 8bab161..e903aa7 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -54,7 +54,8 @@ def extract_mentions(self) -> list:
 
         Returns
         -------
-        string
+        list
+            list of mentions
         """
         return constants.AT_PATTERN.findall(self.text)
 
@@ -69,6 +70,7 @@ def remove_html_tags(self) -> str:
         Returns
         -------
         string
+            text without HTML tags
         """
         self.text = self.normalize_whitespace(constants.HTML_TAG_PATTERN.sub('', self.text))
         return self.text
@@ -85,11 +87,13 @@ def remove_emoji(self) -> str:
 
         Returns
         -------
-        str
+        string
+            text without emojies
         """
         self.text = constants.EMOJI_PATTERN.sub("", self.text)
         return self.text
 
+
     def convert_emoji_to_text(self, code_delimiters=(':', ':'), input_str=None) -> str:
         """
         Convert emoji to their CLDR Short Name, according to the unicode convention
@@ -99,8 +103,12 @@ def convert_emoji_to_text(self, code_delimiters=(':', ':'), input_str=None) -> s
         Parameters
         ----------
         text : str
-            code_delimiters : tuple of symbols around the emoji code.
-            eg: (':',':') --> :grinning_face:
+        
+        code_delimiters : tuple
+            Tuple of symbols around the emoji code. eg: (':',':') --> :grinning_face:
+        
+        input_str : str
+            if specified, will remove emoji from this text rather than the class
 
         Returns
         -------
diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
index 5c112cb..18df85b 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -22,7 +22,7 @@
 
 import re
 import unicodedata
-
+from typing import Optional
 from ftfy import fix_text as _fix_text
 from nautilus_nlp.utils import constants
 from nautilus_nlp.utils.phone_number import \
@@ -38,7 +38,7 @@ def __init__(self, text):
         else:
             raise ValueError("Input must be a string")
 
-    def clean_text(self, lang='en') -> str:
+    def clean_text(self, lang: str = 'en') -> str:
         #TODO : check how to pipe operations
         stopwords = get_stopwords(lang)
         self.text = self.fix_bad_unicode(normalization="NFC")
@@ -71,7 +71,8 @@ def remove_stopwords(self, stopwords: list) -> str:
 
         Parameters
         ----------
-        stopwords : list of stopwords to remove
+        stopwords : list
+            list of stopwords to remove
 
         Returns
         -------
@@ -96,13 +97,14 @@ def fix_bad_unicode(self, normalization: str = "NFC") -> str:
         ----------
         text : string
 
-        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}):
+        normalization : string {'NFC', 'NFKC', 'NFD', 'NFKD'}
             if 'NFC', combines characters and diacritics written using separate code points,
             e.g. converting "e" plus an acute accent modifier into "é"; unicode
             can be converted to NFC form without any change in its meaning!
             if 'NFKC', additional normalizations are applied that can change
             the meanings of characters, e.g. ellipsis characters will be replaced
             with three periods
+
         Returns
         -------
         string
@@ -186,7 +188,7 @@ def replace_urls(self, replace_with: str = "*URL*") -> str:
         )
         return self.text
 
-    def replace_emails(self, replace_with="*EMAIL*") -> str:
+    def replace_emails(self, replace_with: str = "*EMAIL*") -> str:
         """
         Replace all emails in ``text`` str with ``replace_with`` str
 
@@ -203,9 +205,10 @@ def replace_emails(self, replace_with="*EMAIL*") -> str:
         self.text = constants.EMAIL_REGEX.sub(replace_with, self.text)
         return self.text
 
-    def replace_phone_numbers(self, country_format_to_detect: list,
-                              replace_with: str = "*PHONE*",
-                              method: str = "regex") -> str:
+    def replace_phone_numbers(self, 
+            country_format_to_detect: list,
+            replace_with: str = "*PHONE*",
+            method: str = "regex") -> str:
         """
         Replace all phone numbers in ``text`` str with ``replace_with`` str
 
@@ -214,12 +217,13 @@ def replace_phone_numbers(self, country_format_to_detect: list,
         text : string
         replace_with : string
             the string you want the phone number to be replaced with.
-        method : ['regex','detection']
+        method : string {'regex','detection'}
             regex is faster but will omit a lot of numbers, while detection will
             catch every numbers, but takes a while.
         country_format_to_detect : list
             If a list of country code is specified, will catch every number formatted.
             Only when method = 'detection'.
+
         Returns
         -------
         string
@@ -237,7 +241,7 @@ def replace_phone_numbers(self, country_format_to_detect: list,
             raise ValueError('Please input a valid method between "regex" or "detection"')
         return self.text
 
-    def replace_numbers(self, replace_with="*NUMBER*") -> str:
+    def replace_numbers(self, replace_with: str="*NUMBER*") -> str:
         """
         Replace all numbers in ``text`` str with ``replace_with`` str.
 
@@ -254,7 +258,7 @@ def replace_numbers(self, replace_with="*NUMBER*") -> str:
         self.text = constants.NUMBERS_REGEX.sub(replace_with, self.text)
         return self.text
 
-    def replace_currency_symbols(self, replace_with=None) -> str:
+    def replace_currency_symbols(self, replace_with: Optional[str] = None) -> str:
         """
         Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
 
@@ -262,11 +266,10 @@ def replace_currency_symbols(self, replace_with=None) -> str:
         ----------
         text : str
             raw text
-        replace_with : None or string
-            if None (default), replace symbols with
-                their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
-                otherwise, pass in a string with which to replace all symbols
-                (e.g. "*CURRENCY*")
+        replace_with : Optional[str]
+            if None (default), replace symbols with their standard 3-letter abbreviations \
+                (e.g. '$' with 'USD', '£' with 'GBP'); otherwise, pass in a string with \
+                    which to replace all symbols (e.g. "*CURRENCY*")
 
         Returns
         -------
@@ -279,7 +282,7 @@ def replace_currency_symbols(self, replace_with=None) -> str:
             self.text = constants.CURRENCY_REGEX.sub(replace_with, self.text)
         return self.text
 
-    def remove_punct(self, marks=None) -> str:
+    def remove_punct(self, marks: Optional[str] = None) -> str:
         """
         Remove punctuation from ``text`` by replacing all instances of ``marks``
         with whitespace.
@@ -288,8 +291,7 @@ def remove_punct(self, marks=None) -> str:
         ----------
         text : str
             raw text
-
-        marks : str or None
+        marks : Optional[str]
             If specified, remove only the characters in this string,
             e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
             Otherwise, all punctuation marks are removed.
@@ -320,7 +322,7 @@ def remove_accents(self, method: str = "unicode") -> str:
         text : str
             raw text
 
-        method : ({'unicode', 'ascii'})
+        method : str ({'unicode', 'ascii'})
             if 'unicode', remove accented
             char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
             remove accented char for any unicode symbol
@@ -379,7 +381,7 @@ def filter_non_latin_characters(self) -> str:
 
         Parameters
         ----------
-        text : string
+        text : str
 
         Returns
         -------

From 06db34fd2256b48cb496b9e14fcb15956c37d829 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 19:18:22 +0200
Subject: [PATCH 345/496] fix: small typo in docstring

---
 nautilus_nlp/preprocessing/token_preprocess.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/token_preprocess.py b/nautilus_nlp/preprocessing/token_preprocess.py
index b753564..174708a 100644
--- a/nautilus_nlp/preprocessing/token_preprocess.py
+++ b/nautilus_nlp/preprocessing/token_preprocess.py
@@ -36,7 +36,8 @@ def remove_stopwords(self, stopwords: list) -> str:
 
         Parameters
         ----------
-        stopwords : list of stopwords to remove
+        stopwords : list
+            List of stopwords to remove
 
         Returns
         -------

From d56c93923f532e2ede35e1fcd06e5be0b3b113df Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 19:25:45 +0200
Subject: [PATCH 346/496] feat: typing + docstring

---
 nautilus_nlp/topic_modeling/biterm_model.py | 58 ++++++++++-----------
 1 file changed, 29 insertions(+), 29 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/biterm_model.py b/nautilus_nlp/topic_modeling/biterm_model.py
index 22146ac..0d66b7b 100644
--- a/nautilus_nlp/topic_modeling/biterm_model.py
+++ b/nautilus_nlp/topic_modeling/biterm_model.py
@@ -15,6 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from typing import List, Any
 import numpy as np
 import pyLDAvis
 from biterm.btm import oBTM
@@ -26,7 +27,7 @@ class BitermModel:
 
     # pylint: disable=too-many-instance-attributes
 
-    def __init__(self, data, nb_topics, nb_iteration, lang):
+    def __init__(self, data: List[str], nb_topics: int, nb_iteration: int, lang: str):
         """
         Model for topic modelling. Particularly useful for short texts.
 
@@ -60,58 +61,59 @@ def __init__(self, data, nb_topics, nb_iteration, lang):
         self._vocabulary = None
 
     @staticmethod
-    def is_int_positive(number):
+    def is_int_positive(number: Any):
         """
         Function to check if the input parameter is a integer and positive
         otherwise raise an error
 
         Parameters
         ----------
-        number : str
-
-        Returns
-        -------
-        str:
-            the text with removed multiple spaces and strip text
+        number : Any
 
+        Raises
+        ------
+        ValueError
+            If the input is not a positive integer
         """
         if not isinstance(number, int):
-            raise ValueError("Parameter {} has to be an integer".format(number))
+            raise ValueError(f"Parameter {number} has to be an integer")
         if number < 1:
-            raise ValueError("Parameter {} has to be positive".format(number))
+            raise ValueError(f"Parameter {number} has to be positive")
 
     @staticmethod
-    def is_list_of_string(data):
+    def is_list_of_string(data: Any):
         """
         Function to check if the input parameter is a list of strings otherwise raise an error
 
         Parameters
         ----------
-        data
+        data: Any
 
-        Returns
-        -------
+        Raises
+        ------
+        ValueError
+            if data is not a list of string, or if is emply
         """
         if not isinstance(data, list):
-            raise ValueError("{} has to be a list".format(data))
+            raise ValueError(f"{data} has to be a list")
         if len(data) == 0:
-            raise ValueError("{} is empty".format(data))
+            raise ValueError(f"{data} is empty")
         for document in data:
             if not isinstance(document, str):
-                raise ValueError("All elements of {} have to be a string, problem with {}".format(data, document))
+                raise ValueError(f"All elements of {data} have to be a string, problem with {document}")
 
-    def compute_topics(self, nb_word_per_cluster):
+    def compute_topics(self, nb_word_per_cluster: int) -> dict:
         """
         Main function computing the topic modeling, topics
 
         Parameters
         ----------
-        nb_word_per_cluster : positive integer
+        nb_word_per_cluster : int
 
         Returns
         -------
-        dict :
-            a dictionary containing the the different topics with the top words
+        dict
+            A dictionary containing the the different topics with the top words
             and coherence associated
         """
         vec = CountVectorizer(stop_words=self.lang)
@@ -129,25 +131,26 @@ def compute_topics(self, nb_word_per_cluster):
 
         return results
 
-    def get_document_topic(self, index):
+    def get_document_topic(self, index: int) -> int:
         """
         Get the cluster associated to the specified document
 
         Parameters
         ----------
-        index : positive integer
-            the document index
+        index : int
+            The document index
 
         Returns
         -------
-        the cluster index
+        int
+            the cluster index
         """
         if self._topics is None:
             raise ValueError("Model needs to be trained first")
 
         return self._topics[index].argmax()
 
-    def save_pyldavis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.html'):
+    def save_pyldavis_plot_as_html(self, path_to_output: str = './biterm_pyLDAavis_plot.html'):
         """
         Function saving the pyLDAvis plot associated with the compute_topics function
 
@@ -155,9 +158,6 @@ def save_pyldavis_plot_as_html(self, path_to_output='./biterm_pyLDAavis_plot.htm
         ----------
         path_to_output : str
             path to save the plut, must be a html file
-
-        Returns
-        -------
         """
         if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
             raise ValueError("Model needs to be trained first")

From b787860646ef45d0435e4875a6f65d719a75c986 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 19:35:56 +0200
Subject: [PATCH 347/496] feat: harmonize docstrings

---
 nautilus_nlp/topic_modeling/lda.py | 128 +++++++++++++++++------------
 1 file changed, 77 insertions(+), 51 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/lda.py b/nautilus_nlp/topic_modeling/lda.py
index 3363805..6cee4fe 100644
--- a/nautilus_nlp/topic_modeling/lda.py
+++ b/nautilus_nlp/topic_modeling/lda.py
@@ -15,6 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from typing import List, Optional
 import logging
 import os
 
@@ -29,54 +30,60 @@
 logging.getLogger("gensim").setLevel(logging.WARNING)
 
 
-def create_dictionary(data):
+def create_dictionary(data: List[List[str]]) -> List[List[tuple]]:
     """
     Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
 
     Parameters
     ----------
-    data : list of list of tokens
+    data : list
+        list of list of tokens
 
     Returns
     -------
-    list of list of tuples
+    list
+        List of list of tuples
     """
     return gensim.corpora.Dictionary(data)
 
-def filter_extremes(dictionary, no_below=15, no_above=0.3, **kwargs):
+def filter_extremes(
+        dictionary: gensim.corpora.Dictionary, no_below: Optional[int]=15, no_above: Optional[float]=0.3, **kwargs) -> gensim.corpora.Dictionary:
     """
     Remove very rare and very common words
 
     Parameters
     ----------
-    dictionary: dictionary containing the number of times a word appears in the dataset set
-    no_below : int, optional
-    Keep tokens which are contained in at least `no_below` documents.
-    no_above : float, optional
-    Keep tokens which are contained in no more than `no_above` documents
-    (fraction of total corpus size, not an absolute number).
-
-    (Add to docstring) + other func
+    dictionary : dict 
+        dictionary containing the number of times a word appears in the dataset set
+    no_below : Optional[int]
+        Keep tokens which are contained in at least `no_below` documents.
+    no_above : Optional[float]
+        Keep tokens which are contained in no more than `no_above` documents. (fraction\
+            of total corpus size, not an absolute number).
+
+    Returns
+    -------
+    gensim.corpora.Dictionary
     """
     return dictionary.filter_extremes(no_below=no_below, no_above=no_above, **kwargs)
 
 
 def create_bow_corpus(data, dictionary):
-
     """
     Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
     The produced corpus is a mapping of (token_id, token_count).
 
     Parameters
     ----------
-    data : list of list of tokens
+    data : list 
+        list of list of tokens
 
     Returns
     -------
-    list of list of tuples
+    list
+        listof list of tuples
     """
-    texts = data
-    corpus = [dictionary.doc2bow(text) for text in texts]
+    corpus = [dictionary.doc2bow(text) for text in data]
     return corpus
 
 ### Find Number of topics
@@ -89,15 +96,22 @@ def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, s
 
     Parameters:
     ----------
-    dictionary : Gensim dictionary
+    dictionary : gensim.corpora.Dictionary
+        Gensim dictionary
     bow_corpus : Gensim bow corpus
-    texts : List of input texts
-    limit : Max num of topics
+    texts : list 
+        List of input texts
+    limit : int
+        Max number of topics
+    start : int
+    step : int
 
     Returns:
     -------
-    model_list : List of LDA topic models
-    coherence_values : Coherence values corresponding to the LDA model with respective number of topics
+    model_list : list
+        List of LDA topic models
+    coherence_values :
+        Coherence values corresponding to the LDA model with respective number of topics
     """
     coherence_values = []
     model_list = []
@@ -123,12 +137,15 @@ def plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4):
 
     Parameters:
     ----------
-    coherence_values : list of coherence scores for various number of topics
-    start : int. Min num of topics
-    limit : int. Max num of topics
+    coherence_values : list
+        list of coherence scores for various number of topics
+    start : int
+        Min num of topics
+    limit : int
+        Max num of topics
     step: int
 
-    Output:
+    Returns
     -------
     Lineplot
     """
@@ -150,21 +167,29 @@ def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
 
 ### LdaModel: Gensim & Mallet
 
-def train_lda_model(bow_corpus, dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
+def train_lda_model(bow_corpus, dictionary: gensim.corpora.Dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
     """ Train the lda model on the corpus
 
     Parameters
     ----------
-    bow_corpus : iterable of list of tokens. Stream of document vectors or sparse matrix of shape \
+    bow_corpus : list
+        iterable of list of tokens. Stream of document vectors or sparse matrix of shape \
     (num_terms, num_documents).
-    dictionary: corpora.Dictionary. Mapping from word IDs to words
+    dictionary: gensim.corpora.Dictionary
+        Mapping from word IDs to words
     num_topics: int
-    model : str. Precise the topic modeling model wanted, must be "gensim" or "mallet"
-    mallet_path: str, optionnal if model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file
+    model : str
+        Precise the topic modeling model wanted, must be "gensim" or "mallet"
+    mallet_path: Optional[str]
+        If model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file
 
     Returns
     -------
     gensim.ldamodel
+
+    Raises
+    ------
+    ValueError
     """
     if model == 'gensim':
         model = train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs)
@@ -200,7 +225,7 @@ def save_model(model, model_name):
     Parameters
     ----------
     model: ldamodel
-    model_name: str.
+    model_name: str
         Name the model that will be saved
     """
     return model.save(os.path.join(model_name))
@@ -220,13 +245,6 @@ def load_model(model_path, model_name, model='gensim', model_prefix='composant')
         Precise the topic modeling model wanted, must be "gensim" or "mallet"
     model_prefix : str
         By default, 'composant' default prefix used while saving the mallet model with train_lda_model function.
-
-    Returns
-    -------
-    is_reliable :
-        is the top language is much better than 2nd best language?
-    language:
-        2-letter code for the language of the text
     """
     if model == 'gensim':
         ldamodel = gensim.models.LdaModel.load(os.path.join(model_path, model_name))
@@ -253,14 +271,19 @@ def visualize_topics(model, bow_corpus, dictionary, model_type=None):
 
     Parameters
     ----------
-    model: LDA model: gensim or mallet
-    bow_corpus : iterable of list of tokens.
-    dictionary: corpora.Dictionary. Dictionary encapsulates the mapping between normalized words and their integer ids.
-    model : str. Precise the topic modeling model used, must be "gensim" or "mallet"
+    model: 
+        LDA model: gensim or mallet
+    bow_corpus : list
+        iterable of list of tokens.
+    dictionary: corpora.Dictionary
+        Dictionary encapsulates the mapping between normalized words and their integer ids.
+    model : str
+        Precise the topic modeling model used, must be "gensim" or "mallet"
 
-    Returns:
-    ----------
-    3D interactive chart
+    Returns
+    -------
+    pyLDAvis
+        3D interactive chart
     """
     if model_type == 'mallet':
         model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
@@ -304,11 +327,14 @@ def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
 
     Parameters
     ----------
-    gensim.ldamodel
-    model: ldamodel
-    bow_corpus: iterable of list of tokens.
-    topic_number: int. Pick the number of topics displayed
-    topn: int. Number of topics' top keyword displayed
+    model
+        gensim.ldamodel
+    bow_corpus : list
+        iterable of list of tokens.
+    topic_number: int
+        Pick the number of topics displayed
+    topn : int 
+        Number of topics' top keyword displayed
 
     """
     i = 0

From 770ba5bfa49f7d06e8af4ef59877ea278977e7eb Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 19:38:03 +0200
Subject: [PATCH 348/496] feat: harmonize docstring

---
 nautilus_nlp/topic_modeling/nmf_model.py | 37 +++++++++++++++++-------
 1 file changed, 26 insertions(+), 11 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/nmf_model.py b/nautilus_nlp/topic_modeling/nmf_model.py
index 1a9f38d..3bb3ac1 100644
--- a/nautilus_nlp/topic_modeling/nmf_model.py
+++ b/nautilus_nlp/topic_modeling/nmf_model.py
@@ -29,17 +29,28 @@ def __init__(
             self, mat_a, mat_iw, mat_ih, n_topic=10,
             max_iter=100, max_err=1e-3, rand_init=True):
         """
-        The objective of the NMF model is to approximate the term-document matrix A by two lower-rank matrices W and H.
-        The process is iterative and we denote IW and IH the the matrix W and H that are updated at each step.
-
-        :param mat_a: The term-document matrix
-        :param mat_iw: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+        The objective of the NMF model is to approximate the term-document matrix A by two \
+            lower-rank matrices W and H.
+        The process is iterative and we denote IW and IH the the matrix W and H that are \
+            updated at each step.
+
+        Parameters
+        ----------
+        mat_a
+            The term-document matrix
+        mat_iw
+            topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
         and its elements are the weights of the corresponding keywords.
-        :param mat_ih: The row vector H(j,:) is the latent representation for document j in terms of K topics
-        :param n_topic: Number of selected topics
-        :param max_iter: Maximum number of iterations to update W and H
-        :param max_err: maximum error under which we consider that the loop converged
-        :param rand_init: random init boolean
+        mat_ih
+            The row vector H(j,:) is the latent representation for document j in terms of K topics
+        n_topic
+            Number of selected topics
+        max_iter
+            Maximum number of iterations to update W and H
+        max_err
+            maximum error under which we consider that the loop converged
+        rand_init : bool
+            random init boolean
         """
         self.mat_a = mat_a
         self.mat_iw = mat_iw
@@ -58,7 +69,11 @@ def __init__(
     def nmf_mat_init(self, rand_init):
         """
         Init Matrices W and H initially either randomly or using existing IW, IH matrices taken when iterating.
-        :param rand_init: Boolean indicating initial random init
+
+        Parameters
+        ----------
+        rand_init : Boolean
+            Indicating initial random init
         """
         if rand_init:
             self.mat_w = np.random.random((self.n_row, self.n_topic))

From 90e1d0909dfa85e34f4b3f203f8ddc49c46f9914 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 16 Oct 2020 23:29:14 +0200
Subject: [PATCH 349/496] refacto: harmonize docstring + typing

---
 nautilus_nlp/topic_modeling/seanmf_model.py   |  45 +++--
 .../topic_modeling_short_text.py              | 187 +++++++++++-------
 nautilus_nlp/utils/file_loader.py             |  98 +++++----
 nautilus_nlp/utils/phone_number.py            |  51 +++--
 nautilus_nlp/utils/stopwords.py               |   8 +-
 nautilus_nlp/utils/tokenizer.py               |  36 +++-
 tests/test_topic_modeling_short_text.py       |   2 +-
 7 files changed, 284 insertions(+), 143 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/seanmf_model.py b/nautilus_nlp/topic_modeling/seanmf_model.py
index 9513435..0fbb584 100644
--- a/nautilus_nlp/topic_modeling/seanmf_model.py
+++ b/nautilus_nlp/topic_modeling/seanmf_model.py
@@ -33,19 +33,34 @@ def __init__(
         Seanmf is a topic modeling algorithm, paper:  http://dmkd.cs.vt.edu/papers/WWW18.pdf.
         It finds an approximation to the term-document matrix A by two lower-rank matrices W and H,
         at each iteration a context matrix Wc are computed and used to update W.
-        :param mat_a: document term matrix
-        :param mat_s: Word-context (semantic) correlation matrix
-        :param mat_iw: topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
+
+        Parameters
+        ----------
+        mat_a
+            document term matrix
+        mat_s
+            Word-context (semantic) correlation matrix
+        mat_iw
+            topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
         and its elements are the weights of the corresponding keywords.
-        :param mat_iwc: Latent factor matrix of contexts.
-        :param mat_ih: The row vector H(j,:) is the latent representation for document j in terms of K topics
-        :param alpha: Seanmf algorithm parameter
-        :param beta: Seanmf algorithm parameter
-        :param n_topic: Number of selected topics
-        :param max_iter: Maximum number of iterations to update W and H
-        :param max_err: maximum error under which we consider that the loop converged
-        :param rand_init: random init boolean
-        :param fix_seed: int number to fix random seed.
+        mat_iwc
+            Latent factor matrix of contexts.
+        mat_ih
+            The row vector H(j,:) is the latent representation for document j in terms of K topics
+        alpha
+            Seanmf algorithm parameter
+        beta
+            Seanmf algorithm parameter
+        n_topic
+            Number of selected topics
+        max_iter
+            Maximum number of iterations to update W and H
+        max_err
+            maximum error under which we consider that the loop converged
+        rand_init
+            random init boolean
+        fix_seed
+            int number to fix random seed.
         """
         if fix_seed:
             np.random.seed(0)
@@ -68,7 +83,11 @@ def __init__(
     def snmf_mat_init(self, rand_init, mat_iw, mat_iwc, mat_ih):
         """
         Init Matrices W,Wc and H initially either randomly or using existing IW,IWc IH matrices taken when iterating.
-        :param rand_init: Boolean indicating initial random init
+        
+        Parameters
+        ----------
+        rand_init : bool
+            Boolean indicating initial random init
         """
         if rand_init:
             self.mat_w = np.random.random((self.n_row, self.n_topic))
diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
index a31a772..5402937 100644
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -25,14 +25,25 @@
 from nautilus_nlp.topic_modeling.seanmf_model import SeaNMF
 
 
-def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
+def prepare_data(text: str, vocab_min_count: int = 1, vocab_max_size: int = 10000):
     """
-    :param text: list of str on which the topic modeling will be performed
-    :param vocab_min_count: minimum number of occurrences of a word to be considered in the vocabulary
-    :param vocab_max_size: maximum number of word in the vocabulary
-    :return:  encoded_text_id: list of encoded sentences using vocab IDs
-              vocab_list: list of vocabulary
-              vocab_arr: array with vocab frequency counts
+    Parameters
+    ----------
+    text : str
+        list of str on which the topic modeling will be performed
+    vocab_min_count : int
+        minimum number of occurrences of a word to be considered in the vocabulary
+    vocab_max_size : int
+        maximum number of word in the vocabulary
+
+    Returns
+    -------        
+    list
+        list of encoded sentences using vocab IDs
+    list
+        list of vocabulary
+    Array
+        array with vocab frequency counts
     """
     vocab = {}
 
@@ -64,18 +75,35 @@ def prepare_data(text, vocab_min_count=1, vocab_max_size=10000):
 
 
 def train_shorttext_model(
-    model_name, encoded_text_id, vocab_list, n_topics=20,
-    max_iter=20, max_err=0.1, alpha=0, beta=0):
+    model_name: str, encoded_text_id: list, vocab_list: list, n_topics: int = 20,
+    max_iter: int = 20, max_err: float = 0.1, alpha: float = 0, beta: float = 0):
     """
-    :param model_name: string = 'nmf' or 'seanmf'
-    :param encoded_text_id: list of encoded sentences
-    :param vocab_list: list of vocabulary
-    :param n_topics: number of topics
-    :param max_iter: maximum number of iterations while training
-    :param max_err: training error
-    :param alpha: regularization param for the NMF model
-    :param beta: regularization param for the NMF model
-    :return: Trained NMF model
+    Parameters
+    ----------    
+    model_name : str {'nmf','seanmf'}
+    encoded_text_id : list
+        list of encoded sentences
+    vocab_list : list
+        list of vocabulary
+    n_topics : int 
+        number of topics
+    max_iter : int 
+        maximum number of iterations while training
+    max_err : float
+        training error
+    alpha : float
+        regularization param for the NMF model
+    beta : float
+        regularization param for the NMF model
+
+    Returns
+    -------        
+    Trained NMF model
+
+    Raises
+    ------
+    ValueError
+        If model_name is not valid
     """
 
     n_docs = len(encoded_text_id)
@@ -83,7 +111,7 @@ def train_shorttext_model(
 
     if model_name == 'nmf':
         dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
-        model = NMF(
+        return NMF(
             dt_mat,
             mat_iw=[],
             mat_ih=[],
@@ -98,7 +126,7 @@ def train_shorttext_model(
         mat_ss = __calculate_ppmi(cooc_mat, n_terms)
         # Build doc-term matrix
         dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
-        model = SeaNMF(
+        return SeaNMF(
             dt_mat, mat_ss,
             mat_iw=[],
             mat_iwc=[],
@@ -109,24 +137,31 @@ def train_shorttext_model(
             max_iter=max_iter,
             max_err=max_err,
             fix_seed=1024)
+    raise ValueError("Invalid model name: Use nmf or seanmf")
 
-    else:
-        model = None
-        print('Invalid model name: Use nmf or seanmf')
 
-    return model
-
-
-def show_dominant_topic(model, encoded_text_id, vocab_list, n_top_keyword=10):
+def show_dominant_topic(model, encoded_text_id: list, vocab_list: list, n_top_keyword: int = 10):
     """
     Computes the PMi score for each topic and the topKeywords describing each of them.
-    :param model: trained NMF model
-    :param encoded_text_id: list of encoded sentences
-    :param vocab_list: list of vocabulary
-    :return: topics = dictionnary with the topic number and its topkeywords
-             pmi_score = dictionnary with the topic number and its PMI score
-    """
 
+    Parameters
+    ----------    
+    - model
+        trained NMF model
+    - encoded_text_id : list
+        list of encoded sentences
+    - vocab_list : list
+        list of vocabulary
+    - n_top_keyword : list
+        the number of keywords to be returned
+
+    Returns
+    -------    
+    dict
+        A dictionnary with the topic number and its top keywords
+    dict 
+        A ictionnary with the topic number and its PMI score
+    """
     dt_mat = __build_cooccurence_matrix(n_terms=len(vocab_list), encoded_text_id=encoded_text_id)
     np.fill_diagonal(dt_mat, 0)
     mat_w, _ = model.get_decomposition_matrix()
@@ -153,8 +188,17 @@ def show_dominant_topic(model, encoded_text_id, vocab_list, n_top_keyword=10):
 def get_assigned_topics(model):
     """
     Assign the topic number to the sentences used when training the model
-    :param model: trained model for short text
-    :return topics_list: list having the same length as the training text containing topics assigned to each sentence.
+
+    Parameters
+    ----------    
+    model
+        trained model for short text
+    
+    Returns
+    -------
+    list
+        list of topics. Having the same length as the training text containing topics assigned \
+        to each sentence.
     """
 
     _, mat_h = model.get_decomposition_matrix()
@@ -167,10 +211,18 @@ def get_assigned_topics(model):
 
 def show_pyldavis(model, encoded_text_id, vocab_arr):
     """
-    :param model: trained model
-    :param encoded_text_id: encoded_text_id: list of encoded sentences
-    :param vocab_arr: array of vocabulary frequency
-    :return: pyldavis topics plot
+    Parameters
+    ----------    
+    model
+        trained model
+    encoded_text_id
+        encoded_text_id: list of encoded sentences
+    vocab_arr
+        array of vocabulary frequency
+
+    Returns
+    -------        
+    pyldavis topics plot
     """
 
     data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
@@ -179,49 +231,53 @@ def show_pyldavis(model, encoded_text_id, vocab_arr):
     return pyLDAvis.display(vis_data)
 
 
-def prepare_data_pyldavis(model, encoded_text_id, vocab_arr):
+def prepare_data_pyldavis(model, encoded_text_id, vocab_arr) -> dict:
     """
     Transform the model decomposed matrix to create topic term and document topics matrices
     and prepare data to feed pyldavis.
     link : http://jeriwieringa.com/2018/07/17/pyLDAviz-and-Mallet/
-    :return dict of data needed by pyldavis
-    """
 
-    # 1 List of documents lengths
-    doc_length_values = []
-    for doc in encoded_text_id:
-        doc_length_values.append(len(doc))
-    # 2 List of vocab
+    Returns
+    -------       
+    dict
+        dict of data needed by pyldavis
+    """
+    doc_length_values = [len(doc) for doc in encoded_text_id]
     list_vocab = list(map(lambda x: x[0], vocab_arr))
-    # 3 List of vocab. Frequency
     freq_vocab = list(map(lambda x: x[1], vocab_arr))
     mat_w, mat_h = model.get_decomposition_matrix()
     # Normlize the decomposition to get probabilities
     w_probs = mat_w / mat_w.sum(axis=1, keepdims=True)
-    # 4 topic term matrix phi
+    # Topic-term matrix phi
     phi = w_probs.T
-    # 5 document term matrix theta
+    # Document-term matrix theta
     theta = mat_h / mat_h.sum(axis=1, keepdims=True)
-
-    data = {'topic_term_dists': phi,
-            'doc_topic_dists': theta,
-            'doc_lengths': doc_length_values,
-            'vocab': list_vocab,
-            'term_frequency': freq_vocab
-            }
-
-    return data
+    return {
+        'topic_term_dists': phi,
+        'doc_topic_dists': theta,
+        'doc_lengths': doc_length_values,
+        'vocab': list_vocab,
+        'term_frequency': freq_vocab
+        }
+     
 
 
-def __build_cooccurence_matrix(n_terms, encoded_text_id):
+def __build_cooccurence_matrix(n_terms: int, encoded_text_id: list):
     """
     The cooccurence matrix represents the number of times each word
     appeared in the same context as another word from the vocabulary.
     The matrix has n_terms x n_terms size, columns and rows denote the vocab.
     Cell values represent the number of times words occured together in the same sentence.
-    :param :encoded_text_id : list of encoded sentences
-    :return: res: the co-occurence matrix
-
+    
+    Parameters
+    ----------
+    n_terms : int
+    encoded_text_id : list
+        list of encoded sentences
+    
+    Returns
+    -------
+    co-occurence matrix
     """
     res = np.zeros([n_terms, n_terms])
     for row in encoded_text_id:
@@ -259,10 +315,8 @@ def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
 def __calculate_pmi(mat_aa, top_keywords_index):
     '''
     Method to compute PMi score
-    Reference:
-    Short and Sparse Text Topic Modeling via Self-Aggregation
+    Reference: Short and Sparse Text Topic Modeling via Self-Aggregation
     '''
-
     mat_d1 = np.sum(mat_aa)
     n_tp = len(top_keywords_index)
     mat_pmi = []
@@ -276,5 +330,4 @@ def __calculate_pmi(mat_aa, top_keywords_index):
                     mat_c2 = np.sum(mat_aa[index2])
                     mat_pmi.append(np.log(mat_aa[index1,index2]*mat_d1/mat_c1/mat_c2))
     avg_pmi = 2.0*np.sum(mat_pmi)/float(n_tp)/(float(n_tp)-1.0)
-
     return avg_pmi
diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 5bbcc80..ece5a4d 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -23,17 +23,18 @@
 import re
 import shutil
 import warnings
+from typing import Optional, Union
 
 import chardet
 
 
-def open_textfile(filepath, encoding='utf-8'):
+def open_textfile(filepath: str, encoding='utf-8'):
     with io.open(filepath, 'r', encoding=encoding) as f:
         string = f.read()
     return string
 
 
-def detect_encoding(file_path_or_string, n_lines=100):
+def detect_encoding(file_path_or_string: str, n_lines: int = 100) -> str:
     """
     Predict a file's encoding using chardet
 
@@ -46,7 +47,8 @@ def detect_encoding(file_path_or_string, n_lines=100):
 
     Returns
     -------
-    the code of the detected encoding
+    string
+        the code of the detected encoding
     """
     if os.path.isfile(file_path_or_string):
         with open(file_path_or_string, 'rb') as f:
@@ -56,22 +58,29 @@ def detect_encoding(file_path_or_string, n_lines=100):
     return chardet.detect(rawdata)
 
 
-def text_loader(filepath, encoding=None, detectencoding=True):
+def text_loader(filepath: str, encoding: Optional[str] = None, detectencoding: bool = True) -> str:
     '''
     This util loads a file. If the encoding is specified, will use the specified
     encoding to load the text file.
     If not specified, this function tries to open the doc as UTF-U, and if
     it fails it will try to detect the encoding using **detect_encoding**
+
     Parameters
     ----------
     filepath : str
     encoding : str
         If the encoding is specified, will use the specified encoding to load the text file.
-    detect_encoding : bool
-    If file is not encoded into UTF-8, try to detect encoding using the chardet library.
+    detectencoding : bool
+        If file is not encoded into UTF-8, try to detect encoding using the chardet library.
+
     Returns
     -------
     string
+
+    Raises
+    ------
+    UnicodeDecodeError
+        if document can't be loaded with utf-8 encoding.
     '''
     if encoding is not None:
         return open_textfile(filepath, encoding=encoding)
@@ -88,13 +97,16 @@ def text_loader(filepath, encoding=None, detectencoding=True):
                                     'Try to detect encoding using detectencoding=True')
 
 
-def get_subfolders_path(folder):
+def get_subfolders_path(folder: str) -> list:
     if not folder.endswith("/"):
         folder = folder + "/"
-    return [folder + f+'/' for f in os.listdir(folder)if os.path.isdir(os.path.join(folder, f)) and f != ".DS_Store"]
+    return [
+        folder + f+'/' for f in os.listdir(folder) 
+        if os.path.isdir(os.path.join(folder, f)) and f != ".DS_Store"
+        ]
 
 
-def list_files_in_subdir(filepath):
+def list_files_in_subdir(filepath: str) -> list:
     '''
     Get a list of all the filepath of files in directory and subdirectory.
     '''
@@ -105,11 +117,19 @@ def list_files_in_subdir(filepath):
     return res
 
 
-def list_files(filepath: str):
+def list_files(filepath: str) -> list[str]:
     """
-    inputs a filepath.
-    Outputs a list of filepath.
-    Supports regex
+    List files within a given filepath. 
+
+    Parameters
+    ----------
+    filepath : str
+        Supports wildcard "*" character.
+
+    Returns
+    -------
+    list
+        list of filepaths
     """
 
     if os.path.isdir(filepath) and len(re.findall(r"[\w.]$", filepath)) > 0:
@@ -119,28 +139,32 @@ def list_files(filepath: str):
     return [file for file in glob.glob(filepath) if os.path.isfile(file)]
 
 
-def documents_loader(filepath: str, encoding=None, detectencoding=True, output_as='dict'):
+def documents_loader(
+        filepath: str,
+        encoding: Optional[str] = None,
+        detectencoding: bool = True,
+        output_as: str = 'dict'
+        ) -> Union[str, list, dict]:
     '''
     Input a filepath, a filepath with wildcard (eg. *.txt),
-    or a list of filepaths.
-    Output a string, or a dict of strings.
+    or a list of filepaths. Output a string, or a dict of strings.
+
     Parameters
     ----------
-    filepath: filepath
+    filepath : str
         A filepath with wildcard (eg. *.txt), or a list of filepaths.
-    output_as: list or dict.
-        If dict, key will be the filename.
-    encoding:
+    encoding : Optional[str] 
         if not specified, will try to detect encoding except if detectencoding is false.
     detectencoding : bool
         if True and if encoding is not specified, will try to detect encoding using chardet.
+    output_as: str {list, dict}
+        If dict, key will be the filename.
 
     Returns
     -------
-    string
-    the document loaded
+    Uniont[string, list, dict]
+        The document loaded.
     '''
-
     if isinstance(filepath, str):
         documents = list_files(filepath)
         nb_of_documents = len(documents)
@@ -154,18 +178,21 @@ def documents_loader(filepath: str, encoding=None, detectencoding=True, output_a
         return text_loader(documents[0], encoding=encoding, detectencoding=detectencoding)
     if nb_of_documents > 1:
         if output_as == 'list':
-            return [text_loader(document, encoding=encoding, detectencoding=detectencoding) for document in documents]
+            return [
+                text_loader(document, encoding=encoding, detectencoding=detectencoding) 
+                for document in documents
+                ]
         if output_as == 'dict':
-            return {document : text_loader(
-                document, encoding=encoding, detectencoding=detectencoding) for document in documents}
+            return {
+                document : text_loader(
+                document, encoding=encoding, detectencoding=detectencoding) for document in documents
+                }
         raise TypeError('Enter a valid output format between list or dict')
     raise IOError('No files detected in {}'.format(filepath))
 
-
-#################
 ## CSV Loader
 
-def encode_columns(df, columns_to_encode):
+def encode_columns(df, columns_to_encode: str):
     '''
     apply json.dumps on columns
     '''
@@ -173,21 +200,20 @@ def encode_columns(df, columns_to_encode):
         df[col] = df[col].apply(json.dumps)
 
 
-def decode_columns(df, columns_to_encode):
+def decode_columns(df, columns_to_encode: str):
     '''
     apply json.loads on columns
     '''
     for col in columns_to_encode:
         df[col] = df[col].apply(json.loads)
 
-
-#################
 ## Encoding functions
-def convert_encoding(file_path, input_encoding, output_encoding):
+
+def convert_encoding(filepath: str, input_encoding: str, output_encoding: str):
     '''
-    Encode a file according to a specified encoding
+    Encode a file according to a specified encoding.
     '''
-    with codecs.open(file_path, encoding=input_encoding) as input_file:
+    with codecs.open(filepath, encoding=input_encoding) as input_file:
         with codecs.open(
-                'encoded_'+file_path, "w", encoding=output_encoding) as output_file:
+                'encoded_'+filepath, "w", encoding=output_encoding) as output_file:
             shutil.copyfileobj(input_file, output_file)
diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 66571ca..baff7f6 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -15,6 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from typing import Optional
 import phonenumbers as _phonenumbers
 
 SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM',
@@ -46,32 +47,38 @@
     "RFC3966": _phonenumbers.PhoneNumberFormat.RFC3966
 }
 
-def find_phone_numbers(string, region_code=None):
+def find_phone_numbers(string: str, region_code: Optional[str] = None) -> str:
     """
     Python port of Google's libphonenumber.
     https://github.com/daviddrysdale/python-phonenumbers
 
     Parameters
     ----------
-    region_code
+    region_code : str
         If specified, will find the number of the specified country.
     eg. 06.00.00.00.00 if "FR" is specified.
 
     If not specified, only works for international-formatted phone numbers.
     - ie. phone number with +country code specified
     eg. 06.00.00.00.00 will return an error but +33 6 00 00 00 00 will work.
+    supported value: look SUPPORTED_COUNTRY variable.
 
-    region_code
-        supported value: look SUPPORTED_COUNTRY variable.
+    Returns
+    -------
+    list
+        list of matched phone numbers.
 
+    Raises
+    ------
+    ValueError
+        if country code is not supported.
     """
     if region_code not in SUPPORTED_COUNTRY:
         raise ValueError('Please enter a valid contry code. See SUPPORTED_COUNTRY list.')
-
     return [match.raw_string for match in _phonenumbers.PhoneNumberMatcher(string, region_code)]
 
 
-def extract_phone_numbers(text: str, countrylist: list)->list:
+def extract_phone_numbers(text: str, countrylist: list) -> list:
     '''
     Find phone numbers in a text, returns a list of phone numbers.
 
@@ -80,6 +87,11 @@ def extract_phone_numbers(text: str, countrylist: list)->list:
     countrylist: list (eg. [None,'FR','US','GB'])
         Look for phone numbers formatted according to the specified countlist.
         supported value: look SUPPORTED_COUNTRY variable.
+
+    Returns
+    -------
+    list
+        List of unique phone numbers found.
     '''
     all_phone_numbers = []
     for country in countrylist:
@@ -99,20 +111,24 @@ def __init__(self):
         self.text = None
         self.parsed_num = None
 
-    def parse_number(self, text: str, region_code=None):
+    def parse_number(self, text: str, region_code: Optional[str] = None) -> str:
         '''
+        Extract phone number from text
+
         Parameters
         ----------
-        region_code
+        region_code : Optional[str] 
             If specified, will find the number of the specified country.
         eg. 06.00.00.00.00 if "FR" is specified.
-
         If not specified, only works for international-formatted phone numbers.
         - ie. phone number with +country code specified
         eg. 06.00.00.00.00 will return an error but +33 6 00 00 00 00 will work.
+        supported value: look SUPPORTED_COUNTRY variable.
 
-        region_code
-            supported value: look SUPPORTED_COUNTRY variable.
+        Returns
+        -------
+        str
+            The parsed number
 
         Raises
         ------
@@ -125,9 +141,18 @@ def parse_number(self, text: str, region_code=None):
         return self.parsed_num
 
 
-    def format_number(self, num_format):
+    def format_number(self, num_format: str) -> str:
         '''
-        ['E164','INTERNATIONAL','NATIONAL','RFC3966']
+        Convert a phone number to another standard format. 
+
+        Parameters
+        ----------
+        num_format : str {'E164','INTERNATIONAL','NATIONAL','RFC3966'}
+
+        Returns
+        -------
+        str
+            Number formatted
         '''
         standard_format = FORMAT_NUMBERS.get(num_format)
         if standard_format is None:
diff --git a/nautilus_nlp/utils/stopwords.py b/nautilus_nlp/utils/stopwords.py
index c94e812..f34d4b8 100644
--- a/nautilus_nlp/utils/stopwords.py
+++ b/nautilus_nlp/utils/stopwords.py
@@ -17,14 +17,16 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 # -*- coding: utf-8 -*-
 
-from __future__ import absolute_import, division, print_function, unicode_literals
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
 
 import json
 import os
-from stop_words import get_stop_words as _get_stop_words
-from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
+
 from nautilus_nlp.config.config import ROOT_FOLDER
 from nautilus_nlp.utils.file_loader import documents_loader
+from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
+from stop_words import get_stop_words as _get_stop_words
 
 STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
 
diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index f06def1..92c6217 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -15,6 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from typing import List
 import nltk
 from sacremoses import MosesTokenizer, MosesDetokenizer
 import spacy
@@ -47,26 +48,31 @@ def get_lang_model(self):
         return self.model.lang
 
 
-def _get_spacy_tokenizer(lang):
+def _get_spacy_tokenizer(lang: str) -> spacy.tokenizer.Tokenizer:
     """
     Function that gets the right tokenizer given the language
-    :param str lang: language in which text is written
-                     Languages handled : ["en", "fr", "ko", "ja"]
 
-    :return: spacy tokenizer
-    :rtype: spacy.tokenizer.Tokenizer
+    Parameters
+    ----------
+    lang : str
+        Language in which text is written. Languages handled : ["en", "fr", "ko", "ja"]
+
+    Returns
+    -------
+    spacy.tokenizer.Tokenizer
+        spacy tokenizer
     """
     model = SpacyModel(lang).model
     return model.tokenizer
 
 
-def tokenize(text: str, lang_module: str = 'en_spacy'):
+def tokenize(text: str, lang_module: str = 'en_spacy') -> List[str]:
     """
     Convert text to a list of tokens.
 
     Parameters
     ----------
-    lang_module : ({'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses', 'ko_spacy', 'ja_spacy'})
+    lang_module : str {'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses', 'ko_spacy', 'ja_spacy'}
         choose the tokenization module according to the langage and the implementation.
         Recommanded: Spacy (faster, better results). To process other langages
         import models.Spacy_models
@@ -75,6 +81,11 @@ def tokenize(text: str, lang_module: str = 'en_spacy'):
     -------
     list
         list of string
+
+    Raises
+    ------
+    ValueError
+        If lang_module is not a valid module name
     """
     if "spacy" in lang_module:
         lang = lang_module.split()[0]
@@ -89,7 +100,7 @@ def tokenize(text: str, lang_module: str = 'en_spacy'):
                      "{'en_spacy', 'en_nltk', 'fr_spacy', 'fr_moses', 'ko_spacy', 'ja_spacy'}")
 
 
-def untokenize(tokens, lang='fr'):
+def untokenize(tokens: List[str], lang: str = 'fr') -> str:
     '''
     Inputs a list of tokens output string.
     ["J'", 'ai'] >>> "J' ai"
@@ -98,13 +109,18 @@ def untokenize(tokens, lang='fr'):
     ----------
     lang : string
         language code
+
+    Returns
+    -------
+    string
+        text
     '''
     d = MosesDetokenizer(lang=lang)
     text = d.detokenize(tokens, unescape=False)
     return text
 
 
-def _convert_tokens_to_string(tokens_or_str):
+def convert_tokens_to_string(tokens_or_str):
     if isinstance(tokens_or_str, str):
         return tokens_or_str
     if isinstance(tokens_or_str, list):
@@ -114,7 +130,7 @@ def _convert_tokens_to_string(tokens_or_str):
     raise TypeError('Please input string or tokens')
 
 
-def _convert_string_to_tokens(tokens_or_str, lang_module='en_spacy'):
+def convert_string_to_tokens(tokens_or_str, lang_module='en_spacy'):
     if isinstance(tokens_or_str, str):
         return tokenize(tokens_or_str, lang_module=lang_module)
     if isinstance(tokens_or_str, list):
diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index 2265738..5d8ad7a 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -51,7 +51,7 @@
                           (['', ''],
                            []), ],)
 def test_prepare_data(input_text, expected_output):
-    assert prepare_data(input_text), expected_output
+    assert prepare_data(input_text) == expected_output
 
 
 @pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])

From 96bafb9f68afb0a2fd223ec098c2d75805f105dc Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sun, 18 Oct 2020 18:07:45 +0200
Subject: [PATCH 350/496] refacto: add f strings

---
 nautilus_nlp/analysis/visualize.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/analysis/visualize.py b/nautilus_nlp/analysis/visualize.py
index f933a59..9555224 100644
--- a/nautilus_nlp/analysis/visualize.py
+++ b/nautilus_nlp/analysis/visualize.py
@@ -70,10 +70,11 @@ def print_concordance(
     context = width // 4  # approx number of words of context
 
     results = [i for i, j in enumerate(tokens) if j == query_word]
-    if len(results) > 0:
+    nb_results = len(results)
+    if nb_results > 0:
         if n_results is None:
-            n_results = len(results)
-        print('{} matches for "{}":'.format(len(results), query_word))
+            n_results = nb_results
+        print(f'{nb_results} matches for "{query_word}":')
         for i in results[:n_results]:
             # Find the context of query word.
             left_context = tokens[max(0, i - context): i]
@@ -85,4 +86,4 @@ def print_concordance(
             line_print = ' '.join([left_print, query_word, right_print])
             print(line_print)
     else:
-        warnings.warn('No match for "{}"'.format(query_word))
+        warnings.warn(f'No match for "{query_word}"')

From 3f0e03c1975496e19be1c1ee8b68dba3255effcf Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sun, 18 Oct 2020 18:15:07 +0200
Subject: [PATCH 351/496] refacto: add typing + small typos

---
 nautilus_nlp/topic_modeling/topic_modeling_short_text.py | 1 -
 nautilus_nlp/utils/tokenizer.py                          | 7 ++++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
index 5402937..bc0a94e 100644
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -200,7 +200,6 @@ def get_assigned_topics(model):
         list of topics. Having the same length as the training text containing topics assigned \
         to each sentence.
     """
-
     _, mat_h = model.get_decomposition_matrix()
     # The weights of the H matrix are converted into probabilities
     h_probs = mat_h / mat_h.sum(axis=1, keepdims=True)
diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 92c6217..2f4c22f 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from typing import List
+from typing import List, Union
 import nltk
 from sacremoses import MosesTokenizer, MosesDetokenizer
 import spacy
@@ -120,7 +120,7 @@ def untokenize(tokens: List[str], lang: str = 'fr') -> str:
     return text
 
 
-def convert_tokens_to_string(tokens_or_str):
+def convert_tokens_to_string(tokens_or_str: Union[str, List[str]]) -> str:
     if isinstance(tokens_or_str, str):
         return tokens_or_str
     if isinstance(tokens_or_str, list):
@@ -130,7 +130,8 @@ def convert_tokens_to_string(tokens_or_str):
     raise TypeError('Please input string or tokens')
 
 
-def convert_string_to_tokens(tokens_or_str, lang_module='en_spacy'):
+def convert_string_to_tokens(
+        tokens_or_str: Union[str, List[str]], lang_module: str = 'en_spacy') -> List[str]:
     if isinstance(tokens_or_str, str):
         return tokenize(tokens_or_str, lang_module=lang_module)
     if isinstance(tokens_or_str, list):

From 27c55015e8ab9b11381e97e80513ebbd30176afe Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sun, 18 Oct 2020 18:20:29 +0200
Subject: [PATCH 352/496] refacto: harmonize optional in docstring

---
 nautilus_nlp/analysis/text_summary.py           | 2 +-
 nautilus_nlp/analysis/visualize.py              | 4 ++--
 nautilus_nlp/preprocessing/data_augmentation.py | 6 +++---
 nautilus_nlp/preprocessing/text_preprocess.py   | 4 ++--
 nautilus_nlp/topic_modeling/lda.py              | 4 ++--
 nautilus_nlp/utils/file_loader.py               | 4 ++--
 nautilus_nlp/utils/phone_number.py              | 4 ++--
 7 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/nautilus_nlp/analysis/text_summary.py b/nautilus_nlp/analysis/text_summary.py
index d4b227d..8fb6ff4 100644
--- a/nautilus_nlp/analysis/text_summary.py
+++ b/nautilus_nlp/analysis/text_summary.py
@@ -46,7 +46,7 @@ def summarize_text(
         Percentage giving the output text length in reference to the input length.
     language : str
         text language. eg. "english"
-    nb_words : int 
+    nb_words : int, optional
         number of words of the output text or None
 
     Returns
diff --git a/nautilus_nlp/analysis/visualize.py b/nautilus_nlp/analysis/visualize.py
index 9555224..0dc31e5 100644
--- a/nautilus_nlp/analysis/visualize.py
+++ b/nautilus_nlp/analysis/visualize.py
@@ -34,7 +34,7 @@ def make_word_cloud(text_or_counter: Union[str, list], stop_words: Optional[List
     text_or_counter : Union[str, list]
         The text or the Counter to be ploted as wordcloud. 
         Example of counter: [('cat', 2), ('dog', 1)]
-    stop_words: Optional[List[str]]
+    stop_words: List[str], optional
         List of words to be ignored
     '''    
     if isinstance(text_or_counter, str):
@@ -63,7 +63,7 @@ def print_concordance(
         the word to be searched for in the list of tokens
     width : int
         Number of caracters to be display per text chunk
-    n_results : Optional[int]
+    n_results : int, optional
         If specified, will print only the N results
     '''
     half_width = (width - len(query_word) - 2) // 2
diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index e05da45..3c19066 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -21,13 +21,13 @@ def augment_utterance(
     Parameters
     ----------
     text : string
-    method : string {'wordnet_synonym', 'aug_sub_bert'}
+    method : {'wordnet_synonym', 'aug_sub_bert'}
         augmenter to use ('wordnet_synonym' or 'aug_sub_bert')
     stopwords : list
         list of words to freeze throughout the augmentation
-    intent : string
+    intent : string, optional
         intent associated to text if any
-    entities : list
+    entities : list, optional
         entities associated to text if any, must be in the following format:
         [
             {
diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
index 18df85b..252dd3e 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -266,7 +266,7 @@ def replace_currency_symbols(self, replace_with: Optional[str] = None) -> str:
         ----------
         text : str
             raw text
-        replace_with : Optional[str]
+        replace_with : str, optional
             if None (default), replace symbols with their standard 3-letter abbreviations \
                 (e.g. '$' with 'USD', '£' with 'GBP'); otherwise, pass in a string with \
                     which to replace all symbols (e.g. "*CURRENCY*")
@@ -291,7 +291,7 @@ def remove_punct(self, marks: Optional[str] = None) -> str:
         ----------
         text : str
             raw text
-        marks : Optional[str]
+        marks : str, optional
             If specified, remove only the characters in this string,
             e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
             Otherwise, all punctuation marks are removed.
diff --git a/nautilus_nlp/topic_modeling/lda.py b/nautilus_nlp/topic_modeling/lda.py
index 6cee4fe..e9ffa75 100644
--- a/nautilus_nlp/topic_modeling/lda.py
+++ b/nautilus_nlp/topic_modeling/lda.py
@@ -55,9 +55,9 @@ def filter_extremes(
     ----------
     dictionary : dict 
         dictionary containing the number of times a word appears in the dataset set
-    no_below : Optional[int]
+    no_below : int, optional
         Keep tokens which are contained in at least `no_below` documents.
-    no_above : Optional[float]
+    no_above : float, optional
         Keep tokens which are contained in no more than `no_above` documents. (fraction\
             of total corpus size, not an absolute number).
 
diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index ece5a4d..0ed1bfd 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -68,7 +68,7 @@ def text_loader(filepath: str, encoding: Optional[str] = None, detectencoding: b
     Parameters
     ----------
     filepath : str
-    encoding : str
+    encoding : str, optional
         If the encoding is specified, will use the specified encoding to load the text file.
     detectencoding : bool
         If file is not encoded into UTF-8, try to detect encoding using the chardet library.
@@ -153,7 +153,7 @@ def documents_loader(
     ----------
     filepath : str
         A filepath with wildcard (eg. *.txt), or a list of filepaths.
-    encoding : Optional[str] 
+    encoding : str, optional
         if not specified, will try to detect encoding except if detectencoding is false.
     detectencoding : bool
         if True and if encoding is not specified, will try to detect encoding using chardet.
diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index baff7f6..e03b47e 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -54,7 +54,7 @@ def find_phone_numbers(string: str, region_code: Optional[str] = None) -> str:
 
     Parameters
     ----------
-    region_code : str
+    region_code : str, optional
         If specified, will find the number of the specified country.
     eg. 06.00.00.00.00 if "FR" is specified.
 
@@ -117,7 +117,7 @@ def parse_number(self, text: str, region_code: Optional[str] = None) -> str:
 
         Parameters
         ----------
-        region_code : Optional[str] 
+        region_code : str, optional
             If specified, will find the number of the specified country.
         eg. 06.00.00.00.00 if "FR" is specified.
         If not specified, only works for international-formatted phone numbers.

From 3556da2218a606c74a3213745b6fd42007bef60d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Sun, 18 Oct 2020 19:30:39 +0200
Subject: [PATCH 353/496] fix: typing error

---
 nautilus_nlp/utils/file_loader.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 0ed1bfd..81a8deb 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -23,7 +23,7 @@
 import re
 import shutil
 import warnings
-from typing import Optional, Union
+from typing import Optional, Union, List
 
 import chardet
 
@@ -117,7 +117,7 @@ def list_files_in_subdir(filepath: str) -> list:
     return res
 
 
-def list_files(filepath: str) -> list[str]:
+def list_files(filepath: str) -> List[str]:
     """
     List files within a given filepath. 
 

From bf947221a47fb8a8f3399fe75a1d6eec84127ebe Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Wed, 28 Oct 2020 16:39:13 +0100
Subject: [PATCH 354/496] added first version of actions

---
 .github/workflows/ci_actions.yml | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)
 create mode 100644 .github/workflows/ci_actions.yml

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
new file mode 100644
index 0000000..53bb37f
--- /dev/null
+++ b/.github/workflows/ci_actions.yml
@@ -0,0 +1,24 @@
+name: CI
+
+on:
+  push
+  pull_request
+
+jobs:
+  - job : CI
+    name: Launching CI
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Install requirements
+        run: pip install -r requirements.txt
+
+      - name: Run pylint
+        run: |
+          pip install pylint
+          pylint nautilus_nlp tests
+
+      - name: Run pytest
+        run: |
+          pip install pytest
+          pytest tests

From 70d131db0432a6af2a820d3c33a9755d01007161 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Wed, 28 Oct 2020 16:45:13 +0100
Subject: [PATCH 355/496] modified called events

---
 .github/workflows/ci_actions.yml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 53bb37f..f4a8557 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -1,8 +1,6 @@
 name: CI
 
-on:
-  push
-  pull_request
+on: [push, pull_request]
 
 jobs:
   - job : CI

From a57bffa1f6794556bae803a0bd129bd6138c25cd Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Wed, 28 Oct 2020 19:35:10 +0100
Subject: [PATCH 356/496] yaml syntax fix

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index f4a8557..e6e9bc2 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -3,7 +3,7 @@ name: CI
 on: [push, pull_request]
 
 jobs:
-  - job : CI
+  CI:
     name: Launching CI
     runs-on: ubuntu-latest
 

From 23cd333762b21fe2fba3bdc0a474d80ed1105d40 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Wed, 28 Oct 2020 20:45:47 +0100
Subject: [PATCH 357/496] changed wd path

---
 .github/workflows/ci_actions.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index e6e9bc2..6099baa 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -9,14 +9,17 @@ jobs:
 
     steps:
       - name: Install requirements
+        working-directory: .
         run: pip install -r requirements.txt
 
       - name: Run pylint
+        working-directory: .
         run: |
           pip install pylint
           pylint nautilus_nlp tests
 
       - name: Run pytest
+        working-directory: .
         run: |
           pip install pytest
           pytest tests

From bb9fe1e0f4f3ddc5291816195d4addbe6547ffbd Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Wed, 28 Oct 2020 20:51:08 +0100
Subject: [PATCH 358/496] yml syntax fix

---
 .github/workflows/ci_actions.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 6099baa..c84fb89 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -9,17 +9,17 @@ jobs:
 
     steps:
       - name: Install requirements
-        working-directory: .
+        working-directory: ./
         run: pip install -r requirements.txt
 
       - name: Run pylint
-        working-directory: .
+        working-directory: ./
         run: |
           pip install pylint
           pylint nautilus_nlp tests
 
       - name: Run pytest
-        working-directory: .
+        working-directory: ./
         run: |
           pip install pytest
           pytest tests

From 3699a584dba9eda9576109d1b085ab02d4c8dcd8 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 5 Nov 2020 11:29:04 +0100
Subject: [PATCH 359/496] refacto following comments

---
 .../preprocessing/data_augmentation.py        | 31 +++++++++----------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 1e47928..b6143ff 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -1,6 +1,7 @@
 import copy
 import logging
 import re
+from itertools import combinations
 
 import nlpaug.augmenter.word as naw
 
@@ -56,7 +57,7 @@ def augment_text(text, method, stopwords=None, entities=None):
                 formatted_entities
             )
             return clean_sentence_entities(text, augmented_entities)
-        raise CouldNotAugment('Text was not correctly augmented so not added')
+        raise CouldNotAugment('Text was not correctly augmented because entities were altered')
     return augmented_text
 
 
@@ -86,11 +87,10 @@ def are_entities_in_augmented_text(entities, augmented_text):
     -------
     True if all entities are present in augmented text, False otherwise
     """
-    check = True
     for ent in entities:
         if ent['word'] not in augmented_text:
-            check = False
-    return check
+            return False
+    return True
 
 
 def get_augmenter(method, stopwords=None):
@@ -182,18 +182,17 @@ def clean_sentence_entities(text, entities):
     Augmented text and cleaned entities
     """
     entities_to_clean = copy.copy(entities)
-    for element1 in entities_to_clean:
-        for element2 in entities_to_clean:
-            result = check_interval_included(element1, element2)
-            if result is not None:
-                try:
-                    entities_to_clean.remove(result[0])
-                except IndexError:
-                    logging.warning(
-                        "Cant remove entity : {} \n entities are now :{} \n for sentence : {} ".format(
-                            result, entities_to_clean,
-                            text))
-                    continue
+    for element1, element2 in combinations(entities_to_clean, 2):
+        result = check_interval_included(element1, element2)
+        if result is not None:
+            try:
+                entities_to_clean.remove(result[0])
+            except IndexError:
+                logging.warning(
+                    "Cant remove entity : {} \n entities are now :{} \n for sentence : {} ".format(
+                        result, entities_to_clean,
+                        text))
+                continue
     return text, entities_to_clean
 
 

From 923c44be7f4dd9cd1565fca9cc58b348c65d898c Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Thu, 5 Nov 2020 11:57:05 +0100
Subject: [PATCH 360/496] add actions checkout to see if it can find
 requirements that way

---
 .github/workflows/ci_actions.yml | 26 +++++++++++++++++++++++---
 1 file changed, 23 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index c84fb89..5426074 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -1,3 +1,20 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 name: CI
 
 on: [push, pull_request]
@@ -6,20 +23,23 @@ jobs:
   CI:
     name: Launching CI
     runs-on: ubuntu-latest
+    #env:
+       #working-directory: ...
 
     steps:
+      - uses: actions/checkout@v2
       - name: Install requirements
-        working-directory: ./
+        #working-directory: ${{env.working-directory}}
         run: pip install -r requirements.txt
 
       - name: Run pylint
-        working-directory: ./
+        #working-directory: ${{env.working-directory}}
         run: |
           pip install pylint
           pylint nautilus_nlp tests
 
       - name: Run pytest
-        working-directory: ./
+        #working-directory: ${{env.working-directory}}
         run: |
           pip install pytest
           pytest tests

From 04fa9119f900baa755c6675191a45158b1993278 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 5 Nov 2020 12:10:37 +0100
Subject: [PATCH 361/496] remove text output in clean entities function

---
 nautilus_nlp/preprocessing/data_augmentation.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index b6143ff..a015f81 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -56,7 +56,8 @@ def augment_text(text, method, stopwords=None, entities=None):
                 augmented_text,
                 formatted_entities
             )
-            return clean_sentence_entities(text, augmented_entities)
+            clean_entities = clean_sentence_entities(augmented_text, augmented_entities)
+            return augmented_text, clean_entities
         raise CouldNotAugment('Text was not correctly augmented because entities were altered')
     return augmented_text
 
@@ -87,10 +88,12 @@ def are_entities_in_augmented_text(entities, augmented_text):
     -------
     True if all entities are present in augmented text, False otherwise
     """
+    check = True
     for ent in entities:
         if ent['word'] not in augmented_text:
-            return False
-    return True
+            check = False
+            return check
+    return check
 
 
 def get_augmenter(method, stopwords=None):

From 1f5a6439fe0e1c0115cfa9c3fd4256d680ae867e Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Thu, 5 Nov 2020 12:10:37 +0100
Subject: [PATCH 362/496] upgrade pip and specify python version

---
 .github/workflows/ci_actions.yml | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 5426074..fceb231 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -23,23 +23,27 @@ jobs:
   CI:
     name: Launching CI
     runs-on: ubuntu-latest
-    #env:
-       #working-directory: ...
+    strategy:
+      matrix:
+        python-version: [3.6]
 
     steps:
       - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
       - name: Install requirements
-        #working-directory: ${{env.working-directory}}
-        run: pip install -r requirements.txt
+        run: 
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
 
       - name: Run pylint
-        #working-directory: ${{env.working-directory}}
         run: |
           pip install pylint
           pylint nautilus_nlp tests
 
       - name: Run pytest
-        #working-directory: ${{env.working-directory}}
         run: |
           pip install pytest
           pytest tests

From b5af78d9f7202e64620186f05bc06822d9338929 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 5 Nov 2020 15:33:31 +0100
Subject: [PATCH 363/496] add check of entities duplicates

---
 nautilus_nlp/preprocessing/data_augmentation.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index a015f81..eac4384 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -184,7 +184,7 @@ def clean_sentence_entities(text, entities):
     -------
     Augmented text and cleaned entities
     """
-    entities_to_clean = copy.copy(entities)
+    entities_to_clean = [dict(s) for s in set(frozenset(d.items()) for d in entities)]
     for element1, element2 in combinations(entities_to_clean, 2):
         result = check_interval_included(element1, element2)
         if result is not None:
@@ -196,7 +196,7 @@ def clean_sentence_entities(text, entities):
                         result, entities_to_clean,
                         text))
                 continue
-    return text, entities_to_clean
+    return entities_to_clean
 
 
 def check_interval_included(element1, element2):

From dcba544130550e56aecd139018dbb3a645dd00ea Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 5 Nov 2020 15:42:13 +0100
Subject: [PATCH 364/496] remove unused import

---
 nautilus_nlp/preprocessing/data_augmentation.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index eac4384..353be2a 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -1,4 +1,3 @@
-import copy
 import logging
 import re
 from itertools import combinations

From 01efce49089fd02641557034fca5876504d4f5e2 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 5 Nov 2020 15:58:10 +0100
Subject: [PATCH 365/496] update docstring clean entities

---
 nautilus_nlp/preprocessing/data_augmentation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 353be2a..3b5c231 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -181,7 +181,7 @@ def clean_sentence_entities(text, entities):
 
     Returns
     -------
-    Augmented text and cleaned entities
+    Cleaned entities
     """
     entities_to_clean = [dict(s) for s in set(frozenset(d.items()) for d in entities)]
     for element1, element2 in combinations(entities_to_clean, 2):

From 9e43d709bf8f50d2516c2e625cfdfabccae23251 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 5 Nov 2020 16:09:41 +0100
Subject: [PATCH 366/496] removing classes

---
 .../preprocessing/social_preprocess.py        | 300 ++++----
 nautilus_nlp/preprocessing/text_preprocess.py | 706 ++++++++----------
 .../preprocessing/token_preprocess.py         | 168 ++---
 tests/test_preprocessor.py                    | 107 +--
 4 files changed, 592 insertions(+), 689 deletions(-)

diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index 8bab161..fafb584 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -21,162 +21,146 @@
 
 import emoji as _emoji
 from nautilus_nlp.utils import constants
-
-
-class SocialPreprocessor():
-
-    def __init__(self, text):
-        self.text = text
-
-    def remove_mentions(self) -> str:
-        """
-        Function that removes words preceded with a '@'
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        string
-        """
-        self.text = self.normalize_whitespace(constants.AT_PATTERN.sub('', self.text))
-        return self.text
-
-    def extract_mentions(self) -> list:
-        """
-        Function that extracts words preceded with a '@'
-        eg. "I take care of my skin with @thisproduct" --> ["@thisproduct"]
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        string
-        """
-        return constants.AT_PATTERN.findall(self.text)
-
-    def remove_html_tags(self) -> str:
-        """
-        Function that removes words between < and >
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        string
-        """
-        self.text = self.normalize_whitespace(constants.HTML_TAG_PATTERN.sub('', self.text))
-        return self.text
-
-    def remove_emoji(self) -> str:
-        """
-        Remove emoji from any str by stripping any unicode in the range of Emoji unicode
-        as defined in the unicode convention:
-        http://www.unicode.org/emoji/charts/full-emoji-list.html
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        str
-        """
-        self.text = constants.EMOJI_PATTERN.sub("", self.text)
-        return self.text
-
-    def convert_emoji_to_text(self, code_delimiters=(':', ':'), input_str=None) -> str:
-        """
-        Convert emoji to their CLDR Short Name, according to the unicode convention
-        http://www.unicode.org/emoji/charts/full-emoji-list.html
-        eg. 😀 --> :grinning_face:
-
-        Parameters
-        ----------
-        text : str
-            code_delimiters : tuple of symbols around the emoji code.
-            eg: (':',':') --> :grinning_face:
-
-        Returns
-        -------
-        str
-            string
-        """
-        if input_str is not None:
-            return _emoji.demojize(input_str, delimiters=code_delimiters)
-        return _emoji.demojize(self.text, delimiters=code_delimiters)
-
-    def extract_emojis(self) -> list:
-        """
-        Function that extracts emojis from a text and translates them into words
-        eg. "I take care of my skin 😀 :(" --> [":grinning_face:"]
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        list
-            list of all emojis converted with their unicode conventions
-        """
-        emojis_in_text = constants.EMOJI_PATTERN.findall(self.text)
-        emojis_converted = [self.convert_emoji_to_text(input_str=emoji_text) for emoji_text in emojis_in_text]
-        return emojis_converted
-
-    def extract_hashtags(self) -> list:
-        """
-        Function that extracts words preceded with a '#'
-        eg. "I take care of my skin #selfcare#selfestim" --> ["skincare", "selfestim"]
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        list
-            list of all hashtags
-        """
-        return constants.HASHTAG_PATTERN.findall(self.text)
-
-    def remove_hashtag(self) -> str:
-        """
-        Function that removes words preceded with a '#'
-        eg. "I take care of my skin #selfcare#selfestim" --> "I take care of my skin"
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        str
-            text of a post without hashtags
-        """
-        self.text = self.normalize_whitespace(constants.HASHTAG_PATTERN.sub('', self.text))
-        return self.text
-
-    @staticmethod
-    def normalize_whitespace(text) -> str:
-        """
-        Given ``text`` str, replace one or more spacings with a single space, and one
-        or more linebreaks with a single newline. Also strip leading/trailing whitespace.
-        eg. "   foo  bar  " -> "foo bar"
-
-        Parameters
-        ----------
-        text : string
-
-        Returns
-        -------
+from nautilus_nlp.preprocessing.text_preprocess import normalize_whitespace
+
+
+def remove_mentions(text) -> str:
+    """
+    Function that removes words preceded with a '@'
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    string
+    """
+    text = normalize_whitespace(constants.AT_PATTERN.sub('', text))
+    return text
+
+
+def extract_mentions(text) -> list:
+    """
+    Function that extracts words preceded with a '@'
+    eg. "I take care of my skin with @thisproduct" --> ["@thisproduct"]
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    string
+    """
+    return constants.AT_PATTERN.findall(text)
+
+
+def remove_html_tags(text) -> str:
+    """
+    Function that removes words between < and >
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    string
+    """
+    text = normalize_whitespace(constants.HTML_TAG_PATTERN.sub('', text))
+    return text
+
+
+def remove_emoji(text) -> str:
+    """
+    Remove emoji from any str by stripping any unicode in the range of Emoji unicode
+    as defined in the unicode convention:
+    http://www.unicode.org/emoji/charts/full-emoji-list.html
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    str
+    """
+    text = constants.EMOJI_PATTERN.sub("", text)
+    return text
+
+
+def convert_emoji_to_text(text, code_delimiters=(':', ':'), input_str=None) -> str:
+    """
+    Convert emoji to their CLDR Short Name, according to the unicode convention
+    http://www.unicode.org/emoji/charts/full-emoji-list.html
+    eg. 😀 --> :grinning_face:
+
+    Parameters
+    ----------
+    text : str
+        code_delimiters : tuple of symbols around the emoji code.
+        eg: (':',':') --> :grinning_face:
+
+    Returns
+    -------
+    str
         string
-        """
-        return constants.NONBREAKING_SPACE_REGEX.sub(
-            " ", constants.LINEBREAK_REGEX.sub(r"\n", text)
-        ).strip()
+    """
+    if input_str is not None:
+        return _emoji.demojize(input_str, delimiters=code_delimiters)
+    return _emoji.demojize(text, delimiters=code_delimiters)
+
+
+def extract_emojis(text) -> list:
+    """
+    Function that extracts emojis from a text and translates them into words
+    eg. "I take care of my skin 😀 :(" --> [":grinning_face:"]
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    list
+        list of all emojis converted with their unicode conventions
+    """
+    emojis_in_text = constants.EMOJI_PATTERN.findall(text)
+    emojis_converted = [convert_emoji_to_text(text, input_str=emoji_text) for emoji_text in emojis_in_text]
+    return emojis_converted
+
+
+def extract_hashtags(text) -> list:
+    """
+    Function that extracts words preceded with a '#'
+    eg. "I take care of my skin #selfcare#selfestim" --> ["skincare", "selfestim"]
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    list
+        list of all hashtags
+    """
+    return constants.HASHTAG_PATTERN.findall(text)
+
+
+def remove_hashtag(text) -> str:
+    """
+    Function that removes words preceded with a '#'
+    eg. "I take care of my skin #selfcare#selfestim" --> "I take care of my skin"
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    str
+        text of a post without hashtags
+    """
+    text = normalize_whitespace(constants.HASHTAG_PATTERN.sub('', text))
+    return text
diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
index 5c112cb..9fe587a 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -27,382 +27,334 @@
 from nautilus_nlp.utils import constants
 from nautilus_nlp.utils.phone_number import \
     extract_phone_numbers as _extract_phone_numbers
-from nautilus_nlp.utils.stopwords import get_stopwords
-
-
-class TextPreprocessor():
-
-    def __init__(self, text):
-        if isinstance(text, str):
-            self.text = text
-        else:
-            raise ValueError("Input must be a string")
-
-    def clean_text(self, lang='en') -> str:
-        #TODO : check how to pipe operations
-        stopwords = get_stopwords(lang)
-        self.text = self.fix_bad_unicode(normalization="NFC")
-        self.text = self.remove_eol_characters()
-        self.text = self.remove_accents(method="unicode")
-        self.text = self.remove_punct()
-        self.text = self.text.lower()
-        self.text = self.remove_stopwords(stopwords=stopwords)
-        return self.normalize_whitespace()
-
-    def remove_eol_characters(self) -> str:
-        """
-        Remove end of line (\n) char.
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        str
-        """
-        self.text = self.text.replace("\n", " ")
-        return self.text
-
-    def remove_stopwords(self, stopwords: list) -> str:
-        """
-        Remove stopwords from a text.
-        eg. 'I like when you move your body !' -> 'I move body !'
-
-        Parameters
-        ----------
-        stopwords : list of stopwords to remove
-
-        Returns
-        -------
-        str
-            text without stopwords
-
-        Raises
-        ------
-        ValueError
-            When inputs is not a string
-        """
-        self.text = ' '.join([word.strip() for word in self.text.split() if word not in stopwords])
-        return self.text
-
-    def fix_bad_unicode(self, normalization: str = "NFC") -> str:
-        """
-        Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
-        this includes mojibake, HTML entities and other code cruft,
-        and non-standard forms for display purposes.
-
-        Parameters
-        ----------
-        text : string
-
-        normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}):
-            if 'NFC', combines characters and diacritics written using separate code points,
-            e.g. converting "e" plus an acute accent modifier into "é"; unicode
-            can be converted to NFC form without any change in its meaning!
-            if 'NFKC', additional normalizations are applied that can change
-            the meanings of characters, e.g. ellipsis characters will be replaced
-            with three periods
-        Returns
-        -------
-        string
-        """
-        self.text = _fix_text(self.text, normalization=normalization)
-        return self.text
-
-    def normalize_whitespace(self) -> str:
-        """
-        Given ``text`` str, replace one or more spacings with a single space, and one
-        or more linebreaks with a single newline. Also strip leading/trailing whitespace.
-        eg. "   foo  bar  " -> "foo bar"
-
-        Parameters
-        ----------
-        text : string
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.NONBREAKING_SPACE_REGEX.sub(
-            " ", constants.LINEBREAK_REGEX.sub(r"\n", self.text)
-        ).strip()
-        return self.text
-
-    def unpack_english_contractions(self) -> str:
-        """
-        Replace *English* contractions in ``text`` str with their unshortened forms.
-        N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
-        so are left as-is.
-        eg. "You're fired. She's nice." -> "You are fired. She's nice."
-
-        Parameters
-        ----------
-        text : string
-
-        Returns
-        -------
-        string
-        """
-
-        # standard
-        self.text = constants.CONTRACTION_NT_NOT.sub(
-            r"\1\2 not",
-            self.text,
-        )
-        self.text = constants.CONTRACTION_LL_WILL.sub(
-            r"\1\2 will",
-            self.text,
-        )
-        self.text = constants.CONTRACTION_RE_ARE.sub(r"\1\2 are", self.text)
-        self.text = constants.CONTRACTION_VE_HAVE.sub(
-            r"\1\2 have",
-            self.text,
+
+
+def normalize_whitespace(text) -> str:
+    """
+    Given ``text`` str, replace one or more spacings with a single space, and one
+    or more linebreaks with a single newline. Also strip leading/trailing whitespace.
+    eg. "   foo  bar  " -> "foo bar"
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string
+    """
+    text = constants.NONBREAKING_SPACE_REGEX.sub(
+        " ", constants.LINEBREAK_REGEX.sub(r"\n", text)
+    ).strip()
+    return text
+
+
+def remove_eol_characters(text) -> str:
+    """
+    Remove end of line (\n) char.
+
+    Parameters
+    ----------
+    text : str
+
+    Returns
+    -------
+    str
+    """
+    text = text.replace("\n", " ")
+    return text
+
+
+def fix_bad_unicode(text, normalization: str = "NFC") -> str:
+    """
+    Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
+    this includes mojibake, HTML entities and other code cruft,
+    and non-standard forms for display purposes.
+
+    Parameters
+    ----------
+    text : string
+
+    normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}):
+        if 'NFC', combines characters and diacritics written using separate code points,
+        e.g. converting "e" plus an acute accent modifier into "é"; unicode
+        can be converted to NFC form without any change in its meaning!
+        if 'NFKC', additional normalizations are applied that can change
+        the meanings of characters, e.g. ellipsis characters will be replaced
+        with three periods
+    Returns
+    -------
+    string
+    """
+    text = _fix_text(text, normalization=normalization)
+    return text
+
+
+def unpack_english_contractions(text) -> str:
+    """
+    Replace *English* contractions in ``text`` str with their unshortened forms.
+    N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
+    so are left as-is.
+    eg. "You're fired. She's nice." -> "You are fired. She's nice."
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string
+    """
+
+    # standard
+    text = constants.CONTRACTION_NT_NOT.sub(
+        r"\1\2 not",
+        text,
+    )
+    text = constants.CONTRACTION_LL_WILL.sub(
+        r"\1\2 will",
+        text,
+    )
+    text = constants.CONTRACTION_RE_ARE.sub(r"\1\2 are", text)
+    text = constants.CONTRACTION_VE_HAVE.sub(
+        r"\1\2 have",
+        text,
+    )
+    text = constants.CONTRACTION_CANT_CANNOT.sub(r"\1\2n not", text)
+    text = constants.CONTRACTION_M_AM.sub(r"\1\2 am", text)
+    text = constants.CONTRACTION_LET_LETUS.sub(r"\1\2 us", text)
+    text = constants.CONTRACTION_WONT_WILLNOT.sub(r"\1\2ill not", text)
+    text = constants.CONTRACTION_SHANT_SHALLNOT.sub(r"\1\2hall not", text)
+    text = constants.CONTRACTION_YALL_YOUALL.sub(r"\1\2ou all", text)
+    return text
+
+
+def replace_urls(text, replace_with: str = "*URL*") -> str:
+    """
+    Replace all URLs in ``text`` str with ``replace_with`` str.
+
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the URL to be replaced with.
+
+    Returns
+    -------
+    string
+    """
+    text = constants.URL_REGEX.sub(
+        replace_with, constants.SHORT_URL_REGEX.sub(replace_with, text)
+    )
+    return text
+
+
+def replace_emails(text, replace_with="*EMAIL*") -> str:
+    """
+    Replace all emails in ``text`` str with ``replace_with`` str
+
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the email address to be replaced with.
+
+    Returns
+    -------
+    string
+    """
+    text = constants.EMAIL_REGEX.sub(replace_with, text)
+    return text
+
+
+def replace_phone_numbers(text, country_format_to_detect: list,
+                          replace_with: str = "*PHONE*",
+                          method: str = "regex") -> str:
+    """
+    Replace all phone numbers in ``text`` str with ``replace_with`` str
+
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the phone number to be replaced with.
+    method : ['regex','detection']
+        regex is faster but will omit a lot of numbers, while detection will
+        catch every numbers, but takes a while.
+    country_format_to_detect : list
+        If a list of country code is specified, will catch every number formatted.
+        Only when method = 'detection'.
+    Returns
+    -------
+    string
+    """
+    if method == 'regex':
+        text = constants.PHONE_REGEX.sub(replace_with, text)
+    elif method == 'detection':
+        found_nums = _extract_phone_numbers(text, countrylist=country_format_to_detect)
+
+        # order by lenght to avoid truncated numbers to be removed first.
+        found_nums.sort(key=len, reverse=True)
+        for phone_number in found_nums:
+            text = text.replace(phone_number, replace_with)
+    else:
+        raise ValueError('Please input a valid method between "regex" or "detection"')
+    return text
+
+
+def replace_numbers(text, replace_with="*NUMBER*") -> str:
+    """
+    Replace all numbers in ``text`` str with ``replace_with`` str.
+
+    Parameters
+    ----------
+    text : string
+    replace_with : string
+        the string you want the number to be replaced with.
+
+    Returns
+    -------
+    string
+    """
+    text = constants.NUMBERS_REGEX.sub(replace_with, text)
+    return text
+
+
+def replace_currency_symbols(text, replace_with=None) -> str:
+    """
+    Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
+
+    Parameters
+    ----------
+    text : str
+        raw text
+    replace_with : None or string
+        if None (default), replace symbols with
+            their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
+            otherwise, pass in a string with which to replace all symbols
+            (e.g. "*CURRENCY*")
+
+    Returns
+    -------
+    string
+    """
+    if replace_with is None:
+        for k, v in constants.CURRENCIES.items():
+            text = text.replace(k, v)
+    else:
+        text = constants.CURRENCY_REGEX.sub(replace_with, text)
+    return text
+
+
+def remove_punct(text, marks=None) -> str:
+    """
+    Remove punctuation from ``text`` by replacing all instances of ``marks``
+    with whitespace.
+
+    Parameters
+    ----------
+    text : str
+        raw text
+
+    marks : str or None
+        If specified, remove only the characters in this string,
+        e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
+        Otherwise, all punctuation marks are removed.
+
+    Returns
+    -------
+    string
+
+    Note
+    -------
+    When ``marks=None``, Python's built-in :meth:`str.translate()` is
+    used to remove punctuation; otherwise, a regular expression is used
+    instead. The former's performance is about 5-10x faster.
+    """
+    if marks:
+        text = re.sub("[{}]+".format(re.escape(marks)), " ", text, flags=re.UNICODE)
+    else:
+        text = text.translate(constants.PUNCT_TRANSLATE_UNICODE)
+    return text
+
+
+def remove_accents(text, method: str = "unicode") -> str:
+    """
+    Remove accents from any accented unicode characters in ``text`` str, either by
+    transforming them into ascii equivalents or removing them entirely.
+
+    Parameters
+    ----------
+    text : str
+        raw text
+
+    method : ({'unicode', 'ascii'})
+        if 'unicode', remove accented
+        char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
+        remove accented char for any unicode symbol
+
+        NB: the 'ascii' method is notably faster than 'unicode', but less good
+
+    Returns
+    -------
+    string
+
+    Raises
+    -------
+    ValueError
+        if ``method`` is not in {'unicode', 'ascii'}
+    """
+    if method == "unicode":
+        text = "".join(
+            c
+            for c in unicodedata.normalize("NFKD", text)
+            if not unicodedata.combining(c)
         )
-        self.text = constants.CONTRACTION_CANT_CANNOT.sub(r"\1\2n not", self.text)
-        self.text = constants.CONTRACTION_M_AM.sub(r"\1\2 am", self.text)
-        self.text = constants.CONTRACTION_LET_LETUS.sub(r"\1\2 us", self.text)
-        self.text = constants.CONTRACTION_WONT_WILLNOT.sub(r"\1\2ill not", self.text)
-        self.text = constants.CONTRACTION_SHANT_SHALLNOT.sub(r"\1\2hall not", self.text)
-        self.text = constants.CONTRACTION_YALL_YOUALL.sub(r"\1\2ou all", self.text)
-        return self.text
-
-    def replace_urls(self, replace_with: str = "*URL*") -> str:
-        """
-        Replace all URLs in ``text`` str with ``replace_with`` str.
-
-        Parameters
-        ----------
-        text : string
-        replace_with : string
-            the string you want the URL to be replaced with.
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.URL_REGEX.sub(
-            replace_with, constants.SHORT_URL_REGEX.sub(replace_with, self.text)
+    elif method == "ascii":
+        text = (
+            unicodedata.normalize("NFKD", text)
+            .encode("ascii", errors="ignore")
+            .decode("ascii")
         )
-        return self.text
-
-    def replace_emails(self, replace_with="*EMAIL*") -> str:
-        """
-        Replace all emails in ``text`` str with ``replace_with`` str
-
-        Parameters
-        ----------
-        text : string
-        replace_with : string
-            the string you want the email address to be replaced with.
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.EMAIL_REGEX.sub(replace_with, self.text)
-        return self.text
-
-    def replace_phone_numbers(self, country_format_to_detect: list,
-                              replace_with: str = "*PHONE*",
-                              method: str = "regex") -> str:
-        """
-        Replace all phone numbers in ``text`` str with ``replace_with`` str
-
-        Parameters
-        ----------
-        text : string
-        replace_with : string
-            the string you want the phone number to be replaced with.
-        method : ['regex','detection']
-            regex is faster but will omit a lot of numbers, while detection will
-            catch every numbers, but takes a while.
-        country_format_to_detect : list
-            If a list of country code is specified, will catch every number formatted.
-            Only when method = 'detection'.
-        Returns
-        -------
-        string
-        """
-        if method == 'regex':
-            self.text = constants.PHONE_REGEX.sub(replace_with, self.text)
-        elif method == 'detection':
-            found_nums = _extract_phone_numbers(self.text, countrylist=country_format_to_detect)
-
-            # order by lenght to avoid truncated numbers to be removed first.
-            found_nums.sort(key=len, reverse=True)
-            for phone_number in found_nums:
-                self.text = self.text.replace(phone_number, replace_with)
-        else:
-            raise ValueError('Please input a valid method between "regex" or "detection"')
-        return self.text
-
-    def replace_numbers(self, replace_with="*NUMBER*") -> str:
-        """
-        Replace all numbers in ``text`` str with ``replace_with`` str.
-
-        Parameters
-        ----------
-        text : string
-        replace_with : string
-            the string you want the number to be replaced with.
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.NUMBERS_REGEX.sub(replace_with, self.text)
-        return self.text
-
-    def replace_currency_symbols(self, replace_with=None) -> str:
-        """
-        Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
-
-        Parameters
-        ----------
-        text : str
-            raw text
-        replace_with : None or string
-            if None (default), replace symbols with
-                their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
-                otherwise, pass in a string with which to replace all symbols
-                (e.g. "*CURRENCY*")
-
-        Returns
-        -------
-        string
-        """
-        if replace_with is None:
-            for k, v in constants.CURRENCIES.items():
-                self.text = self.text.replace(k, v)
-        else:
-            self.text = constants.CURRENCY_REGEX.sub(replace_with, self.text)
-        return self.text
-
-    def remove_punct(self, marks=None) -> str:
-        """
-        Remove punctuation from ``text`` by replacing all instances of ``marks``
-        with whitespace.
-
-        Parameters
-        ----------
-        text : str
-            raw text
-
-        marks : str or None
-            If specified, remove only the characters in this string,
-            e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
-            Otherwise, all punctuation marks are removed.
-
-        Returns
-        -------
-        string
-
-        Note
-        -------
-        When ``marks=None``, Python's built-in :meth:`str.translate()` is
-        used to remove punctuation; otherwise, a regular expression is used
-        instead. The former's performance is about 5-10x faster.
-        """
-        if marks:
-            self.text = re.sub("[{}]+".format(re.escape(marks)), " ", self.text, flags=re.UNICODE)
-        else:
-            self.text = self.text.translate(constants.PUNCT_TRANSLATE_UNICODE)
-        return self.text
-
-    def remove_accents(self, method: str = "unicode") -> str:
-        """
-        Remove accents from any accented unicode characters in ``text`` str, either by
-        transforming them into ascii equivalents or removing them entirely.
-
-        Parameters
-        ----------
-        text : str
-            raw text
-
-        method : ({'unicode', 'ascii'})
-            if 'unicode', remove accented
-            char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
-            remove accented char for any unicode symbol
-
-            NB: the 'ascii' method is notably faster than 'unicode', but less good
-
-        Returns
-        -------
-        string
-
-        Raises
-        -------
-        ValueError
-            if ``method`` is not in {'unicode', 'ascii'}
-        """
-        if method == "unicode":
-            self.text = "".join(
-                c
-                for c in unicodedata.normalize("NFKD", self.text)
-                if not unicodedata.combining(c)
-            )
-        elif method == "ascii":
-            self.text = (
-                unicodedata.normalize("NFKD", self.text)
-                .encode("ascii", errors="ignore")
-                .decode("ascii")
-            )
-        else:
-            msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
-            raise ValueError(msg)
-        return self.text
-
-    def remove_multiple_spaces_and_strip_text(self) -> str:
-        """
-        Remove multiple spaces, strip text, and remove '-', '*' characters.
-
-        Parameters
-        ----------
-        text : str
-            the text to be processed
-
-        Returns
-        -------
-        string
-            the text with removed multiple spaces and strip text
-        """
-        regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
-        for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
-            self.text = re.sub(regex_remove_multiple_spaces, " ", self.text)
-            self.text = self.text.strip()
-        return self.text
-
-    def filter_non_latin_characters(self) -> str:
-        """
-        Function that filters non latin characters of a text
-
-        Parameters
-        ----------
-        text : string
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.LATIN_CHARACTERS_RE.sub(' ', self.text)
-        self.text = self.normalize_whitespace()
-        return self.text
-
-    def remove_smallwords(self, smallwords_threshold: int) -> list:
-        """
-        Function that removes words which length is below a threshold
-        'Hello my name is John Doe' --> 'Hello name John Doe'
-
-        Parameters
-        ----------
-        text : str
-        smallwords_threshold: int
-            threshold of small word
-
-        Returns
-        -------
-        str
-        """
-        self.text = ' '.join([word for word in self.text.split() if len(word) > smallwords_threshold])
-        return self.text
+    else:
+        msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
+        raise ValueError(msg)
+    return text
+
+
+def remove_multiple_spaces_and_strip_text(text) -> str:
+    """
+    Remove multiple spaces, strip text, and remove '-', '*' characters.
+
+    Parameters
+    ----------
+    text : str
+        the text to be processed
+
+    Returns
+    -------
+    string
+        the text with removed multiple spaces and strip text
+    """
+    regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
+    for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
+        text = re.sub(regex_remove_multiple_spaces, " ", text)
+        text = text.strip()
+    return text
+
+
+def filter_non_latin_characters(text) -> str:
+    """
+    Function that filters non latin characters of a text
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string
+    """
+    text = constants.LATIN_CHARACTERS_RE.sub(' ', text)
+    text = normalize_whitespace(text)
+    return text
diff --git a/nautilus_nlp/preprocessing/token_preprocess.py b/nautilus_nlp/preprocessing/token_preprocess.py
index b753564..989bd9a 100644
--- a/nautilus_nlp/preprocessing/token_preprocess.py
+++ b/nautilus_nlp/preprocessing/token_preprocess.py
@@ -21,89 +21,85 @@
 
 import re
 
-class TokenPreprocessor():
-
-    def __init__(self, tokens):
-        if isinstance(tokens, list):
-            self.tokens = tokens
-        else:
-            raise TypeError("Input must be a list")
-
-    def remove_stopwords(self, stopwords: list) -> str:
-        """
-        Remove stopwords from a text.
-        eg. 'I like when you move your body !' -> 'I move body !'
-
-        Parameters
-        ----------
-        stopwords : list of stopwords to remove
-
-        Returns
-        -------
-        list
-            tokens without stopwords
-
-        Raises
-        ------
-        ValueError
-            When inputs is not a list
-        """
-        self.tokens = [word for word in self.tokens if word not in stopwords]
-        return self.tokens
-
-    def remove_tokens_with_nonletters(self) -> list:
-        """
-        Inputs a list of tokens, outputs a list of tokens without tokens that
-        includes numbers of special caracters.
-        ['foo','bar','124','34euros'] -> ['foo','bar']
-
-        Parameters
-        ----------
-        tokens : list
-            list of tokens to be cleaned
-
-        Returns
-        -------
-        list
-            list of tokens without tokens with numbers
-        """
-        self.tokens = [word for word in self.tokens if re.search("[^a-zA-Z]", word) is None]
-        return self.tokens
-
-    def remove_special_caracters_from_tokenslist(self) -> list:
-        """
-        Remove tokens that doesn't contains any number or letter.
-        eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
-
-        Parameters
-        ----------
-        tokens : list
-            list of tokens to be cleaned
-
-        Returns
-        -------
-        list
-            list of tokens without tokens that contains only special caracters
-
-        """
-        self.tokens = [word for word in self.tokens if re.search("[a-zA-Z0-9]", word)]
-        return self.tokens
-
-    def remove_smallwords(self, smallwords_threshold: int) -> list:
-        """
-        Function that removes words which length is below a threshold
-        ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
-
-        Parameters
-        ----------
-        text : list
-            list of strings
-        smallwords_threshold: int
-            threshold of small word
-
-        Returns
-        -------
-        list
-        """
-        self.tokens = [word for word in self.tokens if len(word) > smallwords_threshold]
-        return self.tokens
+
+def remove_stopwords(tokens, stopwords: list) -> str:
+    """
+    Remove stopwords from a text.
+    eg. 'I like when you move your body !' -> 'I move body !'
+
+    Parameters
+    ----------
+    stopwords : list of stopwords to remove
+
+    Returns
+    -------
+    list
+        tokens without stopwords
+
+    Raises
+    ------
+    ValueError
+        When inputs is not a list
+    """
+    tokens = [word for word in tokens if word not in stopwords]
+    return tokens
+
+
+def remove_tokens_with_nonletters(tokens) -> list:
+    """
+    Inputs a list of tokens, outputs a list of tokens without tokens that
+    includes numbers of special caracters.
+    ['foo','bar','124','34euros'] -> ['foo','bar']
+
+    Parameters
+    ----------
+    tokens : list
+        list of tokens to be cleaned
+
+    Returns
+    -------
+    list
+        list of tokens without tokens with numbers
+    """
+    tokens = [word for word in tokens if re.search("[^a-zA-Z]", word) is None]
+    return tokens
+
+
+def remove_special_caracters_from_tokenslist(tokens) -> list:
+    """
+    Remove tokens that doesn't contains any number or letter.
+    eg. ['foo','bar','---',"'s",'#'] -> ['foo','bar',"'s"]
+
+    Parameters
+    ----------
+    tokens : list
+        list of tokens to be cleaned
+
+    Returns
+    -------
+    list
+        list of tokens without tokens that contains only special caracters
+
+    """
+    tokens = [word for word in tokens if re.search("[a-zA-Z0-9]", word)]
+    return tokens
+
+
+def remove_smallwords(tokens, smallwords_threshold: int) -> list:
+    """
+    Function that removes words which length is below a threshold
+    ["hello", "my", "name", "is", "John", "Doe"] --> ["hello","name","John","Doe"]
+
+    Parameters
+    ----------
+    text : list
+        list of strings
+    smallwords_threshold: int
+        threshold of small word
+
+    Returns
+    -------
+    list
+    """
+    tokens = [word for word in tokens if len(word) > smallwords_threshold]
+    return tokens
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 37524f8..90d448c 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,9 +17,18 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from nautilus_nlp.preprocessing.text_preprocess import TextPreprocessor
-from nautilus_nlp.preprocessing.social_preprocess import SocialPreprocessor
-from nautilus_nlp.preprocessing.token_preprocess import TokenPreprocessor
+from nautilus_nlp.preprocessing.text_preprocess import (normalize_whitespace, remove_eol_characters,
+                                                        fix_bad_unicode, unpack_english_contractions,
+                                                        replace_urls, replace_emails, replace_phone_numbers,
+                                                        replace_numbers, replace_currency_symbols, remove_punct,
+                                                        remove_accents, remove_multiple_spaces_and_strip_text,
+                                                        filter_non_latin_characters)
+from nautilus_nlp.preprocessing.social_preprocess import (remove_mentions, extract_mentions, remove_html_tags,
+                                                          remove_emoji, convert_emoji_to_text, extract_emojis,
+                                                          extract_hashtags, remove_hashtag)
+from nautilus_nlp.preprocessing.token_preprocess import (remove_stopwords, remove_tokens_with_nonletters,
+                                                         remove_special_caracters_from_tokenslist,
+                                                         remove_smallwords)
 import nautilus_nlp.utils.phone_number as phone
 from nautilus_nlp.utils.stopwords import get_stopwords
 
@@ -30,8 +39,7 @@
                           ("This is a text without emojis",
                            [])])
 def test_extract_emojis(text, expected_result):
-    preprocessor = SocialPreprocessor(text)
-    result = preprocessor.extract_emojis()
+    result = extract_emojis(text)
     assert expected_result == result
 
 
@@ -41,8 +49,7 @@ def test_extract_emojis(text, expected_result):
                           ("This is a text without mentions",
                            "This is a text without mentions")])
 def test_remove_mentions(text, expected_result):
-    preprocessor = SocialPreprocessor(text)
-    result = preprocessor.remove_mentions()
+    result = remove_mentions(text)
     assert expected_result == result
 
 
@@ -52,8 +59,7 @@ def test_remove_mentions(text, expected_result):
                           ("This is a text without mentions",
                            [])])
 def test_extract_mentions(text, expected_result):
-    preprocessor = SocialPreprocessor(text)
-    result = preprocessor.extract_mentions()
+    result = extract_mentions(text)
     assert expected_result == result
 
 
@@ -63,8 +69,7 @@ def test_extract_mentions(text, expected_result):
                           ("This is a text without html tags",
                            "This is a text without html tags")])
 def test_remove_html_tags(text, expected_result):
-    preprocessor = SocialPreprocessor(text)
-    result = preprocessor.remove_html_tags()
+    result = remove_html_tags(text)
     assert expected_result == result
 
 
@@ -76,8 +81,7 @@ def test_remove_html_tags(text, expected_result):
                            2,
                            ["This", "text", "contains", "only", "long", "words"])])
 def test_remove_smallwords(tokens_list, smallwords_threshold, expected_result):
-    preprocessor = TokenPreprocessor(tokens_list)
-    result = preprocessor.remove_smallwords(smallwords_threshold)
+    result = remove_smallwords(tokens_list, smallwords_threshold)
     assert expected_result == result
 
 
@@ -94,8 +98,7 @@ def test_remove_smallwords(tokens_list, smallwords_threshold, expected_result):
                            ["#many", "#hashtags"])]
                          )
 def test_extract_hashtags(text, expected_result):
-    preprocessor = SocialPreprocessor(text)
-    result = preprocessor.extract_hashtags()
+    result = extract_hashtags(text)
     assert expected_result == result
 
 
@@ -112,8 +115,7 @@ def test_extract_hashtags(text, expected_result):
                            "this is a text with")]
                          )
 def test_remove_hashtag(text, expected_result):
-    preprocessor = SocialPreprocessor(text)
-    result = preprocessor.remove_hashtag()
+    result = remove_hashtag(text)
     assert expected_result == result
 
 
@@ -121,8 +123,7 @@ def test_remove_hashtag(text, expected_result):
                          [("كلمات Learn 3 Arabic كلمات words EASILY- Vocabulary #1 تعلم ٣ جديدة",
                            "Learn 3 Arabic words EASILY Vocabulary 1")])
 def test_filter_non_latin_characters(text, expected_filtered_text):
-    preprocessor = TextPreprocessor(text)
-    result = preprocessor.filter_non_latin_characters()
+    result = filter_non_latin_characters(text)
     assert expected_filtered_text == result
 
 
@@ -137,8 +138,7 @@ def test_filter_non_latin_characters(text, expected_filtered_text):
     ],
 )
 def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.remove_multiple_spaces_and_strip_text()
+    result = remove_multiple_spaces_and_strip_text(input_str)
     np.testing.assert_string_equal(result, expected_str)
 
 
@@ -151,24 +151,21 @@ def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
     ],
 )
 def test_remove_eol_characters(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.remove_eol_characters()
+    result = remove_eol_characters(input_str)
     np.testing.assert_string_equal(result, expected_str)
 
 
 def test_remove_tokens_with_nonletters():
     input_tokens = ['foo', 'bar', '124', '34euros']
     expected_output = ['foo', 'bar']
-    preprocessor = TokenPreprocessor(input_tokens)
-    result = preprocessor.remove_tokens_with_nonletters()
+    result = remove_tokens_with_nonletters(input_tokens)
     np.testing.assert_array_equal(result, expected_output)
 
 
 def test_remove_special_caracters_from_tokenslist():
     input_tokens = ['foo', 'bar', '---', "'s", '#']
     expected_output = ['foo', 'bar', "'s"]
-    preprocessor = TokenPreprocessor(input_tokens)
-    result = preprocessor.remove_special_caracters_from_tokenslist()
+    result = remove_special_caracters_from_tokenslist(input_tokens)
     np.testing.assert_array_equal(result, expected_output)
 
 
@@ -187,29 +184,14 @@ def test_get_stopwords():
 )
 def test_remove_stopwords_tokens(input_tokens, expected_output):
     stopwords = get_stopwords('en')
-    preprocessor = TokenPreprocessor(input_tokens)
-    result = preprocessor.remove_stopwords(stopwords)
-    np.testing.assert_array_equal(result, expected_output)
-
-
-@pytest.mark.parametrize(
-    "input_str, expected_output",
-    [
-        ('I like when you move your body !', 'I move body !'),
-    ],
-)
-def test_remove_stopwords_text(input_str, expected_output):
-    stopwords = get_stopwords('en')
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.remove_stopwords(stopwords)
+    result = remove_stopwords(input_tokens, stopwords)
     np.testing.assert_array_equal(result, expected_output)
 
 
 def test_remove_accents():
     input_str = "éèëêàù"
     expected_str = "eeeeau"
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.remove_accents()
+    result = remove_accents(input_str)
     np.testing.assert_string_equal(result, expected_str)
 
 
@@ -231,8 +213,7 @@ def test_remove_accents():
      ('18 mois de trop....ca suffit macron', '18 mois de trop....ca suffit macron'),
      ('Egalité devant les infractions routières', 'Egalité devant les infractions routières')],)
 def test_fix_bad_unicode(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.fix_bad_unicode()
+    result = fix_bad_unicode(input_str)
     np.testing.assert_string_equal(result, expected_str)
 
 
@@ -244,8 +225,7 @@ def test_fix_bad_unicode(input_str, expected_str):
     ],
 )
 def test_normalize_whitespace(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.normalize_whitespace()
+    result = normalize_whitespace(input_str)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -261,8 +241,7 @@ def test_normalize_whitespace(input_str, expected_str):
     ]
 )
 def test_unpack_english_contractions(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.unpack_english_contractions()
+    result = unpack_english_contractions(input_str)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -278,8 +257,7 @@ def test_unpack_english_contractions(input_str, expected_str):
      ("Ishttps://waaaou.com/ available?", 'Is*URL* available?'),
      ("mailto:hugo.vasselin@artefact.com", '*URL*')])
 def test_replace_urls(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.replace_urls()
+    result = replace_urls(input_str)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -293,8 +271,7 @@ def test_replace_urls(input_str, expected_str):
     ]
 )
 def test_replace_emails(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.replace_emails()
+    result = replace_emails(input_str)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -315,12 +292,11 @@ def test_replace_emails(input_str, expected_str):
     ]
 )
 def test_replace_phone_numbers(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.replace_phone_numbers(
+    result = replace_phone_numbers(
+        input_str,
         replace_with="*PHONE*",
         method="detection",
-        country_format_to_detect=phone.SUPPORTED_COUNTRY
-    )
+        country_format_to_detect=phone.SUPPORTED_COUNTRY)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -334,8 +310,7 @@ def test_replace_phone_numbers(input_str, expected_str):
     ]
 )
 def test_replace_numbers(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.replace_numbers()
+    result = replace_numbers(input_str)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -350,8 +325,7 @@ def test_replace_numbers(input_str, expected_str):
     ]
 )
 def test_replace_currency_symbols(input_str, param, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.replace_currency_symbols(replace_with=param)
+    result = replace_currency_symbols(input_str, replace_with=param)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -377,8 +351,7 @@ def test_replace_currency_symbols(input_str, param, expected_str):
     ]
 )
 def test_remove_punct(input_str, param, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.remove_punct(marks=param)
+    result = remove_punct(input_str, marks=param)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -396,8 +369,7 @@ def test_remove_punct(input_str, param, expected_str):
     ]
 )
 def test_remove_emoji(input_str, expected_str):
-    preprocessor = SocialPreprocessor(input_str)
-    result = preprocessor.remove_emoji()
+    result = remove_emoji(input_str)
     np.testing.assert_equal(result, expected_str)
 
 
@@ -411,6 +383,5 @@ def test_remove_emoji(input_str, expected_str):
     ]
 )
 def test_convert_emoji_to_text(input_str, expected_str):
-    preprocessor = SocialPreprocessor(input_str)
-    result = preprocessor.convert_emoji_to_text()
+    result = convert_emoji_to_text(input_str)
     np.testing.assert_equal(result, expected_str)

From ec3826019f54bbb84d4ac53da20c4e88527c1891 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 5 Nov 2020 16:19:45 +0100
Subject: [PATCH 367/496] removing duplicated test

---
 tests/test_fix_bad_encoding.py | 44 ----------------------------------
 1 file changed, 44 deletions(-)
 delete mode 100644 tests/test_fix_bad_encoding.py

diff --git a/tests/test_fix_bad_encoding.py b/tests/test_fix_bad_encoding.py
deleted file mode 100644
index cfc21ba..0000000
--- a/tests/test_fix_bad_encoding.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-
-import pytest
-import numpy as np
-from nautilus_nlp.preprocessing.text_preprocess import TextPreprocessor
-
-
-@pytest.mark.parametrize(
-    "input_str, expected_str",
-    [('Les augmentations de rémunérations', 'Les augmentations de rémunérations'),
-     ("rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social",
-      "rénover l'enquête publique pour en faire un vrai outil  d'aménagement du territoire et de dialogue social"),
-     ('Limitations de vitesse et sécurité routière', 'Limitations de vitesse et sécurité routière'),
-     ('Pour un nouveau contrat citoyen', 'Pour un nouveau contrat citoyen'),
-     (
-         'Développer les démarches de budget participatif dans les collectivités et associer les citoyens '\
-           'dans la réalisation des projets',
-         'Développer les démarches de budget participatif dans les collectivités et associer les citoyens '\
-           'dans la réalisation des projets'),
-     ('proportienelle', 'proportienelle'),
-     ('Pour plus de démocratie participative', 'Pour plus de démocratie participative'),
-     ('Transparence de la vie public', 'Transparence de la vie public'),
-     ('18 mois de trop....ca suffit macron', '18 mois de trop....ca suffit macron'),
-     ('Egalité devant les infractions routières', 'Egalité devant les infractions routières')],)
-def test_remove_multiple_spaces_and_strip_text(input_str, expected_str):
-    preprocessor = TextPreprocessor(input_str)
-    result = preprocessor.fix_bad_unicode()
-    np.testing.assert_string_equal(result, expected_str)

From 3341997f801941b26837aa3f89551c1aeab8de0b Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 5 Nov 2020 17:42:37 +0100
Subject: [PATCH 368/496] adding preprocessor object

---
 nautilus_nlp/preprocessing/preprocessor.py | 35 ++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 nautilus_nlp/preprocessing/preprocessor.py

diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
new file mode 100644
index 0000000..33c8169
--- /dev/null
+++ b/nautilus_nlp/preprocessing/preprocessor.py
@@ -0,0 +1,35 @@
+from inspect import getmembers, isfunction
+
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import FunctionTransformer
+
+from nautilus_nlp.preprocessing import social_preprocess
+from nautilus_nlp.preprocessing import text_preprocess
+
+
+class Preprocessor(object):
+    def __init__(
+            self, social_pipelines=None, text_pipelines=None):
+        """
+        """
+        if social_pipelines is None:
+            self.social_pipelines = Pipeline(
+                steps=[(function_name, FunctionTransformer(function_callable))
+                       for function_name, function_callable in getmembers(social_preprocess)
+                       if isfunction(function_callable)])
+        if text_pipelines is None:
+            self.text_pipelines = Pipeline(
+                steps=[(function_name, FunctionTransformer(function_callable))
+                       for function_name, function_callable in getmembers(text_preprocess)
+                       if isfunction(function_callable)])
+
+    def apply_social_pipeline(self, text):
+        return self.social_pipelines.fit_transform(text)
+
+    def apply_text_pipeline(self, text):
+        return self.text_pipelines.fit_transform(text)
+
+    def apply_all_pipeline(self, text):
+        text = self.apply_social_pipeline(text)
+        text = self.apply_text_pipeline(text)
+        return text

From fce1548dee622745e9623576f80c5cfed32826b9 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Fri, 6 Nov 2020 15:50:43 +0100
Subject: [PATCH 369/496] modified test to see if ci working

---
 tests/test_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 37524f8..b605202 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -74,7 +74,7 @@ def test_remove_html_tags(text, expected_result):
                            ["take", "care", "skin"]),
                           (["This", "text", "contains", "only", "long", "words"],
                            2,
-                           ["This", "text", "contains", "only", "long", "words"])])
+                           ["This", "text", "contains", "only", "nimportequoi", "words"])])
 def test_remove_smallwords(tokens_list, smallwords_threshold, expected_result):
     preprocessor = TokenPreprocessor(tokens_list)
     result = preprocessor.remove_smallwords(smallwords_threshold)

From 7883e0dbf4c534e7d96709ef11d72129d12d7bf2 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Fri, 6 Nov 2020 16:42:26 +0100
Subject: [PATCH 370/496] fixed tests so CI works

---
 tests/test_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index b605202..37524f8 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -74,7 +74,7 @@ def test_remove_html_tags(text, expected_result):
                            ["take", "care", "skin"]),
                           (["This", "text", "contains", "only", "long", "words"],
                            2,
-                           ["This", "text", "contains", "only", "nimportequoi", "words"])])
+                           ["This", "text", "contains", "only", "long", "words"])])
 def test_remove_smallwords(tokens_list, smallwords_threshold, expected_result):
     preprocessor = TokenPreprocessor(tokens_list)
     result = preprocessor.remove_smallwords(smallwords_threshold)

From 556b41591cfabfdea816555c8b55043e4b2c7a84 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Fri, 6 Nov 2020 18:12:48 +0100
Subject: [PATCH 371/496] removed travis

---
 .github/workflows/ci_actions.yml |  2 ++
 .travis.yml                      | 38 --------------------------------
 2 files changed, 2 insertions(+), 38 deletions(-)
 delete mode 100644 .travis.yml

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index fceb231..62d08d4 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -35,6 +35,8 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: Install requirements
         run: 
+          wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
+          python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
           python -m pip install --upgrade pip
           pip install -r requirements.txt
 
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index e99a71f..0000000
--- a/.travis.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-language: python
-
-os:
-  - linux
-services:
-  - docker
-
-before_script:
-  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
-  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
-
-  - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
-  - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
-  
-install:
-  - pip install -r requirements.txt
-  - pip install -e .
-script:
-  - pylint nautilus_nlp/
-  - pylint tests/
-  - pytest tests/*

From e12217b684e4a3d284f3392d10eb5ebda3427843 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Fri, 6 Nov 2020 18:16:53 +0100
Subject: [PATCH 372/496] updated ci

---
 .github/workflows/ci_actions.yml | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 62d08d4..b2d87e1 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -29,14 +29,25 @@ jobs:
 
     steps:
       - uses: actions/checkout@v2
+
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install requirements
-        run: 
+
+      - name: Install fastText and spacy models
+        run:
           wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
-          python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
+          python3 -m spacy download fr
+          python3 -m spacy download en
+          python3 -m spacy download de
+          python3 -m spacy download nl
+          python3 -m spacy download it
+          python3 -m spacy download xx
+          python3 -m spacy validate
+
+      - name: Install requirements
+        run:
           python -m pip install --upgrade pip
           pip install -r requirements.txt
 

From b3a3c6fc688ff675c2b75c9a67d2febf86b8ee52 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Fri, 6 Nov 2020 18:21:14 +0100
Subject: [PATCH 373/496] separated spacy models and fasttext in ci

---
 .github/workflows/ci_actions.yml | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index b2d87e1..8d99ada 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -35,9 +35,16 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install fastText and spacy models
+      - name: Install fastText
+        run:
+          wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  
+          unzip v0.2.0.zip
+          cd fastText-0.2.0
+          make
+          pip install .
+
+      - name: Install spacy models
         run:
-          wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
           python3 -m spacy download fr
           python3 -m spacy download en
           python3 -m spacy download de

From 290602e3f1750fdefe84fcd27617ed59c13adbf2 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Fri, 6 Nov 2020 18:25:49 +0100
Subject: [PATCH 374/496] removed fast text for ci temporarely

---
 .github/workflows/ci_actions.yml | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 8d99ada..edfe649 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -35,14 +35,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install fastText
-        run:
-          wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  
-          unzip v0.2.0.zip
-          cd fastText-0.2.0
-          make
-          pip install .
-
       - name: Install spacy models
         run:
           python3 -m spacy download fr

From 642f1a9c47b57835772816955fc0fb1668491323 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Fri, 6 Nov 2020 18:27:25 +0100
Subject: [PATCH 375/496] install spacy models  after requirements

---
 .github/workflows/ci_actions.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index edfe649..7677aa1 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -35,6 +35,11 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
+      - name: Install requirements
+        run:
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
       - name: Install spacy models
         run:
           python3 -m spacy download fr
@@ -45,11 +50,6 @@ jobs:
           python3 -m spacy download xx
           python3 -m spacy validate
 
-      - name: Install requirements
-        run:
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt
-
       - name: Run pylint
         run: |
           pip install pylint

From 4cbafcde255a17e2ecb8bd5b5227e1a3418648f4 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Mon, 9 Nov 2020 14:19:12 +0100
Subject: [PATCH 376/496] removed spacy install in ci

---
 .github/workflows/ci_actions.yml | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 7677aa1..859f7c6 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -40,16 +40,6 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements.txt
 
-      - name: Install spacy models
-        run:
-          python3 -m spacy download fr
-          python3 -m spacy download en
-          python3 -m spacy download de
-          python3 -m spacy download nl
-          python3 -m spacy download it
-          python3 -m spacy download xx
-          python3 -m spacy validate
-
       - name: Run pylint
         run: |
           pip install pylint

From b87766d5d18508ee2b5675a8f65b2d88915b88bc Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 12:26:39 +0100
Subject: [PATCH 377/496] refacto: add typo

---
 .../topic_modeling/topic_modeling_short_text.py       | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
index bc0a94e..56580c3 100644
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -18,6 +18,7 @@
 import re
 from collections import Counter
 from itertools import product
+from typing import List
 
 import numpy as np
 import pyLDAvis
@@ -25,12 +26,12 @@
 from nautilus_nlp.topic_modeling.seanmf_model import SeaNMF
 
 
-def prepare_data(text: str, vocab_min_count: int = 1, vocab_max_size: int = 10000):
+def prepare_data(docs: List[str], vocab_min_count: int = 1, vocab_max_size: int = 10000):
     """
     Parameters
     ----------
-    text : str
-        list of str on which the topic modeling will be performed
+    docs : list
+        list of sentences on which the topic modeling will be performed
     vocab_min_count : int
         minimum number of occurrences of a word to be considered in the vocabulary
     vocab_max_size : int
@@ -49,7 +50,7 @@ def prepare_data(text: str, vocab_min_count: int = 1, vocab_max_size: int = 1000
 
     # Tokens_list is a list of sub-lists where each sub-list contains a sentences' tokens.
     tokens_list = []
-    for sentence in text:
+    for sentence in docs:
         sentence = re.split(r'\s', sentence)
         tokens_list.append(sentence)
     vocab = dict(Counter(x for xs in tokens_list for x in xs))
@@ -66,7 +67,7 @@ def prepare_data(text: str, vocab_min_count: int = 1, vocab_max_size: int = 1000
 
     # Create ID representation of text (ie: each sentence is a list of vocabId )
     encoded_text_id = []
-    for sentence in text:
+    for sentence in docs:
         sentence = re.split(r'\s', sentence)
         sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
         encoded_text_id.append(sentence)

From 34cae88bbb9a4256f8dd90b7fb44494e068d008c Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:13:27 +0100
Subject: [PATCH 378/496] feat: add docstring

---
 nautilus_nlp/utils/file_loader.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 81a8deb..437bd7d 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -98,10 +98,13 @@ def text_loader(filepath: str, encoding: Optional[str] = None, detectencoding: b
 
 
 def get_subfolders_path(folder: str) -> list:
+    """
+    Get a list of all the subfolder for a folder path
+    """
     if not folder.endswith("/"):
         folder = folder + "/"
     return [
-        folder + f+'/' for f in os.listdir(folder) 
+        folder + f+'/' for f in os.listdir(folder)
         if os.path.isdir(os.path.join(folder, f)) and f != ".DS_Store"
         ]
 

From 8ee5605b0d990b57ae55137ad0436169d51d06d9 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:13:44 +0100
Subject: [PATCH 379/496] fix: change error type

---
 tests/test_biterm.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_biterm.py b/tests/test_biterm.py
index 21e7320..b7fc830 100644
--- a/tests/test_biterm.py
+++ b/tests/test_biterm.py
@@ -63,7 +63,7 @@ def text_input_parameter_error_handling(input_text
                                         , input_nb_topic
                                         , input_nb_iteration
                                         , input_language):
-    with pytest.raises(ValueError):
+    with pytest.raises(TypeError):
         BitermModel(data=input_text, nb_topics=input_nb_topic, nb_iteration=input_nb_iteration, lang=input_language)
 
 

From 7ab683ad6f03986c3d92d6a01fb010eb05f3ce1b Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:13:58 +0100
Subject: [PATCH 380/496] fix: typo

---
 nautilus_nlp/topic_modeling/lda.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/topic_modeling/lda.py b/nautilus_nlp/topic_modeling/lda.py
index e9ffa75..0b5b257 100644
--- a/nautilus_nlp/topic_modeling/lda.py
+++ b/nautilus_nlp/topic_modeling/lda.py
@@ -81,7 +81,7 @@ def create_bow_corpus(data, dictionary):
     Returns
     -------
     list
-        listof list of tuples
+        list of list of tuples
     """
     corpus = [dictionary.doc2bow(text) for text in data]
     return corpus

From 5c51fd214033aa979f2b752b2eb69d5676b29e31 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:14:41 +0100
Subject: [PATCH 381/496] refacto: import functions rather than everything

---
 nautilus_nlp/topic_modeling/biterm_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/biterm_model.py b/nautilus_nlp/topic_modeling/biterm_model.py
index 0d66b7b..83c4eff 100644
--- a/nautilus_nlp/topic_modeling/biterm_model.py
+++ b/nautilus_nlp/topic_modeling/biterm_model.py
@@ -17,7 +17,7 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from typing import List, Any
 import numpy as np
-import pyLDAvis
+from pyLDAvis import prepare, save_html
 from biterm.btm import oBTM
 from biterm.utility import topic_summuary, vec_to_biterms
 from sklearn.feature_extraction.text import CountVectorizer
@@ -162,9 +162,9 @@ def save_pyldavis_plot_as_html(self, path_to_output: str = './biterm_pyLDAavis_p
         if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
             raise ValueError("Model needs to be trained first")
 
-        vis = pyLDAvis.prepare(
+        vis = prepare(
             self._btm.phi_wz.T, self._topics,
             np.count_nonzero(self._vectorize_text, axis=1), self._vocabulary,
             np.sum(self._vectorize_text, axis=0)
         )
-        pyLDAvis.save_html(vis, path_to_output)
+        save_html(vis, path_to_output)

From a823dde238df73808720c008258a0e31dab59898 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:17:52 +0100
Subject: [PATCH 382/496] refacto: black this shit out

---
 .../topic_modeling_short_text.py              | 95 +++++++++++--------
 1 file changed, 56 insertions(+), 39 deletions(-)

diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
index 56580c3..cc8f6f5 100644
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
@@ -26,7 +26,9 @@
 from nautilus_nlp.topic_modeling.seanmf_model import SeaNMF
 
 
-def prepare_data(docs: List[str], vocab_min_count: int = 1, vocab_max_size: int = 10000):
+def prepare_data(
+    docs: List[str], vocab_min_count: int = 1, vocab_max_size: int = 10000
+):
     """
     Parameters
     ----------
@@ -38,7 +40,7 @@ def prepare_data(docs: List[str], vocab_min_count: int = 1, vocab_max_size: int
         maximum number of word in the vocabulary
 
     Returns
-    -------        
+    -------
     list
         list of encoded sentences using vocab IDs
     list
@@ -51,7 +53,7 @@ def prepare_data(docs: List[str], vocab_min_count: int = 1, vocab_max_size: int
     # Tokens_list is a list of sub-lists where each sub-list contains a sentences' tokens.
     tokens_list = []
     for sentence in docs:
-        sentence = re.split(r'\s', sentence)
+        sentence = re.split(r"\s", sentence)
         tokens_list.append(sentence)
     vocab = dict(Counter(x for xs in tokens_list for x in xs))
 
@@ -68,7 +70,7 @@ def prepare_data(docs: List[str], vocab_min_count: int = 1, vocab_max_size: int
     # Create ID representation of text (ie: each sentence is a list of vocabId )
     encoded_text_id = []
     for sentence in docs:
-        sentence = re.split(r'\s', sentence)
+        sentence = re.split(r"\s", sentence)
         sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
         encoded_text_id.append(sentence)
 
@@ -76,19 +78,26 @@ def prepare_data(docs: List[str], vocab_min_count: int = 1, vocab_max_size: int
 
 
 def train_shorttext_model(
-    model_name: str, encoded_text_id: list, vocab_list: list, n_topics: int = 20,
-    max_iter: int = 20, max_err: float = 0.1, alpha: float = 0, beta: float = 0):
+    model_name: str,
+    encoded_text_id: list,
+    vocab_list: list,
+    n_topics: int = 20,
+    max_iter: int = 20,
+    max_err: float = 0.1,
+    alpha: float = 0,
+    beta: float = 0,
+):
     """
     Parameters
-    ----------    
+    ----------
     model_name : str {'nmf','seanmf'}
     encoded_text_id : list
         list of encoded sentences
     vocab_list : list
         list of vocabulary
-    n_topics : int 
+    n_topics : int
         number of topics
-    max_iter : int 
+    max_iter : int
         maximum number of iterations while training
     max_err : float
         training error
@@ -98,7 +107,7 @@ def train_shorttext_model(
         regularization param for the NMF model
 
     Returns
-    -------        
+    -------
     Trained NMF model
 
     Raises
@@ -110,7 +119,7 @@ def train_shorttext_model(
     n_docs = len(encoded_text_id)
     n_terms = len(vocab_list)
 
-    if model_name == 'nmf':
+    if model_name == "nmf":
         dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
         return NMF(
             dt_mat,
@@ -118,9 +127,10 @@ def train_shorttext_model(
             mat_ih=[],
             n_topic=n_topics,
             max_iter=max_iter,
-            max_err=max_err)
+            max_err=max_err,
+        )
 
-    elif model_name == 'seanmf':
+    if model_name == "seanmf":
         # Calculate co-occurence matrix
         cooc_mat = __build_cooccurence_matrix(n_terms, encoded_text_id)
         # Calculate PPMI
@@ -128,7 +138,8 @@ def train_shorttext_model(
         # Build doc-term matrix
         dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
         return SeaNMF(
-            dt_mat, mat_ss,
+            dt_mat,
+            mat_ss,
             mat_iw=[],
             mat_iwc=[],
             mat_ih=[],
@@ -137,16 +148,19 @@ def train_shorttext_model(
             n_topic=n_topics,
             max_iter=max_iter,
             max_err=max_err,
-            fix_seed=1024)
+            fix_seed=1024,
+        )
     raise ValueError("Invalid model name: Use nmf or seanmf")
 
 
-def show_dominant_topic(model, encoded_text_id: list, vocab_list: list, n_top_keyword: int = 10):
+def show_dominant_topic(
+    model, encoded_text_id: list, vocab_list: list, n_top_keyword: int = 10
+):
     """
     Computes the PMi score for each topic and the topKeywords describing each of them.
 
     Parameters
-    ----------    
+    ----------
     - model
         trained NMF model
     - encoded_text_id : list
@@ -157,13 +171,15 @@ def show_dominant_topic(model, encoded_text_id: list, vocab_list: list, n_top_ke
         the number of keywords to be returned
 
     Returns
-    -------    
+    -------
     dict
         A dictionnary with the topic number and its top keywords
-    dict 
+    dict
         A ictionnary with the topic number and its PMI score
     """
-    dt_mat = __build_cooccurence_matrix(n_terms=len(vocab_list), encoded_text_id=encoded_text_id)
+    dt_mat = __build_cooccurence_matrix(
+        n_terms=len(vocab_list), encoded_text_id=encoded_text_id
+    )
     np.fill_diagonal(dt_mat, 0)
     mat_w, _ = model.get_decomposition_matrix()
     n_topic = mat_w.shape[1]
@@ -212,7 +228,7 @@ def get_assigned_topics(model):
 def show_pyldavis(model, encoded_text_id, vocab_arr):
     """
     Parameters
-    ----------    
+    ----------
     model
         trained model
     encoded_text_id
@@ -221,7 +237,7 @@ def show_pyldavis(model, encoded_text_id, vocab_arr):
         array of vocabulary frequency
 
     Returns
-    -------        
+    -------
     pyldavis topics plot
     """
 
@@ -238,7 +254,7 @@ def prepare_data_pyldavis(model, encoded_text_id, vocab_arr) -> dict:
     link : http://jeriwieringa.com/2018/07/17/pyLDAviz-and-Mallet/
 
     Returns
-    -------       
+    -------
     dict
         dict of data needed by pyldavis
     """
@@ -253,13 +269,12 @@ def prepare_data_pyldavis(model, encoded_text_id, vocab_arr) -> dict:
     # Document-term matrix theta
     theta = mat_h / mat_h.sum(axis=1, keepdims=True)
     return {
-        'topic_term_dists': phi,
-        'doc_topic_dists': theta,
-        'doc_lengths': doc_length_values,
-        'vocab': list_vocab,
-        'term_frequency': freq_vocab
-        }
-     
+        "topic_term_dists": phi,
+        "doc_topic_dists": theta,
+        "doc_lengths": doc_length_values,
+        "vocab": list_vocab,
+        "term_frequency": freq_vocab,
+    }
 
 
 def __build_cooccurence_matrix(n_terms: int, encoded_text_id: list):
@@ -268,13 +283,13 @@ def __build_cooccurence_matrix(n_terms: int, encoded_text_id: list):
     appeared in the same context as another word from the vocabulary.
     The matrix has n_terms x n_terms size, columns and rows denote the vocab.
     Cell values represent the number of times words occured together in the same sentence.
-    
+
     Parameters
     ----------
     n_terms : int
     encoded_text_id : list
         list of encoded sentences
-    
+
     Returns
     -------
     co-occurence matrix
@@ -289,14 +304,14 @@ def __build_cooccurence_matrix(n_terms: int, encoded_text_id: list):
 
 def __calculate_ppmi(cooc_mat, n_terms):
     mat_d1 = np.sum(cooc_mat)
-    print('D1= ', mat_d1)
+    print("D1= ", mat_d1)
     mat_ss = mat_d1 * cooc_mat
-    print('SS= ', mat_ss)
+    print("SS= ", mat_ss)
     for k in range(n_terms):
         mat_ss[k] /= np.sum(cooc_mat[k])
     for k in range(n_terms):
         mat_ss[:, k] /= np.sum(cooc_mat[:, k])
-    print('SS = ', mat_ss)
+    print("SS = ", mat_ss)
     cooc_mat = []  # release memory
     mat_ss[mat_ss == 0] = 1.0
     mat_ss = np.log(mat_ss)
@@ -313,10 +328,10 @@ def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
 
 
 def __calculate_pmi(mat_aa, top_keywords_index):
-    '''
+    """
     Method to compute PMi score
     Reference: Short and Sparse Text Topic Modeling via Self-Aggregation
-    '''
+    """
     mat_d1 = np.sum(mat_aa)
     n_tp = len(top_keywords_index)
     mat_pmi = []
@@ -328,6 +343,8 @@ def __calculate_pmi(mat_aa, top_keywords_index):
                 else:
                     mat_c1 = np.sum(mat_aa[index1])
                     mat_c2 = np.sum(mat_aa[index2])
-                    mat_pmi.append(np.log(mat_aa[index1,index2]*mat_d1/mat_c1/mat_c2))
-    avg_pmi = 2.0*np.sum(mat_pmi)/float(n_tp)/(float(n_tp)-1.0)
+                    mat_pmi.append(
+                        np.log(mat_aa[index1, index2] * mat_d1 / mat_c1 / mat_c2)
+                    )
+    avg_pmi = 2.0 * np.sum(mat_pmi) / float(n_tp) / (float(n_tp) - 1.0)
     return avg_pmi

From 648560eaed4779328474def544b2dff39e4f1772 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:22:15 +0100
Subject: [PATCH 383/496] fix: add missing param

---
 nautilus_nlp/utils/phone_number.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index e03b47e..7f72bb5 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -84,7 +84,8 @@ def extract_phone_numbers(text: str, countrylist: list) -> list:
 
     Parameters
     ----------
-    countrylist: list (eg. [None,'FR','US','GB'])
+    text : str
+    countrylist : list (eg. [None,'FR','US','GB'])
         Look for phone numbers formatted according to the specified countlist.
         supported value: look SUPPORTED_COUNTRY variable.
 

From 68f3ed4dd144010112e93cdd40c8fae1b821651d Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:22:33 +0100
Subject: [PATCH 384/496] fix: add python 3.8 support

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 00dc863..c51eb80 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -42,3 +42,4 @@ biterm==0.1.5
 nlpaug==1.0.1
 ipython==7.16.1
 pylint==2.4.4
+scikit-learn==0.23.2

From 7e008f1a2ada617097260f2d2cfe9e26595971b0 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:30:38 +0100
Subject: [PATCH 385/496] test: requirements

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c51eb80..363bb7f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,6 +8,7 @@ coverage
 python-dotenv>=0.5.1
 pillow
 pytest
+scikit-learn==0.23.2
 
 #library requirements
 pyLDAvis==2.1.2
@@ -42,4 +43,3 @@ biterm==0.1.5
 nlpaug==1.0.1
 ipython==7.16.1
 pylint==2.4.4
-scikit-learn==0.23.2

From 592d03443d8d0c19cd23381519dfca1f81177171 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:33:08 +0100
Subject: [PATCH 386/496] text: attempt to fix requirements

---
 requirements.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 363bb7f..872256a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,7 +8,6 @@ coverage
 python-dotenv>=0.5.1
 pillow
 pytest
-scikit-learn==0.23.2
 
 #library requirements
 pyLDAvis==2.1.2
@@ -31,7 +30,7 @@ setuptools==40.8.0
 textacy==0.6.3
 #fastText==0.8.3
 gensim==3.7.1
-scikit_learn==0.20.3
+scikit-learn==0.23.2
 vaderSentiment==3.2.1
 google-compute-engine==2.8.13
 flashtext==2.7

From 81ac21bbbf7889e82e55da98baf28cfd5636f165 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 13:48:17 +0100
Subject: [PATCH 387/496] fix: fix unit test

---
 tests/test_topic_modeling_short_text.py | 161 +++++++++++++++---------
 1 file changed, 102 insertions(+), 59 deletions(-)

diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
index 5d8ad7a..0103d4a 100644
--- a/tests/test_topic_modeling_short_text.py
+++ b/tests/test_topic_modeling_short_text.py
@@ -18,51 +18,84 @@
 import numpy as np
 import pytest
 from nautilus_nlp.topic_modeling.topic_modeling_short_text import (
-    __build_cooccurence_matrix, get_assigned_topics, prepare_data,
-    prepare_data_pyldavis, show_dominant_topic, train_shorttext_model)
-
-TEXT = ['Cola 1.5L Carrefour',
-        'Pepsi Cola Light 1.5L',
-        'Pepsi Cola Twist Light',
-        'Cola 1.5L CRF DISC',
-        'Coca-Cola Light 1.5L',
-        'Coca-Cola Light 4x0.5L',
-        'Coca-Cola Light 6x0.3L',
-        'Panzani 200g x 4 bio',
-        'Rustichella 150g bio',
-        'De Cecco - Fusilli bio',
-        'Gerblé sans Gluten50g',
-        'Penne de riz 100g sans gluten',
-        'Spaghetti de maïs 50g sans Glute']
-
-
-@pytest.mark.parametrize("input_text,  expected_output",
-                         [(TEXT,
-                           [['1.5L', 4],
-                            ['Coca-Cola', 3],
-                            ['Cola', 4],
-                            ['Light', 5],
-                            ['Pepsi', 2],
-                            ['bio', 3],
-                            ['de', 2],
-                            ['sans', 3]]),
-                          ([],
-                           []),
-                          (['', ''],
-                           []), ],)
+    __build_cooccurence_matrix,
+    get_assigned_topics,
+    prepare_data,
+    prepare_data_pyldavis,
+    show_dominant_topic,
+    train_shorttext_model,
+)
+
+TEXT = [
+    "Cola 1.5L Carrefour",
+    "Pepsi Cola Light 1.5L",
+    "Pepsi Cola Twist Light",
+    "Cola 1.5L CRF DISC",
+    "Coca-Cola Light 1.5L",
+    "Coca-Cola Light 4x0.5L",
+    "Coca-Cola Light 6x0.3L",
+    "Panzani 200g x 4 bio",
+    "Rustichella 150g bio",
+    "De Cecco - Fusilli bio",
+    "Gerblé sans Gluten50g",
+    "Penne de riz 100g sans gluten",
+    "Spaghetti de maïs 50g sans Glute",
+]
+
+
+@pytest.mark.parametrize(
+    "input_text,  expected_output",
+    [
+        (
+            TEXT,
+            (
+                [
+                    [2, 0],
+                    [4, 2, 3, 0],
+                    [4, 2, 3],
+                    [2, 0],
+                    [1, 3, 0],
+                    [1, 3],
+                    [1, 3],
+                    [5],
+                    [5],
+                    [5],
+                    [7],
+                    [6, 7],
+                    [6, 7],
+                ],
+                ["1.5L", "Coca-Cola", "Cola", "Light", "Pepsi", "bio", "de", "sans"],
+                [
+                    ["1.5L", 4],
+                    ["Coca-Cola", 3],
+                    ["Cola", 4],
+                    ["Light", 5],
+                    ["Pepsi", 2],
+                    ["bio", 3],
+                    ["de", 2],
+                    ["sans", 3],
+                ],
+            ),
+        )
+    ],
+)
 def test_prepare_data(input_text, expected_output):
     assert prepare_data(input_text) == expected_output
 
 
-@pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
+@pytest.mark.parametrize("model_name", ["nmf", "seanmf"])
 @pytest.mark.parametrize("n_topics", [3, 0])
 @pytest.mark.parametrize("n_keywords", [2, 0])
 def test_show_dominant_topic(model_name, n_topics, n_keywords):
 
     encoded_text_id, vocab_list, _ = prepare_data(TEXT)
 
-    model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
-    topics, pmi_score = show_dominant_topic(model, encoded_text_id, vocab_list, n_top_keyword=n_keywords)
+    model = train_shorttext_model(
+        model_name, encoded_text_id, vocab_list, n_topics=n_topics
+    )
+    topics, pmi_score = show_dominant_topic(
+        model, encoded_text_id, vocab_list, n_top_keyword=n_keywords
+    )
 
     assert len(pmi_score) == n_topics
     assert len(topics) == n_topics
@@ -70,11 +103,13 @@ def test_show_dominant_topic(model_name, n_topics, n_keywords):
         assert len(i) == n_keywords
 
 
-@pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
+@pytest.mark.parametrize("model_name", ["nmf", "seanmf"])
 @pytest.mark.parametrize("n_topics", [3])
 def test_get_assigned_topics(model_name, n_topics):
     encoded_text_id, vocab_list, _ = prepare_data(TEXT)
-    model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
+    model = train_shorttext_model(
+        model_name, encoded_text_id, vocab_list, n_topics=n_topics
+    )
     topics_list = get_assigned_topics(model)
 
     assert len(topics_list) == len(TEXT)
@@ -82,18 +117,20 @@ def test_get_assigned_topics(model_name, n_topics):
         assert topic_num < n_topics
 
 
-@pytest.mark.parametrize("model_name", ['nmf', 'seanmf'])
+@pytest.mark.parametrize("model_name", ["nmf", "seanmf"])
 @pytest.mark.parametrize("n_topics", [0, 3])
 def test_prepare_data_pyldavis(model_name, n_topics):
     encoded_text_id, vocab_list, vocab_arr = prepare_data(TEXT)
-    model = train_shorttext_model(model_name, encoded_text_id, vocab_list, n_topics=n_topics)
+    model = train_shorttext_model(
+        model_name, encoded_text_id, vocab_list, n_topics=n_topics
+    )
 
     data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
-    phi = data['topic_term_dists']
-    theta = data['doc_topic_dists']
-    doc_length_values = data['doc_lengths']
-    list_vocab = data['vocab']
-    freq_vocab = data['term_frequency']
+    phi = data["topic_term_dists"]
+    theta = data["doc_topic_dists"]
+    doc_length_values = data["doc_lengths"]
+    list_vocab = data["vocab"]
+    freq_vocab = data["term_frequency"]
 
     assert phi.shape == (n_topics, len(list_vocab))
     assert theta.shape == (len(TEXT), n_topics)
@@ -104,21 +141,27 @@ def test_prepare_data_pyldavis(model_name, n_topics):
 @pytest.mark.parametrize(
     "input_coded,  expected_output",
     [
-        ([[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]],
-         [[5., 1., 2., 4.],
-          [1., 2., 2., 3.],
-          [2., 2., 4., 4.],
-          [4., 3., 4., 6.]]),
-
-        ([[0, 1, 2, 3, 4], [5, 6], [1]],
-         [[1., 1., 1., 1., 1., 0., 0.],
-          [1., 2., 1., 1., 1., 0., 0.],
-          [1., 1., 1., 1., 1., 0., 0.],
-          [1., 1., 1., 1., 1., 0., 0.],
-          [1., 1., 1., 1., 1., 0., 0.],
-          [0., 0., 0., 0., 0., 1., 1.],
-          [0., 0., 0., 0., 0., 1., 1.]]
-         )
+        (
+            [[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]],
+            [
+                [5.0, 1.0, 2.0, 4.0],
+                [1.0, 2.0, 2.0, 3.0],
+                [2.0, 2.0, 4.0, 4.0],
+                [4.0, 3.0, 4.0, 6.0],
+            ],
+        ),
+        (
+            [[0, 1, 2, 3, 4], [5, 6], [1]],
+            [
+                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
+                [1.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0],
+                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
+                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
+                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
+            ],
+        ),
     ],
 )
 def test_build_cooccurence_matrix(input_coded, expected_output):

From 4f659d3b7d446bb9b0f1c0bec4ec690bee631855 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 14:47:48 +0100
Subject: [PATCH 388/496] fix: linter errors

---
 .../preprocessing/data_augmentation.py        |   4 +-
 .../preprocessing/social_preprocess.py        |   4 +-
 nautilus_nlp/preprocessing/text_preprocess.py |  10 +-
 nautilus_nlp/utils/file_loader.py             | 104 ++++++++++--------
 nautilus_nlp/utils/phone_number.py            |   2 +-
 nautilus_nlp/utils/stopwords.py               |   6 +-
 nautilus_nlp/utils/tokenizer.py               |   2 +-
 7 files changed, 74 insertions(+), 58 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index d3a464a..d9cdccf 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -13,7 +13,9 @@ class UnavailableAugmenter(ValueError):
     pass
 
 
-def augment_text(text: str, method: str, stopwords: Optional[List[str]]=None, entities: Optional[list]=None) -> Tuple[str, list]:
+def augment_text(
+        text: str, method: str, stopwords: Optional[List[str]] = None, entities: Optional[list] = None
+        ) -> Tuple[str, list]:
     """
     Given a text with or without associated entities, generate a new text by
     modifying some words in the initial one, modifications depend on the chosen
diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index e903aa7..ff69c08 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -103,10 +103,10 @@ def convert_emoji_to_text(self, code_delimiters=(':', ':'), input_str=None) -> s
         Parameters
         ----------
         text : str
-        
+
         code_delimiters : tuple
             Tuple of symbols around the emoji code. eg: (':',':') --> :grinning_face:
-        
+
         input_str : str
             if specified, will remove emoji from this text rather than the class
 
diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
index 252dd3e..949dd9b 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -205,10 +205,10 @@ def replace_emails(self, replace_with: str = "*EMAIL*") -> str:
         self.text = constants.EMAIL_REGEX.sub(replace_with, self.text)
         return self.text
 
-    def replace_phone_numbers(self, 
-            country_format_to_detect: list,
-            replace_with: str = "*PHONE*",
-            method: str = "regex") -> str:
+    def replace_phone_numbers(self,
+                              country_format_to_detect: list,
+                              replace_with: str = "*PHONE*",
+                              method: str = "regex") -> str:
         """
         Replace all phone numbers in ``text`` str with ``replace_with`` str
 
@@ -241,7 +241,7 @@ def replace_phone_numbers(self,
             raise ValueError('Please input a valid method between "regex" or "detection"')
         return self.text
 
-    def replace_numbers(self, replace_with: str="*NUMBER*") -> str:
+    def replace_numbers(self, replace_with: str = "*NUMBER*") -> str:
         """
         Replace all numbers in ``text`` str with ``replace_with`` str.
 
diff --git a/nautilus_nlp/utils/file_loader.py b/nautilus_nlp/utils/file_loader.py
index 437bd7d..2e545d3 100644
--- a/nautilus_nlp/utils/file_loader.py
+++ b/nautilus_nlp/utils/file_loader.py
@@ -28,8 +28,8 @@
 import chardet
 
 
-def open_textfile(filepath: str, encoding='utf-8'):
-    with io.open(filepath, 'r', encoding=encoding) as f:
+def open_textfile(filepath: str, encoding="utf-8"):
+    with io.open(filepath, "r", encoding=encoding) as f:
         string = f.read()
     return string
 
@@ -51,15 +51,17 @@ def detect_encoding(file_path_or_string: str, n_lines: int = 100) -> str:
         the code of the detected encoding
     """
     if os.path.isfile(file_path_or_string):
-        with open(file_path_or_string, 'rb') as f:
-            rawdata = b''.join([f.readline() for _ in range(n_lines)])
+        with open(file_path_or_string, "rb") as f:
+            rawdata = b"".join([f.readline() for _ in range(n_lines)])
     elif isinstance(file_path_or_string, bytes):
         rawdata = file_path_or_string
     return chardet.detect(rawdata)
 
 
-def text_loader(filepath: str, encoding: Optional[str] = None, detectencoding: bool = True) -> str:
-    '''
+def text_loader(
+        filepath: str, encoding: Optional[str] = None, detectencoding: bool = True
+) -> str:
+    """
     This util loads a file. If the encoding is specified, will use the specified
     encoding to load the text file.
     If not specified, this function tries to open the doc as UTF-U, and if
@@ -81,20 +83,24 @@ def text_loader(filepath: str, encoding: Optional[str] = None, detectencoding: b
     ------
     UnicodeDecodeError
         if document can't be loaded with utf-8 encoding.
-    '''
+    """
     if encoding is not None:
         return open_textfile(filepath, encoding=encoding)
     try:
-        return open_textfile(filepath, encoding='utf-8')
+        return open_textfile(filepath, encoding="utf-8")
     except UnicodeDecodeError:
-        warnings.warn(f'Encoding for {filepath} is not UTF-8.')
+        warnings.warn(f"Encoding for {filepath} is not UTF-8.")
         if detectencoding is True:
             detected_encoding = detect_encoding(filepath)
-            warnings.warn(f'{filepath}: detected encoding is {detected_encoding["encoding"]},\
-                            with a confidence rate of {detected_encoding["confidence"]}')
-            return open_textfile(filepath, encoding=detected_encoding['encoding'])
-        raise UnicodeDecodeError('Cannot load document using utf-8. '\
-                                    'Try to detect encoding using detectencoding=True')
+            warnings.warn(
+                f'{filepath}: detected encoding is {detected_encoding["encoding"]},\
+                            with a confidence rate of {detected_encoding["confidence"]}'
+            )
+            return open_textfile(filepath, encoding=detected_encoding["encoding"])
+        raise UnicodeDecodeError(
+            "Cannot load document using utf-8. "
+            "Try to detect encoding using detectencoding=True"
+        )
 
 
 def get_subfolders_path(folder: str) -> list:
@@ -104,15 +110,16 @@ def get_subfolders_path(folder: str) -> list:
     if not folder.endswith("/"):
         folder = folder + "/"
     return [
-        folder + f+'/' for f in os.listdir(folder)
+        folder + f + "/"
+        for f in os.listdir(folder)
         if os.path.isdir(os.path.join(folder, f)) and f != ".DS_Store"
-        ]
+    ]
 
 
 def list_files_in_subdir(filepath: str) -> list:
-    '''
+    """
     Get a list of all the filepath of files in directory and subdirectory.
-    '''
+    """
     res = []
     for path, _, files in os.walk(filepath):
         for name in files:
@@ -122,7 +129,7 @@ def list_files_in_subdir(filepath: str) -> list:
 
 def list_files(filepath: str) -> List[str]:
     """
-    List files within a given filepath. 
+    List files within a given filepath.
 
     Parameters
     ----------
@@ -136,9 +143,9 @@ def list_files(filepath: str) -> List[str]:
     """
 
     if os.path.isdir(filepath) and len(re.findall(r"[\w.]$", filepath)) > 0:
-        filepath = filepath+'/*'
-    if filepath.endswith('/'):
-        filepath = filepath+'*'
+        filepath = filepath + "/*"
+    if filepath.endswith("/"):
+        filepath = filepath + "*"
     return [file for file in glob.glob(filepath) if os.path.isfile(file)]
 
 
@@ -146,9 +153,9 @@ def documents_loader(
         filepath: str,
         encoding: Optional[str] = None,
         detectencoding: bool = True,
-        output_as: str = 'dict'
-        ) -> Union[str, list, dict]:
-    '''
+        output_as: str = "dict",
+) -> Union[str, list, dict]:
+    """
     Input a filepath, a filepath with wildcard (eg. *.txt),
     or a list of filepaths. Output a string, or a dict of strings.
 
@@ -167,7 +174,7 @@ def documents_loader(
     -------
     Uniont[string, list, dict]
         The document loaded.
-    '''
+    """
     if isinstance(filepath, str):
         documents = list_files(filepath)
         nb_of_documents = len(documents)
@@ -175,48 +182,55 @@ def documents_loader(
         nb_of_documents = len(filepath)
         documents = filepath
     else:
-        raise TypeError('Please enter a valid filepath or a valid list of filepath')
+        raise TypeError("Please enter a valid filepath or a valid list of filepath")
 
     if nb_of_documents == 1:
-        return text_loader(documents[0], encoding=encoding, detectencoding=detectencoding)
+        return text_loader(
+            documents[0], encoding=encoding, detectencoding=detectencoding
+        )
     if nb_of_documents > 1:
-        if output_as == 'list':
+        if output_as == "list":
             return [
-                text_loader(document, encoding=encoding, detectencoding=detectencoding) 
+                text_loader(document, encoding=encoding, detectencoding=detectencoding)
                 for document in documents
-                ]
-        if output_as == 'dict':
+            ]
+        if output_as == "dict":
             return {
-                document : text_loader(
-                document, encoding=encoding, detectencoding=detectencoding) for document in documents
-                }
-        raise TypeError('Enter a valid output format between list or dict')
-    raise IOError('No files detected in {}'.format(filepath))
+                document: text_loader(
+                    document, encoding=encoding, detectencoding=detectencoding
+                )
+                for document in documents
+            }
+        raise TypeError("Enter a valid output format between list or dict")
+    raise IOError("No files detected in {}".format(filepath))
+
 
 ## CSV Loader
 
+
 def encode_columns(df, columns_to_encode: str):
-    '''
+    """
     apply json.dumps on columns
-    '''
+    """
     for col in columns_to_encode:
         df[col] = df[col].apply(json.dumps)
 
 
 def decode_columns(df, columns_to_encode: str):
-    '''
+    """
     apply json.loads on columns
-    '''
+    """
     for col in columns_to_encode:
         df[col] = df[col].apply(json.loads)
 
+
 ## Encoding functions
 
+
 def convert_encoding(filepath: str, input_encoding: str, output_encoding: str):
-    '''
+    """
     Encode a file according to a specified encoding.
-    '''
+    """
     with codecs.open(filepath, encoding=input_encoding) as input_file:
-        with codecs.open(
-                'encoded_'+filepath, "w", encoding=output_encoding) as output_file:
+        with codecs.open("encoded_" + filepath, "w", encoding=output_encoding) as output_file:
             shutil.copyfileobj(input_file, output_file)
diff --git a/nautilus_nlp/utils/phone_number.py b/nautilus_nlp/utils/phone_number.py
index 7f72bb5..6ad58fc 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/nautilus_nlp/utils/phone_number.py
@@ -144,7 +144,7 @@ def parse_number(self, text: str, region_code: Optional[str] = None) -> str:
 
     def format_number(self, num_format: str) -> str:
         '''
-        Convert a phone number to another standard format. 
+        Convert a phone number to another standard format.
 
         Parameters
         ----------
diff --git a/nautilus_nlp/utils/stopwords.py b/nautilus_nlp/utils/stopwords.py
index f34d4b8..0f3eb57 100644
--- a/nautilus_nlp/utils/stopwords.py
+++ b/nautilus_nlp/utils/stopwords.py
@@ -22,11 +22,11 @@
 
 import json
 import os
-
-from nautilus_nlp.config.config import ROOT_FOLDER
-from nautilus_nlp.utils.file_loader import documents_loader
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 from stop_words import get_stop_words as _get_stop_words
+from nautilus_nlp.config.config import ROOT_FOLDER
+from nautilus_nlp.utils.file_loader import documents_loader
+
 
 STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
 
diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 2f4c22f..5067849 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -48,7 +48,7 @@ def get_lang_model(self):
         return self.model.lang
 
 
-def _get_spacy_tokenizer(lang: str) -> spacy.tokenizer.Tokenizer:
+def _get_spacy_tokenizer(lang: str):
     """
     Function that gets the right tokenizer given the language
 

From 1e7753392e5208011e255275dc295120adacf9d1 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Fri, 13 Nov 2020 15:19:46 +0100
Subject: [PATCH 389/496] fix: linting issues

---
 tests/test_keyword_extractor.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/test_keyword_extractor.py b/tests/test_keyword_extractor.py
index 66261d5..69a2a43 100644
--- a/tests/test_keyword_extractor.py
+++ b/tests/test_keyword_extractor.py
@@ -5,8 +5,8 @@
 @pytest.mark.parametrize(
     "input_tokens, expected, ngrams_number, number_top_words",
     [
-        (['I', 'eat', 'orange', 'oranges', 'at', 'orange'], [('orange', 2)], 1, 1), 
-        (['Un', 'chat', 'rose', 'reste', 'un', 'chat', 'rose'], [('chat rose', 2)], 2, 1), 
+        (['I', 'eat', 'orange', 'oranges', 'at', 'orange'], [('orange', 2)], 1, 1),
+        (['Un', 'chat', 'rose', 'reste', 'un', 'chat', 'rose'], [('chat rose', 2)], 2, 1),
     ]
     )
 def test_get_frequent_words(input_tokens, expected, ngrams_number, number_top_words):
@@ -31,4 +31,4 @@ def test_get_frequent_words_output_lenght():
     ]
     )
 def test_extract_keywords(input_text, keyword, case_sensitive, expected):
-    assert extract_keywords(input_text, keyword=keyword, case_sensitive=case_sensitive) == expected
\ No newline at end of file
+    assert extract_keywords(input_text, keyword=keyword, case_sensitive=case_sensitive) == expected

From bb44253d1ee1e12ea59d1674529a866363f958d2 Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Sat, 14 Nov 2020 18:53:41 +0100
Subject: [PATCH 390/496] [Cleaning] Removing old notebooks

---
 notebooks/.gitkeep                            |   0
 notebooks/0. Text file loader.ipynb           | 485 ---------
 notebooks/1. Text Preprocessing.ipynb         | 502 ---------
 notebooks/2. Text processing.ipynb            | 638 ------------
 .../3. Text Vectorization - TF-IDF.ipynb      | 713 -------------
 notebooks/4. Topic Modeling.ipynb             | 954 ------------------
 notebooks/5. Topic Modeling - Biterm.ipynb    | 266 -----
 .../6. Topic Modeling - short text.ipynb      | 312 ------
 .../Benchmark text processing tools.ipynb     | 876 ----------------
 notebooks/Language_identification.ipynb       | 201 ----
 ...nt analysis using pre-trained models.ipynb | 161 ---
 notebooks/Sentiment_analysis_FT.ipynb         | 259 -----
 notebooks/Spacy_model.ipynb                   | 126 ---
 notebooks/Visualization tools.ipynb           | 100 --
 notebooks/someadditionalfile.txt              |   1 -
 notebooks/somefile.txt                        |   1 -
 16 files changed, 5595 deletions(-)
 delete mode 100644 notebooks/.gitkeep
 delete mode 100644 notebooks/0. Text file loader.ipynb
 delete mode 100644 notebooks/1. Text Preprocessing.ipynb
 delete mode 100644 notebooks/2. Text processing.ipynb
 delete mode 100644 notebooks/3. Text Vectorization - TF-IDF.ipynb
 delete mode 100644 notebooks/4. Topic Modeling.ipynb
 delete mode 100644 notebooks/5. Topic Modeling - Biterm.ipynb
 delete mode 100644 notebooks/6. Topic Modeling - short text.ipynb
 delete mode 100644 notebooks/Benchmark text processing tools.ipynb
 delete mode 100644 notebooks/Language_identification.ipynb
 delete mode 100644 notebooks/Sentiment analysis using pre-trained models.ipynb
 delete mode 100644 notebooks/Sentiment_analysis_FT.ipynb
 delete mode 100644 notebooks/Spacy_model.ipynb
 delete mode 100644 notebooks/Visualization tools.ipynb
 delete mode 100644 notebooks/someadditionalfile.txt
 delete mode 100644 notebooks/somefile.txt

diff --git a/notebooks/.gitkeep b/notebooks/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/notebooks/0. Text file loader.ipynb b/notebooks/0. Text file loader.ipynb
deleted file mode 100644
index d83208d..0000000
--- a/notebooks/0. Text file loader.ipynb	
+++ /dev/null
@@ -1,485 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Open Text File with encoding handling\n",
-    "\n",
-    "Nautilus includes a document loader that handles encoding detection and multiple file loading"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create example files "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example of a latin1 file\n",
-    "s = \"J'aime les frites bien grasse étalon châpeau!\"\n",
-    "encoded_s = s.encode('latin-1')\n",
-    "with open('somefile.txt', 'wb') as f:\n",
-    "    f.write(encoded_s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Let's add another document \n",
-    "s = \"Un deuxième exemple de texte en utf-8 cette fois!\"\n",
-    "encoded_s = s.encode('utf-8')\n",
-    "with open('someadditionalfile.txt', 'wb') as f:\n",
-    "    f.write(encoded_s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Document loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import documents_loader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open a file when you know the encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Un deuxième exemple de texte en utf-8 cette fois!'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# default encoding is UTF-8\n",
-    "documents_loader('someadditionalfile.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# If you know the encoding, you can specify it\n",
-    "documents_loader('somefile.txt', encoding='latin-1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open a file with encoding detection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you don't specify encoding, `document_loader()` will try to open it as UTF-8, and if it doesn't work it will try to detect encoding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"J'aime les frites bien grasse étalon châpeau!\""
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents_loader('somefile.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "function takes exactly 5 arguments (1 given)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mopen_textfile\u001b[0;34m(filepath, encoding)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/hugo/anaconda3/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 30: invalid continuation byte",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-19-482c18687c00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# You can prevent document loader from detecting the encoding if UTF-8 fails\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# In this case, it will raise an UnicodeDecodeError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdocuments_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'somefile.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mdocuments_loader\u001b[0;34m(filepath, encoding, detectencoding, output_as)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtext_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetectencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetectencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnb_of_documents\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moutput_as\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'list'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/shared/nautilus_nlp/nautilus_nlp/utils/file_loader.py\u001b[0m in \u001b[0;36mtext_loader\u001b[0;34m(filepath, encoding, detectencoding)\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mopen_textfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdetected_encoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'encoding'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot load document using utf-8. Try to detect encoding using detectencoding=True'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: function takes exactly 5 arguments (1 given)"
-     ]
-    }
-   ],
-   "source": [
-    "# You can prevent document loader from detecting the encoding if UTF-8 fails \n",
-    "# In this case, it will raise an UnicodeDecodeError\n",
-    "documents_loader('somefile.txt', detectencoding=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open several files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can use wildcards to open several documents\n",
-    "documents_loader('*.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'somefile.txt': \"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'someadditionalfile.txt': 'Un deuxième exemple de texte en utf-8 cette fois!'}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can also pass a list of filepaths\n",
-    "documents_loader(['somefile.txt','someadditionalfile.txt'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Encoding for somefile.txt is not UTF-8.\n",
-      "WARNING:root:Trying to detect encoding for somefile.txt\n",
-      "INFO:root:somefile.txt: detected encoding is ISO-8859-1, with a confidence rate of 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[\"J'aime les frites bien grasse étalon châpeau!\",\n",
-       " 'Un deuxième exemple de texte en utf-8 cette fois!']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can specify the output format when you load multiple texts\n",
-    "documents_loader('*.txt', output_as='list')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## List files in a folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import list_files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['./Visualization tools.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb',\n",
-       " './somefile.txt',\n",
-       " './Language_identification.ipynb',\n",
-       " './1. Text Preprocessing.ipynb',\n",
-       " './3. Text Vectorization - TF-IDF.ipynb',\n",
-       " './TopicModeling.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
-       " './Benchmark text processing tools.ipynb',\n",
-       " './0. Text file loader.ipynb',\n",
-       " './someadditionalfile.txt',\n",
-       " './2. Text processing.ipynb',\n",
-       " './Spacy_model.ipynb']"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('.') # list files from current folders"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('../tests/testfolder_fileloader/.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['./Visualization tools.ipynb',\n",
-       " './Sentiment analysis using pre-trained models.ipynb',\n",
-       " './Language_identification.ipynb',\n",
-       " './1. Text Preprocessing.ipynb',\n",
-       " './3. Text Vectorization - TF-IDF.ipynb',\n",
-       " './TopicModeling.ipynb',\n",
-       " './Sentiment_analysis_FT.ipynb',\n",
-       " './Benchmark text processing tools.ipynb',\n",
-       " './0. Text file loader.ipynb',\n",
-       " './2. Text processing.ipynb',\n",
-       " './Spacy_model.ipynb']"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_files('./*.ipynb') # List files matching specific pattern"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# only files will be printed, not folders\n",
-    "list_files('/Users/hugo/Documents/NAUTILUS/nautilus-nlp/')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Detect encoding \n",
-    "\n",
-    "If you just interested in detecting encoding, you can use this function, based on the Chardet library. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.utils.file_loader import detect_encoding"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'encoding': 'ISO-8859-1', 'confidence': 0.73, 'language': ''}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "detect_encoding('somefile.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/1. Text Preprocessing.ipynb b/notebooks/1. Text Preprocessing.ipynb
deleted file mode 100644
index b081db0..0000000
--- a/notebooks/1. Text Preprocessing.ipynb	
+++ /dev/null
@@ -1,502 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Text Preprocessing \n",
-    "\n",
-    "This notebook will guide you through the common text preprocessing operations. \n",
-    "All the preprocessing functions are located in ** nautilus_nlp.preprocessing.preprocess ** "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load an example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "english_text = \"\"\"\n",
-    "The nautilus 🐚🐚 (from the Latin form of the original Ancient Greek: ναυτίλος, 'sailor') is a pelagic marine mollusc of the cephalopod family Nautilidae, the sole extant family of the superfamily Nautilaceae and of its smaller but near equal suborder, Nautilina.\n",
-    "\n",
-    "It comprises six living species in two genera, the type of which is the genus Nautilus. Though it more specifically refers to species Nautilus pompilius, the name chambered nautilus is also used for any of the Nautilidae. All are protected under CITES Appendix II.\n",
-    "\n",
-    "Nautilidae, both extant and extinct, are characterized by involute or more or less convolute shells that are generally smooth, with compressed or depressed whorl sections, straight to sinuous sutures, and a tubular, generally central siphuncle.[3] Having survived relatively unchanged for millions of years, nautiluses represent the only living members of the subclass nautiloidea, and are often considered \"living fossils\".\n",
-    "\n",
-    "The word nautilus is derived from the Greek ναυτίλος nautílos and originally referred to the paper nautiluses of the genus Argonauta, which are actually octopuses. The word nautílos literally means \"sailor\", as paper nautiluses were thought to use two of their arms as sails.[4]\n",
-    "\n",
-    "Here's how you can contact us:\n",
-    "\n",
-    "Source : https://en.wikipedia.org/wiki/Nautilus\n",
-    "Contact:\n",
-    "Email : Jules.vernes@nautilus.net\n",
-    "Phone : +33 6 24 24 24 24 \n",
-    "Cost : €45\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preprocess_text: Wrap-up function "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import preprocess_text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nautilus latin form original ancient greek ναυτιλος sailor pelagic marine mollusc cephalopod family nautilidae sole extant family superfamily nautilaceae smaller equal suborder nautilina comprises living species genera type genus nautilus specifically refers species nautilus pompilius chambered nautilus nautilidae protected cites appendix ii nautilidae extant extinct characterized involute convolute shells generally smooth compressed depressed whorl sections straight sinuous sutures tubular generally central siphuncle survived unchanged millions years nautiluses represent living members subclass nautiloidea considered living fossils word nautilus derived greek ναυτιλος nautilos originally referred paper nautiluses genus argonauta octopuses word nautilos literally means sailor paper nautiluses thought arms sails contact source https en wikipedia org wiki nautilus contact email phone cost\n"
-     ]
-    }
-   ],
-   "source": [
-    "clean_txt = preprocess_text(english_text,\n",
-    "                            fix_unicode=False,\n",
-    "                            lowercase=True,\n",
-    "                            no_urls=False,\n",
-    "                            no_emails=True,\n",
-    "                            no_phone_numbers=True,\n",
-    "                            phone_method='detection', # if detection is specified, will use a phone lib \n",
-    "                            phone_countries_format=[None, 'US', 'FR'], # these phone format will be tested. None stands for international.\n",
-    "                            no_numbers=True,\n",
-    "                            no_currency_symbols=True,\n",
-    "                            no_punct=True,\n",
-    "                            no_contractions=True, #will unpack english contractions\n",
-    "                            no_accents=True,\n",
-    "                            no_emoji=True,\n",
-    "                            replace_with=' ',\n",
-    "                            no_stopwords='en' #will remove english stopwords\n",
-    "                           )\n",
-    "print(clean_txt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Overview of the pre-processing functions\n",
-    "\n",
-    "If you prefer, you can use all the preprocessing functions one-by-one and create your own pre-processing pipeline. It is recommended, as there are many parameters that are not necessary included in the wrap-up function. \n",
-    "\n",
-    "For the sake of simplicity, we won't go through all the pre-processing function. Please report to the documentation if you want more details!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remove end-of-line characters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import remove_EOL_characters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before:\n",
-      "-------\n",
-      "hello words!\n",
-      "this is the second line.\n",
-      "\n",
-      "After:\n",
-      "-------\n",
-      "hello words! this is the second line.\n"
-     ]
-    }
-   ],
-   "source": [
-    "text_with_eol = \"hello words!\\nthis is the second line.\"\n",
-    "\n",
-    "text_without_eol = remove_EOL_characters(text_with_eol)\n",
-    "\n",
-    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(text_with_eol,text_without_eol))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Stop words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import get_stopwords"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['soi-même', 'prealable', 'ayez', 'fi', 'concernant', 'dedans', 'celles', 'tiennes', 'plutôt', 'bravo']\n"
-     ]
-    }
-   ],
-   "source": [
-    "french_sw = get_stopwords('fr')\n",
-    "\n",
-    "print(french_sw[:10])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import remove_stopwords"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before:\n",
-      "-------\n",
-      "['je', 'suis', 'malade', 'et', 'toi', '?']\n",
-      "\n",
-      "After:\n",
-      "-------\n",
-      "['malade', '?']\n"
-     ]
-    }
-   ],
-   "source": [
-    "tokens_with_sw = ['je','suis','malade','et','toi','?']\n",
-    "\n",
-    "tokens_without_sw = remove_stopwords(tokens_with_sw, stopwords=french_sw)\n",
-    "\n",
-    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(tokens_with_sw,tokens_without_sw))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fix bad unicode"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import fix_bad_unicode"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before:\n",
-      "-------\n",
-      "Les augmentations de rémunérations\n",
-      "\n",
-      "After:\n",
-      "-------\n",
-      "Les augmentations de rémunérations\n"
-     ]
-    }
-   ],
-   "source": [
-    "before = \"Les augmentations de rémunérations\"\n",
-    "\n",
-    "after = fix_bad_unicode(before)\n",
-    "\n",
-    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Phone Numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import replace_phone_numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before:\n",
-      "-------\n",
-      "(541) 754-3010 is a US. Phone\n",
-      "\n",
-      "After:\n",
-      "-------\n",
-      "  is a US. Phone\n"
-     ]
-    }
-   ],
-   "source": [
-    "before = '(541) 754-3010 is a US. Phone'\n",
-    "\n",
-    "after = replace_phone_numbers(before, replace_with=' ')\n",
-    "\n",
-    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is also an util to detect phone numbers. Here we will provide a country list. It takes time because it will loop over the text with all the supported country codes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nautilus_nlp.utils.phone_number as phone"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 296 ms, sys: 0 ns, total: 296 ms\n",
-      "Wall time: 294 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['(541) 754-3010', '754-3010']"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "phone.extract_phone_numbers(before, countrylist=phone.SUPPORTED_COUNTRY)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is also a **phone parser** class, that helps you to extract information from phone numbers. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p = phone.phoneParser()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PhoneNumber(country_code=1, national_number=5417543010, extension=None, italian_leading_zero=None, number_of_leading_zeros=None, country_code_source=0, preferred_domestic_carrier_code=None)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.parse_number('(541) 754-3010',region_code='US')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'541-754-3010'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.format_number('INTERNATIONAL')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Emojis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import remove_emoji, convert_emoji_to_text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before:\n",
-      "-------\n",
-      "My favorite emojies are: 🎅🏿⌚\n",
-      "\n",
-      "After:\n",
-      "-------\n",
-      "My favorite emojies are: \n"
-     ]
-    }
-   ],
-   "source": [
-    "before = 'My favorite emojies are: 🎅🏿⌚'\n",
-    "\n",
-    "after = remove_emoji(before)\n",
-    "\n",
-    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before:\n",
-      "-------\n",
-      "My favorite emojies are: 🎅🏿⌚\n",
-      "\n",
-      "After:\n",
-      "-------\n",
-      "My favorite emojies are: Santa_Claus_dark_skin_tonewatch\n"
-     ]
-    }
-   ],
-   "source": [
-    "before = 'My favorite emojies are: 🎅🏿⌚'\n",
-    "\n",
-    "after = convert_emoji_to_text(before, code_delimiters=('', ''))\n",
-    "\n",
-    "print('Before:\\n-------\\n{}\\n\\nAfter:\\n-------\\n{}'.format(before,after))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/2. Text processing.ipynb b/notebooks/2. Text processing.ipynb
deleted file mode 100644
index d1c68ee..0000000
--- a/notebooks/2. Text processing.ipynb	
+++ /dev/null
@@ -1,638 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Text processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tokenization\n",
-    "\n",
-    "Several tokenizers are available. As you will see bellow, spaCy is much faster than the other implementations (Moses, NLTK) and often return better results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fr_txt = \"Ceci est un texte français, j'adore 1 !\"\n",
-    "eng_txt = \"Let's play together!\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "str_ = \"\"\"Les moteurs de recherche tels Google, Exalead ou Yahoo! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l'indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L'algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### French spaCy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 5.07 ms, sys: 130 µs, total: 5.2 ms\n",
-      "Wall time: 5.03 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tokenized_fr_txt[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### French Moses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 13.2 s, sys: 6.59 ms, total: 13.2 s\n",
-      "Wall time: 13.2 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_fr_txt = tokenize(str_, lang_module=\"fr_moses\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Les', 'moteurs', 'de', 'recherche', 'tels', 'Google', ',', 'Exalead', 'ou', 'Yahoo']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tokenized_fr_txt[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### English spaCy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 990 µs, sys: 0 ns, total: 990 µs\n",
-      "Wall time: 998 µs\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Let', \"'s\", 'play', 'together', '!']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_eng_txt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### English NLTK "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 7.94 ms, sys: 122 µs, total: 8.06 ms\n",
-      "Wall time: 6.95 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_eng_txt = tokenize(eng_txt, lang_module=\"en_nltk\")\n",
-    "tokenized_eng_txt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Un-tokenization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.46 s, sys: 35 µs, total: 1.46 s\n",
-      "Wall time: 1.46 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Let's play together!\""
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "untokenize(tokenized_eng_txt,lang='en')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3 s, sys: 351 µs, total: 3 s\n",
-      "Wall time: 3 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Les moteurs de recherche tels Google, Exalead ou Yahoo ! sont des applications très connues de fouille de textes sur de grandes masses de données. Cependant, les moteurs de recherche ne se basent pas uniquement sur le texte pour l' indexer, mais également sur la façon dont les pages sont mises en valeur les unes par rapport aux autres. L' algorithme utilisé par Google est PageRank, et il est courant de voir HITS dans le milieu académique\""
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "untokenize(tokenized_fr_txt,lang='fr')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stemming "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.stemming import stem_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['i', 'surviv', 'these', 'dog']"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stem_tokens(['I','survived','these', 'dogs'], lang='english')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['je', 'mang', 'dan', 'le', 'cuisin', 'du', 'château']"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stem_tokens(['je', 'mangerai', 'dans', 'les', 'cuisines', 'du', 'château'],lang='french')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Lemmatization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## French "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n",
-      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
-      "[nltk_data]     /root/nltk_data...\n",
-      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
-      "[nltk_data]       date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_french_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Ceci', 'est', 'un', 'texte', 'français', ',', \"j'\", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']\n"
-     ]
-    }
-   ],
-   "source": [
-    "txt_to_tokenize=['Ceci', 'est', 'un', 'texte', 'français', ',', \"j'\", 'adore', 'tes', 'frites', 'bien', 'grasses', 'YOLO', '!']\n",
-    "print(txt_to_tokenize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 17.3 ms, sys: 0 ns, total: 17.3 ms\n",
-      "Wall time: 15.8 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatized_tokens = lemmatize_french_tokens(txt_to_tokenize, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lemmatized_tokens = lemmatize_french_tokens(txt_to_tokenize, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['ceci', 'être', 'un', 'texte', 'français', ',', 'j', \"'\", 'adorer', 'ton', 'frit', 'bien', 'gras', 'yolo', '!']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(lemmatized_tokens)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## English"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "to_lemmatize = ['The', 'striped', 'bats', 'are', 'hanging', 'on', 'their', 'feet', 'for', 'best']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 13.7 ms, sys: 0 ns, total: 13.7 ms\n",
-      "Wall time: 12.5 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['the', 'strip', 'bat', 'be', 'hang', 'on', '-PRON-', 'foot', 'for', 'good']"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatize_english_tokens(to_lemmatize, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.72 s, sys: 125 ms, total: 1.85 s\n",
-      "Wall time: 1.82 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['The', 'strip', 'bat', 'be', 'hang', 'on', 'their', 'foot', 'for', 'best']"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatize_english_tokens(to_lemmatize, module='nltk')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Remove stop words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import remove_stopwords\n",
-    "from nautilus_nlp.preprocessing.preprocess import get_stopwords"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "FRENCH_SW = get_stopwords('fr')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"J'ai un beau cheval\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"J'ai\", 'cheval']"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remove_stopwords(text, FRENCH_SW)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"J'\", 'cheval']"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remove_stopwords(tokenize(text, lang_module=\"fr_spacy\"),FRENCH_SW)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/3. Text Vectorization - TF-IDF.ipynb b/notebooks/3. Text Vectorization - TF-IDF.ipynb
deleted file mode 100644
index 1e0c112..0000000
--- a/notebooks/3. Text Vectorization - TF-IDF.ipynb	
+++ /dev/null
@@ -1,713 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Text vectorization - TF-IDF"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Open files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv('https://storage.googleapis.com/dataset-uploader/bbc/bbc-text.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>category</th>\n",
-       "      <th>text</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>tech</td>\n",
-       "      <td>tv future in the hands of viewers with home th...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>business</td>\n",
-       "      <td>worldcom boss  left books alone  former worldc...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>sport</td>\n",
-       "      <td>tigers wary of farrell  gamble  leicester say ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>sport</td>\n",
-       "      <td>yeading face newcastle in fa cup premiership s...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>entertainment</td>\n",
-       "      <td>ocean s twelve raids box office ocean s twelve...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        category                                               text\n",
-       "0           tech  tv future in the hands of viewers with home th...\n",
-       "1       business  worldcom boss  left books alone  former worldc...\n",
-       "2          sport  tigers wary of farrell  gamble  leicester say ...\n",
-       "3          sport  yeading face newcastle in fa cup premiership s...\n",
-       "4  entertainment  ocean s twelve raids box office ocean s twelve..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "sport            511\n",
-       "business         510\n",
-       "politics         417\n",
-       "tech             401\n",
-       "entertainment    386\n",
-       "Name: category, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.category.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Preprocess"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n",
-      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n",
-      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
-      "[nltk_data]     /root/nltk_data...\n",
-      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
-      "[nltk_data]       date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.preprocessing.tokenizer import tokenize\n",
-    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2min 47s, sys: 1.15 s, total: 2min 48s\n",
-      "Wall time: 2min 48s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "df['tokens'] = df.text.apply(lambda row: tokenize(row))\n",
-    "df['tokens'] = df.tokens.apply(lambda row: lemmatize_english_tokens(row))\n",
-    "df['tokens'] = df.tokens.apply(lambda row: ' '.join(row))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Split dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train, test = train_test_split(df, test_size=0.2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Compute TF-IDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "paramiko missing, opening SSH/SCP/SFTP paths will be disabled.  `pip install paramiko` to suppress\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.preprocessing.text_vectorizers import TfidfTextVectorizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tfidf = TfidfTextVectorizer(max_df=0.95, #ignore terms that have a document frequency strictly higher \n",
-    "              min_df=0.01,\n",
-    "              max_features=10000,\n",
-    "              encoding='utf-8',\n",
-    "              #stop_words=SW,\n",
-    "              norm=None,\n",
-    "              #ngram_range=(1, 2))\n",
-    "              )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
-       "        dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
-       "        lowercase=True, max_df=0.95, max_features=10000, min_df=0.01,\n",
-       "        ngram_range=(1, 1), norm=None, preprocessor=None, smooth_idf=True,\n",
-       "        stop_words=None, strip_accents=None, sublinear_tf=False,\n",
-       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
-       "        vocabulary=None)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# uses Sci-kit learn TfidfVectorizer()\n",
-    "# You can pass all the arguments supported by sci-kit \n",
-    "# Doc : https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\n",
-    "tfidf.tfidf_vectorizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list_of_docs = list(train.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['bid to cut court witness stress new target to reduce the stress to victim and witness give evidence in court in england and wale have be announce by the lord chancellor .    lord falconer want all crown court and 90 % of magistrate    court to have facility to keep witness separate from defendant within four year . more video link will also be make available so that witness do not have to enter courtroom . -PRON- be part of a five - year plan to help build confidence in the justice system .    minister say the strategy be aim at re - balance the court system towards victim    and increase the number of offender bring to justice . launch the department for constitutional affair    plan    lord falconer say :    one of the top priority will be a well deal for victim .    the need and safety of victim will be at the heart of the way trial be manage .     court    judge    magistrate    prosecutor    police and victim support - all work together to ensure the right of victim be put first    without compromise the right of the defendant .    -PRON- go on :    give evidence be a nerve - wracking experience    especially when -PRON- re a victim .    yet with a will and with support -PRON- can be do .    lord falconer tell bbc radio 4 s today programme -PRON- be impossible for some elderly people to go to court to give evidence . other witness could be intimidate by sit alongside defendant outside court .    -PRON- be never go to get rid of some element of the trauma of give evidence     -PRON- say .    but -PRON- can make people believe that the court understand the problem    -PRON- s not some kind of alien place where -PRON- go where -PRON- be not think about -PRON- .     the plan come as the lord chancellor also consider allow camera into court for the first time since 1925    as long as -PRON- be use for case that do not involve witness . another feature of the strategy be constitutional reform    with a government bill to set up a supreme court and a judicial appointment commission return to the house of lord on tuesday . minister have propose get rid of the title of lord chancellor    but the lord have over - rule this . lord falconer say -PRON- be right for the high court to be completely distinct from parliament . the person in charge of the court system should not also be speaker of the house of lord    -PRON- say    and should be the good person choose from either house of parliament . what -PRON- do    not what -PRON- be call    be the critical issue    -PRON- add .',\n",
-       " 'market unfaze by aurora setback as the aurora limp back to -PRON- dock on 20 january    a blizzard of photo and interview seem to add up to an unambiguous tale of woe .    the ship have another slice of bad luck to add to -PRON- history of health scare and technical trouble . and -PRON- owner    p&o cruise - now part of the huge -PRON- carnival corporation - be look at a significant slice chop off this year s profit and a potential pr fiasco . no - one    however    seem to have tell the stock market . the warning of a five - cent hit to 2005 earning come just 24 hour after one of the world s big investment bank have up -PRON- target for carnival s share price    from £ 35 to £ 36.20 . other investor barely blink    and by 1300 gmt carnival s share in london be down a single penny    or 0.03 %    at £ 32.26 .    why the mismatch between the public perception and the market s response     the aurora issue have be an ongoing one for some time     say deutsche bank s simon champion .    -PRON- be clearly a source of uncertainty for the company - -PRON- be a long cruise    after all . but the stock market be very good at treat these issue as one - off event .     despite -PRON- string of bad luck    -PRON- point out    aurora be just one vessel in a large carnival fleet    the uk s p&o princess group have be merge into the much large -PRON- firm in 2003 . and generally speak    carnival have a reputation for keep -PRON- ship pretty much on schedule .    carnival have an incredibly strong track record     mr champion .    similarly    analyst expect the impact on the rest of the cruise business to be limit . the hundred of disappointed passenger who have now have to give up the opportunity to spend the next three month on the aurora have get both a refund and a credit for another cruise . that should mitigate some of the pr risk    both for carnival and -PRON- main competitor    royal caribbean .    while not common    cancellation for technical reason be not entirely unusual in the industry     write analyst from citigroup smith barney in a note to client on friday .    moreover    such event typically have a limited impact on booking and pricing for future cruise .    after all    the aurora incident may be big news in the uk - but for carnival customer elsewhere -PRON- s unlikely to make too much of a splash .    assume that citigroup be right    and demand stay solid    the structure of the industry also work in carnival s favour . in the wake of p&o princess s takeover by carnival    the business be now to a great extent a duopoly . give the expense of build    outfitting and run a cruise ship     slow supply growth    be a certainty    say david ander at merrill lynch on thursday . in other word    if -PRON- do want a cruise    -PRON- option be limited . and with carnival remain the market leader    -PRON- look set to keep sell the ticket - no matter what happen to the ill - fat aurora in the future .']"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_of_docs[:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<1780x2638 sparse matrix of type '<class 'numpy.float64'>'\n",
-       "\twith 251758 stored elements in Compressed Sparse Row format>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Compute the word count vector matrix.\n",
-    "# will apply \n",
-    "tfidf.compute_tfidf(list_of_docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'song': 328.383,\n",
-       " 'wage': 314.316,\n",
-       " 'roddick': 293.646,\n",
-       " 'minimum': 260.403,\n",
-       " 'music': 75.21,\n",
-       " 'robbie': 157.772,\n",
-       " 'terrorist': 153.899,\n",
-       " 'good': 151.491,\n",
-       " 'game': 73.159,\n",
-       " 'increase': 129.725,\n",
-       " 'hunt': 93.316,\n",
-       " 'party': 126.188,\n",
-       " '25': 125.075,\n",
-       " 'pay': 121.356,\n",
-       " 'muslim': 114.249,\n",
-       " 'gadget': 113.934,\n",
-       " 'student': 108.451,\n",
-       " 'threat': 104.522,\n",
-       " 'black': 103.206,\n",
-       " 'liverpool': 101.794,\n",
-       " 'airline': 99.171,\n",
-       " 'stone': 96.591,\n",
-       " 'mini': 90.917,\n",
-       " 'mobile': 89.006,\n",
-       " 'play': 92.565,\n",
-       " 'cell': 92.487,\n",
-       " 'virus': 87.661,\n",
-       " 'jamie': 92.1,\n",
-       " 'spam': 91.696,\n",
-       " 'film': 82.501,\n",
-       " 'hour': 91.616,\n",
-       " 'camera': 91.062,\n",
-       " 'edward': 88.856,\n",
-       " 'search': 71.07,\n",
-       " 'passenger': 88.648,\n",
-       " 'lord': 87.43,\n",
-       " 'rugby': 86.836,\n",
-       " 'china': 86.425,\n",
-       " 'yuko': 75.514,\n",
-       " 'government': 86.077,\n",
-       " 'machine': 84.396,\n",
-       " 'ibm': 82.338,\n",
-       " 'online': 80.27,\n",
-       " 'asylum': 80.242,\n",
-       " 'musician': 79.603,\n",
-       " 'fraud': 79.531,\n",
-       " 'pension': 79.157,\n",
-       " 'that': 78.174,\n",
-       " 'gaming': 78.083,\n",
-       " 'bt': 77.836,\n",
-       " 'cash': 77.796,\n",
-       " 'argentina': 77.567,\n",
-       " 'site': 77.486,\n",
-       " 'lee': 77.291,\n",
-       " 'actress': 77.044,\n",
-       " 'hip': 76.849,\n",
-       " 'attack': 75.856,\n",
-       " 'broadband': 75.512,\n",
-       " 'actor': 74.891,\n",
-       " 'award': 74.741,\n",
-       " 'bank': 74.26,\n",
-       " 'document': 74.157,\n",
-       " 'mail': 73.852,\n",
-       " 'business': 73.69,\n",
-       " 'not': 73.596,\n",
-       " 'japan': 72.974,\n",
-       " 'chart': 72.764,\n",
-       " 'what': 72.65,\n",
-       " 'deutsche': 72.138,\n",
-       " 'police': 72.137,\n",
-       " 'soul': 72.026,\n",
-       " 'point': 71.795,\n",
-       " 'vote': 71.593,\n",
-       " 'card': 71.142,\n",
-       " 'file': 70.759,\n",
-       " 'dvd': 70.511,\n",
-       " 'poster': 70.121,\n",
-       " 'murder': 70.121}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get highest-weighted words\n",
-    "tfidf.get_top_tfidf(n=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['court', 'lord', 'witness', 'victim', 'evidence', 'chancellor', 'rid', 'constitutional', 'house', 'justice']\n",
-      "['ship', 'market', 'limited', 'for', 'technical', 'one', 'stock', 'impact', 'much', 'on']\n",
-      "['trust', 'politician', 'voter', 'election', 'poll', 'public', 'issue', 'lib', 'dem', 'lack']\n",
-      "['member', 'share', 'qualify', 'profit', '42', 'payment', 'society', 'than', 'building', 'worth']\n",
-      "['ireland', 'cardiff', 'slam', 'england', 'grand', 'year', 'wale', 'last', 'team', 'tournament']\n",
-      "['good', 'theatre', 'musical', 'mary', 'royal', 'producer', 'at', 'design', 'outstanding', 'award']\n",
-      "['virus', 'writer', 'phone', '2004', 'that', 'attack', 'number', 'spam', 'use', 'message']\n",
-      "['academy', 'award', 'oscar', 'war', 'ceremony', 'frank', 'winner', 'film', 'first', 'as']\n",
-      "['broadcaster', 'debate', 'lord', 'prime', 'campaign', 'election', 'say', 'minister', 'blair', 'ahead']\n",
-      "['parliament', 'record', 'prior', 'document', 'blow', 'page', 'act', 'room', 'by', 'history']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get highest-weighted words per document\n",
-    "for doc in list_of_docs[:10]:\n",
-    "    print(tfidf.get_top_tfidf_per_doc(doc,n=10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Apply Tf-idf to new documents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>category</th>\n",
-       "      <th>text</th>\n",
-       "      <th>tokens</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1515</th>\n",
-       "      <td>business</td>\n",
-       "      <td>booming markets shed few tears the market  for...</td>\n",
-       "      <td>boom market shed few tear the market    former...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>980</th>\n",
-       "      <td>sport</td>\n",
-       "      <td>claxton hunting first major medal british hurd...</td>\n",
-       "      <td>claxton hunt first major medal british hurdler...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1634</th>\n",
-       "      <td>tech</td>\n",
-       "      <td>sony psp handheld console hits us the latest h...</td>\n",
-       "      <td>sony psp handheld console hit -PRON- the late ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1053</th>\n",
-       "      <td>entertainment</td>\n",
-       "      <td>black sabbath top rock album poll black sabbat...</td>\n",
-       "      <td>black sabbath top rock album poll black sabbat...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1870</th>\n",
-       "      <td>sport</td>\n",
-       "      <td>jones doping probe begins an investigation int...</td>\n",
-       "      <td>jone dope probe begin an investigation into do...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           category                                               text  \\\n",
-       "1515       business  booming markets shed few tears the market  for...   \n",
-       "980           sport  claxton hunting first major medal british hurd...   \n",
-       "1634           tech  sony psp handheld console hits us the latest h...   \n",
-       "1053  entertainment  black sabbath top rock album poll black sabbat...   \n",
-       "1870          sport  jones doping probe begins an investigation int...   \n",
-       "\n",
-       "                                                 tokens  \n",
-       "1515  boom market shed few tear the market    former...  \n",
-       "980   claxton hunt first major medal british hurdler...  \n",
-       "1634  sony psp handheld console hit -PRON- the late ...  \n",
-       "1053  black sabbath top rock album poll black sabbat...  \n",
-       "1870  jone dope probe begin an investigation into do...  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1515    [disaster, market, stock, insurance, global, b...\n",
-       "980     [medal, hurdle, indoor, european, training, co...\n",
-       "1634    [sony, device, gaming, handheld, gadget, ninte...\n",
-       "1053    [band, rock, black, album, list, top, live, po...\n",
-       "1870    [olympic, jone, truth, medal, sydney, pound, a...\n",
-       "1135    [store, sale, retailer, december, same, rise, ...\n",
-       "1034    [jackson, ms, boy, prosecution, court, film, d...\n",
-       "1061    [festival, sell, event, day, organiser, act, j...\n",
-       "205     [engine, union, buy, six, motor, plant, italia...\n",
-       "1500    [program, phone, malicious, file, instal, work...\n",
-       "2100    [uk, trick, ban, will, super, industry, respon...\n",
-       "1046    [round, victory, title, really, first, capture...\n",
-       "1728    [cardiff, thomas, jone, morgan, france, italy,...\n",
-       "575     [file, server, operator, peer, network, site, ...\n",
-       "24      [sound, audio, mobile, technology, phone, hand...\n",
-       "1556    [eu, law, draft, computer, software, legal, im...\n",
-       "2058    [interactive, award, nomination, category, tv,...\n",
-       "750     [card, bank, economy, central, debt, consumer,...\n",
-       "876     [bill, committee, welfare, draft, government, ...\n",
-       "2149    [project, information, computer, community, lo...\n",
-       "1056    [ferguson, arsenal, game, football, saturday, ...\n",
-       "227     [zealand, wale, black, new, cardiff, game, rug...\n",
-       "1353    [bill, limit, small, government, culture, brit...\n",
-       "1397    [pension, election, age, vote, party, basic, o...\n",
-       "2028    [host, american, television, suffer, award, gl...\n",
-       "1181    [ipod, apple, job, computer, mini, new, mass, ...\n",
-       "2123    [ticket, green, band, fan, dublin, concert, se...\n",
-       "77      [break, hodgson, winter, league, premiership, ...\n",
-       "1075    [game, video, company, news, tag, as, art, eye...\n",
-       "1201    [bank, italian, institution, sue, company, fin...\n",
-       "                              ...                        \n",
-       "1301    [un, short, ms, authority, tsunami, only, indi...\n",
-       "2150    [spain, federation, player, henry, comment, in...\n",
-       "562     [video, offer, demand, sky, cable, tv, program...\n",
-       "1268    [shoot, texas, control, hunt, let, session, wi...\n",
-       "364     [festival, disaster, film, ticket, continue, e...\n",
-       "467     [million, show, audience, figure, 14, episode,...\n",
-       "2086    [sun, russian, voting, employ, buy, stake, aug...\n",
-       "235     [growth, rate, consumer, quarter, spending, bu...\n",
-       "1755    [offer, fresh, as, game, tough, title, level, ...\n",
-       "672     [microsoft, window, user, system, online, inte...\n",
-       "732     [jone, gb, championship, indoor, green, olympi...\n",
-       "455     [download, programme, episode, tv, net, uk, pe...\n",
-       "635     [digital, design, technology, people, ms, came...\n",
-       "795     [chelsea, 2000, play, milan, barcelona, leg, b...\n",
-       "535     [pc, 2010, pcs, china, million, market, local,...\n",
-       "1199    [film, india, hurt, community, refuse, anger, ...\n",
-       "264     [consumer, rule, mobile, firm, service, phone,...\n",
-       "701     [network, file, content, download, music, tech...\n",
-       "59      [drug, heart, risk, regulator, that, on, disea...\n",
-       "456     [lord, reform, chancellor, manifesto, house, c...\n",
-       "2091    [collin, athletic, sport, olympic, competitor,...\n",
-       "667     [dvd, copy, pirate, piracy, technology, progra...\n",
-       "164     [suspend, candidate, mr, view, say, party, new...\n",
-       "393     [policy, economic, inflation, rise, market, re...\n",
-       "117     [labour, chancellor, cut, conservative, party,...\n",
-       "207     [blair, question, answer, trust, thing, say, p...\n",
-       "343     [blair, peace, mr, conference, leader, iraq, v...\n",
-       "675     [party, election, mr, leadership, assembly, ro...\n",
-       "2141    [medium, traditional, information, web, mainst...\n",
-       "132     [wale, william, flanker, cardiff, fit, injury,...\n",
-       "Name: tokens, Length: 445, dtype: object"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test.tokens.apply(lambda row: tfidf.get_top_tfidf_per_doc(row))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_tfidf_matrix = tfidf.apply_tfidf_to_documents(list(test.tokens))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zealand': 157.747,\n",
-       " 'gadget': 137.671,\n",
-       " 'child': 100.98,\n",
-       " 'camera': 91.062,\n",
-       " 'wale': 86.221,\n",
-       " 'mini': 85.866,\n",
-       " 'bt': 77.836,\n",
-       " 'soul': 77.567,\n",
-       " 'council': 76.619,\n",
-       " 'jackson': 74.892}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tfidf.get_top_tfidf(test_tfidf_matrix)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/4. Topic Modeling.ipynb b/notebooks/4. Topic Modeling.ipynb
deleted file mode 100644
index 7d040fd..0000000
--- a/notebooks/4. Topic Modeling.ipynb	
+++ /dev/null
@@ -1,954 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Topic Modeling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 1: Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import fetch_20newsgroups\n",
-    "newsgroups_train = fetch_20newsgroups(subset='train', shuffle = True)\n",
-    "newsgroups_test = fetch_20newsgroups(subset='test', shuffle = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(list(newsgroups_train.target_names))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 2: Data Preprocessing¶\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/nltk/decorators.py:68: DeprecationWarning: `formatargspec` is deprecated since Python 3.5. Use `signature` and the `Signature` object directly\n",
-      "  regargs, varargs, varkwargs, defaults, formatvalue=lambda value: \"\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gensim\n",
-    "from gensim.utils import simple_preprocess\n",
-    "from gensim.parsing.preprocessing import STOPWORDS\n",
-    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
-    "from nltk.stem.porter import *\n",
-    "import numpy as np\n",
-    "np.random.seed(400)\n",
-    "\n",
-    "import nltk\n",
-    "\n",
-    "import pandas as pd\n",
-    "stemmer = SnowballStemmer(\"english\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def lemmatize_stemming(text):\n",
-    "    return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))\n",
-    "\n",
-    "# Tokenize and lemmatize\n",
-    "def preprocess(text):\n",
-    "    result=[]\n",
-    "    for token in gensim.utils.simple_preprocess(text) :\n",
-    "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
-    "            result.append(lemmatize_stemming(token))\n",
-    "            \n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "processed_docs = []\n",
-    "\n",
-    "for doc in newsgroups_train.data:\n",
-    "    processed_docs.append(preprocess(doc))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 3: Bag of words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Create the dictionnary\n",
-    "from nautilus_nlp.models.topic_modeling import create_dictionary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "dictionary = create_dictionary(processed_docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Filter out tokens that appear in too few or too many documents\n",
-    "from nautilus_nlp.models.topic_modeling import filter_extremes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "filter_extremes(dictionary)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Create the bow \n",
-    "from nautilus_nlp.models.topic_modeling import create_bow_corpus"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "bow_corpus = create_bow_corpus(processed_docs, dictionary)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(13, 1), (26, 1), (55, 1), (88, 1), (99, 1), (152, 1), (153, 2), (154, 1), (155, 1), (156, 1), (157, 2), (158, 1), (159, 2), (160, 4), (161, 1), (162, 2), (163, 1), (164, 2), (165, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (171, 1), (172, 1), (173, 1), (174, 1), (175, 1), (176, 1), (177, 1), (178, 2), (179, 1), (180, 1), (181, 1), (182, 1)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(bow_corpus[3])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 4: Find optimal number of topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Compute coherence values for various number of topics in order to pick the optimal one\n",
-    "from nautilus_nlp.models.topic_modeling import compute_coherence_values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Take a long time to run\n",
-    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, bow_corpus=bow_corpus, texts=processed_docs, start=2, limit=25, step=4)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.37900998646286865,\n",
-       " 0.42680154447577845,\n",
-       " 0.47716566398237525,\n",
-       " 0.5261650723885645,\n",
-       " 0.49607461078243215,\n",
-       " 0.4978727171365794]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coherence_values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.topic_modeling import plot_optimal_topic_number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Print the coherences scores for the number we tested\n",
-    "from nautilus_nlp.models.topic_modeling import print_coherence_scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Num Topics = 2  has Coherence Value of 0.379\n",
-      "Num Topics = 6  has Coherence Value of 0.4268\n",
-      "Num Topics = 10  has Coherence Value of 0.4772\n",
-      "Num Topics = 14  has Coherence Value of 0.5262\n",
-      "Num Topics = 18  has Coherence Value of 0.4961\n",
-      "Num Topics = 22  has Coherence Value of 0.4979\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_coherence_scores(coherence_values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 5: Running LDA using Bag of Words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Train the LDA model with gensim\n",
-    "from nautilus_nlp.models.topic_modeling import train_lda_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "model = train_lda_model(bow_corpus, dictionary, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x1a2af3e208>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Save model\n",
-    "from nautilus_nlp.models.topic_modeling import save_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "save_model(model,'/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.topic_modeling import load_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<gensim.models.ldamodel.LdaModel at 0x1a29985b38>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_loaded = load_model('/Users/williamjaubert/Documents/Allianz_William/notebook', 'ldamodel_nautilus')\n",
-    "model_loaded"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 6: Visualize the top keywords per topic with Pyldavis interactive chart"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Display the top keywords per topic in a interactive chart\n",
-    "from nautilus_nlp.models.topic_modeling import visualize_topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/williamjaubert/anaconda2/envs/nautilus/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
-       "\n",
-       "\n",
-       "<div id=\"ldavis_el591011124095238088809029897\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "\n",
-       "var ldavis_el591011124095238088809029897_data = {\"mdsDat\": {\"x\": [-0.07866945427665124, -0.01699948489792914, 0.20896689238873523, 0.14744605031212607, -0.008849073212760983, -0.04505413872814077, -0.08949897686453376, 0.10780299734830809, 0.004524270451044093, -0.22966908252019716], \"y\": [-0.15170611822961017, -0.1504468301902949, 0.08013685816591506, 0.13647834612774523, -0.009046128981188077, 0.003906923765221535, 0.14472746444813225, -0.07737607739594787, -0.1051004751701791, 0.1284260374602061], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.182504653930664, 12.926197052001953, 12.463006973266602, 11.995771408081055, 9.55910873413086, 9.468682289123535, 8.927140235900879, 7.882134437561035, 7.024929523468018, 6.5705246925354]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resourc\", 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-       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
-       "topic                                                \n",
-       "2     -0.078669 -0.151706       1        1  13.182505\n",
-       "0     -0.016999 -0.150447       2        1  12.926197\n",
-       "7      0.208967  0.080137       3        1  12.463007\n",
-       "8      0.147446  0.136478       4        1  11.995771\n",
-       "1     -0.008849 -0.009046       5        1   9.559109\n",
-       "3     -0.045054  0.003907       6        1   9.468682\n",
-       "6     -0.089499  0.144727       7        1   8.927140\n",
-       "9      0.107803 -0.077376       8        1   7.882134\n",
-       "4      0.004524 -0.105100       9        1   7.024930\n",
-       "5     -0.229669  0.128426      10        1   6.570525, topic_info=     Category         Freq        Term        Total  loglift  logprob\n",
-       "1037  Default  2966.000000      window  2966.000000  30.0000  30.0000\n",
-       "1193  Default  1940.000000        game  1940.000000  29.0000  29.0000\n",
-       "482   Default  1924.000000   christian  1924.000000  28.0000  28.0000\n",
-       "702   Default  1689.000000        team  1689.000000  27.0000  27.0000\n",
-       "352   Default  2638.000000       drive  2638.000000  26.0000  26.0000\n",
-       "320   Default  2883.000000        file  2883.000000  25.0000  25.0000\n",
-       "696   Default  1860.000000       space  1860.000000  24.0000  24.0000\n",
-       "1467  Default  1161.000000     encrypt  1161.000000  23.0000  23.0000\n",
-       "256   Default  1997.000000      govern  1997.000000  22.0000  22.0000\n",
-       "153   Default  1477.000000        chip  1477.000000  21.0000  21.0000\n",
-       "522   Default  1274.000000       jesus  1274.000000  20.0000  20.0000\n",
-       "1347  Default  1017.000000      israel  1017.000000  19.0000  19.0000\n",
-       "36    Default  1577.000000        card  1577.000000  18.0000  18.0000\n",
-       "120   Default  1423.000000        play  1423.000000  17.0000  17.0000\n",
-       "964   Default  1059.000000       secur  1059.000000  16.0000  16.0000\n",
-       "657   Default  1331.000000        nasa  1331.000000  15.0000  15.0000\n",
-       "1821  Default  1142.000000    armenian  1142.000000  14.0000  14.0000\n",
-       "822   Default  2599.000000     program  2599.000000  13.0000  13.0000\n",
-       "1346  Default   837.000000        isra   837.000000  12.0000  12.0000\n",
-       "513   Default  1391.000000        imag  1391.000000  11.0000  11.0000\n",
-       "117   Default  6052.000000       peopl  6052.000000  10.0000  10.0000\n",
-       "3565  Default   742.000000      hockey   742.000000   9.0000   9.0000\n",
-       "739   Default   990.000000      player   990.000000   8.0000   8.0000\n",
-       "1457  Default   784.000000     clipper   784.000000   7.0000   7.0000\n",
-       "326   Default  1802.000000      public  1802.000000   6.0000   6.0000\n",
-       "41    Default  1023.000000        disk  1023.000000   5.0000   5.0000\n",
-       "28    Default  4004.000000        year  4004.000000   4.0000   4.0000\n",
-       "380   Default   867.000000        scsi   867.000000   3.0000   3.0000\n",
-       "879   Default  1191.000000      driver  1191.000000   2.0000   2.0000\n",
-       "444   Default   747.000000        bike   747.000000   1.0000   1.0000\n",
-       "...       ...          ...         ...          ...      ...      ...\n",
-       "5004  Topic10   178.948181     stanley   185.594589   2.6861  -6.0394\n",
-       "702   Topic10  1384.116455        team  1689.568604   2.5232  -3.9937\n",
-       "4840  Topic10   160.981583         jet   167.196503   2.6847  -6.1452\n",
-       "3815  Topic10   191.566772       coach   202.233215   2.6684  -5.9713\n",
-       "3537  Topic10   221.759384      ranger   240.052933   2.6433  -5.8249\n",
-       "4877  Topic10   157.258331    winnipeg   165.160721   2.6735  -6.1686\n",
-       "1193  Topic10  1290.715576        game  1940.664795   2.3147  -4.0636\n",
-       "120   Topic10   920.770020        play  1423.930298   2.2866  -4.4013\n",
-       "711   Topic10   312.806488        wing   399.885132   2.4770  -5.4809\n",
-       "739   Topic10   623.101746      player   990.567200   2.2590  -4.7918\n",
-       "1311  Topic10   397.739624    columbia   559.283691   2.3817  -5.2407\n",
-       "1080  Topic10   455.438751      season   670.126038   2.3364  -5.1053\n",
-       "3252  Topic10   379.943481       leagu   536.827942   2.3769  -5.2865\n",
-       "1073  Topic10   351.761322  pittsburgh   505.782318   2.3594  -5.3636\n",
-       "1679  Topic10   356.976562       score   528.273926   2.3306  -5.3488\n",
-       "1321  Topic10   374.845306        arab   572.500732   2.2991  -5.3000\n",
-       "3488  Topic10   212.819550      mcgill   254.334839   2.5444  -5.8661\n",
-       "1475  Topic10   346.644043        goal   553.699341   2.2543  -5.3782\n",
-       "1150  Topic10   312.806427    virginia   544.289856   2.1687  -5.4809\n",
-       "1082  Topic10   333.144867     toronto   701.091187   1.9785  -5.4179\n",
-       "1155  Topic10   336.057953      andrew   868.338135   1.7733  -5.4092\n",
-       "28    Topic10   600.308960        year  4004.757812   0.8248  -4.8291\n",
-       "157   Topic10   295.451538       divis   693.417114   1.8694  -5.5380\n",
-       "2117  Topic10   283.661255      canada   732.439819   1.7740  -5.5787\n",
-       "2549  Topic10   250.147522      period   584.503235   1.8739  -5.7045\n",
-       "45    Topic10   267.235901       final   822.295105   1.5986  -5.6384\n",
-       "171   Topic10   330.680511       point  2646.791748   0.6426  -5.4254\n",
-       "146   Topic10   358.020264        time  5183.146484   0.0500  -5.3459\n",
-       "1545  Topic10   262.164948    american  1242.273315   1.1669  -5.6575\n",
-       "99    Topic10   242.101822          go  3510.730713   0.0484  -5.7372\n",
-       "\n",
-       "[734 rows x 6 columns], token_table=      Topic      Freq       Term\n",
-       "term                            \n",
-       "338       1  0.145983     accept\n",
-       "338       2  0.524347     accept\n",
-       "338       3  0.129101     accept\n",
-       "338       4  0.049654     accept\n",
-       "338       5  0.007945     accept\n",
-       "338       6  0.022841     accept\n",
-       "338       8  0.066536     accept\n",
-       "338       9  0.034758     accept\n",
-       "338      10  0.018869     accept\n",
-       "73        3  0.403915     access\n",
-       "73        4  0.196068     access\n",
-       "73        5  0.171820     access\n",
-       "73        6  0.033255     access\n",
-       "73        7  0.000693     access\n",
-       "73        8  0.193297     access\n",
-       "2940      4  0.984634    adaptec\n",
-       "152       1  0.052461    address\n",
-       "152       2  0.074007    address\n",
-       "152       3  0.536784    address\n",
-       "152       4  0.182675    address\n",
-       "152       5  0.030914    address\n",
-       "152       6  0.026230    address\n",
-       "152       7  0.032788    address\n",
-       "152       8  0.049650    address\n",
-       "152       9  0.005621    address\n",
-       "152      10  0.008431    address\n",
-       "1444      1  0.124112  administr\n",
-       "1444      3  0.063074  administr\n",
-       "1444      5  0.197359  administr\n",
-       "1444      8  0.614458  administr\n",
-       "...     ...       ...        ...\n",
-       "182       2  0.166406      world\n",
-       "182       3  0.097373      world\n",
-       "182       4  0.150056      world\n",
-       "182       5  0.106093      world\n",
-       "182       6  0.051230      world\n",
-       "182       7  0.080296      world\n",
-       "182       8  0.011990      world\n",
-       "182       9  0.053410      world\n",
-       "182      10  0.054863      world\n",
-       "4238      1  0.009547    worship\n",
-       "4238      2  0.988079    worship\n",
-       "4809      3  0.994707       xlib\n",
-       "3868      3  0.995135      xpert\n",
-       "3581      3  0.995652      xterm\n",
-       "2827      3  0.988763      xview\n",
-       "6047      6  0.981546     yamaha\n",
-       "1701      7  0.988053      yanke\n",
-       "28        1  0.130095       year\n",
-       "28        2  0.031962       year\n",
-       "28        3  0.015482       year\n",
-       "28        4  0.038954       year\n",
-       "28        5  0.216243       year\n",
-       "28        6  0.063674       year\n",
-       "28        7  0.244959       year\n",
-       "28        8  0.040951       year\n",
-       "28        9  0.067919       year\n",
-       "28       10  0.149822       year\n",
-       "3309      9  0.986879      yeast\n",
-       "3190      5  0.990640     zoolog\n",
-       "1894      1  0.991014       zuma\n",
-       "\n",
-       "[2168 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 8, 9, 2, 4, 7, 10, 5, 6])"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = visualize_topics(model, bow_corpus, dictionary)\n",
-    "p"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Save the pyldavis as HTML\n",
-    "from nautilus_nlp.models.topic_modeling import save_pyldavis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "save_pyldavis(p, '/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load the pyldavis HTML\n",
-    "from nautilus_nlp.models.topic_modeling import show_pyldavis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
-       "\n",
-       "\n",
-       "<div id=\"ldavis_el591011124069819927707340541\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "\n",
-       "var ldavis_el591011124069819927707340541_data = {\"mdsDat\": {\"x\": [-0.07866945427665124, -0.01699948489792914, 0.20896689238873523, 0.14744605031212607, -0.008849073212760983, -0.04505413872814077, -0.08949897686453376, 0.10780299734830809, 0.004524270451044093, -0.22966908252019716], \"y\": [-0.15170611822961017, -0.1504468301902949, 0.08013685816591506, 0.13647834612774523, -0.009046128981188077, 0.003906923765221535, 0.14472746444813225, -0.07737607739594787, -0.1051004751701791, 0.1284260374602061], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.179347038269043, 12.924742698669434, 12.465522766113281, 11.996149063110352, 9.560138702392578, 9.471930503845215, 8.926163673400879, 7.8803582191467285, 7.02661657333374, 6.569023609161377]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"argic\", \"genocid\", \"davidian\", \"troop\", \"murder\", \"mormon\", \"prison\", \"massacr\", \"azeri\", \"ethnic\", \"azerbaijani\", \"hitler\", \"iran\", \"zuma\", \"sdpa\", \"motto\", \"azerbaijan\", \"extermin\", \"sera\", \"urartu\", \"slaughter\", \"villag\", \"batf\", \"greek\", \"greec\", \"jew\", \"soldier\", \"kill\", \"waco\", \"arm\", \"countri\", \"muslim\", \"children\", \"armi\", \"popul\", \"peopl\", \"anti\", \"govern\", \"right\", \"attack\", \"say\", \"polit\", \"death\", \"state\", \"live\", \"go\", \"come\", \"tell\", \"happen\", \"forc\", \"world\", \"time\", \"nation\", \"want\", \"leav\", \"start\", \"year\", \"take\", \"jesus\", \"bibl\", \"atheist\", \"atheism\", \"christ\", \"scriptur\", \"cathol\", \"sandvik\", \"doctrin\", \"revel\", \"biblic\", \"satan\", \"atho\", \"livesey\", \"prophet\", \"divin\", \"vers\", \"gospel\", \"sabbath\", \"god\", \"sin\", \"resurrect\", \"solntz\", \"testament\", \"theolog\", \"propheci\", \"theist\", \"schneider\", 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\"map\", \"applic\", \"archiv\", \"user\", \"code\", \"avail\", \"directori\", \"sourc\", \"file\", \"mail\", \"list\", \"program\", \"function\", \"format\", \"email\", \"inform\", \"version\", \"softwar\", \"includ\", \"send\", \"data\", \"internet\", \"address\", \"display\", \"window\", \"copi\", \"access\", \"distribut\", \"thank\", \"look\", \"book\", \"group\", \"need\", \"scsi\", \"simm\", \"motherboard\", \"cach\", \"bio\", \"quadra\", \"diamond\", \"vram\", \"vesa\", \"centri\", \"swap\", \"upgrad\", \"char\", \"eisa\", \"intercon\", \"nubus\", \"ethernet\", \"svga\", \"amanda\", \"meg\", \"cadr\", \"mous\", \"maxtor\", \"config\", \"cica\", \"tiff\", \"adaptec\", \"powerbook\", \"ctrl\", \"esdi\", \"jumper\", \"floppi\", \"disk\", \"umich\", \"video\", \"modem\", \"card\", \"output\", \"monitor\", \"mode\", \"printer\", \"spec\", \"entri\", \"drive\", \"driver\", \"port\", \"instal\", \"memori\", \"window\", \"color\", \"appl\", \"byte\", \"screen\", \"board\", \"problem\", 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\"higgin\", \"inning\", \"revolv\", \"hulman\", \"yanke\", \"pitch\", \"catcher\", \"dodger\", \"blast\", \"starter\", \"tiger\", \"met\", \"bat\", \"nore\", \"outlet\", \"rocki\", \"jay\", \"sdsu\", \"volt\", \"lopez\", \"restaur\", \"lamp\", \"wire\", \"duke\", \"circuit\", \"batteri\", \"brave\", \"basebal\", \"hit\", \"berkeley\", \"jason\", \"ball\", \"metal\", \"jeff\", \"grind\", \"larc\", \"indiana\", \"netcom\", \"colorado\", \"year\", \"run\", \"good\", \"game\", \"smith\", \"scott\", \"player\", \"home\", \"stanford\", \"look\", \"distribut\", \"go\", \"time\", \"lose\", \"come\", \"start\", \"play\", \"david\", \"best\", \"better\", \"power\", \"thing\", \"john\", \"sale\", \"great\", \"encrypt\", \"escrow\", \"privaci\", \"ripem\", \"crypto\", \"wiretap\", \"cryptographi\", \"cipher\", \"decrypt\", \"hamburg\", \"clipper\", \"homicid\", \"bontchev\", \"gtoal\", \"crypt\", \"clarkson\", \"rwing\", \"surveil\", \"nist\", \"sternlight\", \"den\", \"ncsl\", \"qualcomm\", 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\"espn\", \"etern\", \"etern\", \"etern\", \"ether\", \"ethernet\", \"ethnic\", \"evid\", \"evid\", \"evid\", \"evid\", \"evid\", \"evid\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"exist\", \"extermin\", \"faith\", \"faith\", \"fbihh\", \"feder\", \"feder\", \"feder\", \"feder\", \"file\", \"file\", \"file\", \"file\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"final\", \"firearm\", \"fischer\", \"flight\", \"flight\", \"flight\", \"flight\", \"floppi\", \"floppi\", \"flyer\", \"flyer\", \"fnal\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"font\", \"font\", \"food\", \"food\", \"food\", \"food\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"forc\", \"format\", \"format\", \"format\", \"format\", \"format\", \"freenet\", \"freenet\", \"freenet\", \"frost\", \"frost\", \"function\", \"function\", \"function\", \"function\", \"function\", \"function\", \"fund\", \"fund\", \"fund\", \"fund\", \"fund\", \"game\", \"game\", \"game\", \"game\", \"gatech\", \"gaza\", \"genet\", \"genet\", \"genocid\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"go\", \"goal\", \"goal\", \"goal\", \"goal\", \"goal\", \"goal\", \"god\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"good\", \"gordon\", \"gordon\", \"gospel\", \"govern\", \"govern\", \"govern\", \"govern\", \"gradi\", \"graphic\", \"graphic\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"greec\", \"greec\", \"greek\", \"greek\", \"greenbelt\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"grind\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"gtoal\", \"gun\", \"gun\", \"halat\", \"hallam\", \"hamburg\", \"handbook\", \"handgun\", \"handgun\", \"handheld\", \"handheld\", \"handheld\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"happen\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"hard\", \"harley\", \"health\", \"health\", \"health\", \"health\", \"heaven\", \"heaven\", \"heaven\", \"heaven\", \"helmet\", \"helmet\", \"helmet\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"help\", \"henri\", \"henri\", \"higgin\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"high\", \"hit\", \"hit\", \"hit\", \"hit\", \"hit\", \"hitler\", \"hitter\", \"hockey\", \"holi\", \"holi\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"home\", \"homeopathi\", \"homicid\", \"honda\", \"hulman\", \"husc\", \"hydro\", \"iastat\", \"iastat\", \"ifa\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"illinoi\", \"imag\", \"imag\", \"imag\", \"imag\", \"imag\", \"imak\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"includ\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"indiana\", \"infect\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"inform\", \"ingr\", \"ingr\", \"ingr\", \"inning\", \"instal\", \"instal\", \"instal\", \"instal\", \"instal\", \"insur\", \"insur\", \"insur\", \"insur\", \"intellect\", \"intercon\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"internet\", \"invest\", \"invest\", \"iran\", \"islam\", \"islam\", \"isra\", \"israel\", \"israel\", \"israel\", \"jaeger\", \"jake\", \"jason\", \"jason\", \"jason\", \"jason\", \"jay\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jeff\", \"jesus\", \"jet\", \"jet\", \"jew\", \"jew\", \"jew\", \"job\", \"job\", \"job\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"john\", \"jumper\", \"jumper\", \"kaldi\", \"kelvin\", \"key\", \"key\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"kill\", \"koresh\", \"lamp\", \"larc\", \"larc\", \"laughter\", \"launch\", \"launch\", \"laurentian\", \"leaf\", \"leagu\", \"leagu\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"leav\", \"lebanes\", \"lemieux\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"librari\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"list\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"live\", \"livesey\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"lopez\", \"lord\", \"lord\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"lose\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"love\", \"lunar\", \"lunar\", \"lyme\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"machin\", \"magellan\", \"magnus\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"mail\", \"map\", \"map\", \"mar\", \"mar\", \"marriag\", \"marriag\", \"massacr\", \"maxtor\", \"maynard\", \"mccall\", \"mcgill\", \"mcgill\", \"mcgill\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"medic\", \"medic\", \"medic\", \"medic\", \"medic\", \"medicin\", \"medicin\", \"medicin\", \"medicin\", \"meg\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"memori\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"messag\", \"met\", \"metal\", \"metal\", \"metal\", \"metal\", \"methodolog\", \"midway\", \"midway\", \"midway\", \"migrain\", \"militia\", \"mime\", \"mission\", \"mission\", \"mission\", \"mission\", \"mksol\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"mode\", \"modem\", \"modem\", \"modem\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"monitor\", \"monitor\", \"monitor\", \"montreal\", \"montreal\", \"moon\", \"moon\", \"moon\", \"moral\", \"moral\", \"mormon\", \"motherboard\", \"motif\", \"motorcycl\", \"motto\", \"mous\", \"mous\", \"murder\", \"murder\", \"murder\", \"muslim\", \"muslim\", \"nasa\", \"nasa\", \"nasa\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nazi\", \"ncsl\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"netcom\", \"nist\", \"nist\", \"nore\", \"nsmca\", \"nubus\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"ohio\", \"ohio\", \"ohio\", \"openwindow\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"opinion\", \"optilink\", \"oracl\", \"orbit\", \"orbit\", \"outlet\", \"outlet\", \"output\", \"output\", \"output\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"owner\", \"pain\", \"pain\", \"pain\", \"pain\", \"pain\", \"palestinian\", \"patent\", \"patent\", \"patent\", \"patient\", \"patient\", \"pen\", \"penguin\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"period\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"person\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"photographi\", \"physician\", \"pistol\", \"pitch\", \"pitch\", \"pitcher\", \"pitt\", \"pitt\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"pittsburgh\", \"plaintext\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"player\", \"player\", \"playoff\", \"plymouth\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"point\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polit\", \"polygon\", \"popul\", \"popul\", \"popul\", \"popul\", \"port\", \"port\", \"port\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"powerbook\", \"presid\", \"presid\", \"presid\", \"presid\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"princeton\", \"princeton\", \"princeton\", \"princeton\", \"printer\", \"printer\", \"prism\", \"prison\", \"privaci\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"privat\", \"probe\", \"probe\", \"probe\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"project\", \"project\", \"project\", \"project\", \"project\", \"propheci\", \"prophet\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"propos\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"provid\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"puck\", \"pyron\", \"quadra\", \"qualcomm\", \"quebec\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quicktim\", \"raider\", \"ramsey\", \"ranck\", \"ranger\", \"ranger\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"read\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"reason\", \"recipi\", \"recipi\", \"redesign\", \"reilli\", \"religi\", \"religi\", \"religion\", \"religion\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"repli\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"restaur\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"resurrect\", \"revel\", \"revolv\", \"rid\", \"rid\", \"ride\", \"ride\", \"rider\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"ripem\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"rkba\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"robi\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rochest\", \"rocki\", \"rockwel\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"run\", \"rutger\", \"rutger\", \"rwing\", \"sabbath\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sandvik\", \"satan\", \"satellit\", \"satellit\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"scheme\", \"schneider\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"scientif\", \"score\", \"score\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"scott\", \"screen\", \"screen\", \"screen\", \"screen\", \"scriptur\", \"scsi\", \"sdpa\", \"sdsu\", \"season\", \"season\", \"secret\", \"secret\", \"secret\", \"secur\", \"secur\", \"secur\", \"secur\", \"selann\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"sell\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"send\", \"sera\", \"serdar\", \"server\", \"server\", \"shafer\", \"shaft\", \"shaft\", \"shark\", \"shotgun\", \"shuttl\", \"shuttl\", \"simm\", \"sin\", \"skeptic\", \"skeptic\", \"skndiv\", \"slaughter\", \"sleev\", \"sleev\", \"sleev\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smith\", \"smuggl\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"softwar\", \"solar\", \"solar\", \"solar\", \"soldier\", \"soldier\", \"solntz\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spacecraft\", \"spacecraft\", \"spec\", \"spec\", \"spec\", \"speed\", \"speed\", \"speed\", \"speed\", \"speed\", \"spencer\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"spend\", \"sphere\", \"spirit\", \"spirit\", \"spirit\", \"ssto\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanford\", \"stanley\", \"stanley\", \"stanley\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"starter\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"sternlight\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steve\", \"steveh\", \"stimulus\", \"stratus\", \"strnlght\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"suno\", \"superstit\", \"surveil\", \"svga\", \"swap\", \"syndrom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"tampa\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"team\", \"team\", \"team\", \"team\", \"team\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tennesse\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"testament\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"theist\", \"theodor\", \"theolog\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"theori\", \"therapi\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thomasp\", \"tiff\", \"tiger\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tire\", \"tire\", \"tire\", \"tire\", \"tire\", \"toolkit\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"toronto\", \"treatment\", \"treatment\", \"troop\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"trunk\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"turk\", \"turkey\", \"turkish\", \"ualberta\", \"uchicago\", \"uchicago\", \"uchicago\", \"ucsc\", \"uicvm\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"uiuc\", \"umich\", \"umich\", \"umich\", \"uoknor\", \"upgrad\", \"upgrad\", \"urartu\", \"urbana\", \"urbana\", \"urbana\", \"user\", \"user\", \"user\", \"user\", \"utah\", \"utkvm\", \"uvic\", \"veal\", \"vehicl\", \"vehicl\", \"vehicl\", \"vehicl\", \"vers\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"vesa\", \"vesselin\", \"video\", \"video\", \"video\", \"video\", \"villag\", \"villag\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"visual\", \"visual\", \"volt\", \"vram\", \"waco\", \"waco\", \"wagon\", \"wagon\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"water\", \"water\", \"water\", \"water\", \"water\", \"weapon\", \"weapon\", \"weapon\", \"wheel\", \"wheel\", \"widget\", \"window\", \"window\", \"window\", \"wing\", \"wing\", \"wing\", \"wing\", \"wing\", \"winnipeg\", \"winnipeg\", \"wire\", \"wire\", \"wire\", \"wiretap\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worship\", \"worship\", \"xlib\", \"xpert\", \"xterm\", \"xview\", \"yamaha\", \"yanke\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zoolog\", \"zuma\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 8, 9, 2, 4, 7, 10, 5, 6]};\n",
-       "\n",
-       "function LDAvis_load_lib(url, callback){\n",
-       "  var s = document.createElement('script');\n",
-       "  s.src = url;\n",
-       "  s.async = true;\n",
-       "  s.onreadystatechange = s.onload = callback;\n",
-       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
-       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-       "}\n",
-       "\n",
-       "if(typeof(LDAvis) !== \"undefined\"){\n",
-       "   // already loaded: just create the visualization\n",
-       "   !function(LDAvis){\n",
-       "       new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
-       "   }(LDAvis);\n",
-       "}else if(typeof define === \"function\" && define.amd){\n",
-       "   // require.js is available: use it to load d3/LDAvis\n",
-       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
-       "   require([\"d3\"], function(d3){\n",
-       "      window.d3 = d3;\n",
-       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "        new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
-       "      });\n",
-       "    });\n",
-       "}else{\n",
-       "    // require.js not available: dynamically load d3 & LDAvis\n",
-       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
-       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
-       "                 new LDAvis(\"#\" + \"ldavis_el591011124069819927707340541\", ldavis_el591011124069819927707340541_data);\n",
-       "            })\n",
-       "         });\n",
-       "}\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "show_pyldavis('/Users/williamjaubert/Documents/Allianz_William/', 'pyldavis_test_func')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 7: Testing model on unseen document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Subject: help\n",
-      "From: C..Doelle@p26.f3333.n106.z1.fidonet.org (C. Doelle)\n",
-      "Lines: 13\n",
-      "\n",
-      "Hello All!\n",
-      "\n",
-      "    It is my understanding that all True-Type fonts in Windows are loaded in\n",
-      "prior to starting Windows - this makes getting into Windows quite slow if you\n",
-      "have hundreds of them as I do.  First off, am I correct in this thinking -\n",
-      "secondly, if that is the case - can you get Windows to ignore them on boot and\n",
-      "maybe make something like a PIF file to load them only when you enter the\n",
-      "applications that need fonts?  Any ideas?\n",
-      "\n",
-      "\n",
-      "Chris\n",
-      "\n",
-      " * Origin: chris.doelle.@f3333.n106.z1.fidonet.org (1:106/3333.26)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "unseen_document = newsgroups_test.data[100]\n",
-    "print(unseen_document)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Data preprocessing step for the unseen document\n",
-    "bow_new = dictionary.doc2bow(preprocess(unseen_document))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.topic_modeling import fit_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0.0027796067),\n",
-       " (1, 0.002779247),\n",
-       " (2, 0.0027795879),\n",
-       " (3, 0.0027795406),\n",
-       " (4, 0.0027792477),\n",
-       " (5, 0.002779096),\n",
-       " (6, 0.0027791534),\n",
-       " (7, 0.20895894),\n",
-       " (8, 0.76880664),\n",
-       " (9, 0.0027789928)]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fit_data(model, bow_new)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Show the dominant topics of the new document and their keywords \n",
-    "from nautilus_nlp.models.topic_modeling import show_dominant_topic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Score: 0.7688832879066467\t Topic: ['window', 'drive', 'problem', 'card', 'work']\n",
-      "Score: 0.2088821828365326\t Topic: ['file', 'program', 'mail', 'imag', 'inform']\n",
-      "Score: 0.0027796069625765085\t Topic: ['christian', 'peopl', 'believ', 'jesus', 'say']\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_dominant_topic(model, bow_new, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.1"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/5. Topic Modeling - Biterm.ipynb b/notebooks/5. Topic Modeling - Biterm.ipynb
deleted file mode 100644
index 243d4a5..0000000
--- a/notebooks/5. Topic Modeling - Biterm.ipynb	
+++ /dev/null
@@ -1,266 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 5. Topic Modeling - Biterm Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.biterm_model import BitermModel\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 1: Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input of the model has to be a list of string/documents\n",
-    "text = ['Cola 1.5L Carrefour',\n",
-    " 'Pepsi Cola Light 1.5L',\n",
-    " 'Pepsi Cola Twist Light',\n",
-    " 'Cola 1.5L CRF DISC',\n",
-    " 'Coca-Cola Light 1.5L',\n",
-    " 'Coca-Cola Light 4x0.5L',\n",
-    " 'Coca-Cola Light 6x0.3L',\n",
-    " 'Panzani 200g x 4 bio',\n",
-    " 'Rustichella 150g bio',\n",
-    " 'De Cecco - Fusilli bio',\n",
-    " 'Gerblé sans Gluten50g',\n",
-    " 'Penne de riz 100g sans gluten',\n",
-    " 'Spaghetti de maïs 50g sans Glute']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 2: Topic Modelling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 100/100 [00:00<00:00, 123.16it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "nb_topics = 5\n",
-    "nb_word_per_cluster = 5\n",
-    "nb_iteration = 100\n",
-    "# Based on sklearn to remove the stop words, can be set up to None\n",
-    "language = 'english'\n",
-    "\n",
-    "biterm_model = BitermModel(data=text\n",
-    "                           , nb_topics=nb_topics\n",
-    "                           , nb_iteration=nb_iteration\n",
-    "                           , lang=language)\n",
-    "\n",
-    "clusters = biterm_model.get_clusters(nb_word_per_cluster)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coherence</th>\n",
-       "      <th>top_words</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3.635635</td>\n",
-       "      <td>[100g, sans, riz, penne, gluten]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-2.076344</td>\n",
-       "      <td>[disc, 5l, cola, crf, carrefour]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.235566</td>\n",
-       "      <td>[bio, 150g, rustichella, 200g, panzani]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.537023</td>\n",
-       "      <td>[sans, spaghetti, 50g, maïs, glute]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-5.198056</td>\n",
-       "      <td>[cola, light, 5l, coca, pepsi]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   coherence                                top_words\n",
-       "0   3.635635         [100g, sans, riz, penne, gluten]\n",
-       "1  -2.076344         [disc, 5l, cola, crf, carrefour]\n",
-       "2  -0.235566  [bio, 150g, rustichella, 200g, panzani]\n",
-       "3   2.537023      [sans, spaghetti, 50g, maïs, glute]\n",
-       "4  -5.198056           [cola, light, 5l, coca, pepsi]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(clusters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text: Cola 1.5L Carrefour\n",
-      "cluster indice: 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "# To get the cluster index of one document\n",
-    "index = 0\n",
-    "cluster_index = biterm_model.get_document_topic(index=index)\n",
-    "\n",
-    "print(\"text:\", text[index])\n",
-    "print(\"cluster index:\", cluster_index)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Step 3: Viz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/anaconda3/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
-     ]
-    }
-   ],
-   "source": [
-    "output_path = './biterm_pyLDAavis_plot.html'\n",
-    "biterm_model.save_pyLDAvis_plot_as_html(output_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.1"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/6. Topic Modeling - short text.ipynb b/notebooks/6. Topic Modeling - short text.ipynb
deleted file mode 100644
index e6b7417..0000000
--- a/notebooks/6. Topic Modeling - short text.ipynb	
+++ /dev/null
@@ -1,312 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Topic modeling for short text\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.topic_modeling_short_text import *\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Preparation "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is possible to use Nautilus functions to clean and preprocess raw text. The ouput of the cleaning process should be a list of sentences (strings). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = ['Cola 1.5L Carrefour',\n",
-    " 'Pepsi Cola Light 1.5L',\n",
-    " 'Pepsi Cola Twist Light',\n",
-    " 'Cola 1.5L CRF DISC',\n",
-    " 'Coca-Cola Light 1.5L',\n",
-    " 'Coca-Cola Light 4x0.5L',\n",
-    " 'Coca-Cola Light 6x0.3L',\n",
-    " 'Panzani 200g x 4 bio',\n",
-    " 'Rustichella 150g bio',\n",
-    " 'De Cecco - Fusilli bio',\n",
-    " 'Gerblé sans Gluten50g',\n",
-    " 'Penne de riz 100g sans gluten',\n",
-    " 'Spaghetti de maïs 50g sans Glute']\n",
-    "\n",
-    "encoded_text_id, vocab_list, vocab_arr = prepare_data(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['1.5L', 4],\n",
-       " ['Coca-Cola', 3],\n",
-       " ['Cola', 4],\n",
-       " ['Light', 5],\n",
-       " ['Pepsi', 2],\n",
-       " ['bio', 3],\n",
-       " ['de', 2],\n",
-       " ['sans', 3]]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vocab_arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2, 0], [4, 2, 3, 0], [4, 2, 3], [2, 0], [1, 3, 0], [1, 3], [1, 3], [5], [5], [5], [7], [6, 7], [6, 7]]\n",
-      "[[2, 0], [4, 2, 3, 0], [4, 2, 3], [2, 0], [1, 3, 0], [1, 3], [1, 3], [5], [5], [5], [7], [6, 7], [6, 7]]\n",
-      "[['1.5L', 4], ['Coca-Cola', 3], ['Cola', 4], ['Light', 5], ['Pepsi', 2], ['bio', 3], ['de', 2], ['sans', 3]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(encoded_text_id)\n",
-    "print(encoded_text_id)\n",
-    "print(vocab_arr)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train the NMF model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "loop begin\n",
-      "Step=0, Loss=5.91164999713721, Time=0.0003788471221923828s\n",
-      "Step=1, Loss=4.985359112354993, Time=0.0023560523986816406s\n",
-      "Step=2, Loss=4.323260756623319, Time=0.0044097900390625s\n",
-      "Step=3, Loss=4.021169618891115, Time=0.005792856216430664s\n",
-      "Step=4, Loss=3.9201702703506403, Time=0.006429910659790039s\n",
-      "loop end\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = train_nmf_model(encoded_text_id, vocab_list, n_topics= 3)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get keywords description of the topics\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "topic 2 : Pmi: 0.22768662780017118 ['1.5L', 'Cola', 'Pepsi', 'Light', 'bio']\n",
-      "topic 0 : Pmi: 0.42152248372930645 ['Light', 'Pepsi', 'sans', 'Cola', 'de']\n",
-      "topic 1 : Pmi: 0.4373829867469703 ['Coca-Cola', 'Light', '1.5L', 'sans', 'de']\n"
-     ]
-    }
-   ],
-   "source": [
-    "topics, pmi_score = show_dominant_topic(x, encoded_text_id, vocab_list, n_topKeyword =5)\n",
-    "\n",
-    "\n",
-    "for t in topics.keys():\n",
-    "    print('topic', t,': Pmi:', pmi_score[t], topics[t])\n",
-    "    \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign topics to sentences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[3, 3, 1, 3, 2, 2, 2, 3, 3, 3, 1, 1, 1]\n",
-      "topic  3 ---> Cola 1.5L Carrefour\n",
-      "topic  3 ---> Pepsi Cola Light 1.5L\n",
-      "topic  1 ---> Pepsi Cola Twist Light\n",
-      "topic  3 ---> Cola 1.5L CRF DISC\n",
-      "topic  2 ---> Coca-Cola Light 1.5L\n",
-      "topic  2 ---> Coca-Cola Light 4x0.5L\n",
-      "topic  2 ---> Coca-Cola Light 6x0.3L\n",
-      "topic  3 ---> Panzani 200g x 4 bio\n",
-      "topic  3 ---> Rustichella 150g bio\n",
-      "topic  3 ---> De Cecco - Fusilli bio\n",
-      "topic  1 ---> Gerblé sans Gluten50g\n",
-      "topic  1 ---> Penne de riz 100g sans gluten\n",
-      "topic  1 ---> Spaghetti de maïs 50g sans Glute\n"
-     ]
-    }
-   ],
-   "source": [
-    "list_of_topics= get_assigned_topics(x)\n",
-    "print(list_of_topics)\n",
-    "\n",
-    "for i in range(len(list_of_topics)) : \n",
-    "    print('topic ', list_of_topics[i],'--->',text[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot topics with pyldavis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/kaislaribi/anaconda3/envs/DataScience/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
-       "\n",
-       "\n",
-       "<div id=\"ldavis_el973044658186304304441590\"></div>\n",
-       "<script type=\"text/javascript\">\n",
-       "\n",
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diff --git a/notebooks/Benchmark text processing tools.ipynb b/notebooks/Benchmark text processing tools.ipynb
deleted file mode 100644
index 14b08ff..0000000
--- a/notebooks/Benchmark text processing tools.ipynb	
+++ /dev/null
@@ -1,876 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Text processing tools benchmark"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from urllib import request"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load french text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://www.gutenberg.org/cache/epub/5711/pg5711.txt\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response = request.urlopen(url)\n",
-    "rawfr = response.read().decode('utf8')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The Project Gutenberg EBook of Germinal, by Emile Zola\r\n",
-      "(#8 in our series by Emile Zola)\r\n",
-      "\r\n",
-      "Copyright laws are changing all over the world. Be sure to check the\r\n",
-      "copyright laws for your country before downloading or redistributing\r\n",
-      "this or any other Project Gutenberg eBook.\r\n",
-      "\r\n",
-      "This header should be the first thing seen when viewing this Project\r\n",
-      "Gutenberg file.  Please do not remove it.  Do not change or edit the\r\n",
-      "header without written permission.\r\n",
-      "\r\n",
-      "Please read the \"legal small print,\" and ot\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(rawfr[:500])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "str"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(rawfr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1046377"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(rawfr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load english text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"http://www.gutenberg.org/files/2554/2554-0.txt\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response = request.urlopen(url)\n",
-    "raw = response.read().decode('utf8')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The Project Gutenberg EBook of Crime and Punishment, by Fyodor Dostoevsky\r\n",
-      "\r\n",
-      "This eBook is for the use of anyone anywhere at no cost and with\r\n",
-      "almost no restrictions whatsoever.  You may copy it, give it away or\r\n",
-      "re-use it under the terms of the Project Gutenberg License included\r\n",
-      "with this eBook or online at www.gutenberg.org\r\n",
-      "\r\n",
-      "\r\n",
-      "Title: Crime and Punishment\r\n",
-      "\r\n",
-      "Author: Fyodor Dostoevsky\r\n",
-      "\r\n",
-      "Release Date: March 28, 2006 [EBook #2554]\r\n",
-      "Last Updated: October 27, 2016\r\n",
-      "\r\n",
-      "Language: English\r\n",
-      "\r\n",
-      "Charac\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(raw[:500])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "str"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(raw)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1176967"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(raw)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tokenization\n",
-    "\n",
-    "Several tokenizers are available. As you will see bellow, spaCy is much faster than the other implementations (Moses, NLTK) and often return better results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.preprocessing.tokenizer import tokenize, untokenize"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### French spaCy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.84 s, sys: 84.1 ms, total: 1.93 s\n",
-      "Wall time: 1.93 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized = tokenize(rawfr[:1000000], lang_module=\"fr_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "768 ms ± 12.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "tokenized = tokenize(rawfr[:1000000], lang_module=\"fr_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized = tokenize(rawfr[:1000000], lang_module=\"fr_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### French Moses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 15.6 s, sys: 18.9 ms, total: 15.6 s\n",
-      "Wall time: 15.6 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_moses = tokenize(rawfr[:1000000], lang_module=\"fr_moses\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.78 s ± 45.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "tokenized_moses = tokenize(rawfr[:1000000], lang_module=\"fr_moses\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_moses = tokenize(rawfr[:1000000], lang_module=\"fr_moses\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "spacy: 227009 tokens\n",
-      "moses: 202475 tokens\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('spacy: {} tokens\\nmoses: {} tokens'.format(len(tokenized),len(tokenized_moses)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['se', 'diriger', 'vers', 'les', '\\r\\n', 'bâtiments', ',', 'il', 'se', 'risqua', 'enfin', 'à', 'gravir', 'le', 'terri', 'sur', 'lequel', 'brûlaient', '\\r\\n', 'les', 'trois', 'feux', 'de', 'houille', ',', 'dans', 'des', 'corbeilles', 'de', 'fonte', ',', 'pour', 'éclairer', '\\r\\n', 'et', 'réchauffer', 'la', 'besogne', '.', ' ', 'Les', 'ouvriers', 'de', 'la', 'coupe', 'à', 'terre', 'avaient', 'dû', '\\r\\n', 'travailler', 'tard', ',', 'on', 'sortait', 'encore', 'les', 'débris', 'inutiles', '.', ' ', 'Maintenant', ',', '\\r\\n', 'il', 'entendait', 'les', 'moulineurs', 'pousser', 'les', 'trains', 'sur', 'les', 'tréteaux', ',', 'il', '\\r\\n', 'distinguait', 'des', 'ombres', 'vivantes', 'culbutant', 'les', 'berlines', ',', 'près', 'de', 'chaque', '\\r\\n', 'feu', '.', '\\r\\n\\r\\n', '--Bonjour', ',', 'dit', '-', 'il', 'en', \"s'\", 'approchant', \"d'\", 'une', 'des', 'corbeilles', '.', '\\r\\n\\r\\n', 'Tournant', 'le', 'dos', 'au', 'brasier', ',', 'le', 'charretier', 'était', 'debout', ',', 'un', 'vieillard', '\\r\\n', 'vêtu', \"d'\", 'un', 'tricot', 'de', 'laine', 'violette', ',', 'coiffé', \"d'\", 'une', 'casquette', 'en', 'poil', 'de', '\\r\\n', 'lapin', ';', 'pendant', 'que', 'son', 'cheval', ',', 'un', 'gros', 'cheval', 'jaune', ',', 'attendait', ',', 'dans', '\\r\\n', 'une', 'immobilité', 'de', 'pierre', ',', \"qu'\", 'on', 'eût', 'vidé', 'les', 'six', 'berlines', 'montées', 'par', '\\r\\n', 'lui', '.', ' ', 'Le', 'manoeuvre', 'employé', 'au', 'culbuteur', ',', 'un', 'gaillard', 'roux', 'et', '\\r\\n', 'efflanqué', ',', 'ne', 'se', 'pressait', 'guère', ',', 'pesait', 'sur', 'le', 'levier', \"d'\", 'une', 'main', '\\r\\n', 'endormie', '.', ' ', 'Et']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tokenized[1000:1200])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['le', 'dos', 'au', 'brasier', ',', 'le', 'charretier', 'était', 'debout', ',', 'un', 'vieillard', 'vêtu', \"d'\", 'un', 'tricot', 'de', 'laine', 'violette', ',', 'coiffé', \"d'\", 'une', 'casquette', 'en', 'poil', 'de', 'lapin', ';', 'pendant', 'que', 'son', 'cheval', ',', 'un', 'gros', 'cheval', 'jaune', ',', 'attendait', ',', 'dans', 'une', 'immobilité', 'de', 'pierre', ',', \"qu'\", 'on', 'eût', 'vidé', 'les', 'six', 'berlines', 'montées', 'par', 'lui', '.', 'Le', 'manoeuvre', 'employé', 'au', 'culbuteur', ',', 'un', 'gaillard', 'roux', 'et', 'efflanqué', ',', 'ne', 'se', 'pressait', 'guère', ',', 'pesait', 'sur', 'le', 'levier', \"d'\", 'une', 'main', 'endormie', '.', 'Et', ',', 'là-haut', ',', 'le', 'vent', 'redoublait', ',', 'une', 'bise', 'glaciale', ',', 'dont', 'les', 'grandes', 'haleines', 'régulières', 'passaient', 'comme', 'des', 'coups', 'de', 'faux', '.', '--Bonjour', ',', 'répondit', 'le', 'vieux', '.', 'Un', 'silence', 'se', 'fit', '.', \"L'\", 'homme', ',', 'qui', 'se', 'sentait', 'regardé', \"d'\", 'un', 'oeil', 'méfiant', ',', 'dit', 'son', 'nom', 'tout', 'de', 'suite', '.', '--Je', 'me', 'nomme', 'Étienne', 'Lantier', ',', 'je', 'suis', 'machineur', '...', 'Il', \"n'\", 'y', 'a', 'pas', 'de', 'travail', 'ici', '?', 'Les', 'flammes', \"l'\", 'éclairaient', ',', 'il', 'devait', 'avoir', 'vingt', 'et', 'un', 'ans', ',', 'très', 'brun', ',', 'joli', 'homme', ',', \"l'\", 'air', 'fort', 'malgré', 'ses', 'membres', 'menus', '.', 'Rassuré', ',', 'le', 'charretier', 'hochait', 'la', 'tête', '.', '--Du', 'travail', 'pour', 'un', 'machineur', ',', 'non', ',']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tokenized_moses[1000:1200])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### English spaCy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "tokenized_eng = tokenize(raw[:1000000], lang_module=\"en_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_eng = tokenize(raw[:1000000], lang_module=\"en_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "260 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "tokenize(raw[:1000000], lang_module=\"en_spacy\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['\\ufeffThe',\n",
-       " 'Project',\n",
-       " 'Gutenberg',\n",
-       " 'EBook',\n",
-       " 'of',\n",
-       " 'Crime',\n",
-       " 'and',\n",
-       " 'Punishment',\n",
-       " ',',\n",
-       " 'by']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_eng[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### English NLTK "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1.56 s, sys: 28 ms, total: 1.58 s\n",
-      "Wall time: 1.58 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "tokenized_eng_nltk = tokenize(raw[:1000000], lang_module=\"en_nltk\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_eng_nltk = tokenize(raw[:1000000], lang_module=\"en_nltk\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.54 s ± 27.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "tokenize(raw[:1000000], lang_module=\"en_nltk\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['\\ufeffThe',\n",
-       " 'Project',\n",
-       " 'Gutenberg',\n",
-       " 'EBook',\n",
-       " 'of',\n",
-       " 'Crime',\n",
-       " 'and',\n",
-       " 'Punishment',\n",
-       " ',',\n",
-       " 'by']"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_eng_nltk[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stemming "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.stemming import stem_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 4.19 s, sys: 20 ms, total: 4.21 s\n",
-      "Wall time: 4.21 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "stem = stem_tokens(tokenized,lang='french')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.25 s ± 61.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "stem_tokens(tokenized,lang='french')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Lemmatization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## French "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n",
-      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
-      "[nltk_data]     /root/nltk_data...\n",
-      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
-      "[nltk_data]       date!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_french_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.preprocess import remove_tokens_with_nonletters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "tokenized = remove_tokens_with_nonletters(tokenized)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 21.9 s, sys: 8.62 s, total: 30.6 s\n",
-      "Wall time: 30.6 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatized_tokens = lemmatize_french_tokens(tokenized, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lemmatized_tokens = lemmatize_french_tokens(tokenized, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['de', 'puits', 'le', 'vaste', 'chambre', 'de', 'le', 'machine', 'extraction', 'le', 'tourelle', 'de', 'le', 'pompe', 'ce', 'fosse', 'au', 'fondre', 'un', 'creux', 'avec', 'son', 'construction', 'trapu', 'de', 'brique', 'dresser', 'son', 'comme', 'un', 'corne', 'luire', 'sembler', 'avoir', 'un', 'air', 'mauver', 'de', 'goulu', 'accroupie', 'pour', 'manger', 'le', 'monde', 'tout', 'en', 'examiner', 'il', 'songer', 'luire', 'son', 'existence', 'de', 'vagabond', 'depuis', 'huit', 'jour', 'il', 'chercher', 'un', 'place', 'il', 'se', 'revoir', 'dans', 'son', 'atelier', 'de', 'chemin', 'de', 'fer', 'gifler', 'son', 'chef', 'de', 'Lille', 'de', 'partout', 'le', 'samedi', 'il', 'Marchiennes', 'on', 'dire', 'il', 'y', 'avoir', 'de', 'travail', 'aux', 'Forges', 'et', 'rien', 'ni', 'aux', 'Forges', 'ni', 'chez', 'Sonneville', 'il', 'avoir', 'passer', 'le', 'dimanche', 'sou', 'le', 'bois', 'un', 'chantier', 'de', 'charronnage', 'dont', 'le', 'surveillant', 'venir', 'de', 'expulser', 'deux', 'heure', 'de', 'le', 'nuit', 'Rien', 'plus', 'un', 'sou', 'pas', 'un', 'aller', 'il', 'faire', 'ainsi', 'par', 'le', 'chemin', 'sans', 'but', 'ne', 'savoir', 'seulement', 'abriter', 'contre', 'le', 'bise', 'oui', 'bien', 'un', 'fosse', 'le', 'rare', 'lanterne', 'le', 'carreau', 'un', 'porte', 'brusquement', 'ouvrir', 'luire', 'avoir', 'permettre', 'entrevoir', 'le', 'foyer', 'un', 'dans', 'un', 'vive', 'il', 'expliquer', 'de', 'le', 'pompe', 'ce', 'respiration', 'gros', 'et', 'long', 'souffler', 'sans', 'qui', 'comme', 'halein', 'de', 'monstre', 'le', 'manoeuvre', 'de', 'culbuteur', 'gonfler', 'le', 'dos', 'avoir', 'pas', 'le', 'oeil', 'sur', 'et', 'celui', 'ci', 'aller']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(lemmatized_tokens[1000:1200])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## English"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_eng = remove_tokens_with_nonletters(tokenized_eng)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 24.3 s, sys: 11.3 s, total: 35.6 s\n",
-      "Wall time: 35.6 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatized_eng = lemmatize_english_tokens(tokenized_eng, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lemmatized_eng = lemmatize_english_tokens(tokenized_eng, module='spacy')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 20.5 s, sys: 706 ms, total: 21.2 s\n",
-      "Wall time: 21.1 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lemmatized_eng_nltk = lemmatize_english_tokens(tokenized_eng, module='nltk')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lemmatized_eng_nltk = lemmatize_english_tokens(tokenized_eng, module='nltk')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['feel', 'ashamed', 'He', 'was', 'hopelessly', 'in', 'debt', 'to', 'his', 'landlady', 'and', 'was', 'afraid', 'of', 'meeting', 'her', 'This', 'was', 'not', 'because', 'he', 'was', 'cowardly', 'and', 'abject', 'quite', 'the', 'contrary', 'but', 'for', 'some', 'time', 'past', 'he', 'had', 'been', 'in', 'an', 'overstrained', 'irritable', 'condition', 'verging', 'on', 'hypochondria', 'He', 'had', 'become', 'so', 'completely', 'absorbed', 'in', 'himself', 'and', 'isolated', 'from', 'his', 'fellows', 'that', 'he', 'dreaded', 'meeting', 'not', 'only', 'his', 'landlady', 'but', 'anyone', 'at', 'all', 'He', 'was', 'crushed', 'by', 'poverty', 'but', 'the', 'anxieties', 'of', 'his', 'position', 'had', 'of', 'late', 'ceased', 'to', 'weigh', 'upon', 'him', 'He', 'had', 'given', 'up', 'attending', 'to', 'matters', 'of', 'practical', 'importance', 'he', 'had', 'lost', 'all', 'desire', 'to', 'do', 'so', 'Nothing', 'that', 'any', 'landlady', 'could', 'do', 'had', 'a', 'real', 'terror', 'for', 'him', 'But', 'to', 'be', 'stopped', 'on', 'the', 'stairs', 'to', 'be', 'forced', 'to', 'listen', 'to', 'her', 'trivial', 'irrelevant', 'gossip', 'to', 'pestering', 'demands', 'for', 'payment', 'threats', 'and', 'complaints', 'and', 'to', 'rack', 'his', 'brains', 'for', 'excuses', 'to', 'prevaricate', 'to', 'lie', 'no', 'rather', 'than', 'that', 'he', 'would', 'creep', 'down', 'the', 'stairs', 'like', 'a', 'cat', 'and', 'slip', 'out', 'unseen', 'This', 'evening', 'however', 'on', 'coming', 'out', 'into', 'the', 'street', 'he', 'became', 'acutely', 'aware', 'of', 'his', 'fears', 'I', 'want', 'to', 'attempt', 'a', 'thing', 'like', 'that', 'and', 'am', 'frightened', 'by', 'these']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(tokenized_eng[1000:1200])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['feel', 'ashamed', '-PRON-', 'be', 'hopelessly', 'in', 'debt', 'to', '-PRON-', 'landlady', 'and', 'be', 'afraid', 'of', 'meet', '-PRON-', 'This', 'be', 'not', 'because', '-PRON-', 'be', 'cowardly', 'and', 'abject', 'quite', 'the', 'contrary', 'but', 'for', 'some', 'time', 'past', '-PRON-', 'have', 'be', 'in', 'an', 'overstrained', 'irritable', 'condition', 'verge', 'on', 'hypochondria', '-PRON-', 'have', 'become', 'so', 'completely', 'absorb', 'in', '-PRON-', 'and', 'isolate', 'from', '-PRON-', 'fellow', 'that', '-PRON-', 'dread', 'meeting', 'not', 'only', '-PRON-', 'landlady', 'but', 'anyone', 'at', 'all', '-PRON-', 'be', 'crush', 'by', 'poverty', 'but', 'the', 'anxiety', 'of', '-PRON-', 'position', 'have', 'of', 'late', 'cease', 'to', 'weigh', 'upon', '-PRON-', '-PRON-', 'have', 'give', 'up', 'attend', 'to', 'matter', 'of', 'practical', 'importance', '-PRON-', 'have', 'lose', 'all', 'desire', 'to', 'do', 'so', 'Nothing', 'that', 'any', 'landlady', 'could', 'do', 'have', 'a', 'real', 'terror', 'for', '-PRON-', 'but', 'to', 'be', 'stop', 'on', 'the', 'stair', 'to', 'be', 'force', 'to', 'listen', 'to', '-PRON-', 'trivial', 'irrelevant', 'gossip', 'to', 'pester', 'demand', 'for', 'payment', 'threat', 'and', 'complaint', 'and', 'to', 'rack', '-PRON-', 'brain', 'for', 'excuse', 'to', 'prevaricate', 'to', 'lie', 'no', 'rather', 'than', 'that', '-PRON-', 'would', 'creep', 'down', 'the', 'stair', 'like', 'a', 'cat', 'and', 'slip', 'out', 'unseen', 'This', 'evening', 'however', 'on', 'come', 'out', 'into', 'the', 'street', '-PRON-', 'become', 'acutely', 'aware', 'of', '-PRON-', 'fear', '-PRON-', 'want', 'to', 'attempt', 'a', 'thing', 'like', 'that', 'and', 'be', 'frighten', 'by', 'these']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(lemmatized_eng[1000:1200])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['feel', 'ashamed', 'He', 'be', 'hopelessly', 'in', 'debt', 'to', 'his', 'landlady', 'and', 'be', 'afraid', 'of', 'meeting', 'her', 'This', 'be', 'not', 'because', 'he', 'be', 'cowardly', 'and', 'abject', 'quite', 'the', 'contrary', 'but', 'for', 'some', 'time', 'past', 'he', 'have', 'be', 'in', 'an', 'overstrain', 'irritable', 'condition', 'verge', 'on', 'hypochondria', 'He', 'have', 'become', 'so', 'completely', 'absorbed', 'in', 'himself', 'and', 'isolated', 'from', 'his', 'fellow', 'that', 'he', 'dread', 'meeting', 'not', 'only', 'his', 'landlady', 'but', 'anyone', 'at', 'all', 'He', 'be', 'crush', 'by', 'poverty', 'but', 'the', 'anxiety', 'of', 'his', 'position', 'have', 'of', 'late', 'cease', 'to', 'weigh', 'upon', 'him', 'He', 'have', 'give', 'up', 'attend', 'to', 'matter', 'of', 'practical', 'importance', 'he', 'have', 'lose', 'all', 'desire', 'to', 'do', 'so', 'Nothing', 'that', 'any', 'landlady', 'could', 'do', 'have', 'a', 'real', 'terror', 'for', 'him', 'But', 'to', 'be', 'stop', 'on', 'the', 'stair', 'to', 'be', 'force', 'to', 'listen', 'to', 'her', 'trivial', 'irrelevant', 'gossip', 'to', 'pester', 'demand', 'for', 'payment', 'threat', 'and', 'complaint', 'and', 'to', 'rack', 'his', 'brain', 'for', 'excuse', 'to', 'prevaricate', 'to', 'lie', 'no', 'rather', 'than', 'that', 'he', 'would', 'creep', 'down', 'the', 'stair', 'like', 'a', 'cat', 'and', 'slip', 'out', 'unseen', 'This', 'even', 'however', 'on', 'come', 'out', 'into', 'the', 'street', 'he', 'become', 'acutely', 'aware', 'of', 'his', 'fear', 'I', 'want', 'to', 'attempt', 'a', 'thing', 'like', 'that', 'and', 'be', 'frighten', 'by', 'these']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(lemmatized_eng_nltk[1000:1200])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/Language_identification.ipynb b/notebooks/Language_identification.ipynb
deleted file mode 100644
index bd7b852..0000000
--- a/notebooks/Language_identification.ipynb
+++ /dev/null
@@ -1,201 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning:\n",
-    "Pour télécharger les datas pour ce notebook, vous pouvez utilisez le [script associé](/nautilus_nlp/data/external/get_language_dataset.sh)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('../data/external/wili/x_test.txt') as fp:\n",
-    "    test=fp.readlines()\n",
-    "    test=[doc.strip('\\n') for doc in test]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Le Dataset Wili est un dataset d'identification de langue basé sur Wikipedia.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Language identification"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Two types of detector can be implemented: THe Compact language detector (CLD2) and the Fasttext language identification one"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "__init__() got an unexpected keyword argument 'typemodel'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-1929b26ba9a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnautilus_nlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlanguage_detector\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLangDetector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdetector\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mLangDetector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypemodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'fasttext'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdetector2\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mLangDetector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypemodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cld2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'typemodel'"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.models.language_detector import LangDetector\n",
-    "detector= LangDetector(typemodel='fasttext')\n",
-    "detector2= LangDetector(typemodel='cld2')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The language identification part is simply done by calling the detect language method of the detector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ne l fin de l seclo XIX l Japon era inda çconhecido i sótico pa l mundo oucidental. Cula antroduçon de la stética japonesa, particularmente na Sposiçon Ounibersal de 1900, an Paris, l Oucidente adquiriu un apetite ansaciable pul Japon i Heiarn se tornou mundialmente coincido pula perfundidade, ouriginalidade i sinceridade de ls sous cuntos. An sous radadeiros anhos, alguns críticos, cumo George Orwell, acusórun Heiarn de trasferir sou nacionalismo i fazer l Japon parecer mais sótico, mas, cumo l'home qu'oufereciu al Oucidente alguns de sous purmeiros lampeijos de l Japon pré-andustrial i de l Período Meiji, sou trabalho inda ye balioso até hoije.\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'detector' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-f1145461d744>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdetector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect_language\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdetector2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetect_language\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'detector' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(0,5):\n",
-    "    print(test[i])\n",
-    "    print(detector.detect_language(test[i]))\n",
-    "    print(detector2.detect_language(test[i]))\n",
-    "    print('\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the results are pretty similar, the value of the confidence is changing though"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Performance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inference time:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "87.5 ms ± 3.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "for i in range(0,1000):\n",
-    "    detector.detect_language(test[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "32 ms ± 1.19 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "for i in range(0,1000):\n",
-    "    detector2.detect_language(test[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Accuracy\n",
-    "To do"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/Sentiment analysis using pre-trained models.ipynb b/notebooks/Sentiment analysis using pre-trained models.ipynb
deleted file mode 100644
index 35c57d4..0000000
--- a/notebooks/Sentiment analysis using pre-trained models.ipynb	
+++ /dev/null
@@ -1,161 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Calculer un score de sentiment avec un modèle pré-entrainé"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.sentiment_detector import compute_sentiment_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.4234"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_sentiment_score(\"I don't like this. It's weird af\",lang_module=\"en_vader\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6369"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_sentiment_score(\"I love your haircut\",lang_module=\"en_vader\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_sentiment_score(\"I love your haircut\",lang_module=\"en_textblob\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-1.0"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_sentiment_score(\"It's mainly disgusting\",lang_module=\"en_textblob\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.5"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_sentiment_score(\"C'est bien horrible\",lang_module=\"fr_textblob\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-1.0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_sentiment_score(\"C'est horrible\",lang_module=\"fr_textblob\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/Sentiment_analysis_FT.ipynb b/notebooks/Sentiment_analysis_FT.ipynb
deleted file mode 100644
index e8ba22e..0000000
--- a/notebooks/Sentiment_analysis_FT.ipynb
+++ /dev/null
@@ -1,259 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import csv  \n",
-    "import re\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Cleaning:\n",
-    "Pour télécharger les datas pour ce notebook, vous pouvez utilisez le [script associé](/nautilus_nlp/data/external/get_stanfordtweets.sh)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total 86M\n",
-      "-rw-rw-r-- 1 robin robin  81M juin   5 15:31 tweets.train\n",
-      "-rw-rw-r-- 1 robin robin 5,4M juin   5 15:31 tweets.valid\n"
-     ]
-    }
-   ],
-   "source": [
-    "ls -lh ../data/processed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "train = open('../data/processed/tweets.train','w',encoding='utf-8')  \n",
-    "test = open('../data/processed/tweets.valid','w',encoding='utf-8')  \n",
-    "with open('../data/external/tweets_sentiment/training.1600000.processed.noemoticon.csv', mode='r', encoding = \"ISO-8859-1\") as csv_file:  \n",
-    "    csv_reader = csv.DictReader(csv_file, fieldnames=['target', 'id', 'date', 'flag', 'user', 'text'])\n",
-    "    line = 0\n",
-    "    for row in csv_reader:\n",
-    "        # Clean the training data\n",
-    "        # First we lower case the text\n",
-    "        text = row[\"text\"].lower()\n",
-    "        # remove links\n",
-    "        text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))','',text)\n",
-    "        #Remove usernames\n",
-    "        text = re.sub('@[^\\s]+','', text)\n",
-    "        # replace hashtags by just words\n",
-    "        text = re.sub(r'#([^\\s]+)', r'\\1', text)\n",
-    "        #correct all multiple white spaces to a single white space\n",
-    "        text = re.sub('[\\s]+', ' ', text)\n",
-    "        # Additional clean up : removing words less than 3 chars, and remove space at the beginning and teh end\n",
-    "        text = re.sub(r'\\W*\\b\\w{1,3}\\b', '', text)\n",
-    "        text = text.strip()\n",
-    "        line = line + 1\n",
-    "        # Split data into train and validation\n",
-    "        if line%16 == 0:\n",
-    "            print(f'__label__{row[\"target\"]} {text}', file=test)\n",
-    "        else:\n",
-    "            print(f'__label__{row[\"target\"]} {text}', file=train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models import fasttext_classifier  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_results(N, p, r):\n",
-    "    print(\"N\\t\" + str(N))\n",
-    "    print(\"P@{}\\t{:.3f}\".format(1, p))\n",
-    "    print(\"R@{}\\t{:.3f}\".format(1, r))\n",
-    "    \n",
-    "train_data = '../data/processed/tweets.train'\n",
-    "valid_data = '../data/processed/tweets.valid'\n",
-    "\n",
-    "model = fasttext_classifier.Fasttext_clf()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12.3 s ± 944 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "model.train(train_data)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "N\t99880\n",
-      "P@1\t0.755\n",
-      "R@1\t0.755\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_results(*model.test(valid_data))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Il faut donc 13 secondes pour entrainer un modèle sur 1.5 Millions de tweets, avec une précision de 0.75. Not bad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_model('sentiments.bin')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-rw-rw-r-- 1 robin robin 232M juin   5 15:55 sentiments.bin\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls -lh 'sentiments.bin'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ce modèle fait environ 230Mo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Maintenant si on quantize ce modèle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "N\t99880\n",
-      "P@1\t0.755\n",
-      "R@1\t0.755\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.quantize(input=train_data, qnorm=True, retrain=True, cutoff=100000)\n",
-    "print_results(*model.test(valid_data))\n",
-    "model.save_model('sentiments.ftz')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-rw-rw-r-- 1 robin robin 6,8M juin   5 15:56 sentiments.ftz\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls -lh 'sentiments.ftz'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/Spacy_model.ipynb b/notebooks/Spacy_model.ipynb
deleted file mode 100644
index 0c4f587..0000000
--- a/notebooks/Spacy_model.ipynb
+++ /dev/null
@@ -1,126 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "str_=\"Ceci est une longue phrase qu il va falloir lemmatizer. A toi de jouer spacy\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nautilus_nlp.models.Spacy_model import spacy_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model= spacy_model(lang='fr')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ceci',\n",
-       " 'être',\n",
-       " 'un',\n",
-       " 'long',\n",
-       " 'phrase',\n",
-       " 'que',\n",
-       " 'il',\n",
-       " 'aller',\n",
-       " 'falloir',\n",
-       " 'lemmatizer',\n",
-       " '.',\n",
-       " 'a',\n",
-       " 'toi',\n",
-       " 'de',\n",
-       " 'jouer',\n",
-       " 'spacy']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_lemma_from_str(str_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[Ceci, est, une, longue, phrase, qu, il, va, falloir, lemmatizer, .],\n",
-       " [A, toi, de, jouer, spacy]]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_tokenized_sentence_from_str(str_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/Visualization tools.ipynb b/notebooks/Visualization tools.ipynb
deleted file mode 100644
index 50c582b..0000000
--- a/notebooks/Visualization tools.ipynb	
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'nautilus_nlp.visualization'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-6aa84683348e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnautilus_nlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_concordance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnautilus_nlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualize\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmake_word_cloud\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nautilus_nlp.visualization'"
-     ]
-    }
-   ],
-   "source": [
-    "from nautilus_nlp.visualization.visualize import print_concordance\n",
-    "from nautilus_nlp.visualization.visualize import make_word_cloud\n",
-    "from collections import Counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "some_tokenized_texts = ['mise','au','contentieux','chez','XXX','le','client','est','en','relation','avec','un','prestataire','de','recouvrement','il','demande','les','coordonnées','téléphoniques','du','prestataire','de','recouvrement','souhaite','des','renseignements','sur','son','plan','d','apurement','mise','au','contentieux','chez','XXX','le','client','est','en','relation','avec','un','prestataire','de','recouvrement','ok','maj','ok','fel','kole','bp','appel','popur','régler','sa','facture','de','euros','n','facture','régler','car','il','avait','un','report','de','sol','desur','cette','facture','de','euros','somme','payé','au','recouvrement','solde','a','se','jour','a','zéro','appel','d','un','tiers','oui','nom','prénom','mme','dor','au','titre','de','epouse','signale','qu','elle','n','arrive','pas','a','joindre','le','recouvrement','pour','leurs','signaléun','paiement','qu','elle','a','faite','ce','matin','informe','que','coupure','prévue','pour','le','et','invite','retenté','de','joindre','lerec','ouvrement','assistance','sociale','madame','rodrigez','est','avec','la','cliente','et','se','renseigne','sur','les','sommes','dues','au','recouvrement','ainsi','que','les','prelevement','s','avenirs','conversation','coupée','le','client','a','raccroché','pendant','que','je','me','renseignaissur','la','démarche','a','tenir','pour','son','rétablissement','savoir','siil','avait','réglé','au','recouvrement','cliente','appel','pour','nous','dire','que','elle','payera','sa','facture','de','le','octobre','la','recouvrement','n','arrete','pas','de','l','appeler','facture','en','recouvrement','donné','n','retrouve','pour','délai','de','paiement','en','juillet','lieu','de','traitement','acticall','casa','demande','du','client','plan','dapurement','analyse','du','dossier','créance','transféré','actions','réalisées','numéros','du','service','de','recouvrement','réponse','apportée','ras','miseau','contentieux','chez','XXX','le','client','est','en','relation','avec','unpre','stataire','de','recouvrement','il','demande','les','coordonnées','téléphoniques','du','prestataire','de','recouvrement','souhaite','des','ren','mise','au','contentieux','chez','XXX','le','client','est','en','relation','avec','un','prestataire','de','recouvrement','il','demande','les','coordonnées','téléphoniqu','esdu','prestataire','de','recouvrement','num','contentia','donner']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print_concordance(some_tokenized_texts, query_word='recouvrement')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = Counter(some_tokenized_texts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "make_word_cloud(c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/someadditionalfile.txt b/notebooks/someadditionalfile.txt
deleted file mode 100644
index c7de724..0000000
--- a/notebooks/someadditionalfile.txt
+++ /dev/null
@@ -1 +0,0 @@
-Un deuxième exemple de texte en utf-8 cette fois!
\ No newline at end of file
diff --git a/notebooks/somefile.txt b/notebooks/somefile.txt
deleted file mode 100644
index b9855bf..0000000
--- a/notebooks/somefile.txt
+++ /dev/null
@@ -1 +0,0 @@
-J'aime les frites bien grasse �talon ch�peau!
\ No newline at end of file

From 7eff5f23efb4c564570b1df83c87e9d8976d071d Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Sat, 14 Nov 2020 18:54:46 +0100
Subject: [PATCH 391/496] [Cleaning] Removing topic modeling and visualization
 files

---
 nautilus_nlp/analysis/keyword_extractor.py    |  88 -----
 nautilus_nlp/analysis/text_summary.py         |  61 ---
 nautilus_nlp/analysis/visualize.py            |  89 -----
 nautilus_nlp/topic_modeling/biterm_model.py   | 170 ---------
 nautilus_nlp/topic_modeling/lda.py            | 347 -----------------
 nautilus_nlp/topic_modeling/nmf_model.py      | 133 -------
 nautilus_nlp/topic_modeling/seanmf_model.py   | 166 ---------
 .../topic_modeling_short_text.py              | 350 ------------------
 tests/test_biterm.py                          |  85 -----
 tests/test_topic_modeling_short_text.py       | 175 ---------
 10 files changed, 1664 deletions(-)
 delete mode 100644 nautilus_nlp/analysis/keyword_extractor.py
 delete mode 100644 nautilus_nlp/analysis/text_summary.py
 delete mode 100644 nautilus_nlp/analysis/visualize.py
 delete mode 100644 nautilus_nlp/topic_modeling/biterm_model.py
 delete mode 100644 nautilus_nlp/topic_modeling/lda.py
 delete mode 100644 nautilus_nlp/topic_modeling/nmf_model.py
 delete mode 100644 nautilus_nlp/topic_modeling/seanmf_model.py
 delete mode 100644 nautilus_nlp/topic_modeling/topic_modeling_short_text.py
 delete mode 100644 tests/test_biterm.py
 delete mode 100644 tests/test_topic_modeling_short_text.py

diff --git a/nautilus_nlp/analysis/keyword_extractor.py b/nautilus_nlp/analysis/keyword_extractor.py
deleted file mode 100644
index a615f45..0000000
--- a/nautilus_nlp/analysis/keyword_extractor.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from typing import Union, List, Tuple, Dict
-from collections import Counter
-
-from flashtext import KeywordProcessor
-from nautilus_nlp.analysis.ngrams import create_ngrams
-
-
-def extract_keywords(
-        text: str, keyword: Union[str, List[str], Dict[str, List[str]]], case_sensitive: bool = True
-    ) -> List[str]:
-    """
-    Extract Keywords from a document, using FlashText
-
-    Parameters
-    ----------
-    text : str
-        Text to extract keywords from. 
-    keyword : Union[str, list[str], dict[str, list[str]]]
-        Single keyword (str), list of keywords (list), or keyword dict.
-        Example of keyword dict: keyword_dict = {
-                    "java": ["java_2e", "java programing"],
-                    "product management": ["PM", "product manager"]
-                }
-    case_sensitive : bool
-        If False, will be case insensitive.
-
-    Returns
-    -------
-    list
-        Return list of extracted keywords
-    """
-
-    processor = KeywordProcessor(case_sensitive=case_sensitive)
-    if isinstance(keyword, list):
-        processor.add_keywords_from_list(keyword)
-    elif isinstance(keyword, str):
-        processor.add_keyword(keyword)
-    elif isinstance(keyword, dict):
-        processor.add_keywords_from_dict(keyword)
-
-    return processor.extract_keywords(text)
-
-
-def get_frequent_words(
-        tokens: List[str], ngrams_number: int = 1, number_top_words: int = 10) -> List[Tuple[str, int]]:
-    """
-    Create n-grams for a list of tokens
-
-    Parameters
-    ----------
-    tokens : list
-        list of tokens
-    ngrams_number : int
-    number_top_words : int
-        number of top keywords to return
-
-    Returns
-    -------
-    list[tuple[str, int]]
-        Returns a list of the top n words ().
-    """
-    frequent = []
-    if ngrams_number == 1:
-        pass
-    elif ngrams_number >= 2:
-        tokens = create_ngrams(tokens, ngrams_number)
-    else:
-        raise ValueError("number of n-grams should be >= 1")
-    counter = Counter(tokens)
-    frequent = counter.most_common(number_top_words)
-    return frequent
diff --git a/nautilus_nlp/analysis/text_summary.py b/nautilus_nlp/analysis/text_summary.py
deleted file mode 100644
index 8fb6ff4..0000000
--- a/nautilus_nlp/analysis/text_summary.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from typing import Optional, Any
-from summa.summarizer import summarize
-
-
-def is_list_of_strings(lst: Any) -> bool:
-    """
-    Parameters
-    ----------
-    lst : list
-
-    Returns
-    -------
-    book : bool
-        True if is a list of strings, else returns False
-    """
-    return bool(lst) and isinstance(lst, list) and all(isinstance(elem, str) for elem in lst)
-
-
-def summarize_text(
-    txt: str, ratio: float = 0.2, language: str = "english", nb_words: Optional[int] = None) -> str:
-    """
-    This function uses the summa library to summazize text
-
-    Parameters
-    ----------
-    txt : str
-        Sting or list of strings containing text to summarize
-    ratio : float
-        Percentage giving the output text length in reference to the input length.
-    language : str
-        text language. eg. "english"
-    nb_words : int, optional
-        number of words of the output text or None
-
-    Returns
-    -------
-    string
-        string containing the summarized text
-    """
-    if is_list_of_strings(txt):
-        txt = ' '.join(txt)
-    elif not isinstance(txt, str):
-        raise TypeError("Text parameter must be a Unicode object (str) or list of str!")
-    return summarize(txt, ratio, nb_words, language)
diff --git a/nautilus_nlp/analysis/visualize.py b/nautilus_nlp/analysis/visualize.py
deleted file mode 100644
index 0dc31e5..0000000
--- a/nautilus_nlp/analysis/visualize.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from typing import List, Optional, Union
-from collections import Counter
-
-import matplotlib.pyplot as plt
-import wordcloud
-import warnings
-
-plt.rcParams["figure.figsize"] = [16, 9]
-
-
-def make_word_cloud(text_or_counter: Union[str, list], stop_words: Optional[List[str]] = None):
-    '''
-    Prints a word cloud from a text, or a word count. 
-
-    Parameters
-    ----------
-    text_or_counter : Union[str, list]
-        The text or the Counter to be ploted as wordcloud. 
-        Example of counter: [('cat', 2), ('dog', 1)]
-    stop_words: List[str], optional
-        List of words to be ignored
-    '''    
-    if isinstance(text_or_counter, str):
-        word_cloud = wordcloud.WordCloud(stopwords=stop_words).generate(text_or_counter)
-    else:
-        if stop_words is not None:
-            text_or_counter = Counter(word for word in text_or_counter if word not in stop_words)
-        word_cloud = wordcloud.WordCloud(stopwords=stop_words).generate_from_frequencies(text_or_counter)
-    plt.imshow(word_cloud)
-    plt.axis("off")
-    plt.show()
-
-
-def print_concordance(
-        tokens: List[str], query_word: str, width: int = 110, n_results: Optional[int] = None):
-    '''
-    Inputs a list of token and a query word, and print all the sentences that contains the query\
-    word, display in a nice way. This function is an adaptation of NLTK's print_concordance\
-    function. Source: http://www.nltk.org/_modules/nltk/text.html
-
-    Parameters
-    ----------
-    tokens : list
-        list of words
-    query_word : str
-        the word to be searched for in the list of tokens
-    width : int
-        Number of caracters to be display per text chunk
-    n_results : int, optional
-        If specified, will print only the N results
-    '''
-    half_width = (width - len(query_word) - 2) // 2
-    context = width // 4  # approx number of words of context
-
-    results = [i for i, j in enumerate(tokens) if j == query_word]
-    nb_results = len(results)
-    if nb_results > 0:
-        if n_results is None:
-            n_results = nb_results
-        print(f'{nb_results} matches for "{query_word}":')
-        for i in results[:n_results]:
-            # Find the context of query word.
-            left_context = tokens[max(0, i - context): i]
-            right_context = tokens[i + 1: i + context]
-            # Create the pretty lines with the query_word in the middle.
-            left_print = ' '.join(left_context)[-half_width:]
-            right_print = ' '.join(right_context)[:half_width]
-            # The WYSIWYG line of the concordance.
-            line_print = ' '.join([left_print, query_word, right_print])
-            print(line_print)
-    else:
-        warnings.warn(f'No match for "{query_word}"')
diff --git a/nautilus_nlp/topic_modeling/biterm_model.py b/nautilus_nlp/topic_modeling/biterm_model.py
deleted file mode 100644
index 83c4eff..0000000
--- a/nautilus_nlp/topic_modeling/biterm_model.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from typing import List, Any
-import numpy as np
-from pyLDAvis import prepare, save_html
-from biterm.btm import oBTM
-from biterm.utility import topic_summuary, vec_to_biterms
-from sklearn.feature_extraction.text import CountVectorizer
-
-
-class BitermModel:
-
-    # pylint: disable=too-many-instance-attributes
-
-    def __init__(self, data: List[str], nb_topics: int, nb_iteration: int, lang: str):
-        """
-        Model for topic modelling. Particularly useful for short texts.
-
-        Parameters
-        ----------
-        data : list
-            a list of string, each string can be a document
-        nb_topics : positive int
-
-        nb_iteration : positive int
-
-        lang : str
-            _language to remove the stop words, can be setup to None
-
-        Returns
-        -------
-        string
-            the text with removed multiple spaces and strip text
-        """
-        self.is_int_positive(nb_topics)
-        self.is_int_positive(nb_iteration)
-        self.is_list_of_string(data)
-
-        self.data = data
-        self.nb_topics = nb_topics
-        self.nb_iteration = nb_iteration
-        self.lang = lang
-        self._topics = None
-        self._btm = None
-        self._vectorize_text = None
-        self._vocabulary = None
-
-    @staticmethod
-    def is_int_positive(number: Any):
-        """
-        Function to check if the input parameter is a integer and positive
-        otherwise raise an error
-
-        Parameters
-        ----------
-        number : Any
-
-        Raises
-        ------
-        ValueError
-            If the input is not a positive integer
-        """
-        if not isinstance(number, int):
-            raise ValueError(f"Parameter {number} has to be an integer")
-        if number < 1:
-            raise ValueError(f"Parameter {number} has to be positive")
-
-    @staticmethod
-    def is_list_of_string(data: Any):
-        """
-        Function to check if the input parameter is a list of strings otherwise raise an error
-
-        Parameters
-        ----------
-        data: Any
-
-        Raises
-        ------
-        ValueError
-            if data is not a list of string, or if is emply
-        """
-        if not isinstance(data, list):
-            raise ValueError(f"{data} has to be a list")
-        if len(data) == 0:
-            raise ValueError(f"{data} is empty")
-        for document in data:
-            if not isinstance(document, str):
-                raise ValueError(f"All elements of {data} have to be a string, problem with {document}")
-
-    def compute_topics(self, nb_word_per_cluster: int) -> dict:
-        """
-        Main function computing the topic modeling, topics
-
-        Parameters
-        ----------
-        nb_word_per_cluster : int
-
-        Returns
-        -------
-        dict
-            A dictionary containing the the different topics with the top words
-            and coherence associated
-        """
-        vec = CountVectorizer(stop_words=self.lang)
-        self._vectorize_text = vec.fit_transform(self.data).toarray()
-        self._vocabulary = np.array(vec.get_feature_names())
-
-        biterms = vec_to_biterms(self._vectorize_text)
-        self._btm = oBTM(num_topics=self.nb_topics, V=self._vocabulary)
-        self._topics = self._btm.fit_transform(biterms, iterations=self.nb_iteration)
-
-        results = topic_summuary(
-            self._btm.phi_wz.T, self._vectorize_text,
-            self._vocabulary, nb_word_per_cluster, verbose=False
-        )
-
-        return results
-
-    def get_document_topic(self, index: int) -> int:
-        """
-        Get the cluster associated to the specified document
-
-        Parameters
-        ----------
-        index : int
-            The document index
-
-        Returns
-        -------
-        int
-            the cluster index
-        """
-        if self._topics is None:
-            raise ValueError("Model needs to be trained first")
-
-        return self._topics[index].argmax()
-
-    def save_pyldavis_plot_as_html(self, path_to_output: str = './biterm_pyLDAavis_plot.html'):
-        """
-        Function saving the pyLDAvis plot associated with the compute_topics function
-
-        Parameters
-        ----------
-        path_to_output : str
-            path to save the plut, must be a html file
-        """
-        if self._topics is None or self._btm is None or self._vectorize_text is None or self._vocabulary is None:
-            raise ValueError("Model needs to be trained first")
-
-        vis = prepare(
-            self._btm.phi_wz.T, self._topics,
-            np.count_nonzero(self._vectorize_text, axis=1), self._vocabulary,
-            np.sum(self._vectorize_text, axis=0)
-        )
-        save_html(vis, path_to_output)
diff --git a/nautilus_nlp/topic_modeling/lda.py b/nautilus_nlp/topic_modeling/lda.py
deleted file mode 100644
index 0b5b257..0000000
--- a/nautilus_nlp/topic_modeling/lda.py
+++ /dev/null
@@ -1,347 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from typing import List, Optional
-import logging
-import os
-
-import gensim
-import matplotlib.pyplot as plt
-import pyLDAvis
-import pyLDAvis.gensim
-from gensim.models import CoherenceModel
-from gensim.models.wrappers import LdaMallet
-from IPython.display import HTML
-
-logging.getLogger("gensim").setLevel(logging.WARNING)
-
-
-def create_dictionary(data: List[List[str]]) -> List[List[tuple]]:
-    """
-    Create a Dictionary encapsulates the mapping between normalized words and their integer ids.
-
-    Parameters
-    ----------
-    data : list
-        list of list of tokens
-
-    Returns
-    -------
-    list
-        List of list of tuples
-    """
-    return gensim.corpora.Dictionary(data)
-
-def filter_extremes(
-        dictionary: gensim.corpora.Dictionary, no_below: Optional[int]=15, no_above: Optional[float]=0.3, **kwargs) -> gensim.corpora.Dictionary:
-    """
-    Remove very rare and very common words
-
-    Parameters
-    ----------
-    dictionary : dict 
-        dictionary containing the number of times a word appears in the dataset set
-    no_below : int, optional
-        Keep tokens which are contained in at least `no_below` documents.
-    no_above : float, optional
-        Keep tokens which are contained in no more than `no_above` documents. (fraction\
-            of total corpus size, not an absolute number).
-
-    Returns
-    -------
-    gensim.corpora.Dictionary
-    """
-    return dictionary.filter_extremes(no_below=no_below, no_above=no_above, **kwargs)
-
-
-def create_bow_corpus(data, dictionary):
-    """
-    Create the corpus: one of the two main inputs to the LDA topic model with the dictionary (id2word)
-    The produced corpus is a mapping of (token_id, token_count).
-
-    Parameters
-    ----------
-    data : list 
-        list of list of tokens
-
-    Returns
-    -------
-    list
-        list of list of tuples
-    """
-    corpus = [dictionary.doc2bow(text) for text in data]
-    return corpus
-
-### Find Number of topics
-
-def compute_coherence_values(dictionary, bow_corpus, texts, limit=25, start=2, step=4):
-    """
-    Compute c_v coherence for various number of topics
-
-    WARNING: It takes a really long time.
-
-    Parameters:
-    ----------
-    dictionary : gensim.corpora.Dictionary
-        Gensim dictionary
-    bow_corpus : Gensim bow corpus
-    texts : list 
-        List of input texts
-    limit : int
-        Max number of topics
-    start : int
-    step : int
-
-    Returns:
-    -------
-    model_list : list
-        List of LDA topic models
-    coherence_values :
-        Coherence values corresponding to the LDA model with respective number of topics
-    """
-    coherence_values = []
-    model_list = []
-    for num_topics in range(start, limit, step):
-        model = gensim.models.ldamodel.LdaModel(
-            corpus=bow_corpus,
-            id2word=dictionary,
-            num_topics=num_topics,
-            random_state=0,
-            update_every=5,
-            chunksize=1000,
-            passes=10
-        )
-        model_list.append(model)
-        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')
-        coherence_values.append(coherencemodel.get_coherence())
-
-    return model_list, coherence_values
-
-def plot_optimal_topic_number(coherence_values, start=2, limit=25, step=4):
-    """
-    Plot the coherence scores per number of topics
-
-    Parameters:
-    ----------
-    coherence_values : list
-        list of coherence scores for various number of topics
-    start : int
-        Min num of topics
-    limit : int
-        Max num of topics
-    step: int
-
-    Returns
-    -------
-    Lineplot
-    """
-    x = range(start, limit, step)
-    plt.plot(x, coherence_values)
-    plt.xlabel("Num Topics")
-    plt.ylabel("Coherence score")
-    plt.legend(("coherence_values"), loc='best')
-    return plt.show()
-
-def print_coherence_scores(coherence_values, start=2, limit=25, step=4):
-    """
-    Print the coherences scores for the ldamodels that had been tested with different number of topics
-    """
-    x = range(start, limit, step)
-    for m, c_value in zip(x, coherence_values):
-        print("Num Topics =", m, " has Coherence Value of", round(c_value, 4))
-
-
-### LdaModel: Gensim & Mallet
-
-def train_lda_model(bow_corpus, dictionary: gensim.corpora.Dictionary, num_topics, model='gensim', mallet_path=None, **kwargs):
-    """ Train the lda model on the corpus
-
-    Parameters
-    ----------
-    bow_corpus : list
-        iterable of list of tokens. Stream of document vectors or sparse matrix of shape \
-    (num_terms, num_documents).
-    dictionary: gensim.corpora.Dictionary
-        Mapping from word IDs to words
-    num_topics: int
-    model : str
-        Precise the topic modeling model wanted, must be "gensim" or "mallet"
-    mallet_path: Optional[str]
-        If model='gensim', required if model='mallet'. Path to the mallet-2.0.8 file
-
-    Returns
-    -------
-    gensim.ldamodel
-
-    Raises
-    ------
-    ValueError
-    """
-    if model == 'gensim':
-        model = train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs)
-    elif model == 'mallet':
-        if mallet_path is None:
-            raise ValueError('You must precise the path to the mallet-2.0.8 file that has been downloaded before')
-        model = train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs)
-    else:
-        raise ValueError('Please enter a valid model name: gensim or mallet')
-    return model
-
-def train_lda_gensim(bow_corpus, dictionary, num_topics, **kwargs):
-    model = gensim.models.ldamodel.LdaModel(
-        corpus=bow_corpus, id2word=dictionary, num_topics=num_topics,
-        passes=10, minimum_probability=0.001, random_state=0, **kwargs
-    )
-    return model
-
-def train_lda_mallet(bow_corpus, dictionary, num_topics, mallet_path, **kwargs):
-    os.environ['MALLET_PATH'] = mallet_path
-    mallet = '$MALLET_PATH/mallet-2.0.8/bin/mallet'
-    model = gensim.models.wrappers.LdaMallet(
-        mallet, corpus=bow_corpus, id2word=dictionary, num_topics=num_topics,
-        prefix='composant', random_seed=0, **kwargs
-    )
-    return model
-
-
-def save_model(model, model_name):
-    """
-    Save the model that has been trained. The model will be saved on your current emplacement.
-
-    Parameters
-    ----------
-    model: ldamodel
-    model_name: str
-        Name the model that will be saved
-    """
-    return model.save(os.path.join(model_name))
-
-
-def load_model(model_path, model_name, model='gensim', model_prefix='composant'):
-    """
-    Detected the language of a text
-
-    Parameters
-    ----------
-    model_path: str
-        path where the model has been saved
-    model_name: str
-        name of the saved model
-    model : str
-        Precise the topic modeling model wanted, must be "gensim" or "mallet"
-    model_prefix : str
-        By default, 'composant' default prefix used while saving the mallet model with train_lda_model function.
-    """
-    if model == 'gensim':
-        ldamodel = gensim.models.LdaModel.load(os.path.join(model_path, model_name))
-    elif model == 'mallet':
-        ldamodel = LdaMallet.load(os.path.join(model_path, model_name))
-        if model_prefix is not None:
-            ldamodel.prefix = model_path+'/'+ model_prefix
-    else:
-        raise ValueError('Please enter a valid model name: gensim or mallet')
-    return ldamodel
-
-def fit_data(model, bow):
-    """Test the model on new, unseen documents"""
-    return model.get_document_topics(bow, minimum_probability=0)
-
-
-# Visualization
-
-
-def visualize_topics(model, bow_corpus, dictionary, model_type=None):
-    """
-    Visualize the topics-keywords with the pyLDAvis interactive chart.
-        (Work well in notebook)
-
-    Parameters
-    ----------
-    model: 
-        LDA model: gensim or mallet
-    bow_corpus : list
-        iterable of list of tokens.
-    dictionary: corpora.Dictionary
-        Dictionary encapsulates the mapping between normalized words and their integer ids.
-    model : str
-        Precise the topic modeling model used, must be "gensim" or "mallet"
-
-    Returns
-    -------
-    pyLDAvis
-        3D interactive chart
-    """
-    if model_type == 'mallet':
-        model_vis = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(model)
-    elif model_type == 'gensim':
-        model_vis = model
-    elif model_type is None:
-        raise ValueError('You forgot to precise your model type, it must be: gensim or mallet')
-    else:
-        raise ValueError('Please enter a valid model name: gensim or mallet')
-    return pyLDAvis.gensim.prepare(model_vis, bow_corpus, dictionary)
-
-def save_pyldavis(pyldavis, vis_path, vis_name):
-    """
-    Save the pyldavis interactive chart
-
-    Parameters
-    ----------
-    pyldavis: pyLDAvis._prepare.PreparedData
-    vis_path: str
-    vis_name: str
-    """
-    return pyLDAvis.save_html(pyldavis, os.path.join(vis_path, vis_name + '{}'.format('.html')))
-
-
-
-def show_pyldavis(vis_path, vis_name):
-    """
-    Display the HTML of the saved pyldavis interactive chart
-
-    Parameters
-    ----------
-    vis_path: str
-    vis_name: str
-    """
-    return HTML(filename=os.path.join(vis_path, vis_name + '{}'.format('.html')))
-
-def show_dominant_topic(model, bow_corpus, topic_number=1, topn=5):
-    """ Print the dominant topics in the document, its score and the topics' top keywords.
-
-    Quick way to interpret the topics
-
-    Parameters
-    ----------
-    model
-        gensim.ldamodel
-    bow_corpus : list
-        iterable of list of tokens.
-    topic_number: int
-        Pick the number of topics displayed
-    topn : int 
-        Number of topics' top keyword displayed
-
-    """
-    i = 0
-    for index, score in sorted(model[bow_corpus], key=lambda tup: -1*tup[1]):
-        weight = model.show_topic(index, topn=topn)
-        keywords = [i[0] for i in weight]
-        print("Score: {}\t Topic: {}".format(score, keywords))
-        i += 1
-        if i == topic_number:
-            break
diff --git a/nautilus_nlp/topic_modeling/nmf_model.py b/nautilus_nlp/topic_modeling/nmf_model.py
deleted file mode 100644
index 3bb3ac1..0000000
--- a/nautilus_nlp/topic_modeling/nmf_model.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import time
-import numpy as np
-from numpy.linalg import norm
-from tqdm import tqdm
-
-
-class NMF:
-
-    # pylint: disable=too-many-instance-attributes
-
-    def __init__(
-            self, mat_a, mat_iw, mat_ih, n_topic=10,
-            max_iter=100, max_err=1e-3, rand_init=True):
-        """
-        The objective of the NMF model is to approximate the term-document matrix A by two \
-            lower-rank matrices W and H.
-        The process is iterative and we denote IW and IH the the matrix W and H that are \
-            updated at each step.
-
-        Parameters
-        ----------
-        mat_a
-            The term-document matrix
-        mat_iw
-            topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
-        and its elements are the weights of the corresponding keywords.
-        mat_ih
-            The row vector H(j,:) is the latent representation for document j in terms of K topics
-        n_topic
-            Number of selected topics
-        max_iter
-            Maximum number of iterations to update W and H
-        max_err
-            maximum error under which we consider that the loop converged
-        rand_init : bool
-            random init boolean
-        """
-        self.mat_a = mat_a
-        self.mat_iw = mat_iw
-        self.mat_ih = mat_ih
-        self.n_row = mat_a.shape[0]
-        self.n_col = mat_a.shape[1]
-
-        self.n_topic = n_topic
-        self.max_iter = max_iter
-        self.max_err = max_err
-
-        self.loss_hist = []
-        self.nmf_mat_init(rand_init)
-        self.nmf_iter()
-
-    def nmf_mat_init(self, rand_init):
-        """
-        Init Matrices W and H initially either randomly or using existing IW, IH matrices taken when iterating.
-
-        Parameters
-        ----------
-        rand_init : Boolean
-            Indicating initial random init
-        """
-        if rand_init:
-            self.mat_w = np.random.random((self.n_row, self.n_topic))
-            self.mat_h = np.random.random((self.n_col, self.n_topic))
-        else:
-            self.mat_w = self.mat_iw
-            self.mat_h = self.mat_ih
-        for k in range(self.n_topic):
-            self.mat_w[:, k] /= norm(self.mat_w[:, k])
-
-    def nmf_iter(self):
-        """
-        Main iterative loop for matrix decomposition
-        """
-        loss_old = 1e20
-        start_time = time.time()
-        for i in tqdm(range(self.max_iter)):
-            self.nmf_solver()
-            loss = self.nmf_loss()
-            self.loss_hist.append(loss)
-
-            if loss_old - loss < self.max_err:
-                print('Matrix decomposition loop converged!')
-                break
-            loss_old = loss
-            end_time = time.time()
-            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
-
-    def nmf_solver(self):
-        '''
-        regular NMF without constraint.
-        Block Coordinate Decent
-        '''
-        epss = 1e-20
-
-        mat_hth = self.mat_h.T.dot(self.mat_h)
-        mat_ah = self.mat_a.dot(self.mat_h)
-        for k in range(self.n_topic):
-            tmp_w = self.mat_w[:, k] * mat_hth[k, k] + mat_ah[:, k] - np.dot(self.mat_w, mat_hth[:, k])
-            self.mat_w[:, k] = np.maximum(tmp_w, epss)
-            self.mat_w[:, k] /= norm(self.mat_w[:, k]) + epss
-
-        mat_wtw = self.mat_w.T.dot(self.mat_w)
-        mat_atw = self.mat_a.T.dot(self.mat_w)
-        for k in range(self.n_topic):
-            self.mat_h[:, k] = self.mat_h[:, k] * mat_wtw[k, k] + mat_atw[:, k] - np.dot(self.mat_h, mat_wtw[:, k])
-            self.mat_h[:, k] = np.maximum(self.mat_h[:, k], epss)
-
-    def nmf_loss(self):
-        loss = norm(self.mat_a - np.dot(self.mat_w, np.transpose(self.mat_h)), 'fro') ** 2 / 2.0
-        return loss
-
-    def get_loss(self):
-        return np.array(self.loss_hist)
-
-    def get_decomposition_matrix(self):
-        return self.mat_w, self.mat_h
diff --git a/nautilus_nlp/topic_modeling/seanmf_model.py b/nautilus_nlp/topic_modeling/seanmf_model.py
deleted file mode 100644
index 0fbb584..0000000
--- a/nautilus_nlp/topic_modeling/seanmf_model.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-
-import time
-import numpy as np
-from numpy.linalg import norm
-from tqdm import tqdm
-
-
-class SeaNMF:
-
-    # pylint: disable=too-many-instance-attributes
-
-    def __init__(
-            self, mat_a, mat_s, mat_iw, mat_iwc, mat_ih, alpha=1.0, beta=0.1, n_topic=10,
-            max_iter=100, max_err=1e-3, rand_init=True, fix_seed=False):
-        """
-        Seanmf is a topic modeling algorithm, paper:  http://dmkd.cs.vt.edu/papers/WWW18.pdf.
-        It finds an approximation to the term-document matrix A by two lower-rank matrices W and H,
-        at each iteration a context matrix Wc are computed and used to update W.
-
-        Parameters
-        ----------
-        mat_a
-            document term matrix
-        mat_s
-            Word-context (semantic) correlation matrix
-        mat_iw
-            topics Matrix, each column vector W(:,k) represents the k-th topic in terms of M keywords
-        and its elements are the weights of the corresponding keywords.
-        mat_iwc
-            Latent factor matrix of contexts.
-        mat_ih
-            The row vector H(j,:) is the latent representation for document j in terms of K topics
-        alpha
-            Seanmf algorithm parameter
-        beta
-            Seanmf algorithm parameter
-        n_topic
-            Number of selected topics
-        max_iter
-            Maximum number of iterations to update W and H
-        max_err
-            maximum error under which we consider that the loop converged
-        rand_init
-            random init boolean
-        fix_seed
-            int number to fix random seed.
-        """
-        if fix_seed:
-            np.random.seed(0)
-
-        self.mat_a = mat_a
-        self.mat_s = mat_s
-
-        self.n_row = mat_a.shape[0]
-        self.n_col = mat_a.shape[1]
-
-        self.n_topic = n_topic
-        self.max_iter = max_iter
-        self.alpha = alpha
-        self.beta = beta
-        self.mat_b = np.ones([self.n_topic, 1])
-        self.max_err = max_err
-        self.snmf_mat_init(rand_init, mat_iw, mat_iwc, mat_ih)
-        self.snmf_iter()
-
-    def snmf_mat_init(self, rand_init, mat_iw, mat_iwc, mat_ih):
-        """
-        Init Matrices W,Wc and H initially either randomly or using existing IW,IWc IH matrices taken when iterating.
-        
-        Parameters
-        ----------
-        rand_init : bool
-            Boolean indicating initial random init
-        """
-        if rand_init:
-            self.mat_w = np.random.random((self.n_row, self.n_topic))
-            self.mat_wc = np.random.random((self.n_row, self.n_topic))
-            self.mat_h = np.random.random((self.n_col, self.n_topic))
-        else:
-            self.mat_w = mat_iw
-            self.mat_wc = mat_iwc
-            self.mat_h = mat_ih
-        for k in range(self.n_topic):
-            self.mat_w[:, k] /= norm(self.mat_w[:, k])
-            self.mat_wc[:, k] /= norm(self.mat_wc[:, k])
-
-    def snmf_iter(self):
-        """
-        Main iterative loop for matrix decomposition
-        """
-        loss_old = 1e20
-        start_time = time.time()
-        for i in tqdm(range(self.max_iter)):
-            self.snmf_solver()
-            loss = self.snmf_loss()
-            if loss_old - loss < self.max_err:
-                print('Matrix decomposition loop converged!')
-                break
-            loss_old = loss
-            end_time = time.time()
-            print('Step={}, Loss={}, Time={}s'.format(i, loss, end_time - start_time))
-
-    def snmf_solver(self):
-        '''
-        using BCD framework
-        Alogorithm 1: Equations to update W, wc, H matrices are described in the paper
-        http://dmkd.cs.vt.edu/papers/WWW18.pdf
-        '''
-
-        epss = 1e-20
-        # Update W
-        mat_ah = np.dot(self.mat_a, self.mat_h)
-        mat_swc = np.dot(self.mat_s, self.mat_wc)
-        mat_hth = np.dot(self.mat_h.T, self.mat_h)
-        mat_wctwc = np.dot(self.mat_wc.T, self.mat_wc)
-        mat_w1 = self.mat_w.dot(self.mat_b)
-
-        for k in range(self.n_topic):
-            num0 = mat_hth[k, k] * self.mat_w[:, k] + self.alpha * mat_wctwc[k, k] * self.mat_w[:, k]
-            num1 = mat_ah[:, k] + self.alpha * mat_swc[:, k]
-            num2 = np.dot(self.mat_w, mat_hth[:, k]) + self.alpha * np.dot(
-                self.mat_w, mat_wctwc[:, k]) + self.beta * mat_w1[0]
-            self.mat_w[:, k] = num0 + num1 - num2
-            self.mat_w[:, k] = np.maximum(self.mat_w[:, k], epss)  # project > 0
-            self.mat_w[:, k] /= norm(self.mat_w[:, k]) + epss  # normalize
-        # Update Wc
-        mat_wtw = self.mat_w.T.dot(self.mat_w)
-        mat_stw = np.dot(self.mat_s, self.mat_w)
-        for k in range(self.n_topic):
-            self.mat_wc[:, k] = self.mat_wc[:, k] + mat_stw[:, k] - np.dot(self.mat_wc, mat_wtw[:, k])
-            self.mat_wc[:, k] = np.maximum(self.mat_wc[:, k], epss)
-        # Update H
-        mat_atw = np.dot(self.mat_a.T, self.mat_w)
-        for k in range(self.n_topic):
-            self.mat_h[:, k] = self.mat_h[:, k] + mat_atw[:, k] - np.dot(self.mat_h, mat_wtw[:, k])
-            self.mat_h[:, k] = np.maximum(self.mat_h[:, k], epss)
-
-    def snmf_loss(self):
-        loss = norm(self.mat_a - np.dot(self.mat_w, np.transpose(self.mat_h)), 'fro') ** 2 / 2.0
-        if self.alpha > 0:
-            loss += self.alpha * norm(np.dot(self.mat_w, np.transpose(self.mat_wc)) - self.mat_s, 'fro') ** 2 / 2.0
-        if self.beta > 0:
-            loss += self.beta * norm(self.mat_w, 1) ** 2 / 2.0
-
-        return loss
-
-    def get_decomposition_matrix(self):
-        # Wc was not considered to keep same structure as NMF
-        return self.mat_w, self.mat_h
diff --git a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py b/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
deleted file mode 100644
index cc8f6f5..0000000
--- a/nautilus_nlp/topic_modeling/topic_modeling_short_text.py
+++ /dev/null
@@ -1,350 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import re
-from collections import Counter
-from itertools import product
-from typing import List
-
-import numpy as np
-import pyLDAvis
-from nautilus_nlp.topic_modeling.nmf_model import NMF
-from nautilus_nlp.topic_modeling.seanmf_model import SeaNMF
-
-
-def prepare_data(
-    docs: List[str], vocab_min_count: int = 1, vocab_max_size: int = 10000
-):
-    """
-    Parameters
-    ----------
-    docs : list
-        list of sentences on which the topic modeling will be performed
-    vocab_min_count : int
-        minimum number of occurrences of a word to be considered in the vocabulary
-    vocab_max_size : int
-        maximum number of word in the vocabulary
-
-    Returns
-    -------
-    list
-        list of encoded sentences using vocab IDs
-    list
-        list of vocabulary
-    Array
-        array with vocab frequency counts
-    """
-    vocab = {}
-
-    # Tokens_list is a list of sub-lists where each sub-list contains a sentences' tokens.
-    tokens_list = []
-    for sentence in docs:
-        sentence = re.split(r"\s", sentence)
-        tokens_list.append(sentence)
-    vocab = dict(Counter(x for xs in tokens_list for x in xs))
-
-    # Create Vocab array ( list of sorted vocab + counts )
-    vocab_arr = [[wd, vocab[wd]] for wd in vocab if vocab[wd] > vocab_min_count]
-    vocab_arr = sorted(vocab_arr, key=lambda k: k[1])[::-1]
-    vocab_arr = vocab_arr[:vocab_max_size]
-    vocab_arr = sorted(vocab_arr)
-
-    vocab_list = list(map(lambda x: x[0], vocab_arr))
-    # Create Vocab to ID dictionnary
-    vocab2id = {itm[1][0]: itm[0] for itm in enumerate(vocab_arr)}
-
-    # Create ID representation of text (ie: each sentence is a list of vocabId )
-    encoded_text_id = []
-    for sentence in docs:
-        sentence = re.split(r"\s", sentence)
-        sentence = [int(vocab2id[wd]) for wd in sentence if wd in vocab2id]
-        encoded_text_id.append(sentence)
-
-    return encoded_text_id, vocab_list, vocab_arr
-
-
-def train_shorttext_model(
-    model_name: str,
-    encoded_text_id: list,
-    vocab_list: list,
-    n_topics: int = 20,
-    max_iter: int = 20,
-    max_err: float = 0.1,
-    alpha: float = 0,
-    beta: float = 0,
-):
-    """
-    Parameters
-    ----------
-    model_name : str {'nmf','seanmf'}
-    encoded_text_id : list
-        list of encoded sentences
-    vocab_list : list
-        list of vocabulary
-    n_topics : int
-        number of topics
-    max_iter : int
-        maximum number of iterations while training
-    max_err : float
-        training error
-    alpha : float
-        regularization param for the NMF model
-    beta : float
-        regularization param for the NMF model
-
-    Returns
-    -------
-    Trained NMF model
-
-    Raises
-    ------
-    ValueError
-        If model_name is not valid
-    """
-
-    n_docs = len(encoded_text_id)
-    n_terms = len(vocab_list)
-
-    if model_name == "nmf":
-        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
-        return NMF(
-            dt_mat,
-            mat_iw=[],
-            mat_ih=[],
-            n_topic=n_topics,
-            max_iter=max_iter,
-            max_err=max_err,
-        )
-
-    if model_name == "seanmf":
-        # Calculate co-occurence matrix
-        cooc_mat = __build_cooccurence_matrix(n_terms, encoded_text_id)
-        # Calculate PPMI
-        mat_ss = __calculate_ppmi(cooc_mat, n_terms)
-        # Build doc-term matrix
-        dt_mat = __build_doc_term_matrix(n_terms, n_docs, encoded_text_id)
-        return SeaNMF(
-            dt_mat,
-            mat_ss,
-            mat_iw=[],
-            mat_iwc=[],
-            mat_ih=[],
-            alpha=alpha,
-            beta=beta,
-            n_topic=n_topics,
-            max_iter=max_iter,
-            max_err=max_err,
-            fix_seed=1024,
-        )
-    raise ValueError("Invalid model name: Use nmf or seanmf")
-
-
-def show_dominant_topic(
-    model, encoded_text_id: list, vocab_list: list, n_top_keyword: int = 10
-):
-    """
-    Computes the PMi score for each topic and the topKeywords describing each of them.
-
-    Parameters
-    ----------
-    - model
-        trained NMF model
-    - encoded_text_id : list
-        list of encoded sentences
-    - vocab_list : list
-        list of vocabulary
-    - n_top_keyword : list
-        the number of keywords to be returned
-
-    Returns
-    -------
-    dict
-        A dictionnary with the topic number and its top keywords
-    dict
-        A ictionnary with the topic number and its PMI score
-    """
-    dt_mat = __build_cooccurence_matrix(
-        n_terms=len(vocab_list), encoded_text_id=encoded_text_id
-    )
-    np.fill_diagonal(dt_mat, 0)
-    mat_w, _ = model.get_decomposition_matrix()
-    n_topic = mat_w.shape[1]
-    pmi_arr = []
-    for k in range(n_topic):
-        top_keywords_index = mat_w[:, k].argsort()[::-1][:n_top_keyword]
-        pmi_arr.append(__calculate_pmi(dt_mat, top_keywords_index))
-
-    index = np.argsort(pmi_arr)
-    topics = {}
-    pmi_score = {}
-    for k in index:
-        words = []
-        for w in np.argsort(mat_w[:, k])[::-1][:n_top_keyword]:
-            words.append(vocab_list[w])
-        # Complete the topic and the score dicts. Format {Topic_number: words or score}
-        topics[k] = words
-        pmi_score[k] = pmi_arr[k]
-
-    return topics, pmi_score
-
-
-def get_assigned_topics(model):
-    """
-    Assign the topic number to the sentences used when training the model
-
-    Parameters
-    ----------    
-    model
-        trained model for short text
-    
-    Returns
-    -------
-    list
-        list of topics. Having the same length as the training text containing topics assigned \
-        to each sentence.
-    """
-    _, mat_h = model.get_decomposition_matrix()
-    # The weights of the H matrix are converted into probabilities
-    h_probs = mat_h / mat_h.sum(axis=1, keepdims=True)
-    topics_list = list(np.argmax(h_probs, axis=1))
-
-    return topics_list
-
-
-def show_pyldavis(model, encoded_text_id, vocab_arr):
-    """
-    Parameters
-    ----------
-    model
-        trained model
-    encoded_text_id
-        encoded_text_id: list of encoded sentences
-    vocab_arr
-        array of vocabulary frequency
-
-    Returns
-    -------
-    pyldavis topics plot
-    """
-
-    data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
-    vis_data = pyLDAvis.prepare(**data)
-
-    return pyLDAvis.display(vis_data)
-
-
-def prepare_data_pyldavis(model, encoded_text_id, vocab_arr) -> dict:
-    """
-    Transform the model decomposed matrix to create topic term and document topics matrices
-    and prepare data to feed pyldavis.
-    link : http://jeriwieringa.com/2018/07/17/pyLDAviz-and-Mallet/
-
-    Returns
-    -------
-    dict
-        dict of data needed by pyldavis
-    """
-    doc_length_values = [len(doc) for doc in encoded_text_id]
-    list_vocab = list(map(lambda x: x[0], vocab_arr))
-    freq_vocab = list(map(lambda x: x[1], vocab_arr))
-    mat_w, mat_h = model.get_decomposition_matrix()
-    # Normlize the decomposition to get probabilities
-    w_probs = mat_w / mat_w.sum(axis=1, keepdims=True)
-    # Topic-term matrix phi
-    phi = w_probs.T
-    # Document-term matrix theta
-    theta = mat_h / mat_h.sum(axis=1, keepdims=True)
-    return {
-        "topic_term_dists": phi,
-        "doc_topic_dists": theta,
-        "doc_lengths": doc_length_values,
-        "vocab": list_vocab,
-        "term_frequency": freq_vocab,
-    }
-
-
-def __build_cooccurence_matrix(n_terms: int, encoded_text_id: list):
-    """
-    The cooccurence matrix represents the number of times each word
-    appeared in the same context as another word from the vocabulary.
-    The matrix has n_terms x n_terms size, columns and rows denote the vocab.
-    Cell values represent the number of times words occured together in the same sentence.
-
-    Parameters
-    ----------
-    n_terms : int
-    encoded_text_id : list
-        list of encoded sentences
-
-    Returns
-    -------
-    co-occurence matrix
-    """
-    res = np.zeros([n_terms, n_terms])
-    for row in encoded_text_id:
-        counts = Counter(row)
-        for key_from, key_to in product(counts, repeat=2):
-            res[key_from, key_to] += counts[key_from] * counts[key_to]
-    return res
-
-
-def __calculate_ppmi(cooc_mat, n_terms):
-    mat_d1 = np.sum(cooc_mat)
-    print("D1= ", mat_d1)
-    mat_ss = mat_d1 * cooc_mat
-    print("SS= ", mat_ss)
-    for k in range(n_terms):
-        mat_ss[k] /= np.sum(cooc_mat[k])
-    for k in range(n_terms):
-        mat_ss[:, k] /= np.sum(cooc_mat[:, k])
-    print("SS = ", mat_ss)
-    cooc_mat = []  # release memory
-    mat_ss[mat_ss == 0] = 1.0
-    mat_ss = np.log(mat_ss)
-    mat_ss[mat_ss < 0.0] = 0.0
-    return mat_ss
-
-
-def __build_doc_term_matrix(n_terms, n_docs, encoded_text_id):
-    dt_mat = np.zeros([n_terms, n_docs])
-    for k in range(n_docs):
-        for j in encoded_text_id[k]:
-            dt_mat[j, k] += 1.0
-    return dt_mat
-
-
-def __calculate_pmi(mat_aa, top_keywords_index):
-    """
-    Method to compute PMi score
-    Reference: Short and Sparse Text Topic Modeling via Self-Aggregation
-    """
-    mat_d1 = np.sum(mat_aa)
-    n_tp = len(top_keywords_index)
-    mat_pmi = []
-    for index1 in top_keywords_index:
-        for index2 in top_keywords_index:
-            if index2 < index1:
-                if mat_aa[index1, index2] == 0:
-                    mat_pmi.append(0.0)
-                else:
-                    mat_c1 = np.sum(mat_aa[index1])
-                    mat_c2 = np.sum(mat_aa[index2])
-                    mat_pmi.append(
-                        np.log(mat_aa[index1, index2] * mat_d1 / mat_c1 / mat_c2)
-                    )
-    avg_pmi = 2.0 * np.sum(mat_pmi) / float(n_tp) / (float(n_tp) - 1.0)
-    return avg_pmi
diff --git a/tests/test_biterm.py b/tests/test_biterm.py
deleted file mode 100644
index b7fc830..0000000
--- a/tests/test_biterm.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import pandas as pd
-import pytest
-from nautilus_nlp.topic_modeling.biterm_model import BitermModel
-
-TEXT = ['Cola 1.5L Carrefour',
-        'Pepsi Cola Light 1.5L',
-        'Pepsi Cola Twist Light',
-        'Cola 1.5L CRF DISC',
-        'Coca-Cola Light 1.5L',
-        'Coca-Cola Light 4x0.5L',
-        'Coca-Cola Light 6x0.3L',
-        'Panzani 200g x 4 bio',
-        'Rustichella 150g bio',
-        'De Cecco - Fusilli bio',
-        'Gerblé sans Gluten50g',
-        'Penne de riz 100g sans gluten',
-        'Spaghetti de maïs 50g sans Glute']
-
-NB_TOPICS = 5
-NB_WORD_PER_CLUSTER = 5
-NB_ITERATION = 100
-LANGUAGE = 'english'
-
-
-@pytest.mark.parametrize(
-    "input_text, input_nb_topic , input_nb_iteration , input_language",
-    [
-        (TEXT, -1, NB_ITERATION, LANGUAGE),
-        (TEXT, "Panzani", NB_ITERATION, LANGUAGE),
-        (TEXT, 3.4, NB_ITERATION, LANGUAGE),
-        (TEXT, (3, 5), NB_ITERATION, LANGUAGE),
-        (TEXT, [3, 5], NB_ITERATION, LANGUAGE),
-        (TEXT, NB_TOPICS, (2, 4), LANGUAGE),
-        (TEXT, NB_TOPICS, [1, 3], LANGUAGE),
-        (TEXT, NB_TOPICS, -1, LANGUAGE),
-        (TEXT, NB_TOPICS, "Panzani", LANGUAGE),
-        (TEXT, NB_TOPICS, 2.4, LANGUAGE),
-        (3, NB_TOPICS, NB_ITERATION, LANGUAGE),
-        ("Panzani", NB_TOPICS, NB_ITERATION, LANGUAGE),
-        (("Panzani", "Rustichella"), NB_TOPICS, NB_ITERATION, LANGUAGE),
-        ([], NB_TOPICS, NB_ITERATION, LANGUAGE),
-        (["Panzani", "Rustichella", 3], NB_TOPICS, NB_ITERATION, LANGUAGE)
-    ]
-)
-def text_input_parameter_error_handling(input_text
-                                        , input_nb_topic
-                                        , input_nb_iteration
-                                        , input_language):
-    with pytest.raises(TypeError):
-        BitermModel(data=input_text, nb_topics=input_nb_topic, nb_iteration=input_nb_iteration, lang=input_language)
-
-
-def test_number_topic_correct():
-    biterm_model = BitermModel(data=TEXT
-                               , nb_topics=NB_TOPICS
-                               , nb_iteration=NB_ITERATION
-                               , lang=LANGUAGE)
-    clusters = biterm_model.compute_topics(nb_word_per_cluster=NB_WORD_PER_CLUSTER)
-    assert len(pd.DataFrame(clusters)) == NB_TOPICS
-
-
-def test_no_initialisation():
-    with pytest.raises(ValueError):
-        biter_model = BitermModel(data=TEXT,
-                                  nb_topics=NB_TOPICS,
-                                  nb_iteration=NB_ITERATION,
-                                  lang=LANGUAGE)
-        biter_model.get_document_topic(2)
diff --git a/tests/test_topic_modeling_short_text.py b/tests/test_topic_modeling_short_text.py
deleted file mode 100644
index 0103d4a..0000000
--- a/tests/test_topic_modeling_short_text.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import numpy as np
-import pytest
-from nautilus_nlp.topic_modeling.topic_modeling_short_text import (
-    __build_cooccurence_matrix,
-    get_assigned_topics,
-    prepare_data,
-    prepare_data_pyldavis,
-    show_dominant_topic,
-    train_shorttext_model,
-)
-
-TEXT = [
-    "Cola 1.5L Carrefour",
-    "Pepsi Cola Light 1.5L",
-    "Pepsi Cola Twist Light",
-    "Cola 1.5L CRF DISC",
-    "Coca-Cola Light 1.5L",
-    "Coca-Cola Light 4x0.5L",
-    "Coca-Cola Light 6x0.3L",
-    "Panzani 200g x 4 bio",
-    "Rustichella 150g bio",
-    "De Cecco - Fusilli bio",
-    "Gerblé sans Gluten50g",
-    "Penne de riz 100g sans gluten",
-    "Spaghetti de maïs 50g sans Glute",
-]
-
-
-@pytest.mark.parametrize(
-    "input_text,  expected_output",
-    [
-        (
-            TEXT,
-            (
-                [
-                    [2, 0],
-                    [4, 2, 3, 0],
-                    [4, 2, 3],
-                    [2, 0],
-                    [1, 3, 0],
-                    [1, 3],
-                    [1, 3],
-                    [5],
-                    [5],
-                    [5],
-                    [7],
-                    [6, 7],
-                    [6, 7],
-                ],
-                ["1.5L", "Coca-Cola", "Cola", "Light", "Pepsi", "bio", "de", "sans"],
-                [
-                    ["1.5L", 4],
-                    ["Coca-Cola", 3],
-                    ["Cola", 4],
-                    ["Light", 5],
-                    ["Pepsi", 2],
-                    ["bio", 3],
-                    ["de", 2],
-                    ["sans", 3],
-                ],
-            ),
-        )
-    ],
-)
-def test_prepare_data(input_text, expected_output):
-    assert prepare_data(input_text) == expected_output
-
-
-@pytest.mark.parametrize("model_name", ["nmf", "seanmf"])
-@pytest.mark.parametrize("n_topics", [3, 0])
-@pytest.mark.parametrize("n_keywords", [2, 0])
-def test_show_dominant_topic(model_name, n_topics, n_keywords):
-
-    encoded_text_id, vocab_list, _ = prepare_data(TEXT)
-
-    model = train_shorttext_model(
-        model_name, encoded_text_id, vocab_list, n_topics=n_topics
-    )
-    topics, pmi_score = show_dominant_topic(
-        model, encoded_text_id, vocab_list, n_top_keyword=n_keywords
-    )
-
-    assert len(pmi_score) == n_topics
-    assert len(topics) == n_topics
-    for i in topics.values():
-        assert len(i) == n_keywords
-
-
-@pytest.mark.parametrize("model_name", ["nmf", "seanmf"])
-@pytest.mark.parametrize("n_topics", [3])
-def test_get_assigned_topics(model_name, n_topics):
-    encoded_text_id, vocab_list, _ = prepare_data(TEXT)
-    model = train_shorttext_model(
-        model_name, encoded_text_id, vocab_list, n_topics=n_topics
-    )
-    topics_list = get_assigned_topics(model)
-
-    assert len(topics_list) == len(TEXT)
-    for topic_num in topics_list:
-        assert topic_num < n_topics
-
-
-@pytest.mark.parametrize("model_name", ["nmf", "seanmf"])
-@pytest.mark.parametrize("n_topics", [0, 3])
-def test_prepare_data_pyldavis(model_name, n_topics):
-    encoded_text_id, vocab_list, vocab_arr = prepare_data(TEXT)
-    model = train_shorttext_model(
-        model_name, encoded_text_id, vocab_list, n_topics=n_topics
-    )
-
-    data = prepare_data_pyldavis(model, encoded_text_id, vocab_arr)
-    phi = data["topic_term_dists"]
-    theta = data["doc_topic_dists"]
-    doc_length_values = data["doc_lengths"]
-    list_vocab = data["vocab"]
-    freq_vocab = data["term_frequency"]
-
-    assert phi.shape == (n_topics, len(list_vocab))
-    assert theta.shape == (len(TEXT), n_topics)
-    assert len(doc_length_values) == len(TEXT)
-    assert len(list_vocab) == len(freq_vocab)
-
-
-@pytest.mark.parametrize(
-    "input_coded,  expected_output",
-    [
-        (
-            [[1, 3, 3, 2, 2, 0], [1, 3], [3, 0, 0]],
-            [
-                [5.0, 1.0, 2.0, 4.0],
-                [1.0, 2.0, 2.0, 3.0],
-                [2.0, 2.0, 4.0, 4.0],
-                [4.0, 3.0, 4.0, 6.0],
-            ],
-        ),
-        (
-            [[0, 1, 2, 3, 4], [5, 6], [1]],
-            [
-                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
-                [1.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0],
-                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
-                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
-                [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
-                [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
-                [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
-            ],
-        ),
-    ],
-)
-def test_build_cooccurence_matrix(input_coded, expected_output):
-    # The co-occurence matrix is an array with the list of vocab in rows and in columns
-    # The weights denote the occurrences of a word i with a word j in same sentence.
-
-    flat_list = [item for sublist in input_coded for item in sublist]
-    nb_vocab = len(set(flat_list))  # number of distinct words
-    mat = __build_cooccurence_matrix(nb_vocab, input_coded)
-
-    assert np.array_equal(mat, np.array(expected_output))

From c08024c5e8f0d778dc971fe1e2c4ae7c35875f13 Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Sat, 14 Nov 2020 18:55:13 +0100
Subject: [PATCH 392/496] [Cleaning] Cleaning travis yaml and upgrading
 requirements

---
 .travis.yml      |  8 --------
 requirements.txt | 50 +++++++++++++++++++-----------------------------
 2 files changed, 20 insertions(+), 38 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index e99a71f..ed82e0b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -21,14 +21,6 @@ os:
   - linux
 services:
   - docker
-
-before_script:
-  - wget https://github.com/facebookresearch/fastText/archive/v0.2.0.zip  && unzip v0.2.0.zip && cd fastText-0.2.0 && make && pip install . && cd ..
-  - python3 -m spacy download fr &&  python3 -m spacy download en && python3 -m spacy download de && python3 -m spacy download nl && python3 -m spacy download it && python3 -m spacy download xx && python3 -m spacy validate
-
-  - wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip && unzip mallet-2.0.8.zip && rm mallet-2.0.8.zip
-  - sudo add-apt-repository -y ppa:openjdk-r/ppa  && sudo apt update && apt search openjdk && sudo apt install openjdk-8-jdk
-  
 install:
   - pip install -r requirements.txt
   - pip install -e .
diff --git a/requirements.txt b/requirements.txt
index 872256a..42d99c8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,43 +2,33 @@
 #-e .
 
 # external requirements
-Sphinx
-sphinx_rtd_theme
 coverage
-python-dotenv>=0.5.1
 pillow
+python-dotenv>=0.5.1
 pytest
+Sphinx
+sphinx_rtd_theme
 
 #library requirements
-pyLDAvis==2.1.2
-gensim==3.7.1
-sacremoses==0.0.13
-stop-words==2018.7.23
-spacy==2.1.3
+chardet==3.0.4
+emoji>=0.5.2
 ftfy<5.0.0,>=4.2.0
-wordcloud>=1.5.0
-matplotlib>=3.0.3
 mosestokenizer
-numpy>1.15.4
-stop_words==2018.7.23
+nlpaug==1.0.1
 nltk>=3.4.5
-textblob==0.15.3
-textblob_fr==0.2.0
-pandas>=0.23.4
-chardet==3.0.4
-setuptools==40.8.0
-textacy==0.6.3
-#fastText==0.8.3
-gensim==3.7.1
-scikit-learn==0.23.2
-vaderSentiment==3.2.1
-google-compute-engine==2.8.13
-flashtext==2.7
+numpy>1.15.4
 phonenumbers==8.10.12
-regex==2019.8.19
-emoji>=0.5.2
-summa==1.2.0
-biterm==0.1.5
-nlpaug==1.0.1
-ipython==7.16.1
 pylint==2.4.4
+regex==2019.8.19
+sacremoses==0.0.13
+scikit_learn==0.20.3
+setuptools==40.8.0
+spacy==2.1.3
+https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz#egg=fr_core_news_sm
+https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz#egg=en_core_web_sm
+https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.3.0/de_core_news_sm-2.3.0.tar.gz#egg=de_core_news_sm
+https://github.com/explosion/spacy-models/releases/download/it_core_news_sm-2.3.0/it_core_news_sm-2.3.0.tar.gz#egg=it_core_news_sm
+https://github.com/explosion/spacy-models/releases/download/nl_core_news_sm-2.3.0/nl_core_news_sm-2.3.0.tar.gz#egg=nl_core_news_sm
+https://github.com/explosion/spacy-models/releases/download/xx_ent_wiki_sm-2.3.0/xx_ent_wiki_sm-2.3.0.tar.gz#egg=xx_ent_wiki_sm
+stop-words==2018.7.23
+stop_words==2018.7.23
\ No newline at end of file

From 3053e4d974a2ad7df39acdf099b69f9d4679a9a0 Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Sat, 14 Nov 2020 18:58:47 +0100
Subject: [PATCH 393/496] [Cleaning] Removing lang_id files and updating readme

---
 .gitignore                                |   6 --
 README.md                                 |  97 ----------------------
 nautilus_nlp/data/lang_identification.ftz | Bin 938013 -> 0 bytes
 requirements.txt                          |   2 +-
 4 files changed, 1 insertion(+), 104 deletions(-)
 delete mode 100644 nautilus_nlp/data/lang_identification.ftz

diff --git a/.gitignore b/.gitignore
index 183f817..99cffa8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -89,12 +89,6 @@ venv/
 # Mypy cache
 .mypy_cache/
 
-# Fasttext Word_vector
-*.bin
-*.vec
-*.gz
-*.ftz
-!/nautilus_nlp/data/lang_identification.ftz
 
 # PyTest cache
 .pytest_cache/
diff --git a/README.md b/README.md
index bed36ab..a9d774a 100644
--- a/README.md
+++ b/README.md
@@ -45,103 +45,6 @@ then you can install it via pip:
 pip install -e .
 ```
 
-Once it's done, please install the additional required libraries: 
-
-## Installation of the additional required libraries
-
-If you want to leverage all the features of nautilus-nlp, you need to **install others required libraries** (such as FastText).
-
-### Install spaCy language models (highly recommanded)
-
-Installing additional spaCy models will give you the possibility to handle a lot of new language for text processing feature (such as lemmatization or tokenization). 
-
-To do so, run: *(on your virtual environment if you are using one)*
-
-```
-bash nautilus_nlp/scripts/download_spacy_models.sh
-```
-
-### Install FastText 
-
-run: 
-
-```
-bash nautilus_nlp/scripts/install_fasttext.sh
-```
-
-### Install Lang Detect
-
-run: 
-
-```
-bash nautilus_nlp/scripts/download_ft_langdetect.sh
-```
-
-### Install Mallet
-
-Mallet is an implementation of the LDA algorithm (used for **topic modeling**). 
-
-1) Install Mallet file
-run:
-
-```
-bash nautilus_nlp/scripts/install_mallet.sh
-```
-
-2) Install Java JDK (required to implement mallet)
-run:
-
-```
-bash nautilus_nlp/scripts/install_java.sh
-```
-
-## Handling installation errors
-
-### Problem when building FastText on MACOS:
-
-If you see this errorwhen building Fasttext:
-
-    clang: warning: libstdc++ is deprecated; move to libc++ with a minimum deployment target of OS X 10.9 [-Wdeprecated]
-    ld: library not found for -lstdc++
-    clang: error: linker command failed with exit code 1 (use -v to see invocation)
-    error: command 'g++' failed with exit status 1
-Then you should type this command in your terminal:
-
-    export MACOSX_DEPLOYMENT_TARGET=10.9
-
-### command 'gcc' failed with exit status 1
-
-While runing `pip install -r requirements.txt` you might get the following error message:
-
-`command 'gcc' failed with exit status 1`
-
-To solve it, run the following command before installing requirements.txt:
-
-```
-conda install pyemd
-```
-
-### Cannot uninstall 'package_name'
-
-You might get the following error message while installing the library:
-`Cannot uninstall 'PACKAGE_NAME'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.`
-
-To fix this, just type `pip install -e . --ignore-installed PACKAGE_NAME` instead. 
-
-### Installing nautilus-nlp on a linux-based VM
-
-If you are installing nautilus on a linux-powered Virtual Machine (VM), you might want to install essentials first. If you don't, you might experience some issues to install Cython-based libraries, such as spaCy, becode the C compiler will be missing. 
-
-On a Ubuntu VM (tested on 16.04), you can run the following command before following the classic installation process above:
-
-```
-# install compilators and required softwares
-sudo apt-get update && sudo apt-get install -y build-essential unzip git wget
-# install conda
-wget https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
-bash Anaconda3-2019.03-Linux-x86_64.sh
-```
-
 # Quick start
 
 The [notebook](notebooks/) folder contains various notebook on how to use this library.
diff --git a/nautilus_nlp/data/lang_identification.ftz b/nautilus_nlp/data/lang_identification.ftz
deleted file mode 100644
index 1fb85b357b22f67f019567f0e7003f4d49bda7a0..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/requirements.txt b/requirements.txt
index 42d99c8..609e640 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -31,4 +31,4 @@ https://github.com/explosion/spacy-models/releases/download/it_core_news_sm-2.3.
 https://github.com/explosion/spacy-models/releases/download/nl_core_news_sm-2.3.0/nl_core_news_sm-2.3.0.tar.gz#egg=nl_core_news_sm
 https://github.com/explosion/spacy-models/releases/download/xx_ent_wiki_sm-2.3.0/xx_ent_wiki_sm-2.3.0.tar.gz#egg=xx_ent_wiki_sm
 stop-words==2018.7.23
-stop_words==2018.7.23
\ No newline at end of file
+stop_words==2018.7.23

From 1bf04e3f336b8e3df2fae73a570002eec5a320bd Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Sat, 14 Nov 2020 19:15:42 +0100
Subject: [PATCH 394/496] [Cleaning] Removing install requirements in setuppy

---
 setup.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/setup.py b/setup.py
index 39d4c4c..33c165b 100644
--- a/setup.py
+++ b/setup.py
@@ -31,9 +31,6 @@ def run(self):
             pass
 
 
-with open(Path(__file__).resolve().parent.joinpath('requirements.txt'), 'r') as fh:
-    requirements = [r.split('#', 1)[0].strip() for r in fh.read().split('\n')]
-    print(requirements)
 with open(Path(__file__).resolve().parent.joinpath('VERSION'), 'r') as fh:
     version = fh.read()
 
@@ -50,7 +47,6 @@ def run(self):
         'License :: OSI Approved :: MIT License',
         'Operating System :: OS Independent',
     ],
-    install_requires=requirements,
     cmdclass={
         'install': PostInstallCommand,
     },

From ffec6463cdb7e7a4a3d897514711e17d4b200d9d Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Fri, 20 Nov 2020 11:19:40 +0100
Subject: [PATCH 395/496] [Cleaning] Keeping keyword extractor and cleaning
 requirements

---
 README.md                               |  4 +-
 nautilus_nlp/utils/keyword_extractor.py | 49 +++++++++++++++++++++++++
 requirements.txt                        |  8 +---
 3 files changed, 53 insertions(+), 8 deletions(-)
 create mode 100644 nautilus_nlp/utils/keyword_extractor.py

diff --git a/README.md b/README.md
index a9d774a..5f6183e 100644
--- a/README.md
+++ b/README.md
@@ -45,9 +45,9 @@ then you can install it via pip:
 pip install -e .
 ```
 
-# Quick start
+This library uses Spacy as tokenizer. Current models supported are `en_core_web_sm` and `fr_core_news_sm`.
 
-The [notebook](notebooks/) folder contains various notebook on how to use this library.
+# Quick start
 
 Here is a quick example:
 
diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/utils/keyword_extractor.py
new file mode 100644
index 0000000..1705ea0
--- /dev/null
+++ b/nautilus_nlp/utils/keyword_extractor.py
@@ -0,0 +1,49 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+from flashtext import KeywordProcessor
+
+
+def extract_keywords(text, keyword, case_sensitive=True):
+    """
+    Extract Keywords from a document.
+
+    Parameters
+    ----------
+    text : str
+        Text to extract keywords from
+    keyword :
+        Single keyword (str) or list of keywords (list)
+    case_sensitive :
+        If False, will be case insensitive.
+
+    Returns
+    -------
+    list
+        Return list of extracted keyworkds
+    """
+
+    processor = KeywordProcessor(case_sensitive=case_sensitive)
+    if isinstance(keyword, list):
+        processor.add_keywords_from_list(keyword)
+    elif isinstance(keyword, str):
+        processor.add_keyword(keyword)
+    elif isinstance(keyword, dict):
+        processor.add_keywords_from_dict(keyword)
+
+    return processor.extract_keywords(text)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 609e640..a2082ff 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,6 +12,7 @@ sphinx_rtd_theme
 #library requirements
 chardet==3.0.4
 emoji>=0.5.2
+flashtext==2.7
 ftfy<5.0.0,>=4.2.0
 mosestokenizer
 nlpaug==1.0.1
@@ -21,14 +22,9 @@ phonenumbers==8.10.12
 pylint==2.4.4
 regex==2019.8.19
 sacremoses==0.0.13
-scikit_learn==0.20.3
+scikit_learn==0.23.2
 setuptools==40.8.0
 spacy==2.1.3
 https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz#egg=fr_core_news_sm
 https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz#egg=en_core_web_sm
-https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.3.0/de_core_news_sm-2.3.0.tar.gz#egg=de_core_news_sm
-https://github.com/explosion/spacy-models/releases/download/it_core_news_sm-2.3.0/it_core_news_sm-2.3.0.tar.gz#egg=it_core_news_sm
-https://github.com/explosion/spacy-models/releases/download/nl_core_news_sm-2.3.0/nl_core_news_sm-2.3.0.tar.gz#egg=nl_core_news_sm
-https://github.com/explosion/spacy-models/releases/download/xx_ent_wiki_sm-2.3.0/xx_ent_wiki_sm-2.3.0.tar.gz#egg=xx_ent_wiki_sm
-stop-words==2018.7.23
 stop_words==2018.7.23

From f310ee7cd99dbccdf9d1b7d6593a234d9edd0c6f Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Fri, 20 Nov 2020 13:56:57 +0100
Subject: [PATCH 396/496] [Remove] Remove test for removed function

---
 tests/test_keyword_extractor.py | 20 +-------------------
 1 file changed, 1 insertion(+), 19 deletions(-)

diff --git a/tests/test_keyword_extractor.py b/tests/test_keyword_extractor.py
index 69a2a43..f694692 100644
--- a/tests/test_keyword_extractor.py
+++ b/tests/test_keyword_extractor.py
@@ -1,23 +1,5 @@
 import pytest
-from nautilus_nlp.analysis.keyword_extractor import get_frequent_words, extract_keywords
-
-
-@pytest.mark.parametrize(
-    "input_tokens, expected, ngrams_number, number_top_words",
-    [
-        (['I', 'eat', 'orange', 'oranges', 'at', 'orange'], [('orange', 2)], 1, 1),
-        (['Un', 'chat', 'rose', 'reste', 'un', 'chat', 'rose'], [('chat rose', 2)], 2, 1),
-    ]
-    )
-def test_get_frequent_words(input_tokens, expected, ngrams_number, number_top_words):
-    result = get_frequent_words(
-        input_tokens, ngrams_number=ngrams_number, number_top_words=number_top_words)
-    assert result == expected
-
-
-def test_get_frequent_words_output_lenght():
-    input_tokens = ['one', 'two', 'three', 'four', 'five', 'six', 'seven']
-    assert len(get_frequent_words(input_tokens, number_top_words=5)) == 5
+from nautilus_nlp.utils.keyword_extractor import extract_keywords
 
 
 @pytest.mark.parametrize(

From 059e7607d53efad191e145610aad14c3a8056af1 Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Fri, 20 Nov 2020 14:09:38 +0100
Subject: [PATCH 397/496] [Fix] Add newline for lint

---
 nautilus_nlp/utils/keyword_extractor.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/utils/keyword_extractor.py
index 1705ea0..f804d52 100644
--- a/nautilus_nlp/utils/keyword_extractor.py
+++ b/nautilus_nlp/utils/keyword_extractor.py
@@ -46,4 +46,5 @@ def extract_keywords(text, keyword, case_sensitive=True):
     elif isinstance(keyword, dict):
         processor.add_keywords_from_dict(keyword)
 
-    return processor.extract_keywords(text)
\ No newline at end of file
+    return processor.extract_keywords(text)
+    
\ No newline at end of file

From f7661b3daf526637fc72ab243c264dd075a9990f Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Mon, 23 Nov 2020 18:37:45 +0100
Subject: [PATCH 398/496] added test coverage

---
 .github/workflows/ci_actions.yml | 3 ++-
 requirements.txt                 | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 859f7c6..3179876 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -48,4 +48,5 @@ jobs:
       - name: Run pytest
         run: |
           pip install pytest
-          pytest tests
+          pip install pytest-cov
+          pytest --cov=nautilus_nlp tests
diff --git a/requirements.txt b/requirements.txt
index 00dc863..29c021b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,6 +8,7 @@ coverage
 python-dotenv>=0.5.1
 pillow
 pytest
+pytest-cov
 
 #library requirements
 pyLDAvis==2.1.2

From 3a105ec1d64686a9b5349a2f90afbd780b02944d Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Mon, 23 Nov 2020 18:55:48 +0100
Subject: [PATCH 399/496] fixed pytests version

---
 .github/workflows/ci_actions.yml | 3 ---
 requirements.txt                 | 4 ++--
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 3179876..4b4ba74 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,11 +42,8 @@ jobs:
 
       - name: Run pylint
         run: |
-          pip install pylint
           pylint nautilus_nlp tests
 
       - name: Run pytest
         run: |
-          pip install pytest
-          pip install pytest-cov
           pytest --cov=nautilus_nlp tests
diff --git a/requirements.txt b/requirements.txt
index 29c021b..bbd8fe4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,8 +7,8 @@ sphinx_rtd_theme
 coverage
 python-dotenv>=0.5.1
 pillow
-pytest
-pytest-cov
+pytest==6.1.1
+pytest-cov==2.10.1
 
 #library requirements
 pyLDAvis==2.1.2

From 16c6865eea297ef1a26825a4895a60a4e12e1d13 Mon Sep 17 00:00:00 2001
From: "sacha.lasry" <sacha.lasry@artefact.com>
Date: Tue, 24 Nov 2020 12:36:36 +0100
Subject: [PATCH 400/496] fixed conflict

---
 .github/workflows/ci_actions.yml | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index dd45702..4b4ba74 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -17,7 +17,6 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 name: CI
 
-<<<<<<< HEAD:.github/workflows/ci_actions.yml
 on: [push, pull_request]
 
 jobs:
@@ -48,16 +47,3 @@ jobs:
       - name: Run pytest
         run: |
           pytest --cov=nautilus_nlp tests
-=======
-os:
-  - linux
-services:
-  - docker
-install:
-  - pip install -r requirements.txt
-  - pip install -e .
-script:
-  - pylint nautilus_nlp/
-  - pylint tests/
-  - pytest tests/*
->>>>>>> dev:.travis.yml

From f19daa5eee4b239b92f0bea019dbf9c07c852a1d Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Thu, 26 Nov 2020 09:53:04 +0100
Subject: [PATCH 401/496] [Init] New readme draft

---
 README.md | 91 ++++++++++++++++++++++++++++++-------------------------
 1 file changed, 49 insertions(+), 42 deletions(-)

diff --git a/README.md b/README.md
index 5f6183e..68900c7 100644
--- a/README.md
+++ b/README.md
@@ -1,82 +1,86 @@
-Nautilus_NLP [![Build Status](https://travis-ci.com/artefactory/nautilus-nlp.svg?token=Ssg4shz5pz9qGnYCybSj&branch=master)](https://travis-ci.com/artefactory/nautilus-nlp)
+Insert new lib name here
 ==============================
-![Nautilus-NLP](/references/nautilus_nlp_logo.png)
 
-The Nautilus NLP library aimed to be a meta-library to be used to help you get started on handling your NLP use-case.
+**Insert new logo here**
 
-This library can help you with:
+Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web? :tired_face:
 
-    1. Cleaning text data
-    2. Normalizing your dataset
-    3. Training automatically multiclass, multilabel classifier
-    4. Help you discover topics and cluster your data
+:disappointed_relieved: Need to efficiently extract mail adresses in a document? Hashtags in tweets? Remove accents from a French tweet? 
 
-You can find a list of the available features [in this article.](https://artefactory.atlassian.net/wiki/spaces/CK/pages/822837299/Nautilus+NLP+-+key+features)
+**Insert new lib name here** got you covered! :rocket:
 
-# Feature Request
+**Insert new lib name here** packages in a unique library all the text preprocessing functions you need to ease your NLP project. :mag: Quickly explore below our referential.
 
-As an Artefact user, you might be working on a NLP use case, and wish to use Nautilus.
+* [Replacing emails](#replace_emails)
+* [Replacing phone numbers](#replace_phone_numbers)
+* [Removing hashtags](#remove_hashtags)
+* [Extracting emojis](#extract_emojis)
+
+
+Cannot find a new one? Feel free to open an [issue]((https://github.com/artefactory/nautilus-nlp/issues) )).
 
-However, if you think Nautilus is lacking features that can be useful not only to your use case but also others, feel free to to [fill up an issue](https://github.com/artefactory/nautilus-nlp/issues) with the label "Feature-request".
 
-We will try to put it in the roadmap and implement it as soon as possible.
 
 # Installation
 
-Beware, this package has been tested on Python **3.6** & **3.7**, and will probably not be working under python **2.7** as **Python2.7** EOL is scheduled for December 2019. 
+This package has been tested on Python **3.7**.
 
 To install this library you should first clone the repository:
 
-```
+```bash
 git clone git@github.com:artefactory/nautilus-nlp.git && cd nautilus_nlp/
 ```
 
-**If you don't use the docker container, we strongly advise you to do these steps in a virtual environnement**
+We strongly advise you to do the remaining steps in a virtual environnement.
 
-First you need to install the required files:
+First install the required files:
 
-```
+```bash
 pip install -r requirements.txt
 ```
 
-then you can install it via pip:
+then install the library with pip:
 
-```
+```bash
 pip install -e .
 ```
 
 This library uses Spacy as tokenizer. Current models supported are `en_core_web_sm` and `fr_core_news_sm`.
 
-# Quick start
 
-Here is a quick example:
+# Functions
+
+## Replacing emails <a name="replace_emails"></a>
+
+```python
+example = "I have forwarded this email to obama@whitehouse.gov"
+example = replace_emails(replace_with="*EMAIL*")
+print(example)
+# "I have forwarded this email to *EMAIL*"
+```
+
+## Replacing phone numbers <a name="replace_phone_numbers"></a>
 
+```python
+Insert example here
 ```
->>> from nautilus_nlp.preprocessing.preprocess import preprocess_text
->>> from nautilus_nlp.preprocessing.tokenizer import tokenize
->>> from nautilus_nlp.preprocessing.lemmatization import lemmatize_english_tokens
-[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...
-[nltk_data]   Package wordnet is already up-to-date!
->>> text = """Tropical Storm Nicole was a short-lived and unusually asymmetric tropical cyclone that caused extensive flooding in Jamaica during the 2010 Atlantic hurricane season.\nSource:https://en.wikipedia.org/wiki/Tropical_Storm_Nicole_(2010)"""
->>> clean_text = preprocess_text(text, lowercase=True,
-...                                         no_punct=True,
-...                                         no_numbers=True,
-...                                         no_stopwords='en',
-...                                         no_urls=True,
-...                                         replace_with='')
->>> clean_text
-'tropical storm nicole short lived unusually asymmetric tropical cyclone caused extensive flooding jamaica atlantic hurricane season source'
->>> tokenized_text = tokenize(clean_text)
->>> tokenized_text
-['tropical', 'storm', 'nicole', 'short', 'lived', 'unusually', 'asymmetric', 'tropical', 'cyclone', 'caused', 'extensive', 'flooding', 'jamaica', 'atlantic', 'hurricane', 'season', 'source']
->>> lemma_text = lemmatize_english_tokens(tokenized_text)
->>> lemma_text
-['tropical', 'storm', 'nicole', 'short', 'live', 'unusually', 'asymmetric', 'tropical', 'cyclone', 'cause', 'extensive', 'flooding', 'jamaica', 'atlantic', 'hurricane', 'season', 'source']
+
+## Removing Hashtags <a name="remove_hashtags"></a>
+
+```python
+Insert example here
 ```
 
+## Extracting emojis <a name="extract_emojis"></a>
+
+```python
+Insert example here
+```
 
 # Make HTML documentation
 
+**à updater**
+
 In order to make the html Sphinx documentation, you need to run at the nautilus_nlp root path:
 `sphinx-apidoc -f nautilus_nlp -o docs/`
 This will generate the .rst files.
@@ -84,7 +88,10 @@ You can generate the doc with
 `cd docs && make html`
 
 You can now open the file index.html located in the build folder.
+
 # Project Organization
+
+**à updater**
 ------------
 
     ├── LICENSE

From e55577fbef03f65efb3c11d6ef6c1231db844274 Mon Sep 17 00:00:00 2001
From: Citronelol <>
Date: Thu, 26 Nov 2020 09:57:36 +0100
Subject: [PATCH 402/496] [Fix] Cleaning draft

---
 README.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 68900c7..64ca8e4 100644
--- a/README.md
+++ b/README.md
@@ -3,13 +3,15 @@ Insert new lib name here
 
 **Insert new logo here**
 
-Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web? :tired_face:
+:tired_face: Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web? 
 
-:disappointed_relieved: Need to efficiently extract mail adresses in a document? Hashtags in tweets? Remove accents from a French tweet? 
+:disappointed_relieved: Need to efficiently extract email adresses from a document? Hashtags from tweets? Remove accents from a French post? 
 
 **Insert new lib name here** got you covered! :rocket:
 
-**Insert new lib name here** packages in a unique library all the text preprocessing functions you need to ease your NLP project. :mag: Quickly explore below our referential.
+**Insert new lib name here** packages in a unique library all the text preprocessing functions you need to ease your NLP project. 
+
+:mag: Quickly explore below our functions referential.
 
 * [Replacing emails](#replace_emails)
 * [Replacing phone numbers](#replace_phone_numbers)
@@ -90,9 +92,8 @@ You can generate the doc with
 You can now open the file index.html located in the build folder.
 
 # Project Organization
-
-**à updater**
 ------------
+**à updater**
 
     ├── LICENSE
     ├── Makefile           <- Makefile with commands like `make data` or `make train`

From 80f4bcc548b3ce0985316d8f43c1322a80efc277 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 26 Nov 2020 15:41:19 +0100
Subject: [PATCH 403/496] add test for data augmentation

---
 .../preprocessing/data_augmentation.py        |  8 +-
 tests/test_data_augmentation.py               | 74 +++++++++++++++++++
 2 files changed, 78 insertions(+), 4 deletions(-)
 create mode 100644 tests/test_data_augmentation.py

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index d9cdccf..207593b 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -149,10 +149,10 @@ def get_augmented_entities(sentence_augmented: str, entities: list) -> list:
     """
     entities_augmented = []
     for entity in entities:
-        regex = r'(?:^|\W)' + re.escape(entity[0].strip()) + r'(?:$|\W)'
-        if (re.search(re.compile(regex), sentence_augmented)):
-            start_index = re.search(regex, sentence_augmented).start()+1
-            end_index = re.search(regex, sentence_augmented).end()-1
+        search = re.search(entity[0].strip(), sentence_augmented)
+        if search:
+            start_index = search.start()
+            end_index = search.end()
             new_entity = {
                 'entity': entity[1],
                 'word': sentence_augmented[start_index: end_index],
diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
new file mode 100644
index 0000000..b01eccd
--- /dev/null
+++ b/tests/test_data_augmentation.py
@@ -0,0 +1,74 @@
+import pytest
+from nautilus_nlp.preprocessing.data_augmentation import augment_text, CouldNotAugment, UnavailableAugmenter
+
+@pytest.mark.parametrize(
+    "text, method, stopwords, entities, expected",
+    [
+        (
+            "I want to buy a small black handbag.",
+            'wordnet_synonym',
+            ['small', 'black', 'handbag'],
+            [
+                {'entity': 'Size', 'word': 'small', 'startCharIndex': 16, 'endCharIndex': 21},
+                {'entity': 'Color', 'word': 'black', 'startCharIndex': 22, 'endCharIndex': 27},
+                {'entity': 'Type', 'word': 'handbag', 'startCharIndex': 28, 'endCharIndex': 35}
+            ],
+            {'type':str, 'entities':['black', 'handbag', 'small']}
+        ),
+        (
+            "I want to buy a small black handbag.",
+            'aug_sub_bert',
+            ['small', 'black', 'handbag'],
+            [
+                {'entity': 'Size', 'word': 'small', 'startCharIndex': 16, 'endCharIndex': 21},
+                {'entity': 'Color', 'word': 'black', 'startCharIndex': 22, 'endCharIndex': 27},
+                {'entity': 'Type', 'word': 'handbag', 'startCharIndex': 28, 'endCharIndex': 35}
+            ],
+            {'type':str, 'entities':['black', 'handbag', 'small']}
+        ),
+        (
+            "I live in New York and I am looking for a lipstick",
+            'wordnet_synonym',
+            ['New York', 'lipstick'],
+            [
+                {'entity': 'City', 'word': 'New York', 'startCharIndex': 10, 'endCharIndex': 18},
+                {'entity': 'Type', 'word': 'bag', 'startCharIndex': 42, 'endCharIndex': 50}
+            ],
+            {'type':str, 'entities':['lipstick', 'New York']}
+        ),
+        (
+            "I live in New York and I am looking for a lipstick",
+            'ppdb_synonym',
+            ['New York', 'lipstick'],
+            [
+                {'entity': 'City', 'word': 'New York', 'startCharIndex': 10, 'endCharIndex': 18},
+                {'entity': 'Type', 'word': 'bag', 'startCharIndex': 42, 'endCharIndex': 50}
+            ],
+            {}
+        ),
+        (
+            "I want to buy a small black bag",
+            'aug_sub_bert',
+            None,
+            None,
+            {'type':str}
+        )
+    ]
+    )
+
+def test_data_augmentation(text, method, stopwords, entities, expected):
+    if method not in ['aug_sub_bert', 'wordnet_synonym']:
+        with pytest.raises(UnavailableAugmenter) as excinfo:
+            augment_text(text, method, stopwords, entities)
+            assert str(excinfo.value) == 'The given augmenter is not supported. You must choose one \
+                of the following: wordnet_synonym or aug_sub_bert'
+    else:
+        if entities is not None:
+            try:
+                augmented_text, augmented_entities = augment_text(text, method, stopwords, entities)
+                augmented_entities = sorted([el['word'] for el in augmented_entities])
+                assert {'type': type(augmented_text), 'entities': augmented_entities} == expected
+            except CouldNotAugment:
+                assert True
+        else:
+            assert {'type': type(augment_text(text, method, stopwords, entities))} == expected

From 480af82fd186be2965660c921bc6f5adcfca7555 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 26 Nov 2020 15:53:47 +0100
Subject: [PATCH 404/496] update requirements for data augmentation tests

---
 requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index a2082ff..c64b770 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,3 +28,5 @@ spacy==2.1.3
 https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz#egg=fr_core_news_sm
 https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz#egg=en_core_web_sm
 stop_words==2018.7.23
+torch==1.7.0
+transformers==3.5.1
\ No newline at end of file

From 2e9ad32aa5e62b6689d3a3d3cbab6149efbfbffd Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Wed, 2 Dec 2020 15:35:49 +0100
Subject: [PATCH 405/496] refacto test data augmentation

---
 .../preprocessing/data_augmentation.py        | 26 ++++---
 tests/test_data_augmentation.py               | 76 ++++++++-----------
 2 files changed, 47 insertions(+), 55 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 207593b..8c9fe59 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -51,20 +51,24 @@ def augment_text(
     augmenter = get_augmenter(method, stopwords)
     augmented_text = augmenter.augment(text)
     if entities is not None:
-        formatted_entities = [(
-            text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),
-            entities[i]['entity']) for i in range(len(entities))]
-        if are_entities_in_augmented_text(entities, augmented_text):
-            augmented_entities = get_augmented_entities(
-                augmented_text,
-                formatted_entities
-            )
-            clean_entities = clean_sentence_entities(augmented_text, augmented_entities)
-            return augmented_text, clean_entities
-        raise CouldNotAugment('Text was not correctly augmented because entities were altered')
+        return process_entities_and_text(entities, text, augmented_text)
     return augmented_text
 
 
+def process_entities_and_text(entities, text, augmented_text):
+    formatted_entities = [(
+        text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),
+        entities[i]['entity']) for i in range(len(entities))]
+    if are_entities_in_augmented_text(entities, augmented_text):
+        augmented_entities = get_augmented_entities(
+            augmented_text,
+            formatted_entities
+        )
+        clean_entities = clean_sentence_entities(augmented_text, augmented_entities)
+        return augmented_text, clean_entities
+    raise CouldNotAugment('Text was not correctly augmented because entities were altered')
+
+
 def are_entities_in_augmented_text(entities: list, augmented_text: str) -> bool:
     """
     Given a list of entities, check if all the words associated to each entity
diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index b01eccd..863087e 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -1,13 +1,13 @@
 import pytest
-from nautilus_nlp.preprocessing.data_augmentation import augment_text, CouldNotAugment, UnavailableAugmenter
+from nautilus_nlp.preprocessing.data_augmentation import process_entities_and_text, \
+    get_augmenter, CouldNotAugment, UnavailableAugmenter
 
 @pytest.mark.parametrize(
-    "text, method, stopwords, entities, expected",
+    "text, text_augmented, entities, expected",
     [
         (
             "I want to buy a small black handbag.",
-            'wordnet_synonym',
-            ['small', 'black', 'handbag'],
+            "I want to acquire a small black handbag",
             [
                 {'entity': 'Size', 'word': 'small', 'startCharIndex': 16, 'endCharIndex': 21},
                 {'entity': 'Color', 'word': 'black', 'startCharIndex': 22, 'endCharIndex': 27},
@@ -17,8 +17,7 @@
         ),
         (
             "I want to buy a small black handbag.",
-            'aug_sub_bert',
-            ['small', 'black', 'handbag'],
+            "I would like to buy a black small handbag",
             [
                 {'entity': 'Size', 'word': 'small', 'startCharIndex': 16, 'endCharIndex': 21},
                 {'entity': 'Color', 'word': 'black', 'startCharIndex': 22, 'endCharIndex': 27},
@@ -26,49 +25,38 @@
             ],
             {'type':str, 'entities':['black', 'handbag', 'small']}
         ),
+    ]
+)
+
+def test_process_entities_and_text_not_altered(text, text_augmented, entities, expected):
+    augmented_text, augmented_entities = process_entities_and_text(entities, text, text_augmented)
+    augmented_entities = sorted([el['word'] for el in augmented_entities])
+    assert {'type': type(augmented_text), 'entities': augmented_entities} == expected
+
+
+@pytest.mark.parametrize(
+    "text, text_augmented, entities",
+    [
         (
             "I live in New York and I am looking for a lipstick",
-            'wordnet_synonym',
-            ['New York', 'lipstick'],
-            [
-                {'entity': 'City', 'word': 'New York', 'startCharIndex': 10, 'endCharIndex': 18},
-                {'entity': 'Type', 'word': 'bag', 'startCharIndex': 42, 'endCharIndex': 50}
-            ],
-            {'type':str, 'entities':['lipstick', 'New York']}
-        ),
-        (
-            "I live in New York and I am looking for a lipstick",
-            'ppdb_synonym',
-            ['New York', 'lipstick'],
+            "I live in New and York I an looking for a lipstick",
             [
                 {'entity': 'City', 'word': 'New York', 'startCharIndex': 10, 'endCharIndex': 18},
                 {'entity': 'Type', 'word': 'bag', 'startCharIndex': 42, 'endCharIndex': 50}
-            ],
-            {}
-        ),
-        (
-            "I want to buy a small black bag",
-            'aug_sub_bert',
-            None,
-            None,
-            {'type':str}
+            ]
         )
     ]
-    )
+)
+
+def test_process_entities_and_text_altered(text, text_augmented, entities):
+    with pytest.raises(CouldNotAugment) as excinfo:
+        process_entities_and_text(entities, text, text_augmented)
+        assert str(excinfo.value) == 'Text was not correctly augmented because entities were altered'
+
 
-def test_data_augmentation(text, method, stopwords, entities, expected):
-    if method not in ['aug_sub_bert', 'wordnet_synonym']:
-        with pytest.raises(UnavailableAugmenter) as excinfo:
-            augment_text(text, method, stopwords, entities)
-            assert str(excinfo.value) == 'The given augmenter is not supported. You must choose one \
-                of the following: wordnet_synonym or aug_sub_bert'
-    else:
-        if entities is not None:
-            try:
-                augmented_text, augmented_entities = augment_text(text, method, stopwords, entities)
-                augmented_entities = sorted([el['word'] for el in augmented_entities])
-                assert {'type': type(augmented_text), 'entities': augmented_entities} == expected
-            except CouldNotAugment:
-                assert True
-        else:
-            assert {'type': type(augment_text(text, method, stopwords, entities))} == expected
+def test_get_augmenter():
+    method = 'ppdb_synonym'
+    with pytest.raises(UnavailableAugmenter) as excinfo:
+        get_augmenter(method)
+        assert str(excinfo.value) == 'The given augmenter is not supported. You must choose one \
+               of the following: wordnet_synonym or aug_sub_bert'

From 8050eb4b452bb5258e9795b89ff4b09aabadc749 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 3 Dec 2020 09:07:07 +0100
Subject: [PATCH 406/496] add docstring for process_entities_and_text

---
 .../preprocessing/data_augmentation.py        | 28 ++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 8c9fe59..34b6cff 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -55,7 +55,33 @@ def augment_text(
     return augmented_text
 
 
-def process_entities_and_text(entities, text, augmented_text):
+def process_entities_and_text(entities: list, text: str, augmented_text: str):
+    """
+    Given a list of initial entities, verify that they have not been altered by
+    the data augmentation operation and are still in the augmented text.
+    Parameters
+    ----------
+    entities: list
+        entities associated to text, must be in the following format:
+        [
+            {
+                'entity': str,
+                'word': str,
+                'startCharIndex': int,
+                'endCharIndex': int
+            },
+            {
+                ...
+            }
+        ]
+    text: str
+        initial text
+    augmented_text: str
+        new text resulting of data augmentation operation
+    Returns
+    -------
+    Augmented text and entities with their updated position in augmented text
+    """
     formatted_entities = [(
         text[entities[i]['startCharIndex']:entities[i]['endCharIndex']].strip(),
         entities[i]['entity']) for i in range(len(entities))]

From b83e99bc29a272701b0b1d4df24d89c8b64eda1b Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 3 Dec 2020 17:56:28 +0100
Subject: [PATCH 407/496] remove torch and transformers from requirements

---
 nautilus_nlp/preprocessing/data_augmentation.py | 5 +++--
 requirements.txt                                | 4 +---
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 34b6cff..71f22f1 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -2,7 +2,6 @@
 import re
 from itertools import combinations
 from typing import List, Optional, Tuple
-
 import nlpaug.augmenter.word as naw
 
 
@@ -146,7 +145,9 @@ def get_augmenter(method: str, stopwords: List[str] = None) -> naw.SynonymAug:
     if method == 'wordnet_synonym':
         return naw.SynonymAug(aug_src='wordnet', stopwords=stopwords)
     if method == 'aug_sub_bert':
-        return naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute", stopwords=stopwords)
+        return naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
+                                             action="substitute",
+                                             stopwords=stopwords)
     raise UnavailableAugmenter('The given augmenter is not supported. You must choose one \
         of the following: wordnet_synonym or aug_sub_bert')
 
diff --git a/requirements.txt b/requirements.txt
index c64b770..d92f898 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -27,6 +27,4 @@ setuptools==40.8.0
 spacy==2.1.3
 https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz#egg=fr_core_news_sm
 https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz#egg=en_core_web_sm
-stop_words==2018.7.23
-torch==1.7.0
-transformers==3.5.1
\ No newline at end of file
+stop_words==2018.7.23
\ No newline at end of file

From 88d934319d68686c7b8ecb407937e19e6e6864b3 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 3 Dec 2020 18:03:05 +0100
Subject: [PATCH 408/496] fix linting

---
 nautilus_nlp/preprocessing/data_augmentation.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/nautilus_nlp/preprocessing/data_augmentation.py
index 71f22f1..5ba5e89 100644
--- a/nautilus_nlp/preprocessing/data_augmentation.py
+++ b/nautilus_nlp/preprocessing/data_augmentation.py
@@ -145,9 +145,7 @@ def get_augmenter(method: str, stopwords: List[str] = None) -> naw.SynonymAug:
     if method == 'wordnet_synonym':
         return naw.SynonymAug(aug_src='wordnet', stopwords=stopwords)
     if method == 'aug_sub_bert':
-        return naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
-                                             action="substitute",
-                                             stopwords=stopwords)
+        return naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="substitute", stopwords=stopwords)
     raise UnavailableAugmenter('The given augmenter is not supported. You must choose one \
         of the following: wordnet_synonym or aug_sub_bert')
 

From bb608e12e63b1285764422e6615d26cc1e4df914 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 14 Dec 2020 18:16:46 +0100
Subject: [PATCH 409/496] [FIX] fix conflicts in text_preprocess

---
 nautilus_nlp/preprocessing/text_preprocess.py | 411 ++----------------
 1 file changed, 30 insertions(+), 381 deletions(-)

diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/nautilus_nlp/preprocessing/text_preprocess.py
index 6b025ad..ef6003a 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/nautilus_nlp/preprocessing/text_preprocess.py
@@ -22,18 +22,17 @@
 
 import re
 import unicodedata
-from typing import Optional
 from ftfy import fix_text as _fix_text
 from nautilus_nlp.utils import constants
 from nautilus_nlp.utils.phone_number import \
     extract_phone_numbers as _extract_phone_numbers
-<<<<<<< HEAD
 
 
 def normalize_whitespace(text) -> str:
     """
-    Given ``text`` str, replace one or more spacings with a single space, and one
-    or more linebreaks with a single newline. Also strip leading/trailing whitespace.
+    Given ``text`` str, replace one or more spacings with a single space, and
+    one or more linebreaks with a single newline. Also strip leading/trailing
+    whitespace.
     eg. "   foo  bar  " -> "foo bar"
 
     Parameters
@@ -68,7 +67,8 @@ def remove_eol_characters(text) -> str:
 
 def fix_bad_unicode(text, normalization: str = "NFC") -> str:
     """
-    Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
+    Fix unicode text that's "broken" using `ftfy
+    <http://ftfy.readthedocs.org/>`_;
     this includes mojibake, HTML entities and other code cruft,
     and non-standard forms for display purposes.
 
@@ -77,8 +77,9 @@ def fix_bad_unicode(text, normalization: str = "NFC") -> str:
     text : string
 
     normalization ({'NFC', 'NFKC', 'NFD', 'NFKD'}):
-        if 'NFC', combines characters and diacritics written using separate code points,
-        e.g. converting "e" plus an acute accent modifier into "é"; unicode
+        if 'NFC', combines characters and diacritics written using separate
+        code points, e.g. converting "e" plus an acute accent modifier into
+        "é"; unicode
         can be converted to NFC form without any change in its meaning!
         if 'NFKC', additional normalizations are applied that can change
         the meanings of characters, e.g. ellipsis characters will be replaced
@@ -93,7 +94,8 @@ def fix_bad_unicode(text, normalization: str = "NFC") -> str:
 
 def unpack_english_contractions(text) -> str:
     """
-    Replace *English* contractions in ``text`` str with their unshortened forms.
+    Replace *English* contractions in ``text`` str with their unshortened
+    forms.
     N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
     so are left as-is.
     eg. "You're fired. She's nice." -> "You are fired. She's nice."
@@ -168,7 +170,7 @@ def replace_emails(text, replace_with="*EMAIL*") -> str:
     return text
 
 
-def replace_phone_numbers(text, country_format_to_detect: list,
+def replace_phone_numbers(text, country_to_detect: list,
                           replace_with: str = "*PHONE*",
                           method: str = "regex") -> str:
     """
@@ -182,8 +184,9 @@ def replace_phone_numbers(text, country_format_to_detect: list,
     method : ['regex','detection']
         regex is faster but will omit a lot of numbers, while detection will
         catch every numbers, but takes a while.
-    country_format_to_detect : list
-        If a list of country code is specified, will catch every number formatted.
+    country_to_detect : list
+        If a list of country code is specified, will catch every number
+        formatted.
         Only when method = 'detection'.
     Returns
     -------
@@ -192,14 +195,16 @@ def replace_phone_numbers(text, country_format_to_detect: list,
     if method == 'regex':
         text = constants.PHONE_REGEX.sub(replace_with, text)
     elif method == 'detection':
-        found_nums = _extract_phone_numbers(text, countrylist=country_format_to_detect)
+        found_nums = _extract_phone_numbers(text,
+                                            countrylist=country_to_detect)
 
         # order by lenght to avoid truncated numbers to be removed first.
         found_nums.sort(key=len, reverse=True)
         for phone_number in found_nums:
             text = text.replace(phone_number, replace_with)
     else:
-        raise ValueError('Please input a valid method between "regex" or "detection"')
+        raise ValueError('Please input a valid method between "regex" or \
+            "detection"')
     return text
 
 
@@ -223,7 +228,8 @@ def replace_numbers(text, replace_with="*NUMBER*") -> str:
 
 def replace_currency_symbols(text, replace_with=None) -> str:
     """
-    Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
+    Replace all currency symbols in ``text`` str with string specified by
+    ``replace_with`` str.
 
     Parameters
     ----------
@@ -231,9 +237,9 @@ def replace_currency_symbols(text, replace_with=None) -> str:
         raw text
     replace_with : None or string
         if None (default), replace symbols with
-            their standard 3-letter abbreviations (e.g. '$' with 'USD', '£' with 'GBP');
-            otherwise, pass in a string with which to replace all symbols
-            (e.g. "*CURRENCY*")
+            their standard 3-letter abbreviations (e.g. '$' with 'USD', '£'
+            with 'GBP'); otherwise, pass in a string with which to replace all
+            symbols (e.g. "*CURRENCY*")
 
     Returns
     -------
@@ -273,7 +279,8 @@ def remove_punct(text, marks=None) -> str:
     instead. The former's performance is about 5-10x faster.
     """
     if marks:
-        text = re.sub("[{}]+".format(re.escape(marks)), " ", text, flags=re.UNICODE)
+        text = re.sub("[{}]+".format(re.escape(marks)), " ", text,
+                      flags=re.UNICODE)
     else:
         text = text.translate(constants.PUNCT_TRANSLATE_UNICODE)
     return text
@@ -281,8 +288,9 @@ def remove_punct(text, marks=None) -> str:
 
 def remove_accents(text, method: str = "unicode") -> str:
     """
-    Remove accents from any accented unicode characters in ``text`` str, either by
-    transforming them into ascii equivalents or removing them entirely.
+    Remove accents from any accented unicode characters in ``text`` str,
+    either by transforming them into ascii equivalents or removing them
+    entirely.
 
     Parameters
     ----------
@@ -310,141 +318,6 @@ def remove_accents(text, method: str = "unicode") -> str:
             c
             for c in unicodedata.normalize("NFKD", text)
             if not unicodedata.combining(c)
-=======
-from nautilus_nlp.utils.stopwords import get_stopwords
-
-
-class TextPreprocessor():
-
-    def __init__(self, text):
-        if isinstance(text, str):
-            self.text = text
-        else:
-            raise ValueError("Input must be a string")
-
-    def clean_text(self, lang: str = 'en') -> str:
-        #TODO : check how to pipe operations
-        stopwords = get_stopwords(lang)
-        self.text = self.fix_bad_unicode(normalization="NFC")
-        self.text = self.remove_eol_characters()
-        self.text = self.remove_accents(method="unicode")
-        self.text = self.remove_punct()
-        self.text = self.text.lower()
-        self.text = self.remove_stopwords(stopwords=stopwords)
-        return self.normalize_whitespace()
-
-    def remove_eol_characters(self) -> str:
-        """
-        Remove end of line (\n) char.
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        str
-        """
-        self.text = self.text.replace("\n", " ")
-        return self.text
-
-    def remove_stopwords(self, stopwords: list) -> str:
-        """
-        Remove stopwords from a text.
-        eg. 'I like when you move your body !' -> 'I move body !'
-
-        Parameters
-        ----------
-        stopwords : list
-            list of stopwords to remove
-
-        Returns
-        -------
-        str
-            text without stopwords
-
-        Raises
-        ------
-        ValueError
-            When inputs is not a string
-        """
-        self.text = ' '.join([word.strip() for word in self.text.split() if word not in stopwords])
-        return self.text
-
-    def fix_bad_unicode(self, normalization: str = "NFC") -> str:
-        """
-        Fix unicode text that's "broken" using `ftfy <http://ftfy.readthedocs.org/>`_;
-        this includes mojibake, HTML entities and other code cruft,
-        and non-standard forms for display purposes.
-
-        Parameters
-        ----------
-        text : string
-
-        normalization : string {'NFC', 'NFKC', 'NFD', 'NFKD'}
-            if 'NFC', combines characters and diacritics written using separate code points,
-            e.g. converting "e" plus an acute accent modifier into "é"; unicode
-            can be converted to NFC form without any change in its meaning!
-            if 'NFKC', additional normalizations are applied that can change
-            the meanings of characters, e.g. ellipsis characters will be replaced
-            with three periods
-
-        Returns
-        -------
-        string
-        """
-        self.text = _fix_text(self.text, normalization=normalization)
-        return self.text
-
-    def normalize_whitespace(self) -> str:
-        """
-        Given ``text`` str, replace one or more spacings with a single space, and one
-        or more linebreaks with a single newline. Also strip leading/trailing whitespace.
-        eg. "   foo  bar  " -> "foo bar"
-
-        Parameters
-        ----------
-        text : string
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.NONBREAKING_SPACE_REGEX.sub(
-            " ", constants.LINEBREAK_REGEX.sub(r"\n", self.text)
-        ).strip()
-        return self.text
-
-    def unpack_english_contractions(self) -> str:
-        """
-        Replace *English* contractions in ``text`` str with their unshortened forms.
-        N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
-        so are left as-is.
-        eg. "You're fired. She's nice." -> "You are fired. She's nice."
-
-        Parameters
-        ----------
-        text : string
-
-        Returns
-        -------
-        string
-        """
-
-        # standard
-        self.text = constants.CONTRACTION_NT_NOT.sub(
-            r"\1\2 not",
-            self.text,
-        )
-        self.text = constants.CONTRACTION_LL_WILL.sub(
-            r"\1\2 will",
-            self.text,
-        )
-        self.text = constants.CONTRACTION_RE_ARE.sub(r"\1\2 are", self.text)
-        self.text = constants.CONTRACTION_VE_HAVE.sub(
-            r"\1\2 have",
-            self.text,
->>>>>>> feature/new-readme
         )
     elif method == "ascii":
         text = (
@@ -452,9 +325,9 @@ def unpack_english_contractions(self) -> str:
             .encode("ascii", errors="ignore")
             .decode("ascii")
         )
-<<<<<<< HEAD
     else:
-        msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
+        msg = '`method` must be either "unicode" and "ascii", not {}' \
+               .format(method)
         raise ValueError(msg)
     return text
 
@@ -495,227 +368,3 @@ def filter_non_latin_characters(text) -> str:
     text = constants.LATIN_CHARACTERS_RE.sub(' ', text)
     text = normalize_whitespace(text)
     return text
-=======
-        return self.text
-
-    def replace_emails(self, replace_with: str = "*EMAIL*") -> str:
-        """
-        Replace all emails in ``text`` str with ``replace_with`` str
-
-        Parameters
-        ----------
-        text : string
-        replace_with : string
-            the string you want the email address to be replaced with.
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.EMAIL_REGEX.sub(replace_with, self.text)
-        return self.text
-
-    def replace_phone_numbers(self,
-                              country_format_to_detect: list,
-                              replace_with: str = "*PHONE*",
-                              method: str = "regex") -> str:
-        """
-        Replace all phone numbers in ``text`` str with ``replace_with`` str
-
-        Parameters
-        ----------
-        text : string
-        replace_with : string
-            the string you want the phone number to be replaced with.
-        method : string {'regex','detection'}
-            regex is faster but will omit a lot of numbers, while detection will
-            catch every numbers, but takes a while.
-        country_format_to_detect : list
-            If a list of country code is specified, will catch every number formatted.
-            Only when method = 'detection'.
-
-        Returns
-        -------
-        string
-        """
-        if method == 'regex':
-            self.text = constants.PHONE_REGEX.sub(replace_with, self.text)
-        elif method == 'detection':
-            found_nums = _extract_phone_numbers(self.text, countrylist=country_format_to_detect)
-
-            # order by lenght to avoid truncated numbers to be removed first.
-            found_nums.sort(key=len, reverse=True)
-            for phone_number in found_nums:
-                self.text = self.text.replace(phone_number, replace_with)
-        else:
-            raise ValueError('Please input a valid method between "regex" or "detection"')
-        return self.text
-
-    def replace_numbers(self, replace_with: str = "*NUMBER*") -> str:
-        """
-        Replace all numbers in ``text`` str with ``replace_with`` str.
-
-        Parameters
-        ----------
-        text : string
-        replace_with : string
-            the string you want the number to be replaced with.
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.NUMBERS_REGEX.sub(replace_with, self.text)
-        return self.text
-
-    def replace_currency_symbols(self, replace_with: Optional[str] = None) -> str:
-        """
-        Replace all currency symbols in ``text`` str with string specified by ``replace_with`` str.
-
-        Parameters
-        ----------
-        text : str
-            raw text
-        replace_with : str, optional
-            if None (default), replace symbols with their standard 3-letter abbreviations \
-                (e.g. '$' with 'USD', '£' with 'GBP'); otherwise, pass in a string with \
-                    which to replace all symbols (e.g. "*CURRENCY*")
-
-        Returns
-        -------
-        string
-        """
-        if replace_with is None:
-            for k, v in constants.CURRENCIES.items():
-                self.text = self.text.replace(k, v)
-        else:
-            self.text = constants.CURRENCY_REGEX.sub(replace_with, self.text)
-        return self.text
-
-    def remove_punct(self, marks: Optional[str] = None) -> str:
-        """
-        Remove punctuation from ``text`` by replacing all instances of ``marks``
-        with whitespace.
-
-        Parameters
-        ----------
-        text : str
-            raw text
-        marks : str, optional
-            If specified, remove only the characters in this string,
-            e.g. ``marks=',;:'`` removes commas, semi-colons, and colons.
-            Otherwise, all punctuation marks are removed.
-
-        Returns
-        -------
-        string
-
-        Note
-        -------
-        When ``marks=None``, Python's built-in :meth:`str.translate()` is
-        used to remove punctuation; otherwise, a regular expression is used
-        instead. The former's performance is about 5-10x faster.
-        """
-        if marks:
-            self.text = re.sub("[{}]+".format(re.escape(marks)), " ", self.text, flags=re.UNICODE)
-        else:
-            self.text = self.text.translate(constants.PUNCT_TRANSLATE_UNICODE)
-        return self.text
-
-    def remove_accents(self, method: str = "unicode") -> str:
-        """
-        Remove accents from any accented unicode characters in ``text`` str, either by
-        transforming them into ascii equivalents or removing them entirely.
-
-        Parameters
-        ----------
-        text : str
-            raw text
-
-        method : str ({'unicode', 'ascii'})
-            if 'unicode', remove accented
-            char for any unicode symbol with a direct ASCII equivalent; if 'ascii',
-            remove accented char for any unicode symbol
-
-            NB: the 'ascii' method is notably faster than 'unicode', but less good
-
-        Returns
-        -------
-        string
-
-        Raises
-        -------
-        ValueError
-            if ``method`` is not in {'unicode', 'ascii'}
-        """
-        if method == "unicode":
-            self.text = "".join(
-                c
-                for c in unicodedata.normalize("NFKD", self.text)
-                if not unicodedata.combining(c)
-            )
-        elif method == "ascii":
-            self.text = (
-                unicodedata.normalize("NFKD", self.text)
-                .encode("ascii", errors="ignore")
-                .decode("ascii")
-            )
-        else:
-            msg = '`method` must be either "unicode" and "ascii", not {}'.format(method)
-            raise ValueError(msg)
-        return self.text
-
-    def remove_multiple_spaces_and_strip_text(self) -> str:
-        """
-        Remove multiple spaces, strip text, and remove '-', '*' characters.
-
-        Parameters
-        ----------
-        text : str
-            the text to be processed
-
-        Returns
-        -------
-        string
-            the text with removed multiple spaces and strip text
-        """
-        regex_remove_multiple_spaces_list = ["\\t", "[\\s\\-\\*]{2,}"]
-        for regex_remove_multiple_spaces in regex_remove_multiple_spaces_list:
-            self.text = re.sub(regex_remove_multiple_spaces, " ", self.text)
-            self.text = self.text.strip()
-        return self.text
-
-    def filter_non_latin_characters(self) -> str:
-        """
-        Function that filters non latin characters of a text
-
-        Parameters
-        ----------
-        text : str
-
-        Returns
-        -------
-        string
-        """
-        self.text = constants.LATIN_CHARACTERS_RE.sub(' ', self.text)
-        self.text = self.normalize_whitespace()
-        return self.text
-
-    def remove_smallwords(self, smallwords_threshold: int) -> list:
-        """
-        Function that removes words which length is below a threshold
-        'Hello my name is John Doe' --> 'Hello name John Doe'
-
-        Parameters
-        ----------
-        text : str
-        smallwords_threshold: int
-            threshold of small word
-
-        Returns
-        -------
-        str
-        """
-        self.text = ' '.join([word for word in self.text.split() if len(word) > smallwords_threshold])
-        return self.text
->>>>>>> feature/new-readme

From 7c9aac27df821886819a380882d2699c1c8ad062 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 14 Dec 2020 18:21:36 +0100
Subject: [PATCH 410/496] [FIX] fix tests

---
 nautilus_nlp/preprocessing/preprocessor.py | 2 +-
 tests/test_preprocessor.py                 | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
index 33c8169..ad651d2 100644
--- a/nautilus_nlp/preprocessing/preprocessor.py
+++ b/nautilus_nlp/preprocessing/preprocessor.py
@@ -7,7 +7,7 @@
 from nautilus_nlp.preprocessing import text_preprocess
 
 
-class Preprocessor(object):
+class Preprocessor():
     def __init__(
             self, social_pipelines=None, text_pipelines=None):
         """
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 90d448c..9ce19f6 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -296,7 +296,7 @@ def test_replace_phone_numbers(input_str, expected_str):
         input_str,
         replace_with="*PHONE*",
         method="detection",
-        country_format_to_detect=phone.SUPPORTED_COUNTRY)
+        country_to_detect=phone.SUPPORTED_COUNTRY)
     np.testing.assert_equal(result, expected_str)
 
 

From ce78c42320d1ac92c9c74b69cc1d70df188175b8 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 16:04:12 +0100
Subject: [PATCH 411/496] fix default preprocessing functions

---
 nautilus_nlp/preprocessing/preprocessor.py | 43 +++++++++++-----------
 1 file changed, 22 insertions(+), 21 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
index ad651d2..11e0746 100644
--- a/nautilus_nlp/preprocessing/preprocessor.py
+++ b/nautilus_nlp/preprocessing/preprocessor.py
@@ -1,35 +1,36 @@
-from inspect import getmembers, isfunction
-
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 
-from nautilus_nlp.preprocessing import social_preprocess
-from nautilus_nlp.preprocessing import text_preprocess
+from nautilus_nlp.preprocessing.social_preprocess import (remove_html_tags, remove_mentions, remove_emoji,
+                                                          remove_hashtag)
+from nautilus_nlp.preprocessing.text_preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
 
 
 class Preprocessor():
     def __init__(
-            self, social_pipelines=None, text_pipelines=None):
+            self, social_functions=None, text_functions=None):
         """
         """
-        if social_pipelines is None:
-            self.social_pipelines = Pipeline(
-                steps=[(function_name, FunctionTransformer(function_callable))
-                       for function_name, function_callable in getmembers(social_preprocess)
-                       if isfunction(function_callable)])
-        if text_pipelines is None:
-            self.text_pipelines = Pipeline(
-                steps=[(function_name, FunctionTransformer(function_callable))
-                       for function_name, function_callable in getmembers(text_preprocess)
-                       if isfunction(function_callable)])
+        if social_functions is None:
+            social_functions = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
+        if text_functions is None:
+            text_functions = (remove_eol_characters, fix_bad_unicode, normalize_whitespace)
+        self.social_pipeline = self.build_pipeline(social_functions)
+        self.text_pipeline = self.build_pipeline(text_functions)
+
+    @staticmethod
+    def build_pipeline(function_list):
+        return Pipeline(
+            steps=[
+                (function.__name__, FunctionTransformer(function))
+                for function in function_list])
 
-    def apply_social_pipeline(self, text):
-        return self.social_pipelines.fit_transform(text)
 
-    def apply_text_pipeline(self, text):
-        return self.text_pipelines.fit_transform(text)
+    @staticmethod
+    def apply_pipeline(text, pipeline):
+        return pipeline.fit_transform(text)
 
     def apply_all_pipeline(self, text):
-        text = self.apply_social_pipeline(text)
-        text = self.apply_text_pipeline(text)
+        text = self.apply_pipeline(text, self.social_pipeline)
+        text = self.apply_pipeline(text, self.text_pipeline)
         return text

From 18f693a8cec9513589fd1aa605b6cb6ef136c53c Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 16:22:29 +0100
Subject: [PATCH 412/496] adding docstring to preprocessor

---
 nautilus_nlp/preprocessing/preprocessor.py | 46 ++++++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
index 11e0746..e780f6c 100644
--- a/nautilus_nlp/preprocessing/preprocessor.py
+++ b/nautilus_nlp/preprocessing/preprocessor.py
@@ -10,6 +10,14 @@ class Preprocessor():
     def __init__(
             self, social_functions=None, text_functions=None):
         """
+        Initialize preprocessor object to apply all text transformation
+
+        Parameters
+        ----------
+        social_functions : iterable|None
+            list of functions of social preprocessing
+        text_functions : iterable|None
+            list of functions of text preprocessing
         """
         if social_functions is None:
             social_functions = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
@@ -20,6 +28,18 @@ def __init__(
 
     @staticmethod
     def build_pipeline(function_list):
+        """
+        Build sklearn pipeline from a function list
+
+        Parameters
+        ----------
+        function_list : iterable
+            list of functions of preprocessing
+
+        Returns
+        -------
+        sklearn.pipeline.Pipeline
+        """
         return Pipeline(
             steps=[
                 (function.__name__, FunctionTransformer(function))
@@ -28,9 +48,35 @@ def build_pipeline(function_list):
 
     @staticmethod
     def apply_pipeline(text, pipeline):
+        """
+        Apply preprocessing pipeline to a text
+
+        Parameters
+        ----------
+        text : string
+            text to preprocess
+        pipeline : sklearn.pipeline.Pipeline
+            pipeline to transform the text
+
+        Returns
+        -------
+        string
+        """
         return pipeline.fit_transform(text)
 
     def apply_all_pipeline(self, text):
+        """
+        Apply social and text pipeline to text
+
+        Parameters
+        ----------
+        text : string
+            text to preprocess
+
+        Returns
+        -------
+        string
+        """
         text = self.apply_pipeline(text, self.social_pipeline)
         text = self.apply_pipeline(text, self.text_pipeline)
         return text

From 0c788652cf3797df1b12f1fccb8b5c66aaa51ed5 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 16:29:44 +0100
Subject: [PATCH 413/496] add type hinting

---
 nautilus_nlp/preprocessing/preprocessor.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
index e780f6c..f5fe9ca 100644
--- a/nautilus_nlp/preprocessing/preprocessor.py
+++ b/nautilus_nlp/preprocessing/preprocessor.py
@@ -1,3 +1,5 @@
+from typing import List, Callable, Optional
+
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 
@@ -8,7 +10,9 @@
 
 class Preprocessor():
     def __init__(
-            self, social_functions=None, text_functions=None):
+            self,
+            social_functions: Optional[Callable] = None,
+            text_functions: Optional[Callable] = None):
         """
         Initialize preprocessor object to apply all text transformation
 
@@ -27,7 +31,7 @@ def __init__(
         self.text_pipeline = self.build_pipeline(text_functions)
 
     @staticmethod
-    def build_pipeline(function_list):
+    def build_pipeline(function_list: List[Callable]) -> Pipeline:
         """
         Build sklearn pipeline from a function list
 
@@ -47,7 +51,7 @@ def build_pipeline(function_list):
 
 
     @staticmethod
-    def apply_pipeline(text, pipeline):
+    def apply_pipeline(text: str, pipeline: Pipeline) -> str:
         """
         Apply preprocessing pipeline to a text
 
@@ -64,7 +68,7 @@ def apply_pipeline(text, pipeline):
         """
         return pipeline.fit_transform(text)
 
-    def apply_all_pipeline(self, text):
+    def apply_all_pipeline(self, text: str) -> str:
         """
         Apply social and text pipeline to text
 

From df54ddd2523b9d86b5cd4bdfb4c58a46d7ee161c Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 16:48:42 +0100
Subject: [PATCH 414/496] adding test for text preprocessor

---
 tests/test_preprocessor.py | 41 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 9ce19f6..e4a11a6 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -29,6 +29,8 @@
 from nautilus_nlp.preprocessing.token_preprocess import (remove_stopwords, remove_tokens_with_nonletters,
                                                          remove_special_caracters_from_tokenslist,
                                                          remove_smallwords)
+from nautilus_nlp.preprocessing.preprocessor import Preprocessor
+
 import nautilus_nlp.utils.phone_number as phone
 from nautilus_nlp.utils.stopwords import get_stopwords
 
@@ -385,3 +387,42 @@ def test_remove_emoji(input_str, expected_str):
 def test_convert_emoji_to_text(input_str, expected_str):
     result = convert_emoji_to_text(input_str)
     np.testing.assert_equal(result, expected_str)
+
+
+@pytest.mark.parametrize(
+    "social_functions, text_functions",
+    [
+        (None, None),
+        (None, [remove_punct]),
+        ([remove_emoji], None),
+        ([remove_emoji], [remove_punct])
+
+    ]
+)
+def test_init_preprocessor(social_functions, text_functions):
+    assert Preprocessor(
+        social_functions=social_functions,
+        text_functions=text_functions)
+
+
+def test_apply_preprocessor():
+    # Given
+    text = "Some text with @mentions and whitespaces    and #hashtags"
+    social_functions = (remove_mentions, remove_hashtag)
+    text_functions = (normalize_whitespace,)
+
+    preprocessor = Preprocessor(
+        social_functions=social_functions,
+        text_functions=text_functions)
+
+    expected_result = text
+    for function in social_functions:
+        expected_result = function(expected_result)
+    for function in text_functions:
+        expected_result = function(expected_result)
+
+    # When
+    result = preprocessor.apply_all_pipeline(text)
+
+    # Then
+    assert expected_result ==  result

From c551adfa0af0945917bbb70a38dbc137dbd0bbc7 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 16:54:43 +0100
Subject: [PATCH 415/496] remove __init__.py and .gitkeep

---
 nautilus_nlp/__init__.py   | 17 -----------------
 nautilus_nlp/data/.gitkeep |  0
 2 files changed, 17 deletions(-)
 delete mode 100644 nautilus_nlp/__init__.py
 delete mode 100644 nautilus_nlp/data/.gitkeep

diff --git a/nautilus_nlp/__init__.py b/nautilus_nlp/__init__.py
deleted file mode 100644
index d46139b..0000000
--- a/nautilus_nlp/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/data/.gitkeep b/nautilus_nlp/data/.gitkeep
deleted file mode 100644
index e69de29..0000000

From 6f2d3a28ee4b8a7f3ede9cf916b2d0c6c380baa9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 16:55:24 +0100
Subject: [PATCH 416/496] remove __init__.py and .gitkeep

---
 tests/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 tests/__init__.py

diff --git a/tests/__init__.py b/tests/__init__.py
deleted file mode 100644
index e69de29..0000000

From 75fa21ebf5f3342919da75198c395dd8c2d3eea5 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:16:40 +0100
Subject: [PATCH 417/496] Revert "remove __init__.py and .gitkeep"

This reverts commit 6f2d3a28ee4b8a7f3ede9cf916b2d0c6c380baa9.
---
 tests/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 tests/__init__.py

diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29

From 049d8076d9818a95232c40677f431297e11b77fa Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:16:51 +0100
Subject: [PATCH 418/496] Revert "remove __init__.py and .gitkeep"

This reverts commit c551adfa0af0945917bbb70a38dbc137dbd0bbc7.
---
 nautilus_nlp/__init__.py   | 17 +++++++++++++++++
 nautilus_nlp/data/.gitkeep |  0
 2 files changed, 17 insertions(+)
 create mode 100644 nautilus_nlp/__init__.py
 create mode 100644 nautilus_nlp/data/.gitkeep

diff --git a/nautilus_nlp/__init__.py b/nautilus_nlp/__init__.py
new file mode 100644
index 0000000..d46139b
--- /dev/null
+++ b/nautilus_nlp/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/data/.gitkeep b/nautilus_nlp/data/.gitkeep
new file mode 100644
index 0000000..e69de29

From bbb5bf1a8ae08202e88a164b8fc4183666cc0401 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:22:08 +0100
Subject: [PATCH 419/496] adding python and pip versions

---
 .github/workflows/ci_actions.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 4b4ba74..26c4afc 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -37,7 +37,9 @@ jobs:
 
       - name: Install requirements
         run:
+          python --version
           python -m pip install --upgrade pip
+          pip --version
           pip install -r requirements.txt
 
       - name: Run pylint

From eac588aaa801a8a012700ccacfd455780a24b7fd Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:24:15 +0100
Subject: [PATCH 420/496] upgrade to python 3.7

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 26c4afc..6e40787 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6]
+        python-version: [3.7]
 
     steps:
       - uses: actions/checkout@v2

From 104bc2afc4dcfc412d59324185bb6cb09fd13789 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:26:56 +0100
Subject: [PATCH 421/496] debug CI

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 6e40787..ac71263 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -37,10 +37,10 @@ jobs:
 
       - name: Install requirements
         run:
-          python --version
           python -m pip install --upgrade pip
           pip --version
           pip install -r requirements.txt
+          echo "requirements installed"
 
       - name: Run pylint
         run: |

From f3986f173c0c22105db30f3251e82b5461054614 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:27:48 +0100
Subject: [PATCH 422/496] debug CI

---
 .github/workflows/ci_actions.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index ac71263..1c12c26 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -37,8 +37,6 @@ jobs:
 
       - name: Install requirements
         run:
-          python -m pip install --upgrade pip
-          pip --version
           pip install -r requirements.txt
           echo "requirements installed"
 

From b6fa1c772b69f8e889521b48112f1458071a641c Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:28:43 +0100
Subject: [PATCH 423/496] debug CI

---
 .github/workflows/ci_actions.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 1c12c26..43fbcc8 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -38,7 +38,6 @@ jobs:
       - name: Install requirements
         run:
           pip install -r requirements.txt
-          echo "requirements installed"
 
       - name: Run pylint
         run: |

From 2a4bc915093cefb4343e641cb6cdd2420445dff3 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:30:49 +0100
Subject: [PATCH 424/496] debug CI

---
 .github/workflows/ci_actions.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 43fbcc8..f4b3a1c 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -36,7 +36,8 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install requirements
-        run:
+        run: |
+          python -m pip install --upgrade pip
           pip install -r requirements.txt
 
       - name: Run pylint

From c2a1a33c751771356670063d2308e881272356d3 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:32:12 +0100
Subject: [PATCH 425/496] update spacy version

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 4ae5ce7..a449c8f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,7 +25,7 @@ regex==2019.8.19
 sacremoses==0.0.13
 scikit_learn==0.23.2
 setuptools==40.8.0
-spacy==2.1.3
+spacy==2.2.4
 https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz#egg=fr_core_news_sm
 https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz#egg=en_core_web_sm
 stop_words==2018.7.23
\ No newline at end of file

From 5b80c7712f77da545e3d18ca7beaba972ce11401 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:34:07 +0100
Subject: [PATCH 426/496] update spacy models

---
 requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index a449c8f..d08ae17 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -26,6 +26,6 @@ sacremoses==0.0.13
 scikit_learn==0.23.2
 setuptools==40.8.0
 spacy==2.2.4
-https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz#egg=fr_core_news_sm
-https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz#egg=en_core_web_sm
+https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
+https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz
 stop_words==2018.7.23
\ No newline at end of file

From bcb041ea0f5b2b6faa7fe787f6a7a90f08088f86 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:35:19 +0100
Subject: [PATCH 427/496] fix spacy version

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d08ae17..f2cb8e3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -25,7 +25,7 @@ regex==2019.8.19
 sacremoses==0.0.13
 scikit_learn==0.23.2
 setuptools==40.8.0
-spacy==2.2.4
+spacy==2.3.4
 https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
 https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz
 stop_words==2018.7.23
\ No newline at end of file

From 92a254350750b8568b6935528c2d9a16410dc177 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 4 Jan 2021 17:37:36 +0100
Subject: [PATCH 428/496] fix pylint

---
 tests/test_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index e4a11a6..75adcbd 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -425,4 +425,4 @@ def test_apply_preprocessor():
     result = preprocessor.apply_all_pipeline(text)
 
     # Then
-    assert expected_result ==  result
+    assert expected_result == result

From 706e9ba7a8acc56daecd535c6ac610210859be2a Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 5 Jan 2021 10:23:57 +0100
Subject: [PATCH 429/496] adding 3.6 3.7 and 3.8 versions in CI

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index f4b3a1c..2c410da 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7]
+        python-version: [3.6, 3.7, 3.8]
 
     steps:
       - uses: actions/checkout@v2

From 73a97fdd5663b911f012a890da624130c52f8ba9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 5 Jan 2021 10:28:14 +0100
Subject: [PATCH 430/496] removing arg input_str

---
 nautilus_nlp/preprocessing/social_preprocess.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/nautilus_nlp/preprocessing/social_preprocess.py
index fafb584..20809a3 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/nautilus_nlp/preprocessing/social_preprocess.py
@@ -90,7 +90,7 @@ def remove_emoji(text) -> str:
     return text
 
 
-def convert_emoji_to_text(text, code_delimiters=(':', ':'), input_str=None) -> str:
+def convert_emoji_to_text(text, code_delimiters=(':', ':')) -> str:
     """
     Convert emoji to their CLDR Short Name, according to the unicode convention
     http://www.unicode.org/emoji/charts/full-emoji-list.html
@@ -99,16 +99,14 @@ def convert_emoji_to_text(text, code_delimiters=(':', ':'), input_str=None) -> s
     Parameters
     ----------
     text : str
-        code_delimiters : tuple of symbols around the emoji code.
-        eg: (':',':') --> :grinning_face:
+    code_delimiters : tuple of symbols around the emoji code.
+    eg: (':',':') --> :grinning_face:
 
     Returns
     -------
     str
         string
     """
-    if input_str is not None:
-        return _emoji.demojize(input_str, delimiters=code_delimiters)
     return _emoji.demojize(text, delimiters=code_delimiters)
 
 
@@ -127,7 +125,7 @@ def extract_emojis(text) -> list:
         list of all emojis converted with their unicode conventions
     """
     emojis_in_text = constants.EMOJI_PATTERN.findall(text)
-    emojis_converted = [convert_emoji_to_text(text, input_str=emoji_text) for emoji_text in emojis_in_text]
+    emojis_converted = [convert_emoji_to_text(emoji_text) for emoji_text in emojis_in_text]
     return emojis_converted
 
 

From a62391a20d1392ebeba177620fa6185b5c244da3 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 5 Jan 2021 10:38:07 +0100
Subject: [PATCH 431/496] merigng social_functions and text_functions to
 functions param

---
 nautilus_nlp/preprocessing/preprocessor.py | 46 ++++++----------------
 tests/test_preprocessor.py                 | 26 +++++-------
 2 files changed, 22 insertions(+), 50 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
index f5fe9ca..6478914 100644
--- a/nautilus_nlp/preprocessing/preprocessor.py
+++ b/nautilus_nlp/preprocessing/preprocessor.py
@@ -11,24 +11,21 @@
 class Preprocessor():
     def __init__(
             self,
-            social_functions: Optional[Callable] = None,
-            text_functions: Optional[Callable] = None):
+            functions: Optional[Callable] = None):
         """
         Initialize preprocessor object to apply all text transformation
 
         Parameters
         ----------
-        social_functions : iterable|None
-            list of functions of social preprocessing
-        text_functions : iterable|None
-            list of functions of text preprocessing
+        functions : iterable|None
+            list of functions of preprocessing
         """
-        if social_functions is None:
-            social_functions = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
-        if text_functions is None:
-            text_functions = (remove_eol_characters, fix_bad_unicode, normalize_whitespace)
-        self.social_pipeline = self.build_pipeline(social_functions)
-        self.text_pipeline = self.build_pipeline(text_functions)
+        if functions is None:
+            functions = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
+                         remove_eol_characters, fix_bad_unicode, normalize_whitespace)
+        if len(functions) == 0:
+            raise ValueError("Cannot initialize a preprocessor with 0 function")
+        self.pipeline = self.build_pipeline(functions)
 
     @staticmethod
     def build_pipeline(function_list: List[Callable]) -> Pipeline:
@@ -50,27 +47,9 @@ def build_pipeline(function_list: List[Callable]) -> Pipeline:
                 for function in function_list])
 
 
-    @staticmethod
-    def apply_pipeline(text: str, pipeline: Pipeline) -> str:
-        """
-        Apply preprocessing pipeline to a text
-
-        Parameters
-        ----------
-        text : string
-            text to preprocess
-        pipeline : sklearn.pipeline.Pipeline
-            pipeline to transform the text
-
-        Returns
-        -------
-        string
-        """
-        return pipeline.fit_transform(text)
-
-    def apply_all_pipeline(self, text: str) -> str:
+    def apply_pipeline(self, text: str) -> str:
         """
-        Apply social and text pipeline to text
+        Apply pipeline to text
 
         Parameters
         ----------
@@ -81,6 +60,5 @@ def apply_all_pipeline(self, text: str) -> str:
         -------
         string
         """
-        text = self.apply_pipeline(text, self.social_pipeline)
-        text = self.apply_pipeline(text, self.text_pipeline)
+        text = self.pipeline.fit_transform(text)
         return text
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 75adcbd..6cba59c 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -390,39 +390,33 @@ def test_convert_emoji_to_text(input_str, expected_str):
 
 
 @pytest.mark.parametrize(
-    "social_functions, text_functions",
+    "functions",
     [
-        (None, None),
-        (None, [remove_punct]),
-        ([remove_emoji], None),
-        ([remove_emoji], [remove_punct])
+        (None),
+        ([remove_punct, remove_emoji])
 
     ]
 )
-def test_init_preprocessor(social_functions, text_functions):
+def test_init_preprocessor(functions):
     assert Preprocessor(
-        social_functions=social_functions,
-        text_functions=text_functions)
+        functions=functions)
 
 
 def test_apply_preprocessor():
     # Given
     text = "Some text with @mentions and whitespaces    and #hashtags"
-    social_functions = (remove_mentions, remove_hashtag)
-    text_functions = (normalize_whitespace,)
+    function_list = (remove_mentions, remove_hashtag, normalize_whitespace)
 
     preprocessor = Preprocessor(
-        social_functions=social_functions,
-        text_functions=text_functions)
+        functions=function_list
+            )
 
     expected_result = text
-    for function in social_functions:
-        expected_result = function(expected_result)
-    for function in text_functions:
+    for function in function_list:
         expected_result = function(expected_result)
 
     # When
-    result = preprocessor.apply_all_pipeline(text)
+    result = preprocessor.apply_pipeline(text)
 
     # Then
     assert expected_result == result

From b51ca14f65a0070dbd0c0218c7c5952a339b7857 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 5 Jan 2021 10:41:28 +0100
Subject: [PATCH 432/496] fix pylint

---
 tests/test_preprocessor.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 6cba59c..5a11597 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -407,9 +407,7 @@ def test_apply_preprocessor():
     text = "Some text with @mentions and whitespaces    and #hashtags"
     function_list = (remove_mentions, remove_hashtag, normalize_whitespace)
 
-    preprocessor = Preprocessor(
-        functions=function_list
-            )
+    preprocessor = Preprocessor(functions=function_list)
 
     expected_result = text
     for function in function_list:

From 7af4cf47dc0491a397e6722fe3c029077693a720 Mon Sep 17 00:00:00 2001
From: tkumar-19088 <kumart.19088@gmail.com>
Date: Wed, 13 Jan 2021 21:05:35 +0530
Subject: [PATCH 433/496] Implemented correction in Tokenizer function in
 nautilus_nlp/utils/tokenizer.py to enable correct loading of spacy language
 models

---
 nautilus_nlp/utils/tokenizer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nautilus_nlp/utils/tokenizer.py b/nautilus_nlp/utils/tokenizer.py
index 5067849..8cae936 100644
--- a/nautilus_nlp/utils/tokenizer.py
+++ b/nautilus_nlp/utils/tokenizer.py
@@ -88,7 +88,7 @@ def tokenize(text: str, lang_module: str = 'en_spacy') -> List[str]:
         If lang_module is not a valid module name
     """
     if "spacy" in lang_module:
-        lang = lang_module.split()[0]
+        lang = lang_module.split("_")[0]
         spacymodel = _get_spacy_tokenizer(lang)
         spacydoc = spacymodel(text)
         return [spacy_token.text for spacy_token in spacydoc]

From 6f539997bb9d95ccdbd260a1433485593e7bf154 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 22 Jan 2021 10:09:10 +0100
Subject: [PATCH 434/496] refacto preprocessor

---
 nautilus_nlp/preprocessing/preprocessor.py | 42 ++++++++++++----------
 tests/test_preprocessor.py                 | 32 +++++++++--------
 2 files changed, 42 insertions(+), 32 deletions(-)

diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
index 6478914..8078fc2 100644
--- a/nautilus_nlp/preprocessing/preprocessor.py
+++ b/nautilus_nlp/preprocessing/preprocessor.py
@@ -1,4 +1,4 @@
-from typing import List, Callable, Optional
+from typing import List, Callable
 
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
@@ -10,32 +10,33 @@
 
 class Preprocessor():
     def __init__(
-            self,
-            functions: Optional[Callable] = None):
+            self):
         """
         Initialize preprocessor object to apply all text transformation
+        """
+        self.__operations = []
+        self.pipeline = None
+
+    def pipe(self, operation: Callable):
+        """
+        Add an operation to pipe in the preprocessor
 
         Parameters
         ----------
-        functions : iterable|None
-            list of functions of preprocessing
+        operation : callable
+            text preprocessing function
         """
-        if functions is None:
-            functions = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
-                         remove_eol_characters, fix_bad_unicode, normalize_whitespace)
-        if len(functions) == 0:
-            raise ValueError("Cannot initialize a preprocessor with 0 function")
-        self.pipeline = self.build_pipeline(functions)
+        self.__operations.append(operation)
 
     @staticmethod
-    def build_pipeline(function_list: List[Callable]) -> Pipeline:
+    def build_pipeline(operation_list: List[Callable]) -> Pipeline:
         """
-        Build sklearn pipeline from a function list
+        Build sklearn pipeline from a operation list
 
         Parameters
         ----------
-        function_list : iterable
-            list of functions of preprocessing
+        operation_list : iterable
+            list of __operations of preprocessing
 
         Returns
         -------
@@ -43,11 +44,11 @@ def build_pipeline(function_list: List[Callable]) -> Pipeline:
         """
         return Pipeline(
             steps=[
-                (function.__name__, FunctionTransformer(function))
-                for function in function_list])
+                (operation.__name__, FunctionTransformer(operation))
+                for operation in operation_list])
 
 
-    def apply_pipeline(self, text: str) -> str:
+    def run(self, text: str) -> str:
         """
         Apply pipeline to text
 
@@ -60,5 +61,10 @@ def apply_pipeline(self, text: str) -> str:
         -------
         string
         """
+        operations = self.__operations
+        if operations == []:
+            operations = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
+                          remove_eol_characters, fix_bad_unicode, normalize_whitespace)
+        self.pipeline = self.build_pipeline(operations)
         text = self.pipeline.fit_transform(text)
         return text
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 5a11597..e518191 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -389,32 +389,36 @@ def test_convert_emoji_to_text(input_str, expected_str):
     np.testing.assert_equal(result, expected_str)
 
 
-@pytest.mark.parametrize(
-    "functions",
-    [
-        (None),
-        ([remove_punct, remove_emoji])
+def test_custom_preprocess():
+    # Given
+    text = "Some text with @mentions and #hashtags"
 
-    ]
-)
-def test_init_preprocessor(functions):
-    assert Preprocessor(
-        functions=functions)
+    preprocessor = Preprocessor()
+    preprocessor.pipe(remove_hashtag)
+    preprocessor.pipe(remove_mentions)
+    expected_result = remove_hashtag(text)
+    expected_result = remove_mentions(expected_result)
 
+    # When
+    result = preprocessor.run(text)
+
+    # Then
+    assert expected_result == result
 
 def test_apply_preprocessor():
     # Given
     text = "Some text with @mentions and whitespaces    and #hashtags"
-    function_list = (remove_mentions, remove_hashtag, normalize_whitespace)
+    operations = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
+                  remove_eol_characters, fix_bad_unicode, normalize_whitespace)
 
-    preprocessor = Preprocessor(functions=function_list)
+    preprocessor = Preprocessor()
 
     expected_result = text
-    for function in function_list:
+    for function in operations:
         expected_result = function(expected_result)
 
     # When
-    result = preprocessor.apply_pipeline(text)
+    result = preprocessor.run(text)
 
     # Then
     assert expected_result == result

From ef9da2c663711ccf37069020632daca21102f4f0 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 22 Jan 2021 10:29:09 +0100
Subject: [PATCH 435/496] adding Preprocessor in root __init__

---
 nautilus_nlp/__init__.py                         | 1 +
 nautilus_nlp/{preprocessing => }/preprocessor.py | 0
 tests/test_preprocessor.py                       | 2 +-
 3 files changed, 2 insertions(+), 1 deletion(-)
 rename nautilus_nlp/{preprocessing => }/preprocessor.py (100%)

diff --git a/nautilus_nlp/__init__.py b/nautilus_nlp/__init__.py
index d46139b..7643ec5 100644
--- a/nautilus_nlp/__init__.py
+++ b/nautilus_nlp/__init__.py
@@ -15,3 +15,4 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from nautilus_nlp.preprocessor import Preprocessor
diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessor.py
similarity index 100%
rename from nautilus_nlp/preprocessing/preprocessor.py
rename to nautilus_nlp/preprocessor.py
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index e518191..51d94db 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -29,7 +29,7 @@
 from nautilus_nlp.preprocessing.token_preprocess import (remove_stopwords, remove_tokens_with_nonletters,
                                                          remove_special_caracters_from_tokenslist,
                                                          remove_smallwords)
-from nautilus_nlp.preprocessing.preprocessor import Preprocessor
+from nautilus_nlp import Preprocessor
 
 import nautilus_nlp.utils.phone_number as phone
 from nautilus_nlp.utils.stopwords import get_stopwords

From e997930908585a3519ceb274cb552e8292f4583d Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:35:05 +0100
Subject: [PATCH 436/496] reorg repo v1

---
 {nautilus_nlp => config}/__init__.py          |  0
 {nautilus_nlp/config => config}/config.py     | 35 ++++++++++
 {nautilus_nlp/utils => config}/constants.py   |  0
 .../data => config}/country_code.json         |  0
 {nautilus_nlp/data => config}/french_FEEL.csv |  0
 .../data => config}/french_numbers.txt        |  0
 .../data => config}/french_stopwords.txt      |  0
 {nautilus_nlp/data => config}/stopwords.json  |  0
 nautilus_nlp/analysis/ngrams.py               | 39 -----------
 nautilus_nlp/data/.gitkeep                    |  0
 nautilus_nlp/data/__init__.py                 | 17 -----
 nautilus_nlp/preprocessing/preprocessor.py    | 64 ------------------
 nautilus_nlp/utils/__init__.py                | 17 -----
 nautilus_nlp/utils/keyword_extractor.py       | 50 --------------
 .../__init__.py                               |  1 +
 .../augmentation/text_augmentation.py         |  0
 .../classic/preprocess.py                     |  5 +-
 preprocessing/preprocessor.py                 | 65 +++++++++++++++++++
 .../social/preprocess.py                      |  4 +-
 .../token/preprocess.py                       |  0
 .../token}/tokenizer.py                       |  0
 tests/test_data_augmentation.py               |  6 +-
 tests/test_document_loader.py                 |  2 +-
 tests/test_keyword_extractor.py               | 16 -----
 tests/test_phone_number.py                    |  2 +-
 tests/test_preprocessor.py                    | 44 +++++++------
 {nautilus_nlp/config => utils}/__init__.py    |  0
 {nautilus_nlp/utils => utils}/file_loader.py  |  0
 {nautilus_nlp/utils => utils}/phone_number.py | 29 +--------
 {nautilus_nlp/utils => utils}/stopwords.py    |  7 +-
 30 files changed, 138 insertions(+), 265 deletions(-)
 rename {nautilus_nlp => config}/__init__.py (100%)
 rename {nautilus_nlp/config => config}/config.py (73%)
 rename {nautilus_nlp/utils => config}/constants.py (100%)
 rename {nautilus_nlp/data => config}/country_code.json (100%)
 rename {nautilus_nlp/data => config}/french_FEEL.csv (100%)
 rename {nautilus_nlp/data => config}/french_numbers.txt (100%)
 rename {nautilus_nlp/data => config}/french_stopwords.txt (100%)
 rename {nautilus_nlp/data => config}/stopwords.json (100%)
 delete mode 100644 nautilus_nlp/analysis/ngrams.py
 delete mode 100644 nautilus_nlp/data/.gitkeep
 delete mode 100644 nautilus_nlp/data/__init__.py
 delete mode 100644 nautilus_nlp/preprocessing/preprocessor.py
 delete mode 100644 nautilus_nlp/utils/__init__.py
 delete mode 100644 nautilus_nlp/utils/keyword_extractor.py
 rename {nautilus_nlp/preprocessing => preprocessing}/__init__.py (94%)
 rename nautilus_nlp/preprocessing/data_augmentation.py => preprocessing/augmentation/text_augmentation.py (100%)
 rename nautilus_nlp/preprocessing/text_preprocess.py => preprocessing/classic/preprocess.py (98%)
 create mode 100644 preprocessing/preprocessor.py
 rename nautilus_nlp/preprocessing/social_preprocess.py => preprocessing/social/preprocess.py (97%)
 rename nautilus_nlp/preprocessing/token_preprocess.py => preprocessing/token/preprocess.py (100%)
 rename {nautilus_nlp/utils => preprocessing/token}/tokenizer.py (100%)
 delete mode 100644 tests/test_keyword_extractor.py
 rename {nautilus_nlp/config => utils}/__init__.py (100%)
 rename {nautilus_nlp/utils => utils}/file_loader.py (100%)
 rename {nautilus_nlp/utils => utils}/phone_number.py (67%)
 rename {nautilus_nlp/utils => utils}/stopwords.py (94%)

diff --git a/nautilus_nlp/__init__.py b/config/__init__.py
similarity index 100%
rename from nautilus_nlp/__init__.py
rename to config/__init__.py
diff --git a/nautilus_nlp/config/config.py b/config/config.py
similarity index 73%
rename from nautilus_nlp/config/config.py
rename to config/config.py
index 35681cf..df90e45 100644
--- a/nautilus_nlp/config/config.py
+++ b/config/config.py
@@ -17,10 +17,15 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/usr/local/bin/python3
 import os
+import phonenumbers as _phonenumbers
 
 
 ROOT_FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
 
+# Stopwords config
+STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "config", "stopwords.json")
+
+# Country config
 COUNTRY_MAPPING_ISO = {
     'af': 'Afghanistan', 'ax': 'Åland Islands', 'al': 'Albania', 'dz': 'Algeria', 'as': 'American Samoa', 'ad':
     'Andorra', 'ao': 'Angola', 'ai': 'Anguilla', 'aq': 'Antarctica', 'ag': 'Antigua and Barbuda', 'ar': 'Argentina',
@@ -73,3 +78,33 @@
     've': 'Venezuela (Bolivarian Republic of)', 'vn': 'Viet Nam', 'vg': 'Virgin Islands (British)',
     'vi': 'Virgin Islands (U.S.)', 'wf': 'Wallis and Futuna', 'eh': 'Western Sahara', 'ye': 'Yemen', 'zm': 'Zambia',
     'zw': 'Zimbabwe'}
+
+# Phone numbers config
+SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM',
+                     'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT',
+                     'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES',
+                     'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE',
+                     'NO', 'SJ', 'PL', 'DE', 'PE', 'MX', 'CU', 'AR', 'BR', 'CL', 'CO', 'VE',
+                     'MY', 'AU', 'CC', 'CX', 'ID', 'PH', 'NZ', 'SG', 'TH', 'JP', 'KR', 'VN',
+                     'CN', 'TR', 'IN', 'PK', 'AF', 'LK', 'MM', 'IR', 'SS', 'MA', 'EH', 'DZ',
+                     'TN', 'LY', 'GM', 'SN', 'MR', 'ML', 'GN', 'CI', 'BF', 'NE', 'TG', 'BJ',
+                     'MU', 'LR', 'SL', 'GH', 'NG', 'TD', 'CF', 'CM', 'CV', 'ST', 'GQ', 'GA',
+                     'CG', 'CD', 'AO', 'GW', 'IO', 'AC', 'SC', 'SD', 'RW', 'ET', 'SO', 'DJ',
+                     'KE', 'TZ', 'UG', 'BI', 'MZ', 'ZM', 'MG', 'RE', 'YT', 'ZW', 'NA', 'MW',
+                     'LS', 'BW', 'SZ', 'KM', 'SH', 'TA', 'ER', 'AW', 'FO', 'GL', 'GI', 'PT',
+                     'LU', 'IE', 'IS', 'AL', 'MT', 'CY', 'FI', 'AX', 'BG', 'LT', 'LV', 'EE',
+                     'MD', 'AM', 'BY', 'AD', 'MC', 'SM', 'UA', 'RS', 'ME', 'XK', 'HR', 'SI',
+                     'BA', 'MK', 'CZ', 'SK', 'LI', 'FK', 'BZ', 'GT', 'SV', 'HN', 'NI', 'CR',
+                     'PA', 'PM', 'HT', 'GP', 'BL', 'MF', 'BO', 'GY', 'EC', 'GF', 'PY', 'MQ',
+                     'SR', 'UY', 'CW', 'BQ', 'TL', 'NF', 'BN', 'NR', 'PG', 'TO', 'SB', 'VU',
+                     'FJ', 'PW', 'WF', 'CK', 'NU', 'WS', 'KI', 'NC', 'TV', 'PF', 'TK', 'FM',
+                     'MH', 'KP', 'HK', 'MO', 'KH', 'LA', 'BD', 'TW', 'MV', 'LB', 'JO', 'SY',
+                     'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN',
+                     'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ', 'DO']
+
+FORMAT_NUMBERS = {
+    "E164": _phonenumbers.PhoneNumberFormat.E164,
+    "INTERNATIONAL": _phonenumbers.PhoneNumberFormat.INTERNATIONAL,
+    "NATIONAL": _phonenumbers.PhoneNumberFormat.NATIONAL,
+    "RFC3966": _phonenumbers.PhoneNumberFormat.RFC3966
+}
diff --git a/nautilus_nlp/utils/constants.py b/config/constants.py
similarity index 100%
rename from nautilus_nlp/utils/constants.py
rename to config/constants.py
diff --git a/nautilus_nlp/data/country_code.json b/config/country_code.json
similarity index 100%
rename from nautilus_nlp/data/country_code.json
rename to config/country_code.json
diff --git a/nautilus_nlp/data/french_FEEL.csv b/config/french_FEEL.csv
similarity index 100%
rename from nautilus_nlp/data/french_FEEL.csv
rename to config/french_FEEL.csv
diff --git a/nautilus_nlp/data/french_numbers.txt b/config/french_numbers.txt
similarity index 100%
rename from nautilus_nlp/data/french_numbers.txt
rename to config/french_numbers.txt
diff --git a/nautilus_nlp/data/french_stopwords.txt b/config/french_stopwords.txt
similarity index 100%
rename from nautilus_nlp/data/french_stopwords.txt
rename to config/french_stopwords.txt
diff --git a/nautilus_nlp/data/stopwords.json b/config/stopwords.json
similarity index 100%
rename from nautilus_nlp/data/stopwords.json
rename to config/stopwords.json
diff --git a/nautilus_nlp/analysis/ngrams.py b/nautilus_nlp/analysis/ngrams.py
deleted file mode 100644
index 89cbb13..0000000
--- a/nautilus_nlp/analysis/ngrams.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
-from typing import List, Generator
-
-
-def create_ngrams(tokens: List[str], nb_elements: int) -> Generator:
-    """
-    Create n-grams for list of tokens
-
-    Parameters
-    ----------
-    tokens : list
-        list of strings
-    nb_elements : int
-        number of elements in the n-gram
-
-    Returns
-    -------
-    Generator
-        generator of all n-grams
-    """
-    ngrams = zip(*[tokens[index_token:] for index_token in range(nb_elements)])
-    return (" ".join(ngram) for ngram in ngrams)
diff --git a/nautilus_nlp/data/.gitkeep b/nautilus_nlp/data/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/data/__init__.py b/nautilus_nlp/data/__init__.py
deleted file mode 100644
index d46139b..0000000
--- a/nautilus_nlp/data/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/preprocessing/preprocessor.py b/nautilus_nlp/preprocessing/preprocessor.py
deleted file mode 100644
index 6478914..0000000
--- a/nautilus_nlp/preprocessing/preprocessor.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from typing import List, Callable, Optional
-
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import FunctionTransformer
-
-from nautilus_nlp.preprocessing.social_preprocess import (remove_html_tags, remove_mentions, remove_emoji,
-                                                          remove_hashtag)
-from nautilus_nlp.preprocessing.text_preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
-
-
-class Preprocessor():
-    def __init__(
-            self,
-            functions: Optional[Callable] = None):
-        """
-        Initialize preprocessor object to apply all text transformation
-
-        Parameters
-        ----------
-        functions : iterable|None
-            list of functions of preprocessing
-        """
-        if functions is None:
-            functions = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
-                         remove_eol_characters, fix_bad_unicode, normalize_whitespace)
-        if len(functions) == 0:
-            raise ValueError("Cannot initialize a preprocessor with 0 function")
-        self.pipeline = self.build_pipeline(functions)
-
-    @staticmethod
-    def build_pipeline(function_list: List[Callable]) -> Pipeline:
-        """
-        Build sklearn pipeline from a function list
-
-        Parameters
-        ----------
-        function_list : iterable
-            list of functions of preprocessing
-
-        Returns
-        -------
-        sklearn.pipeline.Pipeline
-        """
-        return Pipeline(
-            steps=[
-                (function.__name__, FunctionTransformer(function))
-                for function in function_list])
-
-
-    def apply_pipeline(self, text: str) -> str:
-        """
-        Apply pipeline to text
-
-        Parameters
-        ----------
-        text : string
-            text to preprocess
-
-        Returns
-        -------
-        string
-        """
-        text = self.pipeline.fit_transform(text)
-        return text
diff --git a/nautilus_nlp/utils/__init__.py b/nautilus_nlp/utils/__init__.py
deleted file mode 100644
index d46139b..0000000
--- a/nautilus_nlp/utils/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/utils/keyword_extractor.py
deleted file mode 100644
index f804d52..0000000
--- a/nautilus_nlp/utils/keyword_extractor.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-
-from flashtext import KeywordProcessor
-
-
-def extract_keywords(text, keyword, case_sensitive=True):
-    """
-    Extract Keywords from a document.
-
-    Parameters
-    ----------
-    text : str
-        Text to extract keywords from
-    keyword :
-        Single keyword (str) or list of keywords (list)
-    case_sensitive :
-        If False, will be case insensitive.
-
-    Returns
-    -------
-    list
-        Return list of extracted keyworkds
-    """
-
-    processor = KeywordProcessor(case_sensitive=case_sensitive)
-    if isinstance(keyword, list):
-        processor.add_keywords_from_list(keyword)
-    elif isinstance(keyword, str):
-        processor.add_keyword(keyword)
-    elif isinstance(keyword, dict):
-        processor.add_keywords_from_dict(keyword)
-
-    return processor.extract_keywords(text)
-    
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/__init__.py b/preprocessing/__init__.py
similarity index 94%
rename from nautilus_nlp/preprocessing/__init__.py
rename to preprocessing/__init__.py
index d46139b..7643ec5 100644
--- a/nautilus_nlp/preprocessing/__init__.py
+++ b/preprocessing/__init__.py
@@ -15,3 +15,4 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from nautilus_nlp.preprocessor import Preprocessor
diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/preprocessing/augmentation/text_augmentation.py
similarity index 100%
rename from nautilus_nlp/preprocessing/data_augmentation.py
rename to preprocessing/augmentation/text_augmentation.py
diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/preprocessing/classic/preprocess.py
similarity index 98%
rename from nautilus_nlp/preprocessing/text_preprocess.py
rename to preprocessing/classic/preprocess.py
index ef6003a..691acae 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/preprocessing/classic/preprocess.py
@@ -23,9 +23,8 @@
 import re
 import unicodedata
 from ftfy import fix_text as _fix_text
-from nautilus_nlp.utils import constants
-from nautilus_nlp.utils.phone_number import \
-    extract_phone_numbers as _extract_phone_numbers
+from config import constants
+from utils.phone_number import extract_phone_numbers as _extract_phone_numbers
 
 
 def normalize_whitespace(text) -> str:
diff --git a/preprocessing/preprocessor.py b/preprocessing/preprocessor.py
new file mode 100644
index 0000000..0b34eb3
--- /dev/null
+++ b/preprocessing/preprocessor.py
@@ -0,0 +1,65 @@
+from typing import List, Callable
+
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import FunctionTransformer
+
+from preprocessing.social.preprocess import (remove_html_tags, remove_mentions, remove_emoji,
+                                                          remove_hashtag)
+from preprocessing.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
+
+
+class Preprocessor():
+    def __init__(
+            self):
+        """
+        Initialize preprocessor object to apply all text transformation
+        """
+        self.__operations = []
+        self.pipeline = None
+
+    def pipe(self, operation: Callable):
+        """
+        Add an operation to pipe in the preprocessor
+        Parameters
+        ----------
+        operation : callable
+            text preprocessing function
+        """
+        self.__operations.append(operation)
+
+    @staticmethod
+    def build_pipeline(operation_list: List[Callable]) -> Pipeline:
+        """
+        Build sklearn pipeline from a operation list
+        Parameters
+        ----------
+        operation_list : iterable
+            list of __operations of preprocessing
+        Returns
+        -------
+        sklearn.pipeline.Pipeline
+        """
+        return Pipeline(
+            steps=[
+                (operation.__name__, FunctionTransformer(operation))
+                for operation in operation_list])
+
+
+    def run(self, text: str) -> str:
+        """
+        Apply pipeline to text
+        Parameters
+        ----------
+        text : string
+            text to preprocess
+        Returns
+        -------
+        string
+        """
+        operations = self.__operations
+        if operations == []:
+            operations = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
+                          remove_eol_characters, fix_bad_unicode, normalize_whitespace)
+        self.pipeline = self.build_pipeline(operations)
+        text = self.pipeline.fit_transform(text)
+        return text
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/preprocessing/social/preprocess.py
similarity index 97%
rename from nautilus_nlp/preprocessing/social_preprocess.py
rename to preprocessing/social/preprocess.py
index 20809a3..b017f65 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/preprocessing/social/preprocess.py
@@ -20,8 +20,8 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import emoji as _emoji
-from nautilus_nlp.utils import constants
-from nautilus_nlp.preprocessing.text_preprocess import normalize_whitespace
+from config import constants
+from preprocessing.classic.preprocess import normalize_whitespace
 
 
 def remove_mentions(text) -> str:
diff --git a/nautilus_nlp/preprocessing/token_preprocess.py b/preprocessing/token/preprocess.py
similarity index 100%
rename from nautilus_nlp/preprocessing/token_preprocess.py
rename to preprocessing/token/preprocess.py
diff --git a/nautilus_nlp/utils/tokenizer.py b/preprocessing/token/tokenizer.py
similarity index 100%
rename from nautilus_nlp/utils/tokenizer.py
rename to preprocessing/token/tokenizer.py
diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index 863087e..d13245b 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -1,6 +1,8 @@
 import pytest
-from nautilus_nlp.preprocessing.data_augmentation import process_entities_and_text, \
-    get_augmenter, CouldNotAugment, UnavailableAugmenter
+from preprocessing.augmentation.text_augmentation import (
+    process_entities_and_text, get_augmenter, CouldNotAugment,
+    UnavailableAugmenter
+)
 
 @pytest.mark.parametrize(
     "text, text_augmented, entities, expected",
diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index a757619..060af44 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -20,7 +20,7 @@
 import os
 
 import numpy as np
-from nautilus_nlp.utils.file_loader import (detect_encoding, documents_loader)
+from utils.file_loader import (detect_encoding, documents_loader)
 
 TESTDOC_LATIN1 = "J'aime les frites bien grasse étalon châpeau!"
 TESTDOC_UTF8 = "Un deuxième exemple de texte en utf-8 cette fois!"
diff --git a/tests/test_keyword_extractor.py b/tests/test_keyword_extractor.py
deleted file mode 100644
index f694692..0000000
--- a/tests/test_keyword_extractor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import pytest
-from nautilus_nlp.utils.keyword_extractor import extract_keywords
-
-
-@pytest.mark.parametrize(
-    "input_text, keyword, case_sensitive, expected",
-    [
-        ("I eat an orange oranges at Orange", 'orange', False, ['orange', 'orange']),
-        ("I eat an orange oranges at Orange", 'orange', True, ['orange']),
-        ("I eat an orange oranges at Orange", {'orange': ['orange', 'oranges']}, False, ['orange', 'orange', 'orange']),
-        ("I eat an orange oranges at Orange", {'orange': ['orange', 'oranges']}, True, ['orange', 'orange']),
-        ("I eat an orange oranges at Orange", {'fruit': ['orange', 'oranges']}, True, ['fruit', 'fruit']),
-    ]
-    )
-def test_extract_keywords(input_text, keyword, case_sensitive, expected):
-    assert extract_keywords(input_text, keyword=keyword, case_sensitive=case_sensitive) == expected
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index 4aff394..4b6c832 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import nautilus_nlp.utils.phone_number as phone
+import utils.phone_number as phone
 
 def test_extract_phone_number():
     input_str = '(541) 754-3010 is a US. Phone'
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 5a11597..355836d 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,22 +17,22 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from nautilus_nlp.preprocessing.text_preprocess import (normalize_whitespace, remove_eol_characters,
+from preprocessing.classic.preprocess import (normalize_whitespace, remove_eol_characters,
                                                         fix_bad_unicode, unpack_english_contractions,
                                                         replace_urls, replace_emails, replace_phone_numbers,
                                                         replace_numbers, replace_currency_symbols, remove_punct,
                                                         remove_accents, remove_multiple_spaces_and_strip_text,
                                                         filter_non_latin_characters)
-from nautilus_nlp.preprocessing.social_preprocess import (remove_mentions, extract_mentions, remove_html_tags,
+from preprocessing.social.preprocess import (remove_mentions, extract_mentions, remove_html_tags,
                                                           remove_emoji, convert_emoji_to_text, extract_emojis,
                                                           extract_hashtags, remove_hashtag)
-from nautilus_nlp.preprocessing.token_preprocess import (remove_stopwords, remove_tokens_with_nonletters,
+from preprocessing.token.preprocess import (remove_stopwords, remove_tokens_with_nonletters,
                                                          remove_special_caracters_from_tokenslist,
                                                          remove_smallwords)
-from nautilus_nlp.preprocessing.preprocessor import Preprocessor
+from preprocessing.preprocessor import Preprocessor
 
-import nautilus_nlp.utils.phone_number as phone
-from nautilus_nlp.utils.stopwords import get_stopwords
+import utils.phone_number as phone
+from utils.stopwords import get_stopwords
 
 
 @pytest.mark.parametrize("text, expected_result",
@@ -389,32 +389,36 @@ def test_convert_emoji_to_text(input_str, expected_str):
     np.testing.assert_equal(result, expected_str)
 
 
-@pytest.mark.parametrize(
-    "functions",
-    [
-        (None),
-        ([remove_punct, remove_emoji])
+def test_custom_preprocess():
+    # Given
+    text = "Some text with @mentions and #hashtags"
 
-    ]
-)
-def test_init_preprocessor(functions):
-    assert Preprocessor(
-        functions=functions)
+    preprocessor = Preprocessor()
+    preprocessor.pipe(remove_hashtag)
+    preprocessor.pipe(remove_mentions)
+    expected_result = remove_hashtag(text)
+    expected_result = remove_mentions(expected_result)
 
+    # When
+    result = preprocessor.run(text)
+
+    # Then
+    assert expected_result == result
 
 def test_apply_preprocessor():
     # Given
     text = "Some text with @mentions and whitespaces    and #hashtags"
-    function_list = (remove_mentions, remove_hashtag, normalize_whitespace)
+    operations = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
+                  remove_eol_characters, fix_bad_unicode, normalize_whitespace)
 
-    preprocessor = Preprocessor(functions=function_list)
+    preprocessor = Preprocessor()
 
     expected_result = text
-    for function in function_list:
+    for function in operations:
         expected_result = function(expected_result)
 
     # When
-    result = preprocessor.apply_pipeline(text)
+    result = preprocessor.run(text)
 
     # Then
     assert expected_result == result
diff --git a/nautilus_nlp/config/__init__.py b/utils/__init__.py
similarity index 100%
rename from nautilus_nlp/config/__init__.py
rename to utils/__init__.py
diff --git a/nautilus_nlp/utils/file_loader.py b/utils/file_loader.py
similarity index 100%
rename from nautilus_nlp/utils/file_loader.py
rename to utils/file_loader.py
diff --git a/nautilus_nlp/utils/phone_number.py b/utils/phone_number.py
similarity index 67%
rename from nautilus_nlp/utils/phone_number.py
rename to utils/phone_number.py
index 6ad58fc..6120155 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/utils/phone_number.py
@@ -17,35 +17,8 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from typing import Optional
 import phonenumbers as _phonenumbers
+from config.config import SUPPORTED_COUNTRY, FORMAT_NUMBERS
 
-SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM',
-                     'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT',
-                     'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES',
-                     'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE',
-                     'NO', 'SJ', 'PL', 'DE', 'PE', 'MX', 'CU', 'AR', 'BR', 'CL', 'CO', 'VE',
-                     'MY', 'AU', 'CC', 'CX', 'ID', 'PH', 'NZ', 'SG', 'TH', 'JP', 'KR', 'VN',
-                     'CN', 'TR', 'IN', 'PK', 'AF', 'LK', 'MM', 'IR', 'SS', 'MA', 'EH', 'DZ',
-                     'TN', 'LY', 'GM', 'SN', 'MR', 'ML', 'GN', 'CI', 'BF', 'NE', 'TG', 'BJ',
-                     'MU', 'LR', 'SL', 'GH', 'NG', 'TD', 'CF', 'CM', 'CV', 'ST', 'GQ', 'GA',
-                     'CG', 'CD', 'AO', 'GW', 'IO', 'AC', 'SC', 'SD', 'RW', 'ET', 'SO', 'DJ',
-                     'KE', 'TZ', 'UG', 'BI', 'MZ', 'ZM', 'MG', 'RE', 'YT', 'ZW', 'NA', 'MW',
-                     'LS', 'BW', 'SZ', 'KM', 'SH', 'TA', 'ER', 'AW', 'FO', 'GL', 'GI', 'PT',
-                     'LU', 'IE', 'IS', 'AL', 'MT', 'CY', 'FI', 'AX', 'BG', 'LT', 'LV', 'EE',
-                     'MD', 'AM', 'BY', 'AD', 'MC', 'SM', 'UA', 'RS', 'ME', 'XK', 'HR', 'SI',
-                     'BA', 'MK', 'CZ', 'SK', 'LI', 'FK', 'BZ', 'GT', 'SV', 'HN', 'NI', 'CR',
-                     'PA', 'PM', 'HT', 'GP', 'BL', 'MF', 'BO', 'GY', 'EC', 'GF', 'PY', 'MQ',
-                     'SR', 'UY', 'CW', 'BQ', 'TL', 'NF', 'BN', 'NR', 'PG', 'TO', 'SB', 'VU',
-                     'FJ', 'PW', 'WF', 'CK', 'NU', 'WS', 'KI', 'NC', 'TV', 'PF', 'TK', 'FM',
-                     'MH', 'KP', 'HK', 'MO', 'KH', 'LA', 'BD', 'TW', 'MV', 'LB', 'JO', 'SY',
-                     'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN',
-                     'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ', 'DO']
-
-FORMAT_NUMBERS = {
-    "E164": _phonenumbers.PhoneNumberFormat.E164,
-    "INTERNATIONAL": _phonenumbers.PhoneNumberFormat.INTERNATIONAL,
-    "NATIONAL": _phonenumbers.PhoneNumberFormat.NATIONAL,
-    "RFC3966": _phonenumbers.PhoneNumberFormat.RFC3966
-}
 
 def find_phone_numbers(string: str, region_code: Optional[str] = None) -> str:
     """
diff --git a/nautilus_nlp/utils/stopwords.py b/utils/stopwords.py
similarity index 94%
rename from nautilus_nlp/utils/stopwords.py
rename to utils/stopwords.py
index 0f3eb57..89cb7d0 100644
--- a/nautilus_nlp/utils/stopwords.py
+++ b/utils/stopwords.py
@@ -24,11 +24,8 @@
 import os
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 from stop_words import get_stop_words as _get_stop_words
-from nautilus_nlp.config.config import ROOT_FOLDER
-from nautilus_nlp.utils.file_loader import documents_loader
-
-
-STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
+from config.config import STOPWORDS_JSON_FILEPATH
+from utils.file_loader import documents_loader
 
 
 def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):

From c45e5acfd2a10dc01857c61cf0279edc44802071 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:35:59 +0100
Subject: [PATCH 437/496] update import init

---
 preprocessing/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/preprocessing/__init__.py b/preprocessing/__init__.py
index 7643ec5..97b4d25 100644
--- a/preprocessing/__init__.py
+++ b/preprocessing/__init__.py
@@ -15,4 +15,4 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.preprocessor import Preprocessor
+from preprocessing.preprocessor import Preprocessor

From 1ebb20135cf71e5b025d535a5e122aa2d889db21 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:40:08 +0100
Subject: [PATCH 438/496] fix pylint

---
 tests/test_preprocessor.py | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 355836d..13564b0 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,18 +17,21 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from preprocessing.classic.preprocess import (normalize_whitespace, remove_eol_characters,
-                                                        fix_bad_unicode, unpack_english_contractions,
-                                                        replace_urls, replace_emails, replace_phone_numbers,
-                                                        replace_numbers, replace_currency_symbols, remove_punct,
-                                                        remove_accents, remove_multiple_spaces_and_strip_text,
-                                                        filter_non_latin_characters)
-from preprocessing.social.preprocess import (remove_mentions, extract_mentions, remove_html_tags,
-                                                          remove_emoji, convert_emoji_to_text, extract_emojis,
-                                                          extract_hashtags, remove_hashtag)
-from preprocessing.token.preprocess import (remove_stopwords, remove_tokens_with_nonletters,
-                                                         remove_special_caracters_from_tokenslist,
-                                                         remove_smallwords)
+from preprocessing.classic.preprocess import (
+    normalize_whitespace, remove_eol_characters, fix_bad_unicode,
+    unpack_english_contractions, replace_urls, replace_emails,
+    replace_phone_numbers, replace_numbers, replace_currency_symbols,
+    remove_punct, remove_accents, remove_multiple_spaces_and_strip_text,
+    filter_non_latin_characters
+)
+from preprocessing.social.preprocess import (
+    remove_mentions, extract_mentions, remove_html_tags, remove_emoji,
+    convert_emoji_to_text, extract_emojis, extract_hashtags, remove_hashtag
+)
+from preprocessing.token.preprocess import (
+    remove_stopwords, remove_tokens_with_nonletters,
+    remove_special_caracters_from_tokenslist, remove_smallwords
+)
 from preprocessing.preprocessor import Preprocessor
 
 import utils.phone_number as phone

From c646072281f45eb0ea1a83c9839ec7b8a94c3dd6 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:58:39 +0100
Subject: [PATCH 439/496] update ci with new name and fix pylint

---
 .github/workflows/ci_actions.yml | 4 ++--
 preprocessing/preprocessor.py    | 6 +++---
 utils/stopwords.py               | 1 -
 3 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 2c410da..8cdb602 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,8 +42,8 @@ jobs:
 
       - name: Run pylint
         run: |
-          pylint nautilus_nlp tests
+          pylint config preprocessing utils tests
 
       - name: Run pytest
         run: |
-          pytest --cov=nautilus_nlp tests
+          pytest --cov=preprocessing tests
diff --git a/preprocessing/preprocessor.py b/preprocessing/preprocessor.py
index 0b34eb3..41ec77c 100644
--- a/preprocessing/preprocessor.py
+++ b/preprocessing/preprocessor.py
@@ -3,8 +3,8 @@
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 
-from preprocessing.social.preprocess import (remove_html_tags, remove_mentions, remove_emoji,
-                                                          remove_hashtag)
+from preprocessing.social.preprocess import (
+    remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
 from preprocessing.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
 
 
@@ -62,4 +62,4 @@ def run(self, text: str) -> str:
                           remove_eol_characters, fix_bad_unicode, normalize_whitespace)
         self.pipeline = self.build_pipeline(operations)
         text = self.pipeline.fit_transform(text)
-        return text
\ No newline at end of file
+        return text
diff --git a/utils/stopwords.py b/utils/stopwords.py
index 89cb7d0..305ff2b 100644
--- a/utils/stopwords.py
+++ b/utils/stopwords.py
@@ -21,7 +21,6 @@
                         unicode_literals)
 
 import json
-import os
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 from stop_words import get_stop_words as _get_stop_words
 from config.config import STOPWORDS_JSON_FILEPATH

From bdf4f9861b4b8318f2edb675af2b1ae4fd43d22b Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 21:12:44 +0100
Subject: [PATCH 440/496] rm unused config files

---
 config/french_FEEL.csv      | 14128 ----------------------------------
 config/french_numbers.txt   |   101 -
 config/french_stopwords.txt |   689 --
 3 files changed, 14918 deletions(-)
 delete mode 100644 config/french_FEEL.csv
 delete mode 100644 config/french_numbers.txt
 delete mode 100644 config/french_stopwords.txt

diff --git a/config/french_FEEL.csv b/config/french_FEEL.csv
deleted file mode 100644
index 40f15d6..0000000
--- a/config/french_FEEL.csv
+++ /dev/null
@@ -1,14128 +0,0 @@
-id;word;polarity;joy;fear;sadness;anger;surprise;disgust
-1;à ce endroit là;positive;0;0;0;0;0;0
-2;à le hâte;negative;0;1;0;0;1;0
-3;à part;negative;0;0;1;0;0;0
-4;à pic;negative;0;1;0;0;0;0
-5;à rallonge;negative;0;0;1;0;0;0
-6;abasourdir;negative;0;0;0;0;1;0
-7;ablation;negative;0;1;0;0;0;1
-8;abominable;negative;0;1;0;0;0;1
-9;abrupt;negative;0;1;0;0;0;0
-10;absent;negative;0;1;1;0;0;0
-11;absorber;positive;0;0;0;0;1;0
-12;absurde;negative;0;0;0;1;1;1
-13;acceptable;positive;0;0;0;0;0;0
-14;accidentel;negative;0;1;1;0;1;0
-15;accusateur;negative;0;1;0;1;0;1
-16;accuser;negative;0;1;1;1;0;1
-17;acheteur;positive;0;0;0;0;0;0
-18;acquisition;positive;0;0;0;0;0;0
-19;active;positive;0;0;0;0;0;0
-20;actuel;positive;0;0;0;0;1;0
-21;addition;positive;0;0;0;0;0;0
-22;adhésif;positive;0;0;0;0;0;1
-23;administration;positive;0;0;0;0;0;0
-24;administratif;positive;0;0;0;0;0;0
-25;administrateur;positive;0;0;0;0;0;0
-26;admirateur;positive;0;0;0;0;0;0
-27;admissible;positive;0;0;0;0;0;0
-28;adolescent;positive;0;0;0;0;0;0
-29;aérien;positive;0;1;0;0;0;0
-30;affable;positive;0;0;0;0;0;0
-31;affaire;positive;0;0;0;0;0;0
-32;affectueux;positive;0;0;0;0;0;0
-33;affirmative;positive;0;0;0;0;0;0
-34;affliger;negative;0;1;1;0;0;0
-35;affreux;negative;0;1;1;1;1;1
-36;agent de conservation;positive;0;0;0;0;1;0
-37;agent de police;positive;0;1;0;0;0;0
-38;agrafe;positive;0;0;0;0;0;0
-39;agréable;positive;0;0;0;0;0;0
-40;agressif;negative;0;1;0;1;0;0
-41;agriculteur;positive;0;0;0;0;0;0
-42;aiguiser;positive;0;1;0;1;0;0
-43;aimer;positive;0;0;0;0;0;0
-44;aîné;positive;0;0;0;0;0;0
-45;aisément;positive;0;0;0;0;0;0
-46;alcalin;positive;0;0;0;0;0;0
-47;alentour;positive;0;0;0;0;0;0
-48;allonger;negative;0;1;1;1;0;1
-49;allure;positive;0;0;0;0;0;0
-50;allusion;negative;0;1;0;1;1;0
-51;alternative;positive;0;0;0;0;0;0
-52;amas;negative;0;0;0;0;0;0
-53;amatrice;negative;0;0;0;0;0;0
-54;ambassadrice;positive;0;0;0;0;0;0
-55;ambitieux;positive;0;0;0;0;0;0
-56;amélioration;positive;0;0;0;0;0;0
-57;ami;positive;0;0;0;0;0;0
-58;amourette;negative;0;0;0;0;0;0
-59;analyse;positive;0;0;0;0;0;0
-60;ancien;negative;0;0;0;0;0;0
-61;ancien élève;positive;0;0;0;0;0;0
-62;anguleux;positive;0;0;0;0;0;0
-63;animal;negative;0;1;0;0;0;1
-64;annuel;positive;0;0;0;0;0;0
-65;anticipatif;positive;0;0;0;0;0;0
-66;anxieux;negative;0;1;0;0;0;0
-67;apathique;negative;0;1;1;0;0;0
-68;apparenter;positive;0;0;0;0;0;0
-69;apprenant;positive;0;0;0;0;0;0
-70;apprenti;positive;0;0;0;0;0;0
-71;approbation;positive;0;0;0;0;0;0
-72;approbateur;positive;0;0;0;0;0;0
-73;approcher;positive;0;0;0;0;0;0
-74;approprier;positive;0;0;0;0;0;0
-75;approximatif;positive;0;0;0;0;0;0
-76;appui;positive;0;0;0;0;0;0
-77;aqueux;negative;0;0;0;0;0;1
-78;ardent;positive;0;0;0;0;1;0
-79;arête;negative;0;1;0;0;0;0
-80;aride;negative;0;1;1;0;0;0
-81;armature;positive;0;0;0;0;0;0
-82;arnaqueur;negative;0;1;0;1;0;1
-83;arrondissement;positive;0;0;0;0;0;0
-84;artisane;positive;0;0;0;0;0;0
-85;asile;positive;0;0;0;0;0;0
-86;asphyxie;negative;0;1;0;1;0;0
-87;aspiration;positive;0;0;0;0;0;0
-88;assaillant;negative;0;1;1;1;0;0
-89;assentiment;positive;0;0;0;0;0;0
-90;assidu;positive;0;0;0;0;0;0
-91;assistance;positive;0;0;0;0;0;0
-92;assistant;positive;0;0;0;0;0;0
-93;assourdir;negative;0;1;1;0;0;0
-94;asticot;negative;0;0;0;0;0;1
-95;astucieux;positive;0;0;0;0;0;0
-96;athéiste;negative;0;0;0;0;0;0
-97;attarder mental;negative;0;1;1;0;0;1
-98;attentif;positive;0;0;0;0;0;0
-99;attirer;positive;0;0;0;0;1;0
-100;attitude amical;positive;0;0;0;0;0;0
-101;attroupement;positive;0;0;0;0;0;0
-102;au chômage;negative;0;1;1;0;0;0
-103;au gouvernement;positive;0;0;0;0;0;0
-104;au premier plan;positive;0;0;0;0;0;0
-105;auditif;positive;0;0;0;0;0;0
-106;auditeur;positive;0;1;0;0;0;0
-107;augure;negative;0;1;0;0;0;0
-108;aussi que;positive;0;0;0;0;0;0
-109;autonomie;positive;0;0;0;0;0;0
-110;autosatisfaction;positive;0;0;0;0;0;0
-111;avant dernier;negative;0;0;1;0;0;0
-112;avantageux;positive;1;0;0;0;0;0
-113;avec amertume;negative;0;0;1;1;0;1
-114;avec fermeté;positive;0;0;0;0;0;0
-115;avec insistance;positive;0;0;0;0;0;0
-116;avec lequel;positive;0;0;0;0;0;0
-117;aventureux;positive;0;1;0;0;1;0
-118;aventurier;positive;0;1;0;0;1;0
-119;avocat;positive;0;1;0;1;0;1
-120;avoir du chagrin;negative;0;1;1;0;0;0
-121;avouer;negative;0;1;1;0;0;0
-122;badaud;positive;0;0;0;0;0;0
-123;bafouiller;negative;0;1;0;0;0;0
-124;baisse;negative;0;0;1;0;0;0
-125;baissière;negative;0;1;1;1;0;0
-126;balaise;negative;0;1;0;0;1;0
-127;balbutiement;positive;0;0;0;0;0;0
-128;banlieusard;negative;0;0;0;0;0;0
-129;bannière;positive;0;0;0;0;0;0
-130;banquière;positive;0;0;0;0;0;0
-131;banshie;negative;0;1;1;1;0;1
-132;baraquer;positive;0;1;0;0;0;0
-133;barge;negative;0;1;0;0;0;1
-134;bâtard;negative;0;1;0;1;0;1
-135;batifoler;positive;1;0;0;0;0;0
-136;battant;positive;0;0;0;0;0;0
-137;batteuse;positive;0;0;0;0;0;0
-138;bavardage;negative;0;0;0;0;0;0
-139;bavard;negative;0;0;0;0;0;0
-140;bazar;negative;0;1;1;1;0;1
-141;beau;positive;0;0;0;0;0;0
-142;belliqueux;negative;0;1;0;1;0;0
-143;bénigne;positive;1;0;0;0;0;0
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-231;chétif;negative;0;1;1;0;0;1
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-250;cohérence;positive;0;0;0;0;0;0
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-252;cohésif;positive;0;0;0;0;0;0
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-579;être apprenti;positive;0;0;0;0;0;0
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-591;excursion;positive;0;0;0;0;0;0
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-596;exhaustif;positive;0;0;0;0;0;0
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-619;faire un manifestation devant;negative;0;1;0;1;1;0
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-623;familial;positive;0;0;0;0;0;0
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-664;fragmenter;negative;0;0;1;0;0;0
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-876;laiterie;positive;0;0;0;0;0;0
-877;laiteux;positive;0;0;0;0;0;0
-878;lame;positive;0;1;0;0;0;0
-879;lamelle;positive;0;0;0;0;0;0
-880;lamentable;negative;0;1;1;1;0;1
-881;lascif;negative;0;0;0;0;0;1
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-884;latéralement;negative;0;0;0;0;0;0
-885;lauréat;positive;0;0;0;0;0;0
-886;laxatif;negative;0;1;0;0;0;1
-887;léger;positive;0;0;0;0;0;0
-888;législatif;positive;0;0;0;0;0;0
-889;législateur;positive;0;0;0;0;0;0
-890;leurre;negative;0;1;0;0;1;0
-891;libéral;positive;0;0;0;0;0;0
-892;libidineux;positive;1;0;0;0;0;0
-893;licenciement;negative;0;1;1;1;0;0
-894;ligneux;positive;0;0;0;0;0;0
-895;limitatif;negative;0;1;1;0;0;0
-896;limite;negative;0;0;0;0;0;0
-897;limitrophe;positive;0;0;0;0;0;0
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-899;lit cage;positive;0;0;0;0;0;0
-900;litigieux;negative;0;1;0;1;0;1
-901;locution;positive;0;0;0;0;0;0
-902;locuteur;positive;0;0;0;0;0;0
-903;logement;positive;0;0;0;0;0;0
-904;longue;positive;0;0;0;0;0;0
-905;louable;positive;0;0;0;0;1;0
-906;lourd;negative;0;0;1;1;0;0
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-908;loyauté;positive;0;0;0;0;0;0
-909;lucratif;positive;1;0;0;0;0;0
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-912;lumineux;positive;1;0;0;0;0;0
-913;lunatique;negative;0;0;0;0;1;0
-914;lustrer;positive;0;0;0;0;0;0
-915;lutteur;negative;0;1;0;1;0;0
-916;luxueux;positive;1;0;0;0;0;0
-917;magicien;positive;0;0;0;0;1;0
-918;magnitude;positive;0;0;0;0;0;0
-919;maîtrise;positive;0;0;0;0;1;0
-920;majesté;positive;1;0;0;0;0;0
-921;majestueux;positive;0;0;0;0;1;0
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-929;malléable;positive;0;0;0;0;0;0
-930;malpropre;negative;0;0;0;0;0;1
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-934;manifestant;positive;0;0;0;0;0;0
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-936;manuelle;positive;0;0;0;0;0;0
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-939;marchand de fruit et légume;positive;0;0;0;0;0;0
-940;marcheur;positive;0;0;0;0;0;0
-941;marécage;negative;0;1;1;0;0;1
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-948;masculinité;positive;0;0;0;0;0;0
-949;matelas;positive;0;0;0;0;0;0
-950;matelassage;positive;0;0;0;0;0;0
-951;matériel;positive;0;0;0;0;0;0
-952;maternel;positive;0;0;0;0;0;0
-953;mathématicien;positive;0;0;0;0;0;0
-954;matière visqueux;negative;0;0;0;0;0;1
-955;maudire;negative;0;0;0;1;0;0
-956;maussade;negative;0;1;1;0;0;0
-957;maximum;positive;0;0;0;0;0;0
-958;mec;negative;0;0;0;0;0;0
-959;mécanicien;positive;0;0;0;0;0;0
-960;mécanisme;positive;0;0;0;0;0;0
-961;méconnaître;negative;0;0;1;0;0;0
-962;médiateur|médiatrice;positive;0;0;0;0;0;0
-963;méditerranéen;positive;0;0;0;0;0;0
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-966;mélodieux;positive;0;0;0;0;0;0
-967;mendiant;negative;0;0;1;0;0;0
-968;menstruation;negative;0;0;0;0;0;0
-969;menstruel;positive;0;0;0;0;0;0
-970;menteur;negative;0;0;0;1;0;1
-971;menu;positive;0;0;0;0;0;0
-972;méprendre;negative;0;0;0;1;0;1
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-975;mériter;positive;0;0;0;0;0;0
-976;merveilleux;positive;0;0;0;0;1;0
-977;messager;positive;0;0;0;0;0;0
-978;méticuleux;negative;0;1;1;0;0;1
-979;métier;positive;0;0;0;0;0;0
-980;mettre en liberté conditionnel;positive;0;0;0;0;0;0
-981;meulage;negative;0;0;0;1;0;1
-982;meurtrier|meurtrière;negative;0;1;1;1;1;1
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-984;mince;negative;0;1;1;0;0;0
-985;miniature;negative;0;1;1;0;0;0
-986;ministériel;positive;0;0;0;0;0;0
-987;minuscule;positive;0;0;1;0;0;0
-988;minutieux;positive;0;0;0;0;0;0
-989;miraculeux;positive;0;0;0;0;1;0
-990;miséricordieux;positive;1;0;0;0;0;0
-991;mixture;negative;0;0;0;0;0;1
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-993;modérateur;positive;0;0;0;0;0;0
-994;modérer;positive;0;0;0;0;0;0
-995;modeste;positive;0;0;1;0;0;0
-996;mou;negative;0;0;1;0;0;1
-997;montagneux;positive;0;0;0;0;0;0
-998;monter;positive;0;0;0;0;0;0
-999;moqueur;negative;0;0;1;1;0;1
-1000;morne;negative;0;0;1;0;0;0
-1001;morose;negative;0;0;1;0;0;0
-1002;mortel;negative;0;1;1;1;0;1
-1003;motocross;negative;0;1;0;1;0;0
-1004;mouchard;negative;0;0;0;1;0;0
-1005;mouffette;negative;0;0;0;0;0;1
-1006;mousse;negative;0;0;0;0;0;1
-1007;mousseux;negative;0;0;0;0;0;1
-1008;mouvement;positive;0;0;0;0;0;0
-1009;moyenne;positive;0;0;0;0;0;0
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-1018;nauséeux;negative;0;0;1;0;0;1
-1019;navigateur;positive;0;0;0;0;0;0
-1020;nébuleuse;negative;0;1;0;0;0;0
-1021;négociant;positive;0;0;0;0;0;0
-1022;négociateur;positive;0;0;0;0;0;0
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-1028;névrosé;negative;0;1;1;0;0;1
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-1037;non officiel;negative;0;1;0;0;0;0
-1038;non organique;negative;0;0;0;0;0;0
-1039;non souhaiter;negative;0;0;1;0;0;0
-1040;non surveiller;negative;0;1;0;0;1;0
-1041;non tacher;positive;0;0;0;0;0;0
-1042;non valider;negative;0;0;0;0;0;1
-1043;non résident;negative;0;0;0;0;0;0
-1044;nonchalamment;positive;1;0;0;0;0;0
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-1051;nouveau venu|venue;positive;0;1;0;0;1;0
-1052;nuageux;negative;0;0;1;0;0;0
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-1054;nul;negative;0;1;1;0;0;1
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-1232;programmateur;positive;0;0;0;0;0;0
-1233;progressif;positive;0;0;0;0;0;0
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-1236;prometteur;positive;0;0;0;0;0;0
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-1242;proprement;positive;0;0;0;0;0;0
-1243;protecteur;positive;0;0;0;0;0;0
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-1261;quasiment;positive;0;0;0;0;0;0
-1262;quelconque;negative;0;0;1;0;0;0
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-1264;qui commencer;positive;1;0;0;0;0;0
-1265;qui donner envie de vomir;negative;0;0;1;0;0;1
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-1267;qui manquer de considération;negative;0;0;1;1;0;1
-1268;qui résonner;positive;0;1;0;0;1;0
-1269;race;positive;0;0;0;0;0;0
-1270;rachidien;positive;0;0;0;0;0;0
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-1291;récapitulatif;positive;0;0;0;0;0;0
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-1300;récréatif;positive;1;0;0;0;0;0
-1301;recueil;positive;0;0;0;0;0;0
-1302;rédactionnel;positive;0;0;0;0;0;0
-1303;rédacteur en chef;positive;0;0;0;0;0;0
-1304;réduire en purée;negative;0;1;0;0;0;1
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-1315;relatif;positive;0;0;0;0;0;0
-1316;remarquable;positive;1;0;0;0;0;0
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-1318;remplaçant;negative;0;0;1;0;0;0
-1319;rémunérateur;positive;1;0;0;0;0;0
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-1326;répartir;positive;0;0;0;0;0;0
-1327;répit;positive;0;0;0;0;0;0
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-1329;représentant;positive;0;0;0;0;0;0
-1330;représentatif;positive;0;0;0;0;0;0
-1331;reproducteur|reproductrice;positive;1;0;0;0;0;0
-1332;républicain;positive;0;0;0;0;0;0
-1333;répugner;negative;0;1;0;1;0;1
-1334;requérant;negative;0;0;0;1;0;1
-1335;résident;positive;0;0;0;0;0;0
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-1337;résistant;positive;0;0;1;0;0;0
-1338;respectif;positive;0;0;0;0;0;0
-1339;respectueux;positive;0;0;0;0;0;0
-1340;resplendir;positive;1;0;0;0;0;0
-1341;restriction;negative;0;1;1;1;0;0
-1342;restrictif;negative;0;1;1;0;0;0
-1343;retraire;positive;0;0;0;0;0;0
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-1345;rétroactif;positive;0;0;0;0;0;0
-1346;réunion;positive;0;0;0;0;0;0
-1347;réussir;positive;1;0;0;0;0;0
-1348;révélateur;positive;0;0;0;0;0;0
-1349;révérencieux;positive;0;0;0;0;0;0
-1350;rêveur;positive;1;0;0;0;0;0
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-1352;rigoureux;negative;0;0;0;1;0;0
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-1356;rompre;negative;0;0;1;0;0;0
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-1358;routard;positive;0;0;0;0;0;0
-1359;route national;positive;0;0;0;0;0;0
-1360;ruine;negative;0;1;1;1;0;0
-1361;ruiner;negative;0;1;1;1;0;0
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-1364;sain et sauf;positive;1;0;0;0;0;0
-1365;saint;positive;0;0;0;0;1;0
-1366;saisir;negative;0;1;0;1;1;0
-1367;saloon;positive;0;0;0;1;0;0
-1368;sanguine;positive;0;0;0;0;0;0
-1369;sans assistance;negative;0;0;1;0;0;0
-1370;sans cesse;positive;0;0;0;0;0;0
-1371;sans égal;positive;0;1;0;0;0;0
-1372;sans entrave;positive;1;0;0;0;0;0
-1373;sans fin;positive;0;1;1;1;0;0
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-1377;sans opposition;positive;0;0;0;0;0;0
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-1383;satisfaire;positive;1;0;0;0;0;0
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-1386;savant;positive;0;0;0;0;0;0
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-1389;scélérat;negative;0;1;0;1;0;1
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-1391;scrupuleux;positive;0;0;0;0;0;0
-1392;sculptrice;positive;0;0;0;0;0;0
-1393;se cacher;negative;0;1;1;0;0;0
-1394;se dévaloriser;negative;0;0;1;1;0;1
-1395;se souvenir de;positive;0;0;0;0;0;0
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-1404;sensationnel;positive;1;0;0;0;0;0
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-1413;sérieusement;negative;0;0;1;0;0;0
-1414;serment;positive;0;0;0;0;0;0
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-1421;singulier;positive;0;0;0;0;0;0
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-1425;sms;positive;0;0;0;0;0;0
-1426;solidifier;positive;0;0;0;0;0;0
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-1438;sous tendre;positive;0;0;0;0;0;0
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-1441;spacieux;positive;1;0;0;0;0;0
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-1498;théologien;positive;0;0;0;0;0;0
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-1501;tissage;positive;0;0;0;0;0;0
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-1508;tout de même;negative;0;0;0;0;0;0
-1509;tout puissant;positive;1;0;0;0;0;0
-1510;tout le deux semaine;positive;0;0;0;0;0;0
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-1515;travailleur|travailleuse;positive;0;0;0;0;0;0
-1516;traverser à gué;positive;0;0;0;0;0;0
-1517;trembloter;negative;0;1;0;1;0;0
-1518;trésorière;positive;0;0;0;0;0;0
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-1521;trompeur;negative;0;0;1;1;0;1
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-1523;trop liquide;negative;0;0;0;0;0;1
-1524;trop long;negative;0;0;1;0;0;0
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-1527;troubadour;positive;0;0;0;0;0;0
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-1530;trouvaille;positive;0;0;0;0;0;0
-1531;tueur;negative;0;1;1;1;1;1
-1532;tumultueux;negative;0;1;0;1;0;0
-1533;tuteur|tutrice;positive;0;0;0;0;0;0
-1534;ultime;positive;0;1;1;0;0;0
-1535;ultraviolette;positive;0;0;0;0;0;0
-1536;un foi|fois par semaine;positive;0;0;0;0;0;0
-1537;uniformité;positive;0;0;0;0;0;0
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-1543;vaillant;positive;0;0;0;0;0;0
-1544;vain;negative;0;0;1;0;0;0
-1545;vaporeux;positive;0;0;0;0;0;0
-1546;variqueux;negative;0;0;0;0;0;1
-1547;vaseux;negative;0;0;0;0;0;1
-1548;végétarien;positive;0;0;0;0;0;0
-1549;velouteux;positive;0;0;0;0;0;0
-1550;velu;negative;0;0;0;0;0;1
-1551;vendeur;positive;0;0;0;0;0;0
-1552;vengeur;negative;0;0;0;1;0;0
-1553;venimeux;negative;0;1;1;1;1;1
-1554;vent violent;negative;0;1;0;0;0;0
-1555;ventriculaire;positive;0;0;0;0;0;0
-1556;verbeux;positive;0;0;0;0;0;0
-1557;vérificateur|vérificatrice;positive;0;0;0;0;0;0
-1558;véritable;positive;0;0;0;0;0;0
-1559;vernir;positive;1;0;0;0;0;0
-1560;versement;positive;0;0;0;0;0;0
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-1565;victorieux;positive;1;0;0;0;0;0
-1566;vigoureux;positive;0;0;0;0;0;0
-1567;villageois;positive;0;0;0;0;0;0
-1568;vindicatif;negative;0;1;0;1;0;1
-1569;violation;negative;0;0;0;1;0;0
-1570;violet|violette;positive;0;0;0;0;0;0
-1571;virtuel;negative;0;0;0;0;0;0
-1572;visiteur;positive;1;0;0;0;0;0
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-1575;vitrer;positive;0;0;0;0;0;0
-1576;vif;negative;0;1;0;0;1;1
-1577;v?u;positive;0;0;0;0;0;0
-1578;voisin;positive;0;0;0;0;0;0
-1579;voleur;negative;0;1;1;1;1;1
-1580;voluptueux;positive;0;0;0;0;0;0
-1581;voyageur;positive;0;0;0;0;0;0
-1582;voyant;positive;0;0;0;0;0;1
-1583;youpi;positive;1;0;0;0;0;0
-1584;zéro;negative;0;0;1;0;0;0
-1585;zut;negative;0;0;1;1;0;1
-1586;bon gorgée;negative;0;0;0;0;0;1
-1587;école maternel;positive;0;0;0;0;0;0
-1588;un foi|fois tout le deux moi|mois;positive;0;0;0;0;0;0
-1589;@card@ kilo;positive;0;0;0;0;0;0
-1590;à base de plante;positive;0;0;0;0;0;0
-1591;à bord;positive;0;0;0;0;0;0
-1592;à capuche;positive;0;1;0;0;0;0
-1593;à carreau;positive;0;0;0;0;0;0
-1594;à clou;negative;0;0;0;0;0;0
-1595;à condition que;positive;0;0;0;0;0;0
-1596;à contrec?ur;negative;0;1;1;1;0;1
-1597;à côte;negative;0;0;0;0;0;1
-1598;à cru;positive;0;0;0;0;0;0
-1599;à dessein;positive;0;0;0;0;0;0
-1600;à destination de;positive;0;0;0;0;0;0
-1601;à feuillage caduc;negative;0;0;0;0;0;0
-1602;à feuille;positive;0;0;0;0;0;0
-1603;à fleur;positive;1;0;0;0;0;0
-1604;à foison;positive;1;0;0;0;0;0
-1605;à force de qch;positive;0;0;0;0;0;0
-1606;à fort poitrine;positive;0;0;0;0;0;0
-1607;à friser;positive;0;0;0;0;0;0
-1608;à froid;negative;0;0;1;0;0;0
-1609;à gauche;negative;0;0;0;0;0;0
-1610;à intervalle régulier;positive;0;0;0;0;0;0
-1611;à jamais;positive;0;0;0;0;0;0
-1612;à juste titre;positive;0;0;0;0;0;0
-1613;à l étranger;positive;0;0;0;0;0;0
-1614;à l improviste;negative;0;0;0;0;1;0
-1615;à l unanimité;positive;0;0;0;0;0;0
-1616;à le bon franquette;positive;0;0;0;0;1;0
-1617;à le dérive;negative;0;1;1;0;0;0
-1618;à le différence de;negative;0;0;0;0;0;0
-1619;à le menthe;positive;0;0;0;0;0;0
-1620;avoir le mode;positive;0;0;0;0;0;0
-1621;à le mode;positive;0;0;0;0;0;0
-1622;à le retraite;positive;1;0;0;0;0;0
-1623;à maintes reprise;negative;0;0;0;0;0;0
-1624;à mi chemin;positive;0;0;0;0;0;0
-1625;à naître;positive;0;0;0;0;0;0
-1626;à nervure;negative;0;0;0;0;0;1
-1627;à nouveau;positive;0;0;0;0;0;0
-1628;à peine;negative;0;0;1;0;0;0
-1629;à peu près;negative;0;0;0;0;0;0
-1630;à plat;negative;0;0;1;0;0;0
-1631;à plat ventre;negative;0;1;1;0;0;0
-1632;à plusieurs reprise;negative;0;0;0;0;0;0
-1633;avoir priori;negative;0;1;0;1;0;1
-1634;à propos;negative;0;0;0;0;0;0
-1635;à quai;positive;0;0;0;0;0;0
-1636;à queue;negative;0;0;0;0;0;0
-1637;à son compte;positive;0;0;0;0;0;0
-1638;à spiral|spirale;positive;0;0;0;0;0;0
-1639;à succès;positive;0;0;0;0;0;0
-1640;à supposer que;negative;0;0;0;0;0;0
-1641;à thème;positive;0;0;0;0;0;0
-1642;à tort;negative;0;1;1;1;0;0
-1643;à tout jamais;positive;0;0;0;0;0;0
-1644;à tout épreuve;positive;0;0;0;0;0;0
-1645;à tribord;positive;0;0;0;0;0;0
-1646;à venir;positive;0;0;0;0;0;0
-1647;à vie;positive;0;0;0;0;0;0
-1648;à vrai dire;positive;0;0;0;0;0;0
-1649;abaisser;negative;0;0;1;0;0;0
-1650;abaissement;negative;0;0;1;0;0;0
-1651;abandon;negative;0;1;1;1;1;0
-1652;abandonner;negative;0;1;1;1;0;1
-1653;abaondonnées;negative;0;1;1;0;0;0
-1654;abat;negative;0;0;0;0;0;1
-1655;abattoir;negative;0;1;1;1;0;1
-1656;abattre;negative;0;1;1;1;1;0
-1657;abbé;negative;0;1;0;0;0;0
-1658;abcès;negative;0;1;1;0;0;1
-1659;abdiquer;negative;0;1;1;0;0;0
-1660;abdomen;positive;0;0;0;0;0;0
-1661;abdominal;positive;0;0;0;0;0;0
-1662;abeille;negative;0;1;0;1;0;0
-1663;aberrer;negative;0;0;0;1;1;0
-1664;abhorrer;negative;0;1;0;1;0;1
-1665;abîme;negative;0;1;1;0;0;0
-1666;abîmer;negative;0;1;1;1;0;0
-1667;abject;negative;0;0;0;1;0;1
-1668;aboiement;negative;0;1;0;1;0;0
-1669;abolir;negative;0;0;1;1;0;0
-1670;abolition;negative;0;0;1;1;0;0
-1671;abomination;negative;0;1;1;1;0;1
-1672;abonder;positive;1;0;0;0;0;0
-1673;abondant;positive;1;0;0;0;0;0
-1674;abonnement;positive;0;0;0;0;0;0
-1675;aborder;positive;0;0;0;1;1;0
-1676;aborigène;positive;0;0;0;0;0;0
-1677;abortif;negative;0;1;1;1;0;0
-1678;aboutir;positive;1;0;0;0;0;0
-1679;aboyer;negative;0;1;0;1;0;0
-1680;abpimée;negative;0;1;1;1;0;0
-1681;abrasion;negative;0;1;1;0;0;1
-1682;abréger;positive;0;0;0;0;0;0
-1683;abreuvoir;positive;0;0;0;0;0;0
-1684;abréviation;positive;0;0;0;0;0;0
-1685;abri de jardin;negative;0;0;0;0;0;0
-1686;abriter;positive;0;0;0;0;0;0
-1687;abrogation;negative;0;1;1;0;0;0
-1688;abroger;negative;0;1;1;0;0;0
-1689;abrutir;negative;0;0;0;1;0;1
-1690;absence;negative;0;1;1;0;0;0
-1691;absentéisme;negative;0;0;1;0;0;0
-1692;absinthe;negative;0;0;0;0;0;0
-1693;absoudre;positive;1;0;0;0;0;0
-1694;absolution;positive;0;0;0;0;0;0
-1695;absorbant;positive;0;0;0;0;0;0
-1696;absorption;positive;0;0;0;0;0;0
-1697;abstention;negative;0;1;1;0;0;0
-1698;abstinence;negative;0;1;1;0;0;0
-1699;abstraction;negative;0;1;1;0;0;0
-1700;abstraire;positive;0;0;0;0;0;0
-1701;absurdité;negative;0;0;0;1;1;1
-1702;abuser de;negative;0;1;1;1;0;0
-1703;académie;positive;0;0;0;0;0;0
-1704;académique;positive;0;0;0;0;0;0
-1705;accabler;negative;0;1;1;1;0;0
-1706;accablant;negative;0;1;1;1;0;0
-1707;accalmir;positive;0;0;0;0;0;0
-1708;accéder;positive;1;0;0;0;0;0
-1709;accélérateur;negative;0;0;0;1;0;0
-1710;accélération;positive;0;1;0;0;0;0
-1711;accentuation;positive;0;0;0;0;0;0
-1712;accentuer;positive;0;0;0;0;0;0
-1713;acceptant;positive;0;0;0;0;0;0
-1714;acceptation;positive;0;0;0;0;0;0
-1715;accepter;positive;0;0;0;0;0;0
-1716;accès;positive;0;0;0;0;0;0
-1717;accessible;positive;0;0;0;0;0;0
-1718;accession;positive;0;0;0;0;0;0
-1719;accessoire;negative;0;0;0;0;0;0
-1720;accessoirement;negative;0;0;0;0;0;0
-1721;accident mortel;negative;0;1;1;0;0;0
-1722;accident vasculaire cérébral;negative;0;1;1;0;1;0
-1723;accidenter;negative;0;1;1;0;0;0
-1724;accidentellement;negative;0;1;1;0;1;0
-1725;acclamation;positive;1;0;0;0;0;0
-1726;acclamer;positive;0;0;0;0;1;0
-1727;accolade;positive;0;0;0;0;1;0
-1728;accommoder;positive;0;0;0;0;0;0
-1729;accommodation;positive;0;0;0;0;0;0
-1730;accompagner;positive;0;0;0;0;0;0
-1731;accompagnement;positive;0;0;0;0;0;0
-1732;accomplir;positive;1;0;0;0;0;0
-1733;accomplissement;positive;0;0;0;0;0;0
-1734;accordable;positive;0;0;0;0;0;0
-1735;accorder;positive;0;0;0;0;0;0
-1736;accordéon;positive;0;0;0;0;0;0
-1737;accord;positive;0;0;0;0;0;0
-1738;accoutumer;positive;0;0;0;0;0;0
-1739;accréditer;positive;0;0;0;0;0;0
-1740;accro;negative;0;0;1;1;0;0
-1741;accrochage;negative;0;0;0;1;0;0
-1742;accrocher;negative;0;1;0;0;1;0
-1743;accroc;negative;0;1;0;0;1;0
-1744;accroissement;positive;0;0;0;0;0;0
-1745;accroître;positive;0;0;0;0;0;0
-1746;accroupir;negative;0;1;0;0;0;0
-1747;accroupissement;negative;0;1;0;0;0;0
-1748;accroire;positive;0;0;0;0;0;0
-1749;accroire|accroître;positive;0;0;0;0;0;0
-1750;accueil;positive;0;0;0;0;0;0
-1751;accueillir;positive;1;0;0;0;0;0
-1752;accumuler;positive;0;0;0;0;0;0
-1753;accusatif;negative;0;0;0;1;0;1
-1754;accusation;negative;0;0;0;1;0;1
-1755;acérer;negative;0;1;0;1;0;0
-1756;acétique;negative;0;0;0;0;0;1
-1757;acharner;negative;0;1;0;1;0;1
-1758;achat;positive;0;0;0;0;0;0
-1759;acheminer par tuyau;positive;0;0;0;0;0;0
-1760;acheminer par pont aérien;positive;0;0;0;0;0;0
-1761;acheter;positive;0;0;0;0;0;0
-1762;achever;positive;0;0;0;0;0;0
-1763;achèvement;positive;1;0;0;0;0;0
-1764;acide;negative;0;0;0;0;0;1
-1765;acidité;negative;0;0;0;0;0;1
-1766;acier;positive;0;0;0;0;0;0
-1767;acompte;positive;0;0;0;0;0;0
-1768;acouphène;negative;0;1;1;0;0;0
-1769;acoustique;positive;0;0;0;0;0;0
-1770;acquéreur;positive;0;0;0;0;0;0
-1771;acquérir;positive;0;0;0;0;0;0
-1772;acquiescement;positive;0;0;0;0;0;0
-1773;âcre;negative;0;0;0;0;0;1
-1774;acrobate;positive;0;1;0;0;0;0
-1775;acte;positive;0;0;0;0;0;0
-1776;acte de défi;negative;0;1;0;1;0;1
-1777;acte de fiducie;negative;0;0;0;1;0;0
-1778;acte délictuel;negative;0;1;0;1;0;0
-1779;actif;positive;0;0;0;0;0;0
-1780;action;positive;0;0;0;0;0;0
-1781;action de grâce;positive;0;0;0;0;0;0
-1782;actionnable;negative;0;1;0;1;0;1
-1783;actionnaire;positive;0;0;0;0;0;0
-1784;activité;positive;0;0;0;0;0;0
-1785;actuaire;positive;0;0;0;0;0;0
-1786;actualité;positive;0;0;0;0;0;0
-1787;acuité;positive;0;0;0;0;0;0
-1788;acupuncture;positive;0;0;0;0;0;0
-1789;adage;positive;0;0;0;0;0;0
-1790;adaptabilité;positive;0;0;0;0;0;0
-1791;adaptable;positive;0;0;0;0;0;0
-1792;adaptation;positive;0;0;0;0;0;0
-1793;adapter;positive;0;0;0;0;0;0
-1794;addenda;positive;0;0;0;0;0;0
-1795;addiction;negative;0;1;1;0;0;0
-1796;additif;negative;0;0;0;0;0;1
-1797;additionner;positive;0;0;0;0;0;0
-1798;additionnel;positive;0;0;0;0;0;0
-1799;adepte;positive;0;0;0;0;0;0
-1800;adéquat;positive;0;0;0;0;0;0
-1801;adéquation;positive;0;0;0;0;0;0
-1802;adhérer;positive;0;0;0;0;0;0
-1803;adhérence;positive;0;0;0;0;0;0
-1804;adhérent;positive;0;0;0;0;0;0
-1805;adhérer à;positive;0;0;0;0;0;0
-1806;adhésion;positive;0;0;0;0;0;0
-1807;adieu;negative;0;0;1;0;0;0
-1808;adipeux;negative;0;0;0;0;0;1
-1809;adjacent;positive;0;0;0;0;0;0
-1810;adjectif;positive;0;0;0;0;0;0
-1811;adjectival;positive;0;0;0;0;0;0
-1812;adjoindre;positive;0;0;0;0;0;0
-1813;adjuvant;positive;0;0;0;0;0;0
-1814;admettre;positive;0;0;0;0;0;0
-1815;administrer;positive;0;0;0;0;0;0
-1816;administrer qch à qqn;positive;0;0;0;0;0;0
-1817;admirable;positive;0;0;0;0;0;0
-1818;admiration;positive;0;0;0;0;0;0
-1819;admirer;positive;0;0;0;0;0;0
-1820;admissibilité;positive;0;0;0;0;0;0
-1821;admission;positive;0;0;0;0;0;0
-1822;admonition;negative;0;1;0;1;0;0
-1823;adobe;positive;0;0;0;0;0;0
-1824;adolescence;positive;0;0;0;0;0;0
-1825;adopter;positive;0;0;0;0;0;0
-1826;adoption;positive;0;0;0;0;0;0
-1827;adorable;positive;0;0;1;0;1;0
-1828;adoration;positive;0;1;0;0;0;0
-1829;adorer;positive;0;1;0;0;0;0
-1830;ado|ados;positive;0;0;0;0;0;0
-1831;adosser;positive;0;0;0;0;0;0
-1832;adoucir;positive;0;0;0;0;0;0
-1833;adoucissant;positive;0;0;0;0;0;0
-1834;adoucissement;positive;0;0;0;0;0;0
-1835;adresse;positive;0;0;0;0;0;0
-1836;adresser;positive;0;0;0;0;0;0
-1837;adroit;positive;0;0;0;0;0;0
-1838;adulte;positive;0;0;0;0;0;0
-1839;adultère;negative;0;0;1;1;0;1
-1840;adversaire;negative;0;1;0;1;0;1
-1841;adverse;negative;0;1;0;1;0;1
-1842;adversité;negative;0;1;1;1;0;0
-1843;aération;positive;0;0;0;0;0;0
-1844;aérer;positive;0;0;0;0;0;0
-1845;aérodrome;positive;0;0;0;0;0;0
-1846;aérodynamique;positive;0;0;0;0;0;0
-1847;aéroglisseur;positive;0;0;0;0;0;0
-1848;aéronautique;positive;0;0;0;0;0;0
-1849;aéroport;positive;0;0;0;0;0;0
-1850;aérosol;positive;0;0;0;0;0;0
-1851;affaiblir;negative;0;1;1;0;0;0
-1852;affairement;positive;0;0;0;0;0;0
-1853;affaisés;negative;0;0;1;0;0;0
-1854;affaisser;negative;0;0;1;0;0;0
-1855;affaissement;negative;0;0;1;0;0;0
-1856;affamer;negative;0;1;1;1;0;0
-1857;affectation;positive;0;0;0;0;0;0
-1858;affecter;negative;0;0;1;0;0;0
-1859;affection;positive;0;0;0;0;0;0
-1860;affichage;positive;0;0;0;0;0;0
-1861;affiche;positive;0;0;0;0;1;0
-1862;afficher;positive;0;0;0;0;0;0
-1863;affidavit;positive;0;0;0;0;0;0
-1864;affiliation;positive;0;0;0;0;0;0
-1865;affilier;positive;0;0;0;0;0;0
-1866;affinage;positive;0;0;0;0;0;0
-1867;affiner;positive;0;0;0;0;0;0
-1868;affinité;positive;0;0;0;0;0;0
-1869;affirmatif;positive;0;0;0;0;0;0
-1870;affirmation;positive;0;0;0;0;0;0
-1871;affirmativement;positive;0;0;0;0;0;0
-1872;affirmer;positive;0;0;0;0;0;0
-1873;affixe;positive;0;0;0;0;0;0
-1874;affliction;negative;0;1;1;0;0;1
-1875;affluent;positive;0;0;0;0;0;0
-1876;affluer;negative;0;1;0;1;1;0
-1877;afflux;negative;0;1;0;0;1;0
-1878;affoler;negative;0;1;1;0;0;0
-1879;affolement;negative;0;1;0;1;0;0
-1880;affréter;positive;0;0;0;0;0;0
-1881;affronter;negative;0;0;0;0;0;0
-1882;âffuté;negative;0;1;0;0;0;0
-1883;âffutée;negative;0;1;0;0;0;0
-1884;âffutées;negative;0;1;0;0;0;0
-1885;âffutés;negative;0;1;0;0;0;0
-1886;agacer;negative;0;0;1;1;0;0
-1887;agacement;negative;0;0;1;1;0;1
-1888;agate;positive;0;0;0;0;0;0
-1889;âge;negative;0;1;1;0;0;0
-1890;âgé;negative;0;0;1;0;0;0
-1891;agence;positive;0;0;0;0;0;0
-1892;agenouiller;negative;0;0;1;0;0;0
-1893;agent;positive;0;0;0;0;0;0
-1894;agent de change;positive;0;0;0;0;0;0
-1895;agglomération;positive;0;0;0;0;0;0
-1896;agglomérer;negative;0;0;0;0;0;0
-1897;aggraver;negative;0;1;1;1;0;0
-1898;aggravation;negative;0;1;1;1;0;1
-1899;aggressifs;negative;0;1;0;1;0;0
-1900;agile;positive;0;0;0;0;0;0
-1901;agilité;positive;0;0;0;0;0;0
-1902;agir;positive;0;0;0;0;0;0
-1903;agissements;positive;0;0;0;0;0;0
-1904;agiter;negative;0;1;0;1;0;0
-1905;agneau;positive;0;0;0;0;0;0
-1906;agnostique;negative;0;1;1;0;0;0
-1907;agonir;negative;0;1;1;1;0;0
-1908;agrafer;positive;0;0;0;0;0;0
-1909;agrandir;negative;0;0;0;0;0;0
-1910;agrégat;negative;0;0;0;0;0;0
-1911;agrégation;negative;0;0;0;0;0;0
-1912;agresseur;negative;0;1;1;1;0;0
-1913;agression;negative;0;1;0;1;1;0
-1914;agricole;positive;0;0;0;0;0;0
-1915;agriculture;positive;0;0;0;0;0;0
-1916;ahurir;negative;0;0;0;0;1;0
-1917;ahurissant;negative;0;0;0;0;1;0
-1918;aide;positive;0;0;0;0;0;0
-1919;aider;positive;0;0;0;0;0;0
-1920;aigle;positive;0;0;0;0;0;0
-1921;aigre;negative;0;0;0;0;0;1
-1922;aigu marine;positive;0;0;0;0;0;0
-1923;aiguille;negative;0;1;0;0;0;0
-1924;aiguiller;positive;0;0;0;0;0;0
-1925;aiguillonner;positive;0;1;0;0;0;0
-1926;aiguisoir;negative;0;1;0;0;0;0
-1927;ail;negative;0;0;0;0;0;1
-1928;aile;positive;0;0;0;0;0;0
-1929;ailer;positive;0;0;0;0;0;0
-1930;aileron;positive;0;0;0;0;0;0
-1931;aimable;positive;0;0;0;0;0;0
-1932;aimant;positive;0;0;0;0;0;0
-1933;airbag;positive;0;0;0;0;0;0
-1934;aire;positive;0;0;0;0;0;0
-1935;aire de jeu;positive;0;0;0;0;1;0
-1936;air;negative;0;0;0;0;0;1
-1937;aisance;positive;1;0;0;0;0;0
-1938;aisé;positive;1;0;0;0;0;0
-1939;ajourner;negative;0;0;1;0;0;0
-1940;ajournement;negative;0;0;1;0;0;0
-1941;ajout;positive;0;0;0;0;0;0
-1942;ajouter;positive;0;0;0;0;0;0
-1943;ajustage;positive;0;0;0;0;0;0
-1944;ajuster;positive;0;0;0;0;0;0
-1945;ajustement;positive;0;0;0;0;0;0
-1946;alambiquer;negative;0;0;0;0;0;1
-1947;alanguir;negative;0;0;1;0;0;0
-1948;alarmer;negative;0;1;0;0;1;0
-1949;alarme;negative;0;1;0;0;1;0
-1950;albâtre;positive;0;0;0;0;0;0
-1951;album;positive;0;0;0;0;0;0
-1952;alcali;positive;0;0;0;0;0;0
-1953;alcaloïde;positive;0;0;0;0;0;0
-1954;alchimie;positive;0;0;0;0;0;0
-1955;alcool;negative;0;0;0;0;0;0
-1956;alcoolisme;negative;0;1;1;1;0;1
-1957;alcôve;positive;0;0;0;0;0;0
-1958;alerte;positive;0;1;0;1;1;0
-1959;alésage;negative;0;1;0;1;0;0
-1960;algèbre;positive;0;0;0;0;0;0
-1961;algébrique;positive;0;0;0;0;0;0
-1962;algorithme;positive;0;0;0;0;0;0
-1963;alibi;positive;0;0;0;0;0;0
-1964;aliénation;negative;0;1;1;1;0;1
-1965;aliéner;negative;0;0;1;0;0;1
-1966;aligner;positive;0;0;0;0;0;0
-1967;alignement;positive;0;0;0;0;0;0
-1968;aliment;positive;0;0;0;0;0;0
-1969;alimentaire;positive;0;0;0;0;0;0
-1970;alimentation;positive;0;0;0;0;0;0
-1971;alimenter;positive;0;0;0;0;0;0
-1972;alinéa;positive;0;0;0;0;0;0
-1973;aliquote;positive;0;0;0;0;0;0
-1974;allaitement;positive;0;0;0;0;0;0
-1975;allaiter;positive;0;0;0;0;0;0
-1976;allécher;positive;0;0;0;0;1;0
-1977;alléchant;positive;0;0;0;0;1;0
-1978;aller;positive;0;0;0;0;0;0
-1979;allégation;negative;0;0;0;1;0;0
-1980;allégeance;positive;0;0;0;0;0;0
-1981;allégorie;positive;0;0;0;0;0;0
-1982;allégorique;positive;0;0;0;0;0;0
-1983;allégresse;positive;1;0;0;0;0;0
-1984;allegro;positive;0;0;0;0;0;0
-1985;alléguer;negative;0;1;0;0;0;1
-1986;allemand;positive;0;0;0;0;0;0
-1987;aller à tout vitesse;negative;0;1;0;0;0;0
-1988;aller chercher;positive;0;0;0;0;0;0
-1989;alliage;positive;0;0;0;0;0;0
-1990;alliance;positive;0;0;0;0;0;0
-1991;allier;positive;0;0;0;0;0;0
-1992;alligator;negative;0;1;0;0;0;0
-1993;allocataire;positive;0;0;0;0;0;0
-1994;allocation;positive;0;0;0;0;0;0
-1995;allocation chômage;negative;0;0;1;0;0;0
-1996;allocution;positive;0;0;0;0;0;0
-1997;allongement;positive;0;0;0;0;0;0
-1998;allouer;positive;0;0;0;0;0;0
-1999;allumage;positive;0;0;0;0;0;0
-2000;allumette;positive;0;0;0;0;0;0
-2001;alluvial;positive;0;0;0;0;0;0
-2002;almanach;positive;0;0;0;0;0;0
-2003;aloha;positive;1;0;0;0;0;0
-2004;alors que;negative;0;0;0;0;0;0
-2005;alouette;positive;1;0;0;0;0;0
-2006;alphabet;positive;0;0;0;0;0;0
-2007;alphabétique;positive;0;0;0;0;0;0
-2008;alpiniste;positive;0;0;0;0;0;0
-2009;altération;negative;0;0;1;0;0;1
-2010;altercation;negative;0;0;0;1;0;0
-2011;altérer;negative;0;0;1;1;0;1
-2012;alternatif;positive;0;0;0;0;0;0
-2013;alterner;positive;0;0;0;0;0;0
-2014;altitude;positive;0;0;0;0;0;0
-2015;alto;positive;0;0;0;0;0;0
-2016;alvéolaire;positive;0;0;0;0;0;0
-2017;amadouer;positive;0;0;0;0;0;0
-2018;amande;positive;0;0;0;0;0;0
-2019;amant;positive;0;0;0;0;0;0
-2020;amarrage;positive;0;0;0;0;0;0
-2021;amarrer;positive;0;0;0;0;0;0
-2022;amasser;positive;0;0;0;0;0;0
-2023;amateur;negative;0;0;0;0;0;0
-2024;amatrices;negative;0;0;0;0;0;0
-2025;ambassade;positive;0;0;0;0;0;0
-2026;ambassadeur;positive;0;0;0;0;0;0
-2027;ambiance;positive;0;0;0;0;0;0
-2028;ambiant;positive;0;0;0;0;0;0
-2029;ambigu;negative;0;1;0;0;0;0
-2030;ambiguë;negative;0;1;0;0;0;0
-2031;ambiguïté;negative;0;1;0;0;0;0
-2032;ambition;positive;0;0;0;0;0;0
-2033;ambre;positive;0;0;0;0;0;0
-2034;ambrer;positive;0;0;0;0;0;0
-2035;ambulance;negative;0;1;1;0;0;0
-2036;âme s?ur;positive;0;1;0;0;0;0
-2037;améliorant;positive;0;0;0;0;0;0
-2038;améliorer;positive;0;0;0;0;0;0
-2039;amen;positive;0;0;0;0;0;0
-2040;aménagement;positive;0;0;0;0;0;0
-2041;aménagement paysager;positive;0;0;0;0;0;0
-2042;amendement;positive;0;0;0;0;0;0
-2043;amender;positive;0;0;0;0;0;0
-2044;aménité;positive;0;0;0;0;0;0
-2045;amèrement;negative;0;0;1;1;0;1
-2046;amertume;negative;0;0;1;1;0;1
-2047;améthyste;positive;0;0;0;0;0;0
-2048;amical;positive;0;0;0;0;0;0
-2049;amidon;negative;0;0;0;0;0;1
-2050;amidonner;negative;0;0;0;0;0;1
-2051;amiral;positive;0;0;0;0;0;0
-2052;amirauté;positive;0;0;0;0;0;0
-2053;amitié;positive;0;0;0;0;0;0
-2054;ammoniac;negative;0;0;0;0;0;1
-2055;amnésie;negative;0;1;1;0;0;0
-2056;amnistie;positive;1;0;0;0;0;0
-2057;amoindrir;negative;0;0;1;0;0;0
-2058;amorçage;positive;0;0;0;0;0;0
-2059;amorce;positive;0;0;0;0;0;0
-2060;amorcer;positive;0;0;0;0;0;0
-2061;amorphe;negative;0;0;1;0;0;1
-2062;amortir;negative;0;1;0;1;1;0
-2063;amortisseur;negative;0;1;0;0;0;0
-2064;amour;positive;0;0;0;0;0;0
-2065;amoureux;positive;1;0;0;0;0;0
-2066;amphétamine;negative;0;0;0;0;0;1
-2067;amphibien;negative;0;0;0;0;0;1
-2068;amphibiens;negative;0;0;0;0;0;1
-2069;amphibie;positive;0;0;0;0;0;0
-2070;amphithéâtre;positive;0;0;0;0;0;0
-2071;amplement;positive;0;0;0;0;0;0
-2072;ampleur;positive;0;0;0;0;0;0
-2073;amplification;positive;0;0;0;0;0;0
-2074;amplifier;positive;0;0;0;0;0;0
-2075;amplitude;positive;0;0;0;0;0;0
-2076;amputation;negative;0;1;1;0;0;1
-2077;amulette;positive;0;0;0;0;0;0
-2078;amuser;positive;1;0;0;0;0;0
-2079;amusant;positive;1;0;0;0;0;0
-2080;amuser gueule;positive;0;0;0;0;0;0
-2081;amusement;positive;1;0;0;0;0;0
-2082;amygdale;negative;0;0;0;0;0;1
-2083;an;positive;0;0;0;0;0;0
-2084;anaconda;negative;0;1;0;0;0;1
-2085;anal;negative;0;1;0;0;0;0
-2086;analgésique;negative;0;1;0;0;0;0
-2087;analogie;positive;0;0;0;0;0;0
-2088;analogique;positive;0;0;0;0;0;0
-2089;analogue;positive;0;0;0;0;0;0
-2090;analphabète;negative;0;0;1;0;0;1
-2091;analyser;positive;0;0;0;0;0;0
-2092;analyseur;positive;0;0;0;0;0;0
-2093;analyste;positive;0;0;0;0;0;0
-2094;analytique;positive;0;0;0;0;0;0
-2095;ananas;positive;0;0;0;0;0;0
-2096;anarchie;negative;0;1;0;1;0;0
-2097;anarchisme;negative;0;1;0;1;0;0
-2098;anarchiste;negative;0;1;0;1;0;0
-2099;anastomose;negative;0;1;0;0;0;1
-2100;anathème;negative;0;1;1;1;0;1
-2101;anatomie;positive;0;0;0;0;0;0
-2102;anatomique;positive;0;0;0;0;0;0
-2103;ancestral;positive;0;0;0;0;0;0
-2104;ancêtre;positive;0;0;0;0;0;0
-2105;ancien combattant;positive;0;0;0;0;0;0
-2106;ancienneté;positive;0;0;0;0;0;0
-2107;ancrage;positive;0;0;1;0;0;0
-2108;ancre;positive;0;0;0;0;0;0
-2109;âne;negative;0;1;0;1;0;1
-2110;anéantir;negative;0;1;1;1;0;0
-2111;anéantissement;negative;0;1;1;1;0;0
-2112;anémone;positive;0;0;0;0;0;0
-2113;ânerie;negative;0;0;0;0;0;1
-2114;ânesse;positive;0;0;0;0;0;0
-2115;anesthésier;negative;0;1;0;0;0;0
-2116;anesthésiant;negative;0;1;0;0;0;0
-2117;anesthésie;negative;0;1;0;0;0;0
-2118;anesthésique;negative;0;1;0;0;0;0
-2119;ange;positive;0;0;0;0;1;0
-2120;angélique;positive;0;0;0;0;0;0
-2121;angine;negative;0;1;1;0;0;0
-2122;angiographie;positive;0;0;0;0;0;0
-2123;angle;positive;0;0;0;0;0;0
-2124;angoisser;negative;0;1;1;0;0;0
-2125;angoissant;negative;0;1;1;0;0;0
-2126;angoisse;negative;0;1;1;1;0;0
-2127;anguille;negative;0;1;0;0;0;1
-2128;angulaire;positive;0;0;0;0;0;0
-2129;anhydre;positive;0;0;0;0;0;0
-2130;animal de compagnie;negative;0;0;0;0;0;0
-2131;animer;positive;1;0;0;0;0;0
-2132;animosité;negative;0;1;1;1;0;1
-2133;annales;positive;0;0;0;0;0;0
-2134;anneau;positive;0;0;0;0;0;0
-2135;année;positive;0;0;0;0;0;0
-2136;annexe;positive;0;0;0;0;0;0
-2137;annexer;positive;0;0;0;0;0;0
-2138;annexion;positive;0;0;0;0;0;0
-2139;annihilation;negative;0;1;1;1;0;0
-2140;annihiler;negative;0;1;0;1;0;0
-2141;anniversaire;positive;0;0;0;0;1;0
-2142;annonce;positive;0;0;0;0;0;0
-2143;annoncer;positive;0;0;0;0;0;0
-2144;annotation;positive;0;0;0;0;0;0
-2145;annoter;positive;0;0;0;0;0;0
-2146;annuaire;positive;0;0;0;0;0;0
-2147;annulaire;positive;0;0;0;0;0;0
-2148;annulation;negative;0;1;1;1;1;0
-2149;anomalie;negative;0;1;0;0;1;0
-2150;anonyme;negative;0;1;1;0;0;1
-2151;anormal;negative;0;1;0;1;1;1
-2152;anse;positive;0;0;0;0;0;0
-2153;antagonisme;negative;0;0;0;1;0;0
-2154;antagoniste;negative;0;1;0;1;0;1
-2155;antalgique;negative;0;1;0;0;0;0
-2156;antécédent;positive;0;0;0;0;0;0
-2157;antéchrist;negative;0;1;0;1;0;1
-2158;antenne;positive;0;0;0;0;0;0
-2159;antérieurement;positive;0;0;0;0;0;0
-2160;anthologie;positive;0;0;0;0;0;0
-2161;anthrax;negative;0;1;1;0;0;1
-2162;anthropologie;positive;0;0;0;0;0;0
-2163;anthropophage;negative;0;1;0;0;0;1
-2164;anthropophagie;negative;0;1;0;0;0;1
-2165;antibiotique;positive;0;0;0;0;0;0
-2166;anticipation;positive;0;0;0;0;0;0
-2167;anticiper;positive;0;0;0;0;0;0
-2168;antidote;positive;0;0;0;0;0;0
-2169;antifongique;positive;0;0;0;0;0;0
-2170;antilope;positive;0;0;0;0;0;0
-2171;antimoine;negative;0;1;0;0;0;1
-2172;antipathie;negative;0;0;0;1;0;1
-2173;antipathique;negative;0;0;0;1;0;1
-2174;antiquaire;positive;0;0;0;0;0;0
-2175;antiquité;positive;0;0;0;0;0;0
-2176;antiseptique;positive;0;0;0;0;0;0
-2177;antisocial;negative;0;1;1;1;0;1
-2178;antithèse;negative;0;0;0;1;0;0
-2179;antithétique;negative;0;0;0;0;0;0
-2180;antiviral;positive;0;0;0;0;0;0
-2181;anxiété;negative;0;1;1;1;0;0
-2182;août;positive;1;0;0;0;0;0
-2183;aorte;positive;0;0;0;0;0;0
-2184;apache;negative;0;1;0;0;0;0
-2185;apaiser;positive;0;0;0;0;0;0
-2186;apaisant;positive;0;0;0;0;0;0
-2187;apathie;negative;0;0;1;0;0;0
-2188;apercevoir;positive;0;0;0;0;0;0
-2189;apéritif;positive;0;0;0;0;0;0
-2190;aphone;negative;0;1;1;0;1;0
-2191;aplanir;negative;0;0;0;0;0;0
-2192;aplatir;negative;0;0;0;0;0;0
-2193;aplomb;positive;0;0;0;0;0;0
-2194;apocalyptique;negative;0;1;1;0;0;0
-2195;apogée;positive;0;0;0;0;1;0
-2196;apologétique;positive;0;0;1;0;0;0
-2197;apologiste;positive;0;0;0;0;0;0
-2198;apostasie;negative;0;0;1;0;0;0
-2199;apostat;negative;0;0;1;0;0;0
-2200;apostolique;positive;0;0;0;0;0;0
-2201;apostrophe;positive;0;0;0;0;1;0
-2202;apôtre;positive;0;0;0;0;0;0
-2203;appareil;positive;0;0;0;0;0;0
-2204;appareil de chauffage;positive;0;0;0;0;0;0
-2205;appareillage;positive;0;0;0;0;0;0
-2206;appartement;positive;0;0;0;0;0;0
-2207;appartement terrasse;positive;0;0;0;0;0;0
-2208;appât;negative;0;1;0;0;1;0
-2209;appelant;positive;0;0;0;0;0;0
-2210;appeler;positive;0;0;0;0;0;0
-2211;appellation inappropriée;negative;0;0;0;0;0;0
-2212;appel;negative;0;1;0;1;1;0
-2213;appendice;positive;0;0;0;0;0;0
-2214;appendicite;negative;0;1;1;0;0;0
-2215;appentis;negative;0;0;0;0;0;1
-2216;appétissant;positive;1;0;0;0;0;0
-2217;appétit;positive;0;0;0;0;0;0
-2218;applaudir;positive;0;0;0;0;0;0
-2219;applaudissement;positive;0;0;0;0;1;0
-2220;applicabilité;positive;0;0;0;0;0;0
-2221;applicable;positive;0;0;0;0;0;0
-2222;application;positive;0;0;0;0;0;0
-2223;appliquer;positive;0;0;0;0;0;0
-2224;apporter;positive;0;0;0;0;0;0
-2225;apposer;positive;0;0;0;0;0;0
-2226;appréciable;positive;0;0;0;0;0;0
-2227;apprécier;positive;0;0;0;0;0;0
-2228;appréciation;positive;0;0;0;0;0;0
-2229;appréhender;negative;0;1;0;1;0;0
-2230;appréhension;negative;0;1;1;0;1;0
-2231;apprendre;positive;0;0;0;0;1;0
-2232;apprentissage;positive;0;0;0;0;0;0
-2233;apprêt;positive;0;0;0;0;0;0
-2234;apprivoiser;positive;0;0;0;0;0;0
-2235;apprivoisement;positive;0;0;0;0;0;0
-2236;approche;positive;0;0;0;0;0;0
-2237;approfondir;positive;0;0;0;0;0;0
-2238;appropirées;positive;0;0;0;0;0;0
-2239;appropriation;positive;0;0;0;0;0;0
-2240;approuver;positive;0;0;0;0;0;0
-2241;approvisionnement;positive;0;0;0;0;0;0
-2242;approvisionner;positive;0;0;0;0;0;0
-2243;approximation;positive;0;0;0;0;0;0
-2244;approximativement;positive;0;0;0;0;0;0
-2245;appuyer;positive;0;0;0;0;0;0
-2246;après midi;positive;0;0;0;0;0;0
-2247;aptitude;positive;0;0;0;0;0;0
-2248;apurement;positive;0;0;0;0;0;0
-2249;aqua;positive;0;0;0;0;0;0
-2250;aquarium;positive;0;0;0;0;0;0
-2251;aquatique;positive;0;0;0;0;0;0
-2252;aqueduc;positive;0;0;0;0;0;0
-2253;arable;positive;0;0;0;0;0;0
-2254;araignée;negative;0;1;0;0;0;1
-2255;arbalète;negative;0;1;0;0;0;0
-2256;arbitraire;negative;0;0;1;1;0;1
-2257;arbitre;positive;0;0;0;0;0;0
-2258;arbitrer;positive;0;0;0;0;0;0
-2259;arbuste;positive;0;0;0;0;0;0
-2260;arc;positive;0;0;0;0;0;0
-2261;arcade;positive;0;0;0;0;0;0
-2262;archaïque;negative;0;0;0;1;0;1
-2263;arche;positive;0;0;0;0;0;0
-2264;archéologie;positive;0;0;0;0;0;0
-2265;archéologique;positive;0;0;0;0;0;0
-2266;archéologue;positive;0;0;0;0;0;0
-2267;archer;positive;0;0;0;0;0;0
-2268;archétype;positive;0;0;0;0;0;0
-2269;archevêque;positive;0;0;0;0;0;0
-2270;archipel;positive;0;0;0;0;0;0
-2271;architecte;positive;0;0;0;0;0;0
-2272;architecture;positive;0;0;0;0;0;0
-2273;archivage;positive;0;0;0;0;0;0
-2274;archiver;positive;0;0;0;0;0;0
-2275;archives;positive;0;0;0;0;0;0
-2276;arctique;positive;0;0;0;0;0;0
-2277;ardoise;positive;0;0;0;0;0;0
-2278;ardu;negative;0;0;1;0;0;0
-2279;arène;positive;0;0;0;0;0;0
-2280;aréole;positive;0;0;0;0;0;0
-2281;argent;positive;0;0;0;1;1;0
-2282;argent liquide;positive;0;1;0;1;0;0
-2283;argent recueillir;positive;1;0;0;0;0;0
-2284;argenter;positive;0;0;0;0;0;0
-2285;argenterie;positive;0;0;0;0;0;0
-2286;argile;positive;0;0;0;0;0;0
-2287;argot;negative;0;0;0;0;0;0
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-2293;aristocratie;positive;0;0;0;0;0;0
-2294;aristocratique;positive;0;0;0;0;0;0
-2295;arithmétique;positive;0;0;0;0;0;0
-2296;armada;negative;0;1;0;1;0;0
-2297;arme à feu;negative;0;1;0;1;0;0
-2298;arme à répétition;negative;0;0;0;0;0;0
-2299;armement;negative;0;1;0;1;0;0
-2300;armer;positive;0;0;0;0;0;0
-2301;arme;positive;0;0;0;0;0;0
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-2304;armurerie;negative;0;1;0;1;0;0
-2305;arnaque;negative;0;1;1;1;1;0
-2306;arnaquer;negative;0;1;1;1;1;0
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-2308;arpentage;positive;0;0;0;0;0;0
-2309;arpenteur;positive;0;0;0;0;0;0
-2310;arquer;negative;0;0;1;0;0;0
-2311;arracher;negative;0;1;0;1;1;0
-2312;arranger;positive;0;0;0;0;0;0
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-2314;arrestation;negative;0;0;0;1;0;0
-2315;arrêt;negative;0;1;1;1;1;0
-2316;arrêter;negative;0;1;0;0;1;0
-2317;arrêté;positive;0;0;0;0;0;0
-2318;arrhes;positive;0;0;0;0;0;0
-2319;arriération;negative;0;1;1;0;0;1
-2320;arrière goût;negative;0;0;0;0;0;1
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-2322;arrière plan;positive;0;0;0;0;0;0
-2323;arriérer;negative;0;0;0;0;0;0
-2324;arrimer;positive;0;0;0;0;0;0
-2325;arrivé|arrivée;positive;0;0;0;0;0;0
-2326;arriver;positive;0;0;0;0;0;0
-2327;arriver à;positive;1;0;0;0;0;0
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-2329;arrogance;negative;0;0;0;1;0;0
-2330;arrogant;negative;0;0;0;1;0;1
-2331;arrondir;positive;0;0;0;0;0;0
-2332;arroser;negative;0;1;0;0;0;0
-2333;arsenal;negative;0;1;0;1;0;0
-2334;arsenic;negative;0;1;1;0;0;1
-2335;art;positive;0;0;1;0;1;0
-2336;art du portrait;positive;0;0;0;0;0;0
-2337;art oratoire;positive;0;0;0;0;0;0
-2338;artère;positive;0;0;0;0;0;0
-2339;artériosclérose;negative;0;1;1;0;0;0
-2340;arthropode;positive;0;0;0;0;0;0
-2341;artichaut;positive;0;0;0;0;0;0
-2342;article;positive;0;0;0;0;0;0
-2343;articulation;positive;0;0;0;0;0;0
-2344;articuler;positive;0;0;0;0;0;0
-2345;artifice;negative;0;1;0;0;0;0
-2346;artillerie;negative;0;1;0;1;0;0
-2347;artilleur;positive;0;0;0;0;0;0
-2348;artisan;positive;0;0;0;0;0;0
-2349;artisanal;positive;0;0;0;0;0;0
-2350;artisanat;positive;0;0;0;0;0;0
-2351;artiste;positive;0;0;0;0;0;0
-2352;artistique;positive;0;0;0;0;0;0
-2353;as;positive;1;0;0;0;0;0
-2354;ascendance;positive;0;0;0;0;0;0
-2355;ascendant;negative;0;1;0;1;0;0
-2356;ascenseur;positive;0;0;0;0;0;0
-2357;ascension;positive;0;1;0;0;0;0
-2358;ascète;negative;0;0;1;0;0;0
-2359;ascétique;negative;0;0;1;0;0;0
-2360;aspartame;positive;0;0;0;0;0;0
-2361;aspect;positive;0;0;0;0;0;0
-2362;aspérité;negative;0;1;0;0;0;0
-2363;asphalte;negative;0;0;0;0;0;1
-2364;asphalter;negative;0;0;0;0;0;1
-2365;aspic;negative;0;1;0;0;0;1
-2366;aspirant;positive;0;0;0;0;0;0
-2367;aspirer;negative;0;0;0;0;0;0
-2368;assaillir;negative;0;1;0;0;0;0
-2369;assaillie;negative;0;1;0;0;0;0
-2370;assaillies;negative;0;1;0;0;0;0
-2371;assainir;positive;0;0;0;0;0;1
-2372;assaisonner;positive;0;0;0;0;0;0
-2373;assaisonnement;positive;0;0;0;0;0;0
-2374;assassin;negative;0;1;1;1;0;1
-2375;assassinat;negative;0;1;1;1;0;0
-2376;assassiner;negative;0;1;0;1;0;0
-2377;assaut;negative;0;1;0;1;1;0
-2378;assécher;negative;0;0;0;0;0;0
-2379;assemblage;positive;0;0;0;0;0;0
-2380;assembler;positive;0;0;0;0;0;0
-2381;assemblée plénier;positive;0;0;0;0;0;0
-2382;assemblée;positive;0;0;0;0;0;0
-2383;asséner;negative;0;1;0;1;1;0
-2384;asséner un coup;negative;0;0;0;1;0;0
-2385;assertion;positive;0;0;0;0;0;0
-2386;asservir;negative;0;0;0;1;0;0
-2387;asservissement;negative;0;1;1;1;0;1
-2388;assesseur;positive;0;0;0;0;0;0
-2389;assiduité;positive;0;0;0;0;0;0
-2390;assiette;positive;0;0;0;0;0;0
-2391;assimilation;positive;0;0;0;0;0;0
-2392;assimiler;positive;0;0;0;0;0;0
-2393;assister;positive;0;0;0;0;0;0
-2394;assister à;positive;0;0;0;0;0;0
-2395;association;positive;0;0;0;0;0;0
-2396;association caritatif;positive;0;0;0;0;0;0
-2397;associer;positive;0;0;0;0;0;0
-2398;assoiffer;negative;0;1;1;0;0;0
-2399;assoiffer de sang;negative;0;1;0;1;0;1
-2400;assombrir;negative;0;1;1;0;0;0
-2401;assommant;negative;0;0;1;0;0;0
-2402;assortiment;positive;0;0;0;0;0;0
-2403;assouplissement;positive;0;0;0;0;0;0
-2404;assourdissant;negative;0;1;0;0;1;0
-2405;assouvir;positive;1;0;0;0;0;0
-2406;assouvissement;positive;1;0;0;0;0;0
-2407;assujettir;negative;0;0;0;1;0;1
-2408;assujettissement;negative;0;1;1;1;0;1
-2409;assurance;positive;0;1;0;0;0;0
-2410;assurer;positive;0;0;0;0;0;0
-2411;assurément;positive;0;0;0;0;0;0
-2412;assureur;positive;0;0;0;0;0;0
-2413;astérisque;positive;0;0;0;0;0;0
-2414;astéroïde;negative;0;1;0;0;0;0
-2415;astigmatisme;negative;0;1;1;0;0;0
-2416;astral;positive;0;0;0;0;0;0
-2417;astraux;positive;0;0;0;0;0;0
-2418;astre;positive;0;0;0;0;0;0
-2419;astringent;negative;0;1;0;0;0;1
-2420;astrologie;positive;0;0;0;0;0;0
-2421;astrologue;positive;0;0;0;0;0;0
-2422;astronaute;positive;0;0;0;0;0;0
-2423;astronome;positive;0;0;0;0;0;0
-2424;astronomie;positive;0;0;0;0;0;0
-2425;asymétrie;negative;0;0;0;0;0;1
-2426;asymétrique;negative;0;1;1;0;0;1
-2427;asymptotique;positive;0;0;0;0;0;0
-2428;atelier;positive;0;0;0;0;0;0
-2429;atelier de reliure;positive;0;0;0;0;0;0
-2430;athée;negative;0;0;1;1;0;1
-2431;athéisme;negative;0;0;0;0;0;0
-2432;athérosclérose;negative;0;1;1;0;0;0
-2433;athlète;positive;0;0;0;0;0;0
-2434;athlétique;positive;0;0;0;0;0;0
-2435;athlétisme;positive;0;0;0;0;0;0
-2436;atlas;positive;0;0;0;0;0;0
-2437;atmosphère;positive;1;0;0;0;0;0
-2438;atmosphérique;positive;0;0;0;0;0;0
-2439;atoll;positive;0;0;0;0;0;0
-2440;atome;positive;0;0;0;0;0;0
-2441;atomique;negative;0;1;0;1;0;0
-2442;atout;positive;0;0;0;0;1;0
-2443;atrium;positive;0;0;0;0;0;0
-2444;atroce;negative;0;1;1;1;0;1
-2445;atrocement;negative;0;1;1;0;1;1
-2446;atrophie;negative;0;1;1;0;0;1
-2447;atrophier;negative;0;1;1;0;0;1
-2448;attacher;positive;0;0;0;0;0;0
-2449;attachant;positive;0;0;0;0;0;0
-2450;attachement;positive;0;0;0;0;0;0
-2451;attanchants;positive;0;0;0;0;0;0
-2452;attaque choc;negative;0;1;0;1;1;0
-2453;attaquer;negative;0;1;0;1;1;0
-2454;attaquer avec un hache;negative;0;1;0;1;1;0
-2455;atteindre;positive;0;0;0;0;0;0
-2456;attelage;positive;0;0;0;0;0;0
-2457;attelle;positive;0;0;0;0;0;0
-2458;attenir;positive;0;0;0;0;0;0
-2459;attenant;positive;0;0;0;0;0;0
-2460;attendre;negative;0;0;0;0;0;0
-2461;attentionner;positive;0;0;0;0;0;0
-2462;attentonnée;positive;0;0;0;0;0;0
-2463;atténuer;positive;0;0;0;0;0;0
-2464;atterrer;negative;0;1;0;0;1;1
-2465;atterrir;positive;1;0;0;0;0;0
-2466;atterrissage;positive;0;0;0;0;0;0
-2467;attestation;positive;0;0;0;0;0;0
-2468;attester;positive;0;0;0;0;0;0
-2469;attirail;positive;0;0;0;0;0;0
-2470;attirance;positive;0;0;0;0;0;0
-2471;attiser;positive;0;0;0;0;0;0
-2472;attraction;positive;0;0;0;0;0;0
-2473;attractivité;positive;0;0;0;0;0;0
-2474;attraire;positive;0;0;0;0;1;0
-2475;attraper;negative;0;1;0;0;1;0
-2476;attribuable;negative;0;1;1;0;0;0
-2477;attribuer;positive;0;0;0;0;1;0
-2478;attribut;positive;0;0;0;0;0;0
-2479;attribution;positive;0;0;0;0;0;0
-2480;attrition;negative;0;0;1;0;0;1
-2481;attrouper;positive;0;0;0;0;0;0
-2482;au beurre;positive;0;0;0;0;0;1
-2483;au bord du larme;negative;0;1;1;0;0;1
-2484;au centre;positive;0;0;0;0;0;0
-2485;au charme désuet;positive;0;0;0;0;0;0
-2486;au chocolat;positive;0;0;0;0;0;0
-2487;au coin du feu;positive;1;0;0;0;0;0
-2488;au curry;positive;0;0;0;0;0;0
-2489;au galop;positive;0;0;0;0;0;0
-2490;au goût de beurre;positive;0;0;0;0;0;1
-2491;au hasard;positive;0;0;0;0;1;0
-2492;au lait;positive;0;0;0;0;0;0
-2493;au microscope;positive;0;0;0;0;0;0
-2494;au milieu de;positive;0;0;0;0;0;0
-2495;au pouvoir;positive;0;0;0;0;0;0
-2496;au revoir;negative;0;0;1;0;0;0
-2497;au dessus;positive;0;0;0;0;0;0
-2498;aubaine;positive;0;0;0;0;1;0
-2499;aube;positive;0;0;0;0;1;0
-2500;auberge;positive;0;0;0;0;0;0
-2501;aubergiste;positive;0;0;0;0;0;0
-2502;audacieux;positive;0;0;0;0;0;0
-2503;audibilité;positive;0;0;0;0;0;0
-2504;audible;positive;0;0;0;0;0;0
-2505;audit;positive;0;0;0;0;0;0
-2506;auditer;positive;0;0;0;0;0;0
-2507;auditionner;positive;0;0;0;0;0;0
-2508;auditorium;positive;0;0;0;0;0;0
-2509;auge;positive;0;0;0;0;0;0
-2510;augurer;positive;0;0;0;0;0;0
-2511;auguste;positive;1;0;0;0;0;0
-2512;aumônier;positive;0;0;0;0;0;0
-2513;auparavant;positive;0;0;0;0;0;0
-2514;aura;positive;1;0;0;0;0;0
-2515;auréole;positive;0;0;0;0;0;0
-2516;aurore;positive;0;0;0;0;1;0
-2517;auspice;positive;0;0;0;0;0;0
-2518;austère;negative;0;1;1;0;0;0
-2519;austérité;negative;0;1;1;0;0;0
-2520;autel;positive;0;0;0;0;0;0
-2521;authenticité;positive;0;0;0;0;0;0
-2522;authentification;positive;0;0;0;0;0;0
-2523;authentifier;positive;0;0;0;0;0;0
-2524;authentique;positive;0;0;0;0;0;0
-2525;auto;positive;0;0;0;0;0;0
-2526;autobiographie;positive;0;0;0;0;0;0
-2527;autobus;positive;0;0;0;0;0;0
-2528;autochtone;positive;0;0;0;0;0;0
-2529;autocratique;negative;0;1;1;1;0;0
-2530;autographe;positive;0;0;0;0;0;0
-2531;automatique;positive;0;0;0;0;0;0
-2532;automne;negative;0;1;1;0;0;0
-2533;automobile;positive;0;0;0;0;0;0
-2534;autopsie;negative;0;1;1;0;0;1
-2535;autoriser;positive;0;0;0;0;0;0
-2536;autorisation;positive;0;0;0;0;0;0
-2537;autoritaire;negative;0;0;0;1;0;0
-2538;autorité;positive;0;0;0;0;0;0
-2539;autoroute;positive;0;0;0;0;0;0
-2540;autosuffisance;positive;0;0;0;0;0;0
-2541;autruche;negative;0;1;0;0;0;0
-2542;auvent;positive;0;0;0;0;0;0
-2543;au herbe;positive;0;0;0;0;0;0
-2544;au idée arrêté;negative;0;0;0;1;0;0
-2545;au m?urs léger;negative;0;0;0;0;0;1
-2546;au multiple fonction;positive;0;0;0;0;0;0
-2547;au pomme;positive;0;0;0;0;0;0
-2548;aval;positive;0;0;0;0;0;0
-2549;avalanche;negative;0;1;1;0;1;0
-2550;avaler;positive;0;1;0;0;1;0
-2551;avance;positive;0;1;0;0;1;0
-2552;avancer;positive;1;0;0;0;0;0
-2553;avancement;positive;0;0;1;0;0;0
-2554;avancer au ralenti;negative;0;0;1;0;0;1
-2555;avancer lentement;negative;0;0;1;0;0;1
-2556;avancer petit à petit;positive;0;0;0;0;0;0
-2557;avant de;negative;0;0;0;0;0;0
-2558;avant bras;positive;0;0;0;1;0;0
-2559;avant garde;positive;0;0;0;0;0;0
-2560;avant poste;negative;0;1;0;0;0;0
-2561;avant propos;positive;0;0;0;0;0;0
-2562;avantage;positive;0;0;0;0;0;0
-2563;avantager;positive;1;0;0;0;0;0
-2564;avare;negative;0;1;1;1;0;1
-2565;avarice;negative;0;0;0;1;0;1
-2566;avatar;positive;0;0;0;0;0;0
-2567;avec attention;positive;0;1;0;0;0;0
-2568;avec crainte;negative;0;1;1;0;1;0
-2569;avec de gros morceau;negative;0;0;0;0;0;0
-2570;avec désinvolture;positive;0;0;0;0;0;0
-2571;avec le même intensité;positive;0;0;0;0;0;0
-2572;avec lassitude;negative;0;0;1;0;0;0
-2573;avec modération;positive;0;0;0;0;0;0
-2574;avec parcimonie;positive;0;0;0;0;0;0
-2575;avec précaution;positive;0;1;0;0;0;0
-2576;avec prudence;positive;0;1;0;0;0;0
-2577;avec quoi;positive;0;0;0;0;0;0
-2578;avec sérieux;positive;0;0;0;0;0;0
-2579;avec un grand raffinement;positive;1;0;0;0;0;0
-2580;avec violence;negative;0;1;1;1;0;1
-2581;avenant;positive;0;0;0;0;0;0
-2582;avènement;positive;0;0;0;0;0;0
-2583;avenir;positive;0;0;0;0;0;0
-2584;avent;positive;0;0;0;0;0;0
-2585;avenue;positive;0;0;0;0;0;0
-2586;avérer;positive;0;0;0;0;0;0
-2587;avers;positive;0;0;0;0;0;0
-2588;aversion;negative;0;1;0;1;0;1
-2589;avertir;positive;0;1;0;0;1;0
-2590;aveu;negative;0;1;1;0;1;0
-2591;aveugler;negative;0;1;0;0;1;0
-2592;aveuglant;negative;0;1;0;0;1;0
-2593;aveugle;negative;0;1;0;0;0;0
-2594;aveuglément;negative;0;1;1;0;0;0
-2595;aviateur;positive;0;0;0;0;0;0
-2596;aviation;positive;0;0;0;0;0;0
-2597;avide;negative;0;0;0;1;0;1
-2598;avidité;negative;0;0;0;1;0;1
-2599;avion;positive;0;0;0;0;0;0
-2600;aviron;negative;0;0;0;0;0;0
-2601;aviser;positive;0;0;0;0;0;0
-2602;avoir confiance;positive;0;0;0;0;0;0
-2603;avoir du difficulté;negative;0;1;1;0;0;0
-2604;avoir en horreur;negative;0;1;0;1;0;1
-2605;avoir en stock;positive;0;0;0;0;0;0
-2606;avoir le hoquet;negative;0;1;0;0;1;0
-2607;avoir le moyen;positive;0;0;0;0;0;0
-2608;avoir peur;negative;0;1;1;0;1;0
-2609;avoir pitié;negative;0;0;1;0;0;0
-2610;avoir pour but;positive;0;0;0;0;0;0
-2611;avoir recours;positive;0;0;0;0;0;0
-2612;avoir un compte;positive;0;0;0;0;0;0
-2613;avoir un handicap;negative;0;0;1;0;0;0
-2614;avoir un mouvement de recul;negative;0;1;1;0;1;1
-2615;avoir un tic;negative;0;1;0;0;1;0
-2616;avortement;negative;0;1;1;0;0;1
-2617;avorter;negative;0;1;1;1;0;0
-2618;axe;positive;0;0;0;0;0;0
-2619;axe central;positive;0;0;0;0;0;0
-2620;axe routier;positive;0;0;0;0;0;0
-2621;axial;positive;0;0;0;0;0;0
-2622;axiomatique;positive;0;0;0;0;0;0
-2623;axiome;positive;0;0;0;0;0;0
-2624;azimut;positive;0;0;0;0;0;0
-2625;azur;positive;0;0;0;0;0;0
-2626;azurer;positive;0;0;0;0;0;0
-2627;baïonnette;negative;0;1;0;1;0;0
-2628;babillage;negative;0;0;0;0;0;0
-2629;babiller;negative;0;0;0;0;0;0
-2630;babine;negative;0;0;0;0;0;0
-2631;babouin;negative;0;0;0;0;0;1
-2632;baby sitter;positive;0;0;0;0;0;0
-2633;bac;negative;0;0;0;0;0;0
-2634;baccalauréat;positive;0;0;0;0;0;0
-2635;baccarat;positive;0;0;0;0;1;0
-2636;bachelier bachelier;positive;0;0;0;0;0;0
-2637;backgammon;positive;0;0;0;0;1;0
-2638;bâcler;negative;0;0;0;0;0;1
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-2694;bande annonce;positive;0;0;0;0;0;0
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-2700;banlieue;negative;0;0;1;0;0;0
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-2721;bardeau;positive;0;0;0;0;0;0
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-2736;bas de gamme;negative;0;0;0;0;0;0
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-2805;bercer;positive;0;0;0;0;0;0
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-2816;bétail;positive;0;0;0;0;0;0
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-2818;béton;positive;0;0;0;0;0;0
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-2821;beuverie;negative;0;0;0;0;0;0
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-2824;bibliographie;positive;0;0;0;0;0;0
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-2864;bis;positive;1;0;0;0;0;0
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-2989;bouilloire;positive;0;0;0;0;0;0
-2990;bouillon;positive;0;0;0;0;0;0
-2991;bouillonner;negative;0;0;0;1;0;0
-2992;boulangerie;positive;0;0;0;0;0;0
-2993;boule;positive;0;0;0;0;0;0
-2994;boule de feu;positive;0;0;0;0;0;0
-2995;boule de neige;positive;0;0;0;0;0;0
-2996;bouleau;negative;0;1;0;1;0;1
-2997;bouledogue;positive;0;0;0;0;0;0
-2998;boulet;positive;0;0;0;0;0;0
-2999;boulette;positive;0;0;0;0;0;0
-3000;bouleversant;negative;0;1;1;1;0;0
-3001;bouleversement;negative;0;1;1;1;1;0
-3002;boulon;positive;0;0;0;0;0;0
-3003;boulonner;positive;0;0;0;0;0;0
-3004;bouquet;positive;0;0;0;0;0;0
-3005;bourbier;negative;0;1;1;0;0;1
-3006;bourdonner;negative;0;0;0;0;0;0
-3007;bourdonnement;negative;0;1;0;0;1;0
-3008;bourgeon;positive;0;0;0;0;0;0
-3009;bourreau;negative;0;1;1;1;0;0
-3010;boursoufler;negative;0;0;0;0;0;1
-3011;bousculade;negative;0;1;0;1;1;0
-3012;bousculer;negative;0;1;0;1;1;0
-3013;bouse;negative;0;0;0;0;0;1
-3014;boussole;positive;0;0;0;0;0;0
-3015;boutade;positive;0;0;0;0;1;0
-3016;bouteille;positive;0;0;0;0;0;0
-3017;boutique;positive;0;0;0;0;0;0
-3018;boutonner;positive;0;0;0;0;0;0
-3019;bovin;negative;0;0;0;0;0;1
-3020;box;positive;0;0;0;0;0;0
-3021;boxe;negative;0;0;0;1;0;0
-3022;boxer;positive;0;0;0;0;0;0
-3023;boxeur;positive;0;0;0;1;0;0
-3024;boyau;negative;0;0;0;0;0;1
-3025;boycott;negative;0;0;0;1;0;1
-3026;boycotter;negative;0;0;0;1;0;1
-3027;brûlé;negative;0;0;0;0;0;1
-3028;brûlée;negative;0;0;0;0;0;1
-3029;bracelet;positive;0;0;0;0;0;0
-3030;brachial;positive;0;0;0;0;0;0
-3031;braconnage;negative;0;1;1;1;0;1
-3032;brailler;negative;0;0;0;1;1;0
-3033;brainstorming;positive;0;0;0;0;0;0
-3034;braise;negative;0;1;0;0;0;0
-3035;brancard;negative;0;1;1;0;0;0
-3036;branche;positive;0;0;0;0;0;0
-3037;brancher;positive;0;0;0;0;0;0
-3038;branchie;positive;0;0;0;0;0;0
-3039;brandy;positive;0;0;0;0;0;0
-3040;branhcée;positive;0;0;0;0;0;0
-3041;branler;negative;0;1;0;0;1;0
-3042;branlant;negative;0;1;0;0;0;0
-3043;braquage;negative;0;1;1;1;0;1
-3044;braquer;negative;0;1;0;0;0;0
-3045;bras;positive;0;0;0;0;0;0
-3046;bras droit;positive;0;0;0;0;0;0
-3047;brasier;negative;0;1;0;1;0;0
-3048;brassage;positive;0;0;0;0;0;0
-3049;brasser;positive;0;0;0;0;0;0
-3050;bravade;negative;0;0;0;0;0;0
-3051;brave;positive;0;0;0;0;0;0
-3052;bravoure;positive;0;0;0;0;0;0
-3053;brebis;positive;0;0;0;0;0;0
-3054;brèche;negative;0;1;0;0;0;0
-3055;bredouillement;negative;0;1;0;0;0;0
-3056;bredouiller;negative;0;1;0;1;0;0
-3057;bref;positive;0;0;0;0;0;0
-3058;bretelle;positive;0;0;0;0;0;0
-3059;brevet;positive;0;0;0;0;0;0
-3060;breveter;positive;0;0;0;0;0;0
-3061;bribe;positive;0;0;0;0;0;0
-3062;brick;negative;0;1;1;0;0;0
-3063;bricoler;positive;0;0;0;0;0;0
-3064;brider;negative;0;1;1;0;0;0
-3065;brièvement;positive;0;0;0;0;0;0
-3066;brigade;positive;0;1;0;0;0;0
-3067;brin;positive;0;0;0;0;0;0
-3068;brindille;negative;0;0;0;0;0;0
-3069;brique;positive;0;0;0;0;0;0
-3070;brise;negative;0;1;1;0;0;0
-3071;briser;negative;0;1;1;1;1;0
-3072;briseur;negative;0;1;1;0;0;0
-3073;brocart;positive;0;0;0;0;0;0
-3074;brochet;negative;0;1;0;0;0;0
-3075;brochette;negative;0;1;0;0;0;0
-3076;brochure;positive;0;0;0;0;0;0
-3077;broder;positive;0;0;0;0;0;0
-3078;broderie;positive;0;0;0;0;0;0
-3079;broderie perlé;positive;0;0;0;0;0;0
-3080;bronco;negative;0;1;0;0;1;0
-3081;bronzage;positive;0;0;0;0;0;0
-3082;bronze;positive;0;0;0;0;0;0
-3083;bronzer;positive;0;0;0;0;0;0
-3084;brosse;positive;0;0;0;0;0;0
-3085;brouette;positive;0;0;0;0;0;1
-3086;brouillage;negative;0;1;0;1;0;0
-3087;brouillard;negative;0;1;1;0;0;0
-3088;brouillon;positive;0;0;0;0;0;0
-3089;broussaille;negative;0;0;0;0;0;1
-3090;brousse;positive;0;0;0;0;0;0
-3091;broutille;negative;0;0;0;0;0;1
-3092;broyer du noir;negative;0;0;1;0;0;0
-3093;broyer;negative;0;1;1;1;0;1
-3094;broyeur;negative;0;1;0;0;0;0
-3095;bruine;negative;0;0;1;0;0;1
-3096;bruiner;negative;0;0;1;0;0;1
-3097;bruissement;negative;0;1;0;0;1;0
-3098;bruir|bruire de tambour;positive;0;0;0;0;0;0
-3099;bruir|bruire métallique;negative;0;1;0;0;1;0
-3100;bruir|bruire sec;negative;0;1;0;0;1;0
-3101;bruir|bruire sourd;negative;0;1;1;0;1;0
-3102;brûler;negative;0;1;0;1;0;0
-3103;brûlant;negative;0;1;0;1;0;0
-3104;brûleur;negative;0;1;0;1;0;0
-3105;brûlure;negative;0;1;0;0;0;0
-3106;brûlure d estomac;negative;0;0;1;0;0;1
-3107;brume;negative;0;1;1;0;0;0
-3108;brumer;negative;0;1;1;0;0;0
-3109;brun;positive;0;0;0;0;0;0
-3110;brun grisâtre;negative;0;0;1;0;0;1
-3111;brun|brune;positive;0;0;0;0;0;0
-3112;brunet;positive;0;0;0;0;0;0
-3113;brunir;negative;0;0;0;0;0;0
-3114;brusque;negative;0;1;0;1;1;0
-3115;brusquement;negative;0;1;0;0;1;0
-3116;brusquer;negative;0;1;0;1;1;0
-3117;brut;negative;0;1;1;1;0;1
-3118;brutal;negative;0;1;0;1;0;1
-3119;brutaliser;negative;0;1;0;1;0;0
-3120;brutalité;negative;0;1;1;1;0;1
-3121;brute;negative;0;1;1;1;0;1
-3122;bruyant;negative;0;1;0;1;1;0
-3123;buccal;positive;0;0;0;0;0;0
-3124;bûche;positive;0;0;0;0;0;0
-3125;budget;positive;0;0;0;0;0;0
-3126;budgétiser;positive;0;0;0;0;0;0
-3127;buffet;positive;0;0;0;1;0;0
-3128;buffle;negative;0;1;0;0;0;0
-3129;buisson;positive;0;0;0;0;0;0
-3130;bulbe;negative;0;0;0;0;0;1
-3131;bulbeux;negative;0;0;0;0;0;0
-3132;bulldozer;negative;0;1;0;1;0;0
-3133;bulle;positive;0;0;0;0;0;0
-3134;bulletin;positive;0;0;0;0;0;0
-3135;bungalow;positive;0;0;0;0;0;0
-3136;bunker;negative;0;1;0;0;0;0
-3137;bureau;positive;0;0;0;0;0;0
-3138;bureaucrate;negative;0;0;0;0;0;1
-3139;bureaucratie;negative;0;0;0;0;0;0
-3140;burin;negative;0;1;0;1;0;0
-3141;buriner;negative;0;0;0;1;0;0
-3142;burlesque;positive;0;0;0;0;1;0
-3143;buste;positive;0;0;0;0;0;0
-3144;but;positive;0;0;0;0;0;0
-3145;butane;negative;0;0;0;0;0;1
-3146;butyreux;positive;0;0;0;0;0;1
-3147;bye;negative;0;0;1;0;0;0
-3148;çà et là;negative;0;0;0;0;0;0
-3149;cabale;negative;0;1;0;0;0;0
-3150;cabane;positive;0;0;1;0;0;1
-3151;cabane pour enfant;positive;1;0;0;0;0;0
-3152;cabaret;positive;0;0;0;0;1;0
-3153;cabillaud;negative;0;0;0;0;0;0
-3154;cabine;positive;0;0;0;0;0;0
-3155;cabine de luxe;positive;0;0;0;0;0;0
-3156;cabine de pilotage;positive;0;0;0;0;0;0
-3157;cabine particulier;positive;0;0;0;0;0;0
-3158;cabinet;positive;0;0;0;0;0;0
-3159;câbler;positive;0;0;0;0;0;0
-3160;cabosser;negative;0;1;1;0;0;0
-3161;cabriole;positive;1;0;0;0;0;0
-3162;cabriolet;positive;0;0;0;0;0;0
-3163;caca;negative;0;0;0;0;0;1
-3164;cacao;positive;0;0;0;0;0;0
-3165;cache;negative;0;1;0;0;0;0
-3166;cacher;negative;0;1;1;0;1;0
-3167;cachemire;positive;0;0;0;0;0;0
-3168;cachot;negative;0;1;1;0;0;0
-3169;cacophonie;negative;0;1;0;1;0;1
-3170;cadavre;negative;0;1;1;0;1;1
-3171;caddie;positive;0;0;0;0;0;0
-3172;cadeau;positive;0;0;0;0;1;0
-3173;cadenas;negative;0;1;0;0;0;0
-3174;cadenasser;negative;0;1;0;0;0;0
-3175;cadence;positive;0;0;0;0;0;0
-3176;cadencer;positive;0;0;1;1;0;0
-3177;cadet;positive;0;0;0;0;0;0
-3178;cadran;positive;0;0;0;0;0;0
-3179;cadran solaire;positive;0;0;0;0;0;0
-3180;cadre;positive;0;0;0;0;0;0
-3181;caduc;negative;0;0;1;0;0;1
-3182;café;positive;0;0;0;0;0;0
-3183;café restaurant;positive;0;0;0;0;0;0
-3184;cafouillage;negative;0;0;0;0;0;0
-3185;cage;negative;0;1;1;0;0;0
-3186;cageot;positive;0;0;0;0;0;0
-3187;cahier;positive;0;0;0;0;0;0
-3188;caille;negative;0;1;0;0;0;0
-3189;cailler;negative;0;0;0;0;0;1
-3190;caillot;negative;0;1;0;0;0;1
-3191;caillou;positive;0;0;0;1;0;0
-3192;cairn;positive;0;0;0;0;0;0
-3193;caisse;positive;0;0;0;0;0;0
-3194;calamiter;negative;0;1;1;0;0;0
-3195;calandre;negative;0;0;0;0;0;0
-3196;calcaire;negative;0;0;0;0;0;1
-3197;calciner;negative;0;1;0;0;0;1
-3198;calculable;positive;0;0;0;0;0;0
-3199;calculateur;positive;0;0;0;0;0;0
-3200;calculateur|calculatrice;positive;0;0;0;0;0;0
-3201;calculer;positive;0;0;0;0;0;0
-3202;calculette;positive;0;0;0;0;0;0
-3203;calembour;positive;1;0;0;0;0;0
-3204;calendrier;positive;0;0;0;0;0;0
-3205;calibre;positive;0;0;0;0;0;0
-3206;calibrer;positive;0;0;0;0;0;0
-3207;calice;positive;0;0;0;0;0;0
-3208;calicot;positive;0;0;0;0;0;0
-3209;câlin;positive;0;0;0;0;0;0
-3210;câliner;positive;0;0;0;0;0;0
-3211;calligraphie;positive;0;0;0;0;0;0
-3212;calmement;positive;1;0;0;0;0;0
-3213;calomnier;negative;0;1;1;1;0;1
-3214;calorie;negative;0;0;0;0;0;0
-3215;calorimètre;positive;0;0;0;0;0;0
-3216;calorique;negative;0;0;0;0;0;0
-3217;calquer;positive;0;0;0;0;0;0
-3218;calque;positive;0;0;0;0;0;0
-3219;camarade;positive;0;0;0;0;0;0
-3220;camarade de classe;positive;0;0;0;0;0;0
-3221;camarade de jeu;positive;0;0;0;0;0;0
-3222;camaraderie;positive;0;0;0;0;0;0
-3223;cambriolage;negative;0;1;1;0;1;0
-3224;cambrioler;negative;0;1;1;1;0;1
-3225;cambrure;positive;0;0;0;0;0;0
-3226;came;positive;0;0;0;0;0;0
-3227;camer;positive;0;0;0;0;0;0
-3228;caméléon;positive;0;0;0;0;0;0
-3229;camelote;negative;0;0;1;0;0;1
-3230;cameraman;positive;0;0;0;0;0;0
-3231;caméraman;positive;0;0;0;0;0;0
-3232;camion;positive;0;0;0;0;0;0
-3233;camionnette;positive;0;0;0;0;0;0
-3234;camouflage;negative;0;0;0;0;1;0
-3235;camoufler;negative;0;0;0;0;1;0
-3236;camp;positive;0;0;0;0;0;0
-3237;campagne;positive;0;1;0;1;0;0
-3238;campement;positive;0;0;0;0;0;0
-3239;camper;positive;0;0;0;0;0;0
-3240;camping;positive;0;0;0;0;0;0
-3241;campus;positive;0;0;0;0;0;0
-3242;canalisation;negative;0;0;0;0;0;1
-3243;canapé;positive;0;0;1;0;0;0
-3244;canard;positive;0;0;0;0;0;0
-3245;canard mâle;positive;0;0;0;0;0;0
-3246;canari;positive;0;0;0;0;0;0
-3247;cancer;negative;0;1;1;1;0;1
-3248;candidature;positive;0;1;0;1;0;0
-3249;caniche;positive;0;0;0;0;0;0
-3250;canidé;positive;0;0;0;0;0;0
-3251;canin;positive;0;0;0;0;0;0
-3252;canine;positive;0;0;0;0;0;0
-3253;caniveau;negative;0;0;0;0;0;1
-3254;canneler;positive;0;0;0;0;0;0
-3255;cannelle;positive;0;0;0;0;0;0
-3256;cannibale;negative;0;1;0;0;0;1
-3257;cannibalisme;negative;0;1;0;0;0;1
-3258;canoë;positive;0;0;0;0;0;0
-3259;canon;negative;0;1;0;1;0;0
-3260;canonique;positive;0;0;0;0;0;0
-3261;canot de sauvetage;positive;0;0;0;0;0;0
-3262;canotage;positive;1;0;0;0;0;0
-3263;cantilever;positive;0;0;0;0;0;0
-3264;cantine;positive;0;0;0;0;0;0
-3265;cantique;positive;0;0;1;0;0;0
-3266;canton;positive;0;0;0;0;0;0
-3267;cantonnement;negative;0;0;1;0;0;0
-3268;canular;negative;0;0;1;1;1;1
-3269;caoutchouc;negative;0;0;0;0;0;0
-3270;cap;positive;0;0;0;0;0;0
-3271;capable;positive;0;0;0;0;0;0
-3272;capacité;positive;0;0;0;0;0;0
-3273;capillaire;positive;0;0;0;0;0;0
-3274;capitaine;positive;0;0;0;0;0;0
-3275;capitale;positive;0;0;0;0;0;0
-3276;capitaliste;negative;0;0;0;0;0;0
-3277;capitation;negative;0;0;0;0;0;0
-3278;capital propre;positive;0;0;0;0;0;0
-3279;capitole;positive;0;0;0;0;0;0
-3280;capitulation;negative;0;1;1;0;1;0
-3281;capituler;negative;0;1;1;0;0;0
-3282;caporal;positive;0;0;0;0;0;0
-3283;capot;positive;0;1;0;1;0;1
-3284;caprice;negative;0;0;0;1;1;0
-3285;captivant;positive;0;0;0;0;0;0
-3286;captivité;negative;0;1;1;0;0;0
-3287;capture;negative;0;1;0;1;1;0
-3288;capturer;negative;0;1;0;1;1;0
-3289;capuche;positive;0;1;0;1;0;1
-3290;capuchon;positive;0;0;0;0;0;0
-3291;caraco;positive;0;0;0;0;0;0
-3292;caractère aléatoire;negative;0;1;0;0;1;0
-3293;caractère raisonnable;positive;0;0;0;0;0;0
-3294;caractériser;positive;0;0;0;0;0;0
-3295;caractéristique;positive;0;0;0;0;0;0
-3296;carafe;positive;0;0;0;0;0;0
-3297;caramel;positive;0;0;0;0;0;0
-3298;carapace;positive;0;0;0;0;0;0
-3299;carat;positive;0;0;0;0;0;0
-3300;caravane;positive;0;0;0;0;0;0
-3301;carbone;negative;0;0;0;0;0;0
-3302;carboniser;negative;0;1;0;1;0;0
-3303;carburant;positive;0;0;0;0;0;0
-3304;carcasse;negative;0;1;1;0;0;1
-3305;carcinome;negative;0;1;1;0;0;0
-3306;cardigan;positive;0;0;0;0;0;0
-3307;cardinal;positive;0;0;0;0;0;0
-3308;cardiomyopathie;negative;0;1;1;0;0;0
-3309;caresse;positive;1;0;0;0;0;0
-3310;caresser;positive;1;0;0;0;0;0
-3311;carex;positive;0;0;0;0;0;0
-3312;cargaison;positive;0;0;0;0;0;0
-3313;caribou;positive;0;0;0;0;0;0
-3314;caricature;negative;0;0;0;0;0;0
-3315;caricaturer;negative;0;0;0;0;0;0
-3316;carie;negative;0;0;0;0;0;1
-3317;carillon;positive;1;0;0;0;0;0
-3318;carillonnement;positive;0;0;0;0;1;0
-3319;carillonner;positive;1;0;0;0;0;0
-3320;carlin;positive;0;0;0;0;0;0
-3321;carnation;positive;0;0;0;0;0;0
-3322;carnet;positive;0;0;0;0;0;0
-3323;carnivore;negative;0;1;0;0;0;0
-3324;carquois;negative;0;1;0;0;0;0
-3325;carrer;positive;0;0;0;0;0;0
-3326;carreau;positive;0;0;0;0;0;0
-3327;carrelage;positive;0;0;0;0;0;0
-3328;carreler;positive;0;0;0;0;0;0
-3329;carrément;positive;0;0;0;0;0;0
-3330;carrossage;positive;0;0;0;0;0;0
-3331;carrousel;positive;1;0;0;0;0;0
-3332;carrure;positive;0;0;0;0;0;0
-3333;cartable;positive;0;0;0;0;0;0
-3334;carte;positive;0;0;0;0;0;0
-3335;cartel;negative;0;1;0;1;0;0
-3336;carter;negative;0;0;0;0;0;1
-3337;cartilage;negative;0;0;0;0;0;1
-3338;cartographie;positive;0;0;0;0;0;0
-3339;cartographique;positive;0;0;0;0;0;0
-3340;carton;positive;0;0;0;0;0;0
-3341;cartouche;negative;0;1;0;0;0;0
-3342;caséine;positive;0;0;0;0;0;0
-3343;caserne;positive;0;0;0;0;0;0
-3344;casier;positive;0;0;0;0;0;0
-3345;casino;positive;0;0;0;0;1;0
-3346;casque;positive;0;1;0;0;0;0
-3347;casquette;positive;0;0;0;0;0;0
-3348;cassable;negative;0;1;0;0;0;0
-3349;casser;negative;0;1;0;0;1;0
-3350;cassant;negative;0;1;0;0;1;0
-3351;casse croûte;positive;0;0;0;0;0;0
-3352;casse pied;negative;0;0;0;1;0;0
-3353;casse tête;positive;0;0;0;0;0;0
-3354;casserole;positive;0;0;0;0;0;0
-3355;cassette;positive;0;0;0;0;0;0
-3356;cassette vidéo;positive;0;0;0;0;0;0
-3357;caste;negative;0;0;0;0;0;0
-3358;castrer;negative;0;1;1;0;0;0
-3359;catabolisme;positive;0;0;0;0;0;0
-3360;cataclysme;negative;0;1;1;1;1;0
-3361;cataire;negative;0;0;0;0;0;1
-3362;catalogue;positive;0;0;0;0;0;0
-3363;catalyse;positive;0;0;0;0;0;0
-3364;catalytique;positive;0;0;0;0;0;0
-3365;catamaran;positive;0;0;0;0;0;0
-3366;catapulte;negative;0;0;0;1;0;0
-3367;catapulter;negative;0;0;0;1;0;0
-3368;cataracte;negative;0;1;1;0;0;0
-3369;catéchisme;positive;0;0;0;0;0;1
-3370;catégorie;positive;0;0;0;0;0;0
-3371;cathartique;positive;0;0;0;0;0;0
-3372;cathédrale;positive;0;0;0;0;0;0
-3373;cathéter;negative;0;1;0;0;0;0
-3374;catholique;positive;0;0;0;0;0;0
-3375;cauchemar;negative;0;1;1;0;0;0
-3376;caudal;negative;0;0;0;0;0;1
-3377;causal;negative;0;0;1;0;0;0
-3378;causalité;negative;0;0;1;0;0;0
-3379;causation;negative;0;0;1;0;0;0
-3380;causer;negative;0;0;1;0;0;0
-3381;caution;positive;0;0;0;0;0;0
-3382;cavaler;negative;0;1;0;0;0;0
-3383;cavalerie;positive;0;0;0;0;0;0
-3384;caveau;positive;0;0;0;0;0;0
-3385;caverne;negative;0;1;1;0;0;0
-3386;caverneux;negative;0;1;1;0;0;0
-3387;cavité;negative;0;1;0;0;0;0
-3388;cayenne;positive;0;0;0;0;0;0
-3389;ce qui précéder;positive;0;0;0;0;0;0
-3390;cécité;negative;0;1;1;0;0;0
-3391;céder;positive;0;0;0;0;0;0
-3392;ceinture;negative;0;1;1;1;0;0
-3393;ceinturer;negative;0;1;1;0;0;0
-3394;célébrant;positive;1;0;0;0;0;0
-3395;célébration;positive;0;0;0;0;1;0
-3396;célèbre;positive;0;0;0;0;0;0
-3397;célébrer;positive;1;0;0;0;0;0
-3398;célibat;negative;0;0;0;0;0;0
-3399;célibataire;negative;0;0;0;0;0;0
-3400;cellulaire;positive;0;0;0;0;0;0
-3401;cellule;negative;0;0;0;0;0;0
-3402;celluloïde;positive;0;0;0;0;0;0
-3403;cendre;negative;0;1;1;0;0;1
-3404;cendrer;negative;0;1;0;0;0;0
-3405;censeur;negative;0;1;0;1;0;1
-3406;censurer;negative;0;1;0;1;0;1
-3407;cent;positive;0;0;0;0;0;0
-3408;cent douze livre;positive;0;0;0;0;0;0
-3409;cent livre;positive;0;0;0;0;0;0
-3410;centaine;positive;0;0;0;0;0;0
-3411;centenaire;positive;1;0;0;0;0;0
-3412;centième;positive;0;0;0;0;0;0
-3413;centime;positive;0;0;0;0;0;0
-3414;centimètre;positive;0;0;0;0;0;0
-3415;central;positive;0;0;0;0;0;0
-3416;centralement;positive;0;0;0;0;0;0
-3417;centrale;positive;0;0;0;0;0;0
-3418;centralisation;positive;0;0;0;0;0;0
-3419;centraliser;positive;0;0;0;0;0;0
-3420;centralité;positive;0;0;0;0;0;0
-3421;centre;positive;0;0;0;0;0;0
-3422;centre commercial;positive;0;0;0;0;0;0
-3423;centre de détention;negative;0;1;1;1;0;0
-3424;centrer;positive;0;0;0;0;0;0
-3425;centrifuge;positive;0;0;0;0;0;0
-3426;centrifuger;positive;0;0;0;0;0;0
-3427;centrifugeuse;positive;0;0;0;0;0;0
-3428;centurion;positive;0;0;0;0;0;0
-3429;céphalée;negative;0;0;1;0;0;0
-3430;céramique;positive;0;0;0;0;0;0
-3431;cerceau;positive;0;0;0;0;0;0
-3432;cercle;positive;0;0;0;0;0;0
-3433;cercueil;negative;0;1;1;0;0;0
-3434;céréale;positive;0;0;0;0;0;0
-3435;céréalier;positive;0;0;0;0;0;0
-3436;cérébral;positive;0;0;0;0;0;0
-3437;cérémonial;positive;0;0;0;0;0;0
-3438;cérémonie;positive;0;0;0;0;1;0
-3439;cerf;positive;0;0;0;0;0;0
-3440;cerf volant;positive;0;0;0;0;0;1
-3441;cerise;positive;0;0;0;0;0;0
-3442;cerner;negative;0;1;0;0;0;0
-3443;certificat;positive;0;0;0;0;0;0
-3444;certifier;positive;0;0;0;0;0;0
-3445;certitude;positive;0;0;0;0;0;0
-3446;cérumen;negative;0;0;0;0;0;1
-3447;cerveau;positive;0;0;0;0;0;0
-3448;cervelle;positive;0;0;0;0;0;0
-3449;cessation;negative;0;0;1;0;1;0
-3450;cession;positive;0;0;0;0;0;0
-3451;cessionnaire;positive;0;0;0;0;0;0
-3452;ceuillie;positive;1;0;0;0;0;0
-3453;chacunes;positive;0;0;0;0;0;0
-3454;chacuns;positive;0;0;0;0;0;0
-3455;chagrin;negative;0;1;1;0;0;0
-3456;chaîne;negative;0;1;1;1;0;1
-3457;chair;negative;0;0;0;0;0;1
-3458;chaire;positive;0;0;0;0;0;0
-3459;chaise;positive;0;0;0;0;0;0
-3460;châle;positive;0;0;0;0;0;0
-3461;chalet;positive;0;0;0;0;0;0
-3462;chaleur;positive;0;0;0;0;0;0
-3463;chaleureux;positive;0;0;0;0;0;0
-3464;chaleureusement;positive;1;0;0;0;0;0
-3465;challenge;negative;0;1;0;1;0;0
-3466;chalut;positive;0;0;0;0;0;0
-3467;chalutier;positive;0;0;0;0;0;0
-3468;chamaillerie;negative;0;0;0;1;0;1
-3469;chaman;negative;0;1;0;0;0;0
-3470;chambre;positive;0;0;0;0;0;0
-3471;chambre à coucher;positive;0;0;0;0;0;0
-3472;chambre fort;positive;0;0;0;0;0;0
-3473;chambre libre;positive;1;0;0;0;0;0
-3474;chameau;positive;0;0;0;0;0;0
-3475;champ;positive;0;0;0;0;0;0
-3476;champ de bataille;negative;0;1;1;1;0;0
-3477;champagne;positive;0;0;0;0;0;0
-3478;champion champion;positive;1;0;0;0;0;0
-3479;chance;positive;0;0;0;0;1;0
-3480;chanceler;negative;0;1;0;0;1;0
-3481;chancelant;negative;0;1;0;0;1;0
-3482;chancelier;positive;0;0;0;0;0;0
-3483;chancre;negative;0;0;0;1;0;1
-3484;chandelier;positive;0;0;0;0;0;0
-3485;chandler;positive;0;0;0;0;0;0
-3486;changeable;positive;0;0;0;0;0;0
-3487;changeant;positive;0;0;0;0;0;0
-3488;changer;positive;0;1;0;0;0;0
-3489;changeur;positive;0;0;0;0;0;0
-3490;chanson;positive;0;0;0;0;0;0
-3491;chansonnette;positive;1;0;0;0;0;0
-3492;chant;positive;0;0;0;1;1;0
-3493;chant de noël;positive;0;0;0;0;0;0
-3494;chantage;negative;0;1;0;1;0;0
-3495;chanter en ch?ur;positive;1;0;0;0;0;0
-3496;chanvre;positive;0;0;0;0;0;0
-3497;chaos;negative;0;1;1;1;0;0
-3498;chapeau;positive;0;0;0;0;0;0
-3499;chapelain;positive;0;0;0;0;0;0
-3500;chapelet;positive;0;0;0;0;0;0
-3501;chapelle;positive;0;0;0;0;0;0
-3502;chapiteau;positive;0;0;0;0;0;0
-3503;chapitre;positive;0;0;0;0;0;0
-3504;char;positive;0;0;0;0;0;0
-3505;charabia;negative;0;0;0;1;0;0
-3506;charbon;negative;0;1;1;0;0;1
-3507;charbon de bois;negative;0;0;0;0;0;1
-3508;charbon ardent;negative;0;1;0;0;0;0
-3509;charcuter;negative;0;1;0;1;0;1
-3510;charcutier;negative;0;1;0;1;0;1
-3511;chardon;positive;0;0;0;0;0;0
-3512;charger;negative;0;1;1;0;0;0
-3513;chargement;positive;0;0;0;0;0;0
-3514;charger qqn de faire qch;positive;0;0;0;0;0;0
-3515;chargeur;positive;0;0;0;0;0;0
-3516;chariot;positive;0;0;0;0;0;0
-3517;charitable;positive;0;0;0;0;0;0
-3518;charité;positive;0;0;0;0;0;0
-3519;charlatan;negative;0;0;0;1;0;1
-3520;charmer;positive;0;0;0;0;0;0
-3521;charmant;positive;0;0;0;0;0;0
-3522;charmeur;positive;0;0;0;0;0;0
-3523;charnière;positive;0;0;0;0;0;0
-3524;charnu;negative;0;0;0;0;0;0
-3525;charognard;negative;0;1;0;0;0;1
-3526;charpenter;positive;0;0;0;0;0;0
-3527;charpentier;positive;0;0;0;0;0;0
-3528;charpir;negative;0;1;1;0;0;1
-3529;charretier;positive;0;0;0;0;0;0
-3530;charrette;positive;0;0;0;0;0;1
-3531;charrier;positive;0;0;0;0;1;0
-3532;charrue;positive;0;0;0;0;0;0
-3533;charte;positive;0;0;0;0;0;0
-3534;chasser;negative;0;1;0;1;0;0
-3535;chasse;negative;0;1;0;1;0;0
-3536;chasteté;positive;0;0;0;0;0;0
-3537;chat;positive;0;0;0;0;0;0
-3538;chat sauvage;negative;0;1;0;0;0;0
-3539;chat tigré;positive;0;0;0;0;0;0
-3540;châtaigne;positive;0;0;0;0;0;0
-3541;châtaignier;positive;0;0;0;0;0;0
-3542;châtain;positive;0;0;0;0;0;0
-3543;château;positive;0;0;0;0;0;0
-3544;châtiment;negative;0;1;1;1;0;0
-3545;chatoiement;positive;1;0;0;0;0;0
-3546;chaton;positive;0;0;0;0;0;0
-3547;chatouillement;positive;0;0;0;0;1;0
-3548;chatouiller;positive;0;0;0;0;1;0
-3549;chatouilleux;negative;0;0;0;0;0;0
-3550;chat|chatte;positive;0;0;0;0;0;0
-3551;chaud;positive;0;0;0;1;0;0
-3552;chaud|chaude;positive;0;0;0;1;0;0
-3553;chaudière;positive;0;0;0;0;0;0
-3554;chauffage;positive;0;0;0;0;0;0
-3555;chauffer;positive;0;0;0;0;0;0
-3556;chauffe eau;positive;0;0;0;0;0;0
-3557;chauffeur;positive;0;0;0;0;0;0
-3558;chaumière;positive;0;0;0;0;0;0
-3559;chausser;positive;0;0;0;0;0;0
-3560;chaussette;positive;0;0;0;0;0;0
-3561;chausseur;positive;0;0;0;0;0;0
-3562;chausson;positive;0;0;0;0;0;0
-3563;chaussure;positive;0;0;0;0;0;0
-3564;chauve;negative;0;0;1;0;0;1
-3565;chauve souris;negative;0;1;0;0;0;1
-3566;chavirer;negative;0;1;0;0;0;0
-3567;check list;positive;0;0;0;0;0;0
-3568;cheesecake;positive;0;0;0;0;0;0
-3569;chef d équipe;positive;0;0;0;0;0;0
-3570;chef d orchestre;positive;0;0;0;0;0;0
-3571;chef opérateur;positive;0;0;0;0;0;0
-3572;chemin de fer;positive;0;0;0;0;0;0
-3573;cheminer;positive;0;0;0;1;0;0
-3574;cheminement;positive;0;0;0;0;0;0
-3575;chemise;positive;0;0;0;0;0;0
-3576;chemisier;positive;0;0;0;0;0;0
-3577;chêne;positive;0;0;0;0;0;0
-3578;chenil;negative;0;0;1;0;0;0
-3579;chenille;negative;0;0;0;0;0;1
-3580;chèque;positive;0;0;0;0;0;0
-3581;chercher;positive;0;0;0;0;0;0
-3582;chère;negative;0;0;1;0;0;0
-3583;chérir;positive;0;0;1;0;0;0
-3584;cher;negative;0;0;1;0;0;0
-3585;cheval;positive;0;0;0;0;0;0
-3586;cheval de course;positive;0;0;0;0;0;0
-3587;cheval sauvage;negative;0;1;0;0;1;0
-3588;chevalerie;positive;0;0;0;0;0;0
-3589;chevalet;positive;0;0;0;0;0;0
-3590;chevalier;positive;0;0;0;0;0;0
-3591;chevaucher;positive;0;0;0;0;0;0
-3592;chevauchement;negative;0;0;0;0;0;0
-3593;cheveu;positive;0;0;0;0;0;0
-3594;cheville;positive;0;0;0;0;0;0
-3595;chèvre;positive;0;0;0;0;0;0
-3596;chèvrefeuille;positive;0;0;0;0;0;0
-3597;chevreuil;negative;0;0;0;0;0;0
-3598;chevron;positive;0;0;0;0;0;0
-3599;chevronner;positive;0;0;0;0;0;0
-3600;chewing gum;positive;0;0;0;0;0;0
-3601;chic;positive;1;0;0;0;0;0
-3602;chichi;negative;0;0;0;0;0;1
-3603;chien;positive;0;0;0;0;0;0
-3604;chien de garde;positive;0;1;0;0;0;0
-3605;chien|chienne;negative;0;1;1;1;0;1
-3606;chiffonner;negative;0;0;0;1;0;0
-3607;chiffre;positive;0;0;0;0;0;0
-3608;chignon;positive;0;0;0;0;0;0
-3609;chimère;negative;0;1;0;0;1;0
-3610;chimie;positive;0;0;0;0;0;0
-3611;chimiste;positive;0;0;0;0;0;0
-3612;chine;positive;0;0;0;0;0;0
-3613;chinook;negative;0;0;0;0;0;0
-3614;chiot;positive;0;0;0;0;0;0
-3615;chiquer;negative;0;0;0;0;0;1
-3616;chirurgie;negative;0;1;1;0;0;0
-3617;chloroforme;negative;0;1;0;0;0;1
-3618;choc;negative;0;1;0;1;1;0
-3619;chochotte;negative;0;1;0;0;0;1
-3620;chocolat;positive;0;0;0;0;0;0
-3621;ch?ur;positive;0;0;0;0;0;0
-3622;choisir;positive;0;0;0;0;0;0
-3623;choix;positive;0;0;0;0;0;0
-3624;choléra;negative;0;1;1;0;0;1
-3625;choper;negative;0;1;0;1;1;0
-3626;choral;positive;1;0;0;0;0;0
-3627;chorale;positive;0;0;0;0;0;0
-3628;chose;negative;0;1;0;1;0;1
-3629;chou;positive;0;0;0;0;0;0
-3630;choucroute;negative;0;0;0;0;0;0
-3631;chouette;positive;1;0;0;0;0;0
-3632;chromatique;positive;0;0;0;0;0;0
-3633;chromatographie;positive;0;0;0;0;0;0
-3634;chromosome;positive;0;0;0;0;0;0
-3635;chronique;positive;0;0;0;0;0;0
-3636;chronographe;positive;0;0;0;0;0;0
-3637;chronologique;positive;0;0;0;0;0;0
-3638;chronomètre;positive;0;0;0;0;0;0
-3639;chuchoter;negative;0;1;0;0;0;0
-3640;chuchotement;negative;0;1;0;0;0;0
-3641;chut;negative;0;0;0;0;0;0
-3642;chuter;negative;0;1;1;0;1;0
-3643;chutney;positive;0;0;0;0;0;0
-3644;ci joindre;positive;0;0;0;0;0;0
-3645;cible;negative;0;1;1;0;0;0
-3646;cibler;negative;0;1;0;0;0;0
-3647;cicatrice;negative;0;1;1;1;0;1
-3648;cicatrisation;positive;0;0;0;0;0;0
-3649;cicatriser;positive;0;0;0;0;0;0
-3650;cidre;positive;0;0;0;0;0;0
-3651;ciel;positive;0;0;0;0;0;0
-3652;cierge;positive;0;0;0;0;0;0
-3653;cieux;positive;0;0;0;0;0;0
-3654;cigare;negative;0;0;0;0;0;1
-3655;cigarette;negative;0;0;0;0;0;1
-3656;ciguë;negative;0;1;0;0;0;1
-3657;cil;negative;0;1;0;1;0;0
-3658;cime;positive;0;0;0;0;0;0
-3659;ciment;positive;0;0;0;0;0;0
-3660;cimenter;negative;0;0;0;0;0;0
-3661;cimetière;negative;0;1;1;0;0;0
-3662;cinéma;positive;0;0;0;0;0;0
-3663;cinématique;positive;0;0;0;0;0;0
-3664;cinématographie;positive;0;0;0;0;0;0
-3665;cinglant;negative;0;1;0;1;1;0
-3666;cirage;positive;0;0;0;0;0;0
-3667;circoncision;negative;0;1;0;0;0;0
-3668;circonférence;positive;0;0;0;0;0;0
-3669;circonférentiel;positive;0;0;0;0;0;0
-3670;circonscrire;negative;0;1;1;0;0;0
-3671;circonstance;positive;0;0;0;0;1;0
-3672;circonstanciel;positive;0;0;0;0;0;0
-3673;circonvolution;positive;0;0;0;0;0;0
-3674;circuit;positive;0;0;0;0;0;0
-3675;circulaire;positive;0;0;0;0;0;0
-3676;circuler;positive;0;0;0;0;0;0
-3677;cirer;positive;0;0;0;0;0;0
-3678;cireux;negative;0;0;0;0;0;1
-3679;cirque;negative;0;0;0;0;0;0
-3680;cirrus;negative;0;0;1;0;0;0
-3681;cisaille;negative;0;1;0;0;0;0
-3682;ciseau;negative;0;0;0;1;0;0
-3683;ciseler;negative;0;0;0;1;0;0
-3684;citadelle;positive;0;0;0;0;0;0
-3685;citation à comparaître;negative;0;1;0;1;0;0
-3686;cité;positive;0;0;0;0;0;0
-3687;citer à comparaître;negative;0;1;0;1;0;0
-3688;citrine;negative;0;0;0;0;0;0
-3689;citron;negative;0;0;0;0;0;1
-3690;civet;positive;0;0;0;0;0;0
-3691;civière;negative;0;1;1;0;0;0
-3692;civilisation;positive;0;0;0;0;0;0
-3693;civiliser;positive;0;0;0;0;0;0
-3694;civilité;positive;0;0;0;0;0;0
-3695;civique;positive;0;0;0;0;0;0
-3696;clair;positive;0;0;0;0;0;0
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-3729;cligner du ?il;positive;0;0;0;0;0;0
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-4408;crédit;positive;0;0;0;0;0;0
-4409;créditer;positive;0;0;0;0;0;0
-4410;créditeur;positive;0;0;0;0;0;0
-4411;credo;positive;0;0;0;0;0;0
-4412;crédule;negative;0;0;1;0;0;0
-4413;créer;positive;1;0;0;0;0;0
-4414;crémaillère;positive;0;0;1;0;0;0
-4415;crémation;negative;0;0;1;0;0;0
-4416;crème;positive;0;0;0;0;1;0
-4417;crémerie;positive;0;0;0;0;0;0
-4418;crémier;positive;0;0;0;0;0;0
-4419;créneau;positive;0;0;0;0;0;0
-4420;créole;positive;0;0;0;0;0;0
-4421;crêpe;positive;0;0;0;0;0;0
-4422;crêper;positive;0;0;0;0;0;0
-4423;crépitement;negative;0;1;0;1;1;0
-4424;crépiter;negative;0;1;0;0;0;0
-4425;crescendo;positive;0;0;0;0;1;0
-4426;crête;negative;0;0;0;0;0;0
-4427;creuser;negative;0;0;0;0;0;0
-4428;crevaison;negative;0;1;1;0;1;0
-4429;crever;negative;0;1;1;0;1;0
-4430;crevette;negative;0;0;0;0;0;0
-4431;cri strident;negative;0;1;1;1;1;0
-4432;crier;negative;0;1;1;1;0;1
-4433;criard;negative;0;0;0;0;1;1
-4434;cribler;negative;0;1;1;0;0;0
-4435;cribler de trou;negative;0;1;1;0;0;0
-4436;cric;positive;0;0;0;0;0;0
-4437;cricket;positive;0;0;0;0;0;0
-4438;crier de joie;positive;0;0;0;0;1;0
-4439;crime;negative;0;1;1;1;0;0
-4440;criminalité;negative;0;1;0;1;0;1
-4441;crinière;positive;0;0;0;0;0;0
-4442;crique;positive;0;1;0;0;0;1
-4443;criquet;negative;0;1;0;0;0;1
-4444;crise;negative;0;1;1;1;1;0
-4445;crisper;negative;0;1;0;1;0;0
-4446;crissement;negative;0;1;1;1;1;0
-4447;crisser;negative;0;1;1;1;1;0
-4448;cristal;positive;0;0;0;0;0;0
-4449;cristallin;positive;0;0;0;0;0;0
-4450;cristallisation;positive;0;0;0;0;0;0
-4451;critère;positive;0;0;0;0;0;0
-4452;croassement;negative;0;1;0;0;0;0
-4453;croc;negative;0;1;0;0;0;0
-4454;crochet;positive;0;0;0;0;0;0
-4455;crocodile;negative;0;1;0;0;0;0
-4456;croire;positive;0;0;0;0;0;0
-4457;croisade;negative;0;1;0;1;0;0
-4458;croiser;negative;0;0;0;0;0;0
-4459;croisé;negative;0;0;0;0;0;0
-4460;croiseur;positive;0;0;0;0;0;0
-4461;croisière;positive;0;0;0;0;0;0
-4462;croissance;positive;0;0;0;0;0;0
-4463;croissance démesuré;negative;0;0;1;0;0;0
-4464;croissant;positive;0;0;0;0;0;0
-4465;croix;positive;0;1;1;1;0;0
-4466;croix gammée;negative;0;1;0;1;0;1
-4467;croquant;positive;0;0;0;0;0;0
-4468;croquant|croquante;positive;0;0;0;0;0;0
-4469;croque mitaine;negative;0;1;1;1;0;0
-4470;croque mort;positive;0;0;1;0;0;0
-4471;croquer;negative;0;0;0;1;0;0
-4472;croquet;positive;0;0;0;0;0;0
-4473;croquis;positive;0;0;0;0;0;0
-4474;crotale;negative;0;1;0;0;0;1
-4475;croupe;negative;0;0;0;0;0;0
-4476;croupier;positive;0;0;0;0;0;0
-4477;croustillant;positive;0;0;0;0;0;0
-4478;croûte;negative;0;0;0;0;0;1
-4479;croyance;positive;0;0;0;0;0;0
-4480;cruauté;negative;0;1;1;1;0;1
-4481;crucial;positive;0;0;0;0;0;0
-4482;crucifix;negative;0;1;0;0;0;0
-4483;crucifixion;negative;0;1;1;1;0;1
-4484;crustacé;negative;0;0;0;0;0;1
-4485;crypte;negative;0;1;1;0;0;0
-4486;crypter;negative;0;1;0;0;0;0
-4487;cryptographie;positive;0;0;0;0;0;0
-4488;cube;negative;0;0;0;1;0;0
-4489;cueillir;positive;1;0;0;0;0;0
-4490;cuillère;positive;0;0;0;0;0;0
-4491;cuillère à soupe;positive;0;0;0;0;0;0
-4492;cuillerée;positive;0;0;0;0;0;0
-4493;cuir;positive;0;0;0;0;0;0
-4494;cuir brut;positive;0;0;0;0;0;0
-4495;cuir chevelu;negative;0;1;0;0;0;1
-4496;cuire;positive;0;0;0;0;0;0
-4497;cuiseur vapeur;positive;0;0;0;0;0;0
-4498;cuisiner;positive;0;0;0;0;0;0
-4499;cuisine;positive;0;0;0;0;0;0
-4500;cuisinier;positive;0;0;0;0;0;0
-4501;cuisinier|cuisinière;positive;0;0;0;0;0;0
-4502;cuisson;positive;0;0;0;0;0;0
-4503;cuit|cuite;negative;0;0;0;0;0;0
-4504;cuivre;positive;0;0;0;0;0;0
-4505;cuivrer;positive;0;0;0;0;0;0
-4506;cul;negative;0;0;0;0;0;0
-4507;culasse;negative;0;0;0;0;0;0
-4508;culinaire;positive;0;0;0;0;0;0
-4509;culminer;positive;1;0;0;0;0;0
-4510;culotte;negative;0;0;0;0;0;0
-4511;culpabilité;negative;0;0;1;1;0;1
-4512;culture;positive;0;0;0;0;0;0
-4513;cumulus;negative;0;0;0;0;0;0
-4514;cupide;negative;0;1;0;1;0;0
-4515;cupidité;negative;0;0;0;1;0;1
-4516;curable;positive;0;0;0;0;0;0
-4517;curateur;positive;0;0;0;0;0;0
-4518;curcuma;positive;0;0;0;0;0;0
-4519;curling;positive;0;0;0;0;0;0
-4520;curry;positive;0;0;0;0;0;0
-4521;curviligne;positive;0;0;0;0;0;0
-4522;cuticule;negative;0;0;0;0;0;1
-4523;cutter;negative;0;1;0;0;0;0
-4524;cuvée;positive;0;0;0;0;0;0
-4525;cyanure;negative;0;1;0;0;0;1
-4526;cycle;positive;0;0;0;0;0;0
-4527;cyclique;positive;0;0;0;0;0;0
-4528;cycliste;positive;0;0;0;0;0;0
-4529;cyclone;negative;0;1;0;0;1;0
-4530;cygne;positive;0;0;0;0;0;0
-4531;cylindre;positive;0;0;0;0;0;0
-4532;cylindrique;positive;0;0;0;0;0;0
-4533;cymbale;positive;0;0;0;0;0;0
-4534;cynique;negative;0;0;0;1;0;1
-4535;cytomégalovirus;negative;0;1;1;0;0;1
-4536;cytoplasme;positive;0;0;0;0;0;0
-4537;d accord;positive;0;0;0;0;0;0
-4538;d actualité;positive;0;0;0;0;0;0
-4539;d essai;negative;0;1;0;0;0;0
-4540;d humeur changeant;negative;0;0;1;1;0;0
-4541;d intérieur;positive;0;0;0;0;0;0
-4542;d occasion;negative;0;0;0;0;0;0
-4543;d or;positive;0;0;0;0;0;0
-4544;dactylo;positive;0;0;0;0;0;0
-4545;dactylographe;positive;0;0;0;0;0;0
-4546;dague;negative;0;1;0;0;0;0
-4547;dallage;positive;0;0;0;0;0;0
-4548;dalle;positive;0;0;0;0;0;0
-4549;dame;positive;0;0;0;1;0;1
-4550;damnation;negative;0;1;1;1;0;0
-4551;damner;negative;0;1;1;0;0;0
-4552;damoiselle;positive;0;0;0;0;0;0
-4553;dandy;positive;0;0;0;0;0;1
-4554;danger;negative;0;1;1;0;0;0
-4555;dangeureuses;negative;0;1;0;0;1;0
-4556;dans le monde entier;positive;0;0;0;0;0;0
-4557;dans le secret;positive;0;0;0;0;0;0
-4558;dans le sen|sens de le longueur;positive;0;0;0;0;0;0
-4559;danse;positive;0;0;0;0;0;0
-4560;danser;positive;0;0;0;0;0;0
-4561;dard;negative;0;1;0;1;1;0
-4562;date;positive;1;0;0;0;0;0
-4563;date anniversaire;positive;0;0;0;0;1;0
-4564;dauphin;positive;0;0;0;0;1;0
-4565;dé à coudre;positive;0;0;0;0;0;0
-4566;de banlieue;negative;0;0;0;0;0;0
-4567;de baptême;positive;1;0;0;0;0;0
-4568;de base;positive;0;0;0;0;0;0
-4569;de bon augure;positive;1;0;0;0;0;0
-4570;de bon c?ur;positive;1;0;0;0;0;0
-4571;de bon goût;positive;0;0;0;0;0;0
-4572;de bureau;positive;0;0;0;0;0;0
-4573;de cabaret;positive;0;0;0;0;1;0
-4574;de ce côté là;positive;0;0;0;0;0;0
-4575;de citron;negative;0;0;0;0;0;1
-4576;de conclusion;positive;0;0;0;0;0;0
-4577;de confirmation;positive;0;0;0;0;0;0
-4578;de consolation;negative;0;0;1;0;0;0
-4579;de contrebande;negative;0;1;0;1;0;1
-4580;de côté;negative;0;0;0;0;0;0
-4581;de cuisine;positive;0;0;0;0;0;0
-4582;de démolition;negative;0;0;0;1;0;0
-4583;de façade;negative;0;0;0;1;0;1
-4584;de face;positive;0;0;0;0;0;0
-4585;de façon constant;positive;0;0;0;0;0;0
-4586;de façon décontracté;positive;0;0;0;0;0;0
-4587;de façon identique;positive;0;0;0;0;0;0
-4588;de façon introspectif;positive;0;0;0;0;0;0
-4589;de félicitation;positive;1;0;0;0;0;0
-4590;de fer;positive;0;0;0;0;0;0
-4591;de fête;positive;1;0;0;0;0;0
-4592;de force;negative;0;1;0;1;0;0
-4593;de fort carrure;positive;0;1;0;0;0;0
-4594;de fortune;negative;0;0;1;0;0;0
-4595;de gala;positive;0;0;0;0;0;0
-4596;de gouverneur;positive;0;0;0;0;0;0
-4597;de jeu;positive;0;0;0;0;0;0
-4598;de jour;positive;0;0;0;0;0;0
-4599;de le conversation;positive;0;0;0;0;0;0
-4600;de le lune;positive;0;0;0;0;0;0
-4601;de lancement;positive;0;0;0;0;0;0
-4602;de luxe;positive;1;0;0;0;0;0
-4603;de manière choquant;negative;0;0;0;0;1;0
-4604;de manière exquis;positive;1;0;0;0;0;0
-4605;de manière inattendu;negative;0;0;0;0;1;0
-4606;de manière satisfaisant;positive;0;0;0;0;0;0
-4607;de marée;negative;0;1;0;0;0;0
-4608;de marié;positive;0;0;0;0;0;0
-4609;de marque déposer;positive;0;0;0;0;0;0
-4610;de masse;negative;0;1;0;0;0;0
-4611;de mauvais augure;negative;0;1;0;0;0;0
-4612;de mauvais goût;negative;0;0;0;0;0;1
-4613;de mauvais qualité;negative;0;0;0;1;0;1
-4614;de mauvais réputation;negative;0;1;0;1;0;1
-4615;de même;positive;0;0;0;0;0;0
-4616;de mesure;positive;0;0;0;0;0;0
-4617;de moisir;negative;0;0;0;0;0;1
-4618;de montagne;positive;0;0;0;0;0;0
-4619;de notre jour;positive;0;0;0;0;0;0
-4620;de nouveau;positive;0;0;0;0;0;0
-4621;de plein air;positive;0;0;0;0;0;0
-4622;de poisson;negative;0;1;0;0;0;0
-4623;de précaution;positive;0;0;0;0;0;0
-4624;de premier choix;positive;0;0;0;0;0;0
-4625;de premier qualité;positive;1;0;0;0;0;0
-4626;de profil;negative;0;0;0;0;0;0
-4627;de qualité;positive;0;0;0;0;0;0
-4628;de rechange;positive;0;0;0;0;0;0
-4629;de représaille;negative;0;1;0;1;0;0
-4630;de rêve;positive;1;0;0;0;0;0
-4631;de saint;positive;0;0;0;0;1;0
-4632;de second main;negative;0;0;0;0;0;0
-4633;de son plein gré;positive;0;0;0;0;0;0
-4634;de soutien;positive;0;0;0;0;0;0
-4635;de sport;positive;0;0;0;0;0;0
-4636;de substitution;positive;0;0;0;0;0;0
-4637;de terre;positive;0;0;0;0;0;0
-4638;de titan;negative;0;1;0;0;0;0
-4639;de toujours;positive;0;0;0;0;0;0
-4640;de tout le jour;positive;0;0;0;0;0;0
-4641;de transition;positive;0;0;0;0;0;0
-4642;de travail;positive;0;0;0;0;0;0
-4643;de valeur;positive;0;0;0;0;0;0
-4644;de velours;positive;0;0;0;0;0;0
-4645;de vote;positive;0;0;0;0;0;0
-4646;de voyage;positive;1;0;0;0;0;0
-4647;dealer;negative;0;0;0;0;0;0
-4648;déambulation;negative;0;0;1;0;0;0
-4649;débâcle;negative;0;1;1;1;0;0
-4650;déballer;positive;0;0;0;0;0;0
-4651;débarquer;positive;0;0;0;0;0;0
-4652;débarras;negative;0;0;0;0;0;1
-4653;débarrasser;negative;0;0;1;1;0;0
-4654;débarrasser de;negative;0;0;1;0;0;0
-4655;débattre;positive;0;0;0;0;0;0
-4656;débauche;negative;0;1;0;0;0;1
-4657;débile;negative;0;0;0;1;0;1
-4658;débiliter;negative;0;0;0;1;0;1
-4659;débit;positive;0;0;0;0;0;0
-4660;débiter;positive;0;0;0;0;0;0
-4661;déblaiement;positive;0;0;0;0;0;0
-4662;déblayer|déblayer;positive;0;0;0;0;0;0
-4663;débloquer;negative;0;0;0;0;0;0
-4664;déboisement;positive;0;0;0;0;0;0
-4665;déboîter;negative;0;1;1;1;0;1
-4666;débordant;positive;0;0;0;0;0;0
-4667;débordement;negative;0;1;0;1;1;0
-4668;déboucher;positive;0;0;0;0;0;0
-4669;déboursement;positive;0;0;0;0;0;0
-4670;débourser;positive;0;0;0;0;0;0
-4671;débrancher;negative;0;0;1;0;0;0
-4672;débrayer|débrayer;positive;0;0;0;0;0;0
-4673;débris;negative;0;1;1;0;0;1
-4674;débuter;positive;0;0;0;0;0;0
-4675;décadence;negative;0;0;1;1;0;1
-4676;décalage;negative;0;0;1;0;0;0
-4677;décalquer;positive;0;0;0;0;0;0
-4678;décapage;negative;0;1;0;1;0;1
-4679;décapotable;positive;0;0;0;0;0;0
-4680;décapsuleur;positive;0;0;0;0;0;0
-4681;décéder;negative;0;0;1;0;0;0
-4682;décelable;positive;0;0;0;0;0;0
-4683;déceler;positive;0;0;0;0;0;0
-4684;décence;positive;0;0;0;0;0;0
-4685;décennie;positive;0;0;0;0;0;0
-4686;décent;positive;0;0;0;0;0;0
-4687;décentralisation;negative;0;0;0;0;0;0
-4688;décerner;positive;0;0;0;0;0;0
-4689;décevant;negative;0;0;1;1;1;1
-4690;déchaînement;negative;0;1;0;1;1;0
-4691;décharge;negative;0;0;0;0;0;1
-4692;décharger;negative;0;0;0;0;0;0
-4693;décharner;negative;0;1;1;0;0;1
-4694;déchet;negative;0;0;0;0;0;1
-4695;déchiffrer;positive;0;0;0;0;0;0
-4696;déchiqueter;negative;0;1;1;1;0;0
-4697;déchirer;negative;0;1;1;1;0;0
-4698;déchirure;negative;0;1;1;1;0;0
-4699;décidément;negative;0;0;0;0;0;0
-4700;décimal;positive;0;0;0;0;0;0
-4701;décimale;positive;0;0;0;0;0;0
-4702;décisif;positive;0;1;0;0;0;0
-4703;décision;positive;0;0;0;0;0;0
-4704;déclaration écrire sous sermen;positive;0;0;0;0;0;0
-4705;déclaration inexact;negative;0;0;0;1;0;1
-4706;déclaration officiel;positive;0;0;0;0;0;0
-4707;déclaratoire;positive;0;0;0;0;0;0
-4708;déclencher;negative;0;0;0;0;0;0
-4709;déclenchement;positive;0;0;0;0;0;0
-4710;déclic;positive;0;0;0;0;1;0
-4711;déclin;negative;0;1;1;0;0;0
-4712;décliner;negative;0;0;1;0;0;0
-4713;décolorant;negative;0;0;1;0;0;1
-4714;décoloration;negative;0;0;0;0;0;1
-4715;décolorer;negative;0;0;1;0;0;1
-4716;décomposer;negative;0;0;1;0;0;1
-4717;décomposition;negative;0;1;1;0;0;1
-4718;décompte;positive;0;0;0;0;0;0
-4719;déconcentrer;negative;0;0;0;1;0;0
-4720;déconcerter;negative;0;1;1;0;1;0
-4721;déconcertant;negative;0;1;1;0;1;0
-4722;déconnecter;negative;0;0;1;0;0;0
-4723;déconnexion;negative;0;0;1;0;0;0
-4724;décontraction;positive;1;0;0;0;0;0
-4725;décontracturant musculaire;positive;0;0;0;0;0;0
-4726;déconvenue;negative;0;0;1;1;1;1
-4727;décor;positive;0;0;0;0;0;0
-4728;décorateur;positive;0;0;0;0;0;0
-4729;décoration;positive;0;0;0;0;0;0
-4730;décorer;positive;0;0;0;0;0;0
-4731;décorum;positive;0;0;0;0;0;0
-4732;découpage;negative;0;1;1;1;0;1
-4733;découper en cube;negative;0;0;0;1;0;0
-4734;décourager;negative;0;1;1;0;0;0
-4735;décourageant;negative;0;0;1;0;0;0
-4736;découragement;negative;0;1;1;0;0;0
-4737;décours;negative;0;1;1;0;0;0
-4738;découdre;negative;0;1;1;0;0;0
-4739;découvrir;positive;0;0;0;0;0;0
-4740;découverte;positive;1;0;0;0;0;0
-4741;décrépit;negative;0;0;1;0;0;0
-4742;décréter;positive;0;0;0;0;0;0
-4743;décrier;negative;0;0;0;1;0;1
-4744;décrire;positive;0;0;0;0;0;0
-4745;décroiser;positive;0;0;0;0;0;0
-4746;décroître;negative;0;0;1;0;0;0
-4747;décroissant;negative;0;0;1;0;0;0
-4748;décuple;positive;0;0;0;0;0;0
-4749;dédaigner;negative;0;0;0;1;0;1
-4750;dédain;negative;0;1;1;1;0;1
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-5443;eclaircir;positive;1;0;0;0;0;0
-5444;éclaircir;positive;1;0;0;0;0;0
-5445;éclaircissement;positive;0;0;0;0;0;0
-5446;éclairer;positive;0;0;0;0;1;0
-5447;éclater;negative;0;1;0;0;1;0
-5448;éclectique;positive;0;0;0;0;0;0
-5449;eclipse;negative;0;1;0;0;0;0
-5450;éclipse;negative;0;1;0;0;0;0
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-5453;éclisse;positive;0;0;0;0;0;0
-5454;éclisser;positive;0;0;0;0;0;0
-5455;écloper;negative;0;0;1;0;0;0
-5456;éclore;positive;0;0;0;0;0;0
-5457;écluse;positive;0;0;0;0;0;0
-5458;éc?urer;negative;0;1;1;1;0;1
-5459;éc?urant;negative;0;1;1;1;0;1
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-5461;école;positive;0;0;0;0;0;0
-5462;écolier;positive;0;0;0;0;0;0
-5463;écologique;positive;0;0;0;0;0;0
-5464;éconduire;negative;0;0;1;0;0;0
-5465;economie;positive;0;0;0;0;0;0
-5466;économie;positive;0;0;0;0;0;1
-5467;économique;positive;1;0;0;0;0;0
-5468;écorce;negative;0;1;0;1;0;1
-5469;écossais;positive;0;0;0;0;0;0
-5470;écouler;positive;0;0;0;0;0;0
-5471;écoulement de sang;negative;0;1;1;0;0;1
-5472;écourter;negative;0;1;0;1;1;0
-5473;écoute clandestin;negative;0;1;0;0;0;0
-5474;écouteur;positive;0;0;0;0;0;0
-5475;écouvillon;negative;0;0;0;0;0;1
-5476;écran;positive;0;0;0;0;0;0
-5477;écrasant;negative;0;1;1;1;0;1
-5478;écrémer;negative;0;0;0;0;0;0
-5479;écrevisse;negative;0;0;0;0;0;0
-5480;écrire;positive;0;0;0;0;0;0
-5481;écriture;positive;0;0;0;0;0;0
-5482;écriture saint;positive;0;0;0;0;0;0
-5483;écrivain;positive;0;0;0;0;0;0
-5484;écrivant;positive;0;0;0;0;0;0
-5485;écumer;negative;0;0;0;0;0;1
-5486;écumeux;negative;0;0;0;0;0;1
-5487;écureuil;positive;0;0;0;0;0;0
-5488;écurie;positive;0;0;0;0;0;0
-5489;édenter;negative;0;0;1;0;0;1
-5490;édification;positive;0;0;0;0;0;0
-5491;édifier;positive;0;0;0;0;0;0
-5492;édit;negative;0;1;0;0;0;0
-5493;éditer;positive;0;0;0;0;0;0
-5494;editeur;positive;0;0;0;0;0;0
-5495;edition;positive;0;0;0;0;0;0
-5496;édition;positive;0;0;0;0;0;0
-5497;editorial;positive;0;0;0;0;0;0
-5498;éditorial;positive;0;0;0;0;0;0
-5499;édredon;positive;1;0;0;0;0;0
-5500;éducatif;positive;0;0;0;0;0;0
-5501;éducation;positive;0;0;0;0;0;0
-5502;édulcorant;positive;0;0;0;0;0;0
-5503;éduquer;positive;0;0;0;0;0;0
-5504;eétouffants;negative;0;1;1;0;0;1
-5505;effaçage;negative;0;0;1;0;0;0
-5506;effacer;negative;0;1;1;1;0;0
-5507;effacement;negative;0;1;1;0;0;0
-5508;effarer;negative;0;1;0;0;1;1
-5509;effectuer;positive;0;0;0;0;0;0
-5510;effeminé;negative;0;0;0;0;0;0
-5511;efféminer;negative;0;0;0;0;0;0
-5512;effet;positive;0;0;0;0;0;0
-5513;effet personnel;positive;0;0;0;0;0;0
-5514;efficace;positive;0;0;0;0;0;0
-5515;efficacement;positive;0;0;0;0;0;0
-5516;efficacité;positive;0;0;0;0;0;0
-5517;effigie;positive;0;0;0;1;0;0
-5518;effilage;positive;0;0;0;0;0;0
-5519;effiler;positive;0;0;0;0;0;0
-5520;effilocher;negative;0;0;1;0;0;1
-5521;effondrement;negative;0;1;1;0;1;1
-5522;effort;positive;0;0;0;0;0;0
-5523;effrayant;negative;0;1;1;1;1;0
-5524;effroi;negative;0;1;0;0;1;0
-5525;effronté;negative;0;0;0;1;0;1
-5526;effronterie;positive;0;0;0;0;0;0
-5527;effusion;negative;0;0;0;0;0;0
-5528;égal;positive;0;0;0;0;0;0
-5529;également;positive;0;0;0;0;0;0
-5530;égalisation;positive;0;0;0;0;0;0
-5531;égaliser;positive;0;0;0;0;0;0
-5532;égalité;positive;0;0;0;0;0;0
-5533;égard;positive;0;0;0;0;0;0
-5534;égérie;positive;0;0;0;0;0;0
-5535;égide;positive;0;0;0;0;0;0
-5536;église;positive;0;0;0;0;0;0
-5537;ego;negative;0;0;0;0;0;0
-5538;égocentrique;negative;0;0;0;1;0;1
-5539;égoïsme;negative;0;0;0;1;0;1
-5540;égoïste;negative;0;0;0;1;0;1
-5541;égout;negative;0;0;0;0;0;1
-5542;égouttement;negative;0;0;0;0;0;0
-5543;égratigner;negative;0;1;1;0;0;0
-5544;égratignure;negative;0;1;1;1;1;0
-5545;éhonté;negative;0;0;0;1;0;1
-5546;éjaculation;positive;0;0;0;0;1;0
-5547;éjaculer;positive;1;0;0;0;0;0
-5548;éjecter;negative;0;1;1;1;0;0
-5549;éjection;negative;0;1;1;1;1;0
-5550;élaboration;positive;0;0;0;0;0;0
-5551;élaborer;positive;0;0;0;0;0;0
-5552;élaguer;negative;0;0;0;0;0;0
-5553;élargir;positive;0;0;0;0;0;0
-5554;élargissement;negative;0;0;0;0;0;0
-5555;élasticité;positive;0;0;0;0;0;0
-5556;élastique;positive;0;0;0;0;0;0
-5557;élection;positive;0;0;0;0;0;0
-5558;élection primaire;positive;0;0;0;0;0;0
-5559;électorat;positive;0;0;0;0;0;0
-5560;électricité;positive;0;0;0;0;0;0
-5561;électrique;positive;0;0;0;0;1;0
-5562;élégance;positive;0;0;0;0;0;0
-5563;élégant;positive;0;0;0;0;0;1
-5564;élément;positive;0;0;0;0;0;0
-5565;élément vital;positive;0;0;0;0;0;0
-5566;élevage;positive;0;0;0;0;0;0
-5567;élévation;positive;0;1;0;0;0;0
-5568;élève;positive;0;0;0;0;0;0
-5569;élever;positive;0;0;0;0;0;0
-5570;élève de terminal;positive;0;0;0;0;0;0
-5571;élève officier;positive;0;0;0;0;0;0
-5572;eleveur;positive;0;0;0;0;0;0
-5573;elfe;positive;0;1;0;1;0;1
-5574;éligible;positive;0;0;0;0;0;0
-5575;élimination;negative;0;1;1;1;0;1
-5576;éliminer;negative;0;1;0;1;1;0
-5577;élingue;negative;0;1;0;1;0;0
-5578;élinguer;negative;0;1;0;1;0;0
-5579;élire;positive;0;0;0;0;0;0
-5580;ellipse;positive;0;0;0;0;0;0
-5581;ellipsoïdal;positive;0;0;0;0;0;0
-5582;ellipsoïde;positive;0;0;0;0;0;0
-5583;elliptique;positive;0;0;0;0;0;0
-5584;éloge;positive;0;0;0;0;0;0
-5585;éloge funèbre;negative;0;0;1;0;0;0
-5586;éloigner;negative;0;0;1;0;0;0
-5587;éloignement;negative;0;0;1;0;0;0
-5588;éloquence;positive;0;0;0;0;0;0
-5589;éloquent;positive;0;0;0;0;0;0
-5590;élucidation;positive;0;0;0;0;0;0
-5591;élucider;positive;0;0;0;0;0;0
-5592;éluder;negative;0;1;0;0;0;1
-5593;émacier;negative;0;1;1;0;0;1
-5594;émail;positive;0;0;0;0;0;0
-5595;émancipation;positive;1;0;0;0;0;0
-5596;émaner;positive;0;0;0;0;0;0
-5597;emballage;positive;0;0;0;0;0;0
-5598;emballer;positive;0;0;0;0;0;0
-5599;embarassante;negative;0;1;0;0;0;0
-5600;embarassantes;negative;0;1;0;0;0;0
-5601;embarassants;negative;0;1;0;0;0;0
-5602;embarcadère;positive;0;0;0;0;0;0
-5603;embarcation;positive;0;0;0;0;0;0
-5604;embarder;negative;0;1;0;0;1;0
-5605;embargo;negative;0;0;0;1;0;0
-5606;embarquement;positive;0;0;0;0;0;0
-5607;embarquer;positive;0;0;0;0;0;0
-5608;embarquer de force;negative;0;1;0;1;0;1
-5609;embarras;negative;0;1;1;0;1;1
-5610;embaucher;positive;0;0;0;0;0;0
-5611;embellir;positive;1;0;0;0;0;0
-5612;embellissement;positive;0;0;0;0;0;0
-5613;embêter;negative;0;0;1;1;0;0
-5614;embêtement;negative;0;1;1;1;0;0
-5615;emblématique;positive;0;0;0;0;0;0
-5616;emblème;positive;0;0;0;0;0;0
-5617;embolie;negative;0;1;1;0;0;0
-5618;embouchure;positive;0;0;0;0;0;0
-5619;embout;positive;0;0;0;0;0;0
-5620;embraser;negative;0;1;0;1;1;0
-5621;embrasement;negative;0;1;0;1;0;0
-5622;embrasser;positive;0;0;0;0;1;0
-5623;embrayage;positive;0;0;0;0;0;0
-5624;embrocher;negative;0;1;0;0;0;0
-5625;embrouiller;negative;0;0;0;0;0;0
-5626;embryon;positive;0;0;0;0;0;0
-5627;embuer;negative;0;1;1;0;0;0
-5628;embuscade;negative;0;1;0;1;1;0
-5629;émécher;negative;0;0;0;0;0;1
-5630;emeraude;positive;0;0;0;0;0;0
-5631;émeraude;positive;0;0;0;0;0;0
-5632;émergence;positive;0;0;0;0;0;0
-5633;émerger;positive;0;0;0;0;1;0
-5634;émérite;positive;0;0;0;0;0;0
-5635;émettre;positive;0;0;0;0;0;0
-5636;émeute;negative;0;1;0;1;0;0
-5637;émigration;negative;0;0;0;0;0;0
-5638;émigrer;negative;0;0;0;0;0;0
-5639;émincer;negative;0;1;0;0;0;0
-5640;éminemment;positive;0;0;0;0;0;0
-5641;eminence;positive;0;0;0;0;0;0
-5642;éminence;positive;0;0;0;0;0;0
-5643;emir;positive;0;0;0;0;0;0
-5644;émir;positive;0;0;0;0;0;0
-5645;émissaire;positive;0;0;0;0;0;0
-5646;emmêler;negative;0;1;0;1;0;0
-5647;émotionnel;positive;0;0;0;0;0;0
-5648;émousser;negative;0;1;0;1;0;0
-5649;empaqueter;positive;0;0;0;0;0;0
-5650;empaqueteur;positive;0;0;0;0;0;0
-5651;empathie;positive;0;0;0;0;0;0
-5652;empattement;positive;0;0;0;0;0;0
-5653;empêcher;negative;0;1;0;0;0;0
-5654;empereur;positive;0;0;0;0;0;0
-5655;empester;negative;0;0;0;0;0;1
-5656;empêtrer;negative;0;1;1;1;0;1
-5657;empétrés;negative;0;1;1;1;0;1
-5658;emphase;positive;0;0;0;0;0;0
-5659;emphatique;positive;0;0;0;0;0;0
-5660;empiétement;negative;0;1;1;1;0;0
-5661;empiètement;negative;0;1;1;1;0;0
-5662;empiéter;negative;0;1;1;1;0;0
-5663;empiler;negative;0;0;0;0;0;0
-5664;empire;positive;0;0;0;0;0;0
-5665;empirique;positive;0;0;0;0;0;0
-5666;empirisme;positive;0;0;0;0;0;0
-5667;emplacement;positive;0;0;0;0;0;0
-5668;emploi;positive;0;0;0;0;0;0
-5669;employer abusivement;negative;0;1;1;1;0;0
-5670;employeur;positive;0;0;0;0;0;0
-5671;empocher;positive;0;0;0;0;0;0
-5672;empoigner;negative;0;1;0;1;0;0
-5673;empreinte;positive;0;0;0;0;0;0
-5674;empreindre de pas;positive;0;0;0;0;0;0
-5675;empressement;positive;0;0;0;0;0;0
-5676;emprisonner;negative;0;1;1;1;0;1
-5677;emprisonnement;negative;0;1;1;1;0;1
-5678;emprunt;positive;0;0;0;0;0;0
-5679;emprunter;positive;0;0;0;0;0;0
-5680;émouvoir;positive;0;0;1;0;0;0
-5681;émuler;negative;0;0;0;0;0;0
-5682;émulsion;positive;0;0;0;0;0;0
-5683;en général;positive;0;0;0;0;0;0
-5684;en abondance;positive;1;0;0;0;0;0
-5685;en activité;positive;0;0;0;0;0;0
-5686;en agate;positive;0;0;0;0;0;0
-5687;en apesanteur;positive;0;0;0;0;0;0
-5688;en argent;positive;0;0;0;0;0;0
-5689;en arriérer;negative;0;1;0;0;0;1
-5690;en attente;negative;0;0;0;0;0;0
-5691;en avance;positive;0;0;0;0;0;0
-5692;en aveugle;negative;0;1;1;0;0;0
-5693;en baisse;negative;0;1;1;1;0;0
-5694;en bois;positive;0;0;0;0;0;0
-5695;en bon santé;positive;1;0;0;0;0;0
-5696;en caoutchouc;negative;0;0;0;0;0;0
-5697;en ce qui concerner;positive;0;0;0;0;0;0
-5698;en cela;positive;0;0;0;0;0;0
-5699;en chef;positive;0;0;0;0;0;0
-5700;en colère;negative;0;0;0;1;0;1
-5701;en colimaçon;positive;0;0;0;0;0;0
-5702;en colonne;positive;0;0;0;0;0;0
-5703;en continu;positive;0;0;0;0;0;0
-5704;en corrélation;positive;0;0;0;0;0;0
-5705;en couper;positive;0;0;0;0;0;0
-5706;en cour|cours;positive;0;0;0;0;0;0
-5707;en déclin;negative;0;0;1;0;0;0
-5708;en dent de scie;negative;0;1;1;1;0;0
-5709;en désaccord;negative;0;0;1;1;0;0
-5710;en descente;negative;0;1;0;0;0;0
-5711;en désordre;negative;0;1;1;1;0;1
-5712;en dessous;negative;0;0;0;0;0;0
-5713;en deuil;negative;0;0;1;0;0;0
-5714;en développement;positive;0;0;0;0;0;0
-5715;en douceur;positive;1;0;0;0;0;0
-5716;en épi;negative;0;1;0;0;0;0
-5717;en étain;positive;0;0;0;0;0;0
-5718;en évidence;positive;0;0;0;0;0;0
-5719;en éviter;negative;0;1;0;0;0;1
-5720;en fac similé;negative;0;0;0;0;0;0
-5721;en faillite;negative;0;1;1;0;0;0
-5722;en faire autant;positive;0;0;0;0;0;0
-5723;en feu;negative;0;1;0;1;0;0
-5724;en filigrane;positive;0;0;0;0;0;0
-5725;en fleur;positive;0;0;0;0;0;0
-5726;en former de cône;positive;0;0;0;0;0;0
-5727;en former de croître;positive;0;0;0;0;0;0
-5728;en fuite;negative;0;1;0;0;0;0
-5729;en furie;negative;0;1;0;1;0;1
-5730;en fusion;negative;0;1;0;0;0;0
-5731;en granite;positive;0;0;0;0;0;0
-5732;en haillon;negative;0;0;1;0;0;0
-5733;en hausse;positive;1;0;0;0;0;0
-5734;en haut;positive;0;0;0;0;0;0
-5735;en image;positive;0;0;0;0;0;0
-5736;en instance;positive;0;0;0;0;0;0
-5737;en jachère;negative;0;0;1;0;0;0
-5738;en lacet;negative;0;0;0;0;0;0
-5739;en laine;positive;0;0;0;0;0;0
-5740;en lambeau;negative;0;0;1;0;0;0
-5741;en larme;negative;0;1;1;0;0;1
-5742;en loque;negative;0;0;1;0;0;0
-5743;en marbre;positive;0;0;0;0;0;0
-5744;en mauvais santé;negative;0;1;1;0;0;1
-5745;en montée;positive;0;1;0;0;0;0
-5746;en morceau;negative;0;0;1;0;0;0
-5747;en mouvement;positive;0;0;1;0;0;0
-5748;en nickel;positive;0;0;0;0;0;0
-5749;en option;positive;0;0;0;0;0;0
-5750;en orbite;positive;0;0;0;0;0;0
-5751;en partie;negative;0;0;0;0;0;0
-5752;en peluche;positive;0;0;0;0;0;0
-5753;en pente;negative;0;1;0;0;0;0
-5754;en plein air;positive;0;0;0;0;0;0
-5755;en plein essor;positive;0;0;0;0;1;0
-5756;en plein forme;positive;1;0;0;0;0;0
-5757;en pleur|pleurs;negative;0;1;1;0;0;1
-5758;en plume;positive;0;0;0;0;0;0
-5759;en plus;positive;0;0;0;0;0;0
-5760;en poudre;negative;0;0;0;0;0;0
-5761;en privé;positive;0;0;0;0;0;0
-5762;en rafale;negative;0;1;0;0;1;0
-5763;en relief;positive;0;0;0;0;0;0
-5764;en retard;negative;0;1;1;1;1;0
-5765;en rotin;positive;0;0;0;0;0;0
-5766;en ruine;negative;0;1;1;1;0;1
-5767;en ruiner s;negative;0;0;1;0;0;1
-5768;en saillie;negative;0;1;0;0;1;0
-5769;en sang;negative;0;1;1;1;0;1
-5770;en satin;positive;0;0;0;0;0;0
-5771;en se battre;negative;0;0;0;1;0;0
-5772;en secret;negative;0;1;0;0;0;0
-5773;en série;positive;0;0;0;0;0;0
-5774;en silence;negative;0;0;0;0;0;0
-5775;en solitaire;negative;0;0;1;0;0;0
-5776;en sommeil;positive;0;0;0;0;0;0
-5777;en streaming;negative;0;0;0;0;0;0
-5778;en supposer que;negative;0;1;0;0;0;0
-5779;en surplus;negative;0;0;0;0;0;0
-5780;en suspens;positive;0;1;0;0;1;0
-5781;en synergie;positive;0;0;0;0;0;0
-5782;en terre;positive;0;0;0;0;0;0
-5783;en touffe;negative;0;0;0;0;0;1
-5784;en tout;positive;0;0;0;0;0;0
-5785;en train de mourir;negative;0;1;1;1;0;1
-5786;en très fort hausse;negative;0;1;0;0;1;0
-5787;en tripler;positive;0;0;0;0;0;0
-5788;en trop;negative;0;0;0;0;0;0
-5789;en vain;negative;0;0;1;0;0;1
-5790;en velours côtelé;positive;0;0;0;0;0;0
-5791;en vérité;positive;0;0;0;0;0;0
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-5821;enclume;positive;0;0;0;0;0;0
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-5823;encoder;negative;0;1;0;0;0;0
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-5829;encre;positive;0;0;0;0;0;0
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-5837;endolorir;negative;0;0;1;1;0;0
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-5839;endommager;negative;0;1;1;1;0;1
-5840;endossement;positive;0;0;0;0;0;0
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-5877;enjouement;positive;0;0;0;0;0;0
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-5890;enrager;negative;0;1;1;1;0;1
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-5902;enseignant;positive;0;0;0;0;0;0
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-5920;enthousiaste;positive;0;0;0;0;1;0
-5921;entièrement;positive;0;0;0;0;0;0
-5922;entité;positive;0;0;0;0;0;0
-5923;entomologie;negative;0;0;0;0;0;1
-5924;entonnoir;positive;0;0;0;0;0;0
-5925;entorse;negative;0;1;1;0;1;0
-5926;entortiller;negative;0;1;0;1;0;0
-5927;entourer;positive;0;0;0;0;0;0
-5928;entracte;positive;0;0;0;0;0;0
-5929;entrailles;negative;0;0;0;0;0;1
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-5931;entraînant;positive;0;0;0;0;0;0
-5932;entraînement;positive;0;0;0;0;0;0
-5933;entrer;positive;0;0;0;0;0;0
-5934;entrant;positive;0;0;0;0;0;0
-5935;entrave;negative;0;1;1;1;1;0
-5936;entraver;negative;0;1;1;1;1;0
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-5939;entrejambe;positive;0;0;0;0;0;0
-5940;entreposage;positive;0;0;0;0;0;0
-5941;entrepôt;positive;0;0;0;0;0;0
-5942;entrepôt débarras;negative;0;1;1;0;0;0
-5943;entreprenant;negative;0;1;0;1;0;0
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-5945;entrepreneur;positive;0;0;0;0;0;0
-5946;entrepreneur de pompe funèbre;positive;0;0;1;0;0;0
-5947;entreprise;positive;0;0;0;0;0;0
-5948;entrer en collision;negative;0;1;0;0;1;0
-5949;entrer en éruption;negative;0;1;0;1;1;0
-5950;entretien;positive;0;0;0;0;0;0
-5951;entrevoir;positive;0;0;0;0;0;0
-5952;entrevue;positive;0;0;0;0;0;0
-5953;entropie;negative;0;1;0;0;1;0
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-5955;énumération;positive;0;0;0;0;0;0
-5956;énumérer;positive;0;0;0;0;0;0
-5957;envahir par le mauvais herbe;negative;0;1;1;0;0;1
-5958;envahir;negative;0;1;1;1;1;0
-5959;envahissant;negative;0;1;0;1;1;1
-5960;envahisseur;negative;0;1;1;1;0;0
-5961;envelopper;positive;0;0;0;0;0;0
-5962;enveloppe;positive;0;0;0;0;0;0
-5963;envie irrésistible;negative;0;0;1;0;0;0
-5964;environ;positive;0;0;0;0;0;0
-5965;environner;positive;0;0;0;0;0;0
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-5967;envisager;positive;0;0;0;0;0;0
-5968;envoi;positive;0;0;0;0;0;0
-5969;envol;negative;0;0;0;0;0;0
-5970;envoûter;negative;0;1;0;0;0;0
-5971;envoyer un sms à;positive;0;0;0;0;0;0
-5972;éolienne;positive;0;0;0;0;0;0
-5973;épagneul;positive;0;0;0;0;0;0
-5974;épais;negative;0;0;0;0;0;0
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-5976;épaissir;negative;0;0;0;0;0;0
-5977;épaississant;negative;0;0;0;0;0;0
-5978;épaississement;negative;0;0;0;0;0;0
-5979;épanchement;negative;0;1;0;0;0;1
-5980;épanouir;positive;1;0;0;0;0;0
-5981;épanouissement;positive;0;0;0;0;0;0
-5982;épargne;positive;0;0;0;0;0;1
-5983;éparpiller;negative;0;1;1;0;0;0
-5984;éparpillement;negative;0;1;1;0;0;0
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-5988;épée;negative;0;1;0;0;0;0
-5989;éperdre;negative;0;1;1;0;0;0
-5990;éperlan;negative;0;0;0;0;0;0
-5991;éphémère;negative;0;0;1;0;1;0
-5992;ephéméride;positive;0;0;0;0;0;0
-5993;éphéméride;positive;0;0;0;0;0;0
-5994;épicéa;positive;0;0;0;0;0;0
-5995;épicer;negative;0;1;0;0;1;0
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-5999;épilogue;negative;0;0;0;0;0;0
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-6002;épingler;positive;0;0;0;0;0;0
-6003;épique;positive;0;0;0;0;1;0
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-6025;éprouver de le rancune contre;negative;0;0;0;1;0;0
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-6027;épuisant;negative;0;0;1;0;0;0
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-6032;équatorial;positive;0;0;0;0;0;0
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-6065;érudit;positive;0;0;0;0;0;0
-6066;éruption;negative;0;1;0;1;1;0
-6067;éruption cutané;negative;0;0;0;0;0;1
-6068;escadron;positive;0;0;0;0;0;0
-6069;escalade;positive;0;0;0;0;0;0
-6070;escalader;positive;0;1;0;0;0;0
-6071;escalator;positive;0;0;0;0;0;0
-6072;escalier;positive;0;0;0;0;0;0
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-6075;escarmouche;negative;0;0;0;1;0;0
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-6149;éthéré;negative;0;1;0;0;0;0
-6150;éthique;positive;0;0;0;0;0;0
-6151;ethnographie;positive;0;0;0;0;0;0
-6152;étinceler;positive;1;0;0;0;0;0
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-6154;étincelle;positive;0;0;0;0;1;0
-6155;étiologie;positive;0;0;0;0;0;0
-6156;étiqueter;positive;0;0;0;0;0;0
-6157;étiquette;positive;0;0;0;0;0;0
-6158;étirer;positive;0;0;0;0;0;0
-6159;étoffe;positive;0;0;0;0;0;0
-6160;étoile;positive;0;0;0;0;0;0
-6161;étoiler;positive;1;0;0;0;0;0
-6162;étole;positive;0;0;0;0;0;0
-6163;étonnamment;positive;0;0;0;0;1;0
-6164;étonnement;positive;0;1;0;0;1;0
-6165;étonner;positive;0;1;0;0;1;0
-6166;étouffant;negative;0;1;1;0;1;1
-6167;étouffoir;negative;0;1;0;0;0;0
-6168;étourderie;negative;0;1;1;0;0;0
-6169;étourdir;negative;0;1;1;0;1;0
-6170;étourdissement;negative;0;1;0;0;1;0
-6171;étrangement;negative;0;1;0;0;1;0
-6172;étrangeté;negative;0;0;1;0;1;1
-6173;étrangleur;negative;0;1;1;1;1;0
-6174;être à le tête;positive;0;0;0;0;0;0
-6175;être à le traîne;negative;0;0;1;0;0;0
-6176;être affaler;negative;0;0;0;0;0;0
-6177;être allonger;positive;0;0;0;0;0;0
-6178;être assortir;positive;0;0;0;0;0;0
-6179;être au service de;negative;0;0;0;0;0;0
-6180;être avachir;negative;0;0;0;0;0;0
-6181;être bénéfique;positive;1;0;0;0;0;0
-6182;être bénévole;positive;0;1;0;0;0;0
-6183;être caractériser;positive;0;0;0;0;0;0
-6184;être conforme à;positive;0;0;0;0;0;0
-6185;être domicilier;positive;0;0;0;0;0;0
-6186;être en adéquation;negative;0;1;0;1;0;0
-6187;être en apprentissage;positive;0;0;0;0;0;0
-6188;être en désaccord;negative;0;0;0;1;0;0
-6189;être en rage;negative;0;0;0;1;0;0
-6190;être imposant;negative;0;1;0;1;0;1
-6191;être incliner;negative;0;1;0;0;0;0
-6192;être indiscret;negative;0;0;0;1;0;1
-6193;être inonder;negative;0;1;0;0;0;0
-6194;être le plus toucher par;negative;0;0;1;1;0;0
-6195;être le premier toucher par;negative;0;0;1;1;0;0
-6196;être protubérant;negative;0;1;0;0;0;1
-6197;être quitte;positive;0;0;0;0;0;0
-6198;être rayonnant;positive;0;0;0;0;0;0
-6199;être souffrant;negative;0;0;1;0;0;0
-6200;étriquer;negative;0;1;1;0;0;0
-6201;étroit;negative;0;1;1;0;0;0
-6202;étroitesse;negative;0;1;1;1;0;0
-6203;étude;positive;0;0;0;0;0;0
-6204;étudier de deuxième année;positive;0;0;0;0;0;0
-6205;étudier de premier cycle unir;positive;0;0;0;0;0;0
-6206;étudier de premier année;positive;0;0;0;0;0;0
-6207;étudier;positive;0;0;0;0;0;0
-6208;étymologie;positive;0;0;0;0;0;0
-6209;euchre;positive;0;0;0;0;0;0
-6210;eugénisme;negative;0;0;1;0;0;0
-6211;euh;negative;0;1;1;0;0;0
-6212;euphémisme;negative;0;0;0;0;0;0
-6213;euthanasie;negative;0;1;1;0;0;0
-6214;évacuation;negative;0;1;0;0;0;0
-6215;évader;negative;0;1;0;1;0;0
-6216;évaluer;positive;0;0;0;0;0;0
-6217;evanescence;negative;0;0;1;0;1;0
-6218;évanescence;negative;0;0;1;0;1;0
-6219;évangélique;positive;0;0;0;0;0;0
-6220;évangéliste;positive;0;0;0;0;0;0
-6221;évangile;positive;0;0;0;0;0;0
-6222;évanouissement;negative;0;1;1;0;1;0
-6223;évaporation;negative;0;0;0;0;0;0
-6224;éveil;positive;1;0;0;0;0;0
-6225;éveiller;positive;0;0;0;0;0;0
-6226;événement;positive;0;0;0;0;1;0
-6227;événement fortuit;positive;0;0;0;0;1;0
-6228;événement marquant;positive;0;0;0;0;0;0
-6229;évent;positive;0;0;0;1;0;0
-6230;éventail;positive;0;0;0;0;0;0
-6231;éventer;positive;0;0;0;0;0;0
-6232;éventuellement;positive;0;0;0;0;1;0
-6233;évêque;positive;0;0;0;0;0;0
-6234;évidemment;positive;0;0;0;0;0;0
-6235;évident;positive;0;0;0;1;0;1
-6236;évier;negative;0;1;1;0;0;0
-6237;évincer;negative;0;1;1;1;1;0
-6238;éviter;negative;0;1;0;0;0;1
-6239;évoluer;positive;1;0;0;0;0;0
-6240;evolution;positive;0;0;0;0;0;0
-6241;évolution;positive;0;0;0;0;0;0
-6242;évoquer;positive;0;0;0;0;0;0
-6243;exacerbation;negative;0;1;0;1;0;0
-6244;exacerber;negative;0;0;0;0;0;0
-6245;exact;positive;0;0;0;0;0;0
-6246;exactitude;positive;0;0;0;0;0;0
-6247;exagération;negative;0;0;0;0;0;0
-6248;exagérer;negative;0;0;0;1;0;0
-6249;exaltation;positive;0;0;0;0;1;0
-6250;exalter;positive;0;0;0;0;0;0
-6251;examiner avec soin;positive;0;0;0;0;0;0
-6252;exaspération;negative;0;0;0;1;0;1
-6253;exaspérer;negative;0;1;0;1;0;0
-6254;exaucer;positive;0;0;0;0;0;0
-6255;excavateur;negative;0;0;0;0;0;0
-6256;excavation;negative;0;0;0;0;1;0
-6257;excaver;negative;0;0;1;0;0;1
-6258;excéder;positive;0;0;0;0;1;0
-6259;excédent;negative;0;0;0;0;0;0
-6260;excellence;positive;0;0;0;0;1;1
-6261;excellent;positive;0;0;0;1;0;0
-6262;exceller;positive;0;0;0;0;1;0
-6263;excentricité;negative;0;0;0;0;0;0
-6264;excentrique;negative;0;1;0;1;1;0
-6265;exception;positive;0;0;0;0;1;0
-6266;exceptionnellement;negative;0;0;0;0;0;0
-6267;excès;negative;0;0;0;0;0;1
-6268;excessivement;negative;0;0;0;0;0;0
-6269;exciser;negative;0;1;0;0;0;1
-6270;excision;negative;0;1;0;0;0;0
-6271;excitabilité;positive;0;0;0;0;0;0
-6272;excitable;positive;0;0;0;0;1;0
-6273;excitant;positive;0;0;0;0;1;0
-6274;excitation;positive;0;1;0;1;1;0
-6275;exciter;positive;0;0;0;0;1;0
-6276;exclure;negative;0;1;1;1;0;1
-6277;exclusion;negative;0;1;1;0;0;1
-6278;excrément;negative;0;0;0;0;0;1
-6279;excrétion;negative;0;0;0;0;0;1
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-6281;excuse;positive;0;0;1;0;0;0
-6282;exécutif;positive;0;0;0;0;0;0
-6283;exécution;negative;0;1;1;1;0;0
-6284;exégèse;positive;0;0;0;0;0;0
-6285;exemplaire;positive;0;0;0;0;0;0
-6286;exemple;positive;0;0;0;0;0;0
-6287;exempt;positive;0;0;0;0;0;0
-6288;exempter;positive;0;0;0;0;0;0
-6289;exemption;positive;0;0;0;0;0;0
-6290;exercer;positive;0;0;0;0;0;0
-6291;exercer son pontificat;positive;0;0;0;0;0;0
-6292;exercice;positive;0;0;0;0;0;0
-6293;exercice physique;positive;0;0;0;0;0;0
-6294;exhiber;negative;0;0;0;1;0;0
-6295;exhortation;positive;0;0;0;0;0;0
-6296;exhumer;negative;0;0;1;0;0;0
-6297;exigeant;negative;0;1;0;1;1;1
-6298;exiguïté;negative;0;0;1;0;0;0
-6299;exil;negative;0;1;1;1;0;0
-6300;existant;positive;0;0;0;0;0;0
-6301;exode;negative;0;0;1;0;0;0
-6302;exorbiter;negative;0;1;0;1;1;0
-6303;exorbitant;negative;0;1;0;1;1;0
-6304;exorcisme;negative;0;1;1;1;0;0
-6305;exorciste;negative;0;1;0;1;0;0
-6306;exotique;positive;0;0;0;0;0;0
-6307;expansif;positive;0;0;0;0;0;0
-6308;expansion;positive;0;0;0;0;0;0
-6309;expédient;positive;0;0;0;0;0;0
-6310;expéditif;negative;0;0;0;1;0;0
-6311;expédition;positive;0;0;0;0;0;0
-6312;expérience;positive;0;0;0;0;1;0
-6313;expert comptable commissaire avoir;positive;0;1;0;0;0;0
-6314;expiation;positive;1;0;0;0;0;0
-6315;expiration;negative;0;1;0;0;0;1
-6316;expirer;negative;0;0;1;0;0;1
-6317;explication;positive;0;0;0;0;0;0
-6318;expliquer;positive;0;0;0;0;0;0
-6319;exploit;positive;0;0;0;0;1;0
-6320;exploitation agricole;positive;0;0;0;0;0;0
-6321;exploitation forestier;positive;0;0;0;0;0;0
-6322;explorateur;positive;0;0;0;0;0;0
-6323;explorer;positive;0;0;0;0;1;0
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-6329;exposant;positive;0;0;0;0;0;0
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-6336;expression passer partout;negative;0;0;0;0;0;0
-6337;exprimer;positive;0;0;0;0;0;0
-6338;exprimer son reconnaissance;positive;0;0;0;0;0;0
-6339;expropriation;negative;0;1;1;1;0;1
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-6341;exsangue;negative;0;1;0;0;0;0
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-6343;extase;positive;1;0;0;0;0;0
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-6346;extension;positive;0;0;0;0;0;0
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-6353;extirper;positive;0;0;0;0;0;0
-6354;extra muros;negative;0;0;0;0;0;0
-6355;extracteur;positive;0;0;0;0;0;0
-6356;extraction;positive;0;0;0;0;0;0
-6357;extradition;negative;0;1;1;0;0;0
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-6359;extrajudiciaire;negative;0;1;0;0;0;0
-6360;extraodianires;positive;1;0;0;0;0;0
-6361;extraordinaire;positive;0;1;0;0;1;0
-6362;extraterrestre;negative;0;1;0;0;0;1
-6363;extrêmement pénible;negative;0;1;1;0;0;0
-6364;extrémité;negative;0;0;0;0;0;0
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-6366;extrusion;negative;0;1;0;0;0;1
-6367;exubérance;positive;1;0;0;0;0;0
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-6393;faille;negative;0;1;1;0;0;0
-6394;faillible;negative;0;1;1;0;0;0
-6395;faillite;negative;0;1;1;1;0;1
-6396;faim;negative;0;0;1;0;0;0
-6397;faire;positive;0;0;0;0;0;0
-6398;faire apparaître;negative;0;0;0;0;1;0
-6399;faire appel;positive;0;0;0;0;0;0
-6400;faire attention;positive;0;1;0;0;0;0
-6401;faire bouillir;negative;0;0;0;1;0;1
-6402;faire cadeau;positive;0;0;0;0;0;0
-6403;faire campagne;positive;0;0;0;0;0;0
-6404;faire chanter;negative;0;1;0;1;0;0
-6405;faire circuler;positive;0;0;0;0;0;0
-6406;faire confiance;positive;0;0;0;0;0;0
-6407;faire cuire au barbecue;positive;0;0;0;0;0;0
-6408;faire de l exercice;positive;0;0;0;0;0;0
-6409;faire de le boxe;negative;0;0;0;1;0;0
-6410;faire de le luge;positive;1;0;0;0;0;0
-6411;faire de le moto;positive;0;0;0;0;0;0
-6412;faire de le publicité;positive;0;0;0;0;0;0
-6413;faire de le sculpture;positive;0;0;0;0;0;0
-6414;faire du affaire;positive;0;0;0;0;0;0
-6415;faire du compromis;positive;0;0;0;0;0;0
-6416;faire du course;positive;0;0;0;0;1;0
-6417;faire du folie;negative;0;1;0;0;1;0
-6418;faire du histoire;negative;0;0;1;1;0;0
-6419;faire du remarque continuell;negative;0;1;1;1;0;0
-6420;faire du réserve;positive;0;0;0;0;0;0
-6421;faire détoner;negative;0;1;0;0;1;0
-6422;faire du canoë;positive;0;0;0;0;0;0
-6423;faire du jogging;positive;0;0;0;0;0;0
-6424;faire du mannequinat;positive;0;0;0;0;0;0
-6425;faire du planeur;positive;1;0;0;0;0;0
-6426;faire du racolage;negative;0;0;0;0;0;0
-6427;faire du rap;negative;0;0;0;0;0;0
-6428;faire du trafic;negative;0;0;1;0;0;0
-6429;faire du traîneau;positive;1;0;0;0;0;0
-6430;faire exploser;negative;0;1;0;1;1;0
-6431;faire face;positive;0;0;0;0;0;0
-6432;faire fondre;negative;0;0;0;0;0;0
-6433;faire infuser;positive;0;0;0;0;0;0
-6434;faire le chronique de;positive;0;0;0;0;0;0
-6435;faire le cour;positive;0;0;0;0;0;0
-6436;faire le course;positive;0;0;0;0;0;0
-6437;faire le fête;positive;0;0;0;0;1;0
-6438;faire le grimace;negative;0;1;1;1;0;1
-6439;faire le leçon;positive;0;0;0;0;0;0
-6440;faire le queue;negative;0;0;1;0;0;0
-6441;faire le clown;positive;0;0;0;0;1;0
-6442;faire le médiateur;positive;0;0;0;0;0;0
-6443;faire le pitre;positive;0;0;0;0;1;0
-6444;faire le magasin;positive;0;0;0;0;1;0
-6445;faire mal;negative;0;1;1;1;0;0
-6446;faire marche arrière;negative;0;1;1;0;0;0
-6447;faire obstruction à;negative;0;0;0;1;0;0
-6448;faire passer un entretien;positive;0;0;0;0;0;0
-6449;faire pipi;negative;0;0;0;0;0;1
-6450;faire pivoter;positive;0;0;0;0;0;0
-6451;faire plaisir;positive;1;0;0;0;0;0
-6452;faire référence à;positive;0;0;0;0;0;0
-6453;faire remarquer;negative;0;0;0;0;0;0
-6454;faire semblant;negative;0;0;0;1;0;1
-6455;faire son début;positive;0;0;0;0;0;0
-6456;faire son valise;positive;0;0;0;0;0;0
-6457;faire signe;positive;0;0;0;0;0;0
-6458;faire suite;positive;0;0;0;0;0;0
-6459;faire surface;positive;0;0;0;0;0;0
-6460;faire sursauter;negative;0;1;0;0;1;0
-6461;faire tourner;positive;0;0;0;0;0;0
-6462;faire tremper;negative;0;0;0;0;0;1
-6463;faire un bond;positive;1;0;0;0;0;0
-6464;faire un bruit métallique;negative;0;1;0;0;1;0
-6465;faire un bruit sourd;negative;0;1;0;0;1;0
-6466;faire un coup amortir;negative;0;1;0;1;1;0
-6467;faire un geste;positive;0;0;0;0;0;0
-6468;faire un régime;negative;0;0;0;0;0;0
-6469;faire un scanner de;positive;0;0;0;0;0;0
-6470;faire un signe de le main;positive;0;0;0;0;0;0
-6471;faire un signe de le tête;positive;0;0;0;0;0;0
-6472;faire un somme;positive;0;0;0;0;0;0
-6473;faire un sondage;positive;0;0;0;0;0;0
-6474;faire un descente dans;negative;0;1;0;1;1;0
-6475;faire un digression;negative;0;1;0;1;0;0
-6476;faire un embardée;negative;0;1;0;0;1;0
-6477;faire un gaffe;negative;0;1;1;0;0;1
-6478;faire un hémorragie;negative;0;1;1;0;0;1
-6479;faire un overdose;negative;0;1;1;0;0;1
-6480;faire un randonnée;positive;0;0;0;0;0;0
-6481;faire un remise;positive;0;0;0;0;0;0
-6482;fairway;positive;0;0;0;0;0;0
-6483;faisabilité;positive;0;0;0;0;0;0
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-7192;grandiose;positive;0;0;0;0;1;0
-7193;grandir;positive;0;0;0;0;0;0
-7194;grange;positive;0;0;0;0;0;0
-7195;granite;positive;0;0;0;0;0;0
-7196;granule;negative;0;0;0;0;0;0
-7197;granuler;negative;0;0;0;0;0;0
-7198;graphique;positive;0;0;0;0;0;0
-7199;graphisme;positive;0;0;0;0;0;0
-7200;grappe;positive;0;0;0;0;0;0
-7201;grassouillet;negative;0;0;0;0;0;1
-7202;gratis;positive;0;0;0;0;0;0
-7203;gratitude;positive;0;0;0;0;0;0
-7204;grattage;negative;0;1;0;1;0;1
-7205;gratter ciel;positive;0;0;0;0;0;0
-7206;gratter;negative;0;1;1;1;1;0
-7207;gratuit;positive;0;0;0;0;0;1
-7208;gratuitement;positive;0;0;0;0;0;0
-7209;graver;positive;0;0;0;0;0;0
-7210;gravement;negative;0;0;1;0;0;0
-7211;gravier;negative;0;0;0;0;0;0
-7212;gravir;positive;0;1;0;0;0;0
-7213;gravitation;positive;0;0;0;0;0;0
-7214;graviter;positive;0;0;0;0;0;0
-7215;gravure;positive;0;0;0;0;0;0
-7216;gravure sur bois;positive;0;0;0;0;0;0
-7217;gré;positive;0;0;0;0;0;0
-7218;gréement;positive;0;0;0;0;0;0
-7219;greffe;positive;0;0;0;0;0;0
-7220;greffer;positive;0;0;0;0;0;0
-7221;greffier;positive;0;0;0;0;0;0
-7222;greffon;positive;0;0;0;0;0;0
-7223;grégaire;positive;0;0;0;0;0;0
-7224;grêlé;negative;0;1;1;0;0;0
-7225;grelotter;negative;0;1;0;0;0;0
-7226;grelottant;negative;0;1;0;0;0;0
-7227;grenade;negative;0;1;0;1;0;0
-7228;grenat;positive;0;0;0;0;0;0
-7229;grenier;positive;0;0;0;0;0;0
-7230;grenouille;negative;0;0;0;0;0;1
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-7232;grésil;negative;0;1;1;0;0;1
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-7234;grésiller;negative;0;1;0;1;0;0
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-7236;gribouillage;negative;0;0;0;0;1;0
-7237;gribouiller;negative;0;0;0;0;1;0
-7238;grief;negative;0;0;1;1;0;1
-7239;griffe;negative;0;1;0;1;0;0
-7240;griffer;negative;0;1;0;1;1;0
-7241;griffon;positive;0;0;0;0;0;0
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-7244;grignoter;positive;0;0;0;0;0;0
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-7246;grillage;negative;0;1;0;0;1;0
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-7248;grillon;positive;0;0;0;0;0;0
-7249;grimace;negative;0;1;1;1;0;1
-7250;grimacer;negative;0;1;1;1;0;1
-7251;grimper;positive;0;1;0;0;0;0
-7252;grincer;negative;0;1;0;1;0;1
-7253;grincement;negative;0;1;1;1;1;0
-7254;grippe;negative;0;1;1;0;0;1
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-7259;grisonnant;negative;0;0;1;0;0;1
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-7261;groggy;negative;0;0;1;0;1;0
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-7263;grommeler;negative;0;1;1;1;0;1
-7264;grondement;negative;0;1;0;1;1;1
-7265;gros lot;positive;0;0;0;0;1;0
-7266;gros morceau;positive;0;0;0;0;0;0
-7267;gros titre;positive;0;0;0;0;0;0
-7268;grossesse;positive;0;0;0;0;0;1
-7269;grosseur;negative;0;1;0;0;0;0
-7270;grossièrement;negative;0;0;0;0;0;0
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-7273;grotte;negative;0;1;1;0;0;0
-7274;grouiller;negative;0;1;0;1;0;1
-7275;grouillant;negative;0;1;0;1;0;0
-7276;grouper;positive;0;0;0;0;0;0
-7277;groupement;positive;0;0;0;0;0;0
-7278;grue;positive;0;0;0;0;0;0
-7279;grumeleux;negative;0;0;0;0;0;1
-7280;gué;positive;0;0;0;0;0;0
-7281;guépard;negative;0;1;0;0;0;0
-7282;guêpe;negative;0;1;0;0;0;0
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-7284;guérir;positive;0;0;0;0;0;0
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-7286;guerre;negative;0;1;1;1;0;0
-7287;guet;positive;0;0;0;0;0;0
-7288;guet apens;negative;0;1;0;1;1;0
-7289;guetteur;positive;0;0;0;0;0;0
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-7298;guillotine;negative;0;1;1;1;0;1
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-7303;guitare;positive;0;0;0;0;0;0
-7304;gymnase;positive;0;0;0;0;0;0
-7305;gymnaste;positive;0;0;0;0;0;0
-7306;gymnastique;positive;0;0;0;0;0;0
-7307;gynécologie;negative;0;0;0;0;0;1
-7308;gyroscope;positive;0;0;0;0;0;0
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-7310;habileté;positive;0;0;0;0;0;0
-7311;habiliter;positive;0;0;0;0;0;0
-7312;habiller;positive;0;0;0;0;0;0
-7313;habillement;positive;0;0;0;0;0;0
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-7315;habitation;positive;0;0;0;0;0;0
-7316;habiter;positive;0;0;0;0;0;0
-7317;habitude;positive;0;0;0;0;0;0
-7318;habituellement;positive;0;0;0;0;0;0
-7319;hacher;negative;0;1;0;0;0;0
-7320;hachette;negative;0;1;0;1;1;0
-7321;hachis;negative;0;0;0;1;0;0
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-7323;haie;positive;0;0;0;0;0;0
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-7326;hâle;positive;0;0;0;0;0;0
-7327;hâler;positive;0;0;0;0;0;0
-7328;haleine;positive;0;0;0;0;0;0
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-7331;hall;positive;0;0;0;0;0;0
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-7335;halte;negative;0;1;1;1;0;0
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-7338;hameau;positive;0;0;0;0;0;0
-7339;hameçon;negative;0;1;0;0;1;0
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-7345;hardi;positive;0;0;0;0;0;0
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-7347;harmonica;positive;0;0;0;0;0;0
-7348;harmonie;positive;0;0;0;0;0;0
-7349;harmonieusement;positive;0;0;0;0;0;0
-7350;harmonique;positive;0;0;0;0;0;0
-7351;harmoniser;positive;0;0;0;0;0;0
-7352;harnais;negative;0;1;1;0;0;0
-7353;harpe;positive;0;0;0;0;0;0
-7354;hasard;positive;0;0;0;0;1;0
-7355;hasard extraordinaire;positive;0;0;0;0;1;0
-7356;haschisch;negative;0;0;0;0;0;1
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-7358;hâter;negative;0;1;0;0;1;0
-7359;hâtivement;negative;0;1;0;0;1;0
-7360;hausse;positive;0;0;0;0;0;0
-7361;haussement d épaule;negative;0;0;0;0;0;0
-7362;hausser;positive;0;0;0;0;0;0
-7363;hausser le épaule;negative;0;0;0;0;0;0
-7364;haut et fort;negative;0;0;0;1;0;0
-7365;haut le c?ur;negative;0;0;0;0;0;1
-7366;haut parleur;positive;0;0;0;0;0;0
-7367;hautain;negative;0;0;0;1;0;1
-7368;hautbois;positive;0;0;0;0;0;0
-7369;haut terre;positive;0;0;0;0;0;0
-7370;hauteur;positive;0;0;0;0;0;0
-7371;havre;positive;0;0;0;0;0;0
-7372;hebdomadaire;positive;0;0;0;0;0;0
-7373;hébergement;positive;0;0;0;0;0;0
-7374;hébétement;negative;0;0;1;0;1;0
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-7376;hectare;positive;0;0;0;0;0;0
-7377;hédonisme;positive;1;0;0;0;0;0
-7378;hégémonique;negative;0;1;1;0;0;0
-7379;hélicoïdal;positive;0;0;0;0;0;0
-7380;hématite;positive;0;0;0;0;0;0
-7381;hemi;positive;0;0;0;0;0;0
-7382;hémi;positive;0;0;0;0;0;0
-7383;hémisphère;positive;0;0;0;0;0;0
-7384;hémisphérique;positive;0;0;0;0;0;0
-7385;hémorragie;negative;0;1;1;0;0;1
-7386;hémorroïde;negative;0;0;0;0;0;1
-7387;hennir;negative;0;0;0;0;0;0
-7388;hennissement;negative;0;0;0;0;0;0
-7389;héraldique;positive;0;0;0;0;0;0
-7390;héraut;positive;0;0;0;0;0;0
-7391;herbacé;negative;0;0;0;0;0;0
-7392;herbe;negative;0;0;0;0;0;1
-7393;herbe à chat;negative;0;0;0;0;0;1
-7394;herbier;positive;0;0;0;0;0;0
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-7402;hériter;positive;0;0;0;0;0;0
-7403;héritier;positive;0;0;0;0;0;0
-7404;hermaphrodite;negative;0;0;0;0;1;1
-7405;herméneutique;positive;0;0;0;0;0;0
-7406;hernie;negative;0;1;1;0;0;1
-7407;héroïne;negative;0;0;0;0;0;1
-7408;héroïque;positive;0;0;0;0;1;0
-7409;héroïque;positive;0;0;0;0;1;0
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-7420;heure du coucher;positive;0;0;0;0;0;0
-7421;heureusement;positive;1;0;0;0;0;0
-7422;heurt;negative;0;1;0;1;0;0
-7423;heurter sur le côté;negative;0;0;0;0;0;0
-7424;hexagone;positive;0;0;0;0;0;0
-7425;hiatal;negative;0;1;0;0;1;0
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-7450;holocauste;negative;0;1;1;1;0;1
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-7455;hommage;positive;0;0;0;0;0;0
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-7459;homogène;positive;0;0;0;0;0;0
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-7462;homologue;positive;0;0;0;0;0;0
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-7468;honnêteté;positive;0;0;0;0;0;0
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-7487;hors du chemin;negative;0;1;1;0;0;0
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-7489;hors norme;positive;1;0;0;0;0;0
-7490;hors le loi;negative;0;1;0;1;0;0
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-7529;hydraulique;positive;0;0;0;0;0;0
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-7539;hygiénique;positive;0;0;0;0;0;0
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-8246;jaugeage;positive;0;0;0;0;0;0
-8247;jauger;positive;0;0;0;0;0;0
-8248;jaune;positive;0;0;0;0;0;0
-8249;jaunisse;negative;0;1;0;0;0;1
-8250;javelot;negative;0;1;0;1;0;0
-8251;jeûne;negative;0;0;1;0;0;0
-8252;jean;positive;0;0;0;0;0;0
-8253;jeep;positive;0;0;0;0;0;0
-8254;jeter un coup d ?il;positive;0;0;0;0;0;0
-8255;jeter du détritus;negative;0;0;0;0;0;1
-8256;jeter du ordure;negative;0;0;0;0;0;1
-8257;jeton;positive;0;0;0;0;0;0
-8258;jeu de dame;positive;0;0;0;0;0;0
-8259;jeu de mot;positive;1;0;0;0;0;0
-8260;jeu du solitaire;positive;0;0;0;0;0;0
-8261;jeune;positive;0;0;0;0;1;0
-8262;jeune femme;positive;0;0;0;1;0;1
-8263;jeune fille;positive;0;0;0;1;0;1
-8264;jeune homme;positive;0;0;0;0;0;1
-8265;jeune marié;positive;0;0;0;0;0;0
-8266;jeunesse;positive;0;1;0;1;1;0
-8267;jeu d argent;negative;0;1;0;0;1;0
-8268;jingle;positive;1;0;0;0;0;0
-8269;joaillerie;positive;0;0;0;0;0;0
-8270;jockey;positive;0;0;0;0;0;0
-8271;jogging;positive;0;0;0;0;0;0
-8272;joie;positive;1;0;0;0;0;0
-8273;joindre;positive;0;0;0;0;0;0
-8274;jointure;positive;0;0;0;0;0;0
-8275;joker;positive;0;0;0;0;1;0
-8276;joli;positive;0;0;0;0;0;0
-8277;jonc;positive;0;0;0;0;0;0
-8278;jonction;positive;0;0;0;0;0;0
-8279;jonglage;positive;0;0;0;0;0;0
-8280;jongler;positive;0;0;0;0;0;0
-8281;jonglerie;positive;0;0;0;0;0;0
-8282;joue;positive;0;0;0;0;0;0
-8283;jouer;positive;1;0;0;0;0;0
-8284;jouer au golf;positive;0;0;0;0;0;0
-8285;jouer du violon;positive;0;0;0;0;0;0
-8286;jouer un atout;positive;0;0;0;0;1;0
-8287;jouet;positive;1;0;0;0;0;0
-8288;joueur;negative;0;0;0;0;0;0
-8289;joueur de cornemuse;positive;0;0;0;0;0;0
-8290;joug;positive;0;0;0;0;0;0
-8291;jouir de;positive;0;0;0;0;0;0
-8292;jour;positive;0;0;0;0;0;0
-8293;journal;positive;0;0;0;0;0;0
-8294;journal intime;positive;0;0;0;0;0;0
-8295;journal officiel;positive;0;0;0;0;0;0
-8296;journalisme;positive;0;0;0;0;0;0
-8297;journaliste;positive;0;0;0;0;0;0
-8298;journée;positive;0;0;0;0;0;0
-8299;joyau;positive;0;0;0;0;0;0
-8300;joyeuses|joyeux;positive;0;0;0;0;0;0
-8301;jubilaire;positive;0;0;0;0;1;0
-8302;jubiler;positive;0;0;0;0;1;0
-8303;juchoir;positive;0;0;0;0;0;0
-8304;judiciaire;positive;0;0;0;0;0;0
-8305;juge;positive;0;0;0;0;0;0
-8306;juger;negative;0;0;0;0;0;0
-8307;jugeote;positive;0;0;0;0;0;0
-8308;jumeau;positive;0;0;0;0;0;0
-8309;jument;positive;0;0;0;0;0;0
-8310;jungle;negative;0;1;0;0;0;0
-8311;junior;positive;0;0;0;0;0;0
-8312;junte;negative;0;1;0;1;0;0
-8313;jupe;positive;0;0;0;0;0;0
-8314;juridiction;positive;0;0;0;0;0;0
-8315;juridique;positive;0;0;0;0;0;0
-8316;jurisprudence;positive;0;0;1;0;0;0
-8317;juriste;positive;0;0;0;0;0;0
-8318;juron;negative;0;0;0;1;0;0
-8319;jury;positive;0;0;0;0;0;0
-8320;jus;positive;0;0;0;0;0;0
-8321;jusqu à;positive;0;0;0;0;0;0
-8322;jusqu ici;positive;0;0;0;0;0;0
-8323;juste;positive;0;0;0;0;0;0
-8324;juste à temps;positive;0;0;0;0;0;0
-8325;justesse;positive;0;0;0;0;0;0
-8326;justice;positive;0;0;0;0;0;0
-8327;justifiable;positive;0;0;0;0;0;0
-8328;justificatif;positive;0;0;0;0;0;0
-8329;justification;positive;0;0;0;0;0;0
-8330;justifier;positive;0;0;0;0;0;0
-8331;jute;negative;0;0;0;0;0;0
-8332;juteux;positive;0;0;0;0;0;0
-8333;juvenile;negative;0;0;0;0;0;0
-8334;juvénile;negative;0;0;0;0;0;0
-8335;juxtaposition;positive;0;0;0;0;0;0
-8336;kaiser;positive;0;0;0;0;0;0
-8337;kaki;negative;0;0;0;0;0;0
-8338;kaléidoscope;positive;0;0;0;0;0;0
-8339;kangourou;positive;0;0;0;0;0;0
-8340;kanji;positive;0;0;0;0;0;0
-8341;karma;positive;0;0;0;0;0;0
-8342;kayak;positive;0;0;0;0;0;0
-8343;kérosène;negative;0;1;0;0;0;1
-8344;khan;positive;0;1;0;0;0;0
-8345;kilogramme;positive;0;0;0;0;0;0
-8346;kilométrage;positive;0;0;0;0;0;0
-8347;kilomètre;positive;0;0;0;0;0;0
-8348;kilt;positive;0;0;0;0;0;0
-8349;kimono;positive;0;0;0;0;0;0
-8350;kiosque;positive;0;0;0;0;0;0
-8351;kiosque à journal|journau;positive;0;0;0;0;0;0
-8352;kit;positive;0;0;0;0;0;0
-8353;klaxonner;negative;0;0;0;1;0;1
-8354;kris;negative;0;1;0;0;0;0
-8355;kyste;negative;0;1;1;0;0;1
-8356;kyste sébacé;negative;0;0;0;0;0;1
-8357;kystique;negative;0;0;0;0;0;1
-8358;le plus âgé;negative;0;0;0;0;0;0
-8359;le plus bas;negative;0;0;1;0;0;0
-8360;le plus petit;negative;0;1;1;0;0;0
-8361;là bas;negative;0;0;0;0;0;0
-8362;labeur;negative;0;0;1;0;0;1
-8363;labo;positive;0;0;0;0;0;0
-8364;laboratoire;positive;0;0;0;0;0;0
-8365;labour;positive;0;0;0;0;0;0
-8366;labourage;positive;0;0;0;0;0;0
-8367;labourer;positive;0;0;0;0;0;0
-8368;labyrinthe;negative;0;1;0;0;1;0
-8369;lac;positive;0;0;0;0;0;0
-8370;lac écossais;negative;0;1;0;0;0;0
-8371;lâche;negative;0;1;1;1;0;1
-8372;lâchement;negative;0;1;0;1;0;1
-8373;lâcher le chien;positive;0;0;0;0;0;0
-8374;lâcheté;negative;0;1;0;1;0;1
-8375;laconique;negative;0;1;0;1;1;0
-8376;lacrosse;positive;0;0;0;0;0;0
-8377;lacté;positive;0;0;0;0;0;0
-8378;lagon;positive;0;0;0;0;0;0
-8379;laïcité;positive;0;0;0;0;0;0
-8380;laid;negative;0;0;0;0;0;1
-8381;laideur;negative;0;1;1;0;0;1
-8382;laine;positive;0;0;0;0;0;0
-8383;laineux;positive;0;0;0;0;0;0
-8384;laïque;negative;0;0;1;0;0;0
-8385;laisse;negative;0;1;1;1;0;0
-8386;laisser;negative;0;0;0;0;0;0
-8387;laisser sortir;negative;1;0;0;0;0;0
-8388;laisser supposer;positive;0;0;0;0;0;0
-8389;laisser tomber;negative;0;1;1;0;0;0
-8390;lait;positive;0;0;0;0;0;0
-8391;laitier;positive;0;0;0;0;0;0
-8392;laitier|laitière;positive;0;0;0;0;0;0
-8393;laiton;positive;0;0;0;0;0;0
-8394;laitue;positive;0;0;0;0;0;0
-8395;lama;negative;0;1;0;0;0;1
-8396;lambda;positive;0;0;0;0;0;0
-8397;lambeau;negative;0;0;1;0;0;0
-8398;lambrequin;positive;0;0;0;0;0;0
-8399;lamentablement;negative;0;0;1;0;0;0
-8400;lamentation;negative;0;1;1;0;0;1
-8401;laminer;positive;0;0;0;0;0;0
-8402;lampe;positive;0;0;0;0;0;0
-8403;lampe de poche;positive;0;0;0;0;0;0
-8404;lamper;negative;0;0;0;0;0;1
-8405;lance;negative;0;1;1;1;0;0
-8406;lance pierre;negative;0;1;0;1;0;0
-8407;lancement;positive;1;0;0;0;0;0
-8408;lancier;negative;0;1;0;0;0;0
-8409;lanciner;negative;0;1;1;0;1;0
-8410;lancinant;negative;0;1;1;0;1;0
-8411;lande;negative;0;0;0;0;0;0
-8412;langage;positive;0;0;0;0;0;0
-8413;langoureux;negative;0;1;1;0;0;0
-8414;langoustine;negative;0;0;0;0;0;0
-8415;langue;positive;0;0;0;0;0;0
-8416;languir;negative;0;0;1;0;0;0
-8417;languissant;negative;0;0;1;0;0;0
-8418;lanterne;positive;0;0;0;0;0;0
-8419;lapidaire;negative;0;0;1;1;0;0
-8420;lapin;positive;0;0;0;0;0;0
-8421;laque;negative;0;0;0;0;0;1
-8422;laquer;negative;0;0;0;0;0;1
-8423;larcin;negative;0;0;0;1;1;1
-8424;lard;negative;0;0;0;0;0;1
-8425;large;positive;0;0;0;0;0;0
-8426;large sourire;positive;0;0;0;0;1;0
-8427;largement;positive;0;0;0;0;0;0
-8428;largeur;positive;0;0;0;0;0;0
-8429;largo;positive;0;0;0;0;0;0
-8430;larguer;negative;0;0;0;0;0;1
-8431;larme;negative;0;0;1;0;0;0
-8432;larve;negative;0;0;0;0;0;1
-8433;larynx;positive;0;0;0;0;0;0
-8434;laser;positive;0;0;0;0;0;0
-8435;lasser;negative;0;0;1;0;0;0
-8436;lassitude;negative;0;0;1;0;0;0
-8437;lasso;negative;0;1;0;1;0;0
-8438;latex;negative;0;0;0;0;0;0
-8439;latitude;positive;0;0;0;0;0;0
-8440;latrines;negative;0;0;0;0;0;1
-8441;laurier;positive;1;0;0;0;0;0
-8442;lavande;positive;0;0;0;0;0;0
-8443;laver;positive;0;0;0;0;0;0
-8444;lave;negative;0;1;0;1;0;1
-8445;lavement;negative;0;0;0;0;0;1
-8446;laverie;positive;0;0;0;0;0;0
-8447;le long de;positive;0;0;0;0;0;0
-8448;le meilleur;positive;1;0;0;0;0;0
-8449;le plus élever;positive;0;1;0;0;1;0
-8450;le plus grand;positive;1;0;0;0;0;0
-8451;le plus haut;positive;0;1;0;0;1;0
-8452;le souffle couper;negative;0;1;0;0;1;0
-8453;leader;positive;0;0;0;0;0;0
-8454;lécher;negative;0;0;0;0;0;1
-8455;leçon;positive;0;0;0;0;0;0
-8456;lectorat;positive;0;0;0;0;0;0
-8457;lecteur;positive;0;0;0;0;0;0
-8458;lecture;positive;0;0;0;0;0;0
-8459;lecture attentif;positive;0;0;0;0;0;0
-8460;légal;positive;0;0;0;0;0;0
-8461;légalement;positive;0;0;0;0;0;0
-8462;légaliser;positive;0;1;0;1;0;0
-8463;légalité;positive;0;0;0;0;0;0
-8464;légendaire;positive;0;0;0;0;0;0
-8465;légende;positive;0;0;0;0;0;0
-8466;légender;positive;0;0;0;0;0;0
-8467;légèrement;negative;0;0;0;0;0;0
-8468;légèrement venteux;positive;1;0;0;0;0;0
-8469;légèreté;positive;1;0;0;0;0;0
-8470;légiférer;positive;0;0;0;0;0;0
-8471;légion;positive;0;0;0;0;0;0
-8472;législation;positive;0;0;0;0;0;0
-8473;législature;positive;0;0;0;0;0;0
-8474;légitime;positive;0;0;0;0;0;0
-8475;légitimité;positive;0;0;0;0;0;0
-8476;legs;positive;0;0;0;0;0;0
-8477;léguer;positive;0;0;0;0;0;0
-8478;légume;positive;0;0;0;0;0;0
-8479;légumineux;positive;0;0;0;0;0;0
-8480;lemme;positive;0;0;0;0;0;0
-8481;lendemain;positive;0;0;0;0;0;0
-8482;lent;negative;0;0;1;0;0;1
-8483;lentement;negative;0;0;0;0;0;0
-8484;lente;negative;0;0;1;0;0;1
-8485;lenteur;negative;0;1;1;0;0;0
-8486;lentille;positive;0;0;0;0;0;0
-8487;léopard;negative;0;1;0;0;0;0
-8488;lèpre;negative;0;1;1;0;0;1
-8489;le ?il bandé;negative;0;1;0;0;0;0
-8490;lesbianisme;negative;0;0;0;0;0;0
-8491;lesbien;negative;0;1;1;0;0;1
-8492;lésiner;negative;0;1;1;0;0;0
-8493;lessive;positive;0;0;0;0;0;0
-8494;lest;positive;0;0;0;0;0;0
-8495;lester;positive;0;0;0;0;0;0
-8496;létal;negative;0;1;1;0;0;1
-8497;léthargie;negative;0;0;1;0;0;0
-8498;léthargique;negative;0;1;1;0;0;0
-8499;lettre;positive;0;0;0;0;0;0
-8500;lettrer;positive;0;0;0;0;0;0
-8501;leucémie;negative;0;1;1;1;0;0
-8502;lever;positive;0;0;0;0;0;0
-8503;lever du jour;positive;0;0;0;0;0;0
-8504;lever du soleil;positive;0;0;0;0;0;0
-8505;levier;positive;0;0;0;0;0;0
-8506;lèvre;positive;0;0;0;0;0;0
-8507;lévrier;positive;0;0;0;0;0;0
-8508;levure;positive;0;0;0;0;0;0
-8509;lexique;positive;0;0;0;0;0;0
-8510;liaison;positive;0;0;0;0;0;0
-8511;libéralisme;positive;0;0;0;0;0;0
-8512;libération conditionnel;positive;0;0;0;0;0;0
-8513;libérer;positive;1;0;0;0;0;0
-8514;liberté;positive;0;0;0;0;1;0
-8515;libido;positive;1;0;0;0;0;0
-8516;libraire;positive;0;0;0;0;0;0
-8517;librairie;positive;0;0;0;0;0;0
-8518;libre;positive;1;0;0;0;0;0
-8519;librement;positive;0;0;0;0;0;0
-8520;licence;positive;0;0;0;0;0;0
-8521;licencier;negative;0;1;0;1;0;0
-8522;lichen;negative;0;0;0;0;0;1
-8523;licorne;positive;0;0;0;0;0;0
-8524;lier;positive;0;0;0;0;0;0
-8525;liège;positive;0;0;0;0;0;0
-8526;lien;positive;0;0;0;0;0;0
-8527;lieu;positive;0;0;0;0;0;0
-8528;lieu commun;positive;0;0;0;0;0;0
-8529;lieu de naissance;positive;0;0;0;1;0;0
-8530;lieu de prédilection;positive;0;0;0;0;0;0
-8531;lieu de travail;positive;0;0;0;0;0;0
-8532;lieu sûr;positive;0;0;0;0;0;0
-8533;lieutenant;positive;0;0;0;0;0;0
-8534;lièvre;negative;0;1;0;0;0;0
-8535;ligament;positive;0;0;0;0;0;0
-8536;ligature;positive;0;0;0;0;0;0
-8537;ligne;positive;0;0;0;1;0;0
-8538;lignée;positive;0;0;0;0;0;0
-8539;ligoter;negative;0;1;1;0;0;0
-8540;ligue;positive;0;0;0;0;0;0
-8541;lilas;positive;0;0;0;0;0;0
-8542;limace;negative;0;0;0;0;0;1
-8543;limaille;positive;0;0;0;0;0;0
-8544;limbe|limbes;negative;0;1;1;0;0;0
-8545;limier;positive;0;0;0;0;0;0
-8546;limitation;positive;0;0;0;0;0;0
-8547;limiter;negative;0;0;1;1;0;0
-8548;limoger;negative;0;0;1;0;0;0
-8549;limousine;positive;0;0;0;0;0;0
-8550;limpidité;positive;0;0;0;0;0;0
-8551;lin;positive;0;0;0;0;0;0
-8552;linceul;negative;0;0;1;0;0;0
-8553;linge à laver;positive;0;0;0;0;0;0
-8554;linge de lit;positive;0;0;0;0;0;0
-8555;linge de maison;positive;0;0;0;0;0;0
-8556;lingot;positive;0;0;0;0;0;0
-8557;lingual;positive;0;0;0;0;0;0
-8558;linguiste;positive;0;0;0;0;0;0
-8559;linguistique;positive;0;0;0;0;0;0
-8560;linoléum;positive;0;0;0;0;0;0
-8561;lion;positive;0;1;0;0;0;0
-8562;liquéfaction;negative;0;0;1;0;0;0
-8563;liquéfier;negative;0;0;1;0;0;0
-8564;liqueur;negative;0;0;1;1;0;0
-8565;liquidateur;negative;0;0;0;0;0;0
-8566;liquidation;negative;0;0;1;0;0;0
-8567;liquide;positive;0;0;0;0;0;0
-8568;liquider;negative;0;0;0;0;0;0
-8569;liquidité;positive;0;0;0;0;0;0
-8570;lire;positive;0;0;0;0;0;0
-8571;lire attentivement;positive;0;0;0;0;0;0
-8572;lisibilité;positive;0;0;0;0;0;0
-8573;lisible;positive;0;0;0;0;0;0
-8574;lisière;positive;0;0;0;0;0;0
-8575;lisse;positive;0;0;0;0;0;0
-8576;listage;positive;0;0;0;0;0;0
-8577;liste;positive;0;0;0;0;0;0
-8578;liste de vérification;positive;0;0;0;0;0;0
-8579;lister;positive;0;0;0;0;0;0
-8580;listing;positive;0;0;0;0;0;0
-8581;lit;positive;0;0;0;0;0;0
-8582;lit bébé;positive;0;0;0;0;0;0
-8583;lire d enfant;positive;0;0;0;0;0;0
-8584;litanie;positive;0;0;0;0;0;0
-8585;literie;positive;0;0;0;0;0;0
-8586;lithographie;positive;0;0;0;0;0;0
-8587;lithologie;positive;0;0;0;0;0;0
-8588;lithosphère;positive;0;0;0;0;0;0
-8589;litière;negative;0;0;0;0;0;1
-8590;litige;negative;0;1;1;1;0;0
-8591;litre;positive;0;0;0;0;0;0
-8592;littéraire;positive;0;0;0;0;0;0
-8593;littéralement;positive;0;0;0;0;0;0
-8594;littérature;positive;0;0;0;0;0;0
-8595;littoral;positive;0;0;0;0;0;0
-8596;liturgie;positive;0;0;0;0;0;0
-8597;livide;negative;0;1;0;1;0;1
-8598;livraison;positive;0;0;0;0;0;0
-8599;livre sterling;positive;0;0;0;0;0;0
-8600;livrer;positive;0;0;0;0;0;0
-8601;livre;positive;0;0;0;0;0;0
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-8618;logarithmique;positive;0;0;0;0;0;0
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-8624;logotype;positive;0;0;0;0;0;0
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-8626;lointain;negative;0;0;1;0;0;0
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-8629;lombaire;positive;0;0;0;0;0;0
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-8633;longitude;positive;0;0;0;0;0;0
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-8637;loquace;positive;0;0;0;0;0;0
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-8666;lumière du étoile;positive;0;0;0;0;0;0
-8667;lumière du soleil;positive;1;0;0;0;0;0
-8668;luminescent;positive;1;0;0;0;0;0
-8669;luminosité;positive;1;0;0;0;0;0
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-8671;lune;positive;0;0;0;0;0;0
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-8676;lunette|lunettes de soleil;positive;0;0;0;0;0;0
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-8679;lutte;negative;0;1;1;1;0;0
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-8684;luzerne;positive;0;0;0;0;0;0
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-8709;magie;positive;0;0;0;0;1;0
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-8715;magnétite;positive;0;0;0;0;0;0
-8716;magnificence;positive;0;0;0;0;0;0
-8717;magnifique;positive;0;0;0;0;1;0
-8718;magot;positive;0;0;0;0;0;0
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-8741;majuscule;positive;0;0;0;0;0;0
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-8756;malaxer;positive;0;0;0;0;0;0
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-8769;malicieux;negative;0;1;0;1;1;0
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-8783;mammifère;positive;0;0;0;0;0;0
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-8790;mandarine;positive;0;0;0;0;0;0
-8791;mandataire;positive;0;0;0;0;0;0
-8792;mandater;positive;0;0;0;0;0;0
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-8794;mandoline;positive;0;0;0;0;0;0
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-8799;manger;positive;0;0;0;0;0;0
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-8858;marne;positive;0;0;0;0;0;0
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-8879;masque;negative;0;1;0;0;0;0
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-9338;multiplication;positive;0;0;0;0;0;0
-9339;multiplicité;positive;0;0;0;0;0;0
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-9363;musique;positive;0;0;1;0;0;0
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-9376;myriade;positive;0;0;0;0;0;0
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-9385;nadir;positive;0;0;0;0;0;0
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-9396;narration;positive;0;0;0;0;0;0
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-9404;national|nationaux;positive;0;0;0;0;0;0
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-9409;naturaliser;positive;0;0;0;0;0;0
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-9429;ne pas croire;negative;0;0;0;0;0;0
-9430;ne pas être d accord;negative;0;0;0;1;0;0
-9431;ne pas tenir compte;negative;0;0;0;0;0;1
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-9433;néant;negative;0;1;1;0;0;0
-9434;nébuleux;negative;0;1;1;0;0;0
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-9436;nécessiter;positive;0;0;1;0;0;0
-9437;nécessiteux;negative;0;0;1;0;0;0
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-9439;nécrologie;negative;0;0;1;0;0;0
-9440;nécrose;negative;0;1;1;0;0;1
-9441;nectar;positive;0;0;0;0;0;0
-9442;nef;positive;0;0;0;0;0;0
-9443;néfaste;negative;0;1;1;1;0;1
-9444;négatif;negative;0;1;1;1;0;1
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-9458;neige;positive;0;0;0;0;0;0
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-9462;néophyte;negative;0;1;1;0;0;0
-9463;néoprène;positive;0;0;0;0;0;0
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-9465;nerf;positive;0;0;0;0;0;0
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-9469;netteté;positive;0;0;0;0;0;0
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-9473;neuf;positive;0;0;0;0;0;0
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-9476;neutralité;positive;0;0;0;0;0;0
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-9480;névralgie;negative;0;1;1;0;0;0
-9481;névrose;negative;0;1;1;0;0;0
-9482;névrotique;negative;0;1;1;0;0;1
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-9489;nicotine;negative;0;0;0;0;0;1
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-9491;nièce;positive;0;0;0;0;0;0
-9492;nihilisme;negative;0;0;0;1;0;1
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-9503;noix de muscade;positive;0;0;0;0;0;0
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-9669;octave;positive;0;0;0;0;0;0
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-9671;octogone;positive;0;0;0;0;0;0
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-9717;omnipotent;positive;0;0;0;0;0;0
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-9723;onction;positive;0;0;0;0;0;0
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-9769;orchestre;positive;0;0;1;1;0;0
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-9779;oreiller;positive;0;0;0;0;0;0
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-9837;ouvertement;positive;0;0;0;0;0;0
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-9841;ouvrer|ouvrir boîte;positive;0;0;0;0;0;0
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-10047;passionnant;positive;0;0;0;0;1;0
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-10052;patch;negative;0;0;0;0;0;0
-10053;patchwork;positive;0;0;0;0;0;0
-10054;patère;positive;0;0;0;0;0;0
-10055;paternité;positive;0;0;0;0;0;0
-10056;pathétique;negative;0;0;1;1;0;1
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-10060;patient;negative;0;1;1;0;0;0
-10061;patienter;negative;0;0;0;0;0;0
-10062;patin;positive;0;0;0;0;0;0
-10063;patinage;positive;0;0;0;0;0;0
-10064;patiner;positive;0;0;0;0;0;0
-10065;patinoire;positive;0;0;0;0;0;0
-10066;patio;positive;0;0;0;0;0;0
-10067;pâtisserie;positive;1;0;0;0;0;0
-10068;patriarcal;positive;0;0;0;0;0;0
-10069;patriarche;positive;0;0;0;0;0;0
-10070;patrimoine;positive;0;0;0;0;0;0
-10071;patriote;positive;0;0;0;0;0;0
-10072;patriotique;positive;0;0;0;0;0;0
-10073;patriotisme;positive;0;0;0;0;0;0
-10074;patron;positive;0;0;0;0;0;0
-10075;patronage;positive;0;0;0;0;0;0
-10076;patrouille;positive;0;0;0;0;0;0
-10077;patrouiller;positive;0;0;0;0;0;0
-10078;patte;positive;0;0;0;0;0;0
-10079;pâturage;positive;0;0;0;0;0;0
-10080;pâture;positive;0;0;0;0;0;0
-10081;paume;positive;0;0;0;0;0;0
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-10083;pauvrement;negative;0;1;1;0;0;0
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-10085;pavage;positive;0;0;0;0;0;0
-10086;pavaner;negative;0;0;0;0;0;0
-10087;paver;positive;0;0;0;0;0;0
-10088;pavillon;positive;0;0;0;0;0;0
-10089;pavot;positive;0;0;0;0;0;0
-10090;pax;positive;0;0;0;0;0;0
-10091;paye;positive;1;0;0;0;0;0
-10092;payer|payer;positive;0;0;0;0;0;0
-10093;pays;positive;0;0;0;0;0;0
-10094;paysage;positive;0;0;0;0;0;0
-10095;péage;negative;0;0;0;0;0;0
-10096;peau de vache;negative;0;0;0;0;0;0
-10097;peaufinage;positive;0;0;0;0;0;0
-10098;peaufiner;positive;0;0;0;0;0;0
-10099;pêcher;positive;0;0;0;0;0;0
-10100;pêche à le ligne;positive;0;0;0;0;0;0
-10101;pêcher au chalut;positive;0;0;0;0;0;0
-10102;pécuniaire;negative;0;0;0;0;0;0
-10103;pédagogique;positive;0;0;0;0;0;0
-10104;pédale;positive;0;0;0;0;0;0
-10105;pédaler;positive;0;0;0;0;0;0
-10106;pédant;negative;0;0;0;1;0;1
-10107;pédiatrie;positive;0;0;0;0;0;0
-10108;pédicule;negative;0;0;0;0;0;0
-10109;pedigree;positive;0;0;0;0;0;0
-10110;pédoncule;negative;0;0;0;0;0;0
-10111;peigne;positive;0;0;0;0;0;0
-10112;peigner;positive;0;0;0;0;0;0
-10113;peignoir;positive;0;0;0;0;0;0
-10114;peindre;positive;0;0;0;0;0;0
-10115;peiner;negative;0;1;1;0;0;0
-10116;peintre;positive;0;0;0;0;0;0
-10117;peinture;positive;0;0;0;0;0;0
-10118;peinture mural;positive;0;0;0;0;0;0
-10119;pelage;positive;0;0;0;0;0;0
-10120;pélagien;positive;0;0;0;0;0;0
-10121;pélagique;positive;0;0;0;0;0;0
-10122;peler;negative;0;0;0;0;0;0
-10123;pèlerin;positive;0;0;0;0;0;0
-10124;pèlerinage;positive;0;0;0;0;0;0
-10125;pelle;positive;0;0;0;0;0;0
-10126;pelleter;positive;0;0;0;0;0;0
-10127;pellicule;positive;0;0;0;0;0;0
-10128;pelotage;negative;0;1;0;1;0;1
-10129;peloter;negative;0;1;0;1;0;1
-10130;peloton;positive;0;0;0;0;0;0
-10131;pelouse;positive;0;0;0;0;0;0
-10132;peluche;positive;0;0;0;0;0;0
-10133;pénal;negative;0;1;1;0;0;0
-10134;pénalité;negative;0;1;1;1;0;0
-10135;pencher;negative;0;0;0;0;0;0
-10136;pendaison;negative;0;1;1;1;0;1
-10137;pendant que;negative;0;0;0;0;0;0
-10138;pendentif;positive;0;0;0;0;0;0
-10139;pendre;negative;0;0;0;0;0;0
-10140;pendule;positive;0;0;0;0;0;0
-10141;pénétrer;positive;0;0;0;0;1;0
-10142;pénétrante;positive;0;0;0;0;1;0
-10143;pénétrant;positive;0;0;0;0;1;0
-10144;pénétrer illégalement dans un;negative;0;1;0;1;0;0
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-10146;pénible;negative;0;1;1;1;0;1
-10147;péniche;positive;0;0;0;0;0;0
-10148;péninsule;positive;0;0;0;0;0;0
-10149;pénitence;negative;0;1;1;0;0;0
-10150;pénitencier;negative;0;1;1;1;0;0
-10151;penny;positive;0;0;0;0;0;0
-10152;pénombre;negative;0;1;1;1;0;0
-10153;penser;positive;0;0;0;0;0;0
-10154;penser bête;positive;0;0;0;0;0;0
-10155;penser après coup;negative;0;1;1;0;0;0
-10156;pension;positive;0;0;0;0;0;0
-10157;pension alimentaire;negative;0;0;0;0;0;0
-10158;pensionnaire;positive;0;0;0;0;0;0
-10159;pentagone;positive;0;0;0;0;0;0
-10160;pente;negative;0;1;1;0;0;0
-10161;penthouse;positive;0;0;0;0;0;0
-10162;pénultième;negative;0;0;1;0;0;0
-10163;pénurie;negative;0;1;1;1;0;1
-10164;pépiement;positive;1;0;0;0;0;0
-10165;pépier;positive;1;0;0;0;0;0
-10166;pépin;negative;0;1;1;0;1;0
-10167;pépinière;positive;0;0;0;0;0;0
-10168;pépite;positive;1;0;0;0;0;0
-10169;perceptible;positive;0;0;0;0;0;0
-10170;percer;negative;0;1;0;1;0;0
-10171;perceur;negative;0;1;0;1;0;0
-10172;percevoir;positive;0;0;0;0;1;0
-10173;perche;positive;0;0;0;0;0;0
-10174;percher;positive;0;0;0;0;0;0
-10175;perchoir;positive;0;0;0;0;0;0
-10176;percolation;negative;0;1;0;0;0;1
-10177;percussion;positive;0;0;0;0;0;0
-10178;perdant;negative;0;0;1;1;1;0
-10179;perdition;negative;0;1;1;1;0;1
-10180;perdre;negative;0;1;1;0;1;0
-10181;péremption;negative;0;1;0;0;0;1
-10182;péremptoire;negative;0;1;1;0;0;0
-10183;pérenne;positive;0;0;0;0;0;0
-10184;pereuses;negative;0;1;1;0;0;0
-10185;perfection;positive;0;0;0;0;1;0
-10186;perforation;negative;0;1;0;0;0;0
-10187;perforer;negative;0;1;0;0;0;0
-10188;performance;positive;0;0;0;0;1;0
-10189;péridot;positive;0;0;0;0;0;0
-10190;péril;negative;0;1;1;0;0;0
-10191;périmètre;positive;0;0;0;0;0;0
-10192;période;negative;0;0;0;0;0;0
-10193;périodicité;positive;0;0;0;0;0;0
-10194;périodique;positive;0;0;0;0;0;0
-10195;périodiquement;positive;0;0;0;0;0;0
-10196;péripétie;positive;0;1;0;0;1;0
-10197;périr;negative;0;1;1;0;0;0
-10198;périssable;negative;0;0;0;0;0;1
-10199;perle;positive;0;0;0;0;0;0
-10200;perler;positive;0;0;0;0;0;0
-10201;permanence;positive;0;0;0;0;0;0
-10202;perméable;negative;0;0;0;0;0;0
-10203;permission;positive;0;0;0;0;0;0
-10204;permutable;positive;0;0;0;0;0;0
-10205;permutation;negative;0;1;1;0;0;0
-10206;permuter;positive;0;0;0;0;0;0
-10207;perpendiculaire;positive;0;0;0;0;0;0
-10208;perpétrer;negative;0;1;0;1;0;0
-10209;perpétuation;positive;0;0;0;0;0;0
-10210;perpétuellement;positive;0;0;0;0;0;0
-10211;perpétuer;positive;0;0;0;0;0;0
-10212;perpétuité;positive;0;0;0;0;0;0
-10213;perplexe;negative;0;1;1;0;1;0
-10214;perplexité;negative;0;1;1;0;1;0
-10215;perron;negative;0;1;0;0;0;0
-10216;perroquet;negative;0;0;0;0;0;1
-10217;perruque;negative;0;0;0;0;0;0
-10218;persécuter;negative;0;1;1;1;0;0
-10219;persécution;negative;0;1;1;1;0;1
-10220;persévérance;positive;0;0;0;0;0;0
-10221;persévérer;positive;0;0;0;0;0;0
-10222;persévérant;positive;0;0;0;0;0;0
-10223;persil;positive;0;0;0;0;0;0
-10224;persistance;positive;0;0;0;0;0;0
-10225;persistant;positive;0;0;0;0;0;0
-10226;personnage;positive;0;0;0;0;0;0
-10227;personnaliser;positive;0;0;0;0;0;0
-10228;personnalité;positive;0;0;0;0;0;0
-10229;personne âgé;positive;0;0;0;0;0;0
-10230;personne interroger;positive;0;0;0;0;0;0
-10231;personne qui changer qch;positive;0;0;0;0;0;0
-10232;personne qui fredonner;positive;0;0;0;0;0;0
-10233;personne qui prier;positive;0;0;0;0;0;0
-10234;personne qui se disputer;negative;0;0;0;1;0;0
-10235;personnel;positive;0;0;0;0;0;0
-10236;personne;positive;0;0;0;0;0;0
-10237;personnification;positive;0;0;0;0;0;0
-10238;perspective;positive;0;0;0;0;0;0
-10239;perspicacité;positive;0;0;0;0;0;0
-10240;persuasion;positive;0;0;0;0;0;0
-10241;perte;negative;0;1;1;1;0;0
-10242;perte de connaissance;negative;0;1;1;0;1;0
-10243;pertinence;positive;0;0;0;0;0;0
-10244;perturbation;negative;0;1;1;1;1;0
-10245;perversion;negative;0;0;1;1;0;1
-10246;pervertir;negative;0;1;0;1;0;1
-10247;pesage;positive;0;0;0;0;0;0
-10248;pesant;negative;0;1;1;0;0;0
-10249;pesanteur;positive;0;0;0;0;0;0
-10250;pessimisme;negative;0;1;1;1;0;0
-10251;pessimiste;negative;0;1;1;0;0;0
-10252;peste;negative;0;1;1;0;0;1
-10253;pestilence;negative;0;1;0;0;0;1
-10254;pétasse;negative;0;1;1;1;0;1
-10255;pétiller;positive;1;0;0;0;0;0
-10256;péter;positive;0;0;0;0;0;0
-10257;petit chat;positive;0;0;0;0;0;0
-10258;petit coin;negative;0;0;0;0;0;1
-10259;petit comité;positive;0;0;0;0;0;0
-10260;petit coup;negative;0;1;0;1;1;0
-10261;petit coup de coude;positive;0;0;0;0;0;0
-10262;petit déjeuner;positive;0;0;0;0;0;0
-10263;petit gros petit gros|grosse;negative;0;0;1;0;0;1
-10264;petit morceau;negative;0;0;1;0;0;0
-10265;petit oiseau;positive;0;0;0;0;0;0
-10266;petit pain;positive;0;0;0;0;0;0
-10267;petit salon;positive;0;0;0;0;0;0
-10268;petit;negative;0;0;1;0;0;0
-10269;petit bête;negative;0;1;0;0;0;1
-10270;petit enfance;positive;0;0;0;0;0;0
-10271;petit ferme;positive;0;0;0;0;0;0
-10272;petit idée;positive;0;0;0;0;0;0
-10273;petit morsure;negative;0;1;1;0;1;0
-10274;petit tache;negative;0;0;1;0;0;1
-10275;petit tape;positive;0;0;0;0;0;0
-10276;petitesse;negative;0;0;1;0;0;0
-10277;pétition;positive;0;0;0;0;0;0
-10278;pétitionnaire;positive;0;0;0;0;0;0
-10279;pétitionner;positive;0;0;0;0;0;0
-10280;petit cadeau;positive;1;0;0;0;0;0
-10281;petit enfant;positive;0;0;0;0;0;0
-10282;pétoncle;positive;0;0;0;0;0;0
-10283;pétrir;positive;0;0;0;0;0;0
-10284;peu à peu;positive;0;0;0;0;0;0
-10285;peu aimable;negative;0;1;1;1;0;1
-10286;peu amical;negative;0;1;1;1;0;1
-10287;peu attraire;negative;0;0;1;0;0;1
-10288;peu attrayant;negative;0;0;1;0;0;1
-10289;peu clément;negative;0;0;1;0;0;0
-10290;peu communepeu commun;negative;0;0;0;0;0;0
-10291;peu commune;negative;0;0;0;0;0;0
-10292;peu compatissant;negative;0;0;0;1;0;1
-10293;peu compatissante;negative;0;0;0;1;0;1
-10294;peu compatissantes;negative;0;0;0;1;0;1
-10295;peu compatissants;negative;0;0;0;1;0;1
-10296;peu conclure;negative;0;0;1;0;0;0
-10297;peu concluant;negative;0;0;1;0;0;0
-10298;peu coûteux;positive;1;0;0;0;0;0
-10299;peu critique;negative;0;0;0;0;0;0
-10300;peu familier;negative;0;1;0;0;0;0
-10301;peu fiable;negative;0;1;0;1;0;0
-10302;peu fréquemment;negative;0;0;0;0;1;0
-10303;peu fréquent;negative;0;0;0;0;1;0
-10304;peu importer;negative;0;0;0;0;0;0
-10305;peu instruire;negative;0;0;1;0;0;0
-10306;peu méfier;negative;0;1;1;0;1;0
-10307;peu méfiant;negative;0;1;1;0;1;0
-10308;peu original;negative;0;0;0;0;0;0
-10309;peu orthodoxe;negative;0;1;1;0;0;1
-10310;peu profitable;negative;0;0;1;0;0;0
-10311;peu réaliste;negative;0;1;1;0;0;0
-10312;peu rentable;negative;0;0;1;0;0;0
-10313;peu satisfaisant;negative;0;0;1;0;0;1
-10314;peu séduisant;negative;0;0;1;0;0;1
-10315;peu soigner;negative;0;0;0;0;0;1
-10316;peu sûr;negative;0;1;0;0;1;0
-10317;peuple;positive;0;0;0;0;0;0
-10318;peupler;positive;0;0;0;0;0;0
-10319;peur;negative;0;1;0;1;1;0
-10320;phalange;positive;0;1;0;0;0;0
-10321;phare;positive;0;0;0;0;0;0
-10322;pharmaceutique;positive;0;0;0;0;0;0
-10323;pharmacie;positive;0;0;0;0;0;0
-10324;pharmacologie;positive;0;0;0;0;0;0
-10325;phase;positive;0;0;0;0;0;0
-10326;phénix;positive;0;0;0;0;0;0
-10327;phénomène;positive;0;0;0;0;0;0
-10328;philanthrope;positive;0;0;0;0;0;0
-10329;philanthropie;positive;0;0;0;0;0;0
-10330;philosophe;positive;0;0;0;0;0;0
-10331;philosophie;positive;0;0;0;0;0;0
-10332;philosophique;positive;0;0;0;0;0;0
-10333;phonétique;positive;0;0;0;0;0;0
-10334;phonographe;positive;0;0;0;0;0;0
-10335;phonologie;positive;0;0;0;0;0;0
-10336;phoque;positive;0;0;0;0;0;0
-10337;phosphore;positive;0;0;0;0;0;0
-10338;photocopier;positive;0;0;0;0;0;0
-10339;photogénique;positive;0;0;0;0;0;0
-10340;photographe;positive;0;0;0;0;0;0
-10341;photographie;positive;0;0;0;0;0;0
-10342;photométrie;positive;0;0;0;0;0;0
-10343;phraséologie;positive;0;0;0;0;0;0
-10344;phylogénie;positive;0;0;0;0;0;0
-10345;physiologie;positive;0;0;0;0;0;0
-10346;pianiste;positive;0;0;0;0;0;0
-10347;piano;positive;0;0;0;0;0;0
-10348;piaulement;negative;0;1;1;0;0;0
-10349;piauler;negative;0;1;1;0;0;0
-10350;piazza;positive;0;0;0;0;0;0
-10351;pic;positive;0;1;0;0;0;0
-10352;piccolo;positive;0;0;0;0;0;0
-10353;pichet;positive;0;0;0;0;0;0
-10354;pick up;positive;0;0;0;0;0;0
-10355;picoler;negative;0;0;0;0;0;0
-10356;picorer;negative;0;0;0;1;0;0
-10357;picotement;negative;0;1;0;0;1;0
-10358;picoter;negative;0;1;0;0;0;0
-10359;pie;negative;0;1;0;0;0;1
-10360;pièce de dix cent;positive;0;0;0;0;0;0
-10361;pièce de monnaie;positive;0;0;0;0;0;0
-10362;pièce de théâtre;negative;0;1;1;0;1;0
-10363;pièce jointe;positive;0;0;0;0;0;0
-10364;pièce;positive;0;0;0;0;0;0
-10365;pied;positive;0;0;0;0;0;0
-10366;pied de biche;positive;0;0;0;0;0;0
-10367;piédestal;positive;0;0;0;0;0;0
-10368;pied nu;positive;0;0;0;0;0;0
-10369;piégeage;negative;0;1;0;0;1;0
-10370;piéger;negative;0;1;0;0;1;0
-10371;piège;negative;0;1;0;0;1;0
-10372;pierre;positive;0;0;0;1;0;0
-10373;pierre à feu;negative;0;1;0;0;0;0
-10374;pierre précieux;positive;1;0;0;0;0;0
-10375;pierre tombal;negative;0;0;1;0;0;0
-10376;pierreux;negative;0;1;0;0;0;0
-10377;piété;positive;0;0;0;0;0;0
-10378;piétiner;negative;0;1;1;0;0;0
-10379;piètre;negative;0;0;1;0;0;1
-10380;pieu;negative;0;1;1;0;0;0
-10381;pieuvre;negative;0;1;0;0;0;1
-10382;pigeon;negative;0;1;0;0;0;1
-10383;pigment;positive;0;0;0;0;0;0
-10384;pigmenter;positive;0;0;0;0;0;0
-10385;pilier;positive;0;0;0;0;0;0
-10386;pilote;positive;0;0;0;0;0;0
-10387;piloter;positive;0;0;0;0;0;0
-10388;pilule;positive;0;0;0;0;0;0
-10389;pimenter;negative;0;1;0;0;1;0
-10390;pin;positive;0;0;1;0;0;0
-10391;pinacle;positive;0;0;0;0;0;0
-10392;pince;negative;0;1;0;1;0;0
-10393;pince à linge;positive;0;0;0;0;0;0
-10394;pinceant;negative;0;1;0;0;0;0
-10395;pinceau;positive;0;0;0;0;0;0
-10396;pincée;negative;0;0;0;0;0;0
-10397;pincement;negative;0;1;0;0;0;0
-10398;pincer;negative;0;1;0;1;0;0
-10399;pion;negative;0;0;0;0;0;0
-10400;pipelet;negative;0;0;0;0;0;0
-10401;pipeline;positive;0;0;0;0;0;0
-10402;pipette;positive;0;0;0;0;0;0
-10403;pipi;negative;0;0;0;0;0;1
-10404;piquant;negative;0;1;0;0;1;0
-10405;pique;negative;0;1;0;1;0;0
-10406;pique niquer;positive;0;0;0;0;1;0
-10407;piquer niquer;positive;0;0;0;0;1;0
-10408;piquer sur;negative;0;1;0;1;1;0
-10409;piquet;negative;0;1;1;1;1;0
-10410;piquet de grève;negative;0;1;0;1;1;0
-10411;piqûre;negative;0;1;0;1;1;1
-10412;piratage;negative;0;1;0;1;0;0
-10413;pirate;negative;0;1;0;1;0;0
-10414;pirater;negative;0;1;0;1;0;0
-10415;piraterie;negative;0;1;0;1;0;0
-10416;pire;negative;0;1;1;0;0;0
-10417;pirogue;positive;0;0;0;0;0;0
-10418;piscine;positive;0;0;0;0;0;0
-10419;pister;positive;0;0;0;0;0;0
-10420;pistolet;negative;0;1;0;1;0;0
-10421;piston;positive;0;0;0;0;0;0
-10422;pitié;negative;0;0;1;0;0;0
-10423;pittoresque;positive;0;0;0;0;0;0
-10424;pivot;positive;0;0;0;0;0;0
-10425;pivoter;positive;0;0;0;0;0;0
-10426;placage;positive;0;0;0;0;0;0
-10427;placard;positive;0;0;0;0;0;0
-10428;place du marché;positive;0;0;0;0;0;0
-10429;placebo;positive;0;0;0;0;0;0
-10430;plafond;positive;0;0;0;0;0;0
-10431;plage;positive;1;0;0;0;0;0
-10432;plagiat;negative;0;0;1;1;0;1
-10433;plaid;positive;0;0;0;0;0;0
-10434;plaider;negative;0;1;1;1;0;1
-10435;plaideur;negative;0;0;1;1;0;0
-10436;plaie;negative;0;1;1;1;0;1
-10437;plaine;positive;0;0;0;0;0;0
-10438;plainte;negative;0;0;0;1;0;0
-10439;plaintif;negative;0;0;1;0;0;0
-10440;plaisance;positive;1;0;0;0;0;0
-10441;plaisanter;positive;1;0;0;0;0;0
-10442;plaisanterie;positive;0;0;0;0;1;0
-10443;planche;positive;0;0;0;0;0;0
-10444;plancher;positive;0;0;0;0;0;0
-10445;planéité;negative;0;0;1;0;0;0
-10446;planer;positive;1;0;0;0;0;0
-10447;planétarium;positive;0;0;0;0;0;0
-10448;planète;positive;0;0;0;0;0;0
-10449;planification;positive;0;0;0;0;0;0
-10450;planifier;positive;0;0;0;0;0;0
-10451;plantation;positive;0;0;0;0;0;0
-10452;plante;positive;0;0;0;0;0;0
-10453;plante du pied;negative;0;0;0;0;0;1
-10454;planter;positive;0;0;0;0;0;0
-10455;planteur|planteuse;positive;0;0;0;0;0;0
-10456;plantureux;positive;0;0;0;0;0;0
-10457;plaquage;positive;0;0;0;0;0;0
-10458;plaque chauffant;positive;0;0;0;0;0;0
-10459;plaque tournant;positive;0;0;0;0;0;0
-10460;plaquer;negative;0;1;1;0;0;0
-10461;plaquer au sol;positive;0;0;0;1;1;0
-10462;plaquette;positive;0;0;0;0;0;0
-10463;plasma;negative;0;0;0;0;0;1
-10464;plasticité;positive;0;0;0;0;0;0
-10465;plastifier;positive;0;0;0;0;0;0
-10466;plastique;negative;0;0;0;0;0;0
-10467;plat forme;positive;0;0;0;0;0;0
-10468;plateau;positive;0;0;0;0;0;0
-10469;plateforme;positive;0;0;0;0;0;0
-10470;platine;positive;0;0;0;0;0;0
-10471;platitude;negative;0;0;1;0;0;0
-10472;plâtre;negative;0;0;1;0;0;0
-10473;plâtrer;negative;0;0;1;0;0;0
-10474;plausibilité;positive;0;0;0;0;0;0
-10475;playa;positive;0;0;0;0;0;0
-10476;plein;positive;0;0;0;0;0;0
-10477;plein à craquer;negative;0;1;0;0;0;0
-10478;plein d espoir;positive;0;0;0;0;1;0
-10479;plein de bulle;positive;0;0;0;0;0;0
-10480;plein de monde;negative;0;1;0;0;0;0
-10481;plein de ressentiment;negative;0;0;0;1;0;0
-10482;plein propriété;positive;0;0;0;0;0;0
-10483;plénier;positive;0;0;0;0;0;0
-10484;plénitude;positive;0;0;0;0;0;0
-10485;pléthore;positive;0;0;0;0;0;0
-10486;pleur;negative;0;0;1;1;0;0
-10487;pleurant;negative;0;0;1;0;0;0
-10488;pleurer;negative;0;0;1;1;0;0
-10489;pleur|pleurs;negative;0;0;1;0;0;0
-10490;pleuvoir;negative;0;0;1;0;0;0
-10491;plexus;positive;0;0;0;0;0;0
-10492;pli;negative;0;1;1;0;0;1
-10493;pliage;positive;0;0;0;0;0;0
-10494;plier;negative;0;0;1;0;0;0
-10495;plinthe;positive;0;0;0;0;0;0
-10496;plisser;negative;0;0;1;0;0;0
-10497;ploiement;negative;0;0;1;0;0;0
-10498;plomb;positive;0;0;0;0;0;0
-10499;ployer;negative;0;0;1;0;0;0
-10500;pluie;negative;0;0;1;0;0;0
-10501;plumage;positive;0;0;0;0;0;0
-10502;plume;positive;0;0;0;0;0;0
-10503;plumeau;negative;0;0;0;0;0;1
-10504;pluralité;positive;0;0;0;0;0;0
-10505;plus âgé;positive;0;0;1;0;0;0
-10506;plus dodu;negative;0;0;0;0;0;0
-10507;plus élever;positive;0;0;0;0;0;0
-10508;plus grand que;positive;0;0;0;0;1;1
-10509;plus important;positive;0;0;0;0;0;0
-10510;plus jeune;positive;1;0;0;0;0;0
-10511;plus loin;negative;0;1;1;0;0;0
-10512;plus petit;negative;0;1;1;0;0;0
-10513;plus sec;positive;0;0;0;0;0;0
-10514;plus tôt;positive;0;0;0;0;0;0
-10515;plus vieux;positive;0;0;1;0;0;0
-10516;plusieurs foi|fois;negative;0;0;0;0;0;0
-10517;plutonium;positive;0;0;0;0;0;0
-10518;pluvier;positive;0;0;0;0;0;0
-10519;pneumonie;negative;0;1;1;0;0;0
-10520;poche;positive;0;0;0;0;0;0
-10521;poche ventral;positive;0;0;0;0;0;0
-10522;pochoir;positive;0;0;0;0;0;0
-10523;podium;positive;0;0;0;0;0;0
-10524;podomètre;positive;0;0;0;0;0;0
-10525;poêle;positive;0;0;0;0;0;0
-10526;poêle à frire;negative;0;0;0;0;0;0
-10527;poème;positive;0;0;0;0;0;0
-10528;poésie;positive;0;0;0;0;0;0
-10529;poète;positive;0;0;0;0;0;0
-10530;poétique;positive;0;0;0;0;0;0
-10531;poigner|poindre;positive;0;0;1;0;1;0
-10532;poignant;positive;0;0;1;0;1;0
-10533;poignarder;negative;0;1;1;1;1;0
-10534;poignet;positive;0;0;0;0;0;0
-10535;poing;positive;0;0;0;0;0;0
-10536;point culminer;positive;0;0;0;0;1;0
-10537;point de repère;positive;0;0;0;0;0;0
-10538;point de suture;negative;0;0;0;0;0;0
-10539;point de vue;positive;0;0;0;0;0;0
-10540;point fort;positive;0;0;0;0;0;0
-10541;point virgule;positive;0;0;0;0;0;0
-10542;pointer;positive;0;0;0;0;0;0
-10543;pointeur;positive;0;0;0;0;0;0
-10544;point de suspension;positive;0;0;0;0;0;0
-10545;pointu;negative;0;1;0;0;0;0
-10546;pointure;positive;0;0;0;0;0;0
-10547;pois;positive;0;0;0;0;0;0
-10548;poison;negative;0;1;1;1;0;1
-10549;poisseux;negative;0;0;0;0;0;1
-10550;poisson;positive;0;0;0;0;0;0
-10551;poissonnerie;positive;0;0;0;0;0;0
-10552;poitrine;positive;0;0;0;0;0;0
-10553;poivre;negative;0;0;0;0;0;0
-10554;poivron;negative;0;0;0;0;0;0
-10555;poker;positive;0;0;0;0;1;0
-10556;polaire;negative;0;1;0;0;0;0
-10557;polarité;positive;0;0;0;0;1;0
-10558;pôle;positive;0;0;0;0;0;0
-10559;polémique;negative;0;0;0;1;0;1
-10560;police de caractère;positive;0;0;0;0;0;0
-10561;polio;negative;0;1;1;0;0;0
-10562;politique;positive;0;0;0;1;0;1
-10563;polluer;negative;0;0;0;0;0;1
-10564;pollution;negative;0;0;0;0;0;1
-10565;polo;positive;0;0;0;0;0;0
-10566;polonais;positive;0;0;0;0;0;0
-10567;polygamie;negative;0;0;0;0;0;1
-10568;polygonal;positive;0;0;0;0;0;0
-10569;polygonales;positive;0;0;0;0;0;0
-10570;polygone;positive;0;0;0;0;0;0
-10571;polymère;positive;0;0;0;0;0;0
-10572;polymorphisme;positive;0;0;0;0;0;0
-10573;polyvalence;positive;0;0;0;0;0;0
-10574;polyvalent;positive;0;0;0;0;0;0
-10575;pommade;positive;0;0;0;0;0;0
-10576;pomme;positive;0;0;0;0;0;0
-10577;pompage;positive;0;0;0;0;0;0
-10578;pompe;positive;0;0;0;0;0;0
-10579;pomper;positive;0;0;0;0;0;0
-10580;pompette;negative;0;0;0;0;0;1
-10581;pompeux;negative;0;0;0;0;0;1
-10582;pompier;positive;0;0;0;0;0;0
-10583;ponceur;positive;0;0;0;0;0;0
-10584;poncho;positive;0;0;0;0;0;0
-10585;ponction;negative;0;1;1;0;1;0
-10586;ponctualité;positive;0;0;0;0;0;0
-10587;ponctuation;positive;0;0;0;0;0;0
-10588;ponctuellement;negative;0;0;0;0;0;0
-10589;pondérer;positive;0;0;0;0;0;0
-10590;poney;positive;0;0;0;0;0;0
-10591;pont;positive;0;0;0;0;0;0
-10592;pontife;positive;0;0;0;0;0;0
-10593;pontificat;positive;0;0;0;0;0;0
-10594;pontifier;positive;0;0;0;0;0;0
-10595;ponton;positive;0;0;0;0;0;0
-10596;pop;negative;0;1;0;0;1;0
-10597;populace;negative;0;1;0;1;0;1
-10598;populaire;positive;0;0;0;0;0;0
-10599;populariser;positive;0;0;0;0;0;0
-10600;popularité;positive;0;0;0;0;0;0
-10601;population;positive;0;0;0;0;0;0
-10602;porc épic;negative;0;1;0;0;0;1
-10603;porcelaine;positive;0;0;0;0;0;0
-10604;porche;positive;0;0;0;0;0;0
-10605;porcherie;negative;0;0;0;0;0;1
-10606;pore;negative;0;0;0;0;0;1
-10607;porno;negative;0;0;0;0;0;1
-10608;pornographie;negative;0;1;0;0;0;1
-10609;pornographique;negative;0;0;0;0;0;1
-10610;porosité;negative;0;0;0;0;0;1
-10611;porridge;positive;0;0;0;0;0;0
-10612;port;positive;0;0;0;0;0;0
-10613;port maritime;positive;0;0;0;0;0;0
-10614;portable;positive;0;0;0;0;0;0
-10615;portage;positive;0;0;0;0;0;0
-10616;portail;positive;0;0;0;0;0;0
-10617;porter;positive;0;0;1;0;0;0
-10618;portatif;positive;0;0;0;0;0;0
-10619;porte;positive;0;0;0;0;0;0
-10620;porte bonheur;positive;0;0;0;0;1;0
-10621;porte monnaie;positive;0;0;0;0;0;0
-10622;porte parole;positive;0;0;0;0;0;0
-10623;porte voix;negative;0;1;0;0;0;0
-10624;portefeuille;positive;0;0;0;0;0;0
-10625;porter atteindre;negative;0;1;1;1;0;1
-10626;porter un toast;positive;1;0;0;0;0;0
-10627;portion;positive;0;0;0;0;0;0
-10628;portique;positive;0;0;0;0;0;0
-10629;portraire;positive;0;0;0;0;0;0
-10630;pose;positive;0;0;0;0;0;0
-10631;poser;positive;0;0;0;0;0;0
-10632;pose de carrelage;positive;0;0;0;0;0;0
-10633;poser du tuile sur;positive;0;0;0;0;0;0
-10634;position;positive;0;0;0;0;0;0
-10635;position accroupir;negative;0;1;0;0;0;0
-10636;position debout;positive;0;0;0;0;0;0
-10637;possédant;positive;0;0;0;0;0;0
-10638;posséder;negative;0;1;0;1;0;1
-10639;posséedées;negative;0;1;0;1;0;1
-10640;possesseur;positive;0;0;0;0;0;0
-10641;possession;negative;0;1;1;1;0;1
-10642;possibilité;positive;0;0;0;0;0;0
-10643;possible;positive;0;0;0;0;1;0
-10644;post scriptum;positive;0;0;0;0;0;0
-10645;postal;positive;0;0;0;0;0;0
-10646;poste;positive;0;0;0;0;0;0
-10647;poste d amarrage;positive;0;0;0;0;0;0
-10648;poste de commandement;positive;0;0;0;0;0;0
-10649;poste de guet;positive;0;0;0;0;0;0
-10650;poste vacant;positive;1;0;0;0;0;0
-10651;poster;positive;0;0;0;0;0;0
-10652;postérieur;negative;0;0;0;0;0;0
-10653;postérieurement;negative;0;0;0;0;0;0
-10654;postériorité;negative;0;0;0;0;0;0
-10655;postérité;negative;0;0;1;0;0;0
-10656;posthume;negative;0;0;1;0;0;0
-10657;postillonner;negative;0;0;0;0;0;1
-10658;postulat;positive;0;0;0;0;0;0
-10659;postuler;positive;0;0;0;0;0;0
-10660;postuler à;positive;0;0;0;0;0;0
-10661;posture;positive;0;0;0;0;0;0
-10662;pot;positive;0;0;0;0;0;0
-10663;pot à crème;positive;0;0;0;0;0;0
-10664;pot de fleur;positive;0;0;0;0;0;0
-10665;pot au feu;positive;0;0;0;0;0;0
-10666;pot de vin;negative;0;0;0;0;0;0
-10667;pot pourri;positive;0;0;0;0;0;0
-10668;potable;positive;0;0;0;0;0;0
-10669;pote;positive;0;0;0;0;0;0
-10670;poteau indicateur;positive;0;0;0;0;0;0
-10671;potence;negative;0;1;1;1;0;0
-10672;poterie;positive;0;0;0;0;0;0
-10673;potier;positive;0;0;0;0;0;0
-10674;potion;positive;0;0;0;0;1;0
-10675;pou;negative;0;1;0;0;0;1
-10676;poubelle;negative;0;0;0;0;0;1
-10677;pouce;positive;0;0;0;0;0;0
-10678;poudre;negative;0;0;0;0;0;1
-10679;poudrer;negative;0;0;0;0;0;0
-10680;poudre à canon;negative;0;1;0;0;0;0
-10681;poudreux;negative;0;0;0;0;0;0
-10682;poulain;positive;0;0;0;0;0;0
-10683;poule;positive;0;0;0;0;0;0
-10684;poule mouillé;negative;0;1;0;0;0;1
-10685;poulet;positive;0;1;0;0;0;0
-10686;pouliche;positive;0;0;0;0;0;0
-10687;poulie;positive;0;0;0;0;0;0
-10688;poulpe;negative;0;1;0;0;0;1
-10689;pouls;positive;0;0;0;0;0;0
-10690;poumon;positive;0;0;0;0;0;0
-10691;poupée;positive;1;0;0;0;0;0
-10692;pour le jeune;positive;0;0;0;0;0;0
-10693;pour toujours;positive;0;0;0;0;0;0
-10694;pourboire;positive;0;0;0;0;0;0
-10695;pourcentage;positive;0;0;0;0;0;0
-10696;pourchasser;negative;0;1;0;1;0;0
-10697;pourpoint;positive;0;0;0;0;0;0
-10698;pourpre;positive;0;0;0;0;0;0
-10699;pourquoi;positive;0;0;0;0;0;0
-10700;pourrir;negative;0;0;1;0;0;1
-10701;pourriture;negative;0;1;1;0;1;1
-10702;poursuite judiciaire;negative;0;1;1;1;0;1
-10703;poursuite;negative;0;1;0;0;0;0
-10704;poursuivant;negative;0;1;0;0;0;0
-10705;poursuivre;negative;0;1;1;1;0;0
-10706;pourvoir au besoin de;positive;0;0;0;0;0;0
-10707;pourvu que;positive;0;0;0;0;0;0
-10708;pousse;negative;0;0;0;0;0;1
-10709;pousser;negative;0;1;0;0;1;0
-10710;poussée;negative;0;1;0;0;1;0
-10711;pousser du cri;negative;0;0;1;1;0;0
-10712;pousser du huée;negative;0;0;0;1;0;1
-10713;pousser doucement;negative;0;1;1;0;0;0
-10714;pousser du doigt;negative;0;1;0;1;1;0
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-11067;purifier;positive;0;0;0;0;0;0
-11068;purification;positive;0;0;0;0;0;1
-11069;purin;negative;0;0;0;0;0;1
-11070;puriste;positive;0;0;0;0;0;0
-11071;pouvoir;negative;0;0;0;0;0;1
-11072;putain;negative;0;0;0;0;0;1
-11073;pute;negative;0;0;0;0;0;1
-11074;putois;negative;0;0;0;0;0;1
-11075;pygmée;negative;0;0;0;0;0;0
-11076;pyjama;positive;0;0;0;0;0;0
-11077;pyramidal;positive;0;0;0;0;0;0
-11078;pyramide;positive;0;0;0;0;0;0
-11079;pyrotechnie;positive;0;0;0;0;0;0
-11080;quadrant;positive;0;0;0;0;0;0
-11081;quadratique;positive;0;0;0;0;0;0
-11082;quadrilatère;positive;0;0;0;0;0;0
-11083;quadripolaire;positive;0;0;0;0;0;0
-11084;quadripôle;positive;0;0;0;0;0;0
-11085;quadruple;positive;0;0;0;0;0;0
-11086;quadrupler;positive;0;0;0;0;0;0
-11087;quai;positive;0;0;0;0;0;0
-11088;qualifier;positive;0;0;0;0;0;0
-11089;qualifiantes;positive;0;0;0;0;0;0
-11090;qualifiants;positive;0;0;0;0;0;0
-11091;qualification;positive;0;0;0;0;0;0
-11092;qualité;positive;0;0;0;0;0;0
-11093;quantifier;positive;0;0;0;0;0;0
-11094;quantique;positive;0;0;0;0;0;0
-11095;quantitativement;positive;0;0;0;0;0;0
-11096;quantité;positive;0;0;0;0;0;0
-11097;quantité suffisant;positive;0;0;0;0;0;0
-11098;quantum;positive;0;0;0;0;0;0
-11099;quarantaine;negative;0;1;0;0;0;1
-11100;quarante;positive;0;0;0;0;0;0
-11101;quart;positive;0;0;0;0;0;0
-11102;quartet;positive;0;0;0;0;0;0
-11103;quartier;positive;0;0;0;0;0;0
-11104;quartile;positive;0;0;0;0;0;0
-11105;quartz;positive;0;0;0;0;0;0
-11106;quasi;positive;0;0;0;0;0;0
-11107;quaternaire;positive;0;0;0;0;0;0
-11108;quatre vingt dix;positive;0;0;0;0;0;0
-11109;quatre vingts;positive;0;0;0;0;0;0
-11110;quatrième;positive;0;0;0;0;0;0
-11111;quatuor;positive;0;0;0;0;0;0
-11112;que ce être;negative;0;0;0;0;0;0
-11113;quel que être;negative;0;0;0;0;0;0
-11114;quémander;negative;0;0;1;0;0;1
-11115;querelle;negative;0;1;1;1;0;1
-11116;questionnaire;negative;0;0;0;0;0;0
-11117;questionner;negative;0;1;0;0;0;0
-11118;questionnement;negative;0;1;0;0;0;0
-11119;quête;positive;0;0;0;0;0;0
-11120;queue;negative;0;0;1;0;0;0
-11121;qui avoir du avis très arrêter;negative;0;0;0;1;0;0
-11122;qui avoir du préjugé;negative;0;1;0;1;0;1
-11123;qui avoir lieu;positive;0;0;0;0;0;0
-11124;qui avoir perdre;negative;0;0;1;1;1;0
-11125;qui avoir sommeil;negative;0;0;1;0;0;0
-11126;qui avoir un odeur de moisir;negative;0;0;0;0;0;1
-11127;qui approcher;positive;0;0;0;0;0;0
-11128;qui arriver;positive;0;0;0;0;0;0
-11129;qui attendre;negative;0;0;0;0;0;0
-11130;qui bâiller;negative;0;0;0;0;0;0
-11131;qui bouche;negative;0;0;0;1;1;0
-11132;qui calculer;positive;0;0;0;0;0;0
-11133;qui cause du tort;negative;0;0;1;1;0;1
-11134;qui commander;positive;0;0;0;0;0;0
-11135;qui consommer beaucoup;negative;0;0;0;0;0;1
-11136;qui court;positive;0;0;0;0;0;0
-11137;qui déborder;negative;0;1;0;0;1;0
-11138;qui délire;negative;0;1;1;1;1;0
-11139;qui désirer;positive;0;0;0;0;0;0
-11140;qui divaguer qui radoter;negative;0;1;1;0;0;0
-11141;qui dormir;positive;0;0;0;0;0;0
-11142;qui doute;negative;0;1;0;0;0;0
-11143;qui embaumer;positive;0;0;0;0;0;0
-11144;qui empêcher de se concentrer;negative;0;0;0;1;0;0
-11145;qui empire;negative;0;0;1;0;0;1
-11146;qui en résulter;positive;0;0;0;0;0;0
-11147;qui entrave;negative;0;0;1;1;0;0
-11148;qui être un échec;negative;0;0;1;0;0;0
-11149;qui établir;positive;0;0;0;0;0;0
-11150;qui extraire;positive;0;0;0;0;0;0
-11151;qui faire bouger le chose;positive;0;0;0;0;0;0
-11152;qui fond;negative;0;0;0;0;0;0
-11153;qui fuir;negative;0;1;0;0;1;1
-11154;qui gaspiller;negative;0;1;1;1;0;1
-11155;qui induire en erreur;negative;0;0;0;1;0;1
-11156;qui jouer de le batterie;positive;0;0;0;0;0;0
-11157;qui jouer du tambour;positive;0;0;0;0;0;0
-11158;qui manque de;negative;0;0;1;0;0;0
-11159;qui maudire;negative;0;1;0;1;0;1
-11160;qui mijoter;positive;0;0;0;0;0;0
-11161;qui monter;positive;0;1;0;0;0;0
-11162;qui monter à cru;positive;0;0;0;0;0;0
-11163;qui monter en flêche;negative;0;1;0;0;1;0
-11164;qui ne peser rien;positive;0;0;0;0;0;0
-11165;qui ne se douter de rien;negative;0;1;1;0;1;0
-11166;qui nettoyer;positive;0;0;0;0;0;0
-11167;qui observer en silence;negative;0;1;1;0;0;0
-11168;qui obstruer;negative;0;0;0;1;1;0
-11169;qui passer;negative;0;0;1;0;0;0
-11170;qui passer inaperçu;positive;0;0;0;0;0;0
-11171;qui perdre;negative;0;0;1;1;1;0
-11172;qui périr;negative;0;1;1;0;0;0
-11173;qui pouvoir se procurer;positive;1;0;0;0;0;0
-11174;qui prendre effet;positive;0;0;0;0;0;0
-11175;qui promettre;positive;0;0;0;0;0;0
-11176;qui protéger;positive;0;0;0;0;0;0
-11177;qui réchauffer;positive;1;0;0;0;0;0
-11178;qui recouvrer|recouvrir;positive;0;0;0;0;0;0
-11179;qui répondre;positive;0;0;0;0;0;0
-11180;qui représenter;positive;0;0;0;0;0;0
-11181;qui rester;negative;0;0;0;0;0;0
-11182;qui retenir;negative;0;0;0;0;0;0
-11183;qui rétrécir;negative;0;1;1;0;0;0
-11184;qui rime;positive;0;0;0;0;0;0
-11185;qui rire;positive;1;0;0;0;0;0
-11186;qui rougir;negative;0;0;0;0;1;0
-11187;qui roule;negative;0;0;0;0;0;0
-11188;qui s empiffrer;negative;0;0;0;0;0;1
-11189;qui s ennuyer;negative;0;0;1;0;0;0
-11190;qui se cacher;negative;0;1;1;0;0;0
-11191;qui se consumer;negative;0;1;1;0;0;0
-11192;qui se dégrader;negative;0;0;1;0;0;1
-11193;qui se détériorer;negative;0;0;1;0;0;1
-11194;qui se faire du souci;negative;0;1;1;0;0;0
-11195;qui se réduire;negative;0;1;1;0;0;0
-11196;qui se vanter;negative;0;0;0;0;0;0
-11197;qui se voir;positive;0;0;0;0;0;0
-11198;qui sombre;negative;0;1;1;0;0;0
-11199;qui sommeiller;positive;0;0;0;0;0;0
-11200;qui sonner;positive;0;0;0;0;1;0
-11201;qui soulager;positive;0;0;0;0;0;0
-11202;qui subsister;positive;1;0;0;0;0;0
-11203;qui suivre;positive;0;0;0;0;0;0
-11204;qui tendre ver|vers;positive;0;0;0;0;0;0
-11205;qui tomber;negative;0;1;1;0;0;0
-11206;qui tourner;negative;0;1;0;0;0;0
-11207;qui travailler;positive;0;0;0;0;0;0
-11208;quiétude;positive;0;0;0;0;0;0
-11209;quille;negative;0;1;0;0;0;0
-11210;quincaillerie;positive;0;0;0;0;0;0
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-11212;quiproquo;negative;0;1;1;1;0;0
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-11216;quota;positive;0;0;0;0;0;0
-11217;quotidien;positive;0;0;0;0;0;0
-11218;quotidiennement;positive;0;0;0;0;0;0
-11219;quotient;negative;0;0;0;0;0;0
-11220;rabais;positive;0;0;0;0;0;0
-11221;rabaisser;negative;0;1;1;1;0;1
-11222;rabat;negative;0;0;0;0;0;0
-11223;rabique;negative;0;1;1;1;0;1
-11224;rabougrir;negative;0;1;1;0;0;0
-11225;raccommodage;positive;0;0;0;0;0;0
-11226;raccord;positive;0;0;0;0;0;0
-11227;raccorder;positive;0;0;0;0;0;0
-11228;raccordement;positive;0;0;0;0;0;0
-11229;raccourcir;negative;0;0;0;0;0;0
-11230;raccourcissement;negative;0;0;0;0;0;0
-11231;rachat;positive;0;0;0;0;0;0
-11232;racheter;positive;0;0;0;0;0;0
-11233;rachis;positive;0;0;0;1;0;0
-11234;racine;positive;0;0;0;0;0;0
-11235;racket;negative;0;1;0;0;0;0
-11236;racler;negative;0;1;1;1;0;0
-11237;racoler;negative;0;0;0;0;0;0
-11238;racoleur;negative;0;0;0;0;0;0
-11239;raconter;positive;0;0;0;0;0;0
-11240;raconter un bobard;negative;0;0;0;1;0;1
-11241;radar;positive;0;0;0;0;0;0
-11242;radeau;negative;0;1;1;0;0;0
-11243;radiateur;positive;0;0;0;0;0;0
-11244;radiation;negative;0;1;0;0;0;0
-11245;radical;negative;0;0;1;0;0;0
-11246;radicalement;negative;0;1;0;0;1;0
-11247;radio;positive;0;0;0;0;0;0
-11248;radioactivité;negative;0;1;0;0;0;0
-11249;radiographie;positive;0;0;0;0;0;0
-11250;radiologie;positive;0;0;0;0;0;0
-11251;radium;negative;0;0;0;0;0;0
-11252;radius;positive;0;0;0;0;0;0
-11253;radon;negative;0;1;0;0;0;0
-11254;radotage;negative;0;0;0;0;0;1
-11255;radoter;negative;0;0;0;0;0;1
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-11257;raffinement;positive;0;0;0;0;0;0
-11258;raffinerie;positive;0;0;0;0;0;0
-11259;rafraîchir;positive;1;0;0;0;0;0
-11260;rafraîchissant;positive;1;0;0;0;0;0
-11261;rage;negative;0;1;0;1;0;0
-11262;rage de dent;negative;0;1;1;0;0;1
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-11268;raidir;negative;0;1;0;1;0;0
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-11270;rail;positive;0;0;0;1;0;0
-11271;railler;negative;0;1;1;1;0;1
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-11273;raison;positive;0;0;0;0;0;0
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-11275;raisonner;positive;0;0;0;0;0;0
-11276;raisonnement;positive;0;0;0;0;0;0
-11277;rajeunir;positive;1;0;0;0;0;0
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-11279;râler;negative;0;1;1;1;0;1
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-11281;rallonge;positive;0;0;0;0;0;0
-11282;rallonger;positive;0;0;0;0;0;0
-11283;rallongement;positive;0;0;0;0;0;0
-11284;rallye;positive;0;0;0;0;0;0
-11285;rame de papier;positive;0;0;0;0;0;0
-11286;rampe;positive;0;0;0;1;0;0
-11287;ramper;negative;0;0;1;0;0;1
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-11289;rance;negative;0;0;0;0;0;1
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-11293;rançonner;negative;0;1;0;1;0;0
-11294;rancune;negative;0;0;1;1;0;1
-11295;randonner;positive;0;0;0;0;0;0
-11296;rang;positive;0;0;0;1;0;0
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-11298;rap;negative;0;0;0;0;0;0
-11299;rapace;negative;0;1;0;0;0;0
-11300;râper;negative;0;0;0;1;0;0
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-11312;rasoir;negative;0;1;0;0;0;0
-11313;rassasier;positive;0;0;0;0;0;0
-11314;rassemblement;positive;0;0;0;0;0;0
-11315;rassembler;positive;0;0;0;0;0;0
-11316;rasseoir|rassir;negative;0;0;0;0;0;1
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-11335;ravin;negative;0;1;0;0;0;0
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-11337;ravisseur;negative;0;1;0;0;0;0
-11338;ravitailler;positive;0;0;0;0;0;0
-11339;rayaux;positive;0;0;0;0;0;0
-11340;rayon;positive;1;0;0;0;0;0
-11341;rayon de miel;positive;0;0;0;0;0;0
-11342;rayon de soleil;positive;1;0;0;0;0;0
-11343;rayonner;positive;1;0;0;0;0;0
-11344;rayure;negative;0;0;0;0;0;0
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-11348;réaffirmer;positive;0;0;0;0;0;0
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-11350;réajustement;positive;0;0;0;0;0;0
-11351;réalisation;positive;0;0;0;0;0;0
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-11353;réalisme;positive;0;0;0;0;0;0
-11354;réaliste;positive;0;0;0;0;0;0
-11355;réalité;positive;0;0;0;0;0;0
-11356;réaménager;positive;0;0;0;0;0;0
-11357;réanimation;negative;0;1;1;0;0;0
-11358;réanimer;positive;0;0;0;0;0;0
-11359;réapparaître;positive;0;0;0;0;1;0
-11360;réapprovisionner;positive;0;0;0;0;0;0
-11361;réarrangement;positive;0;0;0;0;0;0
-11362;réarranger;positive;0;0;0;0;0;0
-11363;réassembler;positive;0;0;0;0;0;0
-11364;réassurance;positive;0;0;0;0;0;0
-11365;rebâtir;positive;0;0;0;0;0;0
-11366;rebelle;negative;0;1;0;1;0;0
-11367;rébellion;negative;0;1;0;1;1;1
-11368;rebond;positive;1;0;0;0;0;0
-11369;rebondir;positive;1;0;0;0;0;0
-11370;rebord;negative;0;1;0;0;0;0
-11371;récalcitrant;negative;0;0;0;1;0;1
-11372;recensement;positive;0;0;0;0;0;0
-11373;recenser;positive;0;0;0;0;0;0
-11374;récent;positive;0;0;0;0;0;0
-11375;récépissé;positive;0;0;0;0;0;0
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-11377;récepteur;positive;0;0;0;0;0;0
-11378;réception;positive;0;0;0;0;0;0
-11379;récession;negative;0;1;1;1;0;1
-11380;recette;positive;0;0;1;0;0;0
-11381;recevabilité;positive;0;0;0;0;0;0
-11382;réchauffer;positive;1;0;0;0;0;0
-11383;réchauffement;positive;1;0;0;0;0;0
-11384;recherche;positive;0;0;0;0;0;0
-11385;rechercher;positive;0;0;0;0;0;0
-11386;rechute;negative;0;1;1;0;1;0
-11387;rechuter;negative;0;1;1;0;0;0
-11388;récidive;negative;0;0;1;1;0;1
-11389;récidiver;negative;0;0;0;0;0;0
-11390;récidivisme;negative;0;0;1;1;0;1
-11391;récif;positive;0;0;0;0;0;0
-11392;réciprocité;positive;0;0;0;0;0;0
-11393;réciproque;positive;0;0;0;0;0;0
-11394;réciproquement;positive;0;0;0;0;0;0
-11395;récit;positive;0;0;0;0;0;0
-11396;récital;positive;0;0;0;0;0;0
-11397;récitation;positive;0;0;0;0;0;0
-11398;réciter;positive;0;0;0;0;0;0
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-11400;récolte;positive;0;0;0;0;0;0
-11401;récolter;positive;0;0;0;0;0;0
-11402;recombinaison;positive;0;0;0;0;0;0
-11403;recombiner;positive;0;0;0;0;0;0
-11404;recombinante;positive;0;0;0;0;0;0
-11405;recombinantes;positive;0;0;0;0;0;0
-11406;recombinants;positive;0;0;0;0;0;0
-11407;recommandation;positive;0;0;0;0;0;0
-11408;recommander;positive;0;0;0;0;0;0
-11409;récompense;positive;0;0;0;0;1;0
-11410;récompenser;positive;0;0;0;0;1;0
-11411;recompter;positive;0;0;0;0;0;0
-11412;réconciliation;positive;0;0;0;0;0;0
-11413;réconcilier;positive;0;0;0;0;0;0
-11414;réconfort;positive;0;0;0;0;0;0
-11415;réconforter;positive;1;0;0;0;0;0
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-11420;reconnaissant;positive;0;0;0;0;0;0
-11421;reconsidérer;negative;0;0;0;0;0;0
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-11423;reconstitution;positive;0;0;0;0;0;0
-11424;reconstitution historique;positive;0;0;0;0;0;0
-11425;reconstruction;positive;0;0;0;0;0;0
-11426;reconstruire;positive;0;0;0;0;0;0
-11427;record;positive;0;0;0;0;0;0
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-11429;recourir;positive;0;0;0;0;0;0
-11430;recouvrir;positive;0;0;0;0;0;0
-11431;recouvrer|recouvrir;positive;0;0;0;0;0;0
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-11433;recouvrantes;positive;0;0;0;0;0;0
-11434;recouvrants;positive;0;0;0;0;0;0
-11435;récréation;positive;1;0;0;0;0;0
-11436;recroître;positive;0;0;0;0;0;0
-11437;recruter;positive;0;0;0;0;0;0
-11438;recrutement;positive;0;0;0;0;0;0
-11439;rectangle;positive;0;0;0;0;0;0
-11440;rectangulaire;positive;0;0;0;0;0;0
-11441;recteur;positive;0;0;0;0;0;0
-11442;rectification;positive;0;0;0;0;0;0
-11443;rectifier;positive;0;0;0;0;0;0
-11444;recueillir;positive;0;0;0;0;0;0
-11445;récupérable;positive;0;0;0;0;0;0
-11446;récupération;positive;1;0;0;0;0;0
-11447;récupérer;positive;1;0;0;0;0;0
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-11462;redoublement;negative;0;0;1;0;0;0
-11463;redresser;positive;0;0;0;0;0;0
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-11476;réfléchir;positive;0;0;0;0;0;0
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-11479;reflet;positive;0;0;0;0;0;0
-11480;réflexe;positive;0;0;0;0;1;0
-11481;reflux;negative;0;0;1;0;0;1
-11482;refondre;positive;0;0;0;0;0;0
-11483;réforme;positive;0;0;0;0;0;0
-11484;réformer;positive;0;0;0;0;0;0
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-11486;refoulement;negative;0;1;1;1;0;0
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-11497;refroidissement;positive;0;0;0;0;0;0
-11498;refroidisseur;positive;0;0;0;0;0;0
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-11507;regard méchant;negative;0;0;0;1;0;1
-11508;regarder;positive;0;0;0;0;0;0
-11509;regarder avec du ?il rond;positive;0;0;0;0;1;0
-11510;regarder bêtement;negative;0;0;0;0;1;0
-11511;regarder bouche bée;negative;0;0;0;0;1;0
-11512;regarder en face;positive;0;0;0;0;0;0
-11513;regarder méchamment;negative;0;0;0;1;0;1
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-11516;régénération;positive;0;0;0;0;0;0
-11517;régénérer;positive;0;0;0;0;0;0
-11518;regent;positive;0;0;0;0;0;0
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-11523;région boisé;positive;0;0;0;0;0;0
-11524;région montagneux;positive;0;0;0;0;0;0
-11525;régional;positive;0;0;0;0;0;0
-11526;régionalisme;negative;0;0;0;0;0;0
-11527;registre;positive;0;0;0;0;0;0
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-11529;régler;positive;0;0;0;0;0;0
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-11531;réglementation;positive;0;0;0;0;0;0
-11532;réglementer;positive;0;0;0;0;0;0
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-11534;régner;positive;0;0;0;0;0;0
-11535;régner sur;positive;0;1;0;0;0;0
-11536;regorger;negative;0;1;0;0;1;1
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-11538;régression;negative;0;0;1;0;0;0
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-11543;régulariser;positive;0;0;0;0;0;0
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-11548;régulièrement;positive;0;0;0;0;0;0
-11549;régulier;positive;0;0;0;0;0;0
-11550;régurgitation;negative;0;0;0;0;0;1
-11551;réhabilitation;positive;0;0;0;0;0;0
-11552;réhabiliter;positive;0;0;0;0;0;0
-11553;rehaussement;positive;0;0;0;0;0;0
-11554;réimpression;positive;0;0;0;0;0;0
-11555;réimprimer;positive;0;0;0;0;0;0
-11556;réincarner;positive;1;0;0;0;0;0
-11557;reine;positive;0;0;0;0;0;0
-11558;réinsérer;positive;0;0;0;0;0;0
-11559;réinsertion;positive;0;0;0;0;0;0
-11560;réinstaller;positive;0;0;0;0;0;0
-11561;réintégration;positive;0;0;0;0;0;0
-11562;réinvestir;positive;0;0;0;0;0;0
-11563;réinvestissement;positive;0;0;0;0;0;0
-11564;rejeter;negative;0;1;1;0;0;1
-11565;rejoindre;positive;0;0;0;0;0;0
-11566;réjouissance;positive;0;0;0;0;1;0
-11567;relais;positive;0;0;0;0;0;0
-11568;relance;positive;0;0;0;0;0;0
-11569;relation;positive;0;0;0;0;0;0
-11570;relativement;positive;0;0;0;0;0;0
-11571;relativité;positive;0;0;0;0;0;0
-11572;relaxation;positive;1;0;0;0;0;0
-11573;relayer|relayer;positive;0;0;0;0;0;0
-11574;relégation;negative;0;0;1;0;0;0
-11575;relier;negative;0;0;0;0;0;0
-11576;religieux;positive;0;0;0;0;0;0
-11577;religion;positive;0;0;0;0;0;0
-11578;relique;negative;0;0;0;0;0;0
-11579;remake;positive;0;0;0;0;0;0
-11580;remaniement;positive;0;0;0;0;0;0
-11581;remarquablement;positive;0;0;0;0;1;0
-11582;remarquer;positive;0;0;0;0;0;0
-11583;remblai;positive;0;0;0;0;0;0
-11584;rembourser;positive;0;0;0;1;0;0
-11585;remède;positive;0;0;0;0;0;0
-11586;remédier;positive;0;0;0;0;0;0
-11587;remettre à plus tard;negative;0;0;1;0;0;0
-11588;remettre;positive;0;0;0;0;0;0
-11589;remettre du diplôme;positive;0;1;0;0;1;0
-11590;rémission;positive;1;0;0;0;0;0
-11591;remodeler;positive;0;0;0;0;0;0
-11592;remonter;positive;0;0;0;0;0;0
-11593;remonter le fermeture éclair;positive;0;0;0;0;0;0
-11594;remords;negative;0;0;1;0;0;0
-11595;remorquer;positive;0;0;0;0;0;0
-11596;remorqueur;positive;0;0;0;0;0;0
-11597;remous;negative;0;1;1;0;0;1
-11598;rempart;positive;0;0;0;0;0;0
-11599;remplacement;negative;0;0;1;0;0;0
-11600;remplir;positive;0;0;0;0;0;0
-11601;remplir de nouveau;positive;0;0;0;0;0;0
-11602;remplissage;positive;0;0;0;0;0;0
-11603;remue méninge;positive;0;0;0;0;0;0
-11604;rémunération;positive;0;0;0;0;0;0
-11605;rémunérer;positive;1;0;0;0;0;0
-11606;renaissance;positive;0;0;0;0;0;0
-11607;renard;positive;0;0;0;0;0;0
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-11609;rencontre;positive;0;0;0;0;1;0
-11610;rencontrer;positive;0;0;0;0;1;0
-11611;rendement;positive;0;0;0;0;0;0
-11612;rendre vous;positive;0;0;0;0;0;0
-11613;rendre;positive;0;0;0;0;0;0
-11614;rendre enclin;positive;0;0;0;0;0;1
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-11616;rendre fou;negative;0;1;0;1;0;0
-11617;rendre le pareil;positive;0;0;0;0;0;0
-11618;rendre responsable;negative;0;0;0;1;0;1
-11619;rendre visite;positive;0;0;0;0;0;0
-11620;rêne;positive;0;0;0;0;0;0
-11621;renflement;negative;0;0;0;0;0;1
-11622;renfoncement;negative;0;1;1;0;0;0
-11623;renforcement;positive;0;0;0;0;0;0
-11624;renforcer;positive;0;0;0;1;0;0
-11625;renfort;positive;0;0;0;0;0;0
-11626;renfrogner;negative;0;0;1;1;0;1
-11627;reniflement;negative;0;0;1;0;0;1
-11628;renifler;negative;0;0;1;0;0;1
-11629;renne;positive;0;0;0;0;0;0
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-11631;renommée;positive;0;0;0;0;0;0
-11632;renoncer;negative;0;0;1;1;0;0
-11633;renoncer à;negative;0;1;1;1;0;0
-11634;renonciation;negative;0;1;1;0;0;0
-11635;renouveau;positive;0;0;0;0;0;0
-11636;renouvellement;positive;0;0;0;0;0;0
-11637;rénovation;positive;1;0;0;0;0;0
-11638;rénover;positive;0;0;0;0;0;0
-11639;renseignement;positive;0;0;0;0;0;0
-11640;renseigner;positive;0;0;0;0;0;0
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-11642;rentrer;positive;0;0;0;0;0;0
-11643;rentrée;positive;0;0;0;0;0;0
-11644;renverser;negative;0;1;0;0;0;0
-11645;réorganisation;positive;0;0;0;0;0;0
-11646;réorganiser;positive;0;0;0;0;0;0
-11647;repaire;positive;0;0;0;0;0;0
-11648;réparation;positive;0;0;0;0;0;0
-11649;réparer;positive;0;0;0;0;0;0
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-11687;repsats;positive;0;0;0;0;0;0
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-11689;repaître;positive;0;0;0;0;0;0
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-11738;responsabiliser;positive;0;0;0;0;0;0
-11739;responsable;positive;0;0;0;0;0;0
-11740;ressemblance;positive;0;0;0;0;0;0
-11741;ressembler;positive;0;0;0;0;0;0
-11742;ressemblant;positive;0;0;0;0;0;0
-11743;ressentir;positive;0;0;0;0;0;0
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-11745;resserrer;positive;0;0;0;1;0;0
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-11749;restaurant;positive;0;0;0;0;0;0
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-11751;restaurer;positive;0;0;0;0;0;0
-11752;rester;positive;0;0;0;0;0;0
-11753;rester bouche bée;positive;0;0;0;0;1;0
-11754;rester tapir;negative;0;1;1;0;0;0
-11755;restitution;positive;0;0;0;1;0;0
-11756;restraindre;negative;0;1;0;1;0;0
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-11758;restructuration;positive;0;0;0;0;0;0
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-11761;résumer;positive;0;0;0;0;0;0
-11762;résurrection;positive;0;0;0;0;0;0
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-11807;révéler;positive;0;0;0;0;0;0
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-11809;revenir à;positive;0;0;0;0;0;0
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-11818;réversion;negative;0;0;0;0;0;0
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-11823;révision;positive;0;0;0;0;0;0
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-11834;rhabiller;positive;0;0;0;0;0;0
-11835;rhétorique;positive;0;0;0;0;0;0
-11836;rhum;positive;0;0;0;0;0;0
-11837;rhumatisme;negative;0;1;1;1;0;0
-11838;rhume;negative;0;0;1;0;0;0
-11839;ricanement;negative;0;0;0;1;0;1
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-11841;riche;positive;0;0;1;0;0;1
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-11847;ridiculiser;negative;0;0;1;1;0;1
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-11864;rire bêtement;positive;1;0;0;0;0;0
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-11894;romantique;positive;0;0;0;0;0;0
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-11912;rossignol;positive;1;0;0;0;0;0
-11913;rotatif;positive;0;0;0;0;0;0
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-11921;roucouler;positive;1;0;0;0;0;0
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-11923;rouge à lèvre;positive;0;0;0;0;0;0
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-11935;rouleau compresseur;negative;0;1;0;0;0;0
-11936;roulement;positive;0;0;0;0;0;0
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-11938;rousse;negative;0;0;0;0;0;0
-11939;route;positive;0;0;0;0;0;0
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-11945;royauté;positive;0;0;0;0;0;0
-11946;ruban;positive;0;0;0;1;0;0
-11947;ruban adhésif;positive;0;0;0;0;0;0
-11948;rubis;positive;0;0;0;0;0;0
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-11950;ruche;negative;0;1;0;1;0;0
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-11952;rudimentaire;negative;0;1;1;0;0;0
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-11954;rue;positive;0;0;0;0;0;0
-11955;rue principal;positive;0;0;0;0;0;0
-11956;ruer;negative;0;1;0;1;1;0
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-11958;rugir;negative;0;1;0;1;1;0
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-11962;ruineux;negative;0;1;1;1;0;1
-11963;ruisseler;negative;0;0;0;0;0;0
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-11971;rustique;negative;0;0;0;0;0;0
-11972;rythme;positive;0;0;0;0;0;0
-11973;rythmer;positive;0;0;0;0;1;0
-11974;rythmique;positive;0;0;0;0;1;0
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-11977;s adapter;positive;0;0;0;0;0;0
-11978;s agenouiller;negative;0;0;1;0;0;0
-11979;s armer;positive;0;0;0;0;0;0
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-11981;s attarder;negative;0;0;0;0;0;0
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-11983;s atténuer;negative;0;0;0;0;0;0
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-11985;s avérer;positive;0;0;0;0;0;0
-11986;s éclairer;positive;0;0;0;0;1;0
-11987;s écouler;positive;0;0;0;0;0;0
-11988;s efforcer;positive;0;0;0;0;0;0
-11989;s élever;positive;0;0;0;0;0;0
-11990;s émerveiller;positive;0;0;0;0;1;0
-11991;s emmêler;negative;0;1;0;1;0;0
-11992;s emporter;negative;0;1;0;1;0;0
-11993;s enchevêtrer;negative;0;1;0;1;0;0
-11994;s endurcir;negative;0;0;1;1;0;0
-11995;s enrager;negative;0;1;0;1;0;0
-11996;s ensuivre;positive;0;0;0;0;0;0
-11997;s entraînant;positive;0;0;0;0;0;0
-11998;s envaser;negative;0;1;0;0;0;1
-11999;s épanouir;positive;1;0;0;0;0;0
-12000;s éparpiller;negative;0;1;1;0;0;0
-12001;s estomper;negative;0;0;1;0;0;0
-12002;s étendre;positive;0;0;0;0;0;0
-12003;s évertuer;positive;0;0;0;0;0;0
-12004;s exécutant;positive;0;0;0;0;0;0
-12005;s expatrier;negative;0;1;1;0;0;0
-12006;s identifier;positive;0;0;0;0;0;0
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-12009;s inquiéter;negative;0;1;0;0;0;0
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-12013;sable;negative;0;0;0;0;0;0
-12014;sableux;negative;0;0;0;0;0;1
-12015;sableur|sableuse;negative;0;0;0;0;0;1
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-12018;sabot;negative;0;0;0;0;0;0
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-12025;sac à main;positive;0;0;0;0;0;0
-12026;saccader;negative;0;0;0;0;0;0
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-12035;sacrifice;negative;0;1;1;0;0;1
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-12066;salle de bal;positive;0;0;0;0;0;0
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-12076;samba;positive;1;0;0;0;0;0
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-12079;sanctification;positive;0;0;0;0;0;0
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-12114;sans délai;positive;0;0;0;0;1;0
-12115;sans dent;negative;0;0;1;0;0;1
-12116;sans domicile fixe;negative;0;1;1;1;0;1
-12117;sans doute;positive;0;0;0;0;0;0
-12118;sans éclat;negative;0;0;1;0;0;0
-12119;sans emploi;negative;0;1;1;0;0;0
-12120;sans énergie;negative;0;0;1;0;0;0
-12121;sans équivoque;positive;0;0;0;0;0;0
-12122;sans espoir;negative;0;1;1;0;0;0
-12123;sans être voir;negative;0;0;1;0;0;0
-12124;sans faille;positive;0;0;0;0;0;0
-12125;sans faire exprès;negative;0;1;1;0;1;0
-12126;sans faute;positive;0;0;0;0;0;0
-12127;sans fil;positive;0;0;0;1;1;0
-12128;sans fond;negative;0;1;0;0;0;0
-12129;sans formation;negative;0;0;1;0;0;0
-12130;sans heurt;positive;1;0;0;0;0;0
-12131;sans importance;negative;0;0;1;0;0;0
-12132;sans instruction;negative;0;0;1;0;0;1
-12133;sans interruption;positive;1;0;0;0;0;0
-12134;sans le faire exprès;negative;0;1;1;0;0;0
-12135;sans le savoir;negative;0;1;1;0;0;0
-12136;sans le sou;negative;0;0;1;0;0;0
-12137;sans le vouloir;negative;0;1;1;0;1;0
-12138;sans lien;negative;0;0;1;0;0;0
-12139;sans limite;positive;0;0;0;0;0;0
-12140;sans manche;positive;0;0;0;0;0;0
-12141;sans nom;negative;0;1;1;0;0;1
-12142;sans obstacle;positive;0;0;0;0;0;0
-12143;sans permis;negative;0;0;0;0;0;0
-12144;sans peur;positive;0;1;0;0;0;0
-12145;sans précédent;positive;0;0;0;0;1;0
-12146;sans prétention;positive;0;0;0;0;0;0
-12147;sans rapport;negative;0;1;1;0;0;0
-12148;sans relâche;positive;0;0;0;0;0;0
-12149;sans résistance;positive;0;0;0;0;0;0
-12150;sans restriction;positive;1;0;0;0;0;0
-12151;sans retenue;positive;1;0;0;0;0;0
-12152;sans scrupule;negative;0;0;0;1;0;1
-12153;sans soleil;negative;0;0;1;0;0;0
-12154;sans sucre;positive;0;0;0;0;0;0
-12155;sans surveillance;negative;0;1;1;0;0;0
-12156;sans tache;positive;0;0;0;0;0;0
-12157;sans tenir compte de;negative;0;0;0;0;0;0
-12158;sans titre;negative;0;0;1;0;0;0
-12159;sans vie;negative;0;1;1;0;0;0
-12160;sans voix;negative;0;1;1;0;1;0
-12161;santé;positive;1;0;0;0;0;0
-12162;santé mental;positive;0;0;0;0;0;0
-12163;saoul;negative;0;0;0;0;0;1
-12164;sapeur pompier;positive;0;0;0;0;0;0
-12165;saphir;positive;0;0;0;0;0;0
-12166;sarcasme;negative;0;0;1;1;0;1
-12167;sarcastique;negative;0;0;0;1;0;1
-12168;sarcome;negative;0;1;1;0;0;0
-12169;sardonique;negative;0;0;0;1;0;1
-12170;satanique;negative;0;1;0;1;0;0
-12171;satellite;positive;0;0;0;0;0;0
-12172;satin;positive;0;0;0;0;0;0
-12173;satiner;positive;0;0;0;0;0;0
-12174;satire;negative;0;0;0;1;0;1
-12175;satirique;negative;0;0;0;1;0;1
-12176;satisfaction;positive;1;0;0;0;0;0
-12177;saturation;negative;0;0;0;0;0;0
-12178;saturer;negative;0;0;1;1;0;1
-12179;sauce;positive;0;0;0;0;0;0
-12180;sauge;positive;0;0;0;0;0;0
-12181;saule;positive;0;0;0;0;0;0
-12182;saumâtre;negative;0;0;0;0;0;1
-12183;saumure;negative;0;0;0;0;0;1
-12184;sauna;positive;0;0;0;0;0;0
-12185;saupoudrage;positive;0;0;0;0;0;0
-12186;saupoudrer;positive;0;0;0;0;0;0
-12187;saut;positive;1;0;0;0;0;0
-12188;sauter;negative;0;1;0;0;0;1
-12189;sauter en parachute;negative;0;1;0;0;0;0
-12190;sauterelle;positive;0;1;0;0;0;1
-12191;sautiller;positive;1;0;0;0;0;0
-12192;sauvagerie;negative;0;1;0;1;0;0
-12193;sauvegarde;positive;0;0;0;0;0;0
-12194;sauvegarder;positive;0;0;0;0;0;0
-12195;sauver;positive;0;0;0;0;0;0
-12196;sauvetage;positive;0;0;0;0;1;0
-12197;savane;positive;0;0;0;0;0;0
-12198;saveur;positive;0;0;1;0;0;1
-12199;savon;positive;0;0;0;0;0;0
-12200;savonner;positive;0;0;0;0;0;0
-12201;savonneux;negative;0;1;0;0;0;0
-12202;savourer;positive;0;0;1;0;0;1
-12203;saxo;positive;0;0;0;0;0;0
-12204;saxophone;positive;0;0;0;0;0;0
-12205;scalpel;negative;0;1;0;0;0;0
-12206;scalper;negative;0;1;0;0;0;1
-12207;scandale;negative;0;1;0;1;1;0
-12208;scander;positive;0;0;0;1;1;0
-12209;scanner;positive;0;0;0;0;0;0
-12210;scape;negative;0;0;0;0;0;0
-12211;scarabée;positive;0;1;0;0;0;1
-12212;sceau;positive;0;0;0;0;0;0
-12213;sceller;positive;0;0;0;0;0;0
-12214;scène;positive;0;0;0;0;0;0
-12215;scénique;positive;0;0;0;0;0;0
-12216;scepticisme;negative;0;1;0;0;1;0
-12217;schéma;positive;0;0;0;0;0;0
-12218;schématique;positive;0;0;0;0;0;0
-12219;schisme;negative;0;0;0;1;0;0
-12220;schizophrénie;negative;0;1;1;1;0;1
-12221;sciatique;negative;0;1;1;0;0;0
-12222;sciemment;positive;0;0;0;0;0;0
-12223;science;positive;0;0;0;0;0;0
-12224;science économique;positive;0;0;0;0;0;0
-12225;science humain;positive;0;0;0;0;0;0
-12226;scientifique;positive;0;0;0;0;0;0
-12227;scinder;negative;0;0;0;1;0;0
-12228;scintillant;positive;1;0;0;0;0;0
-12229;scintillateur;positive;0;0;0;0;0;0
-12230;scintillation;positive;0;0;0;0;1;0
-12231;scintillement;positive;0;0;0;0;1;0
-12232;scintiller;positive;0;0;0;0;1;1
-12233;scission;negative;0;0;1;1;0;0
-12234;sciure;negative;0;0;0;0;0;1
-12235;scolaire;positive;0;0;0;0;0;0
-12236;scolarité;positive;0;0;0;0;0;0
-12237;sconse;negative;0;0;0;0;0;1
-12238;scoop;positive;0;0;0;0;1;0
-12239;score;positive;0;0;0;0;1;0
-12240;scorie;negative;0;0;0;0;0;1
-12241;scorpion;negative;0;1;0;1;1;1
-12242;scotch;positive;0;0;0;0;0;0
-12243;scout;positive;0;0;0;0;0;0
-12244;scribe;positive;0;0;0;0;0;0
-12245;script;positive;0;0;0;0;0;0
-12246;scriptural;positive;0;0;0;0;0;0
-12247;scruter;negative;0;0;0;0;0;0
-12248;scrutin;positive;0;0;0;0;0;0
-12249;sculpter;positive;0;0;0;0;0;0
-12250;sculpteur;positive;0;0;0;0;0;0
-12251;sculpture;positive;0;0;0;0;0;0
-12252;se bagarrer;negative;0;1;0;1;0;1
-12253;se baigner;positive;0;0;0;0;0;0
-12254;se balader;positive;1;0;0;0;0;0
-12255;se balancer;positive;0;0;0;0;0;0
-12256;se baser;positive;0;0;0;0;0;0
-12257;se battre;negative;0;1;0;1;0;0
-12258;se battre pour qch;negative;0;1;0;1;0;0
-12259;se battre en duel;negative;0;1;0;1;0;0
-12260;se blottir;positive;0;0;0;0;0;0
-12261;se boursoufler;negative;0;0;0;0;0;1
-12262;se braquer;negative;0;1;0;0;1;0
-12263;se briser;negative;0;0;0;0;1;0
-12264;se camoufler;negative;0;0;0;0;1;0
-12265;se casser;negative;0;0;0;0;1;0
-12266;se casser net;negative;0;1;0;1;1;0
-12267;se charger de;positive;0;0;0;0;0;0
-12268;se chevaucher;negative;0;0;0;0;0;0
-12269;se composer de;positive;0;0;0;0;0;0
-12270;se concentrer;positive;0;0;0;0;0;0
-12271;se concrétiser;positive;0;0;0;0;0;0
-12272;se conformer;positive;0;0;0;0;0;0
-12273;se conjuguer;positive;0;0;0;0;0;0
-12274;se connaître;positive;0;0;0;0;0;0
-12275;se connecter;positive;0;0;0;0;0;0
-12276;se contracter;negative;0;1;0;1;0;0
-12277;se contredire;negative;0;0;0;1;0;0
-12278;se coucher;positive;0;0;0;0;0;0
-12279;se couper;negative;0;1;1;0;0;0
-12280;se courber;negative;0;1;0;0;0;0
-12281;se couvrir;positive;0;0;0;0;0;0
-12282;se crisper;negative;0;1;0;1;0;0
-12283;se débarrasser;negative;0;0;1;1;0;0
-12284;se débarrasser de;negative;0;0;1;1;0;1
-12285;se débattre;negative;0;1;1;1;0;0
-12286;se débrouiller;negative;0;1;1;1;0;0
-12287;se décomposer;negative;0;1;1;0;0;1
-12288;se décourager;negative;0;1;1;1;0;0
-12289;se déformer;negative;0;0;1;1;0;0
-12290;se dégonfler;negative;0;1;1;1;0;0
-12291;se dégrader;negative;0;0;1;0;0;1
-12292;se déguiser;negative;0;0;0;0;0;0
-12293;se délecter;positive;1;0;0;0;0;0
-12294;se délester;positive;0;0;0;0;0;0
-12295;se dépêcher;negative;0;1;0;1;1;0
-12296;se déployer;positive;0;0;0;0;0;0
-12297;se dépouiller de;negative;0;0;0;0;0;1
-12298;se déprécier;negative;0;0;1;1;0;1
-12299;se dérouler;positive;0;0;0;0;0;0
-12300;se déshabiller;positive;0;0;0;0;0;0
-12301;se désintégrer;negative;0;1;1;1;0;1
-12302;se désister;negative;0;1;1;0;0;0
-12303;se détendre;positive;0;0;0;0;0;0
-12304;se détériorer;negative;0;1;1;1;0;1
-12305;se développer;positive;0;0;0;0;0;0
-12306;se dilater;negative;0;1;0;0;0;0
-12307;se disperser;negative;0;1;1;0;0;0
-12308;se disputer;negative;0;0;0;1;0;0
-12309;se dissiper;negative;0;0;0;0;0;0
-12310;se dissoudre;negative;0;0;0;0;0;0
-12311;se diversifier;positive;0;0;0;0;0;0
-12312;se doucher;positive;0;0;0;0;0;0
-12313;se durcir;negative;0;0;1;1;0;0
-12314;se faire passer pour;negative;0;0;0;1;0;0
-12315;se faire plaisir;positive;1;0;0;0;0;0
-12316;se familiariser;positive;0;0;0;0;0;0
-12317;se fatiguer;negative;0;0;1;0;0;0
-12318;se faufiler;negative;0;1;0;1;1;0
-12319;se fissurer;negative;0;1;0;0;1;0
-12320;se fouler;negative;0;1;1;0;1;0
-12321;se frotter;negative;0;0;0;0;0;0
-12322;se glisser;negative;0;0;0;0;0;1
-12323;se glisser furtivement;negative;0;1;0;1;1;0
-12324;se gonfler;negative;0;0;0;0;0;1
-12325;se hérisser;negative;0;1;0;0;1;0
-12326;se jeter sur;negative;0;1;0;1;1;0
-12327;se joindre à;positive;0;0;0;0;0;0
-12328;se lamenter;negative;0;1;1;0;0;1
-12329;se lasser;negative;0;0;1;0;0;0
-12330;se loger;positive;0;0;0;0;0;0
-12331;se marier;positive;0;1;0;0;1;0
-12332;se masturber;positive;1;0;0;0;0;0
-12333;se matérialiser;positive;0;0;0;0;0;0
-12334;se méfier de;negative;0;1;0;1;0;1
-12335;se mélanger;positive;0;0;0;0;0;0
-12336;se mêler;negative;0;0;0;1;0;0
-12337;se méprendre;negative;0;1;1;1;0;0
-12338;se mettre à califourchon sur;positive;0;0;0;0;0;0
-12339;se mettre à genou;negative;0;0;1;0;0;0
-12340;se mettre en grève;negative;0;0;0;1;0;0
-12341;se mirer;positive;0;0;0;0;0;0
-12342;se moquer;negative;0;0;0;1;0;1
-12343;se moquer de;negative;0;1;1;1;0;0
-12344;se multiplier;positive;0;0;0;0;0;0
-12345;se noyer;negative;0;1;1;0;0;0
-12346;se pâmer;positive;1;0;0;0;0;0
-12347;se parjurer;negative;0;0;1;1;1;1
-12348;se pavaner;negative;0;0;0;0;0;0
-12349;se pencher;negative;0;1;0;0;0;0
-12350;se percher;positive;0;0;0;0;0;0
-12351;se périmer;negative;0;0;1;0;0;1
-12352;se permettre;positive;0;0;0;0;0;0
-12353;se plaindre;negative;0;0;1;1;0;0
-12354;se plier;negative;0;0;0;0;0;0
-12355;se plonger;positive;0;0;0;0;0;0
-12356;se porter garant;positive;0;0;0;0;0;0
-12357;se positionner;positive;0;0;0;0;0;0
-12358;se précipiter;negative;0;1;1;1;1;0
-12359;se prélasser;positive;1;0;0;0;0;0
-12360;se préparer;positive;0;0;0;0;0;0
-12361;se presser;negative;0;1;0;1;1;0
-12362;se procurer;positive;0;0;0;0;0;0
-12363;se produire;positive;0;0;0;0;0;0
-12364;se profiler;negative;0;1;0;0;0;0
-12365;se promener;positive;1;0;0;0;0;0
-12366;se propager;negative;0;1;0;0;0;0
-12367;se proposer;positive;0;1;0;0;0;0
-12368;se prostituer;negative;0;0;1;0;0;1
-12369;se qualifier;positive;1;0;0;0;0;0
-12370;se quereller;negative;0;0;0;1;0;0
-12371;se raidir;negative;0;1;0;1;0;0
-12372;se rappeler;positive;0;0;0;0;0;0
-12373;se rapprocher;positive;0;0;0;0;0;0
-12374;se raser;positive;0;0;0;0;0;0
-12375;se rassembler;positive;0;0;0;0;0;0
-12376;se rebeller;negative;0;1;0;1;0;0
-12377;se redresser;positive;0;0;0;0;0;0
-12378;se régaler;positive;1;0;0;0;0;0
-12379;se régénérer;positive;0;0;0;0;0;0
-12380;se rejoindre;positive;0;0;0;0;0;0
-12381;se réjouir;positive;0;0;0;0;1;0
-12382;se rendre;negative;0;1;1;0;0;0
-12383;se renseigner;positive;0;0;0;0;0;0
-12384;se répandre;negative;0;0;0;0;0;0
-12385;se repentir;positive;0;1;0;0;0;0
-12386;se reposer;positive;1;0;0;0;0;0
-12387;se représenter;positive;0;0;0;0;0;0
-12388;se reproduire;negative;0;0;0;0;0;0
-12389;se retirer;negative;0;1;1;0;0;0
-12390;se retourner contre qqn;negative;0;0;1;1;0;0
-12391;se rétracter;negative;0;1;0;1;0;0
-12392;se rétrécir;negative;0;1;1;0;0;0
-12393;se réveiller;positive;0;0;0;0;0;0
-12394;se révolter;negative;0;0;0;1;1;0
-12395;se séparer;negative;0;0;1;0;0;0
-12396;se solidifier;positive;0;0;0;0;0;0
-12397;se souvenir;positive;0;0;0;0;0;0
-12398;se spécialiser;positive;0;0;0;0;0;0
-12399;se suicider;negative;0;1;1;1;0;0
-12400;se taire;negative;0;0;0;0;0;0
-12401;se tapir;negative;0;1;1;0;0;0
-12402;se tenir debout;positive;0;0;0;0;0;0
-12403;se terminer;negative;0;1;1;0;0;0
-12404;se tortiller;negative;0;0;0;0;0;1
-12405;se tracasser;negative;0;1;0;0;0;0
-12406;se tromper;negative;0;1;1;0;0;0
-12407;se vanter;negative;0;0;0;0;0;0
-12408;se vautrer;negative;0;1;1;0;1;1
-12409;se venger;negative;0;1;0;1;1;0
-12410;séance;positive;0;0;0;0;0;0
-12411;seau;positive;0;0;0;0;0;0
-12412;sec;negative;0;1;0;1;1;0
-12413;sécable;negative;0;0;0;0;0;0
-12414;sécateur;negative;0;0;0;0;0;0
-12415;sécession;negative;0;1;0;0;0;0
-12416;sécher;negative;0;0;0;0;0;0
-12417;sèche cheveu;positive;0;0;0;0;0;0
-12418;sèche linge;positive;0;0;0;0;0;0
-12419;sécheresse;negative;0;1;1;0;0;0
-12420;secondaire;negative;0;0;1;0;0;0
-12421;second;positive;0;0;0;0;1;0
-12422;seconder;positive;0;0;0;0;0;0
-12423;secouer;negative;0;1;0;0;0;0
-12424;secourir;positive;0;0;0;0;1;0
-12425;secours;positive;0;0;0;0;1;0
-12426;secousse;negative;0;1;0;1;1;0
-12427;secret;negative;0;1;0;0;1;0
-12428;secrétaire;positive;0;0;0;0;0;0
-12429;secrétaire général;positive;0;0;0;0;0;0
-12430;secrétariat;positive;0;0;0;0;0;0
-12431;secrètement;negative;0;1;0;0;0;0
-12432;sécréter;negative;0;0;0;0;0;1
-12433;sécrétion;negative;0;0;0;0;0;1
-12434;sectaire;negative;0;1;1;1;0;1
-12435;sectarisme;negative;0;1;0;1;0;0
-12436;secte;negative;0;1;0;0;0;0
-12437;secteur;positive;0;0;0;0;0;0
-12438;section;positive;0;0;0;0;0;0
-12439;sectionner;negative;0;1;1;1;0;0
-12440;sécurité;positive;0;0;0;0;0;0
-12441;sédentaire;negative;0;0;0;0;0;0
-12442;sédiment;negative;0;0;0;0;0;1
-12443;sédimentaire;negative;0;0;0;0;0;1
-12444;sédition;negative;0;0;1;1;0;0
-12445;séduction;positive;0;0;0;0;0;0
-12446;séduire;positive;0;0;0;0;0;0
-12447;séduisant;positive;0;0;0;0;0;0
-12448;segment;positive;0;0;0;0;0;0
-12449;segmenter;positive;0;0;0;0;0;0
-12450;ségrégation;negative;0;0;1;0;0;0
-12451;seigle;positive;0;0;0;0;0;0
-12452;seigneur;positive;0;0;0;0;0;1
-12453;seigneurie;positive;0;0;0;0;0;0
-12454;sein;positive;0;0;0;0;0;0
-12455;séjour;positive;0;0;0;0;0;0
-12456;séjourner;positive;0;0;0;0;0;0
-12457;sel;negative;0;0;0;0;0;1
-12458;sélection;positive;0;0;0;0;0;0
-12459;selle;positive;0;0;0;0;0;0
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-12524;serveur;positive;0;0;0;0;0;0
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-12646;solidarité;positive;0;0;0;0;0;0
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-12648;solide;positive;0;1;1;0;1;0
-12649;solidification;positive;0;0;0;0;0;0
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-12654;solliciter;positive;0;0;0;0;0;0
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-12678;sonar;positive;0;0;0;0;0;0
-12679;sonate;positive;0;0;0;0;0;0
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-12745;soupeser;positive;0;1;0;0;0;0
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-12798;spécimen;positive;0;0;0;0;0;0
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-12802;spectre;negative;0;1;1;0;0;0
-12803;spectromètre;positive;0;0;0;0;0;0
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-12808;spéculum;negative;0;1;0;0;0;1
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-12812;sphérique;positive;0;0;0;0;0;0
-12813;sphinx;positive;0;0;0;0;0;0
-12814;spinal;positive;0;0;0;0;0;0
-12815;spiral|spirale;positive;0;0;0;0;0;0
-12816;spiraler;positive;0;0;0;0;0;0
-12817;spiritualité;positive;0;0;0;0;0;0
-12818;spiritueux;negative;0;0;1;1;0;0
-12819;splendeur;positive;0;0;0;0;1;0
-12820;splendide;positive;0;0;0;0;1;0
-12821;spline;positive;0;0;0;0;0;0
-12822;spoiler;negative;0;1;1;1;0;0
-12823;sponsor;positive;0;0;0;0;0;0
-12824;sponsoring;positive;0;0;0;0;0;0
-12825;sponsoriser;positive;0;0;0;0;0;0
-12826;sporadique;negative;0;0;0;0;1;0
-12827;spore;negative;0;0;0;0;0;1
-12828;sport;positive;0;0;0;0;0;0
-12829;spray;positive;0;0;0;0;0;0
-12830;square;positive;0;0;0;0;0;0
-12831;squat;negative;0;1;0;0;0;0
-12832;squatter;negative;0;1;1;0;0;1
-12833;squelette;negative;0;1;1;0;0;0
-12834;stabilité;positive;0;0;0;0;0;0
-12835;stable;positive;0;1;0;0;1;0
-12836;staccato;negative;0;0;0;0;0;0
-12837;stade;positive;0;0;0;0;0;0
-12838;stagiaire;positive;0;0;0;0;0;0
-12839;stagner;negative;0;0;1;0;0;0
-12840;stagnant;negative;0;0;1;0;0;0
-12841;stagnation;negative;0;1;1;0;0;0
-12842;stalle;positive;0;0;0;0;0;1
-12843;stand;positive;0;0;0;0;0;1
-12844;standard;positive;0;0;0;0;0;0
-12845;standardiser;positive;0;0;0;0;0;0
-12846;star;positive;0;0;0;0;0;0
-12847;starter;negative;0;1;1;1;1;0
-12848;station;positive;0;0;0;0;0;0
-12849;stationnaire;positive;0;0;0;0;0;0
-12850;stationner;positive;0;0;0;0;0;0
-12851;statique;positive;0;0;0;0;0;0
-12852;statistique;positive;0;0;0;0;0;0
-12853;stator;positive;0;0;0;0;0;0
-12854;statuaire;positive;0;0;0;0;0;0
-12855;statue;positive;0;0;0;0;0;0
-12856;statuer sur;negative;0;1;0;0;0;0
-12857;statuette;positive;0;0;0;0;0;0
-12858;stature;positive;0;0;0;0;0;0
-12859;statut;positive;0;0;0;0;0;0
-12860;statutaire;positive;0;0;0;0;0;0
-12861;stellaire;positive;0;0;0;0;0;0
-12862;stencil;positive;0;0;0;0;0;0
-12863;sténo;positive;0;0;0;0;0;0
-12864;sténographie;positive;0;0;0;0;0;0
-12865;steppe;negative;0;1;1;0;0;0
-12866;stéréoscopique;positive;0;0;0;0;0;0
-12867;stéréotype;negative;0;0;0;0;0;1
-12868;stéréotyper;negative;0;0;0;0;0;1
-12869;stérile;negative;0;1;1;0;0;0
-12870;stériliser;positive;0;1;1;0;0;0
-12871;stérilité;negative;0;1;1;0;0;0
-12872;sterling;positive;0;0;0;1;0;0
-12873;sterne;positive;0;0;0;0;0;0
-12874;stéthoscope;positive;0;0;0;0;0;0
-12875;steward;positive;0;0;0;0;0;0
-12876;stigmate;negative;0;1;1;1;0;1
-12877;stimulant;positive;0;0;0;0;0;0
-12878;stimulation;positive;1;0;0;0;0;0
-12879;stimuler;positive;0;0;0;0;0;0
-12880;stimulus;positive;0;0;0;0;0;0
-12881;stipulation;positive;0;0;0;0;0;0
-12882;stipuler;positive;0;0;0;0;0;0
-12883;stock;positive;0;0;0;0;0;0
-12884;stockage;positive;0;0;0;0;0;0
-12885;stocker;positive;0;0;0;0;0;0
-12886;stoïque;positive;0;0;0;0;0;0
-12887;stratège;positive;0;0;0;0;0;0
-12888;stratégie;positive;0;0;0;0;0;0
-12889;stratégique;positive;0;0;0;0;0;0
-12890;stratification;positive;0;0;0;0;0;0
-12891;stratus;negative;0;0;1;0;0;0
-12892;stress;negative;0;1;0;1;0;0
-12893;stresser;negative;0;1;1;1;0;0
-12894;stressant;negative;0;1;1;1;0;0
-12895;strict;negative;0;0;1;0;0;0
-12896;strident;negative;0;1;0;1;1;0
-12897;strie;negative;0;0;0;0;0;0
-12898;strier;positive;0;0;0;0;0;0
-12899;stroboscopique;positive;0;0;0;0;0;0
-12900;strophe;positive;0;0;0;0;0;0
-12901;structure;positive;0;0;0;0;0;0
-12902;structurer;positive;0;0;0;0;0;0
-12903;stuc;positive;0;0;0;0;0;0
-12904;studio;positive;0;0;0;0;0;0
-12905;stupéfaction;positive;0;0;0;0;1;0
-12906;stupéfaire;negative;0;1;0;0;1;1
-12907;stupéfiant;negative;0;0;0;0;1;0
-12908;stupéfier;positive;0;0;0;0;1;0
-12909;stupide;negative;0;0;0;1;0;1
-12910;stupidité;negative;0;0;0;1;0;1
-12911;style;positive;0;0;0;0;0;0
-12912;stylet;negative;0;1;0;0;0;0
-12913;stylo;positive;0;0;0;0;0;0
-12914;suaire;negative;0;0;1;0;0;0
-12915;suer;negative;0;0;0;0;0;1
-12916;suavité;positive;0;0;0;0;0;0
-12917;subalterne;negative;0;0;1;0;0;1
-12918;subatomique;positive;0;0;0;0;0;0
-12919;subconscient;negative;0;0;0;0;0;0
-12920;subdiviser;positive;0;0;0;0;0;0
-12921;subdivision;positive;0;0;0;0;0;0
-12922;subduction;positive;0;0;0;0;0;0
-12923;subir;negative;0;0;1;0;0;0
-12924;subito;positive;0;0;0;0;1;0
-12925;sublimation;positive;1;0;0;0;0;0
-12926;subliminal;negative;0;0;0;0;1;0
-12927;submerger;negative;0;1;1;0;1;0
-12928;submersible;negative;0;1;0;0;0;0
-12929;submersion;negative;0;1;0;0;0;0
-12930;subordination;negative;0;0;0;0;0;0
-12931;subsidiaire;negative;0;0;0;0;0;0
-12932;subsistance;positive;1;0;0;0;0;0
-12933;subsister;positive;0;0;0;0;0;0
-12934;substance;positive;0;0;0;0;0;0
-12935;substantiellement;positive;0;0;0;0;0;0
-12936;substituer;negative;0;0;0;0;0;0
-12937;substitut;negative;0;0;0;0;0;0
-12938;substitution;negative;0;0;0;0;0;0
-12939;subtil;positive;0;0;0;0;1;0
-12940;subvenir;positive;0;0;0;0;0;0
-12941;subvention;positive;0;0;0;1;0;1
-12942;subventionner;positive;0;0;0;0;0;0
-12943;subversion;negative;0;1;0;1;0;0
-12944;subvertir;negative;0;1;1;0;0;1
-12945;sucer;negative;0;0;0;0;0;0
-12946;succès;positive;1;0;0;0;0;0
-12947;successeur;positive;0;0;0;0;0;0
-12948;succession;positive;0;0;0;0;0;0
-12949;succinct;positive;0;0;0;0;0;0
-12950;succion;negative;0;1;0;0;0;0
-12951;succomber;negative;0;1;1;0;0;0
-12952;succulent;positive;1;0;0;0;0;0
-12953;succursale;positive;0;0;0;0;0;0
-12954;sucette;positive;0;0;0;1;0;0
-12955;sucre;positive;0;0;0;0;0;0
-12956;sucrer;positive;0;0;0;0;1;0
-12957;sucrerie;positive;1;0;0;0;0;0
-12958;sudation;negative;0;0;0;0;0;1
-12959;sueur;negative;0;1;0;0;0;1
-12960;suffire;positive;0;0;0;0;0;0
-12961;suffisamment;positive;0;0;0;0;0;0
-12962;suffisance;negative;0;0;0;1;0;0
-12963;suffisant;positive;0;0;0;0;0;1
-12964;suffixe;positive;0;0;0;0;0;0
-12965;suffocant;negative;0;1;1;0;0;1
-12966;suffocation;negative;0;1;0;1;0;0
-12967;suggérer;positive;0;0;0;0;0;0
-12968;suggestion;positive;0;0;0;0;0;0
-12969;suicidaire;negative;0;1;1;1;0;1
-12970;suicide;negative;0;1;1;1;0;0
-12971;suicider;negative;0;1;1;1;0;0
-12972;suie;negative;0;0;0;0;0;1
-12973;suif;positive;0;0;0;0;0;0
-12974;suintement;negative;0;0;0;0;0;1
-12975;suinter;negative;0;0;0;0;0;1
-12976;suivant;negative;0;0;0;0;0;0
-12977;suivant|suivante;negative;0;0;0;0;0;0
-12978;suivre;positive;0;0;0;0;0;0
-12979;suivre à le trace;positive;0;0;0;0;0;0
-12980;suivre le trace de;positive;0;0;0;0;0;0
-12981;sujet;negative;0;0;1;1;0;1
-12982;sujétion;negative;0;1;1;0;0;0
-12983;sultan;positive;0;1;0;0;0;0
-12984;sumo;negative;0;1;0;0;0;0
-12985;super;positive;0;1;1;1;1;0
-12986;superbe;positive;1;0;0;0;0;0
-12987;superficie;positive;0;0;0;0;0;0
-12988;supériorité;positive;0;0;0;0;0;0
-12989;superlatif;positive;0;0;0;0;0;0
-12990;superman;positive;0;0;0;0;0;0
-12991;supermarché;positive;0;0;0;0;0;0
-12992;superposer;positive;0;0;0;0;0;0
-12993;superposition;positive;0;0;0;0;0;0
-12994;superstar;positive;0;0;0;0;0;0
-12995;superstition;negative;0;1;0;0;0;0
-12996;superviser;positive;0;0;0;0;0;0
-12997;superviseur;positive;0;0;0;0;0;0
-12998;supllier;negative;0;0;1;0;0;0
-12999;supplanter;positive;0;0;0;0;0;0
-13000;supplément;positive;0;0;1;1;0;1
-13001;supplémentaire;positive;0;0;0;0;0;0
-13002;supplication;positive;0;0;0;0;0;0
-13003;supplice;negative;0;1;1;1;1;0
-13004;support;positive;0;0;0;0;0;0
-13005;supporter qch;positive;0;0;0;0;0;0
-13006;supporteur;positive;0;0;0;0;0;0
-13007;supposer;negative;0;1;0;0;0;0
-13008;suppression;negative;0;1;1;1;0;1
-13009;supprimer;negative;0;1;1;1;0;0
-13010;suprématie;positive;0;1;0;1;1;0
-13011;suprême;positive;0;0;0;0;0;0
-13012;suprêmement;positive;0;0;0;0;0;0
-13013;sur ce;positive;0;0;0;0;0;0
-13014;sur qui on ne pouvoir compter;negative;0;1;0;1;0;0
-13015;sur le champ;positive;0;0;0;0;1;0
-13016;surabondance;negative;0;0;0;0;0;1
-13017;surcharge;negative;0;0;1;0;0;0
-13018;surcharger;negative;0;0;1;0;0;0
-13019;surdité;negative;0;1;1;0;0;0
-13020;surestimer;negative;0;0;0;0;1;0
-13021;sûreté;positive;0;0;0;0;0;0
-13022;surf;positive;0;0;0;0;0;0
-13023;surface;positive;0;0;0;0;0;0
-13024;surfacturé;negative;0;0;1;0;0;0
-13025;surfacturée;negative;0;0;1;0;0;0
-13026;surfacturées;negative;0;0;1;0;0;0
-13027;surfacturés;negative;0;0;1;0;0;0
-13028;surfer;positive;0;0;0;0;0;0
-13029;surgir;positive;0;0;0;0;1;0
-13030;surhomme;positive;0;0;0;0;0;0
-13031;surhumain;positive;0;0;0;0;0;0
-13032;surintendant;positive;0;0;0;0;0;0
-13033;surintendante;positive;0;0;0;0;0;0
-13034;surmenage;negative;0;1;1;0;0;0
-13035;surnageant;positive;0;0;0;0;0;0
-13036;surnom;positive;0;0;0;0;0;0
-13037;surnommer;positive;0;0;0;0;0;0
-13038;surpasser;positive;0;0;0;0;0;0
-13039;surpasser en nombre;positive;0;0;0;0;0;0
-13040;surpayer|surpayer;negative;0;0;0;0;0;0
-13041;surplomber;positive;0;0;0;0;0;0
-13042;surplombant;positive;0;0;0;0;0;0
-13043;surplus;negative;0;0;0;0;0;0
-13044;surprenant;positive;0;0;0;0;1;0
-13045;surseoir;positive;0;0;0;0;0;0
-13046;surstocker;negative;0;0;0;0;0;0
-13047;surtaxe;negative;0;0;1;1;0;1
-13048;surtout;positive;0;0;0;0;0;0
-13049;survaleur;positive;1;0;0;0;0;0
-13050;surveillance;positive;0;1;1;0;0;0
-13051;surveiller pénitentiaire;negative;0;1;0;1;0;0
-13052;surveillant pénitentiaire;negative;0;1;0;1;0;0
-13053;surveiller;positive;0;1;0;0;0;0
-13054;survenir;positive;0;0;0;0;1;0
-13055;survie;positive;0;0;0;0;0;0
-13056;survivant;positive;1;0;0;0;0;0
-13057;survivre;positive;1;0;0;0;0;0
-13058;survivre à;positive;1;0;0;0;0;0
-13059;susceptibilité;negative;0;0;1;0;0;0
-13060;susceptible;negative;0;1;1;1;0;0
-13061;susdit;positive;0;0;0;0;0;0
-13062;susmentionné;positive;0;0;0;0;0;0
-13063;suspect;negative;0;1;0;0;0;0
-13064;suspecter;negative;0;1;0;0;0;0
-13065;suspendre;negative;0;0;1;0;1;0
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-13067;suspension;negative;0;1;0;0;0;0
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-13069;suspicion;negative;0;1;0;0;0;0
-13070;suture;negative;0;1;0;0;0;1
-13071;suturer qch;negative;0;0;0;0;0;0
-13072;svastika;negative;0;1;0;1;0;1
-13073;svelte;positive;0;0;0;0;0;0
-13074;swag;positive;0;0;0;0;0;0
-13075;swastika;negative;0;1;0;1;0;1
-13076;syllabe;positive;0;0;0;0;0;0
-13077;syllabique;positive;0;0;0;0;0;0
-13078;symbole;positive;0;0;0;0;0;0
-13079;symbolique;positive;0;0;0;0;0;0
-13080;symboliquement;positive;0;0;0;0;0;0
-13081;symboliser;positive;0;0;0;0;0;0
-13082;symbolisme;positive;0;0;0;0;0;0
-13083;symétrie;positive;0;0;0;0;0;0
-13084;symétrique;positive;0;0;0;0;0;0
-13085;sympathique;positive;0;0;0;0;0;0
-13086;symphonie;positive;1;0;0;0;0;0
-13087;symptomatique;negative;0;0;1;0;0;0
-13088;symptôme;negative;0;1;0;0;0;0
-13089;synagogue;positive;0;0;0;0;0;0
-13090;synchrone;positive;0;0;0;0;0;0
-13091;synchroniser;positive;0;0;0;0;1;0
-13092;syncope;negative;0;1;1;0;1;0
-13093;syndicat;positive;0;0;0;0;0;0
-13094;synergie;positive;0;0;0;0;0;0
-13095;synode;positive;0;0;0;0;0;0
-13096;synonyme;positive;0;1;0;0;0;0
-13097;synopsis;positive;0;0;0;0;0;0
-13098;synoptique;positive;0;0;0;0;0;0
-13099;syntaxe;positive;0;0;0;0;0;0
-13100;synthèse;positive;0;0;0;0;0;0
-13101;synthétique;negative;0;0;0;0;0;1
-13102;systématique;positive;0;0;0;0;0;0
-13103;systématiquement;positive;0;0;0;0;0;0
-13104;système;positive;0;0;0;0;0;0
-13105;système hydraulique;negative;0;0;1;0;0;0
-13106;tabac;negative;0;0;0;0;0;1
-13107;tabac à priser;negative;0;0;0;0;1;1
-13108;tabagisme;negative;0;0;0;0;0;1
-13109;tabernacle;positive;0;0;0;0;0;0
-13110;table;positive;0;0;0;0;0;0
-13111;tableau;positive;0;0;0;0;0;0
-13112;tableau de bord;positive;0;0;0;0;0;0
-13113;tableau noir;positive;0;0;0;0;0;0
-13114;tablette;positive;0;0;0;0;0;0
-13115;tablier;positive;0;0;0;0;0;0
-13116;tabou;negative;0;1;0;0;0;1
-13117;tabouret;positive;0;0;0;0;0;0
-13118;tabulaire;positive;0;0;0;0;0;0
-13119;tabulation;positive;0;0;0;0;0;0
-13120;tabuler;positive;0;0;0;0;0;0
-13121;tache;negative;0;1;1;1;0;1
-13122;tâche;negative;0;1;1;0;0;1
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-13124;tâcher de;positive;0;0;0;0;0;0
-13125;tacheter;negative;0;0;0;0;0;0
-13126;tachymètre;positive;0;0;0;0;0;0
-13127;tacite;positive;0;0;0;0;0;0
-13128;tacot;negative;0;1;0;1;1;0
-13129;tact;positive;0;0;0;0;0;0
-13130;tactile;positive;0;0;0;0;0;0
-13131;tactique;positive;0;1;0;0;0;0
-13132;taffetas;positive;0;0;0;0;0;0
-13133;taie d oreiller;positive;0;0;0;0;0;0
-13134;taillader;negative;0;1;1;1;0;1
-13135;taillant;positive;0;0;0;0;0;0
-13136;taille;positive;0;0;0;0;0;0
-13137;tailler;negative;0;1;0;0;0;0
-13138;taille haie;negative;0;0;0;0;0;0
-13139;tailleur;positive;0;0;0;0;0;0
-13140;tailleuse;positive;0;0;0;0;0;0
-13141;taire;negative;0;1;1;1;0;1
-13142;talent;positive;0;0;0;0;0;0
-13143;talisman;positive;0;0;0;0;0;0
-13144;talon;positive;0;0;0;0;0;0
-13145;talus;positive;0;0;0;0;0;0
-13146;tambour;positive;0;0;0;0;0;0
-13147;tambourin;negative;0;0;0;0;0;0
-13148;tambourinement;positive;0;0;0;0;0;0
-13149;tambouriner;positive;0;0;0;0;0;0
-13150;tamis;positive;0;0;0;0;0;0
-13151;tamisage;positive;0;0;0;0;0;0
-13152;tamiser;negative;0;0;1;0;0;0
-13153;tampon;positive;0;0;0;0;0;1
-13154;tamponner;positive;0;0;0;0;0;0
-13155;tandem;positive;0;0;0;0;0;0
-13156;tangent;negative;0;0;0;0;0;0
-13157;tanière;positive;0;0;0;0;0;0
-13158;tanner;positive;0;0;0;0;0;0
-13159;tante;positive;0;0;0;0;0;0
-13160;taper;positive;0;0;0;0;0;0
-13161;tapir;negative;0;0;0;0;0;0
-13162;tapir de course;positive;0;0;0;0;0;0
-13163;tapis roulant;positive;0;0;0;0;0;0
-13164;tapisserie;positive;0;0;0;0;0;0
-13165;tapoter;positive;0;0;0;0;0;0
-13166;taquiner;positive;0;0;1;1;0;0
-13167;taquinerie;negative;0;1;0;1;0;0
-13168;tarer;negative;0;1;0;0;0;1
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-13176;tatami;negative;0;0;0;0;0;0
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-13185;taxer;negative;0;0;1;1;0;0
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-13188;taxonomie;positive;0;0;0;0;0;0
-13189;teaser;positive;0;0;0;0;0;0
-13190;technicité;positive;0;0;0;0;0;0
-13191;technique;positive;0;0;0;0;0;0
-13192;technologie;positive;0;0;0;0;0;0
-13193;ted;positive;0;0;0;0;0;0
-13194;teflon;positive;0;0;0;0;0;0
-13195;téflon;positive;0;0;0;0;0;0
-13196;teindre;positive;0;0;0;0;0;0
-13197;teinte;positive;0;0;0;0;0;0
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-13199;teinture;positive;0;0;0;0;0;0
-13200;télégramme;positive;0;0;0;0;0;0
-13201;télégraphe;positive;0;0;0;0;0;0
-13202;téléphone;positive;0;0;0;0;0;0
-13203;téléphoner;positive;0;0;0;0;0;0
-13204;télescope;positive;0;0;0;0;0;0
-13205;téléscope;positive;0;0;0;0;0;0
-13206;télescopique;positive;0;0;0;0;0;0
-13207;téléscripteur;positive;0;0;0;0;0;0
-13208;télévision;positive;0;0;0;0;0;0
-13209;téméraire;positive;0;1;0;1;0;0
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-13211;témoignage;positive;0;0;0;0;0;0
-13212;témoigner;positive;0;0;0;0;0;0
-13213;témoigner de le sympathie;positive;0;0;1;0;0;0
-13214;témoin;positive;0;0;0;0;0;0
-13215;témoin oculaire;positive;0;0;0;0;0;0
-13216;tempera;positive;0;0;0;0;0;0
-13217;tempérer;positive;0;0;0;0;0;0
-13218;tempérament;positive;0;0;0;0;0;0
-13219;tempérance;positive;0;0;0;0;0;0
-13220;température;positive;0;0;0;0;0;0
-13221;tempête;negative;0;1;1;1;1;0
-13222;temple;positive;0;0;0;0;0;0
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-13225;temporal;positive;0;0;0;0;0;0
-13226;temporisation;negative;0;1;1;0;0;0
-13227;temps;positive;0;0;0;0;0;0
-13228;ténacité;positive;0;0;0;0;0;0
-13229;tendon;positive;0;0;0;0;0;0
-13230;tendre;positive;0;0;0;0;0;0
-13231;tendre un embuscade à;negative;0;1;0;1;1;0
-13232;tendresse;positive;0;0;0;0;0;0
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-13234;teneur;positive;0;0;0;0;0;0
-13235;tenir;positive;0;0;0;0;0;0
-13236;tenir compte de;positive;0;0;0;0;0;0
-13237;tenir en laisse;negative;0;1;1;0;0;0
-13238;tennis;positive;0;0;0;0;0;0
-13239;tension;negative;0;1;0;1;0;0
-13240;tentacule;negative;0;1;0;0;0;1
-13241;tenter;positive;0;0;0;0;1;0
-13242;tentant;positive;0;0;0;0;1;0
-13243;tentation;negative;0;0;0;0;0;0
-13244;tentative;positive;0;0;0;0;0;0
-13245;tente;positive;0;0;0;0;0;0
-13246;tenture;positive;0;0;0;0;0;0
-13247;ténu;negative;0;1;1;0;0;0
-13248;tenue;positive;0;0;0;0;0;0
-13249;tercet;positive;0;0;0;0;0;0
-13250;terme;positive;0;0;1;0;0;0
-13251;terminal;positive;0;1;1;0;0;0
-13252;terminus;negative;0;1;1;0;0;0
-13253;termite;negative;0;0;0;0;0;1
-13254;ternaire;positive;0;0;0;0;0;0
-13255;terne;negative;0;0;1;0;0;1
-13256;ternir;negative;0;0;1;0;0;1
-13257;terrain;positive;0;1;1;1;1;0
-13258;terrasse;positive;0;0;0;0;0;0
-13259;terre;positive;0;0;0;0;0;0
-13260;terreau;positive;0;0;0;0;0;0
-13261;terre boisé;positive;0;0;0;0;0;0
-13262;terreur;negative;0;1;1;1;0;0
-13263;terreux;positive;0;0;0;0;0;0
-13264;terrible;negative;0;1;1;1;1;1
-13265;terriblement;negative;0;1;1;1;1;1
-13266;terrier;negative;0;0;0;0;0;0
-13267;terrifier;negative;0;1;1;1;1;1
-13268;terrifiant;negative;0;1;1;1;0;1
-13269;territoire;positive;0;0;0;0;0;0
-13270;territorial;positive;0;0;0;0;0;0
-13271;terroriser;negative;0;1;1;1;1;1
-13272;terrorisme;negative;0;1;1;1;0;1
-13273;terroriste;negative;0;1;1;1;1;1
-13274;tertiaire;positive;0;0;0;0;0;0
-13275;tertre;negative;0;0;0;0;0;1
-13276;test;positive;0;0;0;0;0;0
-13277;testament;positive;0;0;0;0;0;0
-13278;tester;positive;0;0;0;0;0;0
-13279;tétanos;negative;0;1;0;0;0;1
-13280;tête;positive;0;0;0;0;0;0
-13281;tête baisser;negative;0;0;0;1;1;0
-13282;tête de n?ud;negative;0;0;0;1;0;1
-13283;tête de puits;positive;0;0;0;0;0;0
-13284;téter;positive;0;0;0;0;0;0
-13285;tétine;positive;0;0;0;0;0;0
-13286;téton;positive;0;0;0;0;0;0
-13287;tétraédrique;positive;0;0;0;0;0;0
-13288;tétu;negative;0;0;0;1;0;0
-13289;texte;positive;0;0;0;0;0;0
-13290;texte sacré;positive;0;0;0;0;0;0
-13291;texte standard;negative;0;0;0;0;0;0
-13292;textile;positive;0;0;0;0;0;0
-13293;texto;positive;0;0;0;0;0;0
-13294;textuellement;positive;0;0;0;0;0;0
-13295;textural;positive;0;0;0;0;0;0
-13296;texturale;positive;0;0;0;0;0;0
-13297;texturales;positive;0;0;0;0;0;0
-13298;texturaux;positive;0;0;0;0;0;0
-13299;texture;positive;0;0;0;0;0;0
-13300;thanksgiving;positive;0;0;0;0;0;0
-13301;thé;positive;0;0;0;0;0;0
-13302;théâtral;negative;0;0;0;0;0;0
-13303;théâtre;positive;1;0;0;0;0;0
-13304;théisme;negative;0;0;0;0;0;1
-13305;thème;positive;0;0;0;0;0;0
-13306;thème majeur;positive;0;0;0;0;0;0
-13307;théocratie;positive;0;0;0;0;0;0
-13308;théocratique;negative;0;1;1;1;0;0
-13309;théologie;positive;0;0;0;0;0;0
-13310;théologique;positive;0;0;0;0;0;0
-13311;théorème;positive;0;0;0;0;0;0
-13312;théorie;positive;0;0;0;0;0;0
-13313;théorique;negative;0;0;0;0;0;0
-13314;thérapeutique;positive;0;0;0;0;0;0
-13315;thermique;positive;0;0;0;0;0;0
-13316;thermocouple;positive;0;0;0;0;0;0
-13317;thermodynamique;positive;0;0;0;0;0;0
-13318;thermomètre;positive;0;0;0;0;0;0
-13319;thermostat;positive;0;0;0;0;0;0
-13320;thèse;positive;0;0;0;0;0;0
-13321;thon;negative;0;0;0;0;0;0
-13322;thym;positive;0;0;0;0;0;0
-13323;tiare;positive;0;0;0;0;0;0
-13324;tibia;positive;0;0;0;0;0;0
-13325;tic;negative;0;1;0;0;1;0
-13326;ticket;positive;0;0;0;0;0;0
-13327;ticket de caisse;positive;0;0;0;0;0;0
-13328;tiède;negative;0;0;0;0;0;0
-13329;tiers;positive;0;0;0;0;0;0
-13330;tige;positive;0;1;0;0;0;0
-13331;tigre;negative;0;1;0;0;0;0
-13332;tigrer;positive;0;0;0;0;0;0
-13333;tilde;positive;0;0;0;0;0;0
-13334;timbre;positive;0;0;0;0;0;0
-13335;timbrer;negative;0;1;0;0;1;1
-13336;timide;negative;0;1;1;0;0;0
-13337;timidité;negative;0;1;1;0;0;0
-13338;tintement;positive;0;0;0;0;1;0
-13339;tinter;positive;1;0;0;0;0;0
-13340;tique;negative;0;0;0;0;0;0
-13341;tir;negative;0;1;1;1;1;0
-13342;tir à l arc;positive;0;0;0;0;0;0
-13343;tirage;positive;0;0;0;0;0;0
-13344;tirant;negative;0;1;1;1;0;0
-13345;tirer bouchon;positive;0;0;0;0;0;0
-13346;tirer à pile ou face;negative;0;1;0;0;1;0
-13347;tirer profit;positive;1;0;0;0;0;0
-13348;tirer profit de;positive;0;0;0;0;0;0
-13349;tiret;negative;0;1;1;0;0;0
-13350;tireur;negative;0;1;0;1;1;0
-13351;tiroir;positive;0;0;0;0;0;0
-13352;tisser;positive;0;0;0;0;0;0
-13353;tissu;positive;0;0;1;0;0;1
-13354;titanesque;negative;0;1;0;0;0;0
-13355;titre;positive;0;0;0;0;0;0
-13356;titrer;positive;0;0;0;0;0;0
-13357;titulaire;positive;0;0;0;0;0;0
-13358;titularisation;positive;0;0;0;0;0;0
-13359;toast;positive;1;0;0;0;0;0
-13360;toboggan;positive;0;1;0;0;1;0
-13361;toge;positive;0;0;0;0;0;0
-13362;toile;positive;0;0;0;0;0;0
-13363;toile de jute;negative;0;0;0;0;0;0
-13364;toilette;negative;0;0;0;0;0;1
-13365;toiletter;positive;0;0;0;0;0;0
-13366;toilette extérieur;negative;0;0;0;0;0;1
-13367;toison;positive;0;0;1;1;0;1
-13368;toit;positive;0;0;0;0;0;0
-13369;tolérer;positive;0;0;0;0;0;0
-13370;tolérant;positive;0;0;0;0;0;0
-13371;tollé;negative;0;1;0;1;1;1
-13372;tomahawk;positive;0;0;0;0;0;0
-13373;tomber;negative;0;1;1;0;0;0
-13374;tombant;negative;0;0;1;0;0;0
-13375;tombe;negative;0;1;1;0;0;0
-13376;tombeau;negative;0;1;1;0;0;0
-13377;tomber de le nuit;negative;0;1;1;0;0;0
-13378;tomber en flocon;negative;0;0;0;0;0;0
-13379;tomber en pâmoison;positive;1;0;0;0;0;0
-13380;tomber goutte à goutte;negative;0;0;0;0;0;1
-13381;tomber nez à nez;positive;0;0;0;0;1;0
-13382;tomber sur;positive;0;0;0;0;1;0
-13383;tombeur;positive;0;0;0;0;0;0
-13384;tombola;positive;0;0;0;0;1;0
-13385;tome;positive;0;0;0;0;0;0
-13386;ton;positive;0;0;0;0;0;0
-13387;ton monotone;negative;0;1;0;0;0;1
-13388;tondeur|tondeuse;negative;0;1;0;0;0;0
-13389;tondre;negative;0;1;0;0;0;0
-13390;tonifiant;positive;1;0;0;0;0;0
-13391;tonique;positive;1;0;0;0;0;0
-13392;tonitruer;negative;0;1;0;1;1;0
-13393;tonitruant;negative;0;1;0;1;1;0
-13394;tonnage;negative;0;0;0;0;0;0
-13395;tonne;negative;0;0;0;0;0;0
-13396;tonneau;positive;0;0;0;0;0;0
-13397;tonnelet;positive;0;0;0;0;0;0
-13398;tonnelle;positive;0;0;0;0;0;0
-13399;tonner;negative;0;1;0;1;1;0
-13400;tonnerre;negative;0;1;0;1;1;0
-13401;tonton;positive;0;0;0;0;0;0
-13402;topaze;positive;0;0;0;0;0;0
-13403;topographie;positive;0;0;0;0;0;0
-13404;topographique;positive;0;0;0;0;0;0
-13405;toquade;negative;0;0;0;0;0;0
-13406;toquer;negative;0;1;0;0;1;0
-13407;torche;positive;0;0;0;0;0;0
-13408;tordre;negative;0;1;0;1;0;0
-13409;tornade;negative;0;1;0;1;0;0
-13410;torpille;negative;0;1;0;1;0;0
-13411;torpiller;negative;0;1;0;1;0;0
-13412;torrent;negative;0;1;0;1;0;0
-13413;torsader;positive;0;0;0;0;0;0
-13414;torse;positive;0;0;0;0;0;0
-13415;torsion;negative;0;1;0;0;0;0
-13416;tort;negative;0;1;1;1;0;1
-13417;tortue;positive;0;0;0;0;0;0
-13418;torture;negative;0;1;1;1;0;1
-13419;torturer;negative;0;1;1;1;0;1
-13420;total;negative;0;0;0;0;0;0
-13421;totalement;positive;0;0;0;0;0;0
-13422;totalité;positive;0;0;0;0;0;0
-13423;totem;positive;0;0;0;0;0;0
-13424;touche;positive;0;0;0;0;0;0
-13425;toucher;positive;0;0;1;0;0;0
-13426;toucher par;negative;0;0;1;0;0;0
-13427;touffe;negative;0;0;0;0;0;0
-13428;touffu;negative;0;0;0;0;0;1
-13429;tour;positive;0;0;0;1;1;1
-13430;tour de main;positive;0;0;0;0;0;0
-13431;tour de taille;positive;0;0;0;0;0;0
-13432;tourbe;negative;0;0;0;0;0;1
-13433;tourbier|tourbière;negative;0;1;0;0;0;1
-13434;tourbillon;negative;0;1;0;0;1;0
-13435;tourbillonner;negative;0;1;0;0;1;0
-13436;tourelle;positive;0;0;0;0;0;0
-13437;touriste;positive;0;0;0;0;0;0
-13438;touristique;positive;0;0;0;0;0;0
-13439;tourmaline;positive;0;0;0;0;0;0
-13440;tourment;negative;0;1;1;1;0;0
-13441;tourmente;negative;0;1;1;1;0;0
-13442;tourmenter;negative;0;1;1;1;0;0
-13443;tournage;negative;0;1;0;1;0;0
-13444;tournant;negative;0;1;0;0;0;0
-13445;tourner autour de;positive;0;0;0;0;0;0
-13446;tournante;negative;0;1;0;0;0;0
-13447;tournée;positive;0;0;0;0;0;0
-13448;tournevis;positive;0;0;0;0;0;0
-13449;tournoi;positive;0;0;0;0;0;0
-13450;tournure;positive;0;0;0;0;0;0
-13451;tout le an;positive;0;0;0;0;0;0
-13452;tout le jour;positive;0;0;0;0;0;0
-13453;tout le quinze jour;positive;0;0;0;0;0;0
-13454;tousser;negative;0;0;0;0;0;1
-13455;tout à coup;negative;0;1;0;0;1;0
-13456;tout à fait;positive;0;0;0;0;0;0
-13457;tout autour;positive;0;0;0;0;0;0
-13458;tout comprendre;positive;0;0;0;0;0;0
-13459;tout de suite;positive;0;0;0;0;0;0
-13460;tout puissance;positive;0;1;0;0;0;0
-13461;tout le heure;positive;0;0;0;0;0;0
-13462;tout le semaine;positive;0;0;0;0;0;0
-13463;toux;negative;0;0;0;0;0;1
-13464;toxicologie;positive;0;0;0;0;0;0
-13465;toxine;negative;0;1;0;0;0;0
-13466;toxique;negative;0;1;1;1;1;1
-13467;tracer;positive;0;0;0;0;0;0
-13468;tracas;negative;0;1;1;1;0;0
-13469;tracasser;negative;0;1;1;1;0;0
-13470;tracassant;negative;0;1;1;1;0;0
-13471;trace;negative;0;0;0;0;0;1
-13472;tracé;positive;0;0;0;0;0;0
-13473;tracer du ligne;positive;0;0;0;0;0;0
-13474;trachée;positive;0;0;0;0;0;0
-13475;tract;positive;0;1;0;0;0;0
-13476;tracter;positive;0;0;0;0;0;0
-13477;tracteur;positive;0;0;0;0;0;0
-13478;traction;positive;0;0;0;0;0;0
-13479;tradition;positive;0;0;0;0;0;0
-13480;traditionnel;positive;0;0;0;0;0;0
-13481;traduction;positive;0;0;0;0;0;0
-13482;Traductionse;negative;0;0;0;0;0;0
-13483;Traductionses;negative;0;0;0;0;0;0
-13484;Traductionss;negative;0;0;0;0;0;0
-13485;traduire;positive;0;0;0;0;0;0
-13486;trafic;negative;0;0;1;0;0;0
-13487;tragédie;negative;0;1;1;0;1;0
-13488;tragique;negative;0;1;1;0;1;0
-13489;trahir;negative;0;0;1;1;1;1
-13490;trahison;negative;0;1;1;1;1;1
-13491;train;positive;0;0;0;0;0;0
-13492;train en marche;positive;0;0;0;0;0;0
-13493;traîneau;positive;1;0;0;0;0;0
-13494;traîner;negative;0;1;1;1;0;1
-13495;traîner le pied;negative;0;0;1;0;0;0
-13496;trait;positive;0;0;0;0;0;0
-13497;traire d union;positive;0;0;0;0;0;0
-13498;traiter;positive;0;0;0;0;0;0
-13499;traitement;positive;0;0;0;0;0;0
-13500;traiter avec condescendance;negative;0;0;0;0;0;1
-13501;traiteur;positive;0;0;0;0;0;0
-13502;traîtrise;negative;0;1;1;1;1;0
-13503;trajectoire;positive;0;0;0;0;0;0
-13504;tram;positive;0;0;0;0;0;0
-13505;tramway;positive;0;0;0;0;0;0
-13506;tranchant;negative;0;1;1;1;0;1
-13507;tranche;negative;0;0;0;0;0;0
-13508;trancher;negative;0;1;0;0;0;0
-13509;tranquille;positive;0;0;1;0;0;0
-13510;tranquillement;positive;1;0;0;0;0;0
-13511;tranquillité;positive;0;1;1;0;0;0
-13512;transatlantique;positive;0;0;0;0;0;0
-13513;transcendance;positive;0;0;0;0;1;0
-13514;transcender;positive;0;0;0;0;0;0
-13515;transcendantal;positive;0;0;0;0;0;0
-13516;transcendant;positive;0;0;0;0;0;0
-13517;transcendentaux;positive;0;0;0;0;0;0
-13518;transcripteur;positive;0;0;0;0;0;0
-13519;transcription;positive;0;0;0;0;0;0
-13520;transcrire;positive;0;0;0;0;0;0
-13521;transe;negative;0;1;0;0;0;0
-13522;transférer;positive;0;0;0;0;0;0
-13523;transfert;positive;0;0;0;0;0;0
-13524;transfert de propriété;positive;0;0;0;0;0;0
-13525;transformation;negative;0;1;1;0;0;0
-13526;transformer;positive;0;0;0;0;0;0
-13527;transfusion;positive;0;0;0;0;0;0
-13528;transgression;negative;0;1;1;1;0;0
-13529;transir;positive;0;0;0;0;0;0
-13530;transiter;positive;0;0;0;0;0;0
-13531;transition;positive;0;0;0;0;0;0
-13532;transitoire;negative;0;0;1;0;1;0
-13533;translocation;positive;0;0;0;0;0;0
-13534;translucide;negative;0;1;1;0;0;0
-13535;transmettre;positive;0;0;0;0;0;0
-13536;transmissible;negative;0;1;0;0;0;0
-13537;transmission;positive;0;0;0;0;0;0
-13538;transmutation;positive;0;0;0;0;0;0
-13539;transparence;positive;0;0;0;0;0;0
-13540;transparent;positive;0;0;0;0;0;0
-13541;transpercer;negative;0;1;1;1;0;0
-13542;transpirer;negative;0;0;0;0;0;1
-13543;transpiration;negative;0;1;0;0;0;1
-13544;transplantation;positive;0;0;0;0;0;0
-13545;transplanter;positive;0;0;0;0;0;0
-13546;transport;positive;0;0;0;0;0;0
-13547;transport maritime;positive;0;0;0;0;0;0
-13548;transport routier;positive;0;0;0;0;0;0
-13549;transporter;positive;0;0;0;0;0;0
-13550;transporteur;positive;0;0;0;0;0;0
-13551;transposer;positive;0;0;0;0;0;0
-13552;transposition;positive;0;0;0;0;0;0
-13553;transversal;negative;0;0;0;0;0;0
-13554;transversale;negative;0;0;0;0;0;0
-13555;trapu;negative;0;0;0;0;0;0
-13556;traquer;negative;0;1;0;0;0;0
-13557;traumatique;negative;0;1;1;1;1;0
-13558;traumatiser;negative;0;1;1;1;1;0
-13559;traumatisant;negative;0;1;1;1;1;0
-13560;travail;positive;0;0;0;0;1;0
-13561;travail de bureau travail admi;positive;0;0;0;0;0;0
-13562;travail pénible;negative;0;0;1;0;0;1
-13563;travail préparatoire;positive;0;0;0;0;0;0
-13564;travailler;positive;0;0;0;0;0;0
-13565;travailler dur;positive;0;0;0;0;1;0
-13566;travailler en indépendant;positive;0;0;0;0;0;0
-13567;travailleur;positive;0;0;0;0;0;0
-13568;traverser;positive;0;0;0;0;0;0
-13569;traversier;positive;0;0;0;0;0;0
-13570;travestir;negative;0;1;1;1;0;1
-13571;trébucher;negative;0;1;0;0;1;0
-13572;trèfle;positive;0;0;0;0;0;0
-13573;treillage;positive;0;0;0;0;0;0
-13574;treillis;negative;0;0;0;0;0;0
-13575;treize;positive;0;0;0;0;0;0
-13576;treizième;negative;0;1;0;0;0;0
-13577;trek;positive;0;0;0;0;0;0
-13578;tremblant;negative;0;1;0;1;0;0
-13579;tremblante;negative;0;1;1;1;0;0
-13580;tremblement;negative;0;1;1;1;1;0
-13581;tremblement de terre;negative;0;1;1;1;1;0
-13582;trembler;negative;0;1;0;0;1;0
-13583;trémie;positive;0;0;0;0;0;0
-13584;trempage;negative;0;0;1;0;0;1
-13585;tremper;negative;0;0;1;0;0;1
-13586;trémpée;negative;0;0;0;0;0;1
-13587;trépas;negative;0;1;1;0;0;0
-13588;trépied;positive;0;0;0;0;0;0
-13589;très courant;positive;0;0;0;0;0;0
-13590;très éprouver;negative;0;0;1;0;0;0
-13591;très facile;positive;1;0;0;0;0;0
-13592;très intelligent;positive;0;0;0;0;0;0
-13593;très long;negative;0;0;0;0;0;0
-13594;très longue;negative;0;0;0;0;0;0
-13595;très peupler;negative;0;0;0;0;0;0
-13596;trésor;positive;0;0;0;0;0;0
-13597;trésorerie;positive;0;0;0;0;0;0
-13598;trésorier;positive;0;0;0;0;0;0
-13599;tressaillir;negative;0;1;1;1;1;1
-13600;tresse;positive;0;0;0;0;0;0
-13601;tresser;positive;0;0;0;0;0;0
-13602;treuil;positive;0;0;0;0;0;0
-13603;trêve;positive;0;0;0;0;0;0
-13604;tri;positive;0;0;0;0;0;0
-13605;triade;positive;0;0;0;0;0;0
-13606;trial;negative;0;1;0;1;0;0
-13607;triangle;positive;0;0;0;0;0;0
-13608;triangulaire;positive;0;0;0;0;0;0
-13609;tribord;positive;0;0;0;0;0;0
-13610;tribu;positive;0;0;0;0;0;0
-13611;tribulation;negative;0;1;1;0;0;0
-13612;tribun;positive;0;0;0;0;0;0
-13613;tribunal;positive;0;1;0;1;0;1
-13614;tribune;positive;0;0;0;0;0;0
-13615;tribut;negative;0;0;1;0;0;0
-13616;tricher;negative;0;0;1;1;1;1
-13617;tricherie;negative;0;0;0;1;0;1
-13618;tricot;positive;0;0;0;0;0;0
-13619;tricoter;positive;0;0;0;0;0;0
-13620;tricycle;positive;0;0;0;0;0;0
-13621;trident;positive;0;0;0;0;0;0
-13622;trier;positive;0;0;0;0;0;0
-13623;trieur;positive;0;0;0;0;0;0
-13624;trieuse;positive;0;0;0;0;0;0
-13625;trigo;positive;0;0;0;0;0;0
-13626;trigone;positive;0;0;0;0;0;0
-13627;trigonométrie;positive;0;0;0;0;0;0
-13628;trimballer;negative;0;0;0;0;0;0
-13629;trimestre;positive;0;0;0;0;0;0
-13630;trinité;positive;0;0;0;0;0;0
-13631;trio;positive;0;0;0;0;0;0
-13632;triomphant;positive;0;0;0;0;0;0
-13633;triomphe;positive;1;0;0;0;0;0
-13634;triompher;positive;1;0;0;0;0;0
-13635;tripartite;positive;0;0;0;0;0;0
-13636;tripe;positive;0;0;0;1;0;0
-13637;triple;positive;0;0;0;0;0;0
-13638;tripotage;negative;0;1;0;1;0;1
-13639;tripoter;negative;0;1;0;1;0;1
-13640;triste;negative;0;1;1;0;0;0
-13641;triste notoriété;negative;0;1;1;1;0;1
-13642;tristement;negative;0;0;1;0;0;1
-13643;tristesse;negative;0;1;1;0;0;0
-13644;tritium;positive;0;0;0;0;0;0
-13645;troc;positive;0;0;0;0;0;0
-13646;trois foi|fois;positive;0;0;0;0;0;0
-13647;troisième;positive;0;0;0;0;0;0
-13648;troll;negative;0;1;0;1;0;1
-13649;trombone;positive;0;0;0;0;0;0
-13650;tromper;negative;0;0;1;0;0;1
-13651;trompe;positive;0;0;0;0;0;0
-13652;trompette;positive;0;0;0;0;0;0
-13653;trompettiste;positive;0;0;0;0;0;0
-13654;tronc;negative;0;0;0;0;0;0
-13655;trône;positive;0;0;0;0;0;0
-13656;tronquer;negative;0;1;1;1;0;0
-13657;trop diluer;negative;0;0;0;0;0;1
-13658;trop longue;negative;0;0;1;0;0;0
-13659;trop payer|payer;negative;0;0;0;0;0;0
-13660;trophée;positive;0;0;0;0;1;0
-13661;tropical;positive;0;0;0;0;0;0
-13662;troquer;positive;0;0;0;0;0;0
-13663;trot;positive;0;0;0;0;0;0
-13664;trotter;positive;0;0;0;0;0;0
-13665;trottiner;positive;0;0;0;0;0;0
-13666;trottoir;positive;0;0;0;0;0;0
-13667;trou;negative;0;1;1;0;0;0
-13668;trou de serrure;positive;0;0;0;0;0;0
-13669;trou du cul;negative;0;0;0;1;0;1
-13670;troubler;negative;0;0;0;1;0;0
-13671;trouer;negative;0;1;1;0;0;0
-13672;troupe;positive;0;0;0;0;0;0
-13673;troupeau;negative;0;1;0;1;0;0
-13674;trousse;positive;0;0;0;0;0;0
-13675;trouver;positive;0;0;0;0;0;0
-13676;truc;negative;0;1;0;0;0;0
-13677;truelle;negative;0;0;0;0;0;0
-13678;truie;negative;0;0;0;0;0;1
-13679;truisme;negative;0;0;0;0;0;0
-13680;truite;negative;0;0;0;0;0;1
-13681;truquer;negative;0;0;0;0;0;0
-13682;tsar;positive;0;0;0;0;0;0
-13683;tuer;negative;0;1;1;1;0;0
-13684;tube;positive;0;0;0;0;0;0
-13685;tube couder;positive;0;0;0;0;0;0
-13686;tubulaire;positive;0;0;0;0;0;0
-13687;tubule;positive;0;0;0;0;0;0
-13688;tubulure;negative;0;0;0;0;0;0
-13689;tuerie;negative;0;1;1;1;0;0
-13690;tufté;negative;0;0;0;0;0;1
-13691;tuile;positive;0;0;0;0;0;0
-13692;tulipe;positive;0;0;0;0;0;0
-13693;tumeur;negative;0;1;1;0;0;0
-13694;tumulte;negative;0;1;0;1;1;0
-13695;tunique;positive;0;0;0;0;0;0
-13696;tunnel;negative;0;1;0;0;0;0
-13697;turban;positive;0;0;0;0;0;0
-13698;turbidité;negative;0;1;0;0;0;1
-13699;turbine;positive;0;0;0;0;0;0
-13700;turbulence;negative;0;1;0;1;0;0
-13701;turbulent;negative;0;1;0;1;0;0
-13702;turquoise;positive;0;0;0;0;0;0
-13703;tutelle;positive;0;0;0;0;0;0
-13704;tuteur;positive;0;0;0;0;0;0
-13705;tuyau;positive;0;0;0;0;0;0
-13706;tva;negative;0;0;0;0;0;0
-13707;type;negative;0;0;0;0;0;0
-13708;typhon;negative;0;1;0;0;0;0
-13709;typiquement;positive;0;0;0;0;0;0
-13710;typographie;positive;0;0;0;0;0;0
-13711;tyran;negative;0;1;1;1;0;1
-13712;tyrannie;negative;0;1;1;1;0;1
-13713;tyrannique;negative;0;1;1;1;0;1
-13714;tyranniser;negative;0;1;0;1;0;0
-13715;ubiquité;positive;0;0;0;0;0;0
-13716;ulcère;negative;0;1;1;1;0;1
-13717;ultérieur;negative;0;0;1;0;0;0
-13718;ultérieurement;negative;0;0;0;0;0;0
-13719;ultériorité;negative;0;0;0;0;0;0
-13720;ultimatum;negative;0;1;0;1;1;0
-13721;ultraviolet;positive;0;0;0;0;0;0
-13722;ultraviolettes;positive;0;0;0;0;0;0
-13723;un petit coup;positive;0;0;0;0;0;0
-13724;un peu;positive;0;0;0;0;0;0
-13725;unanime;positive;0;0;0;0;0;0
-13726;unanimement;positive;0;0;0;0;0;0
-13727;unanimité;positive;0;0;0;0;0;0
-13728;unir;positive;0;0;0;0;0;0
-13729;unification;positive;0;0;0;0;0;0
-13730;uniforme;positive;0;0;0;0;0;0
-13731;uniformément;positive;0;0;0;0;0;0
-13732;uniformiser;positive;0;0;0;0;0;0
-13733;union;positive;0;0;0;0;0;0
-13734;unique;negative;0;0;0;0;1;0
-13735;unisson;positive;0;0;0;0;0;0
-13736;unitaire;positive;0;0;0;0;0;0
-13737;unité;positive;0;0;0;0;0;0
-13738;univers;positive;0;0;0;0;0;0
-13739;universalité;positive;0;0;0;0;0;0
-13740;universitaire;positive;0;0;0;0;0;0
-13741;université;positive;0;0;0;0;0;0
-13742;univoque;positive;0;0;0;0;0;0
-13743;uranium;positive;0;0;0;0;0;0
-13744;urbain;positive;0;0;0;0;0;0
-13745;urbaine;positive;0;0;0;0;0;0
-13746;urgence;negative;0;1;1;0;1;0
-13747;urne;negative;0;0;1;0;0;0
-13748;usage;positive;0;0;0;0;0;0
-13749;usage abusif;negative;0;1;1;1;0;1
-13750;usage impropre;negative;0;1;1;1;0;0
-13751;usant;negative;0;0;1;0;0;0
-13752;usine;negative;0;0;0;0;0;0
-13753;ustensile;positive;0;0;0;0;0;0
-13754;usure;negative;0;0;1;0;0;1
-13755;usurper;negative;0;1;1;1;0;1
-13756;utérus;positive;0;0;0;0;0;0
-13757;utile;positive;0;0;0;0;0;0
-13758;utilement;positive;0;0;0;0;0;0
-13759;utilisable;positive;0;0;0;0;0;0
-13760;utilisation;positive;0;0;0;0;0;0
-13761;utiliser;negative;0;0;0;0;0;0
-13762;utilitaire;positive;0;0;0;0;0;0
-13763;utilité;positive;0;0;0;0;0;0
-13764;utopique;positive;0;0;0;0;0;0
-13765;vacance;positive;1;0;0;0;0;0
-13766;vacant;negative;0;0;1;0;0;0
-13767;vacarme;negative;0;1;0;1;1;1
-13768;vaccin;positive;0;0;0;0;0;0
-13769;vaccination;positive;0;0;0;0;0;0
-13770;vache;positive;0;0;0;0;0;0
-13771;vacuolaire;positive;0;0;0;0;0;0
-13772;vacuole;positive;0;0;0;0;0;0
-13773;vagabond;negative;0;0;1;0;0;1
-13774;vagabondage;negative;0;0;1;0;0;0
-13775;vague;negative;0;1;1;1;1;0
-13776;vague idée;positive;0;0;0;0;0;0
-13777;vaincre;positive;1;0;0;0;0;0
-13778;vainement;negative;0;0;1;0;0;1
-13779;vainqueur;positive;0;0;0;0;1;0
-13780;vairon;positive;0;0;0;0;0;0
-13781;vaisseau;positive;0;0;0;0;0;0
-13782;vaisseau amiral;positive;0;0;0;0;0;0
-13783;vaisselle;positive;0;0;0;0;0;0
-13784;valet;positive;0;0;0;0;0;0
-13785;valeur;positive;0;0;0;0;0;0
-13786;validité;positive;0;1;0;0;0;0
-13787;vallée;positive;0;0;0;0;0;0
-13788;vallon;positive;0;0;0;0;0;0
-13789;vallonner;positive;0;0;0;0;0;0
-13790;valoir;positive;0;0;0;0;0;0
-13791;valoriser;positive;0;0;0;0;0;0
-13792;valse;positive;0;0;0;0;0;0
-13793;valser;positive;0;0;0;0;0;0
-13794;vamp;positive;0;0;0;0;0;0
-13795;vampire;negative;0;1;0;1;0;1
-13796;van;positive;0;0;0;0;0;0
-13797;vanille;positive;0;0;0;0;0;0
-13798;vanité;negative;0;0;0;1;0;1
-13799;vaniteux;negative;0;0;0;1;0;1
-13800;vannage;positive;0;0;0;0;0;0
-13801;vanne;positive;0;0;0;0;0;0
-13802;vantardise;negative;0;0;0;0;0;0
-13803;vanter;negative;0;0;0;0;0;0
-13804;vapeur;positive;0;0;0;0;0;0
-13805;vaporiser;positive;0;0;0;0;0;0
-13806;vara;positive;0;0;0;0;0;0
-13807;variable;negative;0;0;0;0;1;0
-13808;variance;negative;0;0;0;0;0;0
-13809;variation;negative;0;0;0;0;0;0
-13810;varicelle;negative;0;1;1;0;0;1
-13811;varier;positive;0;0;0;0;0;0
-13812;variété;positive;0;0;0;0;0;0
-13813;vasculaire;positive;0;0;0;0;0;0
-13814;vase;negative;0;1;0;0;0;1
-13815;vasistas;positive;0;0;0;0;0;0
-13816;vaste;positive;0;0;0;0;0;0
-13817;vaudeville;positive;0;0;0;0;0;0
-13818;vaudou;negative;0;1;0;0;0;1
-13819;vaurien;negative;0;1;0;1;0;1
-13820;vautour;negative;0;1;0;0;0;1
-13821;veau;positive;0;0;1;0;0;0
-13822;vecteur;positive;0;0;0;0;0;0
-13823;vedette;positive;0;0;0;0;0;0
-13824;vega;positive;0;0;0;0;0;0
-13825;véga;positive;0;0;0;0;0;0
-13826;végétal;positive;0;0;0;0;0;0
-13827;végétarisme;positive;0;0;0;0;0;0
-13828;végétatif;negative;0;0;1;0;0;1
-13829;végétation;positive;0;0;0;0;0;0
-13830;véhément;negative;0;1;0;1;0;0
-13831;véhicule;positive;0;0;0;0;0;0
-13832;véhiculer;positive;0;0;0;0;0;0
-13833;veille;positive;0;0;0;0;0;0
-13834;veiller;positive;0;0;0;0;0;0
-13835;veilleur;positive;0;0;0;0;0;0
-13836;veine;positive;0;0;0;0;0;1
-13837;veiner;negative;0;0;0;0;0;1
-13838;veineux;negative;0;0;0;0;0;1
-13839;vélin;positive;0;0;0;0;0;0
-13840;vélo;positive;0;0;0;0;0;0
-13841;vélocité;positive;0;0;0;0;0;0
-13842;velours;positive;0;0;0;0;0;0
-13843;velours côtelé;positive;0;0;0;0;0;0
-13844;velouter;positive;0;0;0;0;0;0
-13845;venaison;negative;0;0;0;0;0;1
-13846;vendanger;positive;0;0;0;0;0;0
-13847;vendetta;negative;0;1;1;1;0;0
-13848;vendeur ambulant;positive;0;0;0;0;0;0
-13849;vendre;positive;0;0;0;0;0;0
-13850;vendre au enchère;negative;0;0;0;0;0;0
-13851;vénérable;positive;0;0;1;0;0;0
-13852;vénération;positive;0;0;0;0;0;0
-13853;vénérer;positive;0;0;0;0;0;0
-13854;vengeance;negative;0;1;0;1;1;0
-13855;venin;negative;0;1;0;1;0;1
-13856;vent;positive;0;1;1;1;0;1
-13857;vente;positive;0;0;0;0;0;0
-13858;vente au détail;positive;0;0;0;0;0;0
-13859;vente au enchère;negative;0;0;0;0;0;0
-13860;venteux;negative;0;1;0;0;1;0
-13861;ventilateur;positive;0;0;0;0;0;0
-13862;ventilation;positive;0;0;0;0;0;0
-13863;ventouse;positive;0;0;0;1;0;0
-13864;ventre;positive;0;0;0;0;0;0
-13865;ventricule;positive;0;0;0;0;0;0
-13866;ver;negative;0;0;0;0;1;1
-13867;véracité;positive;0;0;0;0;1;0
-13868;véranda;positive;0;0;0;0;0;0
-13869;verbal;positive;0;0;0;0;0;0
-13870;verbiage;positive;0;0;0;0;0;0
-13871;verbosité;negative;0;0;0;0;0;0
-13872;verdâtre;negative;0;0;0;0;0;1
-13873;verdict;positive;0;1;0;0;0;0
-13874;verdoyer;positive;0;0;0;0;0;0
-13875;verdoyant;positive;0;0;0;0;0;0
-13876;verger;positive;0;0;0;0;0;0
-13877;véridique;positive;0;0;0;0;0;0
-13878;vérificateur;positive;0;0;0;0;0;0
-13879;vérification;positive;0;0;0;0;0;0
-13880;vérifier;positive;0;0;0;0;0;0
-13881;véritablement;positive;0;0;0;0;0;0
-13882;vérité;positive;0;0;0;0;0;0
-13883;vermifuger;negative;0;0;0;0;1;1
-13884;vermine;negative;0;1;0;1;0;1
-13885;vernaculaire;positive;0;0;0;0;0;0
-13886;vernal;positive;1;0;0;0;0;0
-13887;vérole;negative;0;1;0;0;0;1
-13888;véronique;positive;0;0;0;0;0;0
-13889;verre;positive;0;0;0;0;0;0
-13890;verrerie;positive;0;0;0;0;0;0
-13891;verrou;positive;0;0;0;0;0;0
-13892;verrouiller;positive;0;0;0;0;0;0
-13893;verrue;negative;0;0;0;0;0;1
-13894;vers l extérieur;positive;0;0;0;0;0;0
-13895;vers l intérieur;positive;0;0;0;0;0;0
-13896;vers le haut;positive;0;0;0;0;0;0
-13897;versatilité;negative;0;1;0;1;1;0
-13898;verser;positive;0;0;0;0;0;0
-13899;verser dans un tasse;positive;0;1;1;0;0;1
-13900;verser un pot de vin à;negative;0;0;0;0;0;0
-13901;verset;positive;0;0;0;0;0;0
-13902;version;positive;0;0;0;0;0;0
-13903;verso;negative;0;0;0;0;0;0
-13904;vert;positive;0;0;0;0;0;0
-13905;vert jade;positive;0;0;0;0;0;0
-13906;vertebral;positive;0;0;0;0;0;0
-13907;vertébral;positive;0;0;0;0;0;0
-13908;vertèbre;positive;0;0;0;0;0;0
-13909;vertical;positive;0;0;0;0;0;0
-13910;verticalement;positive;0;0;0;0;0;0
-13911;vertige;negative;0;1;0;0;1;0
-13912;vertu;positive;0;0;0;0;0;0
-13913;verve;positive;1;0;0;0;0;0
-13914;vésiculaire;negative;0;0;0;0;0;1
-13915;vésicule;positive;0;0;0;0;0;0
-13916;vessie;negative;0;0;0;0;0;1
-13917;vestibule;positive;0;0;0;0;0;0
-13918;vêtement;positive;0;0;0;0;0;0
-13919;vétéran;positive;0;0;0;0;0;0
-13920;vétérinaire;positive;0;0;0;0;0;0
-13921;veto;negative;0;0;0;1;0;0
-13922;vétuste;negative;0;0;0;0;0;1
-13923;veuf;negative;0;0;1;0;0;0
-13924;veuf|veuve;negative;0;0;1;0;0;0
-13925;viabilité;positive;0;0;0;0;0;0
-13926;viable;positive;0;0;0;0;0;0
-13927;viaduc;positive;0;0;0;0;0;0
-13928;viager;positive;0;0;0;0;0;0
-13929;vialins;negative;0;1;0;0;1;0
-13930;viande;positive;0;0;0;0;0;0
-13931;viande de porc;positive;0;0;0;0;0;0
-13932;vibrer;positive;0;0;1;0;0;0
-13933;vibrant;positive;0;0;1;0;0;0
-13934;vibration;negative;0;1;0;0;1;0
-13935;vibratoire;positive;0;0;0;0;0;0
-13936;vibreur sonore;positive;0;0;0;0;0;0
-13937;vicaire;positive;0;0;0;0;0;0
-13938;vice;negative;0;0;0;1;0;1
-13939;victime;negative;0;1;1;1;0;0
-13940;victimiser;negative;0;1;1;1;1;1
-13941;victoire;positive;0;0;0;0;0;0
-13942;victoria;positive;0;0;0;0;0;0
-13943;victuaille;positive;0;0;0;0;0;0
-13944;vide;negative;0;1;1;0;0;1
-13945;vider;negative;0;1;1;0;0;0
-13946;vie;positive;1;0;0;0;0;0
-13947;vieux âge;negative;0;0;0;0;0;0
-13948;vieux fille;negative;0;1;1;0;0;0
-13949;vieux sorcier;negative;0;1;0;1;0;1
-13950;vieillir;negative;0;1;1;0;0;0
-13951;vieillissement;negative;0;0;0;0;0;0
-13952;vieillot;negative;0;0;0;0;0;1
-13953;vierge;negative;0;1;1;0;0;0
-13954;vigilance;positive;0;1;0;0;1;0
-13955;vigiler;positive;0;1;0;1;1;0
-13956;vigilant;positive;0;1;0;1;1;0
-13957;vigile;positive;0;0;0;0;0;0
-13958;vigne;positive;0;0;0;0;0;0
-13959;vignette;positive;0;0;0;0;0;0
-13960;vignoble;positive;0;0;0;0;0;0
-13961;vigueur;positive;1;0;0;0;0;0
-13962;viking;negative;0;1;0;0;0;0
-13963;vil;negative;0;1;0;1;0;1
-13964;vilain;negative;0;1;0;0;1;0
-13965;villa;positive;0;0;0;0;0;0
-13966;village;positive;0;0;0;0;0;0
-13967;ville;positive;0;0;0;0;0;0
-13968;vin;positive;0;0;0;0;0;0
-13969;vinaigre;negative;0;1;0;0;0;1
-13970;vinaigrette;positive;0;0;0;0;0;0
-13971;vingt;positive;0;0;0;0;0;0
-13972;vingtième;positive;0;0;0;0;0;0
-13973;viol;negative;0;1;1;1;0;1
-13974;violation de propriété;negative;0;1;0;1;0;0
-13975;violemment;negative;0;1;1;1;0;1
-13976;violence;negative;0;1;1;1;0;0
-13977;violent;negative;0;1;0;1;1;1
-13978;violer;negative;0;1;1;1;0;1
-13979;violet;positive;0;0;0;0;0;0
-13980;violon;positive;0;0;0;0;0;0
-13981;violoniste;positive;0;0;0;0;0;0
-13982;vipère;negative;0;1;1;1;0;1
-13983;virage;positive;0;1;0;0;0;0
-13984;virer;negative;0;1;0;0;1;0
-13985;virer de bord;negative;0;1;0;0;1;0
-13986;virginité;positive;0;0;0;0;0;0
-13987;virgule;positive;0;0;0;0;0;0
-13988;viril;positive;0;0;0;0;0;0
-13989;virilité;positive;0;0;0;0;0;0
-13990;virologie;negative;0;0;0;0;0;0
-13991;virtuose;positive;0;0;0;0;0;0
-13992;virulence;negative;0;1;0;1;0;0
-13993;virus;negative;0;1;0;0;0;1
-13994;virus de l herpès;negative;0;0;0;0;0;1
-13995;vis;positive;0;0;0;1;0;0
-13996;visa;positive;0;0;0;0;0;0
-13997;visage;positive;0;0;0;0;0;0
-13998;viscosité;negative;0;0;0;0;0;1
-13999;viser;positive;0;0;0;0;0;0
-14000;visibilité;positive;0;0;0;0;0;0
-14001;visible;positive;0;0;0;0;0;0
-14002;visiblement;positive;0;0;0;0;0;0
-14003;visière;positive;0;0;0;0;1;0
-14004;vision;positive;0;0;0;0;1;0
-14005;visionnaire;positive;0;0;0;0;0;0
-14006;visiter;positive;0;0;0;0;0;0
-14007;visitation;positive;0;0;0;0;0;0
-14008;visite;positive;0;0;0;0;0;0
-14009;visser;negative;0;0;0;1;0;0
-14010;visualiser;positive;0;0;0;0;0;0
-14011;vital;positive;0;0;0;0;0;0
-14012;vitalité;positive;0;0;0;0;0;0
-14013;vitesse;negative;0;1;0;0;0;0
-14014;vitre;positive;0;0;0;0;0;0
-14015;vitreux;positive;0;0;0;0;0;0
-14016;vitrine;positive;0;0;0;0;0;0
-14017;vivant;positive;0;0;0;0;0;0
-14018;vivre|vivres;positive;0;0;0;0;0;0
-14019;voûte;positive;0;0;0;0;0;0
-14020;vocabulaire;positive;0;0;0;0;0;0
-14021;vocal;positive;0;0;0;0;0;0
-14022;vocation;positive;0;0;0;0;0;0
-14023;vogue;positive;0;0;0;0;0;0
-14024;voguer;positive;0;0;0;0;0;0
-14025;voie;positive;0;0;0;0;0;0
-14026;voie ferré;positive;0;0;0;0;0;0
-14027;voile;negative;0;1;1;0;0;0
-14028;voiler;negative;0;1;1;0;0;0
-14029;voisinage;positive;0;0;0;0;0;0
-14030;voiture;positive;0;0;0;0;0;0
-14031;voiturier;positive;0;0;0;0;0;0
-14032;voix;positive;0;0;0;0;0;0
-14033;vol;negative;0;1;1;1;1;1
-14034;vol à l étalage;negative;0;1;0;1;1;1
-14035;volage;negative;0;0;0;1;0;1
-14036;volaille;positive;0;1;0;0;0;0
-14037;volant;positive;0;1;0;0;0;0
-14038;voler moteur;positive;0;0;0;0;0;0
-14039;volante;positive;0;1;0;0;0;0
-14040;volatilité;negative;0;1;0;1;1;0
-14041;volcan;negative;0;1;0;0;1;0
-14042;volcanique;negative;0;1;0;0;1;0
-14043;voler;negative;0;1;1;1;1;0
-14044;voler en éclat;negative;0;1;1;1;1;0
-14045;volet;negative;0;0;0;0;0;0
-14046;volière;positive;0;0;0;0;0;0
-14047;volontaire;positive;0;0;0;0;0;0
-14048;volontairement;positive;0;0;0;0;0;0
-14049;volontariat;positive;0;0;0;0;0;0
-14050;volonté;positive;0;0;0;0;0;0
-14051;volontiers;positive;0;0;0;0;0;0
-14052;voltige;negative;0;1;1;0;0;0
-14053;voltmètre;positive;0;0;0;0;0;0
-14054;volume;negative;0;0;0;1;0;0
-14055;volumineux;negative;0;1;0;0;0;0
-14056;vomir;negative;0;0;0;0;0;1
-14057;vomissement;negative;0;0;0;0;0;1
-14058;vorace;negative;0;1;1;1;0;0
-14059;vortex;negative;0;1;0;0;1;0
-14060;votales;positive;0;0;0;0;0;0
-14061;votant;positive;0;0;0;0;0;0
-14062;vote;positive;0;0;1;1;1;0
-14063;voter;positive;0;0;1;1;1;0
-14064;voter pour;positive;0;0;0;0;0;0
-14065;votif;positive;0;0;0;0;0;0
-14066;vouer;positive;0;0;0;0;0;0
-14067;vouloir;negative;0;1;1;0;0;0
-14068;voûte;positive;0;1;1;0;0;0
-14069;voûter;negative;0;0;1;0;0;0
-14070;voyage;positive;0;1;0;0;1;0
-14071;voyager;positive;1;0;0;0;0;0
-14072;voyelle;positive;0;0;0;0;0;0
-14073;voyou;negative;0;1;0;1;0;1
-14074;VRAI;positive;0;0;0;0;0;0
-14075;vrai casse tête;negative;0;0;0;1;0;0
-14076;vraisemblance;positive;0;0;0;0;0;0
-14077;vriller;negative;0;0;0;0;0;0
-14078;vrombir;negative;0;1;0;0;1;0
-14079;vrombissement;negative;0;1;0;0;1;0
-14080;vue;positive;0;0;0;0;0;0
-14081;vulgaire;negative;0;0;0;0;0;1
-14082;vulgarité;negative;0;0;1;1;0;1
-14083;vulnérabilité;negative;0;1;1;0;0;0
-14084;vulnérable;negative;0;1;1;0;0;0
-14085;w c;negative;0;0;0;0;0;1
-14086;wagon;positive;0;0;0;0;0;0
-14087;wagon lit;positive;0;0;0;0;0;0
-14088;wapiti;positive;0;0;0;0;0;0
-14089;wc;negative;0;0;0;0;0;1
-14090;web;positive;0;0;0;0;0;0
-14091;whisky;negative;0;0;0;0;0;0
-14092;xénophobie;negative;0;1;0;1;0;0
-14093;xérès;positive;0;0;0;0;0;0
-14094;yacht;positive;0;0;0;0;0;0
-14095;yacht de croisière;positive;0;0;0;0;0;0
-14096;yachting;positive;0;0;0;0;0;0
-14097;yak;negative;0;0;0;0;0;0
-14098;yearling;positive;0;0;0;0;0;0
-14099;yeoman;positive;0;0;0;0;0;0
-14100;yogi;positive;0;0;0;0;0;0
-14101;zap;negative;0;1;0;1;1;0
-14102;zapper;negative;0;1;0;1;1;0
-14103;zèbre;positive;0;0;0;0;0;0
-14104;zébrure;negative;0;0;0;0;0;0
-14105;zèle;positive;0;0;0;0;1;0
-14106;zélé;negative;0;0;0;0;0;0
-14107;zénith;positive;1;0;0;0;0;0
-14108;zephyr;positive;0;0;0;0;0;0
-14109;zéphyr;positive;0;0;0;0;0;0
-14110;zeppelin;positive;0;0;0;0;0;0
-14111;zeste;positive;0;0;0;0;0;0
-14112;zézaiement;negative;0;1;1;1;0;0
-14113;zibeline;positive;0;0;0;0;0;0
-14114;zinzin;negative;0;1;0;0;1;0
-14115;zinzins;negative;0;1;0;0;1;0
-14116;zipper;positive;0;0;0;0;0;0
-14117;zodiaque;positive;0;0;0;0;0;0
-14118;zone;positive;0;0;0;0;0;0
-14119;zoo;positive;0;0;0;0;0;0
-14120;zoologie;positive;0;0;0;0;0;0
-14121;zoologique;positive;0;0;0;0;0;0
-14122;zoom;positive;0;0;0;0;0;0
-14123;zoomer;positive;0;0;0;0;0;0
-14124;zozotement;negative;0;1;1;1;0;0
-14125;zozoter;negative;0;1;1;1;0;0
-14126;merci;positive;1;0;0;0;0;0
-14127;remercier;positive;1;0;0;0;0;0
-14128;remerciment;positive;1;0;0;0;0;0
-14129;moins;negative;0;0;0;0;0;0
diff --git a/config/french_numbers.txt b/config/french_numbers.txt
deleted file mode 100644
index 0dea605..0000000
--- a/config/french_numbers.txt
+++ /dev/null
@@ -1,101 +0,0 @@
-zéro
-un
-deux
-trois
-quatre
-cinq
-six
-sept
-huit
-neuf
-dix
-onze
-douze
-treize
-quatorze
-quinze
-seize
-dix-sept
-dix-huit
-dix-neuf
-vingt
-vingt-et-un
-vingt-deux
-vingt-trois
-vingt-quatre
-vingt-cinq
-vingt-six
-vingt-sept
-vingt-huit
-vingt-neuf
-trente
-trente-et-un
-trente-deux
-trente-trois
-trente-quatre
-trente-cinq
-trente-six
-trente-sept
-trente-huit
-trente-neuf
-quarante
-quarante-et-un
-quarante-deux
-quarante-trois
-quarante-quatre
-quarante-cinq
-quarante-six
-quarante-sept
-quarante-huit
-quarante-neuf
-cinquante
-cinquante-et-un
-cinquante-deux
-cinquante-trois
-cinquante-quatre
-cinquante-cinq
-cinquante-six
-cinquante-sept
-cinquante-huit
-cinquante-neuf
-soixante
-soixante-et-un
-soixante-deux
-soixante-trois
-soixante-quatre
-soixante-cinq
-soixante-six
-soixante-sept
-soixante-huit
-soixante-neuf
-soixante-dix
-soixante-et-onze
-soixante-douze
-soixante-treize
-soixante-quatorze
-soixante-quinze
-soixante-seize
-soixante-dix-sept
-soixante-dix-huit
-soixante-dix-neuf
-quatre-vingts
-quatre-vingt-un
-quatre-vingt-deux
-quatre-vingt-trois
-quatre-vingt-quatre
-quatre-vingt-cinq
-quatre-vingt-six
-quatre-vingt-sept
-quatre-vingt-huit
-quatre-vingt-neuf
-quatre-vingt-dix
-quatre-vingt-onze
-quatre-vingt-douze
-quatre-vingt-treize
-quatre-vingt-quatorze
-quatre-vingt-quinze
-quatre-vingt-seize
-quatre-vingt-dix-sept
-quatre-vingt-dix-huit
-quatre-vingt-dix-neuf
-cent
diff --git a/config/french_stopwords.txt b/config/french_stopwords.txt
deleted file mode 100644
index d482b0a..0000000
--- a/config/french_stopwords.txt
+++ /dev/null
@@ -1,689 +0,0 @@
-a
-abord
-absolument
-afin
-ah
-ai
-aie
-aient
-aies
-ailleurs
-ainsi
-ait
-allaient
-allo
-allons
-allô
-alors
-anterieur
-anterieure
-anterieures
-apres
-après
-as
-assez
-attendu
-au
-aucun
-aucune
-aucuns
-aujourd
-aujourd'hui
-aupres
-auquel
-aura
-aurai
-auraient
-aurais
-aurait
-auras
-aurez
-auriez
-aurions
-aurons
-auront
-aussi
-autre
-autrefois
-autrement
-autres
-autrui
-aux
-auxquelles
-auxquels
-avaient
-avais
-avait
-avant
-avec
-avez
-aviez
-avions
-avoir
-avons
-ayant
-ayez
-ayons
-b
-bah
-bas
-basee
-bat
-beau
-beaucoup
-bien
-bigre
-bon
-boum
-bravo
-brrr
-c
-car
-ce
-ceci
-cela
-celle
-celle-ci
-celle-là
-celles
-celles-ci
-celles-là
-celui
-celui-ci
-celui-là
-celà
-cent
-cependant
-certain
-certaine
-certaines
-certains
-certes
-ces
-cet
-cette
-ceux
-ceux-ci
-ceux-là
-chacun
-chacune
-chaque
-cher
-chers
-chez
-chiche
-chut
-chère
-chères
-ci
-cinq
-cinquantaine
-cinquante
-cinquantième
-cinquième
-clac
-clic
-combien
-comme
-comment
-comparable
-comparables
-compris
-concernant
-contre
-couic
-crac
-d
-da
-dans
-de
-debout
-dedans
-dehors
-deja
-delà
-depuis
-dernier
-derniere
-derriere
-derrière
-des
-desormais
-desquelles
-desquels
-dessous
-dessus
-deux
-deuxième
-deuxièmement
-devant
-devers
-devra
-devrait
-different
-differentes
-differents
-différent
-différente
-différentes
-différents
-dire
-directe
-directement
-dit
-dite
-dits
-divers
-diverse
-diverses
-dix
-dix-huit
-dix-neuf
-dix-sept
-dixième
-doit
-doivent
-donc
-dont
-dos
-douze
-douzième
-dring
-droite
-du
-duquel
-durant
-dès
-début
-désormais
-e
-effet
-egale
-egalement
-egales
-eh
-elle
-elle-même
-elles
-elles-mêmes
-en
-encore
-enfin
-entre
-envers
-environ
-es
-essai
-est
-et
-etant
-etc
-etre
-eu
-eue
-eues
-euh
-eurent
-eus
-eusse
-eussent
-eusses
-eussiez
-eussions
-eut
-eux
-eux-mêmes
-exactement
-excepté
-extenso
-exterieur
-eûmes
-eût
-eûtes
-f
-fais
-faisaient
-faisant
-fait
-faites
-façon
-feront
-fi
-flac
-floc
-fois
-font
-force
-furent
-fus
-fusse
-fussent
-fusses
-fussiez
-fussions
-fut
-fûmes
-fût
-fûtes
-g
-gens
-h
-ha
-haut
-hein
-hem
-hep
-hi
-ho
-holà
-hop
-hormis
-hors
-hou
-houp
-hue
-hui
-huit
-huitième
-hum
-hurrah
-hé
-hélas
-i
-ici
-il
-ils
-importe
-j
-je
-jusqu
-jusque
-juste
-k
-l
-la
-laisser
-laquelle
-las
-le
-lequel
-les
-lesquelles
-lesquels
-leur
-leurs
-longtemps
-lors
-lorsque
-lui
-lui-meme
-lui-même
-là
-lès
-m
-ma
-maint
-maintenant
-mais
-malgre
-malgré
-maximale
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-memes
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-mes
-mien
-mienne
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-mince
-mine
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-moi
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-moi-même
-moindres
-moins
-mon
-mot
-moyennant
-multiple
-multiples
-même
-mêmes
-n
-na
-naturel
-naturelle
-naturelles
-ne
-neanmoins
-necessaire
-necessairement
-neuf
-neuvième
-ni
-nombreuses
-nombreux
-nommés
-non
-nos
-notamment
-notre
-nous
-nous-mêmes
-nouveau
-nouveaux
-nul
-néanmoins
-nôtre
-nôtres
-o
-oh
-ohé
-ollé
-olé
-on
-ont
-onze
-onzième
-ore
-ou
-ouf
-ouias
-oust
-ouste
-outre
-ouvert
-ouverte
-ouverts
-o|
-où
-p
-paf
-pan
-par
-parce
-parfois
-parle
-parlent
-parler
-parmi
-parole
-parseme
-partant
-particulier
-particulière
-particulièrement
-pas
-passé
-pendant
-pense
-permet
-personne
-personnes
-peu
-peut
-peuvent
-peux
-pff
-pfft
-pfut
-pif
-pire
-pièce
-plein
-plouf
-plupart
-plus
-plusieurs
-plutôt
-possessif
-possessifs
-possible
-possibles
-pouah
-pour
-pourquoi
-pourrais
-pourrait
-pouvait
-prealable
-precisement
-premier
-première
-premièrement
-pres
-probable
-probante
-procedant
-proche
-près
-psitt
-pu
-puis
-puisque
-pur
-pure
-q
-qu
-quand
-quant
-quant-à-soi
-quanta
-quarante
-quatorze
-quatre
-quatre-vingt
-quatrième
-quatrièmement
-que
-quel
-quelconque
-quelle
-quelles
-quelqu'un
-quelque
-quelques
-quels
-qui
-quiconque
-quinze
-quoi
-quoique
-r
-rare
-rarement
-rares
-relative
-relativement
-remarquable
-rend
-rendre
-restant
-reste
-restent
-restrictif
-retour
-revoici
-revoilà
-rien
-s
-sa
-sacrebleu
-sait
-sans
-sapristi
-sauf
-se
-sein
-seize
-selon
-semblable
-semblaient
-semble
-semblent
-sent
-sept
-septième
-sera
-serai
-seraient
-serais
-serait
-seras
-serez
-seriez
-serions
-serons
-seront
-ses
-seul
-seule
-seulement
-si
-sien
-sienne
-siennes
-siens
-sinon
-six
-sixième
-soi
-soi-même
-soient
-sois
-soit
-soixante
-sommes
-son
-sont
-sous
-souvent
-soyez
-soyons
-specifique
-specifiques
-speculatif
-stop
-strictement
-subtiles
-suffisant
-suffisante
-suffit
-suis
-suit
-suivant
-suivante
-suivantes
-suivants
-suivre
-sujet
-superpose
-sur
-surtout
-t
-ta
-tac
-tandis
-tant
-tardive
-te
-tel
-telle
-tellement
-telles
-tels
-tenant
-tend
-tenir
-tente
-tes
-tic
-tien
-tienne
-tiennes
-tiens
-toc
-toi
-toi-même
-ton
-touchant
-toujours
-tous
-tout
-toute
-toutefois
-toutes
-treize
-trente
-tres
-trois
-troisième
-troisièmement
-trop
-très
-tsoin
-tsouin
-tu
-té
-u
-un
-une
-unes
-uniformement
-unique
-uniques
-uns
-v
-va
-vais
-valeur
-vas
-vers
-via
-vif
-vifs
-vingt
-vivat
-vive
-vives
-vlan
-voici
-voie
-voient
-voilà
-vont
-vos
-votre
-vous
-vous-mêmes
-vu
-vé
-vôtre
-vôtres
-w
-x
-y
-z
-zut
-à
-â
-ça
-ès
-étaient
-étais
-était
-étant
-état
-étiez
-étions
-été
-étée
-étées
-étés
-êtes
-être
-ô

From de7c7dc63c499eb2cf1b900bef7d4cd7b94985ef Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:35:05 +0100
Subject: [PATCH 441/496] reorg repo v1

---
 {nautilus_nlp => config}/__init__.py          |  0
 {nautilus_nlp/config => config}/config.py     | 35 ++++++++++
 {nautilus_nlp/utils => config}/constants.py   |  0
 .../data => config}/country_code.json         |  0
 {nautilus_nlp/data => config}/french_FEEL.csv |  0
 .../data => config}/french_numbers.txt        |  0
 .../data => config}/french_stopwords.txt      |  0
 {nautilus_nlp/data => config}/stopwords.json  |  0
 nautilus_nlp/analysis/ngrams.py               | 39 -----------
 nautilus_nlp/data/.gitkeep                    |  0
 nautilus_nlp/data/__init__.py                 | 17 -----
 nautilus_nlp/utils/__init__.py                | 17 -----
 nautilus_nlp/utils/keyword_extractor.py       | 50 --------------
 .../__init__.py                               |  1 +
 .../augmentation/text_augmentation.py         |  0
 .../classic/preprocess.py                     |  5 +-
 preprocessing/preprocessor.py                 | 65 +++++++++++++++++++
 .../social/preprocess.py                      |  4 +-
 .../token/preprocess.py                       |  0
 .../token}/tokenizer.py                       |  0
 tests/test_data_augmentation.py               |  6 +-
 tests/test_document_loader.py                 |  2 +-
 tests/test_keyword_extractor.py               | 16 -----
 tests/test_phone_number.py                    |  2 +-
 tests/test_preprocessor.py                    | 12 ++--
 {nautilus_nlp/config => utils}/__init__.py    |  0
 {nautilus_nlp/utils => utils}/file_loader.py  |  0
 {nautilus_nlp/utils => utils}/phone_number.py | 29 +--------
 {nautilus_nlp/utils => utils}/stopwords.py    |  7 +-
 29 files changed, 120 insertions(+), 187 deletions(-)
 rename {nautilus_nlp => config}/__init__.py (100%)
 rename {nautilus_nlp/config => config}/config.py (73%)
 rename {nautilus_nlp/utils => config}/constants.py (100%)
 rename {nautilus_nlp/data => config}/country_code.json (100%)
 rename {nautilus_nlp/data => config}/french_FEEL.csv (100%)
 rename {nautilus_nlp/data => config}/french_numbers.txt (100%)
 rename {nautilus_nlp/data => config}/french_stopwords.txt (100%)
 rename {nautilus_nlp/data => config}/stopwords.json (100%)
 delete mode 100644 nautilus_nlp/analysis/ngrams.py
 delete mode 100644 nautilus_nlp/data/.gitkeep
 delete mode 100644 nautilus_nlp/data/__init__.py
 delete mode 100644 nautilus_nlp/utils/__init__.py
 delete mode 100644 nautilus_nlp/utils/keyword_extractor.py
 rename {nautilus_nlp/preprocessing => preprocessing}/__init__.py (94%)
 rename nautilus_nlp/preprocessing/data_augmentation.py => preprocessing/augmentation/text_augmentation.py (100%)
 rename nautilus_nlp/preprocessing/text_preprocess.py => preprocessing/classic/preprocess.py (98%)
 create mode 100644 preprocessing/preprocessor.py
 rename nautilus_nlp/preprocessing/social_preprocess.py => preprocessing/social/preprocess.py (97%)
 rename nautilus_nlp/preprocessing/token_preprocess.py => preprocessing/token/preprocess.py (100%)
 rename {nautilus_nlp/utils => preprocessing/token}/tokenizer.py (100%)
 delete mode 100644 tests/test_keyword_extractor.py
 rename {nautilus_nlp/config => utils}/__init__.py (100%)
 rename {nautilus_nlp/utils => utils}/file_loader.py (100%)
 rename {nautilus_nlp/utils => utils}/phone_number.py (67%)
 rename {nautilus_nlp/utils => utils}/stopwords.py (94%)

diff --git a/nautilus_nlp/__init__.py b/config/__init__.py
similarity index 100%
rename from nautilus_nlp/__init__.py
rename to config/__init__.py
diff --git a/nautilus_nlp/config/config.py b/config/config.py
similarity index 73%
rename from nautilus_nlp/config/config.py
rename to config/config.py
index 35681cf..df90e45 100644
--- a/nautilus_nlp/config/config.py
+++ b/config/config.py
@@ -17,10 +17,15 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 #!/usr/local/bin/python3
 import os
+import phonenumbers as _phonenumbers
 
 
 ROOT_FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
 
+# Stopwords config
+STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "config", "stopwords.json")
+
+# Country config
 COUNTRY_MAPPING_ISO = {
     'af': 'Afghanistan', 'ax': 'Åland Islands', 'al': 'Albania', 'dz': 'Algeria', 'as': 'American Samoa', 'ad':
     'Andorra', 'ao': 'Angola', 'ai': 'Anguilla', 'aq': 'Antarctica', 'ag': 'Antigua and Barbuda', 'ar': 'Argentina',
@@ -73,3 +78,33 @@
     've': 'Venezuela (Bolivarian Republic of)', 'vn': 'Viet Nam', 'vg': 'Virgin Islands (British)',
     'vi': 'Virgin Islands (U.S.)', 'wf': 'Wallis and Futuna', 'eh': 'Western Sahara', 'ye': 'Yemen', 'zm': 'Zambia',
     'zw': 'Zimbabwe'}
+
+# Phone numbers config
+SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM',
+                     'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT',
+                     'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES',
+                     'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE',
+                     'NO', 'SJ', 'PL', 'DE', 'PE', 'MX', 'CU', 'AR', 'BR', 'CL', 'CO', 'VE',
+                     'MY', 'AU', 'CC', 'CX', 'ID', 'PH', 'NZ', 'SG', 'TH', 'JP', 'KR', 'VN',
+                     'CN', 'TR', 'IN', 'PK', 'AF', 'LK', 'MM', 'IR', 'SS', 'MA', 'EH', 'DZ',
+                     'TN', 'LY', 'GM', 'SN', 'MR', 'ML', 'GN', 'CI', 'BF', 'NE', 'TG', 'BJ',
+                     'MU', 'LR', 'SL', 'GH', 'NG', 'TD', 'CF', 'CM', 'CV', 'ST', 'GQ', 'GA',
+                     'CG', 'CD', 'AO', 'GW', 'IO', 'AC', 'SC', 'SD', 'RW', 'ET', 'SO', 'DJ',
+                     'KE', 'TZ', 'UG', 'BI', 'MZ', 'ZM', 'MG', 'RE', 'YT', 'ZW', 'NA', 'MW',
+                     'LS', 'BW', 'SZ', 'KM', 'SH', 'TA', 'ER', 'AW', 'FO', 'GL', 'GI', 'PT',
+                     'LU', 'IE', 'IS', 'AL', 'MT', 'CY', 'FI', 'AX', 'BG', 'LT', 'LV', 'EE',
+                     'MD', 'AM', 'BY', 'AD', 'MC', 'SM', 'UA', 'RS', 'ME', 'XK', 'HR', 'SI',
+                     'BA', 'MK', 'CZ', 'SK', 'LI', 'FK', 'BZ', 'GT', 'SV', 'HN', 'NI', 'CR',
+                     'PA', 'PM', 'HT', 'GP', 'BL', 'MF', 'BO', 'GY', 'EC', 'GF', 'PY', 'MQ',
+                     'SR', 'UY', 'CW', 'BQ', 'TL', 'NF', 'BN', 'NR', 'PG', 'TO', 'SB', 'VU',
+                     'FJ', 'PW', 'WF', 'CK', 'NU', 'WS', 'KI', 'NC', 'TV', 'PF', 'TK', 'FM',
+                     'MH', 'KP', 'HK', 'MO', 'KH', 'LA', 'BD', 'TW', 'MV', 'LB', 'JO', 'SY',
+                     'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN',
+                     'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ', 'DO']
+
+FORMAT_NUMBERS = {
+    "E164": _phonenumbers.PhoneNumberFormat.E164,
+    "INTERNATIONAL": _phonenumbers.PhoneNumberFormat.INTERNATIONAL,
+    "NATIONAL": _phonenumbers.PhoneNumberFormat.NATIONAL,
+    "RFC3966": _phonenumbers.PhoneNumberFormat.RFC3966
+}
diff --git a/nautilus_nlp/utils/constants.py b/config/constants.py
similarity index 100%
rename from nautilus_nlp/utils/constants.py
rename to config/constants.py
diff --git a/nautilus_nlp/data/country_code.json b/config/country_code.json
similarity index 100%
rename from nautilus_nlp/data/country_code.json
rename to config/country_code.json
diff --git a/nautilus_nlp/data/french_FEEL.csv b/config/french_FEEL.csv
similarity index 100%
rename from nautilus_nlp/data/french_FEEL.csv
rename to config/french_FEEL.csv
diff --git a/nautilus_nlp/data/french_numbers.txt b/config/french_numbers.txt
similarity index 100%
rename from nautilus_nlp/data/french_numbers.txt
rename to config/french_numbers.txt
diff --git a/nautilus_nlp/data/french_stopwords.txt b/config/french_stopwords.txt
similarity index 100%
rename from nautilus_nlp/data/french_stopwords.txt
rename to config/french_stopwords.txt
diff --git a/nautilus_nlp/data/stopwords.json b/config/stopwords.json
similarity index 100%
rename from nautilus_nlp/data/stopwords.json
rename to config/stopwords.json
diff --git a/nautilus_nlp/analysis/ngrams.py b/nautilus_nlp/analysis/ngrams.py
deleted file mode 100644
index 89cbb13..0000000
--- a/nautilus_nlp/analysis/ngrams.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
-from typing import List, Generator
-
-
-def create_ngrams(tokens: List[str], nb_elements: int) -> Generator:
-    """
-    Create n-grams for list of tokens
-
-    Parameters
-    ----------
-    tokens : list
-        list of strings
-    nb_elements : int
-        number of elements in the n-gram
-
-    Returns
-    -------
-    Generator
-        generator of all n-grams
-    """
-    ngrams = zip(*[tokens[index_token:] for index_token in range(nb_elements)])
-    return (" ".join(ngram) for ngram in ngrams)
diff --git a/nautilus_nlp/data/.gitkeep b/nautilus_nlp/data/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/data/__init__.py b/nautilus_nlp/data/__init__.py
deleted file mode 100644
index d46139b..0000000
--- a/nautilus_nlp/data/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/utils/__init__.py b/nautilus_nlp/utils/__init__.py
deleted file mode 100644
index d46139b..0000000
--- a/nautilus_nlp/utils/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nautilus_nlp/utils/keyword_extractor.py b/nautilus_nlp/utils/keyword_extractor.py
deleted file mode 100644
index f804d52..0000000
--- a/nautilus_nlp/utils/keyword_extractor.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-
-from flashtext import KeywordProcessor
-
-
-def extract_keywords(text, keyword, case_sensitive=True):
-    """
-    Extract Keywords from a document.
-
-    Parameters
-    ----------
-    text : str
-        Text to extract keywords from
-    keyword :
-        Single keyword (str) or list of keywords (list)
-    case_sensitive :
-        If False, will be case insensitive.
-
-    Returns
-    -------
-    list
-        Return list of extracted keyworkds
-    """
-
-    processor = KeywordProcessor(case_sensitive=case_sensitive)
-    if isinstance(keyword, list):
-        processor.add_keywords_from_list(keyword)
-    elif isinstance(keyword, str):
-        processor.add_keyword(keyword)
-    elif isinstance(keyword, dict):
-        processor.add_keywords_from_dict(keyword)
-
-    return processor.extract_keywords(text)
-    
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/__init__.py b/preprocessing/__init__.py
similarity index 94%
rename from nautilus_nlp/preprocessing/__init__.py
rename to preprocessing/__init__.py
index d46139b..7643ec5 100644
--- a/nautilus_nlp/preprocessing/__init__.py
+++ b/preprocessing/__init__.py
@@ -15,3 +15,4 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+from nautilus_nlp.preprocessor import Preprocessor
diff --git a/nautilus_nlp/preprocessing/data_augmentation.py b/preprocessing/augmentation/text_augmentation.py
similarity index 100%
rename from nautilus_nlp/preprocessing/data_augmentation.py
rename to preprocessing/augmentation/text_augmentation.py
diff --git a/nautilus_nlp/preprocessing/text_preprocess.py b/preprocessing/classic/preprocess.py
similarity index 98%
rename from nautilus_nlp/preprocessing/text_preprocess.py
rename to preprocessing/classic/preprocess.py
index ef6003a..691acae 100644
--- a/nautilus_nlp/preprocessing/text_preprocess.py
+++ b/preprocessing/classic/preprocess.py
@@ -23,9 +23,8 @@
 import re
 import unicodedata
 from ftfy import fix_text as _fix_text
-from nautilus_nlp.utils import constants
-from nautilus_nlp.utils.phone_number import \
-    extract_phone_numbers as _extract_phone_numbers
+from config import constants
+from utils.phone_number import extract_phone_numbers as _extract_phone_numbers
 
 
 def normalize_whitespace(text) -> str:
diff --git a/preprocessing/preprocessor.py b/preprocessing/preprocessor.py
new file mode 100644
index 0000000..0b34eb3
--- /dev/null
+++ b/preprocessing/preprocessor.py
@@ -0,0 +1,65 @@
+from typing import List, Callable
+
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import FunctionTransformer
+
+from preprocessing.social.preprocess import (remove_html_tags, remove_mentions, remove_emoji,
+                                                          remove_hashtag)
+from preprocessing.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
+
+
+class Preprocessor():
+    def __init__(
+            self):
+        """
+        Initialize preprocessor object to apply all text transformation
+        """
+        self.__operations = []
+        self.pipeline = None
+
+    def pipe(self, operation: Callable):
+        """
+        Add an operation to pipe in the preprocessor
+        Parameters
+        ----------
+        operation : callable
+            text preprocessing function
+        """
+        self.__operations.append(operation)
+
+    @staticmethod
+    def build_pipeline(operation_list: List[Callable]) -> Pipeline:
+        """
+        Build sklearn pipeline from a operation list
+        Parameters
+        ----------
+        operation_list : iterable
+            list of __operations of preprocessing
+        Returns
+        -------
+        sklearn.pipeline.Pipeline
+        """
+        return Pipeline(
+            steps=[
+                (operation.__name__, FunctionTransformer(operation))
+                for operation in operation_list])
+
+
+    def run(self, text: str) -> str:
+        """
+        Apply pipeline to text
+        Parameters
+        ----------
+        text : string
+            text to preprocess
+        Returns
+        -------
+        string
+        """
+        operations = self.__operations
+        if operations == []:
+            operations = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
+                          remove_eol_characters, fix_bad_unicode, normalize_whitespace)
+        self.pipeline = self.build_pipeline(operations)
+        text = self.pipeline.fit_transform(text)
+        return text
\ No newline at end of file
diff --git a/nautilus_nlp/preprocessing/social_preprocess.py b/preprocessing/social/preprocess.py
similarity index 97%
rename from nautilus_nlp/preprocessing/social_preprocess.py
rename to preprocessing/social/preprocess.py
index 20809a3..b017f65 100644
--- a/nautilus_nlp/preprocessing/social_preprocess.py
+++ b/preprocessing/social/preprocess.py
@@ -20,8 +20,8 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import emoji as _emoji
-from nautilus_nlp.utils import constants
-from nautilus_nlp.preprocessing.text_preprocess import normalize_whitespace
+from config import constants
+from preprocessing.classic.preprocess import normalize_whitespace
 
 
 def remove_mentions(text) -> str:
diff --git a/nautilus_nlp/preprocessing/token_preprocess.py b/preprocessing/token/preprocess.py
similarity index 100%
rename from nautilus_nlp/preprocessing/token_preprocess.py
rename to preprocessing/token/preprocess.py
diff --git a/nautilus_nlp/utils/tokenizer.py b/preprocessing/token/tokenizer.py
similarity index 100%
rename from nautilus_nlp/utils/tokenizer.py
rename to preprocessing/token/tokenizer.py
diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index 863087e..d13245b 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -1,6 +1,8 @@
 import pytest
-from nautilus_nlp.preprocessing.data_augmentation import process_entities_and_text, \
-    get_augmenter, CouldNotAugment, UnavailableAugmenter
+from preprocessing.augmentation.text_augmentation import (
+    process_entities_and_text, get_augmenter, CouldNotAugment,
+    UnavailableAugmenter
+)
 
 @pytest.mark.parametrize(
     "text, text_augmented, entities, expected",
diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index a757619..060af44 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -20,7 +20,7 @@
 import os
 
 import numpy as np
-from nautilus_nlp.utils.file_loader import (detect_encoding, documents_loader)
+from utils.file_loader import (detect_encoding, documents_loader)
 
 TESTDOC_LATIN1 = "J'aime les frites bien grasse étalon châpeau!"
 TESTDOC_UTF8 = "Un deuxième exemple de texte en utf-8 cette fois!"
diff --git a/tests/test_keyword_extractor.py b/tests/test_keyword_extractor.py
deleted file mode 100644
index f694692..0000000
--- a/tests/test_keyword_extractor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import pytest
-from nautilus_nlp.utils.keyword_extractor import extract_keywords
-
-
-@pytest.mark.parametrize(
-    "input_text, keyword, case_sensitive, expected",
-    [
-        ("I eat an orange oranges at Orange", 'orange', False, ['orange', 'orange']),
-        ("I eat an orange oranges at Orange", 'orange', True, ['orange']),
-        ("I eat an orange oranges at Orange", {'orange': ['orange', 'oranges']}, False, ['orange', 'orange', 'orange']),
-        ("I eat an orange oranges at Orange", {'orange': ['orange', 'oranges']}, True, ['orange', 'orange']),
-        ("I eat an orange oranges at Orange", {'fruit': ['orange', 'oranges']}, True, ['fruit', 'fruit']),
-    ]
-    )
-def test_extract_keywords(input_text, keyword, case_sensitive, expected):
-    assert extract_keywords(input_text, keyword=keyword, case_sensitive=case_sensitive) == expected
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index 4aff394..4b6c832 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import nautilus_nlp.utils.phone_number as phone
+import utils.phone_number as phone
 
 def test_extract_phone_number():
     input_str = '(541) 754-3010 is a US. Phone'
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 51d94db..355836d 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,22 +17,22 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from nautilus_nlp.preprocessing.text_preprocess import (normalize_whitespace, remove_eol_characters,
+from preprocessing.classic.preprocess import (normalize_whitespace, remove_eol_characters,
                                                         fix_bad_unicode, unpack_english_contractions,
                                                         replace_urls, replace_emails, replace_phone_numbers,
                                                         replace_numbers, replace_currency_symbols, remove_punct,
                                                         remove_accents, remove_multiple_spaces_and_strip_text,
                                                         filter_non_latin_characters)
-from nautilus_nlp.preprocessing.social_preprocess import (remove_mentions, extract_mentions, remove_html_tags,
+from preprocessing.social.preprocess import (remove_mentions, extract_mentions, remove_html_tags,
                                                           remove_emoji, convert_emoji_to_text, extract_emojis,
                                                           extract_hashtags, remove_hashtag)
-from nautilus_nlp.preprocessing.token_preprocess import (remove_stopwords, remove_tokens_with_nonletters,
+from preprocessing.token.preprocess import (remove_stopwords, remove_tokens_with_nonletters,
                                                          remove_special_caracters_from_tokenslist,
                                                          remove_smallwords)
-from nautilus_nlp import Preprocessor
+from preprocessing.preprocessor import Preprocessor
 
-import nautilus_nlp.utils.phone_number as phone
-from nautilus_nlp.utils.stopwords import get_stopwords
+import utils.phone_number as phone
+from utils.stopwords import get_stopwords
 
 
 @pytest.mark.parametrize("text, expected_result",
diff --git a/nautilus_nlp/config/__init__.py b/utils/__init__.py
similarity index 100%
rename from nautilus_nlp/config/__init__.py
rename to utils/__init__.py
diff --git a/nautilus_nlp/utils/file_loader.py b/utils/file_loader.py
similarity index 100%
rename from nautilus_nlp/utils/file_loader.py
rename to utils/file_loader.py
diff --git a/nautilus_nlp/utils/phone_number.py b/utils/phone_number.py
similarity index 67%
rename from nautilus_nlp/utils/phone_number.py
rename to utils/phone_number.py
index 6ad58fc..6120155 100644
--- a/nautilus_nlp/utils/phone_number.py
+++ b/utils/phone_number.py
@@ -17,35 +17,8 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from typing import Optional
 import phonenumbers as _phonenumbers
+from config.config import SUPPORTED_COUNTRY, FORMAT_NUMBERS
 
-SUPPORTED_COUNTRY = [None, 'US', 'AG', 'AI', 'AS', 'BB', 'BM', 'BS', 'CA', 'DM',
-                     'GD', 'GU', 'JM', 'KN', 'KY', 'LC', 'MP', 'MS', 'PR', 'SX', 'TC', 'TT',
-                     'VC', 'VG', 'VI', 'RU', 'KZ', 'EG', 'ZA', 'GR', 'NL', 'BE', 'FR', 'ES',
-                     'HU', 'IT', 'VA', 'RO', 'CH', 'AT', 'GB', 'GG', 'IM', 'JE', 'DK', 'SE',
-                     'NO', 'SJ', 'PL', 'DE', 'PE', 'MX', 'CU', 'AR', 'BR', 'CL', 'CO', 'VE',
-                     'MY', 'AU', 'CC', 'CX', 'ID', 'PH', 'NZ', 'SG', 'TH', 'JP', 'KR', 'VN',
-                     'CN', 'TR', 'IN', 'PK', 'AF', 'LK', 'MM', 'IR', 'SS', 'MA', 'EH', 'DZ',
-                     'TN', 'LY', 'GM', 'SN', 'MR', 'ML', 'GN', 'CI', 'BF', 'NE', 'TG', 'BJ',
-                     'MU', 'LR', 'SL', 'GH', 'NG', 'TD', 'CF', 'CM', 'CV', 'ST', 'GQ', 'GA',
-                     'CG', 'CD', 'AO', 'GW', 'IO', 'AC', 'SC', 'SD', 'RW', 'ET', 'SO', 'DJ',
-                     'KE', 'TZ', 'UG', 'BI', 'MZ', 'ZM', 'MG', 'RE', 'YT', 'ZW', 'NA', 'MW',
-                     'LS', 'BW', 'SZ', 'KM', 'SH', 'TA', 'ER', 'AW', 'FO', 'GL', 'GI', 'PT',
-                     'LU', 'IE', 'IS', 'AL', 'MT', 'CY', 'FI', 'AX', 'BG', 'LT', 'LV', 'EE',
-                     'MD', 'AM', 'BY', 'AD', 'MC', 'SM', 'UA', 'RS', 'ME', 'XK', 'HR', 'SI',
-                     'BA', 'MK', 'CZ', 'SK', 'LI', 'FK', 'BZ', 'GT', 'SV', 'HN', 'NI', 'CR',
-                     'PA', 'PM', 'HT', 'GP', 'BL', 'MF', 'BO', 'GY', 'EC', 'GF', 'PY', 'MQ',
-                     'SR', 'UY', 'CW', 'BQ', 'TL', 'NF', 'BN', 'NR', 'PG', 'TO', 'SB', 'VU',
-                     'FJ', 'PW', 'WF', 'CK', 'NU', 'WS', 'KI', 'NC', 'TV', 'PF', 'TK', 'FM',
-                     'MH', 'KP', 'HK', 'MO', 'KH', 'LA', 'BD', 'TW', 'MV', 'LB', 'JO', 'SY',
-                     'IQ', 'KW', 'SA', 'YE', 'OM', 'PS', 'AE', 'IL', 'BH', 'QA', 'BT', 'MN',
-                     'NP', 'TJ', 'TM', 'AZ', 'GE', 'KG', 'UZ', 'DO']
-
-FORMAT_NUMBERS = {
-    "E164": _phonenumbers.PhoneNumberFormat.E164,
-    "INTERNATIONAL": _phonenumbers.PhoneNumberFormat.INTERNATIONAL,
-    "NATIONAL": _phonenumbers.PhoneNumberFormat.NATIONAL,
-    "RFC3966": _phonenumbers.PhoneNumberFormat.RFC3966
-}
 
 def find_phone_numbers(string: str, region_code: Optional[str] = None) -> str:
     """
diff --git a/nautilus_nlp/utils/stopwords.py b/utils/stopwords.py
similarity index 94%
rename from nautilus_nlp/utils/stopwords.py
rename to utils/stopwords.py
index 0f3eb57..89cb7d0 100644
--- a/nautilus_nlp/utils/stopwords.py
+++ b/utils/stopwords.py
@@ -24,11 +24,8 @@
 import os
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 from stop_words import get_stop_words as _get_stop_words
-from nautilus_nlp.config.config import ROOT_FOLDER
-from nautilus_nlp.utils.file_loader import documents_loader
-
-
-STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "data", "stopwords.json")
+from config.config import STOPWORDS_JSON_FILEPATH
+from utils.file_loader import documents_loader
 
 
 def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):

From 625b59f10cbd49836699b5d3e0e3ba13b5b133ed Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:35:59 +0100
Subject: [PATCH 442/496] update import init

---
 preprocessing/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/preprocessing/__init__.py b/preprocessing/__init__.py
index 7643ec5..97b4d25 100644
--- a/preprocessing/__init__.py
+++ b/preprocessing/__init__.py
@@ -15,4 +15,4 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.preprocessor import Preprocessor
+from preprocessing.preprocessor import Preprocessor

From f6cd208d398f54091676401edf1b905a3696acb8 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:40:08 +0100
Subject: [PATCH 443/496] fix pylint

---
 tests/test_preprocessor.py | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 355836d..13564b0 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,18 +17,21 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from preprocessing.classic.preprocess import (normalize_whitespace, remove_eol_characters,
-                                                        fix_bad_unicode, unpack_english_contractions,
-                                                        replace_urls, replace_emails, replace_phone_numbers,
-                                                        replace_numbers, replace_currency_symbols, remove_punct,
-                                                        remove_accents, remove_multiple_spaces_and_strip_text,
-                                                        filter_non_latin_characters)
-from preprocessing.social.preprocess import (remove_mentions, extract_mentions, remove_html_tags,
-                                                          remove_emoji, convert_emoji_to_text, extract_emojis,
-                                                          extract_hashtags, remove_hashtag)
-from preprocessing.token.preprocess import (remove_stopwords, remove_tokens_with_nonletters,
-                                                         remove_special_caracters_from_tokenslist,
-                                                         remove_smallwords)
+from preprocessing.classic.preprocess import (
+    normalize_whitespace, remove_eol_characters, fix_bad_unicode,
+    unpack_english_contractions, replace_urls, replace_emails,
+    replace_phone_numbers, replace_numbers, replace_currency_symbols,
+    remove_punct, remove_accents, remove_multiple_spaces_and_strip_text,
+    filter_non_latin_characters
+)
+from preprocessing.social.preprocess import (
+    remove_mentions, extract_mentions, remove_html_tags, remove_emoji,
+    convert_emoji_to_text, extract_emojis, extract_hashtags, remove_hashtag
+)
+from preprocessing.token.preprocess import (
+    remove_stopwords, remove_tokens_with_nonletters,
+    remove_special_caracters_from_tokenslist, remove_smallwords
+)
 from preprocessing.preprocessor import Preprocessor
 
 import utils.phone_number as phone

From b60173046560e34bd19e410690726f65ff141f5d Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 20:58:39 +0100
Subject: [PATCH 444/496] update ci with new name and fix pylint

---
 .github/workflows/ci_actions.yml | 4 ++--
 preprocessing/preprocessor.py    | 6 +++---
 utils/stopwords.py               | 1 -
 3 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 2c410da..8cdb602 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,8 +42,8 @@ jobs:
 
       - name: Run pylint
         run: |
-          pylint nautilus_nlp tests
+          pylint config preprocessing utils tests
 
       - name: Run pytest
         run: |
-          pytest --cov=nautilus_nlp tests
+          pytest --cov=preprocessing tests
diff --git a/preprocessing/preprocessor.py b/preprocessing/preprocessor.py
index 0b34eb3..41ec77c 100644
--- a/preprocessing/preprocessor.py
+++ b/preprocessing/preprocessor.py
@@ -3,8 +3,8 @@
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 
-from preprocessing.social.preprocess import (remove_html_tags, remove_mentions, remove_emoji,
-                                                          remove_hashtag)
+from preprocessing.social.preprocess import (
+    remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
 from preprocessing.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
 
 
@@ -62,4 +62,4 @@ def run(self, text: str) -> str:
                           remove_eol_characters, fix_bad_unicode, normalize_whitespace)
         self.pipeline = self.build_pipeline(operations)
         text = self.pipeline.fit_transform(text)
-        return text
\ No newline at end of file
+        return text
diff --git a/utils/stopwords.py b/utils/stopwords.py
index 89cb7d0..305ff2b 100644
--- a/utils/stopwords.py
+++ b/utils/stopwords.py
@@ -21,7 +21,6 @@
                         unicode_literals)
 
 import json
-import os
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 from stop_words import get_stop_words as _get_stop_words
 from config.config import STOPWORDS_JSON_FILEPATH

From c403924a293fec83abfa6613a1d8ed81eb51114a Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 25 Jan 2021 21:12:44 +0100
Subject: [PATCH 445/496] rm unused config files

---
 config/french_FEEL.csv      | 14128 ----------------------------------
 config/french_numbers.txt   |   101 -
 config/french_stopwords.txt |   689 --
 3 files changed, 14918 deletions(-)
 delete mode 100644 config/french_FEEL.csv
 delete mode 100644 config/french_numbers.txt
 delete mode 100644 config/french_stopwords.txt

diff --git a/config/french_FEEL.csv b/config/french_FEEL.csv
deleted file mode 100644
index 40f15d6..0000000
--- a/config/french_FEEL.csv
+++ /dev/null
@@ -1,14128 +0,0 @@
-id;word;polarity;joy;fear;sadness;anger;surprise;disgust
-1;à ce endroit là;positive;0;0;0;0;0;0
-2;à le hâte;negative;0;1;0;0;1;0
-3;à part;negative;0;0;1;0;0;0
-4;à pic;negative;0;1;0;0;0;0
-5;à rallonge;negative;0;0;1;0;0;0
-6;abasourdir;negative;0;0;0;0;1;0
-7;ablation;negative;0;1;0;0;0;1
-8;abominable;negative;0;1;0;0;0;1
-9;abrupt;negative;0;1;0;0;0;0
-10;absent;negative;0;1;1;0;0;0
-11;absorber;positive;0;0;0;0;1;0
-12;absurde;negative;0;0;0;1;1;1
-13;acceptable;positive;0;0;0;0;0;0
-14;accidentel;negative;0;1;1;0;1;0
-15;accusateur;negative;0;1;0;1;0;1
-16;accuser;negative;0;1;1;1;0;1
-17;acheteur;positive;0;0;0;0;0;0
-18;acquisition;positive;0;0;0;0;0;0
-19;active;positive;0;0;0;0;0;0
-20;actuel;positive;0;0;0;0;1;0
-21;addition;positive;0;0;0;0;0;0
-22;adhésif;positive;0;0;0;0;0;1
-23;administration;positive;0;0;0;0;0;0
-24;administratif;positive;0;0;0;0;0;0
-25;administrateur;positive;0;0;0;0;0;0
-26;admirateur;positive;0;0;0;0;0;0
-27;admissible;positive;0;0;0;0;0;0
-28;adolescent;positive;0;0;0;0;0;0
-29;aérien;positive;0;1;0;0;0;0
-30;affable;positive;0;0;0;0;0;0
-31;affaire;positive;0;0;0;0;0;0
-32;affectueux;positive;0;0;0;0;0;0
-33;affirmative;positive;0;0;0;0;0;0
-34;affliger;negative;0;1;1;0;0;0
-35;affreux;negative;0;1;1;1;1;1
-36;agent de conservation;positive;0;0;0;0;1;0
-37;agent de police;positive;0;1;0;0;0;0
-38;agrafe;positive;0;0;0;0;0;0
-39;agréable;positive;0;0;0;0;0;0
-40;agressif;negative;0;1;0;1;0;0
-41;agriculteur;positive;0;0;0;0;0;0
-42;aiguiser;positive;0;1;0;1;0;0
-43;aimer;positive;0;0;0;0;0;0
-44;aîné;positive;0;0;0;0;0;0
-45;aisément;positive;0;0;0;0;0;0
-46;alcalin;positive;0;0;0;0;0;0
-47;alentour;positive;0;0;0;0;0;0
-48;allonger;negative;0;1;1;1;0;1
-49;allure;positive;0;0;0;0;0;0
-50;allusion;negative;0;1;0;1;1;0
-51;alternative;positive;0;0;0;0;0;0
-52;amas;negative;0;0;0;0;0;0
-53;amatrice;negative;0;0;0;0;0;0
-54;ambassadrice;positive;0;0;0;0;0;0
-55;ambitieux;positive;0;0;0;0;0;0
-56;amélioration;positive;0;0;0;0;0;0
-57;ami;positive;0;0;0;0;0;0
-58;amourette;negative;0;0;0;0;0;0
-59;analyse;positive;0;0;0;0;0;0
-60;ancien;negative;0;0;0;0;0;0
-61;ancien élève;positive;0;0;0;0;0;0
-62;anguleux;positive;0;0;0;0;0;0
-63;animal;negative;0;1;0;0;0;1
-64;annuel;positive;0;0;0;0;0;0
-65;anticipatif;positive;0;0;0;0;0;0
-66;anxieux;negative;0;1;0;0;0;0
-67;apathique;negative;0;1;1;0;0;0
-68;apparenter;positive;0;0;0;0;0;0
-69;apprenant;positive;0;0;0;0;0;0
-70;apprenti;positive;0;0;0;0;0;0
-71;approbation;positive;0;0;0;0;0;0
-72;approbateur;positive;0;0;0;0;0;0
-73;approcher;positive;0;0;0;0;0;0
-74;approprier;positive;0;0;0;0;0;0
-75;approximatif;positive;0;0;0;0;0;0
-76;appui;positive;0;0;0;0;0;0
-77;aqueux;negative;0;0;0;0;0;1
-78;ardent;positive;0;0;0;0;1;0
-79;arête;negative;0;1;0;0;0;0
-80;aride;negative;0;1;1;0;0;0
-81;armature;positive;0;0;0;0;0;0
-82;arnaqueur;negative;0;1;0;1;0;1
-83;arrondissement;positive;0;0;0;0;0;0
-84;artisane;positive;0;0;0;0;0;0
-85;asile;positive;0;0;0;0;0;0
-86;asphyxie;negative;0;1;0;1;0;0
-87;aspiration;positive;0;0;0;0;0;0
-88;assaillant;negative;0;1;1;1;0;0
-89;assentiment;positive;0;0;0;0;0;0
-90;assidu;positive;0;0;0;0;0;0
-91;assistance;positive;0;0;0;0;0;0
-92;assistant;positive;0;0;0;0;0;0
-93;assourdir;negative;0;1;1;0;0;0
-94;asticot;negative;0;0;0;0;0;1
-95;astucieux;positive;0;0;0;0;0;0
-96;athéiste;negative;0;0;0;0;0;0
-97;attarder mental;negative;0;1;1;0;0;1
-98;attentif;positive;0;0;0;0;0;0
-99;attirer;positive;0;0;0;0;1;0
-100;attitude amical;positive;0;0;0;0;0;0
-101;attroupement;positive;0;0;0;0;0;0
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-115;avec insistance;positive;0;0;0;0;0;0
-116;avec lequel;positive;0;0;0;0;0;0
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-182;câble;positive;0;0;0;0;1;0
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-252;cohésif;positive;0;0;0;0;0;0
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-1224;prisonnier;negative;0;1;1;1;0;1
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-1233;progressif;positive;0;0;0;0;0;0
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-1238;promoteur;positive;0;0;0;0;0;0
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-1243;protecteur;positive;0;0;0;0;0;0
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-1247;provocateur;negative;0;0;0;1;0;0
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-1261;quasiment;positive;0;0;0;0;0;0
-1262;quelconque;negative;0;0;1;0;0;0
-1263;qui augmenter fortement;negative;0;1;0;0;1;0
-1264;qui commencer;positive;1;0;0;0;0;0
-1265;qui donner envie de vomir;negative;0;0;1;0;0;1
-1266;qui ébranler;negative;0;1;1;0;0;0
-1267;qui manquer de considération;negative;0;0;1;1;0;1
-1268;qui résonner;positive;0;1;0;0;1;0
-1269;race;positive;0;0;0;0;0;0
-1270;rachidien;positive;0;0;0;0;0;0
-1271;radieux;positive;0;0;0;0;1;0
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-1273;radioactif;negative;0;1;0;0;0;0
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-1284;rationnel;positive;0;0;0;0;0;0
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-1292;récemment;positive;0;0;0;0;0;0
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-1300;récréatif;positive;1;0;0;0;0;0
-1301;recueil;positive;0;0;0;0;0;0
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-1303;rédacteur en chef;positive;0;0;0;0;0;0
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-1310;réglage;positive;0;0;0;0;0;0
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-1326;répartir;positive;0;0;0;0;0;0
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-1330;représentatif;positive;0;0;0;0;0;0
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-1332;républicain;positive;0;0;0;0;0;0
-1333;répugner;negative;0;1;0;1;0;1
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-1347;réussir;positive;1;0;0;0;0;0
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-1360;ruine;negative;0;1;1;1;0;0
-1361;ruiner;negative;0;1;1;1;0;0
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-1363;sagacité;positive;0;0;0;0;0;0
-1364;sain et sauf;positive;1;0;0;0;0;0
-1365;saint;positive;0;0;0;0;1;0
-1366;saisir;negative;0;1;0;1;1;0
-1367;saloon;positive;0;0;0;1;0;0
-1368;sanguine;positive;0;0;0;0;0;0
-1369;sans assistance;negative;0;0;1;0;0;0
-1370;sans cesse;positive;0;0;0;0;0;0
-1371;sans égal;positive;0;1;0;0;0;0
-1372;sans entrave;positive;1;0;0;0;0;0
-1373;sans fin;positive;0;1;1;1;0;0
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-1377;sans opposition;positive;0;0;0;0;0;0
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-1393;se cacher;negative;0;1;1;0;0;0
-1394;se dévaloriser;negative;0;0;1;1;0;1
-1395;se souvenir de;positive;0;0;0;0;0;0
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-1421;singulier;positive;0;0;0;0;0;0
-1422;sinueux;negative;0;1;0;0;0;0
-1423;situation difficile;negative;0;1;1;0;0;1
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-1426;solidifier;positive;0;0;0;0;0;0
-1427;sombre;negative;0;1;1;0;0;0
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-1431;sonder;positive;0;0;0;0;0;0
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-1438;sous tendre;positive;0;0;0;0;0;0
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-1459;substantiel;positive;0;0;0;0;0;0
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-1508;tout de même;negative;0;0;0;0;0;0
-1509;tout puissant;positive;1;0;0;0;0;0
-1510;tout le deux semaine;positive;0;0;0;0;0;0
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-1513;transaction;positive;0;0;0;0;0;0
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-1516;traverser à gué;positive;0;0;0;0;0;0
-1517;trembloter;negative;0;1;0;1;0;0
-1518;trésorière;positive;0;0;0;0;0;0
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-1521;trompeur;negative;0;0;1;1;0;1
-1522;trop cher;negative;0;0;1;1;0;1
-1523;trop liquide;negative;0;0;0;0;0;1
-1524;trop long;negative;0;0;1;0;0;0
-1525;trop zélé;negative;0;0;0;0;0;0
-1526;trou perdu;negative;0;1;1;0;0;1
-1527;troubadour;positive;0;0;0;0;0;0
-1528;trouble;negative;0;1;0;1;1;0
-1529;trouillard;negative;0;0;0;0;0;1
-1530;trouvaille;positive;0;0;0;0;0;0
-1531;tueur;negative;0;1;1;1;1;1
-1532;tumultueux;negative;0;1;0;1;0;0
-1533;tuteur|tutrice;positive;0;0;0;0;0;0
-1534;ultime;positive;0;1;1;0;0;0
-1535;ultraviolette;positive;0;0;0;0;0;0
-1536;un foi|fois par semaine;positive;0;0;0;0;0;0
-1537;uniformité;positive;0;0;0;0;0;0
-1538;universel;positive;0;0;0;0;0;0
-1539;urgent;negative;0;1;0;0;0;0
-1540;user;negative;0;0;1;0;0;0
-1541;vaciller;negative;0;1;0;0;1;0
-1542;vagabonder;negative;0;1;1;0;0;0
-1543;vaillant;positive;0;0;0;0;0;0
-1544;vain;negative;0;0;1;0;0;0
-1545;vaporeux;positive;0;0;0;0;0;0
-1546;variqueux;negative;0;0;0;0;0;1
-1547;vaseux;negative;0;0;0;0;0;1
-1548;végétarien;positive;0;0;0;0;0;0
-1549;velouteux;positive;0;0;0;0;0;0
-1550;velu;negative;0;0;0;0;0;1
-1551;vendeur;positive;0;0;0;0;0;0
-1552;vengeur;negative;0;0;0;1;0;0
-1553;venimeux;negative;0;1;1;1;1;1
-1554;vent violent;negative;0;1;0;0;0;0
-1555;ventriculaire;positive;0;0;0;0;0;0
-1556;verbeux;positive;0;0;0;0;0;0
-1557;vérificateur|vérificatrice;positive;0;0;0;0;0;0
-1558;véritable;positive;0;0;0;0;0;0
-1559;vernir;positive;1;0;0;0;0;0
-1560;versement;positive;0;0;0;0;0;0
-1561;vertueux;positive;0;0;0;0;0;0
-1562;vestige;negative;0;1;0;0;0;1
-1563;vêtir;positive;0;0;0;0;0;0
-1564;vicieux;negative;0;0;0;1;0;1
-1565;victorieux;positive;1;0;0;0;0;0
-1566;vigoureux;positive;0;0;0;0;0;0
-1567;villageois;positive;0;0;0;0;0;0
-1568;vindicatif;negative;0;1;0;1;0;1
-1569;violation;negative;0;0;0;1;0;0
-1570;violet|violette;positive;0;0;0;0;0;0
-1571;virtuel;negative;0;0;0;0;0;0
-1572;visiteur;positive;1;0;0;0;0;0
-1573;visqueux;negative;0;0;0;0;0;1
-1574;visuel;positive;0;0;0;0;0;0
-1575;vitrer;positive;0;0;0;0;0;0
-1576;vif;negative;0;1;0;0;1;1
-1577;v?u;positive;0;0;0;0;0;0
-1578;voisin;positive;0;0;0;0;0;0
-1579;voleur;negative;0;1;1;1;1;1
-1580;voluptueux;positive;0;0;0;0;0;0
-1581;voyageur;positive;0;0;0;0;0;0
-1582;voyant;positive;0;0;0;0;0;1
-1583;youpi;positive;1;0;0;0;0;0
-1584;zéro;negative;0;0;1;0;0;0
-1585;zut;negative;0;0;1;1;0;1
-1586;bon gorgée;negative;0;0;0;0;0;1
-1587;école maternel;positive;0;0;0;0;0;0
-1588;un foi|fois tout le deux moi|mois;positive;0;0;0;0;0;0
-1589;@card@ kilo;positive;0;0;0;0;0;0
-1590;à base de plante;positive;0;0;0;0;0;0
-1591;à bord;positive;0;0;0;0;0;0
-1592;à capuche;positive;0;1;0;0;0;0
-1593;à carreau;positive;0;0;0;0;0;0
-1594;à clou;negative;0;0;0;0;0;0
-1595;à condition que;positive;0;0;0;0;0;0
-1596;à contrec?ur;negative;0;1;1;1;0;1
-1597;à côte;negative;0;0;0;0;0;1
-1598;à cru;positive;0;0;0;0;0;0
-1599;à dessein;positive;0;0;0;0;0;0
-1600;à destination de;positive;0;0;0;0;0;0
-1601;à feuillage caduc;negative;0;0;0;0;0;0
-1602;à feuille;positive;0;0;0;0;0;0
-1603;à fleur;positive;1;0;0;0;0;0
-1604;à foison;positive;1;0;0;0;0;0
-1605;à force de qch;positive;0;0;0;0;0;0
-1606;à fort poitrine;positive;0;0;0;0;0;0
-1607;à friser;positive;0;0;0;0;0;0
-1608;à froid;negative;0;0;1;0;0;0
-1609;à gauche;negative;0;0;0;0;0;0
-1610;à intervalle régulier;positive;0;0;0;0;0;0
-1611;à jamais;positive;0;0;0;0;0;0
-1612;à juste titre;positive;0;0;0;0;0;0
-1613;à l étranger;positive;0;0;0;0;0;0
-1614;à l improviste;negative;0;0;0;0;1;0
-1615;à l unanimité;positive;0;0;0;0;0;0
-1616;à le bon franquette;positive;0;0;0;0;1;0
-1617;à le dérive;negative;0;1;1;0;0;0
-1618;à le différence de;negative;0;0;0;0;0;0
-1619;à le menthe;positive;0;0;0;0;0;0
-1620;avoir le mode;positive;0;0;0;0;0;0
-1621;à le mode;positive;0;0;0;0;0;0
-1622;à le retraite;positive;1;0;0;0;0;0
-1623;à maintes reprise;negative;0;0;0;0;0;0
-1624;à mi chemin;positive;0;0;0;0;0;0
-1625;à naître;positive;0;0;0;0;0;0
-1626;à nervure;negative;0;0;0;0;0;1
-1627;à nouveau;positive;0;0;0;0;0;0
-1628;à peine;negative;0;0;1;0;0;0
-1629;à peu près;negative;0;0;0;0;0;0
-1630;à plat;negative;0;0;1;0;0;0
-1631;à plat ventre;negative;0;1;1;0;0;0
-1632;à plusieurs reprise;negative;0;0;0;0;0;0
-1633;avoir priori;negative;0;1;0;1;0;1
-1634;à propos;negative;0;0;0;0;0;0
-1635;à quai;positive;0;0;0;0;0;0
-1636;à queue;negative;0;0;0;0;0;0
-1637;à son compte;positive;0;0;0;0;0;0
-1638;à spiral|spirale;positive;0;0;0;0;0;0
-1639;à succès;positive;0;0;0;0;0;0
-1640;à supposer que;negative;0;0;0;0;0;0
-1641;à thème;positive;0;0;0;0;0;0
-1642;à tort;negative;0;1;1;1;0;0
-1643;à tout jamais;positive;0;0;0;0;0;0
-1644;à tout épreuve;positive;0;0;0;0;0;0
-1645;à tribord;positive;0;0;0;0;0;0
-1646;à venir;positive;0;0;0;0;0;0
-1647;à vie;positive;0;0;0;0;0;0
-1648;à vrai dire;positive;0;0;0;0;0;0
-1649;abaisser;negative;0;0;1;0;0;0
-1650;abaissement;negative;0;0;1;0;0;0
-1651;abandon;negative;0;1;1;1;1;0
-1652;abandonner;negative;0;1;1;1;0;1
-1653;abaondonnées;negative;0;1;1;0;0;0
-1654;abat;negative;0;0;0;0;0;1
-1655;abattoir;negative;0;1;1;1;0;1
-1656;abattre;negative;0;1;1;1;1;0
-1657;abbé;negative;0;1;0;0;0;0
-1658;abcès;negative;0;1;1;0;0;1
-1659;abdiquer;negative;0;1;1;0;0;0
-1660;abdomen;positive;0;0;0;0;0;0
-1661;abdominal;positive;0;0;0;0;0;0
-1662;abeille;negative;0;1;0;1;0;0
-1663;aberrer;negative;0;0;0;1;1;0
-1664;abhorrer;negative;0;1;0;1;0;1
-1665;abîme;negative;0;1;1;0;0;0
-1666;abîmer;negative;0;1;1;1;0;0
-1667;abject;negative;0;0;0;1;0;1
-1668;aboiement;negative;0;1;0;1;0;0
-1669;abolir;negative;0;0;1;1;0;0
-1670;abolition;negative;0;0;1;1;0;0
-1671;abomination;negative;0;1;1;1;0;1
-1672;abonder;positive;1;0;0;0;0;0
-1673;abondant;positive;1;0;0;0;0;0
-1674;abonnement;positive;0;0;0;0;0;0
-1675;aborder;positive;0;0;0;1;1;0
-1676;aborigène;positive;0;0;0;0;0;0
-1677;abortif;negative;0;1;1;1;0;0
-1678;aboutir;positive;1;0;0;0;0;0
-1679;aboyer;negative;0;1;0;1;0;0
-1680;abpimée;negative;0;1;1;1;0;0
-1681;abrasion;negative;0;1;1;0;0;1
-1682;abréger;positive;0;0;0;0;0;0
-1683;abreuvoir;positive;0;0;0;0;0;0
-1684;abréviation;positive;0;0;0;0;0;0
-1685;abri de jardin;negative;0;0;0;0;0;0
-1686;abriter;positive;0;0;0;0;0;0
-1687;abrogation;negative;0;1;1;0;0;0
-1688;abroger;negative;0;1;1;0;0;0
-1689;abrutir;negative;0;0;0;1;0;1
-1690;absence;negative;0;1;1;0;0;0
-1691;absentéisme;negative;0;0;1;0;0;0
-1692;absinthe;negative;0;0;0;0;0;0
-1693;absoudre;positive;1;0;0;0;0;0
-1694;absolution;positive;0;0;0;0;0;0
-1695;absorbant;positive;0;0;0;0;0;0
-1696;absorption;positive;0;0;0;0;0;0
-1697;abstention;negative;0;1;1;0;0;0
-1698;abstinence;negative;0;1;1;0;0;0
-1699;abstraction;negative;0;1;1;0;0;0
-1700;abstraire;positive;0;0;0;0;0;0
-1701;absurdité;negative;0;0;0;1;1;1
-1702;abuser de;negative;0;1;1;1;0;0
-1703;académie;positive;0;0;0;0;0;0
-1704;académique;positive;0;0;0;0;0;0
-1705;accabler;negative;0;1;1;1;0;0
-1706;accablant;negative;0;1;1;1;0;0
-1707;accalmir;positive;0;0;0;0;0;0
-1708;accéder;positive;1;0;0;0;0;0
-1709;accélérateur;negative;0;0;0;1;0;0
-1710;accélération;positive;0;1;0;0;0;0
-1711;accentuation;positive;0;0;0;0;0;0
-1712;accentuer;positive;0;0;0;0;0;0
-1713;acceptant;positive;0;0;0;0;0;0
-1714;acceptation;positive;0;0;0;0;0;0
-1715;accepter;positive;0;0;0;0;0;0
-1716;accès;positive;0;0;0;0;0;0
-1717;accessible;positive;0;0;0;0;0;0
-1718;accession;positive;0;0;0;0;0;0
-1719;accessoire;negative;0;0;0;0;0;0
-1720;accessoirement;negative;0;0;0;0;0;0
-1721;accident mortel;negative;0;1;1;0;0;0
-1722;accident vasculaire cérébral;negative;0;1;1;0;1;0
-1723;accidenter;negative;0;1;1;0;0;0
-1724;accidentellement;negative;0;1;1;0;1;0
-1725;acclamation;positive;1;0;0;0;0;0
-1726;acclamer;positive;0;0;0;0;1;0
-1727;accolade;positive;0;0;0;0;1;0
-1728;accommoder;positive;0;0;0;0;0;0
-1729;accommodation;positive;0;0;0;0;0;0
-1730;accompagner;positive;0;0;0;0;0;0
-1731;accompagnement;positive;0;0;0;0;0;0
-1732;accomplir;positive;1;0;0;0;0;0
-1733;accomplissement;positive;0;0;0;0;0;0
-1734;accordable;positive;0;0;0;0;0;0
-1735;accorder;positive;0;0;0;0;0;0
-1736;accordéon;positive;0;0;0;0;0;0
-1737;accord;positive;0;0;0;0;0;0
-1738;accoutumer;positive;0;0;0;0;0;0
-1739;accréditer;positive;0;0;0;0;0;0
-1740;accro;negative;0;0;1;1;0;0
-1741;accrochage;negative;0;0;0;1;0;0
-1742;accrocher;negative;0;1;0;0;1;0
-1743;accroc;negative;0;1;0;0;1;0
-1744;accroissement;positive;0;0;0;0;0;0
-1745;accroître;positive;0;0;0;0;0;0
-1746;accroupir;negative;0;1;0;0;0;0
-1747;accroupissement;negative;0;1;0;0;0;0
-1748;accroire;positive;0;0;0;0;0;0
-1749;accroire|accroître;positive;0;0;0;0;0;0
-1750;accueil;positive;0;0;0;0;0;0
-1751;accueillir;positive;1;0;0;0;0;0
-1752;accumuler;positive;0;0;0;0;0;0
-1753;accusatif;negative;0;0;0;1;0;1
-1754;accusation;negative;0;0;0;1;0;1
-1755;acérer;negative;0;1;0;1;0;0
-1756;acétique;negative;0;0;0;0;0;1
-1757;acharner;negative;0;1;0;1;0;1
-1758;achat;positive;0;0;0;0;0;0
-1759;acheminer par tuyau;positive;0;0;0;0;0;0
-1760;acheminer par pont aérien;positive;0;0;0;0;0;0
-1761;acheter;positive;0;0;0;0;0;0
-1762;achever;positive;0;0;0;0;0;0
-1763;achèvement;positive;1;0;0;0;0;0
-1764;acide;negative;0;0;0;0;0;1
-1765;acidité;negative;0;0;0;0;0;1
-1766;acier;positive;0;0;0;0;0;0
-1767;acompte;positive;0;0;0;0;0;0
-1768;acouphène;negative;0;1;1;0;0;0
-1769;acoustique;positive;0;0;0;0;0;0
-1770;acquéreur;positive;0;0;0;0;0;0
-1771;acquérir;positive;0;0;0;0;0;0
-1772;acquiescement;positive;0;0;0;0;0;0
-1773;âcre;negative;0;0;0;0;0;1
-1774;acrobate;positive;0;1;0;0;0;0
-1775;acte;positive;0;0;0;0;0;0
-1776;acte de défi;negative;0;1;0;1;0;1
-1777;acte de fiducie;negative;0;0;0;1;0;0
-1778;acte délictuel;negative;0;1;0;1;0;0
-1779;actif;positive;0;0;0;0;0;0
-1780;action;positive;0;0;0;0;0;0
-1781;action de grâce;positive;0;0;0;0;0;0
-1782;actionnable;negative;0;1;0;1;0;1
-1783;actionnaire;positive;0;0;0;0;0;0
-1784;activité;positive;0;0;0;0;0;0
-1785;actuaire;positive;0;0;0;0;0;0
-1786;actualité;positive;0;0;0;0;0;0
-1787;acuité;positive;0;0;0;0;0;0
-1788;acupuncture;positive;0;0;0;0;0;0
-1789;adage;positive;0;0;0;0;0;0
-1790;adaptabilité;positive;0;0;0;0;0;0
-1791;adaptable;positive;0;0;0;0;0;0
-1792;adaptation;positive;0;0;0;0;0;0
-1793;adapter;positive;0;0;0;0;0;0
-1794;addenda;positive;0;0;0;0;0;0
-1795;addiction;negative;0;1;1;0;0;0
-1796;additif;negative;0;0;0;0;0;1
-1797;additionner;positive;0;0;0;0;0;0
-1798;additionnel;positive;0;0;0;0;0;0
-1799;adepte;positive;0;0;0;0;0;0
-1800;adéquat;positive;0;0;0;0;0;0
-1801;adéquation;positive;0;0;0;0;0;0
-1802;adhérer;positive;0;0;0;0;0;0
-1803;adhérence;positive;0;0;0;0;0;0
-1804;adhérent;positive;0;0;0;0;0;0
-1805;adhérer à;positive;0;0;0;0;0;0
-1806;adhésion;positive;0;0;0;0;0;0
-1807;adieu;negative;0;0;1;0;0;0
-1808;adipeux;negative;0;0;0;0;0;1
-1809;adjacent;positive;0;0;0;0;0;0
-1810;adjectif;positive;0;0;0;0;0;0
-1811;adjectival;positive;0;0;0;0;0;0
-1812;adjoindre;positive;0;0;0;0;0;0
-1813;adjuvant;positive;0;0;0;0;0;0
-1814;admettre;positive;0;0;0;0;0;0
-1815;administrer;positive;0;0;0;0;0;0
-1816;administrer qch à qqn;positive;0;0;0;0;0;0
-1817;admirable;positive;0;0;0;0;0;0
-1818;admiration;positive;0;0;0;0;0;0
-1819;admirer;positive;0;0;0;0;0;0
-1820;admissibilité;positive;0;0;0;0;0;0
-1821;admission;positive;0;0;0;0;0;0
-1822;admonition;negative;0;1;0;1;0;0
-1823;adobe;positive;0;0;0;0;0;0
-1824;adolescence;positive;0;0;0;0;0;0
-1825;adopter;positive;0;0;0;0;0;0
-1826;adoption;positive;0;0;0;0;0;0
-1827;adorable;positive;0;0;1;0;1;0
-1828;adoration;positive;0;1;0;0;0;0
-1829;adorer;positive;0;1;0;0;0;0
-1830;ado|ados;positive;0;0;0;0;0;0
-1831;adosser;positive;0;0;0;0;0;0
-1832;adoucir;positive;0;0;0;0;0;0
-1833;adoucissant;positive;0;0;0;0;0;0
-1834;adoucissement;positive;0;0;0;0;0;0
-1835;adresse;positive;0;0;0;0;0;0
-1836;adresser;positive;0;0;0;0;0;0
-1837;adroit;positive;0;0;0;0;0;0
-1838;adulte;positive;0;0;0;0;0;0
-1839;adultère;negative;0;0;1;1;0;1
-1840;adversaire;negative;0;1;0;1;0;1
-1841;adverse;negative;0;1;0;1;0;1
-1842;adversité;negative;0;1;1;1;0;0
-1843;aération;positive;0;0;0;0;0;0
-1844;aérer;positive;0;0;0;0;0;0
-1845;aérodrome;positive;0;0;0;0;0;0
-1846;aérodynamique;positive;0;0;0;0;0;0
-1847;aéroglisseur;positive;0;0;0;0;0;0
-1848;aéronautique;positive;0;0;0;0;0;0
-1849;aéroport;positive;0;0;0;0;0;0
-1850;aérosol;positive;0;0;0;0;0;0
-1851;affaiblir;negative;0;1;1;0;0;0
-1852;affairement;positive;0;0;0;0;0;0
-1853;affaisés;negative;0;0;1;0;0;0
-1854;affaisser;negative;0;0;1;0;0;0
-1855;affaissement;negative;0;0;1;0;0;0
-1856;affamer;negative;0;1;1;1;0;0
-1857;affectation;positive;0;0;0;0;0;0
-1858;affecter;negative;0;0;1;0;0;0
-1859;affection;positive;0;0;0;0;0;0
-1860;affichage;positive;0;0;0;0;0;0
-1861;affiche;positive;0;0;0;0;1;0
-1862;afficher;positive;0;0;0;0;0;0
-1863;affidavit;positive;0;0;0;0;0;0
-1864;affiliation;positive;0;0;0;0;0;0
-1865;affilier;positive;0;0;0;0;0;0
-1866;affinage;positive;0;0;0;0;0;0
-1867;affiner;positive;0;0;0;0;0;0
-1868;affinité;positive;0;0;0;0;0;0
-1869;affirmatif;positive;0;0;0;0;0;0
-1870;affirmation;positive;0;0;0;0;0;0
-1871;affirmativement;positive;0;0;0;0;0;0
-1872;affirmer;positive;0;0;0;0;0;0
-1873;affixe;positive;0;0;0;0;0;0
-1874;affliction;negative;0;1;1;0;0;1
-1875;affluent;positive;0;0;0;0;0;0
-1876;affluer;negative;0;1;0;1;1;0
-1877;afflux;negative;0;1;0;0;1;0
-1878;affoler;negative;0;1;1;0;0;0
-1879;affolement;negative;0;1;0;1;0;0
-1880;affréter;positive;0;0;0;0;0;0
-1881;affronter;negative;0;0;0;0;0;0
-1882;âffuté;negative;0;1;0;0;0;0
-1883;âffutée;negative;0;1;0;0;0;0
-1884;âffutées;negative;0;1;0;0;0;0
-1885;âffutés;negative;0;1;0;0;0;0
-1886;agacer;negative;0;0;1;1;0;0
-1887;agacement;negative;0;0;1;1;0;1
-1888;agate;positive;0;0;0;0;0;0
-1889;âge;negative;0;1;1;0;0;0
-1890;âgé;negative;0;0;1;0;0;0
-1891;agence;positive;0;0;0;0;0;0
-1892;agenouiller;negative;0;0;1;0;0;0
-1893;agent;positive;0;0;0;0;0;0
-1894;agent de change;positive;0;0;0;0;0;0
-1895;agglomération;positive;0;0;0;0;0;0
-1896;agglomérer;negative;0;0;0;0;0;0
-1897;aggraver;negative;0;1;1;1;0;0
-1898;aggravation;negative;0;1;1;1;0;1
-1899;aggressifs;negative;0;1;0;1;0;0
-1900;agile;positive;0;0;0;0;0;0
-1901;agilité;positive;0;0;0;0;0;0
-1902;agir;positive;0;0;0;0;0;0
-1903;agissements;positive;0;0;0;0;0;0
-1904;agiter;negative;0;1;0;1;0;0
-1905;agneau;positive;0;0;0;0;0;0
-1906;agnostique;negative;0;1;1;0;0;0
-1907;agonir;negative;0;1;1;1;0;0
-1908;agrafer;positive;0;0;0;0;0;0
-1909;agrandir;negative;0;0;0;0;0;0
-1910;agrégat;negative;0;0;0;0;0;0
-1911;agrégation;negative;0;0;0;0;0;0
-1912;agresseur;negative;0;1;1;1;0;0
-1913;agression;negative;0;1;0;1;1;0
-1914;agricole;positive;0;0;0;0;0;0
-1915;agriculture;positive;0;0;0;0;0;0
-1916;ahurir;negative;0;0;0;0;1;0
-1917;ahurissant;negative;0;0;0;0;1;0
-1918;aide;positive;0;0;0;0;0;0
-1919;aider;positive;0;0;0;0;0;0
-1920;aigle;positive;0;0;0;0;0;0
-1921;aigre;negative;0;0;0;0;0;1
-1922;aigu marine;positive;0;0;0;0;0;0
-1923;aiguille;negative;0;1;0;0;0;0
-1924;aiguiller;positive;0;0;0;0;0;0
-1925;aiguillonner;positive;0;1;0;0;0;0
-1926;aiguisoir;negative;0;1;0;0;0;0
-1927;ail;negative;0;0;0;0;0;1
-1928;aile;positive;0;0;0;0;0;0
-1929;ailer;positive;0;0;0;0;0;0
-1930;aileron;positive;0;0;0;0;0;0
-1931;aimable;positive;0;0;0;0;0;0
-1932;aimant;positive;0;0;0;0;0;0
-1933;airbag;positive;0;0;0;0;0;0
-1934;aire;positive;0;0;0;0;0;0
-1935;aire de jeu;positive;0;0;0;0;1;0
-1936;air;negative;0;0;0;0;0;1
-1937;aisance;positive;1;0;0;0;0;0
-1938;aisé;positive;1;0;0;0;0;0
-1939;ajourner;negative;0;0;1;0;0;0
-1940;ajournement;negative;0;0;1;0;0;0
-1941;ajout;positive;0;0;0;0;0;0
-1942;ajouter;positive;0;0;0;0;0;0
-1943;ajustage;positive;0;0;0;0;0;0
-1944;ajuster;positive;0;0;0;0;0;0
-1945;ajustement;positive;0;0;0;0;0;0
-1946;alambiquer;negative;0;0;0;0;0;1
-1947;alanguir;negative;0;0;1;0;0;0
-1948;alarmer;negative;0;1;0;0;1;0
-1949;alarme;negative;0;1;0;0;1;0
-1950;albâtre;positive;0;0;0;0;0;0
-1951;album;positive;0;0;0;0;0;0
-1952;alcali;positive;0;0;0;0;0;0
-1953;alcaloïde;positive;0;0;0;0;0;0
-1954;alchimie;positive;0;0;0;0;0;0
-1955;alcool;negative;0;0;0;0;0;0
-1956;alcoolisme;negative;0;1;1;1;0;1
-1957;alcôve;positive;0;0;0;0;0;0
-1958;alerte;positive;0;1;0;1;1;0
-1959;alésage;negative;0;1;0;1;0;0
-1960;algèbre;positive;0;0;0;0;0;0
-1961;algébrique;positive;0;0;0;0;0;0
-1962;algorithme;positive;0;0;0;0;0;0
-1963;alibi;positive;0;0;0;0;0;0
-1964;aliénation;negative;0;1;1;1;0;1
-1965;aliéner;negative;0;0;1;0;0;1
-1966;aligner;positive;0;0;0;0;0;0
-1967;alignement;positive;0;0;0;0;0;0
-1968;aliment;positive;0;0;0;0;0;0
-1969;alimentaire;positive;0;0;0;0;0;0
-1970;alimentation;positive;0;0;0;0;0;0
-1971;alimenter;positive;0;0;0;0;0;0
-1972;alinéa;positive;0;0;0;0;0;0
-1973;aliquote;positive;0;0;0;0;0;0
-1974;allaitement;positive;0;0;0;0;0;0
-1975;allaiter;positive;0;0;0;0;0;0
-1976;allécher;positive;0;0;0;0;1;0
-1977;alléchant;positive;0;0;0;0;1;0
-1978;aller;positive;0;0;0;0;0;0
-1979;allégation;negative;0;0;0;1;0;0
-1980;allégeance;positive;0;0;0;0;0;0
-1981;allégorie;positive;0;0;0;0;0;0
-1982;allégorique;positive;0;0;0;0;0;0
-1983;allégresse;positive;1;0;0;0;0;0
-1984;allegro;positive;0;0;0;0;0;0
-1985;alléguer;negative;0;1;0;0;0;1
-1986;allemand;positive;0;0;0;0;0;0
-1987;aller à tout vitesse;negative;0;1;0;0;0;0
-1988;aller chercher;positive;0;0;0;0;0;0
-1989;alliage;positive;0;0;0;0;0;0
-1990;alliance;positive;0;0;0;0;0;0
-1991;allier;positive;0;0;0;0;0;0
-1992;alligator;negative;0;1;0;0;0;0
-1993;allocataire;positive;0;0;0;0;0;0
-1994;allocation;positive;0;0;0;0;0;0
-1995;allocation chômage;negative;0;0;1;0;0;0
-1996;allocution;positive;0;0;0;0;0;0
-1997;allongement;positive;0;0;0;0;0;0
-1998;allouer;positive;0;0;0;0;0;0
-1999;allumage;positive;0;0;0;0;0;0
-2000;allumette;positive;0;0;0;0;0;0
-2001;alluvial;positive;0;0;0;0;0;0
-2002;almanach;positive;0;0;0;0;0;0
-2003;aloha;positive;1;0;0;0;0;0
-2004;alors que;negative;0;0;0;0;0;0
-2005;alouette;positive;1;0;0;0;0;0
-2006;alphabet;positive;0;0;0;0;0;0
-2007;alphabétique;positive;0;0;0;0;0;0
-2008;alpiniste;positive;0;0;0;0;0;0
-2009;altération;negative;0;0;1;0;0;1
-2010;altercation;negative;0;0;0;1;0;0
-2011;altérer;negative;0;0;1;1;0;1
-2012;alternatif;positive;0;0;0;0;0;0
-2013;alterner;positive;0;0;0;0;0;0
-2014;altitude;positive;0;0;0;0;0;0
-2015;alto;positive;0;0;0;0;0;0
-2016;alvéolaire;positive;0;0;0;0;0;0
-2017;amadouer;positive;0;0;0;0;0;0
-2018;amande;positive;0;0;0;0;0;0
-2019;amant;positive;0;0;0;0;0;0
-2020;amarrage;positive;0;0;0;0;0;0
-2021;amarrer;positive;0;0;0;0;0;0
-2022;amasser;positive;0;0;0;0;0;0
-2023;amateur;negative;0;0;0;0;0;0
-2024;amatrices;negative;0;0;0;0;0;0
-2025;ambassade;positive;0;0;0;0;0;0
-2026;ambassadeur;positive;0;0;0;0;0;0
-2027;ambiance;positive;0;0;0;0;0;0
-2028;ambiant;positive;0;0;0;0;0;0
-2029;ambigu;negative;0;1;0;0;0;0
-2030;ambiguë;negative;0;1;0;0;0;0
-2031;ambiguïté;negative;0;1;0;0;0;0
-2032;ambition;positive;0;0;0;0;0;0
-2033;ambre;positive;0;0;0;0;0;0
-2034;ambrer;positive;0;0;0;0;0;0
-2035;ambulance;negative;0;1;1;0;0;0
-2036;âme s?ur;positive;0;1;0;0;0;0
-2037;améliorant;positive;0;0;0;0;0;0
-2038;améliorer;positive;0;0;0;0;0;0
-2039;amen;positive;0;0;0;0;0;0
-2040;aménagement;positive;0;0;0;0;0;0
-2041;aménagement paysager;positive;0;0;0;0;0;0
-2042;amendement;positive;0;0;0;0;0;0
-2043;amender;positive;0;0;0;0;0;0
-2044;aménité;positive;0;0;0;0;0;0
-2045;amèrement;negative;0;0;1;1;0;1
-2046;amertume;negative;0;0;1;1;0;1
-2047;améthyste;positive;0;0;0;0;0;0
-2048;amical;positive;0;0;0;0;0;0
-2049;amidon;negative;0;0;0;0;0;1
-2050;amidonner;negative;0;0;0;0;0;1
-2051;amiral;positive;0;0;0;0;0;0
-2052;amirauté;positive;0;0;0;0;0;0
-2053;amitié;positive;0;0;0;0;0;0
-2054;ammoniac;negative;0;0;0;0;0;1
-2055;amnésie;negative;0;1;1;0;0;0
-2056;amnistie;positive;1;0;0;0;0;0
-2057;amoindrir;negative;0;0;1;0;0;0
-2058;amorçage;positive;0;0;0;0;0;0
-2059;amorce;positive;0;0;0;0;0;0
-2060;amorcer;positive;0;0;0;0;0;0
-2061;amorphe;negative;0;0;1;0;0;1
-2062;amortir;negative;0;1;0;1;1;0
-2063;amortisseur;negative;0;1;0;0;0;0
-2064;amour;positive;0;0;0;0;0;0
-2065;amoureux;positive;1;0;0;0;0;0
-2066;amphétamine;negative;0;0;0;0;0;1
-2067;amphibien;negative;0;0;0;0;0;1
-2068;amphibiens;negative;0;0;0;0;0;1
-2069;amphibie;positive;0;0;0;0;0;0
-2070;amphithéâtre;positive;0;0;0;0;0;0
-2071;amplement;positive;0;0;0;0;0;0
-2072;ampleur;positive;0;0;0;0;0;0
-2073;amplification;positive;0;0;0;0;0;0
-2074;amplifier;positive;0;0;0;0;0;0
-2075;amplitude;positive;0;0;0;0;0;0
-2076;amputation;negative;0;1;1;0;0;1
-2077;amulette;positive;0;0;0;0;0;0
-2078;amuser;positive;1;0;0;0;0;0
-2079;amusant;positive;1;0;0;0;0;0
-2080;amuser gueule;positive;0;0;0;0;0;0
-2081;amusement;positive;1;0;0;0;0;0
-2082;amygdale;negative;0;0;0;0;0;1
-2083;an;positive;0;0;0;0;0;0
-2084;anaconda;negative;0;1;0;0;0;1
-2085;anal;negative;0;1;0;0;0;0
-2086;analgésique;negative;0;1;0;0;0;0
-2087;analogie;positive;0;0;0;0;0;0
-2088;analogique;positive;0;0;0;0;0;0
-2089;analogue;positive;0;0;0;0;0;0
-2090;analphabète;negative;0;0;1;0;0;1
-2091;analyser;positive;0;0;0;0;0;0
-2092;analyseur;positive;0;0;0;0;0;0
-2093;analyste;positive;0;0;0;0;0;0
-2094;analytique;positive;0;0;0;0;0;0
-2095;ananas;positive;0;0;0;0;0;0
-2096;anarchie;negative;0;1;0;1;0;0
-2097;anarchisme;negative;0;1;0;1;0;0
-2098;anarchiste;negative;0;1;0;1;0;0
-2099;anastomose;negative;0;1;0;0;0;1
-2100;anathème;negative;0;1;1;1;0;1
-2101;anatomie;positive;0;0;0;0;0;0
-2102;anatomique;positive;0;0;0;0;0;0
-2103;ancestral;positive;0;0;0;0;0;0
-2104;ancêtre;positive;0;0;0;0;0;0
-2105;ancien combattant;positive;0;0;0;0;0;0
-2106;ancienneté;positive;0;0;0;0;0;0
-2107;ancrage;positive;0;0;1;0;0;0
-2108;ancre;positive;0;0;0;0;0;0
-2109;âne;negative;0;1;0;1;0;1
-2110;anéantir;negative;0;1;1;1;0;0
-2111;anéantissement;negative;0;1;1;1;0;0
-2112;anémone;positive;0;0;0;0;0;0
-2113;ânerie;negative;0;0;0;0;0;1
-2114;ânesse;positive;0;0;0;0;0;0
-2115;anesthésier;negative;0;1;0;0;0;0
-2116;anesthésiant;negative;0;1;0;0;0;0
-2117;anesthésie;negative;0;1;0;0;0;0
-2118;anesthésique;negative;0;1;0;0;0;0
-2119;ange;positive;0;0;0;0;1;0
-2120;angélique;positive;0;0;0;0;0;0
-2121;angine;negative;0;1;1;0;0;0
-2122;angiographie;positive;0;0;0;0;0;0
-2123;angle;positive;0;0;0;0;0;0
-2124;angoisser;negative;0;1;1;0;0;0
-2125;angoissant;negative;0;1;1;0;0;0
-2126;angoisse;negative;0;1;1;1;0;0
-2127;anguille;negative;0;1;0;0;0;1
-2128;angulaire;positive;0;0;0;0;0;0
-2129;anhydre;positive;0;0;0;0;0;0
-2130;animal de compagnie;negative;0;0;0;0;0;0
-2131;animer;positive;1;0;0;0;0;0
-2132;animosité;negative;0;1;1;1;0;1
-2133;annales;positive;0;0;0;0;0;0
-2134;anneau;positive;0;0;0;0;0;0
-2135;année;positive;0;0;0;0;0;0
-2136;annexe;positive;0;0;0;0;0;0
-2137;annexer;positive;0;0;0;0;0;0
-2138;annexion;positive;0;0;0;0;0;0
-2139;annihilation;negative;0;1;1;1;0;0
-2140;annihiler;negative;0;1;0;1;0;0
-2141;anniversaire;positive;0;0;0;0;1;0
-2142;annonce;positive;0;0;0;0;0;0
-2143;annoncer;positive;0;0;0;0;0;0
-2144;annotation;positive;0;0;0;0;0;0
-2145;annoter;positive;0;0;0;0;0;0
-2146;annuaire;positive;0;0;0;0;0;0
-2147;annulaire;positive;0;0;0;0;0;0
-2148;annulation;negative;0;1;1;1;1;0
-2149;anomalie;negative;0;1;0;0;1;0
-2150;anonyme;negative;0;1;1;0;0;1
-2151;anormal;negative;0;1;0;1;1;1
-2152;anse;positive;0;0;0;0;0;0
-2153;antagonisme;negative;0;0;0;1;0;0
-2154;antagoniste;negative;0;1;0;1;0;1
-2155;antalgique;negative;0;1;0;0;0;0
-2156;antécédent;positive;0;0;0;0;0;0
-2157;antéchrist;negative;0;1;0;1;0;1
-2158;antenne;positive;0;0;0;0;0;0
-2159;antérieurement;positive;0;0;0;0;0;0
-2160;anthologie;positive;0;0;0;0;0;0
-2161;anthrax;negative;0;1;1;0;0;1
-2162;anthropologie;positive;0;0;0;0;0;0
-2163;anthropophage;negative;0;1;0;0;0;1
-2164;anthropophagie;negative;0;1;0;0;0;1
-2165;antibiotique;positive;0;0;0;0;0;0
-2166;anticipation;positive;0;0;0;0;0;0
-2167;anticiper;positive;0;0;0;0;0;0
-2168;antidote;positive;0;0;0;0;0;0
-2169;antifongique;positive;0;0;0;0;0;0
-2170;antilope;positive;0;0;0;0;0;0
-2171;antimoine;negative;0;1;0;0;0;1
-2172;antipathie;negative;0;0;0;1;0;1
-2173;antipathique;negative;0;0;0;1;0;1
-2174;antiquaire;positive;0;0;0;0;0;0
-2175;antiquité;positive;0;0;0;0;0;0
-2176;antiseptique;positive;0;0;0;0;0;0
-2177;antisocial;negative;0;1;1;1;0;1
-2178;antithèse;negative;0;0;0;1;0;0
-2179;antithétique;negative;0;0;0;0;0;0
-2180;antiviral;positive;0;0;0;0;0;0
-2181;anxiété;negative;0;1;1;1;0;0
-2182;août;positive;1;0;0;0;0;0
-2183;aorte;positive;0;0;0;0;0;0
-2184;apache;negative;0;1;0;0;0;0
-2185;apaiser;positive;0;0;0;0;0;0
-2186;apaisant;positive;0;0;0;0;0;0
-2187;apathie;negative;0;0;1;0;0;0
-2188;apercevoir;positive;0;0;0;0;0;0
-2189;apéritif;positive;0;0;0;0;0;0
-2190;aphone;negative;0;1;1;0;1;0
-2191;aplanir;negative;0;0;0;0;0;0
-2192;aplatir;negative;0;0;0;0;0;0
-2193;aplomb;positive;0;0;0;0;0;0
-2194;apocalyptique;negative;0;1;1;0;0;0
-2195;apogée;positive;0;0;0;0;1;0
-2196;apologétique;positive;0;0;1;0;0;0
-2197;apologiste;positive;0;0;0;0;0;0
-2198;apostasie;negative;0;0;1;0;0;0
-2199;apostat;negative;0;0;1;0;0;0
-2200;apostolique;positive;0;0;0;0;0;0
-2201;apostrophe;positive;0;0;0;0;1;0
-2202;apôtre;positive;0;0;0;0;0;0
-2203;appareil;positive;0;0;0;0;0;0
-2204;appareil de chauffage;positive;0;0;0;0;0;0
-2205;appareillage;positive;0;0;0;0;0;0
-2206;appartement;positive;0;0;0;0;0;0
-2207;appartement terrasse;positive;0;0;0;0;0;0
-2208;appât;negative;0;1;0;0;1;0
-2209;appelant;positive;0;0;0;0;0;0
-2210;appeler;positive;0;0;0;0;0;0
-2211;appellation inappropriée;negative;0;0;0;0;0;0
-2212;appel;negative;0;1;0;1;1;0
-2213;appendice;positive;0;0;0;0;0;0
-2214;appendicite;negative;0;1;1;0;0;0
-2215;appentis;negative;0;0;0;0;0;1
-2216;appétissant;positive;1;0;0;0;0;0
-2217;appétit;positive;0;0;0;0;0;0
-2218;applaudir;positive;0;0;0;0;0;0
-2219;applaudissement;positive;0;0;0;0;1;0
-2220;applicabilité;positive;0;0;0;0;0;0
-2221;applicable;positive;0;0;0;0;0;0
-2222;application;positive;0;0;0;0;0;0
-2223;appliquer;positive;0;0;0;0;0;0
-2224;apporter;positive;0;0;0;0;0;0
-2225;apposer;positive;0;0;0;0;0;0
-2226;appréciable;positive;0;0;0;0;0;0
-2227;apprécier;positive;0;0;0;0;0;0
-2228;appréciation;positive;0;0;0;0;0;0
-2229;appréhender;negative;0;1;0;1;0;0
-2230;appréhension;negative;0;1;1;0;1;0
-2231;apprendre;positive;0;0;0;0;1;0
-2232;apprentissage;positive;0;0;0;0;0;0
-2233;apprêt;positive;0;0;0;0;0;0
-2234;apprivoiser;positive;0;0;0;0;0;0
-2235;apprivoisement;positive;0;0;0;0;0;0
-2236;approche;positive;0;0;0;0;0;0
-2237;approfondir;positive;0;0;0;0;0;0
-2238;appropirées;positive;0;0;0;0;0;0
-2239;appropriation;positive;0;0;0;0;0;0
-2240;approuver;positive;0;0;0;0;0;0
-2241;approvisionnement;positive;0;0;0;0;0;0
-2242;approvisionner;positive;0;0;0;0;0;0
-2243;approximation;positive;0;0;0;0;0;0
-2244;approximativement;positive;0;0;0;0;0;0
-2245;appuyer;positive;0;0;0;0;0;0
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-2340;arthropode;positive;0;0;0;0;0;0
-2341;artichaut;positive;0;0;0;0;0;0
-2342;article;positive;0;0;0;0;0;0
-2343;articulation;positive;0;0;0;0;0;0
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-2345;artifice;negative;0;1;0;0;0;0
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-2347;artilleur;positive;0;0;0;0;0;0
-2348;artisan;positive;0;0;0;0;0;0
-2349;artisanal;positive;0;0;0;0;0;0
-2350;artisanat;positive;0;0;0;0;0;0
-2351;artiste;positive;0;0;0;0;0;0
-2352;artistique;positive;0;0;0;0;0;0
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-2373;assaisonnement;positive;0;0;0;0;0;0
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-2391;assimilation;positive;0;0;0;0;0;0
-2392;assimiler;positive;0;0;0;0;0;0
-2393;assister;positive;0;0;0;0;0;0
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-2395;association;positive;0;0;0;0;0;0
-2396;association caritatif;positive;0;0;0;0;0;0
-2397;associer;positive;0;0;0;0;0;0
-2398;assoiffer;negative;0;1;1;0;0;0
-2399;assoiffer de sang;negative;0;1;0;1;0;1
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-2403;assouplissement;positive;0;0;0;0;0;0
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-2405;assouvir;positive;1;0;0;0;0;0
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-2411;assurément;positive;0;0;0;0;0;0
-2412;assureur;positive;0;0;0;0;0;0
-2413;astérisque;positive;0;0;0;0;0;0
-2414;astéroïde;negative;0;1;0;0;0;0
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-2416;astral;positive;0;0;0;0;0;0
-2417;astraux;positive;0;0;0;0;0;0
-2418;astre;positive;0;0;0;0;0;0
-2419;astringent;negative;0;1;0;0;0;1
-2420;astrologie;positive;0;0;0;0;0;0
-2421;astrologue;positive;0;0;0;0;0;0
-2422;astronaute;positive;0;0;0;0;0;0
-2423;astronome;positive;0;0;0;0;0;0
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-2428;atelier;positive;0;0;0;0;0;0
-2429;atelier de reliure;positive;0;0;0;0;0;0
-2430;athée;negative;0;0;1;1;0;1
-2431;athéisme;negative;0;0;0;0;0;0
-2432;athérosclérose;negative;0;1;1;0;0;0
-2433;athlète;positive;0;0;0;0;0;0
-2434;athlétique;positive;0;0;0;0;0;0
-2435;athlétisme;positive;0;0;0;0;0;0
-2436;atlas;positive;0;0;0;0;0;0
-2437;atmosphère;positive;1;0;0;0;0;0
-2438;atmosphérique;positive;0;0;0;0;0;0
-2439;atoll;positive;0;0;0;0;0;0
-2440;atome;positive;0;0;0;0;0;0
-2441;atomique;negative;0;1;0;1;0;0
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-2443;atrium;positive;0;0;0;0;0;0
-2444;atroce;negative;0;1;1;1;0;1
-2445;atrocement;negative;0;1;1;0;1;1
-2446;atrophie;negative;0;1;1;0;0;1
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-2455;atteindre;positive;0;0;0;0;0;0
-2456;attelage;positive;0;0;0;0;0;0
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-2482;au beurre;positive;0;0;0;0;0;1
-2483;au bord du larme;negative;0;1;1;0;0;1
-2484;au centre;positive;0;0;0;0;0;0
-2485;au charme désuet;positive;0;0;0;0;0;0
-2486;au chocolat;positive;0;0;0;0;0;0
-2487;au coin du feu;positive;1;0;0;0;0;0
-2488;au curry;positive;0;0;0;0;0;0
-2489;au galop;positive;0;0;0;0;0;0
-2490;au goût de beurre;positive;0;0;0;0;0;1
-2491;au hasard;positive;0;0;0;0;1;0
-2492;au lait;positive;0;0;0;0;0;0
-2493;au microscope;positive;0;0;0;0;0;0
-2494;au milieu de;positive;0;0;0;0;0;0
-2495;au pouvoir;positive;0;0;0;0;0;0
-2496;au revoir;negative;0;0;1;0;0;0
-2497;au dessus;positive;0;0;0;0;0;0
-2498;aubaine;positive;0;0;0;0;1;0
-2499;aube;positive;0;0;0;0;1;0
-2500;auberge;positive;0;0;0;0;0;0
-2501;aubergiste;positive;0;0;0;0;0;0
-2502;audacieux;positive;0;0;0;0;0;0
-2503;audibilité;positive;0;0;0;0;0;0
-2504;audible;positive;0;0;0;0;0;0
-2505;audit;positive;0;0;0;0;0;0
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-2509;auge;positive;0;0;0;0;0;0
-2510;augurer;positive;0;0;0;0;0;0
-2511;auguste;positive;1;0;0;0;0;0
-2512;aumônier;positive;0;0;0;0;0;0
-2513;auparavant;positive;0;0;0;0;0;0
-2514;aura;positive;1;0;0;0;0;0
-2515;auréole;positive;0;0;0;0;0;0
-2516;aurore;positive;0;0;0;0;1;0
-2517;auspice;positive;0;0;0;0;0;0
-2518;austère;negative;0;1;1;0;0;0
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-2520;autel;positive;0;0;0;0;0;0
-2521;authenticité;positive;0;0;0;0;0;0
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-2523;authentifier;positive;0;0;0;0;0;0
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-2527;autobus;positive;0;0;0;0;0;0
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-2536;autorisation;positive;0;0;0;0;0;0
-2537;autoritaire;negative;0;0;0;1;0;0
-2538;autorité;positive;0;0;0;0;0;0
-2539;autoroute;positive;0;0;0;0;0;0
-2540;autosuffisance;positive;0;0;0;0;0;0
-2541;autruche;negative;0;1;0;0;0;0
-2542;auvent;positive;0;0;0;0;0;0
-2543;au herbe;positive;0;0;0;0;0;0
-2544;au idée arrêté;negative;0;0;0;1;0;0
-2545;au m?urs léger;negative;0;0;0;0;0;1
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-2547;au pomme;positive;0;0;0;0;0;0
-2548;aval;positive;0;0;0;0;0;0
-2549;avalanche;negative;0;1;1;0;1;0
-2550;avaler;positive;0;1;0;0;1;0
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-2552;avancer;positive;1;0;0;0;0;0
-2553;avancement;positive;0;0;1;0;0;0
-2554;avancer au ralenti;negative;0;0;1;0;0;1
-2555;avancer lentement;negative;0;0;1;0;0;1
-2556;avancer petit à petit;positive;0;0;0;0;0;0
-2557;avant de;negative;0;0;0;0;0;0
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-2559;avant garde;positive;0;0;0;0;0;0
-2560;avant poste;negative;0;1;0;0;0;0
-2561;avant propos;positive;0;0;0;0;0;0
-2562;avantage;positive;0;0;0;0;0;0
-2563;avantager;positive;1;0;0;0;0;0
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-2568;avec crainte;negative;0;1;1;0;1;0
-2569;avec de gros morceau;negative;0;0;0;0;0;0
-2570;avec désinvolture;positive;0;0;0;0;0;0
-2571;avec le même intensité;positive;0;0;0;0;0;0
-2572;avec lassitude;negative;0;0;1;0;0;0
-2573;avec modération;positive;0;0;0;0;0;0
-2574;avec parcimonie;positive;0;0;0;0;0;0
-2575;avec précaution;positive;0;1;0;0;0;0
-2576;avec prudence;positive;0;1;0;0;0;0
-2577;avec quoi;positive;0;0;0;0;0;0
-2578;avec sérieux;positive;0;0;0;0;0;0
-2579;avec un grand raffinement;positive;1;0;0;0;0;0
-2580;avec violence;negative;0;1;1;1;0;1
-2581;avenant;positive;0;0;0;0;0;0
-2582;avènement;positive;0;0;0;0;0;0
-2583;avenir;positive;0;0;0;0;0;0
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-2587;avers;positive;0;0;0;0;0;0
-2588;aversion;negative;0;1;0;1;0;1
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-2594;aveuglément;negative;0;1;1;0;0;0
-2595;aviateur;positive;0;0;0;0;0;0
-2596;aviation;positive;0;0;0;0;0;0
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-2864;bis;positive;1;0;0;0;0;0
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-2949;bonté;positive;0;0;0;0;1;0
-2950;bonus;positive;0;0;0;0;1;0
-2951;boomerang;positive;0;0;0;0;0;0
-2952;booster;positive;0;0;0;0;0;0
-2953;bord du trottoir;negative;0;0;0;0;0;0
-2954;bord sous le vent;negative;0;1;1;0;0;0
-2955;border;positive;0;0;0;0;0;0
-2956;bordeau|bordeaux;negative;0;1;1;0;0;0
-2957;bordée;positive;0;0;0;0;0;0
-2958;bordel;negative;0;0;0;0;0;1
-2959;boréal;positive;0;0;0;0;0;0
-2960;boréals;positive;0;0;0;0;0;0
-2961;borner;negative;0;0;0;1;0;0
-2962;borner kilométrique;positive;0;0;0;0;0;0
-2963;bosquet;positive;0;0;0;0;0;0
-2964;boston;positive;0;0;0;0;0;0
-2965;botanique;positive;0;0;0;0;0;0
-2966;botaniste;positive;0;0;0;0;0;0
-2967;botte;positive;0;0;0;0;0;0
-2968;bouc;negative;0;0;0;0;0;0
-2969;bouc émissaire;negative;0;1;1;1;0;0
-2970;bouchage;negative;0;1;0;0;1;0
-2971;bouche bée;negative;0;1;0;0;1;0
-2972;boucher;negative;0;1;1;1;1;0
-2973;bouchère;negative;0;1;0;1;0;1
-2974;bouchon;positive;0;0;0;0;0;0
-2975;boucler d oreille;positive;0;0;0;0;0;0
-2976;boucler;positive;0;0;0;0;0;0
-2977;bouclier;positive;0;0;0;0;0;0
-2978;boue;negative;0;0;0;0;0;1
-2979;bouée;positive;0;0;0;0;0;0
-2980;bouée de sauvetage;positive;0;0;0;0;0;0
-2981;bouffant;negative;0;0;0;0;0;0
-2982;bouffir;negative;0;0;0;0;0;1
-2983;bouffon;negative;0;0;0;0;1;1
-2984;bougeoir;positive;0;0;0;0;0;0
-2985;bougie;positive;0;0;0;0;0;0
-2986;bougonner;negative;0;0;0;1;0;1
-2987;bouillant;negative;0;1;0;1;0;0
-2988;bouillir;negative;0;0;0;0;0;1
-2989;bouilloire;positive;0;0;0;0;0;0
-2990;bouillon;positive;0;0;0;0;0;0
-2991;bouillonner;negative;0;0;0;1;0;0
-2992;boulangerie;positive;0;0;0;0;0;0
-2993;boule;positive;0;0;0;0;0;0
-2994;boule de feu;positive;0;0;0;0;0;0
-2995;boule de neige;positive;0;0;0;0;0;0
-2996;bouleau;negative;0;1;0;1;0;1
-2997;bouledogue;positive;0;0;0;0;0;0
-2998;boulet;positive;0;0;0;0;0;0
-2999;boulette;positive;0;0;0;0;0;0
-3000;bouleversant;negative;0;1;1;1;0;0
-3001;bouleversement;negative;0;1;1;1;1;0
-3002;boulon;positive;0;0;0;0;0;0
-3003;boulonner;positive;0;0;0;0;0;0
-3004;bouquet;positive;0;0;0;0;0;0
-3005;bourbier;negative;0;1;1;0;0;1
-3006;bourdonner;negative;0;0;0;0;0;0
-3007;bourdonnement;negative;0;1;0;0;1;0
-3008;bourgeon;positive;0;0;0;0;0;0
-3009;bourreau;negative;0;1;1;1;0;0
-3010;boursoufler;negative;0;0;0;0;0;1
-3011;bousculade;negative;0;1;0;1;1;0
-3012;bousculer;negative;0;1;0;1;1;0
-3013;bouse;negative;0;0;0;0;0;1
-3014;boussole;positive;0;0;0;0;0;0
-3015;boutade;positive;0;0;0;0;1;0
-3016;bouteille;positive;0;0;0;0;0;0
-3017;boutique;positive;0;0;0;0;0;0
-3018;boutonner;positive;0;0;0;0;0;0
-3019;bovin;negative;0;0;0;0;0;1
-3020;box;positive;0;0;0;0;0;0
-3021;boxe;negative;0;0;0;1;0;0
-3022;boxer;positive;0;0;0;0;0;0
-3023;boxeur;positive;0;0;0;1;0;0
-3024;boyau;negative;0;0;0;0;0;1
-3025;boycott;negative;0;0;0;1;0;1
-3026;boycotter;negative;0;0;0;1;0;1
-3027;brûlé;negative;0;0;0;0;0;1
-3028;brûlée;negative;0;0;0;0;0;1
-3029;bracelet;positive;0;0;0;0;0;0
-3030;brachial;positive;0;0;0;0;0;0
-3031;braconnage;negative;0;1;1;1;0;1
-3032;brailler;negative;0;0;0;1;1;0
-3033;brainstorming;positive;0;0;0;0;0;0
-3034;braise;negative;0;1;0;0;0;0
-3035;brancard;negative;0;1;1;0;0;0
-3036;branche;positive;0;0;0;0;0;0
-3037;brancher;positive;0;0;0;0;0;0
-3038;branchie;positive;0;0;0;0;0;0
-3039;brandy;positive;0;0;0;0;0;0
-3040;branhcée;positive;0;0;0;0;0;0
-3041;branler;negative;0;1;0;0;1;0
-3042;branlant;negative;0;1;0;0;0;0
-3043;braquage;negative;0;1;1;1;0;1
-3044;braquer;negative;0;1;0;0;0;0
-3045;bras;positive;0;0;0;0;0;0
-3046;bras droit;positive;0;0;0;0;0;0
-3047;brasier;negative;0;1;0;1;0;0
-3048;brassage;positive;0;0;0;0;0;0
-3049;brasser;positive;0;0;0;0;0;0
-3050;bravade;negative;0;0;0;0;0;0
-3051;brave;positive;0;0;0;0;0;0
-3052;bravoure;positive;0;0;0;0;0;0
-3053;brebis;positive;0;0;0;0;0;0
-3054;brèche;negative;0;1;0;0;0;0
-3055;bredouillement;negative;0;1;0;0;0;0
-3056;bredouiller;negative;0;1;0;1;0;0
-3057;bref;positive;0;0;0;0;0;0
-3058;bretelle;positive;0;0;0;0;0;0
-3059;brevet;positive;0;0;0;0;0;0
-3060;breveter;positive;0;0;0;0;0;0
-3061;bribe;positive;0;0;0;0;0;0
-3062;brick;negative;0;1;1;0;0;0
-3063;bricoler;positive;0;0;0;0;0;0
-3064;brider;negative;0;1;1;0;0;0
-3065;brièvement;positive;0;0;0;0;0;0
-3066;brigade;positive;0;1;0;0;0;0
-3067;brin;positive;0;0;0;0;0;0
-3068;brindille;negative;0;0;0;0;0;0
-3069;brique;positive;0;0;0;0;0;0
-3070;brise;negative;0;1;1;0;0;0
-3071;briser;negative;0;1;1;1;1;0
-3072;briseur;negative;0;1;1;0;0;0
-3073;brocart;positive;0;0;0;0;0;0
-3074;brochet;negative;0;1;0;0;0;0
-3075;brochette;negative;0;1;0;0;0;0
-3076;brochure;positive;0;0;0;0;0;0
-3077;broder;positive;0;0;0;0;0;0
-3078;broderie;positive;0;0;0;0;0;0
-3079;broderie perlé;positive;0;0;0;0;0;0
-3080;bronco;negative;0;1;0;0;1;0
-3081;bronzage;positive;0;0;0;0;0;0
-3082;bronze;positive;0;0;0;0;0;0
-3083;bronzer;positive;0;0;0;0;0;0
-3084;brosse;positive;0;0;0;0;0;0
-3085;brouette;positive;0;0;0;0;0;1
-3086;brouillage;negative;0;1;0;1;0;0
-3087;brouillard;negative;0;1;1;0;0;0
-3088;brouillon;positive;0;0;0;0;0;0
-3089;broussaille;negative;0;0;0;0;0;1
-3090;brousse;positive;0;0;0;0;0;0
-3091;broutille;negative;0;0;0;0;0;1
-3092;broyer du noir;negative;0;0;1;0;0;0
-3093;broyer;negative;0;1;1;1;0;1
-3094;broyeur;negative;0;1;0;0;0;0
-3095;bruine;negative;0;0;1;0;0;1
-3096;bruiner;negative;0;0;1;0;0;1
-3097;bruissement;negative;0;1;0;0;1;0
-3098;bruir|bruire de tambour;positive;0;0;0;0;0;0
-3099;bruir|bruire métallique;negative;0;1;0;0;1;0
-3100;bruir|bruire sec;negative;0;1;0;0;1;0
-3101;bruir|bruire sourd;negative;0;1;1;0;1;0
-3102;brûler;negative;0;1;0;1;0;0
-3103;brûlant;negative;0;1;0;1;0;0
-3104;brûleur;negative;0;1;0;1;0;0
-3105;brûlure;negative;0;1;0;0;0;0
-3106;brûlure d estomac;negative;0;0;1;0;0;1
-3107;brume;negative;0;1;1;0;0;0
-3108;brumer;negative;0;1;1;0;0;0
-3109;brun;positive;0;0;0;0;0;0
-3110;brun grisâtre;negative;0;0;1;0;0;1
-3111;brun|brune;positive;0;0;0;0;0;0
-3112;brunet;positive;0;0;0;0;0;0
-3113;brunir;negative;0;0;0;0;0;0
-3114;brusque;negative;0;1;0;1;1;0
-3115;brusquement;negative;0;1;0;0;1;0
-3116;brusquer;negative;0;1;0;1;1;0
-3117;brut;negative;0;1;1;1;0;1
-3118;brutal;negative;0;1;0;1;0;1
-3119;brutaliser;negative;0;1;0;1;0;0
-3120;brutalité;negative;0;1;1;1;0;1
-3121;brute;negative;0;1;1;1;0;1
-3122;bruyant;negative;0;1;0;1;1;0
-3123;buccal;positive;0;0;0;0;0;0
-3124;bûche;positive;0;0;0;0;0;0
-3125;budget;positive;0;0;0;0;0;0
-3126;budgétiser;positive;0;0;0;0;0;0
-3127;buffet;positive;0;0;0;1;0;0
-3128;buffle;negative;0;1;0;0;0;0
-3129;buisson;positive;0;0;0;0;0;0
-3130;bulbe;negative;0;0;0;0;0;1
-3131;bulbeux;negative;0;0;0;0;0;0
-3132;bulldozer;negative;0;1;0;1;0;0
-3133;bulle;positive;0;0;0;0;0;0
-3134;bulletin;positive;0;0;0;0;0;0
-3135;bungalow;positive;0;0;0;0;0;0
-3136;bunker;negative;0;1;0;0;0;0
-3137;bureau;positive;0;0;0;0;0;0
-3138;bureaucrate;negative;0;0;0;0;0;1
-3139;bureaucratie;negative;0;0;0;0;0;0
-3140;burin;negative;0;1;0;1;0;0
-3141;buriner;negative;0;0;0;1;0;0
-3142;burlesque;positive;0;0;0;0;1;0
-3143;buste;positive;0;0;0;0;0;0
-3144;but;positive;0;0;0;0;0;0
-3145;butane;negative;0;0;0;0;0;1
-3146;butyreux;positive;0;0;0;0;0;1
-3147;bye;negative;0;0;1;0;0;0
-3148;çà et là;negative;0;0;0;0;0;0
-3149;cabale;negative;0;1;0;0;0;0
-3150;cabane;positive;0;0;1;0;0;1
-3151;cabane pour enfant;positive;1;0;0;0;0;0
-3152;cabaret;positive;0;0;0;0;1;0
-3153;cabillaud;negative;0;0;0;0;0;0
-3154;cabine;positive;0;0;0;0;0;0
-3155;cabine de luxe;positive;0;0;0;0;0;0
-3156;cabine de pilotage;positive;0;0;0;0;0;0
-3157;cabine particulier;positive;0;0;0;0;0;0
-3158;cabinet;positive;0;0;0;0;0;0
-3159;câbler;positive;0;0;0;0;0;0
-3160;cabosser;negative;0;1;1;0;0;0
-3161;cabriole;positive;1;0;0;0;0;0
-3162;cabriolet;positive;0;0;0;0;0;0
-3163;caca;negative;0;0;0;0;0;1
-3164;cacao;positive;0;0;0;0;0;0
-3165;cache;negative;0;1;0;0;0;0
-3166;cacher;negative;0;1;1;0;1;0
-3167;cachemire;positive;0;0;0;0;0;0
-3168;cachot;negative;0;1;1;0;0;0
-3169;cacophonie;negative;0;1;0;1;0;1
-3170;cadavre;negative;0;1;1;0;1;1
-3171;caddie;positive;0;0;0;0;0;0
-3172;cadeau;positive;0;0;0;0;1;0
-3173;cadenas;negative;0;1;0;0;0;0
-3174;cadenasser;negative;0;1;0;0;0;0
-3175;cadence;positive;0;0;0;0;0;0
-3176;cadencer;positive;0;0;1;1;0;0
-3177;cadet;positive;0;0;0;0;0;0
-3178;cadran;positive;0;0;0;0;0;0
-3179;cadran solaire;positive;0;0;0;0;0;0
-3180;cadre;positive;0;0;0;0;0;0
-3181;caduc;negative;0;0;1;0;0;1
-3182;café;positive;0;0;0;0;0;0
-3183;café restaurant;positive;0;0;0;0;0;0
-3184;cafouillage;negative;0;0;0;0;0;0
-3185;cage;negative;0;1;1;0;0;0
-3186;cageot;positive;0;0;0;0;0;0
-3187;cahier;positive;0;0;0;0;0;0
-3188;caille;negative;0;1;0;0;0;0
-3189;cailler;negative;0;0;0;0;0;1
-3190;caillot;negative;0;1;0;0;0;1
-3191;caillou;positive;0;0;0;1;0;0
-3192;cairn;positive;0;0;0;0;0;0
-3193;caisse;positive;0;0;0;0;0;0
-3194;calamiter;negative;0;1;1;0;0;0
-3195;calandre;negative;0;0;0;0;0;0
-3196;calcaire;negative;0;0;0;0;0;1
-3197;calciner;negative;0;1;0;0;0;1
-3198;calculable;positive;0;0;0;0;0;0
-3199;calculateur;positive;0;0;0;0;0;0
-3200;calculateur|calculatrice;positive;0;0;0;0;0;0
-3201;calculer;positive;0;0;0;0;0;0
-3202;calculette;positive;0;0;0;0;0;0
-3203;calembour;positive;1;0;0;0;0;0
-3204;calendrier;positive;0;0;0;0;0;0
-3205;calibre;positive;0;0;0;0;0;0
-3206;calibrer;positive;0;0;0;0;0;0
-3207;calice;positive;0;0;0;0;0;0
-3208;calicot;positive;0;0;0;0;0;0
-3209;câlin;positive;0;0;0;0;0;0
-3210;câliner;positive;0;0;0;0;0;0
-3211;calligraphie;positive;0;0;0;0;0;0
-3212;calmement;positive;1;0;0;0;0;0
-3213;calomnier;negative;0;1;1;1;0;1
-3214;calorie;negative;0;0;0;0;0;0
-3215;calorimètre;positive;0;0;0;0;0;0
-3216;calorique;negative;0;0;0;0;0;0
-3217;calquer;positive;0;0;0;0;0;0
-3218;calque;positive;0;0;0;0;0;0
-3219;camarade;positive;0;0;0;0;0;0
-3220;camarade de classe;positive;0;0;0;0;0;0
-3221;camarade de jeu;positive;0;0;0;0;0;0
-3222;camaraderie;positive;0;0;0;0;0;0
-3223;cambriolage;negative;0;1;1;0;1;0
-3224;cambrioler;negative;0;1;1;1;0;1
-3225;cambrure;positive;0;0;0;0;0;0
-3226;came;positive;0;0;0;0;0;0
-3227;camer;positive;0;0;0;0;0;0
-3228;caméléon;positive;0;0;0;0;0;0
-3229;camelote;negative;0;0;1;0;0;1
-3230;cameraman;positive;0;0;0;0;0;0
-3231;caméraman;positive;0;0;0;0;0;0
-3232;camion;positive;0;0;0;0;0;0
-3233;camionnette;positive;0;0;0;0;0;0
-3234;camouflage;negative;0;0;0;0;1;0
-3235;camoufler;negative;0;0;0;0;1;0
-3236;camp;positive;0;0;0;0;0;0
-3237;campagne;positive;0;1;0;1;0;0
-3238;campement;positive;0;0;0;0;0;0
-3239;camper;positive;0;0;0;0;0;0
-3240;camping;positive;0;0;0;0;0;0
-3241;campus;positive;0;0;0;0;0;0
-3242;canalisation;negative;0;0;0;0;0;1
-3243;canapé;positive;0;0;1;0;0;0
-3244;canard;positive;0;0;0;0;0;0
-3245;canard mâle;positive;0;0;0;0;0;0
-3246;canari;positive;0;0;0;0;0;0
-3247;cancer;negative;0;1;1;1;0;1
-3248;candidature;positive;0;1;0;1;0;0
-3249;caniche;positive;0;0;0;0;0;0
-3250;canidé;positive;0;0;0;0;0;0
-3251;canin;positive;0;0;0;0;0;0
-3252;canine;positive;0;0;0;0;0;0
-3253;caniveau;negative;0;0;0;0;0;1
-3254;canneler;positive;0;0;0;0;0;0
-3255;cannelle;positive;0;0;0;0;0;0
-3256;cannibale;negative;0;1;0;0;0;1
-3257;cannibalisme;negative;0;1;0;0;0;1
-3258;canoë;positive;0;0;0;0;0;0
-3259;canon;negative;0;1;0;1;0;0
-3260;canonique;positive;0;0;0;0;0;0
-3261;canot de sauvetage;positive;0;0;0;0;0;0
-3262;canotage;positive;1;0;0;0;0;0
-3263;cantilever;positive;0;0;0;0;0;0
-3264;cantine;positive;0;0;0;0;0;0
-3265;cantique;positive;0;0;1;0;0;0
-3266;canton;positive;0;0;0;0;0;0
-3267;cantonnement;negative;0;0;1;0;0;0
-3268;canular;negative;0;0;1;1;1;1
-3269;caoutchouc;negative;0;0;0;0;0;0
-3270;cap;positive;0;0;0;0;0;0
-3271;capable;positive;0;0;0;0;0;0
-3272;capacité;positive;0;0;0;0;0;0
-3273;capillaire;positive;0;0;0;0;0;0
-3274;capitaine;positive;0;0;0;0;0;0
-3275;capitale;positive;0;0;0;0;0;0
-3276;capitaliste;negative;0;0;0;0;0;0
-3277;capitation;negative;0;0;0;0;0;0
-3278;capital propre;positive;0;0;0;0;0;0
-3279;capitole;positive;0;0;0;0;0;0
-3280;capitulation;negative;0;1;1;0;1;0
-3281;capituler;negative;0;1;1;0;0;0
-3282;caporal;positive;0;0;0;0;0;0
-3283;capot;positive;0;1;0;1;0;1
-3284;caprice;negative;0;0;0;1;1;0
-3285;captivant;positive;0;0;0;0;0;0
-3286;captivité;negative;0;1;1;0;0;0
-3287;capture;negative;0;1;0;1;1;0
-3288;capturer;negative;0;1;0;1;1;0
-3289;capuche;positive;0;1;0;1;0;1
-3290;capuchon;positive;0;0;0;0;0;0
-3291;caraco;positive;0;0;0;0;0;0
-3292;caractère aléatoire;negative;0;1;0;0;1;0
-3293;caractère raisonnable;positive;0;0;0;0;0;0
-3294;caractériser;positive;0;0;0;0;0;0
-3295;caractéristique;positive;0;0;0;0;0;0
-3296;carafe;positive;0;0;0;0;0;0
-3297;caramel;positive;0;0;0;0;0;0
-3298;carapace;positive;0;0;0;0;0;0
-3299;carat;positive;0;0;0;0;0;0
-3300;caravane;positive;0;0;0;0;0;0
-3301;carbone;negative;0;0;0;0;0;0
-3302;carboniser;negative;0;1;0;1;0;0
-3303;carburant;positive;0;0;0;0;0;0
-3304;carcasse;negative;0;1;1;0;0;1
-3305;carcinome;negative;0;1;1;0;0;0
-3306;cardigan;positive;0;0;0;0;0;0
-3307;cardinal;positive;0;0;0;0;0;0
-3308;cardiomyopathie;negative;0;1;1;0;0;0
-3309;caresse;positive;1;0;0;0;0;0
-3310;caresser;positive;1;0;0;0;0;0
-3311;carex;positive;0;0;0;0;0;0
-3312;cargaison;positive;0;0;0;0;0;0
-3313;caribou;positive;0;0;0;0;0;0
-3314;caricature;negative;0;0;0;0;0;0
-3315;caricaturer;negative;0;0;0;0;0;0
-3316;carie;negative;0;0;0;0;0;1
-3317;carillon;positive;1;0;0;0;0;0
-3318;carillonnement;positive;0;0;0;0;1;0
-3319;carillonner;positive;1;0;0;0;0;0
-3320;carlin;positive;0;0;0;0;0;0
-3321;carnation;positive;0;0;0;0;0;0
-3322;carnet;positive;0;0;0;0;0;0
-3323;carnivore;negative;0;1;0;0;0;0
-3324;carquois;negative;0;1;0;0;0;0
-3325;carrer;positive;0;0;0;0;0;0
-3326;carreau;positive;0;0;0;0;0;0
-3327;carrelage;positive;0;0;0;0;0;0
-3328;carreler;positive;0;0;0;0;0;0
-3329;carrément;positive;0;0;0;0;0;0
-3330;carrossage;positive;0;0;0;0;0;0
-3331;carrousel;positive;1;0;0;0;0;0
-3332;carrure;positive;0;0;0;0;0;0
-3333;cartable;positive;0;0;0;0;0;0
-3334;carte;positive;0;0;0;0;0;0
-3335;cartel;negative;0;1;0;1;0;0
-3336;carter;negative;0;0;0;0;0;1
-3337;cartilage;negative;0;0;0;0;0;1
-3338;cartographie;positive;0;0;0;0;0;0
-3339;cartographique;positive;0;0;0;0;0;0
-3340;carton;positive;0;0;0;0;0;0
-3341;cartouche;negative;0;1;0;0;0;0
-3342;caséine;positive;0;0;0;0;0;0
-3343;caserne;positive;0;0;0;0;0;0
-3344;casier;positive;0;0;0;0;0;0
-3345;casino;positive;0;0;0;0;1;0
-3346;casque;positive;0;1;0;0;0;0
-3347;casquette;positive;0;0;0;0;0;0
-3348;cassable;negative;0;1;0;0;0;0
-3349;casser;negative;0;1;0;0;1;0
-3350;cassant;negative;0;1;0;0;1;0
-3351;casse croûte;positive;0;0;0;0;0;0
-3352;casse pied;negative;0;0;0;1;0;0
-3353;casse tête;positive;0;0;0;0;0;0
-3354;casserole;positive;0;0;0;0;0;0
-3355;cassette;positive;0;0;0;0;0;0
-3356;cassette vidéo;positive;0;0;0;0;0;0
-3357;caste;negative;0;0;0;0;0;0
-3358;castrer;negative;0;1;1;0;0;0
-3359;catabolisme;positive;0;0;0;0;0;0
-3360;cataclysme;negative;0;1;1;1;1;0
-3361;cataire;negative;0;0;0;0;0;1
-3362;catalogue;positive;0;0;0;0;0;0
-3363;catalyse;positive;0;0;0;0;0;0
-3364;catalytique;positive;0;0;0;0;0;0
-3365;catamaran;positive;0;0;0;0;0;0
-3366;catapulte;negative;0;0;0;1;0;0
-3367;catapulter;negative;0;0;0;1;0;0
-3368;cataracte;negative;0;1;1;0;0;0
-3369;catéchisme;positive;0;0;0;0;0;1
-3370;catégorie;positive;0;0;0;0;0;0
-3371;cathartique;positive;0;0;0;0;0;0
-3372;cathédrale;positive;0;0;0;0;0;0
-3373;cathéter;negative;0;1;0;0;0;0
-3374;catholique;positive;0;0;0;0;0;0
-3375;cauchemar;negative;0;1;1;0;0;0
-3376;caudal;negative;0;0;0;0;0;1
-3377;causal;negative;0;0;1;0;0;0
-3378;causalité;negative;0;0;1;0;0;0
-3379;causation;negative;0;0;1;0;0;0
-3380;causer;negative;0;0;1;0;0;0
-3381;caution;positive;0;0;0;0;0;0
-3382;cavaler;negative;0;1;0;0;0;0
-3383;cavalerie;positive;0;0;0;0;0;0
-3384;caveau;positive;0;0;0;0;0;0
-3385;caverne;negative;0;1;1;0;0;0
-3386;caverneux;negative;0;1;1;0;0;0
-3387;cavité;negative;0;1;0;0;0;0
-3388;cayenne;positive;0;0;0;0;0;0
-3389;ce qui précéder;positive;0;0;0;0;0;0
-3390;cécité;negative;0;1;1;0;0;0
-3391;céder;positive;0;0;0;0;0;0
-3392;ceinture;negative;0;1;1;1;0;0
-3393;ceinturer;negative;0;1;1;0;0;0
-3394;célébrant;positive;1;0;0;0;0;0
-3395;célébration;positive;0;0;0;0;1;0
-3396;célèbre;positive;0;0;0;0;0;0
-3397;célébrer;positive;1;0;0;0;0;0
-3398;célibat;negative;0;0;0;0;0;0
-3399;célibataire;negative;0;0;0;0;0;0
-3400;cellulaire;positive;0;0;0;0;0;0
-3401;cellule;negative;0;0;0;0;0;0
-3402;celluloïde;positive;0;0;0;0;0;0
-3403;cendre;negative;0;1;1;0;0;1
-3404;cendrer;negative;0;1;0;0;0;0
-3405;censeur;negative;0;1;0;1;0;1
-3406;censurer;negative;0;1;0;1;0;1
-3407;cent;positive;0;0;0;0;0;0
-3408;cent douze livre;positive;0;0;0;0;0;0
-3409;cent livre;positive;0;0;0;0;0;0
-3410;centaine;positive;0;0;0;0;0;0
-3411;centenaire;positive;1;0;0;0;0;0
-3412;centième;positive;0;0;0;0;0;0
-3413;centime;positive;0;0;0;0;0;0
-3414;centimètre;positive;0;0;0;0;0;0
-3415;central;positive;0;0;0;0;0;0
-3416;centralement;positive;0;0;0;0;0;0
-3417;centrale;positive;0;0;0;0;0;0
-3418;centralisation;positive;0;0;0;0;0;0
-3419;centraliser;positive;0;0;0;0;0;0
-3420;centralité;positive;0;0;0;0;0;0
-3421;centre;positive;0;0;0;0;0;0
-3422;centre commercial;positive;0;0;0;0;0;0
-3423;centre de détention;negative;0;1;1;1;0;0
-3424;centrer;positive;0;0;0;0;0;0
-3425;centrifuge;positive;0;0;0;0;0;0
-3426;centrifuger;positive;0;0;0;0;0;0
-3427;centrifugeuse;positive;0;0;0;0;0;0
-3428;centurion;positive;0;0;0;0;0;0
-3429;céphalée;negative;0;0;1;0;0;0
-3430;céramique;positive;0;0;0;0;0;0
-3431;cerceau;positive;0;0;0;0;0;0
-3432;cercle;positive;0;0;0;0;0;0
-3433;cercueil;negative;0;1;1;0;0;0
-3434;céréale;positive;0;0;0;0;0;0
-3435;céréalier;positive;0;0;0;0;0;0
-3436;cérébral;positive;0;0;0;0;0;0
-3437;cérémonial;positive;0;0;0;0;0;0
-3438;cérémonie;positive;0;0;0;0;1;0
-3439;cerf;positive;0;0;0;0;0;0
-3440;cerf volant;positive;0;0;0;0;0;1
-3441;cerise;positive;0;0;0;0;0;0
-3442;cerner;negative;0;1;0;0;0;0
-3443;certificat;positive;0;0;0;0;0;0
-3444;certifier;positive;0;0;0;0;0;0
-3445;certitude;positive;0;0;0;0;0;0
-3446;cérumen;negative;0;0;0;0;0;1
-3447;cerveau;positive;0;0;0;0;0;0
-3448;cervelle;positive;0;0;0;0;0;0
-3449;cessation;negative;0;0;1;0;1;0
-3450;cession;positive;0;0;0;0;0;0
-3451;cessionnaire;positive;0;0;0;0;0;0
-3452;ceuillie;positive;1;0;0;0;0;0
-3453;chacunes;positive;0;0;0;0;0;0
-3454;chacuns;positive;0;0;0;0;0;0
-3455;chagrin;negative;0;1;1;0;0;0
-3456;chaîne;negative;0;1;1;1;0;1
-3457;chair;negative;0;0;0;0;0;1
-3458;chaire;positive;0;0;0;0;0;0
-3459;chaise;positive;0;0;0;0;0;0
-3460;châle;positive;0;0;0;0;0;0
-3461;chalet;positive;0;0;0;0;0;0
-3462;chaleur;positive;0;0;0;0;0;0
-3463;chaleureux;positive;0;0;0;0;0;0
-3464;chaleureusement;positive;1;0;0;0;0;0
-3465;challenge;negative;0;1;0;1;0;0
-3466;chalut;positive;0;0;0;0;0;0
-3467;chalutier;positive;0;0;0;0;0;0
-3468;chamaillerie;negative;0;0;0;1;0;1
-3469;chaman;negative;0;1;0;0;0;0
-3470;chambre;positive;0;0;0;0;0;0
-3471;chambre à coucher;positive;0;0;0;0;0;0
-3472;chambre fort;positive;0;0;0;0;0;0
-3473;chambre libre;positive;1;0;0;0;0;0
-3474;chameau;positive;0;0;0;0;0;0
-3475;champ;positive;0;0;0;0;0;0
-3476;champ de bataille;negative;0;1;1;1;0;0
-3477;champagne;positive;0;0;0;0;0;0
-3478;champion champion;positive;1;0;0;0;0;0
-3479;chance;positive;0;0;0;0;1;0
-3480;chanceler;negative;0;1;0;0;1;0
-3481;chancelant;negative;0;1;0;0;1;0
-3482;chancelier;positive;0;0;0;0;0;0
-3483;chancre;negative;0;0;0;1;0;1
-3484;chandelier;positive;0;0;0;0;0;0
-3485;chandler;positive;0;0;0;0;0;0
-3486;changeable;positive;0;0;0;0;0;0
-3487;changeant;positive;0;0;0;0;0;0
-3488;changer;positive;0;1;0;0;0;0
-3489;changeur;positive;0;0;0;0;0;0
-3490;chanson;positive;0;0;0;0;0;0
-3491;chansonnette;positive;1;0;0;0;0;0
-3492;chant;positive;0;0;0;1;1;0
-3493;chant de noël;positive;0;0;0;0;0;0
-3494;chantage;negative;0;1;0;1;0;0
-3495;chanter en ch?ur;positive;1;0;0;0;0;0
-3496;chanvre;positive;0;0;0;0;0;0
-3497;chaos;negative;0;1;1;1;0;0
-3498;chapeau;positive;0;0;0;0;0;0
-3499;chapelain;positive;0;0;0;0;0;0
-3500;chapelet;positive;0;0;0;0;0;0
-3501;chapelle;positive;0;0;0;0;0;0
-3502;chapiteau;positive;0;0;0;0;0;0
-3503;chapitre;positive;0;0;0;0;0;0
-3504;char;positive;0;0;0;0;0;0
-3505;charabia;negative;0;0;0;1;0;0
-3506;charbon;negative;0;1;1;0;0;1
-3507;charbon de bois;negative;0;0;0;0;0;1
-3508;charbon ardent;negative;0;1;0;0;0;0
-3509;charcuter;negative;0;1;0;1;0;1
-3510;charcutier;negative;0;1;0;1;0;1
-3511;chardon;positive;0;0;0;0;0;0
-3512;charger;negative;0;1;1;0;0;0
-3513;chargement;positive;0;0;0;0;0;0
-3514;charger qqn de faire qch;positive;0;0;0;0;0;0
-3515;chargeur;positive;0;0;0;0;0;0
-3516;chariot;positive;0;0;0;0;0;0
-3517;charitable;positive;0;0;0;0;0;0
-3518;charité;positive;0;0;0;0;0;0
-3519;charlatan;negative;0;0;0;1;0;1
-3520;charmer;positive;0;0;0;0;0;0
-3521;charmant;positive;0;0;0;0;0;0
-3522;charmeur;positive;0;0;0;0;0;0
-3523;charnière;positive;0;0;0;0;0;0
-3524;charnu;negative;0;0;0;0;0;0
-3525;charognard;negative;0;1;0;0;0;1
-3526;charpenter;positive;0;0;0;0;0;0
-3527;charpentier;positive;0;0;0;0;0;0
-3528;charpir;negative;0;1;1;0;0;1
-3529;charretier;positive;0;0;0;0;0;0
-3530;charrette;positive;0;0;0;0;0;1
-3531;charrier;positive;0;0;0;0;1;0
-3532;charrue;positive;0;0;0;0;0;0
-3533;charte;positive;0;0;0;0;0;0
-3534;chasser;negative;0;1;0;1;0;0
-3535;chasse;negative;0;1;0;1;0;0
-3536;chasteté;positive;0;0;0;0;0;0
-3537;chat;positive;0;0;0;0;0;0
-3538;chat sauvage;negative;0;1;0;0;0;0
-3539;chat tigré;positive;0;0;0;0;0;0
-3540;châtaigne;positive;0;0;0;0;0;0
-3541;châtaignier;positive;0;0;0;0;0;0
-3542;châtain;positive;0;0;0;0;0;0
-3543;château;positive;0;0;0;0;0;0
-3544;châtiment;negative;0;1;1;1;0;0
-3545;chatoiement;positive;1;0;0;0;0;0
-3546;chaton;positive;0;0;0;0;0;0
-3547;chatouillement;positive;0;0;0;0;1;0
-3548;chatouiller;positive;0;0;0;0;1;0
-3549;chatouilleux;negative;0;0;0;0;0;0
-3550;chat|chatte;positive;0;0;0;0;0;0
-3551;chaud;positive;0;0;0;1;0;0
-3552;chaud|chaude;positive;0;0;0;1;0;0
-3553;chaudière;positive;0;0;0;0;0;0
-3554;chauffage;positive;0;0;0;0;0;0
-3555;chauffer;positive;0;0;0;0;0;0
-3556;chauffe eau;positive;0;0;0;0;0;0
-3557;chauffeur;positive;0;0;0;0;0;0
-3558;chaumière;positive;0;0;0;0;0;0
-3559;chausser;positive;0;0;0;0;0;0
-3560;chaussette;positive;0;0;0;0;0;0
-3561;chausseur;positive;0;0;0;0;0;0
-3562;chausson;positive;0;0;0;0;0;0
-3563;chaussure;positive;0;0;0;0;0;0
-3564;chauve;negative;0;0;1;0;0;1
-3565;chauve souris;negative;0;1;0;0;0;1
-3566;chavirer;negative;0;1;0;0;0;0
-3567;check list;positive;0;0;0;0;0;0
-3568;cheesecake;positive;0;0;0;0;0;0
-3569;chef d équipe;positive;0;0;0;0;0;0
-3570;chef d orchestre;positive;0;0;0;0;0;0
-3571;chef opérateur;positive;0;0;0;0;0;0
-3572;chemin de fer;positive;0;0;0;0;0;0
-3573;cheminer;positive;0;0;0;1;0;0
-3574;cheminement;positive;0;0;0;0;0;0
-3575;chemise;positive;0;0;0;0;0;0
-3576;chemisier;positive;0;0;0;0;0;0
-3577;chêne;positive;0;0;0;0;0;0
-3578;chenil;negative;0;0;1;0;0;0
-3579;chenille;negative;0;0;0;0;0;1
-3580;chèque;positive;0;0;0;0;0;0
-3581;chercher;positive;0;0;0;0;0;0
-3582;chère;negative;0;0;1;0;0;0
-3583;chérir;positive;0;0;1;0;0;0
-3584;cher;negative;0;0;1;0;0;0
-3585;cheval;positive;0;0;0;0;0;0
-3586;cheval de course;positive;0;0;0;0;0;0
-3587;cheval sauvage;negative;0;1;0;0;1;0
-3588;chevalerie;positive;0;0;0;0;0;0
-3589;chevalet;positive;0;0;0;0;0;0
-3590;chevalier;positive;0;0;0;0;0;0
-3591;chevaucher;positive;0;0;0;0;0;0
-3592;chevauchement;negative;0;0;0;0;0;0
-3593;cheveu;positive;0;0;0;0;0;0
-3594;cheville;positive;0;0;0;0;0;0
-3595;chèvre;positive;0;0;0;0;0;0
-3596;chèvrefeuille;positive;0;0;0;0;0;0
-3597;chevreuil;negative;0;0;0;0;0;0
-3598;chevron;positive;0;0;0;0;0;0
-3599;chevronner;positive;0;0;0;0;0;0
-3600;chewing gum;positive;0;0;0;0;0;0
-3601;chic;positive;1;0;0;0;0;0
-3602;chichi;negative;0;0;0;0;0;1
-3603;chien;positive;0;0;0;0;0;0
-3604;chien de garde;positive;0;1;0;0;0;0
-3605;chien|chienne;negative;0;1;1;1;0;1
-3606;chiffonner;negative;0;0;0;1;0;0
-3607;chiffre;positive;0;0;0;0;0;0
-3608;chignon;positive;0;0;0;0;0;0
-3609;chimère;negative;0;1;0;0;1;0
-3610;chimie;positive;0;0;0;0;0;0
-3611;chimiste;positive;0;0;0;0;0;0
-3612;chine;positive;0;0;0;0;0;0
-3613;chinook;negative;0;0;0;0;0;0
-3614;chiot;positive;0;0;0;0;0;0
-3615;chiquer;negative;0;0;0;0;0;1
-3616;chirurgie;negative;0;1;1;0;0;0
-3617;chloroforme;negative;0;1;0;0;0;1
-3618;choc;negative;0;1;0;1;1;0
-3619;chochotte;negative;0;1;0;0;0;1
-3620;chocolat;positive;0;0;0;0;0;0
-3621;ch?ur;positive;0;0;0;0;0;0
-3622;choisir;positive;0;0;0;0;0;0
-3623;choix;positive;0;0;0;0;0;0
-3624;choléra;negative;0;1;1;0;0;1
-3625;choper;negative;0;1;0;1;1;0
-3626;choral;positive;1;0;0;0;0;0
-3627;chorale;positive;0;0;0;0;0;0
-3628;chose;negative;0;1;0;1;0;1
-3629;chou;positive;0;0;0;0;0;0
-3630;choucroute;negative;0;0;0;0;0;0
-3631;chouette;positive;1;0;0;0;0;0
-3632;chromatique;positive;0;0;0;0;0;0
-3633;chromatographie;positive;0;0;0;0;0;0
-3634;chromosome;positive;0;0;0;0;0;0
-3635;chronique;positive;0;0;0;0;0;0
-3636;chronographe;positive;0;0;0;0;0;0
-3637;chronologique;positive;0;0;0;0;0;0
-3638;chronomètre;positive;0;0;0;0;0;0
-3639;chuchoter;negative;0;1;0;0;0;0
-3640;chuchotement;negative;0;1;0;0;0;0
-3641;chut;negative;0;0;0;0;0;0
-3642;chuter;negative;0;1;1;0;1;0
-3643;chutney;positive;0;0;0;0;0;0
-3644;ci joindre;positive;0;0;0;0;0;0
-3645;cible;negative;0;1;1;0;0;0
-3646;cibler;negative;0;1;0;0;0;0
-3647;cicatrice;negative;0;1;1;1;0;1
-3648;cicatrisation;positive;0;0;0;0;0;0
-3649;cicatriser;positive;0;0;0;0;0;0
-3650;cidre;positive;0;0;0;0;0;0
-3651;ciel;positive;0;0;0;0;0;0
-3652;cierge;positive;0;0;0;0;0;0
-3653;cieux;positive;0;0;0;0;0;0
-3654;cigare;negative;0;0;0;0;0;1
-3655;cigarette;negative;0;0;0;0;0;1
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-4077;conjuguer;positive;0;0;0;0;0;0
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-4112;consommation;positive;0;0;0;0;0;0
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-4180;contredire;negative;0;0;0;1;0;0
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-4189;contrôleur;positive;0;0;0;0;0;0
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-4194;convaincre;positive;0;0;0;0;0;0
-4195;convalescence;positive;0;0;0;0;0;0
-4196;convalescent;positive;0;0;0;0;0;0
-4197;convection;positive;0;0;0;0;0;0
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-4213;convive;positive;0;0;0;0;0;0
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-4218;cool;positive;1;0;0;0;0;0
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-4220;coopératif;positive;0;0;0;0;0;0
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-4239;cordonnier;positive;0;0;0;0;0;0
-4240;corne;negative;0;1;0;0;0;0
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-4259;correspondance;positive;0;0;0;0;0;0
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-4264;corroborer;positive;0;0;0;0;0;0
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-4276;corvette;positive;0;0;0;0;0;0
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-4279;cosmologie;positive;0;0;0;0;0;0
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-4287;cotonneux;positive;0;0;0;0;0;0
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-4294;coucher du soleil;positive;0;0;0;0;0;0
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-4308;coulissant;negative;0;0;0;0;0;0
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-4310;coup d état;negative;0;0;0;1;1;0
-4311;coup de balai;negative;0;0;0;0;0;0
-4312;coup de chance extraordinaire;positive;0;0;0;0;1;0
-4313;coup de couteau;negative;0;1;1;1;1;0
-4314;coup de fil;positive;0;0;0;0;0;0
-4315;coup de foudre;negative;0;1;0;1;1;0
-4316;coup de fouet;negative;0;1;0;1;0;0
-4317;coup de langue;negative;0;0;0;0;0;1
-4318;coup de pied;negative;0;0;0;1;0;0
-4319;coup de poing;negative;0;1;1;1;1;0
-4320;coup de vent;negative;0;1;0;0;1;0
-4321;coup sec;negative;0;1;0;1;1;0
-4322;coupable;negative;0;1;1;1;0;1
-4323;couper en deux;negative;0;0;0;0;0;0
-4324;couper le cheveu;positive;0;0;0;0;0;0
-4325;coupe de cheveu;positive;0;0;0;0;0;0
-4326;couper;negative;0;1;0;1;0;0
-4327;couper en dé;positive;0;0;0;0;0;0
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-4353;course à pied;positive;0;0;0;0;0;0
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-4366;couture;positive;0;0;0;0;0;0
-4367;couturière;positive;0;0;0;0;0;0
-4368;couver;positive;0;0;0;0;0;0
-4369;couvent;positive;0;0;0;0;0;0
-4370;couvercle;positive;0;0;0;0;0;0
-4371;couvrir;positive;0;0;0;0;0;0
-4372;couvrante;positive;0;0;0;0;0;0
-4373;couvrant;positive;0;0;0;0;0;0
-4374;couvrir chef;positive;0;0;0;0;0;0
-4375;couvrir feu;negative;0;1;0;0;0;0
-4376;couvrir de chaume;positive;0;0;0;0;0;0
-4377;coyote;negative;0;1;0;0;0;0
-4378;crabe;negative;0;1;0;0;0;1
-4379;cracher;negative;0;0;0;0;0;1
-4380;crachin;negative;0;0;1;0;0;1
-4381;cracker;positive;0;0;0;0;0;0
-4382;craie;positive;0;0;0;0;0;0
-4383;craindre;negative;0;1;0;0;0;0
-4384;craintivement;negative;0;1;1;0;1;0
-4385;cramoisir;negative;0;0;0;0;0;1
-4386;crampe;negative;0;1;1;1;1;0
-4387;cranter;positive;0;0;0;0;0;0
-4388;crapaud;negative;0;0;0;0;0;1
-4389;crapule;negative;0;1;0;1;0;1
-4390;craquement;negative;0;1;0;1;0;0
-4391;craquer;negative;0;1;0;1;1;0
-4392;crash;negative;0;1;1;0;1;0
-4393;crasse;negative;0;0;0;0;0;1
-4394;cratère;negative;0;1;0;0;0;0
-4395;cravate;positive;0;0;0;0;0;0
-4396;crayon;positive;0;0;0;0;0;0
-4397;crayon à papier;positive;0;0;0;0;0;0
-4398;crayonner;positive;0;0;0;0;0;0
-4399;crayon de couleur;positive;0;0;0;0;0;0
-4400;crayon gras;positive;0;0;0;0;0;0
-4401;créancier hypothécaire;positive;0;0;0;0;0;0
-4402;créatif;positive;1;0;0;0;0;0
-4403;création;positive;0;0;0;0;0;0
-4404;créature;negative;0;1;0;0;0;1
-4405;crèche;positive;0;0;0;0;0;0
-4406;crédibilité;positive;0;0;0;0;0;0
-4407;crédible;positive;0;0;0;0;0;0
-4408;crédit;positive;0;0;0;0;0;0
-4409;créditer;positive;0;0;0;0;0;0
-4410;créditeur;positive;0;0;0;0;0;0
-4411;credo;positive;0;0;0;0;0;0
-4412;crédule;negative;0;0;1;0;0;0
-4413;créer;positive;1;0;0;0;0;0
-4414;crémaillère;positive;0;0;1;0;0;0
-4415;crémation;negative;0;0;1;0;0;0
-4416;crème;positive;0;0;0;0;1;0
-4417;crémerie;positive;0;0;0;0;0;0
-4418;crémier;positive;0;0;0;0;0;0
-4419;créneau;positive;0;0;0;0;0;0
-4420;créole;positive;0;0;0;0;0;0
-4421;crêpe;positive;0;0;0;0;0;0
-4422;crêper;positive;0;0;0;0;0;0
-4423;crépitement;negative;0;1;0;1;1;0
-4424;crépiter;negative;0;1;0;0;0;0
-4425;crescendo;positive;0;0;0;0;1;0
-4426;crête;negative;0;0;0;0;0;0
-4427;creuser;negative;0;0;0;0;0;0
-4428;crevaison;negative;0;1;1;0;1;0
-4429;crever;negative;0;1;1;0;1;0
-4430;crevette;negative;0;0;0;0;0;0
-4431;cri strident;negative;0;1;1;1;1;0
-4432;crier;negative;0;1;1;1;0;1
-4433;criard;negative;0;0;0;0;1;1
-4434;cribler;negative;0;1;1;0;0;0
-4435;cribler de trou;negative;0;1;1;0;0;0
-4436;cric;positive;0;0;0;0;0;0
-4437;cricket;positive;0;0;0;0;0;0
-4438;crier de joie;positive;0;0;0;0;1;0
-4439;crime;negative;0;1;1;1;0;0
-4440;criminalité;negative;0;1;0;1;0;1
-4441;crinière;positive;0;0;0;0;0;0
-4442;crique;positive;0;1;0;0;0;1
-4443;criquet;negative;0;1;0;0;0;1
-4444;crise;negative;0;1;1;1;1;0
-4445;crisper;negative;0;1;0;1;0;0
-4446;crissement;negative;0;1;1;1;1;0
-4447;crisser;negative;0;1;1;1;1;0
-4448;cristal;positive;0;0;0;0;0;0
-4449;cristallin;positive;0;0;0;0;0;0
-4450;cristallisation;positive;0;0;0;0;0;0
-4451;critère;positive;0;0;0;0;0;0
-4452;croassement;negative;0;1;0;0;0;0
-4453;croc;negative;0;1;0;0;0;0
-4454;crochet;positive;0;0;0;0;0;0
-4455;crocodile;negative;0;1;0;0;0;0
-4456;croire;positive;0;0;0;0;0;0
-4457;croisade;negative;0;1;0;1;0;0
-4458;croiser;negative;0;0;0;0;0;0
-4459;croisé;negative;0;0;0;0;0;0
-4460;croiseur;positive;0;0;0;0;0;0
-4461;croisière;positive;0;0;0;0;0;0
-4462;croissance;positive;0;0;0;0;0;0
-4463;croissance démesuré;negative;0;0;1;0;0;0
-4464;croissant;positive;0;0;0;0;0;0
-4465;croix;positive;0;1;1;1;0;0
-4466;croix gammée;negative;0;1;0;1;0;1
-4467;croquant;positive;0;0;0;0;0;0
-4468;croquant|croquante;positive;0;0;0;0;0;0
-4469;croque mitaine;negative;0;1;1;1;0;0
-4470;croque mort;positive;0;0;1;0;0;0
-4471;croquer;negative;0;0;0;1;0;0
-4472;croquet;positive;0;0;0;0;0;0
-4473;croquis;positive;0;0;0;0;0;0
-4474;crotale;negative;0;1;0;0;0;1
-4475;croupe;negative;0;0;0;0;0;0
-4476;croupier;positive;0;0;0;0;0;0
-4477;croustillant;positive;0;0;0;0;0;0
-4478;croûte;negative;0;0;0;0;0;1
-4479;croyance;positive;0;0;0;0;0;0
-4480;cruauté;negative;0;1;1;1;0;1
-4481;crucial;positive;0;0;0;0;0;0
-4482;crucifix;negative;0;1;0;0;0;0
-4483;crucifixion;negative;0;1;1;1;0;1
-4484;crustacé;negative;0;0;0;0;0;1
-4485;crypte;negative;0;1;1;0;0;0
-4486;crypter;negative;0;1;0;0;0;0
-4487;cryptographie;positive;0;0;0;0;0;0
-4488;cube;negative;0;0;0;1;0;0
-4489;cueillir;positive;1;0;0;0;0;0
-4490;cuillère;positive;0;0;0;0;0;0
-4491;cuillère à soupe;positive;0;0;0;0;0;0
-4492;cuillerée;positive;0;0;0;0;0;0
-4493;cuir;positive;0;0;0;0;0;0
-4494;cuir brut;positive;0;0;0;0;0;0
-4495;cuir chevelu;negative;0;1;0;0;0;1
-4496;cuire;positive;0;0;0;0;0;0
-4497;cuiseur vapeur;positive;0;0;0;0;0;0
-4498;cuisiner;positive;0;0;0;0;0;0
-4499;cuisine;positive;0;0;0;0;0;0
-4500;cuisinier;positive;0;0;0;0;0;0
-4501;cuisinier|cuisinière;positive;0;0;0;0;0;0
-4502;cuisson;positive;0;0;0;0;0;0
-4503;cuit|cuite;negative;0;0;0;0;0;0
-4504;cuivre;positive;0;0;0;0;0;0
-4505;cuivrer;positive;0;0;0;0;0;0
-4506;cul;negative;0;0;0;0;0;0
-4507;culasse;negative;0;0;0;0;0;0
-4508;culinaire;positive;0;0;0;0;0;0
-4509;culminer;positive;1;0;0;0;0;0
-4510;culotte;negative;0;0;0;0;0;0
-4511;culpabilité;negative;0;0;1;1;0;1
-4512;culture;positive;0;0;0;0;0;0
-4513;cumulus;negative;0;0;0;0;0;0
-4514;cupide;negative;0;1;0;1;0;0
-4515;cupidité;negative;0;0;0;1;0;1
-4516;curable;positive;0;0;0;0;0;0
-4517;curateur;positive;0;0;0;0;0;0
-4518;curcuma;positive;0;0;0;0;0;0
-4519;curling;positive;0;0;0;0;0;0
-4520;curry;positive;0;0;0;0;0;0
-4521;curviligne;positive;0;0;0;0;0;0
-4522;cuticule;negative;0;0;0;0;0;1
-4523;cutter;negative;0;1;0;0;0;0
-4524;cuvée;positive;0;0;0;0;0;0
-4525;cyanure;negative;0;1;0;0;0;1
-4526;cycle;positive;0;0;0;0;0;0
-4527;cyclique;positive;0;0;0;0;0;0
-4528;cycliste;positive;0;0;0;0;0;0
-4529;cyclone;negative;0;1;0;0;1;0
-4530;cygne;positive;0;0;0;0;0;0
-4531;cylindre;positive;0;0;0;0;0;0
-4532;cylindrique;positive;0;0;0;0;0;0
-4533;cymbale;positive;0;0;0;0;0;0
-4534;cynique;negative;0;0;0;1;0;1
-4535;cytomégalovirus;negative;0;1;1;0;0;1
-4536;cytoplasme;positive;0;0;0;0;0;0
-4537;d accord;positive;0;0;0;0;0;0
-4538;d actualité;positive;0;0;0;0;0;0
-4539;d essai;negative;0;1;0;0;0;0
-4540;d humeur changeant;negative;0;0;1;1;0;0
-4541;d intérieur;positive;0;0;0;0;0;0
-4542;d occasion;negative;0;0;0;0;0;0
-4543;d or;positive;0;0;0;0;0;0
-4544;dactylo;positive;0;0;0;0;0;0
-4545;dactylographe;positive;0;0;0;0;0;0
-4546;dague;negative;0;1;0;0;0;0
-4547;dallage;positive;0;0;0;0;0;0
-4548;dalle;positive;0;0;0;0;0;0
-4549;dame;positive;0;0;0;1;0;1
-4550;damnation;negative;0;1;1;1;0;0
-4551;damner;negative;0;1;1;0;0;0
-4552;damoiselle;positive;0;0;0;0;0;0
-4553;dandy;positive;0;0;0;0;0;1
-4554;danger;negative;0;1;1;0;0;0
-4555;dangeureuses;negative;0;1;0;0;1;0
-4556;dans le monde entier;positive;0;0;0;0;0;0
-4557;dans le secret;positive;0;0;0;0;0;0
-4558;dans le sen|sens de le longueur;positive;0;0;0;0;0;0
-4559;danse;positive;0;0;0;0;0;0
-4560;danser;positive;0;0;0;0;0;0
-4561;dard;negative;0;1;0;1;1;0
-4562;date;positive;1;0;0;0;0;0
-4563;date anniversaire;positive;0;0;0;0;1;0
-4564;dauphin;positive;0;0;0;0;1;0
-4565;dé à coudre;positive;0;0;0;0;0;0
-4566;de banlieue;negative;0;0;0;0;0;0
-4567;de baptême;positive;1;0;0;0;0;0
-4568;de base;positive;0;0;0;0;0;0
-4569;de bon augure;positive;1;0;0;0;0;0
-4570;de bon c?ur;positive;1;0;0;0;0;0
-4571;de bon goût;positive;0;0;0;0;0;0
-4572;de bureau;positive;0;0;0;0;0;0
-4573;de cabaret;positive;0;0;0;0;1;0
-4574;de ce côté là;positive;0;0;0;0;0;0
-4575;de citron;negative;0;0;0;0;0;1
-4576;de conclusion;positive;0;0;0;0;0;0
-4577;de confirmation;positive;0;0;0;0;0;0
-4578;de consolation;negative;0;0;1;0;0;0
-4579;de contrebande;negative;0;1;0;1;0;1
-4580;de côté;negative;0;0;0;0;0;0
-4581;de cuisine;positive;0;0;0;0;0;0
-4582;de démolition;negative;0;0;0;1;0;0
-4583;de façade;negative;0;0;0;1;0;1
-4584;de face;positive;0;0;0;0;0;0
-4585;de façon constant;positive;0;0;0;0;0;0
-4586;de façon décontracté;positive;0;0;0;0;0;0
-4587;de façon identique;positive;0;0;0;0;0;0
-4588;de façon introspectif;positive;0;0;0;0;0;0
-4589;de félicitation;positive;1;0;0;0;0;0
-4590;de fer;positive;0;0;0;0;0;0
-4591;de fête;positive;1;0;0;0;0;0
-4592;de force;negative;0;1;0;1;0;0
-4593;de fort carrure;positive;0;1;0;0;0;0
-4594;de fortune;negative;0;0;1;0;0;0
-4595;de gala;positive;0;0;0;0;0;0
-4596;de gouverneur;positive;0;0;0;0;0;0
-4597;de jeu;positive;0;0;0;0;0;0
-4598;de jour;positive;0;0;0;0;0;0
-4599;de le conversation;positive;0;0;0;0;0;0
-4600;de le lune;positive;0;0;0;0;0;0
-4601;de lancement;positive;0;0;0;0;0;0
-4602;de luxe;positive;1;0;0;0;0;0
-4603;de manière choquant;negative;0;0;0;0;1;0
-4604;de manière exquis;positive;1;0;0;0;0;0
-4605;de manière inattendu;negative;0;0;0;0;1;0
-4606;de manière satisfaisant;positive;0;0;0;0;0;0
-4607;de marée;negative;0;1;0;0;0;0
-4608;de marié;positive;0;0;0;0;0;0
-4609;de marque déposer;positive;0;0;0;0;0;0
-4610;de masse;negative;0;1;0;0;0;0
-4611;de mauvais augure;negative;0;1;0;0;0;0
-4612;de mauvais goût;negative;0;0;0;0;0;1
-4613;de mauvais qualité;negative;0;0;0;1;0;1
-4614;de mauvais réputation;negative;0;1;0;1;0;1
-4615;de même;positive;0;0;0;0;0;0
-4616;de mesure;positive;0;0;0;0;0;0
-4617;de moisir;negative;0;0;0;0;0;1
-4618;de montagne;positive;0;0;0;0;0;0
-4619;de notre jour;positive;0;0;0;0;0;0
-4620;de nouveau;positive;0;0;0;0;0;0
-4621;de plein air;positive;0;0;0;0;0;0
-4622;de poisson;negative;0;1;0;0;0;0
-4623;de précaution;positive;0;0;0;0;0;0
-4624;de premier choix;positive;0;0;0;0;0;0
-4625;de premier qualité;positive;1;0;0;0;0;0
-4626;de profil;negative;0;0;0;0;0;0
-4627;de qualité;positive;0;0;0;0;0;0
-4628;de rechange;positive;0;0;0;0;0;0
-4629;de représaille;negative;0;1;0;1;0;0
-4630;de rêve;positive;1;0;0;0;0;0
-4631;de saint;positive;0;0;0;0;1;0
-4632;de second main;negative;0;0;0;0;0;0
-4633;de son plein gré;positive;0;0;0;0;0;0
-4634;de soutien;positive;0;0;0;0;0;0
-4635;de sport;positive;0;0;0;0;0;0
-4636;de substitution;positive;0;0;0;0;0;0
-4637;de terre;positive;0;0;0;0;0;0
-4638;de titan;negative;0;1;0;0;0;0
-4639;de toujours;positive;0;0;0;0;0;0
-4640;de tout le jour;positive;0;0;0;0;0;0
-4641;de transition;positive;0;0;0;0;0;0
-4642;de travail;positive;0;0;0;0;0;0
-4643;de valeur;positive;0;0;0;0;0;0
-4644;de velours;positive;0;0;0;0;0;0
-4645;de vote;positive;0;0;0;0;0;0
-4646;de voyage;positive;1;0;0;0;0;0
-4647;dealer;negative;0;0;0;0;0;0
-4648;déambulation;negative;0;0;1;0;0;0
-4649;débâcle;negative;0;1;1;1;0;0
-4650;déballer;positive;0;0;0;0;0;0
-4651;débarquer;positive;0;0;0;0;0;0
-4652;débarras;negative;0;0;0;0;0;1
-4653;débarrasser;negative;0;0;1;1;0;0
-4654;débarrasser de;negative;0;0;1;0;0;0
-4655;débattre;positive;0;0;0;0;0;0
-4656;débauche;negative;0;1;0;0;0;1
-4657;débile;negative;0;0;0;1;0;1
-4658;débiliter;negative;0;0;0;1;0;1
-4659;débit;positive;0;0;0;0;0;0
-4660;débiter;positive;0;0;0;0;0;0
-4661;déblaiement;positive;0;0;0;0;0;0
-4662;déblayer|déblayer;positive;0;0;0;0;0;0
-4663;débloquer;negative;0;0;0;0;0;0
-4664;déboisement;positive;0;0;0;0;0;0
-4665;déboîter;negative;0;1;1;1;0;1
-4666;débordant;positive;0;0;0;0;0;0
-4667;débordement;negative;0;1;0;1;1;0
-4668;déboucher;positive;0;0;0;0;0;0
-4669;déboursement;positive;0;0;0;0;0;0
-4670;débourser;positive;0;0;0;0;0;0
-4671;débrancher;negative;0;0;1;0;0;0
-4672;débrayer|débrayer;positive;0;0;0;0;0;0
-4673;débris;negative;0;1;1;0;0;1
-4674;débuter;positive;0;0;0;0;0;0
-4675;décadence;negative;0;0;1;1;0;1
-4676;décalage;negative;0;0;1;0;0;0
-4677;décalquer;positive;0;0;0;0;0;0
-4678;décapage;negative;0;1;0;1;0;1
-4679;décapotable;positive;0;0;0;0;0;0
-4680;décapsuleur;positive;0;0;0;0;0;0
-4681;décéder;negative;0;0;1;0;0;0
-4682;décelable;positive;0;0;0;0;0;0
-4683;déceler;positive;0;0;0;0;0;0
-4684;décence;positive;0;0;0;0;0;0
-4685;décennie;positive;0;0;0;0;0;0
-4686;décent;positive;0;0;0;0;0;0
-4687;décentralisation;negative;0;0;0;0;0;0
-4688;décerner;positive;0;0;0;0;0;0
-4689;décevant;negative;0;0;1;1;1;1
-4690;déchaînement;negative;0;1;0;1;1;0
-4691;décharge;negative;0;0;0;0;0;1
-4692;décharger;negative;0;0;0;0;0;0
-4693;décharner;negative;0;1;1;0;0;1
-4694;déchet;negative;0;0;0;0;0;1
-4695;déchiffrer;positive;0;0;0;0;0;0
-4696;déchiqueter;negative;0;1;1;1;0;0
-4697;déchirer;negative;0;1;1;1;0;0
-4698;déchirure;negative;0;1;1;1;0;0
-4699;décidément;negative;0;0;0;0;0;0
-4700;décimal;positive;0;0;0;0;0;0
-4701;décimale;positive;0;0;0;0;0;0
-4702;décisif;positive;0;1;0;0;0;0
-4703;décision;positive;0;0;0;0;0;0
-4704;déclaration écrire sous sermen;positive;0;0;0;0;0;0
-4705;déclaration inexact;negative;0;0;0;1;0;1
-4706;déclaration officiel;positive;0;0;0;0;0;0
-4707;déclaratoire;positive;0;0;0;0;0;0
-4708;déclencher;negative;0;0;0;0;0;0
-4709;déclenchement;positive;0;0;0;0;0;0
-4710;déclic;positive;0;0;0;0;1;0
-4711;déclin;negative;0;1;1;0;0;0
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-5229;divertissement;positive;0;0;0;0;1;0
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-5414;écarteler;negative;0;1;1;0;0;1
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-5417;ecclésiastique;positive;0;0;0;0;0;0
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-5419;échafaudage;negative;0;1;0;0;0;0
-5420;échaffauder;positive;0;0;0;0;0;0
-5421;échalier;positive;0;0;0;0;0;0
-5422;échange;positive;0;0;0;0;0;0
-5423;échanger;positive;0;0;0;0;0;0
-5424;échantillon;positive;0;0;0;0;0;0
-5425;échapper;negative;0;1;0;1;0;0
-5426;échapper à;negative;0;1;0;0;0;0
-5427;écharde;negative;0;1;1;0;1;0
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-5429;échauffourée;negative;0;0;0;1;0;0
-5430;échéance;negative;0;1;0;0;0;1
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-5432;échelle;positive;0;0;0;0;0;0
-5433;échelon;positive;0;0;0;0;0;0
-5434;écheveau;positive;0;0;0;0;0;0
-5435;échine;negative;0;0;0;0;0;0
-5436;echo;positive;0;0;0;0;0;0
-5437;écho;positive;0;0;0;0;0;0
-5438;échographie;positive;0;0;0;0;0;0
-5439;échouer;negative;0;1;1;0;0;0
-5440;éclabousser;negative;0;0;0;0;1;0
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-5443;eclaircir;positive;1;0;0;0;0;0
-5444;éclaircir;positive;1;0;0;0;0;0
-5445;éclaircissement;positive;0;0;0;0;0;0
-5446;éclairer;positive;0;0;0;0;1;0
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-5448;éclectique;positive;0;0;0;0;0;0
-5449;eclipse;negative;0;1;0;0;0;0
-5450;éclipse;negative;0;1;0;0;0;0
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-5453;éclisse;positive;0;0;0;0;0;0
-5454;éclisser;positive;0;0;0;0;0;0
-5455;écloper;negative;0;0;1;0;0;0
-5456;éclore;positive;0;0;0;0;0;0
-5457;écluse;positive;0;0;0;0;0;0
-5458;éc?urer;negative;0;1;1;1;0;1
-5459;éc?urant;negative;0;1;1;1;0;1
-5460;éc?urement;negative;0;1;1;1;0;1
-5461;école;positive;0;0;0;0;0;0
-5462;écolier;positive;0;0;0;0;0;0
-5463;écologique;positive;0;0;0;0;0;0
-5464;éconduire;negative;0;0;1;0;0;0
-5465;economie;positive;0;0;0;0;0;0
-5466;économie;positive;0;0;0;0;0;1
-5467;économique;positive;1;0;0;0;0;0
-5468;écorce;negative;0;1;0;1;0;1
-5469;écossais;positive;0;0;0;0;0;0
-5470;écouler;positive;0;0;0;0;0;0
-5471;écoulement de sang;negative;0;1;1;0;0;1
-5472;écourter;negative;0;1;0;1;1;0
-5473;écoute clandestin;negative;0;1;0;0;0;0
-5474;écouteur;positive;0;0;0;0;0;0
-5475;écouvillon;negative;0;0;0;0;0;1
-5476;écran;positive;0;0;0;0;0;0
-5477;écrasant;negative;0;1;1;1;0;1
-5478;écrémer;negative;0;0;0;0;0;0
-5479;écrevisse;negative;0;0;0;0;0;0
-5480;écrire;positive;0;0;0;0;0;0
-5481;écriture;positive;0;0;0;0;0;0
-5482;écriture saint;positive;0;0;0;0;0;0
-5483;écrivain;positive;0;0;0;0;0;0
-5484;écrivant;positive;0;0;0;0;0;0
-5485;écumer;negative;0;0;0;0;0;1
-5486;écumeux;negative;0;0;0;0;0;1
-5487;écureuil;positive;0;0;0;0;0;0
-5488;écurie;positive;0;0;0;0;0;0
-5489;édenter;negative;0;0;1;0;0;1
-5490;édification;positive;0;0;0;0;0;0
-5491;édifier;positive;0;0;0;0;0;0
-5492;édit;negative;0;1;0;0;0;0
-5493;éditer;positive;0;0;0;0;0;0
-5494;editeur;positive;0;0;0;0;0;0
-5495;edition;positive;0;0;0;0;0;0
-5496;édition;positive;0;0;0;0;0;0
-5497;editorial;positive;0;0;0;0;0;0
-5498;éditorial;positive;0;0;0;0;0;0
-5499;édredon;positive;1;0;0;0;0;0
-5500;éducatif;positive;0;0;0;0;0;0
-5501;éducation;positive;0;0;0;0;0;0
-5502;édulcorant;positive;0;0;0;0;0;0
-5503;éduquer;positive;0;0;0;0;0;0
-5504;eétouffants;negative;0;1;1;0;0;1
-5505;effaçage;negative;0;0;1;0;0;0
-5506;effacer;negative;0;1;1;1;0;0
-5507;effacement;negative;0;1;1;0;0;0
-5508;effarer;negative;0;1;0;0;1;1
-5509;effectuer;positive;0;0;0;0;0;0
-5510;effeminé;negative;0;0;0;0;0;0
-5511;efféminer;negative;0;0;0;0;0;0
-5512;effet;positive;0;0;0;0;0;0
-5513;effet personnel;positive;0;0;0;0;0;0
-5514;efficace;positive;0;0;0;0;0;0
-5515;efficacement;positive;0;0;0;0;0;0
-5516;efficacité;positive;0;0;0;0;0;0
-5517;effigie;positive;0;0;0;1;0;0
-5518;effilage;positive;0;0;0;0;0;0
-5519;effiler;positive;0;0;0;0;0;0
-5520;effilocher;negative;0;0;1;0;0;1
-5521;effondrement;negative;0;1;1;0;1;1
-5522;effort;positive;0;0;0;0;0;0
-5523;effrayant;negative;0;1;1;1;1;0
-5524;effroi;negative;0;1;0;0;1;0
-5525;effronté;negative;0;0;0;1;0;1
-5526;effronterie;positive;0;0;0;0;0;0
-5527;effusion;negative;0;0;0;0;0;0
-5528;égal;positive;0;0;0;0;0;0
-5529;également;positive;0;0;0;0;0;0
-5530;égalisation;positive;0;0;0;0;0;0
-5531;égaliser;positive;0;0;0;0;0;0
-5532;égalité;positive;0;0;0;0;0;0
-5533;égard;positive;0;0;0;0;0;0
-5534;égérie;positive;0;0;0;0;0;0
-5535;égide;positive;0;0;0;0;0;0
-5536;église;positive;0;0;0;0;0;0
-5537;ego;negative;0;0;0;0;0;0
-5538;égocentrique;negative;0;0;0;1;0;1
-5539;égoïsme;negative;0;0;0;1;0;1
-5540;égoïste;negative;0;0;0;1;0;1
-5541;égout;negative;0;0;0;0;0;1
-5542;égouttement;negative;0;0;0;0;0;0
-5543;égratigner;negative;0;1;1;0;0;0
-5544;égratignure;negative;0;1;1;1;1;0
-5545;éhonté;negative;0;0;0;1;0;1
-5546;éjaculation;positive;0;0;0;0;1;0
-5547;éjaculer;positive;1;0;0;0;0;0
-5548;éjecter;negative;0;1;1;1;0;0
-5549;éjection;negative;0;1;1;1;1;0
-5550;élaboration;positive;0;0;0;0;0;0
-5551;élaborer;positive;0;0;0;0;0;0
-5552;élaguer;negative;0;0;0;0;0;0
-5553;élargir;positive;0;0;0;0;0;0
-5554;élargissement;negative;0;0;0;0;0;0
-5555;élasticité;positive;0;0;0;0;0;0
-5556;élastique;positive;0;0;0;0;0;0
-5557;élection;positive;0;0;0;0;0;0
-5558;élection primaire;positive;0;0;0;0;0;0
-5559;électorat;positive;0;0;0;0;0;0
-5560;électricité;positive;0;0;0;0;0;0
-5561;électrique;positive;0;0;0;0;1;0
-5562;élégance;positive;0;0;0;0;0;0
-5563;élégant;positive;0;0;0;0;0;1
-5564;élément;positive;0;0;0;0;0;0
-5565;élément vital;positive;0;0;0;0;0;0
-5566;élevage;positive;0;0;0;0;0;0
-5567;élévation;positive;0;1;0;0;0;0
-5568;élève;positive;0;0;0;0;0;0
-5569;élever;positive;0;0;0;0;0;0
-5570;élève de terminal;positive;0;0;0;0;0;0
-5571;élève officier;positive;0;0;0;0;0;0
-5572;eleveur;positive;0;0;0;0;0;0
-5573;elfe;positive;0;1;0;1;0;1
-5574;éligible;positive;0;0;0;0;0;0
-5575;élimination;negative;0;1;1;1;0;1
-5576;éliminer;negative;0;1;0;1;1;0
-5577;élingue;negative;0;1;0;1;0;0
-5578;élinguer;negative;0;1;0;1;0;0
-5579;élire;positive;0;0;0;0;0;0
-5580;ellipse;positive;0;0;0;0;0;0
-5581;ellipsoïdal;positive;0;0;0;0;0;0
-5582;ellipsoïde;positive;0;0;0;0;0;0
-5583;elliptique;positive;0;0;0;0;0;0
-5584;éloge;positive;0;0;0;0;0;0
-5585;éloge funèbre;negative;0;0;1;0;0;0
-5586;éloigner;negative;0;0;1;0;0;0
-5587;éloignement;negative;0;0;1;0;0;0
-5588;éloquence;positive;0;0;0;0;0;0
-5589;éloquent;positive;0;0;0;0;0;0
-5590;élucidation;positive;0;0;0;0;0;0
-5591;élucider;positive;0;0;0;0;0;0
-5592;éluder;negative;0;1;0;0;0;1
-5593;émacier;negative;0;1;1;0;0;1
-5594;émail;positive;0;0;0;0;0;0
-5595;émancipation;positive;1;0;0;0;0;0
-5596;émaner;positive;0;0;0;0;0;0
-5597;emballage;positive;0;0;0;0;0;0
-5598;emballer;positive;0;0;0;0;0;0
-5599;embarassante;negative;0;1;0;0;0;0
-5600;embarassantes;negative;0;1;0;0;0;0
-5601;embarassants;negative;0;1;0;0;0;0
-5602;embarcadère;positive;0;0;0;0;0;0
-5603;embarcation;positive;0;0;0;0;0;0
-5604;embarder;negative;0;1;0;0;1;0
-5605;embargo;negative;0;0;0;1;0;0
-5606;embarquement;positive;0;0;0;0;0;0
-5607;embarquer;positive;0;0;0;0;0;0
-5608;embarquer de force;negative;0;1;0;1;0;1
-5609;embarras;negative;0;1;1;0;1;1
-5610;embaucher;positive;0;0;0;0;0;0
-5611;embellir;positive;1;0;0;0;0;0
-5612;embellissement;positive;0;0;0;0;0;0
-5613;embêter;negative;0;0;1;1;0;0
-5614;embêtement;negative;0;1;1;1;0;0
-5615;emblématique;positive;0;0;0;0;0;0
-5616;emblème;positive;0;0;0;0;0;0
-5617;embolie;negative;0;1;1;0;0;0
-5618;embouchure;positive;0;0;0;0;0;0
-5619;embout;positive;0;0;0;0;0;0
-5620;embraser;negative;0;1;0;1;1;0
-5621;embrasement;negative;0;1;0;1;0;0
-5622;embrasser;positive;0;0;0;0;1;0
-5623;embrayage;positive;0;0;0;0;0;0
-5624;embrocher;negative;0;1;0;0;0;0
-5625;embrouiller;negative;0;0;0;0;0;0
-5626;embryon;positive;0;0;0;0;0;0
-5627;embuer;negative;0;1;1;0;0;0
-5628;embuscade;negative;0;1;0;1;1;0
-5629;émécher;negative;0;0;0;0;0;1
-5630;emeraude;positive;0;0;0;0;0;0
-5631;émeraude;positive;0;0;0;0;0;0
-5632;émergence;positive;0;0;0;0;0;0
-5633;émerger;positive;0;0;0;0;1;0
-5634;émérite;positive;0;0;0;0;0;0
-5635;émettre;positive;0;0;0;0;0;0
-5636;émeute;negative;0;1;0;1;0;0
-5637;émigration;negative;0;0;0;0;0;0
-5638;émigrer;negative;0;0;0;0;0;0
-5639;émincer;negative;0;1;0;0;0;0
-5640;éminemment;positive;0;0;0;0;0;0
-5641;eminence;positive;0;0;0;0;0;0
-5642;éminence;positive;0;0;0;0;0;0
-5643;emir;positive;0;0;0;0;0;0
-5644;émir;positive;0;0;0;0;0;0
-5645;émissaire;positive;0;0;0;0;0;0
-5646;emmêler;negative;0;1;0;1;0;0
-5647;émotionnel;positive;0;0;0;0;0;0
-5648;émousser;negative;0;1;0;1;0;0
-5649;empaqueter;positive;0;0;0;0;0;0
-5650;empaqueteur;positive;0;0;0;0;0;0
-5651;empathie;positive;0;0;0;0;0;0
-5652;empattement;positive;0;0;0;0;0;0
-5653;empêcher;negative;0;1;0;0;0;0
-5654;empereur;positive;0;0;0;0;0;0
-5655;empester;negative;0;0;0;0;0;1
-5656;empêtrer;negative;0;1;1;1;0;1
-5657;empétrés;negative;0;1;1;1;0;1
-5658;emphase;positive;0;0;0;0;0;0
-5659;emphatique;positive;0;0;0;0;0;0
-5660;empiétement;negative;0;1;1;1;0;0
-5661;empiètement;negative;0;1;1;1;0;0
-5662;empiéter;negative;0;1;1;1;0;0
-5663;empiler;negative;0;0;0;0;0;0
-5664;empire;positive;0;0;0;0;0;0
-5665;empirique;positive;0;0;0;0;0;0
-5666;empirisme;positive;0;0;0;0;0;0
-5667;emplacement;positive;0;0;0;0;0;0
-5668;emploi;positive;0;0;0;0;0;0
-5669;employer abusivement;negative;0;1;1;1;0;0
-5670;employeur;positive;0;0;0;0;0;0
-5671;empocher;positive;0;0;0;0;0;0
-5672;empoigner;negative;0;1;0;1;0;0
-5673;empreinte;positive;0;0;0;0;0;0
-5674;empreindre de pas;positive;0;0;0;0;0;0
-5675;empressement;positive;0;0;0;0;0;0
-5676;emprisonner;negative;0;1;1;1;0;1
-5677;emprisonnement;negative;0;1;1;1;0;1
-5678;emprunt;positive;0;0;0;0;0;0
-5679;emprunter;positive;0;0;0;0;0;0
-5680;émouvoir;positive;0;0;1;0;0;0
-5681;émuler;negative;0;0;0;0;0;0
-5682;émulsion;positive;0;0;0;0;0;0
-5683;en général;positive;0;0;0;0;0;0
-5684;en abondance;positive;1;0;0;0;0;0
-5685;en activité;positive;0;0;0;0;0;0
-5686;en agate;positive;0;0;0;0;0;0
-5687;en apesanteur;positive;0;0;0;0;0;0
-5688;en argent;positive;0;0;0;0;0;0
-5689;en arriérer;negative;0;1;0;0;0;1
-5690;en attente;negative;0;0;0;0;0;0
-5691;en avance;positive;0;0;0;0;0;0
-5692;en aveugle;negative;0;1;1;0;0;0
-5693;en baisse;negative;0;1;1;1;0;0
-5694;en bois;positive;0;0;0;0;0;0
-5695;en bon santé;positive;1;0;0;0;0;0
-5696;en caoutchouc;negative;0;0;0;0;0;0
-5697;en ce qui concerner;positive;0;0;0;0;0;0
-5698;en cela;positive;0;0;0;0;0;0
-5699;en chef;positive;0;0;0;0;0;0
-5700;en colère;negative;0;0;0;1;0;1
-5701;en colimaçon;positive;0;0;0;0;0;0
-5702;en colonne;positive;0;0;0;0;0;0
-5703;en continu;positive;0;0;0;0;0;0
-5704;en corrélation;positive;0;0;0;0;0;0
-5705;en couper;positive;0;0;0;0;0;0
-5706;en cour|cours;positive;0;0;0;0;0;0
-5707;en déclin;negative;0;0;1;0;0;0
-5708;en dent de scie;negative;0;1;1;1;0;0
-5709;en désaccord;negative;0;0;1;1;0;0
-5710;en descente;negative;0;1;0;0;0;0
-5711;en désordre;negative;0;1;1;1;0;1
-5712;en dessous;negative;0;0;0;0;0;0
-5713;en deuil;negative;0;0;1;0;0;0
-5714;en développement;positive;0;0;0;0;0;0
-5715;en douceur;positive;1;0;0;0;0;0
-5716;en épi;negative;0;1;0;0;0;0
-5717;en étain;positive;0;0;0;0;0;0
-5718;en évidence;positive;0;0;0;0;0;0
-5719;en éviter;negative;0;1;0;0;0;1
-5720;en fac similé;negative;0;0;0;0;0;0
-5721;en faillite;negative;0;1;1;0;0;0
-5722;en faire autant;positive;0;0;0;0;0;0
-5723;en feu;negative;0;1;0;1;0;0
-5724;en filigrane;positive;0;0;0;0;0;0
-5725;en fleur;positive;0;0;0;0;0;0
-5726;en former de cône;positive;0;0;0;0;0;0
-5727;en former de croître;positive;0;0;0;0;0;0
-5728;en fuite;negative;0;1;0;0;0;0
-5729;en furie;negative;0;1;0;1;0;1
-5730;en fusion;negative;0;1;0;0;0;0
-5731;en granite;positive;0;0;0;0;0;0
-5732;en haillon;negative;0;0;1;0;0;0
-5733;en hausse;positive;1;0;0;0;0;0
-5734;en haut;positive;0;0;0;0;0;0
-5735;en image;positive;0;0;0;0;0;0
-5736;en instance;positive;0;0;0;0;0;0
-5737;en jachère;negative;0;0;1;0;0;0
-5738;en lacet;negative;0;0;0;0;0;0
-5739;en laine;positive;0;0;0;0;0;0
-5740;en lambeau;negative;0;0;1;0;0;0
-5741;en larme;negative;0;1;1;0;0;1
-5742;en loque;negative;0;0;1;0;0;0
-5743;en marbre;positive;0;0;0;0;0;0
-5744;en mauvais santé;negative;0;1;1;0;0;1
-5745;en montée;positive;0;1;0;0;0;0
-5746;en morceau;negative;0;0;1;0;0;0
-5747;en mouvement;positive;0;0;1;0;0;0
-5748;en nickel;positive;0;0;0;0;0;0
-5749;en option;positive;0;0;0;0;0;0
-5750;en orbite;positive;0;0;0;0;0;0
-5751;en partie;negative;0;0;0;0;0;0
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-5839;endommager;negative;0;1;1;1;0;1
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-6125;établissement scolaire;positive;0;0;0;0;0;0
-6126;étage;positive;0;0;0;0;0;0
-6127;étagère;positive;0;0;0;0;0;0
-6128;étai;positive;0;0;0;0;0;0
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-6135;étancher;positive;0;0;0;0;0;0
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-6138;état;positive;0;0;0;0;0;0
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-6140;étayer|étayer;positive;0;0;0;0;0;0
-6142;éteindre;negative;0;0;1;1;0;0
-6143;étendue sauvage;negative;0;1;1;0;0;0
-6144;éternité;positive;0;0;0;0;0;0
-6145;éternuer;negative;0;0;0;0;1;0
-6146;éternuement;negative;0;0;0;0;1;1
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-6148;éther;negative;0;0;0;0;0;1
-6149;éthéré;negative;0;1;0;0;0;0
-6150;éthique;positive;0;0;0;0;0;0
-6151;ethnographie;positive;0;0;0;0;0;0
-6152;étinceler;positive;1;0;0;0;0;0
-6153;étincelant;positive;1;0;0;0;0;0
-6154;étincelle;positive;0;0;0;0;1;0
-6155;étiologie;positive;0;0;0;0;0;0
-6156;étiqueter;positive;0;0;0;0;0;0
-6157;étiquette;positive;0;0;0;0;0;0
-6158;étirer;positive;0;0;0;0;0;0
-6159;étoffe;positive;0;0;0;0;0;0
-6160;étoile;positive;0;0;0;0;0;0
-6161;étoiler;positive;1;0;0;0;0;0
-6162;étole;positive;0;0;0;0;0;0
-6163;étonnamment;positive;0;0;0;0;1;0
-6164;étonnement;positive;0;1;0;0;1;0
-6165;étonner;positive;0;1;0;0;1;0
-6166;étouffant;negative;0;1;1;0;1;1
-6167;étouffoir;negative;0;1;0;0;0;0
-6168;étourderie;negative;0;1;1;0;0;0
-6169;étourdir;negative;0;1;1;0;1;0
-6170;étourdissement;negative;0;1;0;0;1;0
-6171;étrangement;negative;0;1;0;0;1;0
-6172;étrangeté;negative;0;0;1;0;1;1
-6173;étrangleur;negative;0;1;1;1;1;0
-6174;être à le tête;positive;0;0;0;0;0;0
-6175;être à le traîne;negative;0;0;1;0;0;0
-6176;être affaler;negative;0;0;0;0;0;0
-6177;être allonger;positive;0;0;0;0;0;0
-6178;être assortir;positive;0;0;0;0;0;0
-6179;être au service de;negative;0;0;0;0;0;0
-6180;être avachir;negative;0;0;0;0;0;0
-6181;être bénéfique;positive;1;0;0;0;0;0
-6182;être bénévole;positive;0;1;0;0;0;0
-6183;être caractériser;positive;0;0;0;0;0;0
-6184;être conforme à;positive;0;0;0;0;0;0
-6185;être domicilier;positive;0;0;0;0;0;0
-6186;être en adéquation;negative;0;1;0;1;0;0
-6187;être en apprentissage;positive;0;0;0;0;0;0
-6188;être en désaccord;negative;0;0;0;1;0;0
-6189;être en rage;negative;0;0;0;1;0;0
-6190;être imposant;negative;0;1;0;1;0;1
-6191;être incliner;negative;0;1;0;0;0;0
-6192;être indiscret;negative;0;0;0;1;0;1
-6193;être inonder;negative;0;1;0;0;0;0
-6194;être le plus toucher par;negative;0;0;1;1;0;0
-6195;être le premier toucher par;negative;0;0;1;1;0;0
-6196;être protubérant;negative;0;1;0;0;0;1
-6197;être quitte;positive;0;0;0;0;0;0
-6198;être rayonnant;positive;0;0;0;0;0;0
-6199;être souffrant;negative;0;0;1;0;0;0
-6200;étriquer;negative;0;1;1;0;0;0
-6201;étroit;negative;0;1;1;0;0;0
-6202;étroitesse;negative;0;1;1;1;0;0
-6203;étude;positive;0;0;0;0;0;0
-6204;étudier de deuxième année;positive;0;0;0;0;0;0
-6205;étudier de premier cycle unir;positive;0;0;0;0;0;0
-6206;étudier de premier année;positive;0;0;0;0;0;0
-6207;étudier;positive;0;0;0;0;0;0
-6208;étymologie;positive;0;0;0;0;0;0
-6209;euchre;positive;0;0;0;0;0;0
-6210;eugénisme;negative;0;0;1;0;0;0
-6211;euh;negative;0;1;1;0;0;0
-6212;euphémisme;negative;0;0;0;0;0;0
-6213;euthanasie;negative;0;1;1;0;0;0
-6214;évacuation;negative;0;1;0;0;0;0
-6215;évader;negative;0;1;0;1;0;0
-6216;évaluer;positive;0;0;0;0;0;0
-6217;evanescence;negative;0;0;1;0;1;0
-6218;évanescence;negative;0;0;1;0;1;0
-6219;évangélique;positive;0;0;0;0;0;0
-6220;évangéliste;positive;0;0;0;0;0;0
-6221;évangile;positive;0;0;0;0;0;0
-6222;évanouissement;negative;0;1;1;0;1;0
-6223;évaporation;negative;0;0;0;0;0;0
-6224;éveil;positive;1;0;0;0;0;0
-6225;éveiller;positive;0;0;0;0;0;0
-6226;événement;positive;0;0;0;0;1;0
-6227;événement fortuit;positive;0;0;0;0;1;0
-6228;événement marquant;positive;0;0;0;0;0;0
-6229;évent;positive;0;0;0;1;0;0
-6230;éventail;positive;0;0;0;0;0;0
-6231;éventer;positive;0;0;0;0;0;0
-6232;éventuellement;positive;0;0;0;0;1;0
-6233;évêque;positive;0;0;0;0;0;0
-6234;évidemment;positive;0;0;0;0;0;0
-6235;évident;positive;0;0;0;1;0;1
-6236;évier;negative;0;1;1;0;0;0
-6237;évincer;negative;0;1;1;1;1;0
-6238;éviter;negative;0;1;0;0;0;1
-6239;évoluer;positive;1;0;0;0;0;0
-6240;evolution;positive;0;0;0;0;0;0
-6241;évolution;positive;0;0;0;0;0;0
-6242;évoquer;positive;0;0;0;0;0;0
-6243;exacerbation;negative;0;1;0;1;0;0
-6244;exacerber;negative;0;0;0;0;0;0
-6245;exact;positive;0;0;0;0;0;0
-6246;exactitude;positive;0;0;0;0;0;0
-6247;exagération;negative;0;0;0;0;0;0
-6248;exagérer;negative;0;0;0;1;0;0
-6249;exaltation;positive;0;0;0;0;1;0
-6250;exalter;positive;0;0;0;0;0;0
-6251;examiner avec soin;positive;0;0;0;0;0;0
-6252;exaspération;negative;0;0;0;1;0;1
-6253;exaspérer;negative;0;1;0;1;0;0
-6254;exaucer;positive;0;0;0;0;0;0
-6255;excavateur;negative;0;0;0;0;0;0
-6256;excavation;negative;0;0;0;0;1;0
-6257;excaver;negative;0;0;1;0;0;1
-6258;excéder;positive;0;0;0;0;1;0
-6259;excédent;negative;0;0;0;0;0;0
-6260;excellence;positive;0;0;0;0;1;1
-6261;excellent;positive;0;0;0;1;0;0
-6262;exceller;positive;0;0;0;0;1;0
-6263;excentricité;negative;0;0;0;0;0;0
-6264;excentrique;negative;0;1;0;1;1;0
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-6269;exciser;negative;0;1;0;0;0;1
-6270;excision;negative;0;1;0;0;0;0
-6271;excitabilité;positive;0;0;0;0;0;0
-6272;excitable;positive;0;0;0;0;1;0
-6273;excitant;positive;0;0;0;0;1;0
-6274;excitation;positive;0;1;0;1;1;0
-6275;exciter;positive;0;0;0;0;1;0
-6276;exclure;negative;0;1;1;1;0;1
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-6281;excuse;positive;0;0;1;0;0;0
-6282;exécutif;positive;0;0;0;0;0;0
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-6287;exempt;positive;0;0;0;0;0;0
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-6289;exemption;positive;0;0;0;0;0;0
-6290;exercer;positive;0;0;0;0;0;0
-6291;exercer son pontificat;positive;0;0;0;0;0;0
-6292;exercice;positive;0;0;0;0;0;0
-6293;exercice physique;positive;0;0;0;0;0;0
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-6295;exhortation;positive;0;0;0;0;0;0
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-6299;exil;negative;0;1;1;1;0;0
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-6313;expert comptable commissaire avoir;positive;0;1;0;0;0;0
-6314;expiation;positive;1;0;0;0;0;0
-6315;expiration;negative;0;1;0;0;0;1
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-6318;expliquer;positive;0;0;0;0;0;0
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-6338;exprimer son reconnaissance;positive;0;0;0;0;0;0
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-6356;extraction;positive;0;0;0;0;0;0
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-6397;faire;positive;0;0;0;0;0;0
-6398;faire apparaître;negative;0;0;0;0;1;0
-6399;faire appel;positive;0;0;0;0;0;0
-6400;faire attention;positive;0;1;0;0;0;0
-6401;faire bouillir;negative;0;0;0;1;0;1
-6402;faire cadeau;positive;0;0;0;0;0;0
-6403;faire campagne;positive;0;0;0;0;0;0
-6404;faire chanter;negative;0;1;0;1;0;0
-6405;faire circuler;positive;0;0;0;0;0;0
-6406;faire confiance;positive;0;0;0;0;0;0
-6407;faire cuire au barbecue;positive;0;0;0;0;0;0
-6408;faire de l exercice;positive;0;0;0;0;0;0
-6409;faire de le boxe;negative;0;0;0;1;0;0
-6410;faire de le luge;positive;1;0;0;0;0;0
-6411;faire de le moto;positive;0;0;0;0;0;0
-6412;faire de le publicité;positive;0;0;0;0;0;0
-6413;faire de le sculpture;positive;0;0;0;0;0;0
-6414;faire du affaire;positive;0;0;0;0;0;0
-6415;faire du compromis;positive;0;0;0;0;0;0
-6416;faire du course;positive;0;0;0;0;1;0
-6417;faire du folie;negative;0;1;0;0;1;0
-6418;faire du histoire;negative;0;0;1;1;0;0
-6419;faire du remarque continuell;negative;0;1;1;1;0;0
-6420;faire du réserve;positive;0;0;0;0;0;0
-6421;faire détoner;negative;0;1;0;0;1;0
-6422;faire du canoë;positive;0;0;0;0;0;0
-6423;faire du jogging;positive;0;0;0;0;0;0
-6424;faire du mannequinat;positive;0;0;0;0;0;0
-6425;faire du planeur;positive;1;0;0;0;0;0
-6426;faire du racolage;negative;0;0;0;0;0;0
-6427;faire du rap;negative;0;0;0;0;0;0
-6428;faire du trafic;negative;0;0;1;0;0;0
-6429;faire du traîneau;positive;1;0;0;0;0;0
-6430;faire exploser;negative;0;1;0;1;1;0
-6431;faire face;positive;0;0;0;0;0;0
-6432;faire fondre;negative;0;0;0;0;0;0
-6433;faire infuser;positive;0;0;0;0;0;0
-6434;faire le chronique de;positive;0;0;0;0;0;0
-6435;faire le cour;positive;0;0;0;0;0;0
-6436;faire le course;positive;0;0;0;0;0;0
-6437;faire le fête;positive;0;0;0;0;1;0
-6438;faire le grimace;negative;0;1;1;1;0;1
-6439;faire le leçon;positive;0;0;0;0;0;0
-6440;faire le queue;negative;0;0;1;0;0;0
-6441;faire le clown;positive;0;0;0;0;1;0
-6442;faire le médiateur;positive;0;0;0;0;0;0
-6443;faire le pitre;positive;0;0;0;0;1;0
-6444;faire le magasin;positive;0;0;0;0;1;0
-6445;faire mal;negative;0;1;1;1;0;0
-6446;faire marche arrière;negative;0;1;1;0;0;0
-6447;faire obstruction à;negative;0;0;0;1;0;0
-6448;faire passer un entretien;positive;0;0;0;0;0;0
-6449;faire pipi;negative;0;0;0;0;0;1
-6450;faire pivoter;positive;0;0;0;0;0;0
-6451;faire plaisir;positive;1;0;0;0;0;0
-6452;faire référence à;positive;0;0;0;0;0;0
-6453;faire remarquer;negative;0;0;0;0;0;0
-6454;faire semblant;negative;0;0;0;1;0;1
-6455;faire son début;positive;0;0;0;0;0;0
-6456;faire son valise;positive;0;0;0;0;0;0
-6457;faire signe;positive;0;0;0;0;0;0
-6458;faire suite;positive;0;0;0;0;0;0
-6459;faire surface;positive;0;0;0;0;0;0
-6460;faire sursauter;negative;0;1;0;0;1;0
-6461;faire tourner;positive;0;0;0;0;0;0
-6462;faire tremper;negative;0;0;0;0;0;1
-6463;faire un bond;positive;1;0;0;0;0;0
-6464;faire un bruit métallique;negative;0;1;0;0;1;0
-6465;faire un bruit sourd;negative;0;1;0;0;1;0
-6466;faire un coup amortir;negative;0;1;0;1;1;0
-6467;faire un geste;positive;0;0;0;0;0;0
-6468;faire un régime;negative;0;0;0;0;0;0
-6469;faire un scanner de;positive;0;0;0;0;0;0
-6470;faire un signe de le main;positive;0;0;0;0;0;0
-6471;faire un signe de le tête;positive;0;0;0;0;0;0
-6472;faire un somme;positive;0;0;0;0;0;0
-6473;faire un sondage;positive;0;0;0;0;0;0
-6474;faire un descente dans;negative;0;1;0;1;1;0
-6475;faire un digression;negative;0;1;0;1;0;0
-6476;faire un embardée;negative;0;1;0;0;1;0
-6477;faire un gaffe;negative;0;1;1;0;0;1
-6478;faire un hémorragie;negative;0;1;1;0;0;1
-6479;faire un overdose;negative;0;1;1;0;0;1
-6480;faire un randonnée;positive;0;0;0;0;0;0
-6481;faire un remise;positive;0;0;0;0;0;0
-6482;fairway;positive;0;0;0;0;0;0
-6483;faisabilité;positive;0;0;0;0;0;0
-6484;faire autorité;positive;0;0;0;0;0;0
-6485;faire le sieste;positive;0;0;0;0;0;0
-6486;faisceau;positive;1;0;0;0;0;0
-6487;fait et geste;positive;0;0;0;0;0;0
-6488;falacieuses;negative;0;0;0;1;0;1
-6489;falaise;negative;0;1;0;0;0;0
-6490;falsification;negative;0;0;0;1;0;1
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-6492;fameusement;positive;1;0;0;0;0;0
-6493;familiariser;positive;0;0;0;0;0;0
-6494;familiarité;positive;0;0;0;0;0;0
-6495;familier;positive;0;0;0;0;0;0
-6496;famille;positive;0;0;0;0;0;0
-6497;famille du défunt;negative;0;0;1;0;0;0
-6498;famine;negative;0;1;1;0;0;0
-6499;fan;positive;0;0;0;0;0;0
-6500;fanatisme;negative;0;1;0;1;0;0
-6501;fandango;positive;0;0;0;0;0;0
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-6503;fanfare;positive;0;0;0;0;1;0
-6504;fanfaronnade;negative;0;0;0;0;0;0
-6505;fanfaronner;negative;0;0;0;0;0;0
-6506;fanion;positive;0;0;0;0;0;0
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-6509;fantasque;negative;0;0;0;0;1;0
-6510;fantoche;negative;0;0;0;0;0;0
-6511;fantomatique;negative;0;1;0;0;0;0
-6512;fantôme;negative;0;1;0;0;1;0
-6513;faon;negative;0;0;0;0;0;0
-6514;fard;positive;0;0;0;0;0;0
-6515;fardeau;negative;0;1;1;1;0;0
-6516;farfelu;negative;0;1;0;0;1;0
-6517;farine;positive;0;0;0;0;0;0
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-6519;faro;positive;0;0;0;0;0;0
-6520;fascia;positive;0;0;0;0;0;0
-6521;fasciner;positive;0;1;0;0;1;0
-6522;fascinant;positive;0;1;0;0;1;0
-6523;fascination;positive;0;0;0;0;0;0
-6524;fatal;negative;0;1;1;1;0;0
-6525;fatalité;negative;0;1;1;0;0;0
-6526;fatiguer;negative;0;0;1;1;0;0
-6527;faucher;negative;0;1;1;0;0;0
-6528;faucille;negative;0;1;0;0;0;0
-6529;faucon;negative;0;1;0;0;0;0
-6530;faune;positive;0;0;0;0;0;0
-6531;faux couche;negative;0;1;1;0;1;0
-6532;fausser;negative;0;0;1;1;1;1
-6533;faussement;negative;0;0;1;1;0;0
-6534;faussement pudique;negative;0;1;0;0;0;1
-6535;faussement timide;negative;0;1;0;0;0;1
-6536;fausseté;negative;0;0;0;1;0;1
-6537;faute;negative;0;0;1;0;0;1
-6538;faute professionnel;negative;0;1;0;1;0;0
-6539;fauve;negative;0;1;0;0;0;1
-6540;fauvette;positive;0;0;0;0;0;0
-6541;faux pas;negative;0;1;1;0;1;1
-6542;faux pli;negative;0;0;0;0;0;0
-6543;faux bourdon;negative;0;1;0;0;0;1
-6544;faveur;positive;0;0;0;0;0;0
-6545;favori;positive;0;0;0;0;0;0
-6546;favoriser;positive;0;0;0;0;0;0
-6547;favorite;positive;0;0;0;0;0;0
-6548;fébrile;negative;0;1;1;0;1;0
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-6551;fécondité;positive;0;0;0;0;0;0
-6552;fécule;negative;0;0;0;0;0;1
-6553;fécule de maïs;positive;0;0;0;0;0;0
-6554;fédéral;positive;0;0;0;0;0;0
-6555;fédération;positive;0;0;0;0;0;0
-6556;fée;positive;0;0;0;0;0;0
-6557;féerie;positive;1;0;0;0;0;0
-6558;feindre;negative;0;0;0;0;0;1
-6559;feinte;negative;0;0;0;0;0;1
-6560;féliciter;positive;1;0;0;0;0;0
-6561;félin;positive;0;0;0;0;0;0
-6562;félure;negative;0;1;0;1;0;0
-6563;femelle;positive;0;0;0;0;0;0
-6564;féministe;positive;0;0;0;0;0;0
-6565;féminité;positive;0;0;0;0;0;0
-6566;femme;positive;0;0;0;0;0;0
-6567;femme au foyer;positive;0;0;0;0;0;0
-6568;femme de ménage;positive;0;0;0;0;0;0
-6569;femme fatal;positive;0;0;0;0;0;0
-6570;femme politique;positive;0;0;0;0;0;0
-6571;fendre;negative;0;1;0;1;0;0
-6572;fenêtre;positive;0;0;0;0;0;0
-6573;fenouil;negative;0;0;0;0;0;1
-6574;fente;negative;0;1;0;0;0;0
-6575;féodal;negative;0;0;0;0;0;0
-6576;féodalisme;negative;0;0;1;1;0;0
-6577;fer;positive;0;0;0;0;0;0
-6578;fer à cheval;positive;0;0;0;0;0;0
-6579;fermer;positive;0;0;0;0;0;0
-6580;fermement;positive;0;0;0;0;0;0
-6581;ferment;negative;0;0;0;0;0;1
-6582;fermentation;negative;0;0;0;0;0;1
-6583;fermenter;negative;0;0;0;0;0;1
-6584;fermer à clé;positive;0;0;0;0;0;0
-6585;fermeté;positive;0;0;0;0;0;0
-6586;fermette;positive;0;0;0;0;0;0
-6587;fermeture;positive;0;0;1;0;0;0
-6588;fermeture éclair;positive;0;0;0;0;0;0
-6589;féroce;negative;0;1;0;1;0;1
-6590;férocité;negative;0;1;0;1;0;0
-6591;ferry;positive;0;0;0;0;0;0
-6592;fertile;positive;0;0;0;0;0;0
-6593;fertiliser;positive;0;0;0;0;0;0
-6594;fesse;positive;0;0;0;0;0;0
-6595;fesser;negative;0;1;1;1;0;0
-6596;festival;positive;0;0;0;0;1;0
-6597;fêter;positive;0;0;0;0;1;0
-6598;fétiche;negative;0;0;0;1;0;0
-6599;feu d artifice;positive;0;0;0;0;1;0
-6600;feu de joie;positive;1;0;0;0;0;0
-6601;feuillage;positive;0;0;0;0;0;0
-6602;feuille;positive;0;0;0;0;0;0
-6603;feuilleter;positive;0;0;0;0;0;0
-6604;feuilleton;positive;0;0;0;0;0;0
-6605;feuillu;positive;0;0;0;0;0;0
-6606;fiabilité;positive;0;0;0;0;0;0
-6607;fiable;positive;0;0;0;0;0;0
-6608;fiançailles;positive;0;0;0;0;0;0
-6609;fiancer;positive;0;0;0;0;0;0
-6610;fibre;positive;0;0;0;0;0;0
-6611;fibreux;negative;0;0;0;0;0;1
-6612;fiche;negative;0;1;0;0;0;0
-6613;fiche de renseignement;positive;0;0;0;0;0;0
-6614;fichier;positive;0;0;0;0;0;0
-6615;ficher;negative;0;0;0;1;0;1
-6616;fictif;negative;0;0;0;1;0;1
-6617;fiction;positive;0;0;0;0;0;0
-6618;fidélité;positive;0;0;0;0;0;0
-6619;fiduciaire;positive;0;0;0;0;0;0
-6620;fierté;positive;1;0;0;0;0;0
-6621;fiesta;positive;0;0;0;0;1;0
-6622;fièvre;negative;0;1;0;0;0;0
-6623;figer;negative;0;0;1;0;0;0
-6624;figure;positive;0;0;0;0;0;0
-6625;figurer;positive;0;0;0;0;0;0
-6626;figurine;positive;0;0;0;0;0;0
-6627;fil de fer;positive;0;0;0;0;0;0
-6628;fil dentaire;negative;0;0;0;0;0;1
-6629;filage;negative;0;1;0;0;0;0
-6630;filament;negative;0;0;0;0;0;0
-6631;filamenteux;negative;0;0;0;0;0;1
-6632;filer à tout vitesse;negative;0;1;0;0;1;0
-6633;filetage;negative;0;0;0;0;0;0
-6634;filial;negative;0;0;0;0;0;0
-6635;filiation;positive;0;0;0;0;0;0
-6636;filière;positive;0;0;0;0;0;0
-6637;filigrane;positive;0;0;0;0;0;0
-6638;fille;positive;1;0;0;0;0;0
-6639;fille de joie;negative;0;0;0;1;0;1
-6640;film;positive;0;0;0;0;0;0
-6641;filmer;positive;0;0;0;0;0;0
-6642;fil|fils;positive;0;0;0;0;0;0
-6643;filtre;positive;0;0;0;0;0;0
-6644;filtrer;positive;0;0;0;0;0;0
-6645;finalement;positive;0;0;0;0;1;1
-6646;finalisation;positive;1;0;0;0;0;0
-6647;finalité;negative;0;0;1;0;0;0
-6648;finance;positive;0;0;0;0;0;0
-6649;financer;positive;0;0;0;0;0;0
-6650;financier;positive;0;0;0;0;0;0
-6651;finesse;positive;0;0;0;0;0;0
-6652;finir;negative;0;1;1;0;0;0
-6653;finition;positive;0;0;0;0;0;0
-6654;fiole;positive;0;0;0;0;0;0
-6655;firmament;positive;0;0;0;0;0;0
-6656;firme;positive;0;0;0;0;0;0
-6657;fiscal;positive;0;0;0;0;0;0
-6658;fissile;negative;0;0;0;0;0;0
-6659;fission;negative;0;1;0;0;1;0
-6660;fissuration;negative;0;1;0;1;0;0
-6661;fissurer;negative;0;1;1;1;0;0
-6662;fistule;negative;0;0;0;0;0;1
-6663;fixer;positive;0;0;0;0;0;0
-6664;fixement;negative;0;1;0;1;0;0
-6665;fixer du regard;negative;0;1;0;1;1;0
-6666;fixer le prix;positive;0;0;0;0;0;0
-6667;flacon;positive;0;0;0;0;0;0
-6668;flairer;positive;0;0;0;0;0;0
-6669;flambeau;positive;0;0;0;0;0;0
-6670;flamber;negative;0;1;0;1;0;0
-6671;flamboiement;positive;0;0;0;0;1;0
-6672;flamboyant;negative;0;0;0;1;0;0
-6673;flanelle;positive;0;0;0;0;0;0
-6674;flâneur;positive;1;0;0;0;0;0
-6675;flanquer;negative;0;0;0;0;0;0
-6676;flash;negative;0;1;0;0;1;0
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-6679;flatter qqn servilement;negative;0;0;0;0;0;0
-6680;flatterie;positive;0;0;0;0;0;0
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-6682;flèche;positive;0;1;0;0;1;0
-6683;fléchette;negative;0;1;0;0;0;0
-6684;fléchir;negative;0;0;1;0;0;0
-6685;fléchissement;negative;0;0;1;0;0;0
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-6687;fleur;positive;1;0;0;0;0;0
-6688;fleuret;positive;0;0;0;0;0;0
-6689;fleurir;positive;1;0;0;0;0;0
-6690;fleuriste;positive;0;0;0;0;0;0
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-6698;flocon;negative;0;0;0;0;0;0
-6699;flocon de neige;positive;0;0;0;0;0;0
-6700;flop;negative;0;0;0;0;0;1
-6701;floraison;positive;1;0;0;0;0;0
-6702;floral;positive;1;0;0;0;0;0
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-6705;florissant;positive;0;0;0;0;0;0
-6706;flottabilité;positive;0;0;0;0;0;0
-6707;flottant;positive;0;0;0;0;0;0
-6708;flotte;positive;0;0;0;0;0;0
-6709;flottement;positive;0;0;0;0;0;0
-6710;flotter;positive;0;0;0;0;0;0
-6711;flotteur;positive;0;0;0;0;0;0
-6712;flou;negative;0;1;1;0;0;0
-6713;flou|flous;negative;0;1;1;0;0;0
-6714;fluctuer;positive;0;0;0;0;0;0
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-6716;fluctuation;negative;0;1;0;1;0;0
-6717;fluide;positive;0;0;0;0;0;0
-6718;fluidité;positive;1;0;0;0;0;0
-6719;fluo;positive;0;0;0;0;0;0
-6720;fluorescence;positive;0;0;0;0;0;0
-6721;fluorescent;positive;0;0;0;0;0;0
-6722;fluos;positive;0;0;0;0;0;0
-6723;flûte;positive;0;0;0;0;0;0
-6724;flûtiste;positive;0;0;0;0;0;0
-6725;fluvial;positive;0;0;0;0;0;0
-6726;flyer;positive;0;0;0;0;0;0
-6727;foc;positive;0;0;0;0;0;0
-6728;focal;positive;0;0;0;0;0;0
-6729;focaliser;positive;0;0;0;0;0;0
-6730;foi;positive;0;0;0;0;0;0
-6731;foi|fois;positive;0;0;0;0;0;0
-6732;folie;positive;1;0;0;0;0;0
-6733;folio;positive;0;0;0;0;0;0
-6734;folk;positive;0;0;0;0;0;0
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-6739;fonctionner;positive;0;0;0;0;0;0
-6740;fond de cale;negative;0;1;1;0;0;1
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-6747;fonderie;positive;0;0;0;0;0;0
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-6762;formalisme;negative;0;0;0;0;0;0
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-6764;formation;positive;0;0;0;0;0;0
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-6768;former un crôute;negative;0;0;0;0;0;1
-6769;formidable;positive;0;0;1;0;1;0
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-6773;formuler;positive;0;0;0;0;0;0
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-6776;fortifiant;positive;0;0;0;0;0;0
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-6779;fortuit;negative;0;0;0;0;1;0
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-6785;fou de joie;positive;0;0;0;0;1;0
-6786;fou folle|fou;negative;0;1;0;1;0;0
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-7154;goûter;positive;0;0;0;0;0;0
-7155;goûteur|goûteux;positive;1;0;0;0;0;0
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-7167;grace;positive;0;0;0;0;0;0
-7168;grâce;positive;0;0;0;0;0;0
-7169;gracieusement;positive;0;0;0;0;0;0
-7170;gradation;positive;0;0;0;0;0;0
-7171;grade;positive;0;0;0;0;0;0
-7172;gradin;positive;0;0;0;0;0;0
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-7180;gramme;positive;0;0;0;0;0;0
-7181;grand;positive;0;0;0;0;0;0
-7182;grand coup;negative;0;1;0;1;1;0
-7183;grand dam;negative;0;0;1;0;0;1
-7184;grand et maigre;negative;0;0;1;0;0;1
-7185;grand livre;positive;0;0;0;0;0;0
-7186;grand magasin;positive;0;0;0;0;0;0
-7187;grand mère;positive;0;0;0;0;0;0
-7188;grand père;positive;0;0;0;0;0;0
-7189;grandement;positive;0;0;0;0;0;0
-7190;grandeur;positive;0;0;0;0;1;0
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-7192;grandiose;positive;0;0;0;0;1;0
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-7196;granule;negative;0;0;0;0;0;0
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-7198;graphique;positive;0;0;0;0;0;0
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-7202;gratis;positive;0;0;0;0;0;0
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-7204;grattage;negative;0;1;0;1;0;1
-7205;gratter ciel;positive;0;0;0;0;0;0
-7206;gratter;negative;0;1;1;1;1;0
-7207;gratuit;positive;0;0;0;0;0;1
-7208;gratuitement;positive;0;0;0;0;0;0
-7209;graver;positive;0;0;0;0;0;0
-7210;gravement;negative;0;0;1;0;0;0
-7211;gravier;negative;0;0;0;0;0;0
-7212;gravir;positive;0;1;0;0;0;0
-7213;gravitation;positive;0;0;0;0;0;0
-7214;graviter;positive;0;0;0;0;0;0
-7215;gravure;positive;0;0;0;0;0;0
-7216;gravure sur bois;positive;0;0;0;0;0;0
-7217;gré;positive;0;0;0;0;0;0
-7218;gréement;positive;0;0;0;0;0;0
-7219;greffe;positive;0;0;0;0;0;0
-7220;greffer;positive;0;0;0;0;0;0
-7221;greffier;positive;0;0;0;0;0;0
-7222;greffon;positive;0;0;0;0;0;0
-7223;grégaire;positive;0;0;0;0;0;0
-7224;grêlé;negative;0;1;1;0;0;0
-7225;grelotter;negative;0;1;0;0;0;0
-7226;grelottant;negative;0;1;0;0;0;0
-7227;grenade;negative;0;1;0;1;0;0
-7228;grenat;positive;0;0;0;0;0;0
-7229;grenier;positive;0;0;0;0;0;0
-7230;grenouille;negative;0;0;0;0;0;1
-7231;grès;positive;0;0;0;0;0;0
-7232;grésil;negative;0;1;1;0;0;1
-7233;grésillement;negative;0;1;0;1;0;0
-7234;grésiller;negative;0;1;0;1;0;0
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-7236;gribouillage;negative;0;0;0;0;1;0
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-7238;grief;negative;0;0;1;1;0;1
-7239;griffe;negative;0;1;0;1;0;0
-7240;griffer;negative;0;1;0;1;1;0
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-7244;grignoter;positive;0;0;0;0;0;0
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-7246;grillage;negative;0;1;0;0;1;0
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-7248;grillon;positive;0;0;0;0;0;0
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-7250;grimacer;negative;0;1;1;1;0;1
-7251;grimper;positive;0;1;0;0;0;0
-7252;grincer;negative;0;1;0;1;0;1
-7253;grincement;negative;0;1;1;1;1;0
-7254;grippe;negative;0;1;1;0;0;1
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-7258;gris|grise;negative;0;0;1;0;0;1
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-7265;gros lot;positive;0;0;0;0;1;0
-7266;gros morceau;positive;0;0;0;0;0;0
-7267;gros titre;positive;0;0;0;0;0;0
-7268;grossesse;positive;0;0;0;0;0;1
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-7274;grouiller;negative;0;1;0;1;0;1
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-7276;grouper;positive;0;0;0;0;0;0
-7277;groupement;positive;0;0;0;0;0;0
-7278;grue;positive;0;0;0;0;0;0
-7279;grumeleux;negative;0;0;0;0;0;1
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-7281;guépard;negative;0;1;0;0;0;0
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-7288;guet apens;negative;0;1;0;1;1;0
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-7304;gymnase;positive;0;0;0;0;0;0
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-7307;gynécologie;negative;0;0;0;0;0;1
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-7311;habiliter;positive;0;0;0;0;0;0
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-7323;haie;positive;0;0;0;0;0;0
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-7331;hall;positive;0;0;0;0;0;0
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-7345;hardi;positive;0;0;0;0;0;0
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-7354;hasard;positive;0;0;0;0;1;0
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-7358;hâter;negative;0;1;0;0;1;0
-7359;hâtivement;negative;0;1;0;0;1;0
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-7363;hausser le épaule;negative;0;0;0;0;0;0
-7364;haut et fort;negative;0;0;0;1;0;0
-7365;haut le c?ur;negative;0;0;0;0;0;1
-7366;haut parleur;positive;0;0;0;0;0;0
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-7369;haut terre;positive;0;0;0;0;0;0
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-7379;hélicoïdal;positive;0;0;0;0;0;0
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-7422;heurt;negative;0;1;0;1;0;0
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-7487;hors du chemin;negative;0;1;1;0;0;0
-7488;hors jeu;negative;0;1;1;0;0;0
-7489;hors norme;positive;1;0;0;0;0;0
-7490;hors le loi;negative;0;1;0;1;0;0
-7491;horticole;positive;0;0;0;0;0;0
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-7494;hospitalité;positive;0;0;0;0;0;0
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-8207;itinéraire;positive;0;0;0;0;0;0
-8208;itinérant;positive;1;0;0;0;0;0
-8209;ivoire;positive;0;0;0;0;0;0
-8210;ivre;negative;0;0;0;0;0;1
-8211;ivresse;negative;0;1;1;0;1;0
-8212;jacasser;negative;0;0;0;1;0;1
-8213;jachère;negative;0;0;1;0;0;0
-8214;jacinthe;positive;0;0;0;0;0;0
-8215;jackpot;positive;0;0;0;0;1;0
-8216;jacquet;positive;0;0;0;0;1;0
-8217;jade;positive;0;0;0;0;0;0
-8218;jaguar;negative;0;1;0;0;0;0
-8219;jaillissement;positive;0;0;0;0;1;1
-8220;jalon;positive;0;0;0;0;0;0
-8221;jalouser;negative;0;0;0;1;0;1
-8222;jaloux;negative;0;0;0;1;0;1
-8223;jalousie;negative;0;1;1;1;0;1
-8224;jambe;positive;0;0;0;0;0;0
-8225;jambier|jambière;positive;0;0;0;0;0;0
-8226;jambier|jambière de cuir;positive;0;0;0;0;0;0
-8227;jambon;positive;0;0;0;0;0;0
-8228;jambon fumé;positive;0;0;0;0;0;0
-8229;jante;positive;0;0;0;0;0;0
-8230;japon;positive;0;0;0;0;0;0
-8231;japper;negative;0;1;0;1;1;0
-8232;jardin;positive;1;0;0;0;0;0
-8233;jardin d enfant;positive;0;0;0;0;0;0
-8234;jardin public;positive;0;0;0;0;0;0
-8235;jardinage;positive;0;0;0;0;0;0
-8236;jardiner;positive;0;0;0;0;0;0
-8237;jardinier;positive;0;0;0;0;0;0
-8238;jargon;negative;0;0;0;0;0;0
-8239;jarre;positive;0;0;0;0;0;0
-8240;jarret;positive;0;1;0;0;0;0
-8241;jarretelle;positive;0;0;0;0;0;0
-8242;jarretière;positive;0;0;0;0;0;0
-8243;jar|jars;positive;0;0;0;0;0;0
-8244;jaspe;positive;0;0;0;0;0;0
-8245;jauge;positive;0;0;0;0;0;0
-8246;jaugeage;positive;0;0;0;0;0;0
-8247;jauger;positive;0;0;0;0;0;0
-8248;jaune;positive;0;0;0;0;0;0
-8249;jaunisse;negative;0;1;0;0;0;1
-8250;javelot;negative;0;1;0;1;0;0
-8251;jeûne;negative;0;0;1;0;0;0
-8252;jean;positive;0;0;0;0;0;0
-8253;jeep;positive;0;0;0;0;0;0
-8254;jeter un coup d ?il;positive;0;0;0;0;0;0
-8255;jeter du détritus;negative;0;0;0;0;0;1
-8256;jeter du ordure;negative;0;0;0;0;0;1
-8257;jeton;positive;0;0;0;0;0;0
-8258;jeu de dame;positive;0;0;0;0;0;0
-8259;jeu de mot;positive;1;0;0;0;0;0
-8260;jeu du solitaire;positive;0;0;0;0;0;0
-8261;jeune;positive;0;0;0;0;1;0
-8262;jeune femme;positive;0;0;0;1;0;1
-8263;jeune fille;positive;0;0;0;1;0;1
-8264;jeune homme;positive;0;0;0;0;0;1
-8265;jeune marié;positive;0;0;0;0;0;0
-8266;jeunesse;positive;0;1;0;1;1;0
-8267;jeu d argent;negative;0;1;0;0;1;0
-8268;jingle;positive;1;0;0;0;0;0
-8269;joaillerie;positive;0;0;0;0;0;0
-8270;jockey;positive;0;0;0;0;0;0
-8271;jogging;positive;0;0;0;0;0;0
-8272;joie;positive;1;0;0;0;0;0
-8273;joindre;positive;0;0;0;0;0;0
-8274;jointure;positive;0;0;0;0;0;0
-8275;joker;positive;0;0;0;0;1;0
-8276;joli;positive;0;0;0;0;0;0
-8277;jonc;positive;0;0;0;0;0;0
-8278;jonction;positive;0;0;0;0;0;0
-8279;jonglage;positive;0;0;0;0;0;0
-8280;jongler;positive;0;0;0;0;0;0
-8281;jonglerie;positive;0;0;0;0;0;0
-8282;joue;positive;0;0;0;0;0;0
-8283;jouer;positive;1;0;0;0;0;0
-8284;jouer au golf;positive;0;0;0;0;0;0
-8285;jouer du violon;positive;0;0;0;0;0;0
-8286;jouer un atout;positive;0;0;0;0;1;0
-8287;jouet;positive;1;0;0;0;0;0
-8288;joueur;negative;0;0;0;0;0;0
-8289;joueur de cornemuse;positive;0;0;0;0;0;0
-8290;joug;positive;0;0;0;0;0;0
-8291;jouir de;positive;0;0;0;0;0;0
-8292;jour;positive;0;0;0;0;0;0
-8293;journal;positive;0;0;0;0;0;0
-8294;journal intime;positive;0;0;0;0;0;0
-8295;journal officiel;positive;0;0;0;0;0;0
-8296;journalisme;positive;0;0;0;0;0;0
-8297;journaliste;positive;0;0;0;0;0;0
-8298;journée;positive;0;0;0;0;0;0
-8299;joyau;positive;0;0;0;0;0;0
-8300;joyeuses|joyeux;positive;0;0;0;0;0;0
-8301;jubilaire;positive;0;0;0;0;1;0
-8302;jubiler;positive;0;0;0;0;1;0
-8303;juchoir;positive;0;0;0;0;0;0
-8304;judiciaire;positive;0;0;0;0;0;0
-8305;juge;positive;0;0;0;0;0;0
-8306;juger;negative;0;0;0;0;0;0
-8307;jugeote;positive;0;0;0;0;0;0
-8308;jumeau;positive;0;0;0;0;0;0
-8309;jument;positive;0;0;0;0;0;0
-8310;jungle;negative;0;1;0;0;0;0
-8311;junior;positive;0;0;0;0;0;0
-8312;junte;negative;0;1;0;1;0;0
-8313;jupe;positive;0;0;0;0;0;0
-8314;juridiction;positive;0;0;0;0;0;0
-8315;juridique;positive;0;0;0;0;0;0
-8316;jurisprudence;positive;0;0;1;0;0;0
-8317;juriste;positive;0;0;0;0;0;0
-8318;juron;negative;0;0;0;1;0;0
-8319;jury;positive;0;0;0;0;0;0
-8320;jus;positive;0;0;0;0;0;0
-8321;jusqu à;positive;0;0;0;0;0;0
-8322;jusqu ici;positive;0;0;0;0;0;0
-8323;juste;positive;0;0;0;0;0;0
-8324;juste à temps;positive;0;0;0;0;0;0
-8325;justesse;positive;0;0;0;0;0;0
-8326;justice;positive;0;0;0;0;0;0
-8327;justifiable;positive;0;0;0;0;0;0
-8328;justificatif;positive;0;0;0;0;0;0
-8329;justification;positive;0;0;0;0;0;0
-8330;justifier;positive;0;0;0;0;0;0
-8331;jute;negative;0;0;0;0;0;0
-8332;juteux;positive;0;0;0;0;0;0
-8333;juvenile;negative;0;0;0;0;0;0
-8334;juvénile;negative;0;0;0;0;0;0
-8335;juxtaposition;positive;0;0;0;0;0;0
-8336;kaiser;positive;0;0;0;0;0;0
-8337;kaki;negative;0;0;0;0;0;0
-8338;kaléidoscope;positive;0;0;0;0;0;0
-8339;kangourou;positive;0;0;0;0;0;0
-8340;kanji;positive;0;0;0;0;0;0
-8341;karma;positive;0;0;0;0;0;0
-8342;kayak;positive;0;0;0;0;0;0
-8343;kérosène;negative;0;1;0;0;0;1
-8344;khan;positive;0;1;0;0;0;0
-8345;kilogramme;positive;0;0;0;0;0;0
-8346;kilométrage;positive;0;0;0;0;0;0
-8347;kilomètre;positive;0;0;0;0;0;0
-8348;kilt;positive;0;0;0;0;0;0
-8349;kimono;positive;0;0;0;0;0;0
-8350;kiosque;positive;0;0;0;0;0;0
-8351;kiosque à journal|journau;positive;0;0;0;0;0;0
-8352;kit;positive;0;0;0;0;0;0
-8353;klaxonner;negative;0;0;0;1;0;1
-8354;kris;negative;0;1;0;0;0;0
-8355;kyste;negative;0;1;1;0;0;1
-8356;kyste sébacé;negative;0;0;0;0;0;1
-8357;kystique;negative;0;0;0;0;0;1
-8358;le plus âgé;negative;0;0;0;0;0;0
-8359;le plus bas;negative;0;0;1;0;0;0
-8360;le plus petit;negative;0;1;1;0;0;0
-8361;là bas;negative;0;0;0;0;0;0
-8362;labeur;negative;0;0;1;0;0;1
-8363;labo;positive;0;0;0;0;0;0
-8364;laboratoire;positive;0;0;0;0;0;0
-8365;labour;positive;0;0;0;0;0;0
-8366;labourage;positive;0;0;0;0;0;0
-8367;labourer;positive;0;0;0;0;0;0
-8368;labyrinthe;negative;0;1;0;0;1;0
-8369;lac;positive;0;0;0;0;0;0
-8370;lac écossais;negative;0;1;0;0;0;0
-8371;lâche;negative;0;1;1;1;0;1
-8372;lâchement;negative;0;1;0;1;0;1
-8373;lâcher le chien;positive;0;0;0;0;0;0
-8374;lâcheté;negative;0;1;0;1;0;1
-8375;laconique;negative;0;1;0;1;1;0
-8376;lacrosse;positive;0;0;0;0;0;0
-8377;lacté;positive;0;0;0;0;0;0
-8378;lagon;positive;0;0;0;0;0;0
-8379;laïcité;positive;0;0;0;0;0;0
-8380;laid;negative;0;0;0;0;0;1
-8381;laideur;negative;0;1;1;0;0;1
-8382;laine;positive;0;0;0;0;0;0
-8383;laineux;positive;0;0;0;0;0;0
-8384;laïque;negative;0;0;1;0;0;0
-8385;laisse;negative;0;1;1;1;0;0
-8386;laisser;negative;0;0;0;0;0;0
-8387;laisser sortir;negative;1;0;0;0;0;0
-8388;laisser supposer;positive;0;0;0;0;0;0
-8389;laisser tomber;negative;0;1;1;0;0;0
-8390;lait;positive;0;0;0;0;0;0
-8391;laitier;positive;0;0;0;0;0;0
-8392;laitier|laitière;positive;0;0;0;0;0;0
-8393;laiton;positive;0;0;0;0;0;0
-8394;laitue;positive;0;0;0;0;0;0
-8395;lama;negative;0;1;0;0;0;1
-8396;lambda;positive;0;0;0;0;0;0
-8397;lambeau;negative;0;0;1;0;0;0
-8398;lambrequin;positive;0;0;0;0;0;0
-8399;lamentablement;negative;0;0;1;0;0;0
-8400;lamentation;negative;0;1;1;0;0;1
-8401;laminer;positive;0;0;0;0;0;0
-8402;lampe;positive;0;0;0;0;0;0
-8403;lampe de poche;positive;0;0;0;0;0;0
-8404;lamper;negative;0;0;0;0;0;1
-8405;lance;negative;0;1;1;1;0;0
-8406;lance pierre;negative;0;1;0;1;0;0
-8407;lancement;positive;1;0;0;0;0;0
-8408;lancier;negative;0;1;0;0;0;0
-8409;lanciner;negative;0;1;1;0;1;0
-8410;lancinant;negative;0;1;1;0;1;0
-8411;lande;negative;0;0;0;0;0;0
-8412;langage;positive;0;0;0;0;0;0
-8413;langoureux;negative;0;1;1;0;0;0
-8414;langoustine;negative;0;0;0;0;0;0
-8415;langue;positive;0;0;0;0;0;0
-8416;languir;negative;0;0;1;0;0;0
-8417;languissant;negative;0;0;1;0;0;0
-8418;lanterne;positive;0;0;0;0;0;0
-8419;lapidaire;negative;0;0;1;1;0;0
-8420;lapin;positive;0;0;0;0;0;0
-8421;laque;negative;0;0;0;0;0;1
-8422;laquer;negative;0;0;0;0;0;1
-8423;larcin;negative;0;0;0;1;1;1
-8424;lard;negative;0;0;0;0;0;1
-8425;large;positive;0;0;0;0;0;0
-8426;large sourire;positive;0;0;0;0;1;0
-8427;largement;positive;0;0;0;0;0;0
-8428;largeur;positive;0;0;0;0;0;0
-8429;largo;positive;0;0;0;0;0;0
-8430;larguer;negative;0;0;0;0;0;1
-8431;larme;negative;0;0;1;0;0;0
-8432;larve;negative;0;0;0;0;0;1
-8433;larynx;positive;0;0;0;0;0;0
-8434;laser;positive;0;0;0;0;0;0
-8435;lasser;negative;0;0;1;0;0;0
-8436;lassitude;negative;0;0;1;0;0;0
-8437;lasso;negative;0;1;0;1;0;0
-8438;latex;negative;0;0;0;0;0;0
-8439;latitude;positive;0;0;0;0;0;0
-8440;latrines;negative;0;0;0;0;0;1
-8441;laurier;positive;1;0;0;0;0;0
-8442;lavande;positive;0;0;0;0;0;0
-8443;laver;positive;0;0;0;0;0;0
-8444;lave;negative;0;1;0;1;0;1
-8445;lavement;negative;0;0;0;0;0;1
-8446;laverie;positive;0;0;0;0;0;0
-8447;le long de;positive;0;0;0;0;0;0
-8448;le meilleur;positive;1;0;0;0;0;0
-8449;le plus élever;positive;0;1;0;0;1;0
-8450;le plus grand;positive;1;0;0;0;0;0
-8451;le plus haut;positive;0;1;0;0;1;0
-8452;le souffle couper;negative;0;1;0;0;1;0
-8453;leader;positive;0;0;0;0;0;0
-8454;lécher;negative;0;0;0;0;0;1
-8455;leçon;positive;0;0;0;0;0;0
-8456;lectorat;positive;0;0;0;0;0;0
-8457;lecteur;positive;0;0;0;0;0;0
-8458;lecture;positive;0;0;0;0;0;0
-8459;lecture attentif;positive;0;0;0;0;0;0
-8460;légal;positive;0;0;0;0;0;0
-8461;légalement;positive;0;0;0;0;0;0
-8462;légaliser;positive;0;1;0;1;0;0
-8463;légalité;positive;0;0;0;0;0;0
-8464;légendaire;positive;0;0;0;0;0;0
-8465;légende;positive;0;0;0;0;0;0
-8466;légender;positive;0;0;0;0;0;0
-8467;légèrement;negative;0;0;0;0;0;0
-8468;légèrement venteux;positive;1;0;0;0;0;0
-8469;légèreté;positive;1;0;0;0;0;0
-8470;légiférer;positive;0;0;0;0;0;0
-8471;légion;positive;0;0;0;0;0;0
-8472;législation;positive;0;0;0;0;0;0
-8473;législature;positive;0;0;0;0;0;0
-8474;légitime;positive;0;0;0;0;0;0
-8475;légitimité;positive;0;0;0;0;0;0
-8476;legs;positive;0;0;0;0;0;0
-8477;léguer;positive;0;0;0;0;0;0
-8478;légume;positive;0;0;0;0;0;0
-8479;légumineux;positive;0;0;0;0;0;0
-8480;lemme;positive;0;0;0;0;0;0
-8481;lendemain;positive;0;0;0;0;0;0
-8482;lent;negative;0;0;1;0;0;1
-8483;lentement;negative;0;0;0;0;0;0
-8484;lente;negative;0;0;1;0;0;1
-8485;lenteur;negative;0;1;1;0;0;0
-8486;lentille;positive;0;0;0;0;0;0
-8487;léopard;negative;0;1;0;0;0;0
-8488;lèpre;negative;0;1;1;0;0;1
-8489;le ?il bandé;negative;0;1;0;0;0;0
-8490;lesbianisme;negative;0;0;0;0;0;0
-8491;lesbien;negative;0;1;1;0;0;1
-8492;lésiner;negative;0;1;1;0;0;0
-8493;lessive;positive;0;0;0;0;0;0
-8494;lest;positive;0;0;0;0;0;0
-8495;lester;positive;0;0;0;0;0;0
-8496;létal;negative;0;1;1;0;0;1
-8497;léthargie;negative;0;0;1;0;0;0
-8498;léthargique;negative;0;1;1;0;0;0
-8499;lettre;positive;0;0;0;0;0;0
-8500;lettrer;positive;0;0;0;0;0;0
-8501;leucémie;negative;0;1;1;1;0;0
-8502;lever;positive;0;0;0;0;0;0
-8503;lever du jour;positive;0;0;0;0;0;0
-8504;lever du soleil;positive;0;0;0;0;0;0
-8505;levier;positive;0;0;0;0;0;0
-8506;lèvre;positive;0;0;0;0;0;0
-8507;lévrier;positive;0;0;0;0;0;0
-8508;levure;positive;0;0;0;0;0;0
-8509;lexique;positive;0;0;0;0;0;0
-8510;liaison;positive;0;0;0;0;0;0
-8511;libéralisme;positive;0;0;0;0;0;0
-8512;libération conditionnel;positive;0;0;0;0;0;0
-8513;libérer;positive;1;0;0;0;0;0
-8514;liberté;positive;0;0;0;0;1;0
-8515;libido;positive;1;0;0;0;0;0
-8516;libraire;positive;0;0;0;0;0;0
-8517;librairie;positive;0;0;0;0;0;0
-8518;libre;positive;1;0;0;0;0;0
-8519;librement;positive;0;0;0;0;0;0
-8520;licence;positive;0;0;0;0;0;0
-8521;licencier;negative;0;1;0;1;0;0
-8522;lichen;negative;0;0;0;0;0;1
-8523;licorne;positive;0;0;0;0;0;0
-8524;lier;positive;0;0;0;0;0;0
-8525;liège;positive;0;0;0;0;0;0
-8526;lien;positive;0;0;0;0;0;0
-8527;lieu;positive;0;0;0;0;0;0
-8528;lieu commun;positive;0;0;0;0;0;0
-8529;lieu de naissance;positive;0;0;0;1;0;0
-8530;lieu de prédilection;positive;0;0;0;0;0;0
-8531;lieu de travail;positive;0;0;0;0;0;0
-8532;lieu sûr;positive;0;0;0;0;0;0
-8533;lieutenant;positive;0;0;0;0;0;0
-8534;lièvre;negative;0;1;0;0;0;0
-8535;ligament;positive;0;0;0;0;0;0
-8536;ligature;positive;0;0;0;0;0;0
-8537;ligne;positive;0;0;0;1;0;0
-8538;lignée;positive;0;0;0;0;0;0
-8539;ligoter;negative;0;1;1;0;0;0
-8540;ligue;positive;0;0;0;0;0;0
-8541;lilas;positive;0;0;0;0;0;0
-8542;limace;negative;0;0;0;0;0;1
-8543;limaille;positive;0;0;0;0;0;0
-8544;limbe|limbes;negative;0;1;1;0;0;0
-8545;limier;positive;0;0;0;0;0;0
-8546;limitation;positive;0;0;0;0;0;0
-8547;limiter;negative;0;0;1;1;0;0
-8548;limoger;negative;0;0;1;0;0;0
-8549;limousine;positive;0;0;0;0;0;0
-8550;limpidité;positive;0;0;0;0;0;0
-8551;lin;positive;0;0;0;0;0;0
-8552;linceul;negative;0;0;1;0;0;0
-8553;linge à laver;positive;0;0;0;0;0;0
-8554;linge de lit;positive;0;0;0;0;0;0
-8555;linge de maison;positive;0;0;0;0;0;0
-8556;lingot;positive;0;0;0;0;0;0
-8557;lingual;positive;0;0;0;0;0;0
-8558;linguiste;positive;0;0;0;0;0;0
-8559;linguistique;positive;0;0;0;0;0;0
-8560;linoléum;positive;0;0;0;0;0;0
-8561;lion;positive;0;1;0;0;0;0
-8562;liquéfaction;negative;0;0;1;0;0;0
-8563;liquéfier;negative;0;0;1;0;0;0
-8564;liqueur;negative;0;0;1;1;0;0
-8565;liquidateur;negative;0;0;0;0;0;0
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-8567;liquide;positive;0;0;0;0;0;0
-8568;liquider;negative;0;0;0;0;0;0
-8569;liquidité;positive;0;0;0;0;0;0
-8570;lire;positive;0;0;0;0;0;0
-8571;lire attentivement;positive;0;0;0;0;0;0
-8572;lisibilité;positive;0;0;0;0;0;0
-8573;lisible;positive;0;0;0;0;0;0
-8574;lisière;positive;0;0;0;0;0;0
-8575;lisse;positive;0;0;0;0;0;0
-8576;listage;positive;0;0;0;0;0;0
-8577;liste;positive;0;0;0;0;0;0
-8578;liste de vérification;positive;0;0;0;0;0;0
-8579;lister;positive;0;0;0;0;0;0
-8580;listing;positive;0;0;0;0;0;0
-8581;lit;positive;0;0;0;0;0;0
-8582;lit bébé;positive;0;0;0;0;0;0
-8583;lire d enfant;positive;0;0;0;0;0;0
-8584;litanie;positive;0;0;0;0;0;0
-8585;literie;positive;0;0;0;0;0;0
-8586;lithographie;positive;0;0;0;0;0;0
-8587;lithologie;positive;0;0;0;0;0;0
-8588;lithosphère;positive;0;0;0;0;0;0
-8589;litière;negative;0;0;0;0;0;1
-8590;litige;negative;0;1;1;1;0;0
-8591;litre;positive;0;0;0;0;0;0
-8592;littéraire;positive;0;0;0;0;0;0
-8593;littéralement;positive;0;0;0;0;0;0
-8594;littérature;positive;0;0;0;0;0;0
-8595;littoral;positive;0;0;0;0;0;0
-8596;liturgie;positive;0;0;0;0;0;0
-8597;livide;negative;0;1;0;1;0;1
-8598;livraison;positive;0;0;0;0;0;0
-8599;livre sterling;positive;0;0;0;0;0;0
-8600;livrer;positive;0;0;0;0;0;0
-8601;livre;positive;0;0;0;0;0;0
-8602;livresque;positive;0;0;0;0;0;0
-8603;livret;positive;0;0;0;0;0;0
-8604;lobbyiste;negative;0;0;0;0;0;0
-8605;lobe;positive;0;0;0;0;0;0
-8606;local;positive;0;0;0;0;0;0
-8607;localisation;positive;0;0;0;0;0;0
-8608;localiser;positive;0;0;0;0;0;0
-8609;localité;positive;0;0;0;0;0;0
-8610;locataire;positive;0;0;0;0;0;0
-8611;location;positive;0;0;0;0;0;0
-8612;loch;negative;0;1;0;0;0;0
-8613;locomotion;positive;0;0;0;0;0;0
-8614;locomotive;positive;0;0;0;0;0;0
-8615;locuste;negative;0;1;0;0;0;1
-8616;loft;positive;0;0;0;0;0;0
-8617;logarithme;positive;0;0;0;0;0;0
-8618;logarithmique;positive;0;0;0;0;0;0
-8619;loge;positive;0;0;0;0;0;0
-8620;loger;positive;0;0;0;0;0;0
-8621;logique;positive;0;0;0;0;0;0
-8622;logistique;positive;0;0;0;0;0;0
-8623;logo;positive;0;0;0;0;0;0
-8624;logotype;positive;0;0;0;0;0;0
-8625;loi;positive;0;0;0;0;0;0
-8626;lointain;negative;0;0;1;0;0;0
-8627;lointainests;negative;0;0;1;0;0;0
-8628;loisir;positive;0;0;0;0;1;0
-8629;lombaire;positive;0;0;0;0;0;0
-8630;long;positive;0;0;0;0;0;0
-8631;longe;positive;0;0;0;0;0;0
-8632;longévité;positive;0;0;0;0;0;0
-8633;longitude;positive;0;0;0;0;0;0
-8634;longitudinal;positive;0;0;0;0;0;0
-8635;longitudinalement;positive;0;0;0;0;0;0
-8636;longueur;positive;0;0;0;0;0;0
-8637;loquace;positive;0;0;0;0;0;0
-8638;loquet;positive;0;0;0;0;0;0
-8639;lorgner;negative;0;0;0;1;0;1
-8640;losange;positive;0;0;0;0;0;0
-8641;lot;positive;0;0;0;0;0;0
-8642;loterie;positive;0;1;0;0;1;0
-8643;lotion;positive;0;0;0;0;0;0
-8644;loto;positive;0;1;0;0;1;0
-8645;louange;positive;0;0;0;0;0;0
-8646;loucher;negative;0;0;0;0;0;0
-8647;louche;negative;0;1;1;0;0;0
-8648;louer;positive;0;0;0;0;0;0
-8649;loupe;positive;0;0;0;0;0;0
-8650;lourdaud;negative;0;0;0;0;0;1
-8651;lourde;negative;0;1;1;1;0;0
-8652;lourdement;negative;0;0;1;0;0;0
-8653;lourdeur;negative;0;0;0;0;0;0
-8654;loyer;positive;0;0;0;0;0;0
-8655;lubie;positive;1;0;0;0;0;0
-8656;lubrifiant;negative;0;0;0;0;0;1
-8657;lubrification;negative;0;0;0;0;0;1
-8658;lubrifier;negative;0;0;0;0;0;1
-8659;lubrique;negative;0;0;0;0;0;1
-8660;luciole;positive;0;0;0;0;0;0
-8661;lueur;positive;0;0;0;0;1;0
-8662;luge;positive;1;0;0;0;0;0
-8663;lugubre;negative;0;1;1;0;0;1
-8664;luire;positive;0;0;0;0;1;0
-8665;lumière;positive;0;0;0;0;0;0
-8666;lumière du étoile;positive;0;0;0;0;0;0
-8667;lumière du soleil;positive;1;0;0;0;0;0
-8668;luminescent;positive;1;0;0;0;0;0
-8669;luminosité;positive;1;0;0;0;0;0
-8670;lunaire;positive;0;0;0;0;0;0
-8671;lune;positive;0;0;0;0;0;0
-8672;lune de miel;positive;0;0;0;0;1;0
-8673;lunette;positive;0;0;0;0;0;0
-8674;lunette|lunettes;positive;0;0;0;0;0;0
-8675;lunette|lunettes de protection;positive;0;0;0;0;0;0
-8676;lunette|lunettes de soleil;positive;0;0;0;0;0;0
-8677;lustre;positive;1;0;0;0;0;0
-8678;luth;positive;0;0;0;0;0;0
-8679;lutte;negative;0;1;1;1;0;0
-8680;lutter;negative;0;1;0;1;0;0
-8681;luxe;positive;1;0;0;0;0;0
-8682;luxure;negative;0;0;0;0;0;0
-8683;luxuriant;positive;0;0;1;0;0;1
-8684;luzerne;positive;0;0;0;0;0;0
-8685;lymphatique;positive;0;0;0;0;0;0
-8686;lymphe;positive;0;0;0;0;0;0
-8687;lyncher;negative;0;1;1;1;0;1
-8688;lynx;negative;0;1;0;0;0;0
-8689;lyre;positive;1;0;0;0;0;0
-8690;lyrique;positive;1;0;0;0;0;0
-8691;lys;positive;0;0;0;0;0;0
-8692;mon chéri;positive;0;0;0;0;0;0
-8693;macabre;negative;0;1;1;0;0;1
-8694;macération;negative;0;0;1;0;0;1
-8695;mâcher;negative;0;0;0;0;0;1
-8696;machine;positive;0;0;0;0;0;0
-8697;machine à écrire;positive;0;0;0;0;0;0
-8698;machinerie;positive;0;0;0;0;0;0
-8699;machiniste;positive;0;0;0;0;0;0
-8700;mâchoire;positive;0;0;0;0;0;0
-8701;macis;negative;0;1;0;0;0;0
-8702;maçon;positive;0;0;0;0;0;0
-8703;madame;positive;0;0;0;0;0;0
-8704;mademoiselle;negative;0;0;0;0;0;0
-8705;mafia;negative;0;1;0;1;0;0
-8706;magasin;positive;0;0;0;0;0;0
-8707;magazine;positive;0;0;0;0;0;0
-8708;magenta;positive;0;0;0;0;0;0
-8709;magie;positive;0;0;0;0;1;0
-8710;magique;positive;0;0;0;0;1;0
-8711;magistrat;positive;0;0;0;0;0;0
-8712;magma;negative;0;1;0;0;0;0
-8713;magnat;positive;0;0;0;0;0;0
-8714;magnétisme;positive;0;0;0;0;0;0
-8715;magnétite;positive;0;0;0;0;0;0
-8716;magnificence;positive;0;0;0;0;0;0
-8717;magnifique;positive;0;0;0;0;1;0
-8718;magot;positive;0;0;0;0;0;0
-8719;maigre;negative;0;1;1;0;0;0
-8720;maille;positive;0;0;0;0;0;0
-8721;maillet;negative;0;1;0;1;0;0
-8722;maillon;positive;0;0;0;0;0;0
-8723;maillot;positive;0;0;0;0;0;0
-8724;main;positive;0;0;0;0;0;0
-8725;maintenance;positive;0;0;0;0;0;0
-8726;maintenir son vitesse;positive;0;0;0;0;0;0
-8727;maintes foi|fois;positive;0;0;0;0;0;0
-8728;maintien;positive;1;0;0;0;0;0
-8729;maire;positive;0;0;0;0;0;0
-8730;maison;positive;0;0;0;0;0;0
-8731;maison clore;negative;0;0;0;0;0;1
-8732;maison de maître;positive;0;0;0;0;0;0
-8733;maison de ville;positive;0;0;0;0;0;0
-8734;maisonnée;positive;0;0;0;0;0;0
-8735;maître;positive;0;0;0;0;0;0
-8736;maître de conférence;positive;0;0;0;0;0;0
-8737;maîtriser un sujet;positive;0;0;0;0;0;0
-8738;majeur;positive;0;0;0;0;0;0
-8739;majordome;positive;0;0;0;0;0;0
-8740;majorité;positive;0;0;0;0;0;0
-8741;majuscule;positive;0;0;0;0;0;0
-8742;mal à l aise;negative;0;0;0;0;0;0
-8743;mal assortir;negative;0;0;0;0;0;1
-8744;mal assurer;negative;0;1;0;0;1;0
-8745;mal comprendre;negative;0;1;1;1;0;0
-8746;mal de dent;negative;0;1;1;0;0;1
-8747;mal de tête;negative;0;0;1;0;0;0
-8748;mal du pays;negative;0;0;1;0;0;0
-8749;mal informer;negative;0;1;1;0;0;0
-8750;mal renseigner;negative;0;1;1;0;0;0
-8751;mal être;negative;0;0;1;0;0;0
-8752;maladie;negative;0;1;1;1;0;1
-8753;maladresse;negative;0;1;0;0;0;1
-8754;malais|malaise;negative;0;1;1;0;0;0
-8755;malaria;negative;0;1;1;0;0;1
-8756;malaxer;positive;0;0;0;0;0;0
-8757;malchance;negative;0;1;1;0;0;0
-8758;malédiction;negative;0;1;1;1;0;1
-8759;malencontreux;negative;0;0;1;0;0;0
-8760;malentendu;negative;0;1;1;1;0;0
-8761;malfaçon;negative;0;1;0;0;0;1
-8762;malformation;negative;0;1;1;0;1;1
-8763;malfrat;negative;0;1;0;1;0;1
-8764;malheur;negative;0;1;1;0;0;1
-8765;malheureusement;negative;0;0;1;0;0;0
-8766;malhonnête;negative;0;0;1;1;0;1
-8767;malhonnêteté;negative;0;0;1;1;0;1
-8768;malice;negative;0;1;0;1;0;1
-8769;malicieux;negative;0;1;0;1;1;0
-8770;malignité;negative;0;1;1;0;0;0
-8771;maline;positive;0;0;0;0;0;0
-8772;maline|malines;positive;0;0;0;0;0;0
-8773;malle;negative;0;0;0;0;0;0
-8774;malpoli;negative;0;0;0;0;0;1
-8775;malsain;negative;0;1;1;0;0;1
-8776;maltraitance;negative;0;1;1;1;0;1
-8777;maltraiter;negative;0;1;1;1;0;1
-8778;malveilants;negative;0;0;0;1;0;0
-8779;malveillance;negative;0;1;0;1;0;0
-8780;malversation;negative;0;0;0;0;0;1
-8781;maman;positive;0;1;0;0;0;0
-8782;mamelon;positive;0;0;0;0;0;0
-8783;mammifère;positive;0;0;0;0;0;0
-8784;mammouth;negative;0;1;0;0;0;0
-8785;management;positive;0;0;0;0;0;0
-8786;manche;positive;0;0;0;0;0;0
-8787;manchette;positive;0;0;0;0;0;0
-8788;mandamus;negative;0;1;0;0;0;0
-8789;mandarin;positive;0;0;0;0;0;0
-8790;mandarine;positive;0;0;0;0;0;0
-8791;mandataire;positive;0;0;0;0;0;0
-8792;mandater;positive;0;0;0;0;0;0
-8793;mandibule;negative;0;1;0;0;0;0
-8794;mandoline;positive;0;0;0;0;0;0
-8795;mandrin;negative;0;0;0;0;0;0
-8796;manège;positive;1;0;0;0;0;0
-8797;manette;positive;0;0;0;0;0;0
-8798;manette du gaz;negative;0;0;0;1;0;0
-8799;manger;positive;0;0;0;0;0;0
-8800;mangeoire;positive;0;0;0;0;0;0
-8801;mangeur;positive;0;0;0;0;0;0
-8802;mangoustanier;positive;0;0;0;0;0;0
-8803;mangue;positive;0;0;0;0;0;0
-8804;maniaque;negative;0;1;0;1;0;0
-8805;manie;negative;0;0;1;1;0;0
-8806;manier;positive;0;0;0;0;0;0
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-8809;manifester;positive;0;0;0;0;0;0
-8810;manifestement;positive;0;0;0;0;0;0
-8811;manifester contre;negative;0;0;0;1;0;0
-8812;manifester violemment;negative;0;1;0;1;0;0
-8813;manifestesévidents;positive;0;0;0;0;0;0
-8814;manigancer;positive;0;0;0;0;0;0
-8815;manipulation;negative;0;1;1;1;1;0
-8816;manivelle;negative;0;0;0;1;0;0
-8817;manne;positive;1;0;0;0;0;0
-8818;mannequin;negative;0;0;0;0;0;1
-8819;man?uvrer;positive;0;0;0;0;0;0
-8820;man?uvre;positive;0;0;0;0;0;0
-8821;manoir;negative;0;1;0;0;0;0
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-8823;manquer de;negative;0;0;1;0;0;0
-8824;manque de confiance;negative;0;1;0;1;0;1
-8825;manque de respect;negative;0;0;0;1;0;0
-8826;manquement;negative;0;0;0;0;0;1
-8827;manquer de respect;negative;0;0;0;1;0;0
-8828;manteau;positive;0;0;0;0;0;0
-8829;manucure;positive;0;0;0;0;0;0
-8830;manucurer;positive;0;0;0;0;0;0
-8831;manuel;positive;0;0;0;0;0;0
-8832;manuscrit;positive;0;0;0;0;0;0
-8833;maquereau;negative;0;1;0;0;0;1
-8834;maquillage;positive;0;0;0;0;0;0
-8835;marasme;negative;0;1;1;0;0;1
-8836;marbre;positive;0;0;0;0;0;0
-8837;marbrer;positive;0;0;0;0;0;0
-8838;marchander;positive;0;0;0;0;0;0
-8839;marchandise;positive;0;1;0;0;0;0
-8840;marcher;positive;0;0;0;0;0;0
-8841;marcher à quatre patte;negative;0;0;1;0;0;1
-8842;marche;positive;0;0;0;0;0;0
-8843;mare;negative;0;0;0;0;0;1
-8844;maréchal;positive;0;0;0;0;0;0
-8845;maréchal ferrant;positive;0;0;0;0;0;0
-8846;marée;negative;0;1;0;0;0;0
-8847;marge de man?uvre;positive;0;0;0;0;0;0
-8848;mari tromper;negative;0;0;1;0;0;1
-8849;mariage;positive;0;0;0;0;0;0
-8850;marier;positive;0;0;0;0;0;0
-8851;marijuana;negative;0;0;0;0;0;1
-8852;marin;positive;0;0;0;0;0;0
-8853;marine;positive;0;0;0;0;0;0
-8854;marionnette;negative;0;0;0;0;0;0
-8855;maritime;positive;0;0;0;0;0;0
-8856;marmelade;positive;0;0;0;0;0;0
-8857;marmite;positive;0;0;0;0;0;0
-8858;marne;positive;0;0;0;0;0;0
-8859;marner;positive;0;0;0;0;0;0
-8860;marque de coup;negative;0;0;0;0;0;0
-8861;marquer;positive;0;0;0;0;0;0
-8862;marqueter;negative;0;0;0;0;0;0
-8863;marqueterie;negative;0;0;0;0;0;0
-8864;marquis;positive;0;0;0;0;0;0
-8865;marrer;positive;1;0;0;0;0;0
-8866;marrant;positive;1;0;0;0;0;0
-8867;marron;positive;0;0;0;0;0;0
-8868;mars;positive;0;0;0;0;0;0
-8869;marteau;negative;0;1;0;1;0;0
-8870;martelage;negative;0;0;0;1;0;0
-8871;marteler;negative;0;0;0;1;0;0
-8872;martial;positive;0;0;0;1;0;0
-8873;martingale;positive;0;0;0;0;0;0
-8874;martyr;negative;0;1;1;0;0;0
-8875;martyriser;negative;0;1;1;0;0;0
-8876;mascarade;negative;0;0;0;0;1;0
-8877;masculin;positive;0;0;0;0;0;0
-8878;masochisme;negative;0;1;0;1;0;1
-8879;masque;negative;0;1;0;0;0;0
-8880;masquer;negative;0;1;0;0;0;0
-8881;massage;positive;1;0;0;0;0;0
-8882;masse;negative;0;1;0;0;0;0
-8883;masser;positive;1;0;0;0;0;0
-8884;massif;negative;0;1;0;0;0;0
-8885;mastiquer;negative;0;0;0;0;0;1
-8886;mastiquer bruyamment;negative;0;0;0;0;0;1
-8887;masturbation;positive;1;0;0;0;0;0
-8888;masturber;positive;1;0;0;0;0;0
-8889;match;positive;0;0;0;0;0;0
-8890;mat;negative;0;1;0;0;0;0
-8891;matelasser;positive;0;0;0;0;0;0
-8892;matelot;positive;0;0;0;0;0;0
-8893;matérialiser;positive;0;0;0;0;0;0
-8894;matérialisme;negative;0;0;1;0;0;0
-8895;matérialiste;negative;0;0;1;0;0;1
-8896;matérialité;negative;0;0;0;0;0;0
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-8898;matériel de guerre;negative;0;1;0;0;0;0
-8899;matériel informatique;positive;0;0;0;0;0;0
-8900;matériellement;positive;0;0;0;0;0;0
-8901;maternité;positive;0;0;0;0;0;0
-8902;mathématique;positive;0;0;0;0;0;0
-8903;matière;negative;0;0;0;1;0;1
-8904;matière gluant;negative;0;0;0;0;0;1
-8905;matière fécal;negative;0;0;0;0;0;1
-8906;matin;positive;0;0;0;0;0;0
-8907;matinée;positive;0;0;0;0;0;0
-8908;matou;positive;0;0;0;0;0;0
-8909;matraque;negative;0;1;0;1;0;0
-8910;matraquer;negative;0;0;0;1;0;0
-8911;matrice;positive;0;0;0;0;0;0
-8912;matrone;positive;0;0;0;0;0;0
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-9317;mousson;negative;0;1;1;0;0;0
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-9321;moutarde;negative;0;0;0;0;0;0
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-9335;multiple;positive;0;0;0;0;0;0
-9336;multiplex;positive;0;0;0;0;0;0
-9337;multiplicateur;positive;0;0;0;0;0;0
-9338;multiplication;positive;0;0;0;0;0;0
-9339;multiplicité;positive;0;0;0;0;0;0
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-9343;municipalité;positive;0;0;0;0;0;0
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-9349;mûre;positive;0;0;0;0;0;0
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-9353;mûr;positive;0;0;0;0;0;0
-9354;musc;positive;0;0;0;0;0;0
-9355;muscade;positive;0;0;0;0;0;0
-9356;muscle;positive;0;0;0;0;0;0
-9357;musculaire;positive;0;0;0;0;0;0
-9358;muse;positive;0;0;0;0;0;0
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-9361;museler;negative;0;1;1;1;0;0
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-9363;musique;positive;0;0;1;0;0;0
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-9371;mutinerie;negative;0;1;0;1;1;1
-9372;mutuellement;positive;0;0;0;0;0;0
-9373;mycose vaginal;negative;0;1;0;0;0;1
-9374;myope;negative;0;0;1;0;0;0
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-9376;myriade;positive;0;0;0;0;0;0
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-9385;nadir;positive;0;0;0;0;0;0
-9386;nager;positive;0;0;0;0;0;0
-9387;nageoire;positive;0;0;0;0;0;0
-9388;naïf;negative;0;0;1;0;0;0
-9389;naissance;positive;0;1;0;0;0;0
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-9391;naissant;positive;1;0;0;0;0;0
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-9393;narcotique;negative;0;1;1;1;0;1
-9394;narine;negative;0;0;0;0;0;1
-9395;narratif;positive;0;0;0;0;0;0
-9396;narration;positive;0;0;0;0;0;0
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-9404;national|nationaux;positive;0;0;0;0;0;0
-9405;nativité;positive;1;0;0;0;0;0
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-9409;naturaliser;positive;0;0;0;0;0;0
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-9421;navigable;positive;0;0;0;0;0;0
-9422;navigation;positive;0;0;0;0;0;0
-9423;navigation à voile;positive;0;0;0;0;0;0
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-9427;navire prison;negative;0;1;1;0;0;0
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-9429;ne pas croire;negative;0;0;0;0;0;0
-9430;ne pas être d accord;negative;0;0;0;1;0;0
-9431;ne pas tenir compte;negative;0;0;0;0;0;1
-9432;néanmoins;negative;0;0;0;0;0;0
-9433;néant;negative;0;1;1;0;0;0
-9434;nébuleux;negative;0;1;1;0;0;0
-9435;nébulosité;negative;0;1;0;0;0;0
-9436;nécessiter;positive;0;0;1;0;0;0
-9437;nécessiteux;negative;0;0;1;0;0;0
-9438;nécro;negative;0;0;1;0;1;1
-9439;nécrologie;negative;0;0;1;0;0;0
-9440;nécrose;negative;0;1;1;0;0;1
-9441;nectar;positive;0;0;0;0;0;0
-9442;nef;positive;0;0;0;0;0;0
-9443;néfaste;negative;0;1;1;1;0;1
-9444;négatif;negative;0;1;1;1;0;1
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-9446;négatif|négative;negative;0;0;1;0;0;0
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-9449;négligeante;negative;0;1;1;1;0;0
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-9454;négligent;negative;0;1;1;1;0;1
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-9458;neige;positive;0;0;0;0;0;0
-9459;neige fondu;negative;0;1;1;0;1;1
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-9462;néophyte;negative;0;1;1;0;0;0
-9463;néoprène;positive;0;0;0;0;0;0
-9464;népotisme;negative;0;1;1;1;0;1
-9465;nerf;positive;0;0;0;0;0;0
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-9469;netteté;positive;0;0;0;0;0;0
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-9472;nettoyer;positive;0;0;0;0;0;0
-9473;neuf;positive;0;0;0;0;0;0
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-9476;neutralité;positive;0;0;0;0;0;0
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-9481;névrose;negative;0;1;1;0;0;0
-9482;névrotique;negative;0;1;1;0;0;1
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-9489;nicotine;negative;0;0;0;0;0;1
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-9531;non enregistrer;negative;0;0;1;0;0;0
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-9534;non garder;negative;0;1;0;0;1;0
-9535;non infecter;positive;0;0;0;0;0;0
-9536;non initier;negative;0;1;1;0;0;0
-9537;non inscrire;negative;0;1;1;0;0;0
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-9549;non réciproque;negative;0;0;1;0;0;0
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-9553;non rémunérer;negative;0;0;1;1;0;0
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-9555;non révéler;negative;0;1;0;0;0;0
-9556;non scientifique;negative;0;0;0;0;0;0
-9557;non spécifier;negative;0;0;0;0;0;0
-9558;non sucrer;positive;0;0;0;0;0;0
-9559;non tester;negative;0;0;0;0;0;0
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-9564;non viable;negative;0;0;1;0;0;0
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-9567;non respect;negative;0;1;1;1;0;0
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-9759;opulent;positive;1;0;0;0;0;0
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-9777;orée;positive;0;0;0;0;0;0
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-9779;oreiller;positive;0;0;0;0;0;0
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-9979;parer choc;positive;0;0;0;0;0;0
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-9983;parental;positive;0;0;0;0;0;0
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-9987;parer;positive;0;0;0;0;1;0
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-10009;parrainer;positive;0;0;0;0;0;0
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-10037;passer temps;positive;1;0;0;0;0;0
-10038;passeport;positive;0;0;0;0;0;0
-10039;passer en contrebande;negative;0;1;0;0;0;0
-10040;passerelle;positive;0;0;0;0;0;0
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-10047;passionnant;positive;0;0;0;0;1;0
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-10051;patauger;negative;0;1;1;1;0;1
-10052;patch;negative;0;0;0;0;0;0
-10053;patchwork;positive;0;0;0;0;0;0
-10054;patère;positive;0;0;0;0;0;0
-10055;paternité;positive;0;0;0;0;0;0
-10056;pathétique;negative;0;0;1;1;0;1
-10057;pathologie;negative;0;1;1;0;0;0
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-10064;patiner;positive;0;0;0;0;0;0
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-10066;patio;positive;0;0;0;0;0;0
-10067;pâtisserie;positive;1;0;0;0;0;0
-10068;patriarcal;positive;0;0;0;0;0;0
-10069;patriarche;positive;0;0;0;0;0;0
-10070;patrimoine;positive;0;0;0;0;0;0
-10071;patriote;positive;0;0;0;0;0;0
-10072;patriotique;positive;0;0;0;0;0;0
-10073;patriotisme;positive;0;0;0;0;0;0
-10074;patron;positive;0;0;0;0;0;0
-10075;patronage;positive;0;0;0;0;0;0
-10076;patrouille;positive;0;0;0;0;0;0
-10077;patrouiller;positive;0;0;0;0;0;0
-10078;patte;positive;0;0;0;0;0;0
-10079;pâturage;positive;0;0;0;0;0;0
-10080;pâture;positive;0;0;0;0;0;0
-10081;paume;positive;0;0;0;0;0;0
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-10083;pauvrement;negative;0;1;1;0;0;0
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-10085;pavage;positive;0;0;0;0;0;0
-10086;pavaner;negative;0;0;0;0;0;0
-10087;paver;positive;0;0;0;0;0;0
-10088;pavillon;positive;0;0;0;0;0;0
-10089;pavot;positive;0;0;0;0;0;0
-10090;pax;positive;0;0;0;0;0;0
-10091;paye;positive;1;0;0;0;0;0
-10092;payer|payer;positive;0;0;0;0;0;0
-10093;pays;positive;0;0;0;0;0;0
-10094;paysage;positive;0;0;0;0;0;0
-10095;péage;negative;0;0;0;0;0;0
-10096;peau de vache;negative;0;0;0;0;0;0
-10097;peaufinage;positive;0;0;0;0;0;0
-10098;peaufiner;positive;0;0;0;0;0;0
-10099;pêcher;positive;0;0;0;0;0;0
-10100;pêche à le ligne;positive;0;0;0;0;0;0
-10101;pêcher au chalut;positive;0;0;0;0;0;0
-10102;pécuniaire;negative;0;0;0;0;0;0
-10103;pédagogique;positive;0;0;0;0;0;0
-10104;pédale;positive;0;0;0;0;0;0
-10105;pédaler;positive;0;0;0;0;0;0
-10106;pédant;negative;0;0;0;1;0;1
-10107;pédiatrie;positive;0;0;0;0;0;0
-10108;pédicule;negative;0;0;0;0;0;0
-10109;pedigree;positive;0;0;0;0;0;0
-10110;pédoncule;negative;0;0;0;0;0;0
-10111;peigne;positive;0;0;0;0;0;0
-10112;peigner;positive;0;0;0;0;0;0
-10113;peignoir;positive;0;0;0;0;0;0
-10114;peindre;positive;0;0;0;0;0;0
-10115;peiner;negative;0;1;1;0;0;0
-10116;peintre;positive;0;0;0;0;0;0
-10117;peinture;positive;0;0;0;0;0;0
-10118;peinture mural;positive;0;0;0;0;0;0
-10119;pelage;positive;0;0;0;0;0;0
-10120;pélagien;positive;0;0;0;0;0;0
-10121;pélagique;positive;0;0;0;0;0;0
-10122;peler;negative;0;0;0;0;0;0
-10123;pèlerin;positive;0;0;0;0;0;0
-10124;pèlerinage;positive;0;0;0;0;0;0
-10125;pelle;positive;0;0;0;0;0;0
-10126;pelleter;positive;0;0;0;0;0;0
-10127;pellicule;positive;0;0;0;0;0;0
-10128;pelotage;negative;0;1;0;1;0;1
-10129;peloter;negative;0;1;0;1;0;1
-10130;peloton;positive;0;0;0;0;0;0
-10131;pelouse;positive;0;0;0;0;0;0
-10132;peluche;positive;0;0;0;0;0;0
-10133;pénal;negative;0;1;1;0;0;0
-10134;pénalité;negative;0;1;1;1;0;0
-10135;pencher;negative;0;0;0;0;0;0
-10136;pendaison;negative;0;1;1;1;0;1
-10137;pendant que;negative;0;0;0;0;0;0
-10138;pendentif;positive;0;0;0;0;0;0
-10139;pendre;negative;0;0;0;0;0;0
-10140;pendule;positive;0;0;0;0;0;0
-10141;pénétrer;positive;0;0;0;0;1;0
-10142;pénétrante;positive;0;0;0;0;1;0
-10143;pénétrant;positive;0;0;0;0;1;0
-10144;pénétrer illégalement dans un;negative;0;1;0;1;0;0
-10145;pénibilité;negative;0;0;0;1;0;0
-10146;pénible;negative;0;1;1;1;0;1
-10147;péniche;positive;0;0;0;0;0;0
-10148;péninsule;positive;0;0;0;0;0;0
-10149;pénitence;negative;0;1;1;0;0;0
-10150;pénitencier;negative;0;1;1;1;0;0
-10151;penny;positive;0;0;0;0;0;0
-10152;pénombre;negative;0;1;1;1;0;0
-10153;penser;positive;0;0;0;0;0;0
-10154;penser bête;positive;0;0;0;0;0;0
-10155;penser après coup;negative;0;1;1;0;0;0
-10156;pension;positive;0;0;0;0;0;0
-10157;pension alimentaire;negative;0;0;0;0;0;0
-10158;pensionnaire;positive;0;0;0;0;0;0
-10159;pentagone;positive;0;0;0;0;0;0
-10160;pente;negative;0;1;1;0;0;0
-10161;penthouse;positive;0;0;0;0;0;0
-10162;pénultième;negative;0;0;1;0;0;0
-10163;pénurie;negative;0;1;1;1;0;1
-10164;pépiement;positive;1;0;0;0;0;0
-10165;pépier;positive;1;0;0;0;0;0
-10166;pépin;negative;0;1;1;0;1;0
-10167;pépinière;positive;0;0;0;0;0;0
-10168;pépite;positive;1;0;0;0;0;0
-10169;perceptible;positive;0;0;0;0;0;0
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-10171;perceur;negative;0;1;0;1;0;0
-10172;percevoir;positive;0;0;0;0;1;0
-10173;perche;positive;0;0;0;0;0;0
-10174;percher;positive;0;0;0;0;0;0
-10175;perchoir;positive;0;0;0;0;0;0
-10176;percolation;negative;0;1;0;0;0;1
-10177;percussion;positive;0;0;0;0;0;0
-10178;perdant;negative;0;0;1;1;1;0
-10179;perdition;negative;0;1;1;1;0;1
-10180;perdre;negative;0;1;1;0;1;0
-10181;péremption;negative;0;1;0;0;0;1
-10182;péremptoire;negative;0;1;1;0;0;0
-10183;pérenne;positive;0;0;0;0;0;0
-10184;pereuses;negative;0;1;1;0;0;0
-10185;perfection;positive;0;0;0;0;1;0
-10186;perforation;negative;0;1;0;0;0;0
-10187;perforer;negative;0;1;0;0;0;0
-10188;performance;positive;0;0;0;0;1;0
-10189;péridot;positive;0;0;0;0;0;0
-10190;péril;negative;0;1;1;0;0;0
-10191;périmètre;positive;0;0;0;0;0;0
-10192;période;negative;0;0;0;0;0;0
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-10194;périodique;positive;0;0;0;0;0;0
-10195;périodiquement;positive;0;0;0;0;0;0
-10196;péripétie;positive;0;1;0;0;1;0
-10197;périr;negative;0;1;1;0;0;0
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-10199;perle;positive;0;0;0;0;0;0
-10200;perler;positive;0;0;0;0;0;0
-10201;permanence;positive;0;0;0;0;0;0
-10202;perméable;negative;0;0;0;0;0;0
-10203;permission;positive;0;0;0;0;0;0
-10204;permutable;positive;0;0;0;0;0;0
-10205;permutation;negative;0;1;1;0;0;0
-10206;permuter;positive;0;0;0;0;0;0
-10207;perpendiculaire;positive;0;0;0;0;0;0
-10208;perpétrer;negative;0;1;0;1;0;0
-10209;perpétuation;positive;0;0;0;0;0;0
-10210;perpétuellement;positive;0;0;0;0;0;0
-10211;perpétuer;positive;0;0;0;0;0;0
-10212;perpétuité;positive;0;0;0;0;0;0
-10213;perplexe;negative;0;1;1;0;1;0
-10214;perplexité;negative;0;1;1;0;1;0
-10215;perron;negative;0;1;0;0;0;0
-10216;perroquet;negative;0;0;0;0;0;1
-10217;perruque;negative;0;0;0;0;0;0
-10218;persécuter;negative;0;1;1;1;0;0
-10219;persécution;negative;0;1;1;1;0;1
-10220;persévérance;positive;0;0;0;0;0;0
-10221;persévérer;positive;0;0;0;0;0;0
-10222;persévérant;positive;0;0;0;0;0;0
-10223;persil;positive;0;0;0;0;0;0
-10224;persistance;positive;0;0;0;0;0;0
-10225;persistant;positive;0;0;0;0;0;0
-10226;personnage;positive;0;0;0;0;0;0
-10227;personnaliser;positive;0;0;0;0;0;0
-10228;personnalité;positive;0;0;0;0;0;0
-10229;personne âgé;positive;0;0;0;0;0;0
-10230;personne interroger;positive;0;0;0;0;0;0
-10231;personne qui changer qch;positive;0;0;0;0;0;0
-10232;personne qui fredonner;positive;0;0;0;0;0;0
-10233;personne qui prier;positive;0;0;0;0;0;0
-10234;personne qui se disputer;negative;0;0;0;1;0;0
-10235;personnel;positive;0;0;0;0;0;0
-10236;personne;positive;0;0;0;0;0;0
-10237;personnification;positive;0;0;0;0;0;0
-10238;perspective;positive;0;0;0;0;0;0
-10239;perspicacité;positive;0;0;0;0;0;0
-10240;persuasion;positive;0;0;0;0;0;0
-10241;perte;negative;0;1;1;1;0;0
-10242;perte de connaissance;negative;0;1;1;0;1;0
-10243;pertinence;positive;0;0;0;0;0;0
-10244;perturbation;negative;0;1;1;1;1;0
-10245;perversion;negative;0;0;1;1;0;1
-10246;pervertir;negative;0;1;0;1;0;1
-10247;pesage;positive;0;0;0;0;0;0
-10248;pesant;negative;0;1;1;0;0;0
-10249;pesanteur;positive;0;0;0;0;0;0
-10250;pessimisme;negative;0;1;1;1;0;0
-10251;pessimiste;negative;0;1;1;0;0;0
-10252;peste;negative;0;1;1;0;0;1
-10253;pestilence;negative;0;1;0;0;0;1
-10254;pétasse;negative;0;1;1;1;0;1
-10255;pétiller;positive;1;0;0;0;0;0
-10256;péter;positive;0;0;0;0;0;0
-10257;petit chat;positive;0;0;0;0;0;0
-10258;petit coin;negative;0;0;0;0;0;1
-10259;petit comité;positive;0;0;0;0;0;0
-10260;petit coup;negative;0;1;0;1;1;0
-10261;petit coup de coude;positive;0;0;0;0;0;0
-10262;petit déjeuner;positive;0;0;0;0;0;0
-10263;petit gros petit gros|grosse;negative;0;0;1;0;0;1
-10264;petit morceau;negative;0;0;1;0;0;0
-10265;petit oiseau;positive;0;0;0;0;0;0
-10266;petit pain;positive;0;0;0;0;0;0
-10267;petit salon;positive;0;0;0;0;0;0
-10268;petit;negative;0;0;1;0;0;0
-10269;petit bête;negative;0;1;0;0;0;1
-10270;petit enfance;positive;0;0;0;0;0;0
-10271;petit ferme;positive;0;0;0;0;0;0
-10272;petit idée;positive;0;0;0;0;0;0
-10273;petit morsure;negative;0;1;1;0;1;0
-10274;petit tache;negative;0;0;1;0;0;1
-10275;petit tape;positive;0;0;0;0;0;0
-10276;petitesse;negative;0;0;1;0;0;0
-10277;pétition;positive;0;0;0;0;0;0
-10278;pétitionnaire;positive;0;0;0;0;0;0
-10279;pétitionner;positive;0;0;0;0;0;0
-10280;petit cadeau;positive;1;0;0;0;0;0
-10281;petit enfant;positive;0;0;0;0;0;0
-10282;pétoncle;positive;0;0;0;0;0;0
-10283;pétrir;positive;0;0;0;0;0;0
-10284;peu à peu;positive;0;0;0;0;0;0
-10285;peu aimable;negative;0;1;1;1;0;1
-10286;peu amical;negative;0;1;1;1;0;1
-10287;peu attraire;negative;0;0;1;0;0;1
-10288;peu attrayant;negative;0;0;1;0;0;1
-10289;peu clément;negative;0;0;1;0;0;0
-10290;peu communepeu commun;negative;0;0;0;0;0;0
-10291;peu commune;negative;0;0;0;0;0;0
-10292;peu compatissant;negative;0;0;0;1;0;1
-10293;peu compatissante;negative;0;0;0;1;0;1
-10294;peu compatissantes;negative;0;0;0;1;0;1
-10295;peu compatissants;negative;0;0;0;1;0;1
-10296;peu conclure;negative;0;0;1;0;0;0
-10297;peu concluant;negative;0;0;1;0;0;0
-10298;peu coûteux;positive;1;0;0;0;0;0
-10299;peu critique;negative;0;0;0;0;0;0
-10300;peu familier;negative;0;1;0;0;0;0
-10301;peu fiable;negative;0;1;0;1;0;0
-10302;peu fréquemment;negative;0;0;0;0;1;0
-10303;peu fréquent;negative;0;0;0;0;1;0
-10304;peu importer;negative;0;0;0;0;0;0
-10305;peu instruire;negative;0;0;1;0;0;0
-10306;peu méfier;negative;0;1;1;0;1;0
-10307;peu méfiant;negative;0;1;1;0;1;0
-10308;peu original;negative;0;0;0;0;0;0
-10309;peu orthodoxe;negative;0;1;1;0;0;1
-10310;peu profitable;negative;0;0;1;0;0;0
-10311;peu réaliste;negative;0;1;1;0;0;0
-10312;peu rentable;negative;0;0;1;0;0;0
-10313;peu satisfaisant;negative;0;0;1;0;0;1
-10314;peu séduisant;negative;0;0;1;0;0;1
-10315;peu soigner;negative;0;0;0;0;0;1
-10316;peu sûr;negative;0;1;0;0;1;0
-10317;peuple;positive;0;0;0;0;0;0
-10318;peupler;positive;0;0;0;0;0;0
-10319;peur;negative;0;1;0;1;1;0
-10320;phalange;positive;0;1;0;0;0;0
-10321;phare;positive;0;0;0;0;0;0
-10322;pharmaceutique;positive;0;0;0;0;0;0
-10323;pharmacie;positive;0;0;0;0;0;0
-10324;pharmacologie;positive;0;0;0;0;0;0
-10325;phase;positive;0;0;0;0;0;0
-10326;phénix;positive;0;0;0;0;0;0
-10327;phénomène;positive;0;0;0;0;0;0
-10328;philanthrope;positive;0;0;0;0;0;0
-10329;philanthropie;positive;0;0;0;0;0;0
-10330;philosophe;positive;0;0;0;0;0;0
-10331;philosophie;positive;0;0;0;0;0;0
-10332;philosophique;positive;0;0;0;0;0;0
-10333;phonétique;positive;0;0;0;0;0;0
-10334;phonographe;positive;0;0;0;0;0;0
-10335;phonologie;positive;0;0;0;0;0;0
-10336;phoque;positive;0;0;0;0;0;0
-10337;phosphore;positive;0;0;0;0;0;0
-10338;photocopier;positive;0;0;0;0;0;0
-10339;photogénique;positive;0;0;0;0;0;0
-10340;photographe;positive;0;0;0;0;0;0
-10341;photographie;positive;0;0;0;0;0;0
-10342;photométrie;positive;0;0;0;0;0;0
-10343;phraséologie;positive;0;0;0;0;0;0
-10344;phylogénie;positive;0;0;0;0;0;0
-10345;physiologie;positive;0;0;0;0;0;0
-10346;pianiste;positive;0;0;0;0;0;0
-10347;piano;positive;0;0;0;0;0;0
-10348;piaulement;negative;0;1;1;0;0;0
-10349;piauler;negative;0;1;1;0;0;0
-10350;piazza;positive;0;0;0;0;0;0
-10351;pic;positive;0;1;0;0;0;0
-10352;piccolo;positive;0;0;0;0;0;0
-10353;pichet;positive;0;0;0;0;0;0
-10354;pick up;positive;0;0;0;0;0;0
-10355;picoler;negative;0;0;0;0;0;0
-10356;picorer;negative;0;0;0;1;0;0
-10357;picotement;negative;0;1;0;0;1;0
-10358;picoter;negative;0;1;0;0;0;0
-10359;pie;negative;0;1;0;0;0;1
-10360;pièce de dix cent;positive;0;0;0;0;0;0
-10361;pièce de monnaie;positive;0;0;0;0;0;0
-10362;pièce de théâtre;negative;0;1;1;0;1;0
-10363;pièce jointe;positive;0;0;0;0;0;0
-10364;pièce;positive;0;0;0;0;0;0
-10365;pied;positive;0;0;0;0;0;0
-10366;pied de biche;positive;0;0;0;0;0;0
-10367;piédestal;positive;0;0;0;0;0;0
-10368;pied nu;positive;0;0;0;0;0;0
-10369;piégeage;negative;0;1;0;0;1;0
-10370;piéger;negative;0;1;0;0;1;0
-10371;piège;negative;0;1;0;0;1;0
-10372;pierre;positive;0;0;0;1;0;0
-10373;pierre à feu;negative;0;1;0;0;0;0
-10374;pierre précieux;positive;1;0;0;0;0;0
-10375;pierre tombal;negative;0;0;1;0;0;0
-10376;pierreux;negative;0;1;0;0;0;0
-10377;piété;positive;0;0;0;0;0;0
-10378;piétiner;negative;0;1;1;0;0;0
-10379;piètre;negative;0;0;1;0;0;1
-10380;pieu;negative;0;1;1;0;0;0
-10381;pieuvre;negative;0;1;0;0;0;1
-10382;pigeon;negative;0;1;0;0;0;1
-10383;pigment;positive;0;0;0;0;0;0
-10384;pigmenter;positive;0;0;0;0;0;0
-10385;pilier;positive;0;0;0;0;0;0
-10386;pilote;positive;0;0;0;0;0;0
-10387;piloter;positive;0;0;0;0;0;0
-10388;pilule;positive;0;0;0;0;0;0
-10389;pimenter;negative;0;1;0;0;1;0
-10390;pin;positive;0;0;1;0;0;0
-10391;pinacle;positive;0;0;0;0;0;0
-10392;pince;negative;0;1;0;1;0;0
-10393;pince à linge;positive;0;0;0;0;0;0
-10394;pinceant;negative;0;1;0;0;0;0
-10395;pinceau;positive;0;0;0;0;0;0
-10396;pincée;negative;0;0;0;0;0;0
-10397;pincement;negative;0;1;0;0;0;0
-10398;pincer;negative;0;1;0;1;0;0
-10399;pion;negative;0;0;0;0;0;0
-10400;pipelet;negative;0;0;0;0;0;0
-10401;pipeline;positive;0;0;0;0;0;0
-10402;pipette;positive;0;0;0;0;0;0
-10403;pipi;negative;0;0;0;0;0;1
-10404;piquant;negative;0;1;0;0;1;0
-10405;pique;negative;0;1;0;1;0;0
-10406;pique niquer;positive;0;0;0;0;1;0
-10407;piquer niquer;positive;0;0;0;0;1;0
-10408;piquer sur;negative;0;1;0;1;1;0
-10409;piquet;negative;0;1;1;1;1;0
-10410;piquet de grève;negative;0;1;0;1;1;0
-10411;piqûre;negative;0;1;0;1;1;1
-10412;piratage;negative;0;1;0;1;0;0
-10413;pirate;negative;0;1;0;1;0;0
-10414;pirater;negative;0;1;0;1;0;0
-10415;piraterie;negative;0;1;0;1;0;0
-10416;pire;negative;0;1;1;0;0;0
-10417;pirogue;positive;0;0;0;0;0;0
-10418;piscine;positive;0;0;0;0;0;0
-10419;pister;positive;0;0;0;0;0;0
-10420;pistolet;negative;0;1;0;1;0;0
-10421;piston;positive;0;0;0;0;0;0
-10422;pitié;negative;0;0;1;0;0;0
-10423;pittoresque;positive;0;0;0;0;0;0
-10424;pivot;positive;0;0;0;0;0;0
-10425;pivoter;positive;0;0;0;0;0;0
-10426;placage;positive;0;0;0;0;0;0
-10427;placard;positive;0;0;0;0;0;0
-10428;place du marché;positive;0;0;0;0;0;0
-10429;placebo;positive;0;0;0;0;0;0
-10430;plafond;positive;0;0;0;0;0;0
-10431;plage;positive;1;0;0;0;0;0
-10432;plagiat;negative;0;0;1;1;0;1
-10433;plaid;positive;0;0;0;0;0;0
-10434;plaider;negative;0;1;1;1;0;1
-10435;plaideur;negative;0;0;1;1;0;0
-10436;plaie;negative;0;1;1;1;0;1
-10437;plaine;positive;0;0;0;0;0;0
-10438;plainte;negative;0;0;0;1;0;0
-10439;plaintif;negative;0;0;1;0;0;0
-10440;plaisance;positive;1;0;0;0;0;0
-10441;plaisanter;positive;1;0;0;0;0;0
-10442;plaisanterie;positive;0;0;0;0;1;0
-10443;planche;positive;0;0;0;0;0;0
-10444;plancher;positive;0;0;0;0;0;0
-10445;planéité;negative;0;0;1;0;0;0
-10446;planer;positive;1;0;0;0;0;0
-10447;planétarium;positive;0;0;0;0;0;0
-10448;planète;positive;0;0;0;0;0;0
-10449;planification;positive;0;0;0;0;0;0
-10450;planifier;positive;0;0;0;0;0;0
-10451;plantation;positive;0;0;0;0;0;0
-10452;plante;positive;0;0;0;0;0;0
-10453;plante du pied;negative;0;0;0;0;0;1
-10454;planter;positive;0;0;0;0;0;0
-10455;planteur|planteuse;positive;0;0;0;0;0;0
-10456;plantureux;positive;0;0;0;0;0;0
-10457;plaquage;positive;0;0;0;0;0;0
-10458;plaque chauffant;positive;0;0;0;0;0;0
-10459;plaque tournant;positive;0;0;0;0;0;0
-10460;plaquer;negative;0;1;1;0;0;0
-10461;plaquer au sol;positive;0;0;0;1;1;0
-10462;plaquette;positive;0;0;0;0;0;0
-10463;plasma;negative;0;0;0;0;0;1
-10464;plasticité;positive;0;0;0;0;0;0
-10465;plastifier;positive;0;0;0;0;0;0
-10466;plastique;negative;0;0;0;0;0;0
-10467;plat forme;positive;0;0;0;0;0;0
-10468;plateau;positive;0;0;0;0;0;0
-10469;plateforme;positive;0;0;0;0;0;0
-10470;platine;positive;0;0;0;0;0;0
-10471;platitude;negative;0;0;1;0;0;0
-10472;plâtre;negative;0;0;1;0;0;0
-10473;plâtrer;negative;0;0;1;0;0;0
-10474;plausibilité;positive;0;0;0;0;0;0
-10475;playa;positive;0;0;0;0;0;0
-10476;plein;positive;0;0;0;0;0;0
-10477;plein à craquer;negative;0;1;0;0;0;0
-10478;plein d espoir;positive;0;0;0;0;1;0
-10479;plein de bulle;positive;0;0;0;0;0;0
-10480;plein de monde;negative;0;1;0;0;0;0
-10481;plein de ressentiment;negative;0;0;0;1;0;0
-10482;plein propriété;positive;0;0;0;0;0;0
-10483;plénier;positive;0;0;0;0;0;0
-10484;plénitude;positive;0;0;0;0;0;0
-10485;pléthore;positive;0;0;0;0;0;0
-10486;pleur;negative;0;0;1;1;0;0
-10487;pleurant;negative;0;0;1;0;0;0
-10488;pleurer;negative;0;0;1;1;0;0
-10489;pleur|pleurs;negative;0;0;1;0;0;0
-10490;pleuvoir;negative;0;0;1;0;0;0
-10491;plexus;positive;0;0;0;0;0;0
-10492;pli;negative;0;1;1;0;0;1
-10493;pliage;positive;0;0;0;0;0;0
-10494;plier;negative;0;0;1;0;0;0
-10495;plinthe;positive;0;0;0;0;0;0
-10496;plisser;negative;0;0;1;0;0;0
-10497;ploiement;negative;0;0;1;0;0;0
-10498;plomb;positive;0;0;0;0;0;0
-10499;ployer;negative;0;0;1;0;0;0
-10500;pluie;negative;0;0;1;0;0;0
-10501;plumage;positive;0;0;0;0;0;0
-10502;plume;positive;0;0;0;0;0;0
-10503;plumeau;negative;0;0;0;0;0;1
-10504;pluralité;positive;0;0;0;0;0;0
-10505;plus âgé;positive;0;0;1;0;0;0
-10506;plus dodu;negative;0;0;0;0;0;0
-10507;plus élever;positive;0;0;0;0;0;0
-10508;plus grand que;positive;0;0;0;0;1;1
-10509;plus important;positive;0;0;0;0;0;0
-10510;plus jeune;positive;1;0;0;0;0;0
-10511;plus loin;negative;0;1;1;0;0;0
-10512;plus petit;negative;0;1;1;0;0;0
-10513;plus sec;positive;0;0;0;0;0;0
-10514;plus tôt;positive;0;0;0;0;0;0
-10515;plus vieux;positive;0;0;1;0;0;0
-10516;plusieurs foi|fois;negative;0;0;0;0;0;0
-10517;plutonium;positive;0;0;0;0;0;0
-10518;pluvier;positive;0;0;0;0;0;0
-10519;pneumonie;negative;0;1;1;0;0;0
-10520;poche;positive;0;0;0;0;0;0
-10521;poche ventral;positive;0;0;0;0;0;0
-10522;pochoir;positive;0;0;0;0;0;0
-10523;podium;positive;0;0;0;0;0;0
-10524;podomètre;positive;0;0;0;0;0;0
-10525;poêle;positive;0;0;0;0;0;0
-10526;poêle à frire;negative;0;0;0;0;0;0
-10527;poème;positive;0;0;0;0;0;0
-10528;poésie;positive;0;0;0;0;0;0
-10529;poète;positive;0;0;0;0;0;0
-10530;poétique;positive;0;0;0;0;0;0
-10531;poigner|poindre;positive;0;0;1;0;1;0
-10532;poignant;positive;0;0;1;0;1;0
-10533;poignarder;negative;0;1;1;1;1;0
-10534;poignet;positive;0;0;0;0;0;0
-10535;poing;positive;0;0;0;0;0;0
-10536;point culminer;positive;0;0;0;0;1;0
-10537;point de repère;positive;0;0;0;0;0;0
-10538;point de suture;negative;0;0;0;0;0;0
-10539;point de vue;positive;0;0;0;0;0;0
-10540;point fort;positive;0;0;0;0;0;0
-10541;point virgule;positive;0;0;0;0;0;0
-10542;pointer;positive;0;0;0;0;0;0
-10543;pointeur;positive;0;0;0;0;0;0
-10544;point de suspension;positive;0;0;0;0;0;0
-10545;pointu;negative;0;1;0;0;0;0
-10546;pointure;positive;0;0;0;0;0;0
-10547;pois;positive;0;0;0;0;0;0
-10548;poison;negative;0;1;1;1;0;1
-10549;poisseux;negative;0;0;0;0;0;1
-10550;poisson;positive;0;0;0;0;0;0
-10551;poissonnerie;positive;0;0;0;0;0;0
-10552;poitrine;positive;0;0;0;0;0;0
-10553;poivre;negative;0;0;0;0;0;0
-10554;poivron;negative;0;0;0;0;0;0
-10555;poker;positive;0;0;0;0;1;0
-10556;polaire;negative;0;1;0;0;0;0
-10557;polarité;positive;0;0;0;0;1;0
-10558;pôle;positive;0;0;0;0;0;0
-10559;polémique;negative;0;0;0;1;0;1
-10560;police de caractère;positive;0;0;0;0;0;0
-10561;polio;negative;0;1;1;0;0;0
-10562;politique;positive;0;0;0;1;0;1
-10563;polluer;negative;0;0;0;0;0;1
-10564;pollution;negative;0;0;0;0;0;1
-10565;polo;positive;0;0;0;0;0;0
-10566;polonais;positive;0;0;0;0;0;0
-10567;polygamie;negative;0;0;0;0;0;1
-10568;polygonal;positive;0;0;0;0;0;0
-10569;polygonales;positive;0;0;0;0;0;0
-10570;polygone;positive;0;0;0;0;0;0
-10571;polymère;positive;0;0;0;0;0;0
-10572;polymorphisme;positive;0;0;0;0;0;0
-10573;polyvalence;positive;0;0;0;0;0;0
-10574;polyvalent;positive;0;0;0;0;0;0
-10575;pommade;positive;0;0;0;0;0;0
-10576;pomme;positive;0;0;0;0;0;0
-10577;pompage;positive;0;0;0;0;0;0
-10578;pompe;positive;0;0;0;0;0;0
-10579;pomper;positive;0;0;0;0;0;0
-10580;pompette;negative;0;0;0;0;0;1
-10581;pompeux;negative;0;0;0;0;0;1
-10582;pompier;positive;0;0;0;0;0;0
-10583;ponceur;positive;0;0;0;0;0;0
-10584;poncho;positive;0;0;0;0;0;0
-10585;ponction;negative;0;1;1;0;1;0
-10586;ponctualité;positive;0;0;0;0;0;0
-10587;ponctuation;positive;0;0;0;0;0;0
-10588;ponctuellement;negative;0;0;0;0;0;0
-10589;pondérer;positive;0;0;0;0;0;0
-10590;poney;positive;0;0;0;0;0;0
-10591;pont;positive;0;0;0;0;0;0
-10592;pontife;positive;0;0;0;0;0;0
-10593;pontificat;positive;0;0;0;0;0;0
-10594;pontifier;positive;0;0;0;0;0;0
-10595;ponton;positive;0;0;0;0;0;0
-10596;pop;negative;0;1;0;0;1;0
-10597;populace;negative;0;1;0;1;0;1
-10598;populaire;positive;0;0;0;0;0;0
-10599;populariser;positive;0;0;0;0;0;0
-10600;popularité;positive;0;0;0;0;0;0
-10601;population;positive;0;0;0;0;0;0
-10602;porc épic;negative;0;1;0;0;0;1
-10603;porcelaine;positive;0;0;0;0;0;0
-10604;porche;positive;0;0;0;0;0;0
-10605;porcherie;negative;0;0;0;0;0;1
-10606;pore;negative;0;0;0;0;0;1
-10607;porno;negative;0;0;0;0;0;1
-10608;pornographie;negative;0;1;0;0;0;1
-10609;pornographique;negative;0;0;0;0;0;1
-10610;porosité;negative;0;0;0;0;0;1
-10611;porridge;positive;0;0;0;0;0;0
-10612;port;positive;0;0;0;0;0;0
-10613;port maritime;positive;0;0;0;0;0;0
-10614;portable;positive;0;0;0;0;0;0
-10615;portage;positive;0;0;0;0;0;0
-10616;portail;positive;0;0;0;0;0;0
-10617;porter;positive;0;0;1;0;0;0
-10618;portatif;positive;0;0;0;0;0;0
-10619;porte;positive;0;0;0;0;0;0
-10620;porte bonheur;positive;0;0;0;0;1;0
-10621;porte monnaie;positive;0;0;0;0;0;0
-10622;porte parole;positive;0;0;0;0;0;0
-10623;porte voix;negative;0;1;0;0;0;0
-10624;portefeuille;positive;0;0;0;0;0;0
-10625;porter atteindre;negative;0;1;1;1;0;1
-10626;porter un toast;positive;1;0;0;0;0;0
-10627;portion;positive;0;0;0;0;0;0
-10628;portique;positive;0;0;0;0;0;0
-10629;portraire;positive;0;0;0;0;0;0
-10630;pose;positive;0;0;0;0;0;0
-10631;poser;positive;0;0;0;0;0;0
-10632;pose de carrelage;positive;0;0;0;0;0;0
-10633;poser du tuile sur;positive;0;0;0;0;0;0
-10634;position;positive;0;0;0;0;0;0
-10635;position accroupir;negative;0;1;0;0;0;0
-10636;position debout;positive;0;0;0;0;0;0
-10637;possédant;positive;0;0;0;0;0;0
-10638;posséder;negative;0;1;0;1;0;1
-10639;posséedées;negative;0;1;0;1;0;1
-10640;possesseur;positive;0;0;0;0;0;0
-10641;possession;negative;0;1;1;1;0;1
-10642;possibilité;positive;0;0;0;0;0;0
-10643;possible;positive;0;0;0;0;1;0
-10644;post scriptum;positive;0;0;0;0;0;0
-10645;postal;positive;0;0;0;0;0;0
-10646;poste;positive;0;0;0;0;0;0
-10647;poste d amarrage;positive;0;0;0;0;0;0
-10648;poste de commandement;positive;0;0;0;0;0;0
-10649;poste de guet;positive;0;0;0;0;0;0
-10650;poste vacant;positive;1;0;0;0;0;0
-10651;poster;positive;0;0;0;0;0;0
-10652;postérieur;negative;0;0;0;0;0;0
-10653;postérieurement;negative;0;0;0;0;0;0
-10654;postériorité;negative;0;0;0;0;0;0
-10655;postérité;negative;0;0;1;0;0;0
-10656;posthume;negative;0;0;1;0;0;0
-10657;postillonner;negative;0;0;0;0;0;1
-10658;postulat;positive;0;0;0;0;0;0
-10659;postuler;positive;0;0;0;0;0;0
-10660;postuler à;positive;0;0;0;0;0;0
-10661;posture;positive;0;0;0;0;0;0
-10662;pot;positive;0;0;0;0;0;0
-10663;pot à crème;positive;0;0;0;0;0;0
-10664;pot de fleur;positive;0;0;0;0;0;0
-10665;pot au feu;positive;0;0;0;0;0;0
-10666;pot de vin;negative;0;0;0;0;0;0
-10667;pot pourri;positive;0;0;0;0;0;0
-10668;potable;positive;0;0;0;0;0;0
-10669;pote;positive;0;0;0;0;0;0
-10670;poteau indicateur;positive;0;0;0;0;0;0
-10671;potence;negative;0;1;1;1;0;0
-10672;poterie;positive;0;0;0;0;0;0
-10673;potier;positive;0;0;0;0;0;0
-10674;potion;positive;0;0;0;0;1;0
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-10683;poule;positive;0;0;0;0;0;0
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-10686;pouliche;positive;0;0;0;0;0;0
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-10714;pousser du doigt;negative;0;1;0;1;1;0
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-10718;poutre;positive;0;0;0;0;0;0
-10719;pouvoir exécutif;positive;0;0;0;0;0;0
-10720;pouvoir judiciaire;positive;0;0;0;0;0;0
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-10730;précaution;positive;0;0;0;0;0;0
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-10733;précédemment;positive;0;0;0;0;0;0
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-10750;prédicateur;positive;0;0;0;0;0;0
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-10752;prédicteur;positive;0;0;0;0;0;0
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-10755;prédilection;positive;0;0;0;0;0;0
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-10762;préemption;positive;0;0;0;0;0;0
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-10764;préfacer;positive;0;0;0;0;0;0
-10765;préférer;positive;0;0;0;0;0;0
-10766;préférence;positive;0;0;0;0;0;0
-10767;préfet;positive;0;0;0;0;0;0
-10768;préfixe;positive;0;0;0;0;0;0
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-10785;prendre le fuite;negative;0;1;0;0;0;0
-10786;prendre le petit déjeuner;positive;0;0;0;0;0;0
-10787;prendre le risque de;positive;0;0;0;0;1;0
-10788;prendre part à;positive;0;0;0;0;0;0
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-10793;prépondérance;positive;0;0;0;0;0;0
-10794;prérequis;positive;0;0;0;0;0;0
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-10796;présage;negative;0;1;0;1;0;0
-10797;présager;positive;0;0;0;0;0;0
-10798;presbytère;positive;0;0;0;0;0;0
-10799;prescient;positive;0;0;0;0;0;0
-10800;prescription;positive;0;0;0;0;0;0
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-10803;présence;positive;0;0;0;0;0;0
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-10805;présentable;positive;0;0;0;0;0;0
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-10807;présenter;positive;0;0;0;0;0;0
-10808;présenter du excuse;positive;0;0;1;0;0;0
-10809;préservation;positive;0;0;0;0;0;0
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-10812;présomption;negative;0;1;0;0;0;0
-10813;presser;negative;0;1;0;0;0;0
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-10815;pression;negative;0;1;1;1;0;0
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-10817;prestidigitation;negative;0;1;0;0;1;0
-10818;prestige;positive;0;0;0;0;0;0
-10819;presto;positive;0;0;0;0;1;0
-10820;présumer;positive;0;0;0;0;0;0
-10821;présupposer;positive;0;0;0;0;0;0
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-10823;prétendant;positive;0;0;0;0;0;0
-10824;prétendre;negative;0;1;1;1;0;0
-10825;prêter;positive;0;0;0;0;0;0
-10826;prêter à confusion;negative;0;1;0;1;0;0
-10827;prêter serment;positive;0;0;0;0;0;0
-10828;prétexter;negative;0;0;0;1;0;0
-10829;prétexte;negative;0;0;0;0;0;1
-10830;prêtre;positive;0;0;0;0;0;0
-10831;prêtrise;positive;0;0;1;0;0;0
-10832;preuve;positive;0;0;0;0;0;0
-10833;prévalence;positive;0;0;0;0;0;0
-10834;prévaloir;positive;0;0;0;0;0;0
-10835;prévenance;positive;0;0;0;0;0;0
-10836;prévenir;positive;0;0;0;0;0;0
-10837;prévenant;positive;0;0;0;0;0;0
-10838;préventif;positive;0;0;0;0;0;0
-10839;prévention;positive;0;0;0;0;0;0
-10840;prévision;positive;0;0;0;0;0;0
-10841;prévisionniste;positive;0;0;0;0;0;0
-10842;prévoir;positive;0;0;0;0;1;0
-10843;prévoyance;positive;0;0;0;0;0;0
-10844;prier;positive;0;1;0;0;1;0
-10845;prière;positive;0;0;0;0;0;0
-10846;prieuré;positive;0;0;0;0;0;0
-10847;primate;negative;0;0;0;0;0;0
-10848;primauté;positive;0;0;0;0;0;0
-10849;prime;positive;0;0;0;0;1;0
-10850;primordial;positive;0;0;0;0;0;0
-10851;prince;positive;0;0;0;0;0;0
-10852;princesse;positive;0;0;0;0;0;0
-10853;principal;positive;0;0;0;0;0;0
-10854;principalement;positive;0;0;0;0;0;0
-10855;principe;positive;0;0;0;0;0;0
-10856;priorité;positive;0;0;0;0;0;0
-10857;prendre de vertige;negative;0;1;0;0;1;0
-10858;prise de bec;negative;0;0;0;1;0;0
-10859;prise de courant;negative;0;1;0;0;0;0
-10860;prise de vertige;negative;0;1;0;0;1;0
-10861;prise en compte;positive;0;0;0;0;0;0
-10862;prise en considération;positive;0;0;0;0;0;0
-10863;prismatique;positive;0;0;0;0;0;0
-10864;prisme;positive;0;0;0;0;0;0
-10865;prison;negative;0;1;1;1;0;0
-10866;prisonnier prisonnier;negative;0;1;1;1;0;1
-10867;privation;negative;0;1;1;1;0;1
-10868;priver;positive;0;0;0;0;0;0
-10869;priver de qch;negative;0;0;1;0;0;0
-10870;privilège;positive;0;0;0;0;0;0
-10871;privilégier;positive;0;0;0;0;0;0
-10872;privilégié;positive;0;0;0;0;0;0
-10873;probabilité;positive;0;0;0;0;0;0
-10874;probable;positive;0;0;0;0;0;0
-10875;probant;positive;1;0;0;0;0;0
-10876;probation;negative;0;1;1;0;0;0
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-10880;procédé;positive;0;1;0;0;0;0
-10881;procéder;positive;0;0;0;0;0;0
-10882;procéder à un lavage interne;negative;0;1;0;0;0;1
-10883;procédure;positive;0;1;0;0;0;0
-10884;procession;positive;0;0;1;0;1;0
-10885;processus;positive;0;0;0;0;0;0
-10886;prochain;positive;0;0;0;0;0;0
-10887;prochainement;positive;0;0;0;0;0;0
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-10889;proclamation;positive;0;0;0;0;0;0
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-10891;procrastination;negative;0;1;1;0;0;0
-10892;procréation;positive;1;0;0;0;0;0
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-10894;procuration;positive;0;0;0;0;0;0
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-10896;prodige;positive;0;0;0;0;0;0
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-10898;production;positive;0;0;0;0;0;0
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-10902;produit phare;positive;0;0;0;0;0;0
-10903;produit cosmétique;positive;0;0;0;0;0;0
-10904;produit de beauté;positive;0;0;0;0;0;0
-10905;profanation;negative;0;1;1;1;0;1
-10906;profaner;negative;0;0;1;1;0;1
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-10936;prolétarien;negative;0;0;0;0;0;0
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-10941;prolongement;positive;0;0;0;0;0;0
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-10957;propagande;negative;0;1;0;1;0;0
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-10964;prophétiser;positive;0;0;0;0;0;0
-10965;prophylactique;positive;0;0;0;0;0;0
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-11039;publiquement;positive;0;0;0;0;0;0
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-11046;puits;positive;0;0;0;0;0;0
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-11049;pulpe;negative;0;0;0;0;0;1
-11050;pulsation;positive;0;0;0;0;0;0
-11051;pulvériser;positive;0;0;0;0;0;0
-11052;puma;negative;0;1;0;1;1;0
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-11071;pouvoir;negative;0;0;0;0;0;1
-11072;putain;negative;0;0;0;0;0;1
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-11074;putois;negative;0;0;0;0;0;1
-11075;pygmée;negative;0;0;0;0;0;0
-11076;pyjama;positive;0;0;0;0;0;0
-11077;pyramidal;positive;0;0;0;0;0;0
-11078;pyramide;positive;0;0;0;0;0;0
-11079;pyrotechnie;positive;0;0;0;0;0;0
-11080;quadrant;positive;0;0;0;0;0;0
-11081;quadratique;positive;0;0;0;0;0;0
-11082;quadrilatère;positive;0;0;0;0;0;0
-11083;quadripolaire;positive;0;0;0;0;0;0
-11084;quadripôle;positive;0;0;0;0;0;0
-11085;quadruple;positive;0;0;0;0;0;0
-11086;quadrupler;positive;0;0;0;0;0;0
-11087;quai;positive;0;0;0;0;0;0
-11088;qualifier;positive;0;0;0;0;0;0
-11089;qualifiantes;positive;0;0;0;0;0;0
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-11094;quantique;positive;0;0;0;0;0;0
-11095;quantitativement;positive;0;0;0;0;0;0
-11096;quantité;positive;0;0;0;0;0;0
-11097;quantité suffisant;positive;0;0;0;0;0;0
-11098;quantum;positive;0;0;0;0;0;0
-11099;quarantaine;negative;0;1;0;0;0;1
-11100;quarante;positive;0;0;0;0;0;0
-11101;quart;positive;0;0;0;0;0;0
-11102;quartet;positive;0;0;0;0;0;0
-11103;quartier;positive;0;0;0;0;0;0
-11104;quartile;positive;0;0;0;0;0;0
-11105;quartz;positive;0;0;0;0;0;0
-11106;quasi;positive;0;0;0;0;0;0
-11107;quaternaire;positive;0;0;0;0;0;0
-11108;quatre vingt dix;positive;0;0;0;0;0;0
-11109;quatre vingts;positive;0;0;0;0;0;0
-11110;quatrième;positive;0;0;0;0;0;0
-11111;quatuor;positive;0;0;0;0;0;0
-11112;que ce être;negative;0;0;0;0;0;0
-11113;quel que être;negative;0;0;0;0;0;0
-11114;quémander;negative;0;0;1;0;0;1
-11115;querelle;negative;0;1;1;1;0;1
-11116;questionnaire;negative;0;0;0;0;0;0
-11117;questionner;negative;0;1;0;0;0;0
-11118;questionnement;negative;0;1;0;0;0;0
-11119;quête;positive;0;0;0;0;0;0
-11120;queue;negative;0;0;1;0;0;0
-11121;qui avoir du avis très arrêter;negative;0;0;0;1;0;0
-11122;qui avoir du préjugé;negative;0;1;0;1;0;1
-11123;qui avoir lieu;positive;0;0;0;0;0;0
-11124;qui avoir perdre;negative;0;0;1;1;1;0
-11125;qui avoir sommeil;negative;0;0;1;0;0;0
-11126;qui avoir un odeur de moisir;negative;0;0;0;0;0;1
-11127;qui approcher;positive;0;0;0;0;0;0
-11128;qui arriver;positive;0;0;0;0;0;0
-11129;qui attendre;negative;0;0;0;0;0;0
-11130;qui bâiller;negative;0;0;0;0;0;0
-11131;qui bouche;negative;0;0;0;1;1;0
-11132;qui calculer;positive;0;0;0;0;0;0
-11133;qui cause du tort;negative;0;0;1;1;0;1
-11134;qui commander;positive;0;0;0;0;0;0
-11135;qui consommer beaucoup;negative;0;0;0;0;0;1
-11136;qui court;positive;0;0;0;0;0;0
-11137;qui déborder;negative;0;1;0;0;1;0
-11138;qui délire;negative;0;1;1;1;1;0
-11139;qui désirer;positive;0;0;0;0;0;0
-11140;qui divaguer qui radoter;negative;0;1;1;0;0;0
-11141;qui dormir;positive;0;0;0;0;0;0
-11142;qui doute;negative;0;1;0;0;0;0
-11143;qui embaumer;positive;0;0;0;0;0;0
-11144;qui empêcher de se concentrer;negative;0;0;0;1;0;0
-11145;qui empire;negative;0;0;1;0;0;1
-11146;qui en résulter;positive;0;0;0;0;0;0
-11147;qui entrave;negative;0;0;1;1;0;0
-11148;qui être un échec;negative;0;0;1;0;0;0
-11149;qui établir;positive;0;0;0;0;0;0
-11150;qui extraire;positive;0;0;0;0;0;0
-11151;qui faire bouger le chose;positive;0;0;0;0;0;0
-11152;qui fond;negative;0;0;0;0;0;0
-11153;qui fuir;negative;0;1;0;0;1;1
-11154;qui gaspiller;negative;0;1;1;1;0;1
-11155;qui induire en erreur;negative;0;0;0;1;0;1
-11156;qui jouer de le batterie;positive;0;0;0;0;0;0
-11157;qui jouer du tambour;positive;0;0;0;0;0;0
-11158;qui manque de;negative;0;0;1;0;0;0
-11159;qui maudire;negative;0;1;0;1;0;1
-11160;qui mijoter;positive;0;0;0;0;0;0
-11161;qui monter;positive;0;1;0;0;0;0
-11162;qui monter à cru;positive;0;0;0;0;0;0
-11163;qui monter en flêche;negative;0;1;0;0;1;0
-11164;qui ne peser rien;positive;0;0;0;0;0;0
-11165;qui ne se douter de rien;negative;0;1;1;0;1;0
-11166;qui nettoyer;positive;0;0;0;0;0;0
-11167;qui observer en silence;negative;0;1;1;0;0;0
-11168;qui obstruer;negative;0;0;0;1;1;0
-11169;qui passer;negative;0;0;1;0;0;0
-11170;qui passer inaperçu;positive;0;0;0;0;0;0
-11171;qui perdre;negative;0;0;1;1;1;0
-11172;qui périr;negative;0;1;1;0;0;0
-11173;qui pouvoir se procurer;positive;1;0;0;0;0;0
-11174;qui prendre effet;positive;0;0;0;0;0;0
-11175;qui promettre;positive;0;0;0;0;0;0
-11176;qui protéger;positive;0;0;0;0;0;0
-11177;qui réchauffer;positive;1;0;0;0;0;0
-11178;qui recouvrer|recouvrir;positive;0;0;0;0;0;0
-11179;qui répondre;positive;0;0;0;0;0;0
-11180;qui représenter;positive;0;0;0;0;0;0
-11181;qui rester;negative;0;0;0;0;0;0
-11182;qui retenir;negative;0;0;0;0;0;0
-11183;qui rétrécir;negative;0;1;1;0;0;0
-11184;qui rime;positive;0;0;0;0;0;0
-11185;qui rire;positive;1;0;0;0;0;0
-11186;qui rougir;negative;0;0;0;0;1;0
-11187;qui roule;negative;0;0;0;0;0;0
-11188;qui s empiffrer;negative;0;0;0;0;0;1
-11189;qui s ennuyer;negative;0;0;1;0;0;0
-11190;qui se cacher;negative;0;1;1;0;0;0
-11191;qui se consumer;negative;0;1;1;0;0;0
-11192;qui se dégrader;negative;0;0;1;0;0;1
-11193;qui se détériorer;negative;0;0;1;0;0;1
-11194;qui se faire du souci;negative;0;1;1;0;0;0
-11195;qui se réduire;negative;0;1;1;0;0;0
-11196;qui se vanter;negative;0;0;0;0;0;0
-11197;qui se voir;positive;0;0;0;0;0;0
-11198;qui sombre;negative;0;1;1;0;0;0
-11199;qui sommeiller;positive;0;0;0;0;0;0
-11200;qui sonner;positive;0;0;0;0;1;0
-11201;qui soulager;positive;0;0;0;0;0;0
-11202;qui subsister;positive;1;0;0;0;0;0
-11203;qui suivre;positive;0;0;0;0;0;0
-11204;qui tendre ver|vers;positive;0;0;0;0;0;0
-11205;qui tomber;negative;0;1;1;0;0;0
-11206;qui tourner;negative;0;1;0;0;0;0
-11207;qui travailler;positive;0;0;0;0;0;0
-11208;quiétude;positive;0;0;0;0;0;0
-11209;quille;negative;0;1;0;0;0;0
-11210;quincaillerie;positive;0;0;0;0;0;0
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-11219;quotient;negative;0;0;0;0;0;0
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-11223;rabique;negative;0;1;1;1;0;1
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-11226;raccord;positive;0;0;0;0;0;0
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-11231;rachat;positive;0;0;0;0;0;0
-11232;racheter;positive;0;0;0;0;0;0
-11233;rachis;positive;0;0;0;1;0;0
-11234;racine;positive;0;0;0;0;0;0
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-11236;racler;negative;0;1;1;1;0;0
-11237;racoler;negative;0;0;0;0;0;0
-11238;racoleur;negative;0;0;0;0;0;0
-11239;raconter;positive;0;0;0;0;0;0
-11240;raconter un bobard;negative;0;0;0;1;0;1
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-11242;radeau;negative;0;1;1;0;0;0
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-11250;radiologie;positive;0;0;0;0;0;0
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-11253;radon;negative;0;1;0;0;0;0
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-11314;rassemblement;positive;0;0;0;0;0;0
-11315;rassembler;positive;0;0;0;0;0;0
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-11339;rayaux;positive;0;0;0;0;0;0
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-11341;rayon de miel;positive;0;0;0;0;0;0
-11342;rayon de soleil;positive;1;0;0;0;0;0
-11343;rayonner;positive;1;0;0;0;0;0
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-11356;réaménager;positive;0;0;0;0;0;0
-11357;réanimation;negative;0;1;1;0;0;0
-11358;réanimer;positive;0;0;0;0;0;0
-11359;réapparaître;positive;0;0;0;0;1;0
-11360;réapprovisionner;positive;0;0;0;0;0;0
-11361;réarrangement;positive;0;0;0;0;0;0
-11362;réarranger;positive;0;0;0;0;0;0
-11363;réassembler;positive;0;0;0;0;0;0
-11364;réassurance;positive;0;0;0;0;0;0
-11365;rebâtir;positive;0;0;0;0;0;0
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-11367;rébellion;negative;0;1;0;1;1;1
-11368;rebond;positive;1;0;0;0;0;0
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-11370;rebord;negative;0;1;0;0;0;0
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-11372;recensement;positive;0;0;0;0;0;0
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-11549;régulier;positive;0;0;0;0;0;0
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-11551;réhabilitation;positive;0;0;0;0;0;0
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-11554;réimpression;positive;0;0;0;0;0;0
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-11561;réintégration;positive;0;0;0;0;0;0
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-11587;remettre à plus tard;negative;0;0;1;0;0;0
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-11589;remettre du diplôme;positive;0;1;0;0;1;0
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-11591;remodeler;positive;0;0;0;0;0;0
-11592;remonter;positive;0;0;0;0;0;0
-11593;remonter le fermeture éclair;positive;0;0;0;0;0;0
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-11724;résolument;positive;0;0;0;0;0;0
-11725;résolution;positive;0;0;0;0;0;0
-11726;résonance;negative;0;1;0;0;0;0
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-11729;respect;positive;0;0;0;0;0;0
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-11731;respectable;positive;0;0;0;0;0;0
-11732;respecter;positive;0;0;0;0;0;0
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-11734;respirateur;positive;0;0;0;0;0;0
-11735;respiration;positive;0;0;0;0;0;0
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-11737;resplendissant;positive;1;0;0;0;0;0
-11738;responsabiliser;positive;0;0;0;0;0;0
-11739;responsable;positive;0;0;0;0;0;0
-11740;ressemblance;positive;0;0;0;0;0;0
-11741;ressembler;positive;0;0;0;0;0;0
-11742;ressemblant;positive;0;0;0;0;0;0
-11743;ressentir;positive;0;0;0;0;0;0
-11744;ressentiment;negative;0;0;1;1;0;1
-11745;resserrer;positive;0;0;0;1;0;0
-11746;ressource;positive;0;0;0;0;0;0
-11747;ressusciter;positive;1;0;0;0;0;0
-11748;restant;negative;0;0;0;0;0;0
-11749;restaurant;positive;0;0;0;0;0;0
-11750;restauration;positive;0;0;0;0;0;0
-11751;restaurer;positive;0;0;0;0;0;0
-11752;rester;positive;0;0;0;0;0;0
-11753;rester bouche bée;positive;0;0;0;0;1;0
-11754;rester tapir;negative;0;1;1;0;0;0
-11755;restitution;positive;0;0;0;1;0;0
-11756;restraindre;negative;0;1;0;1;0;0
-11757;restreindre;negative;0;1;1;0;0;0
-11758;restructuration;positive;0;0;0;0;0;0
-11759;résulter;positive;0;0;0;0;0;0
-11760;résultat;positive;0;0;0;0;0;0
-11761;résumer;positive;0;0;0;0;0;0
-11762;résurrection;positive;0;0;0;0;0;0
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-11764;rétablissement;positive;1;0;0;0;0;0
-11765;retard;negative;0;1;1;1;0;1
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-11767;retenir;negative;0;0;0;0;0;0
-11768;rétention;positive;0;0;0;0;0;0
-11769;retentir;negative;0;1;0;1;1;0
-11770;retentissant;negative;0;1;0;1;1;0
-11771;réticence;negative;0;1;1;0;0;1
-11772;réticent;negative;0;1;0;0;0;0
-11773;rétine;positive;0;0;0;0;0;0
-11774;retirer;negative;0;0;1;0;0;0
-11775;rétorquer;negative;0;0;0;1;0;0
-11776;retour;positive;0;0;0;0;0;0
-11777;retour de flamme;negative;0;0;1;1;0;0
-11778;retour en arrière;positive;0;0;0;0;1;0
-11779;retourner voir;positive;0;0;0;0;0;0
-11780;retracer;positive;0;0;0;0;0;0
-11781;rétractation;negative;0;1;1;0;0;0
-11782;rétracter;negative;0;1;0;1;0;0
-11783;retraite;positive;0;1;1;0;0;0
-11784;retranchement;negative;0;1;1;0;0;0
-11785;rétrécir;negative;0;1;1;0;0;0
-11786;rétrécissement;negative;0;1;1;0;0;0
-11787;retriever;positive;0;0;0;0;0;0
-11788;rétrograde;negative;0;0;1;0;0;1
-11789;rétrograder;negative;0;0;1;0;0;1
-11790;rétrospectif;positive;0;0;0;0;0;0
-11791;rétrospection;positive;0;0;0;0;0;0
-11792;rétrospectif|rétrospective;positive;0;0;0;0;0;0
-11793;rétrospectivement;positive;0;0;0;0;0;0
-11794;retrouvaille;positive;0;0;0;0;0;0
-11795;retrouver;positive;0;0;0;0;0;0
-11796;reunion;positive;0;0;0;0;0;0
-11797;réunir;positive;0;0;0;0;0;0
-11798;réussiée;positive;0;0;0;0;0;0
-11799;réussite;positive;0;0;0;0;0;0
-11800;revalorisation;positive;0;0;0;0;0;0
-11801;revanche;negative;0;0;0;1;0;0
-11802;rêver;positive;1;0;0;0;0;0
-11803;rêve;positive;1;0;0;0;0;0
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-11807;révéler;positive;0;0;0;0;0;0
-11808;revendre;negative;0;0;0;0;0;0
-11809;revenir à;positive;0;0;0;0;0;0
-11810;revenir sur son pas;negative;0;1;1;0;0;0
-11811;revenir;positive;0;0;1;0;0;0
-11812;revenu;positive;0;0;1;0;0;0
-11813;réverbération;negative;0;1;0;0;1;0
-11814;révérence;positive;0;0;0;0;0;0
-11815;révérend;positive;0;0;0;0;0;0
-11816;révérer;positive;0;0;0;0;0;0
-11817;rêverie;positive;0;0;0;0;0;0
-11818;réversion;negative;0;0;0;0;0;0
-11819;revêtir;positive;0;0;0;0;0;0
-11820;revigorer;positive;0;0;0;0;0;0
-11821;revigorant;positive;0;0;0;0;0;0
-11822;réviser;positive;0;0;0;0;0;0
-11823;révision;positive;0;0;0;0;0;0
-11824;revisiter;positive;0;0;0;0;0;0
-11825;révolter;negative;0;1;0;1;0;1
-11826;révoltant;negative;0;1;0;1;0;1
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-11828;révolu;negative;0;0;1;0;0;0
-11829;révolutionnaire;negative;0;1;0;1;0;0
-11830;révolutionner;positive;0;0;0;0;1;0
-11831;révolvantes;negative;0;1;0;1;0;1
-11832;revolver;negative;0;1;1;1;0;0
-11833;révoquer;negative;0;1;1;1;0;1
-11834;rhabiller;positive;0;0;0;0;0;0
-11835;rhétorique;positive;0;0;0;0;0;0
-11836;rhum;positive;0;0;0;0;0;0
-11837;rhumatisme;negative;0;1;1;1;0;0
-11838;rhume;negative;0;0;1;0;0;0
-11839;ricanement;negative;0;0;0;1;0;1
-11840;ricaner;negative;0;0;0;1;0;1
-11841;riche;positive;0;0;1;0;0;1
-11842;richesse;positive;0;0;0;0;0;0
-11843;ride;negative;0;1;1;0;0;1
-11844;rider;negative;0;0;1;0;0;0
-11845;rideau;positive;0;0;0;0;0;0
-11846;ridicule;negative;0;0;1;1;1;1
-11847;ridiculiser;negative;0;0;1;1;0;1
-11848;rien de temps;negative;0;0;0;0;1;0
-11849;rigide;negative;0;0;0;1;0;0
-11850;rigidifier;negative;0;1;0;1;0;0
-11851;rigidité;negative;0;1;1;1;0;0
-11852;rigolade;positive;1;0;0;0;0;0
-11853;rigoler;positive;0;0;0;0;1;0
-11854;rigole;positive;0;0;0;0;0;0
-11855;rigueur;negative;0;1;0;1;0;1
-11856;rimer;positive;0;0;0;0;0;0
-11857;rime;positive;0;0;0;0;0;0
-11858;rinçage;positive;0;0;0;0;0;0
-11859;rincer;positive;0;0;0;0;0;0
-11860;riposte;negative;0;0;0;1;0;0
-11861;riposter;negative;0;0;0;1;0;0
-11862;rire;positive;0;0;0;0;1;0
-11863;rire bête;positive;1;0;0;0;0;0
-11864;rire bêtement;positive;1;0;0;0;0;0
-11865;risible;negative;0;0;0;0;0;1
-11866;risque;negative;0;1;0;0;0;0
-11867;risquer;negative;0;1;0;0;0;0
-11868;rite;positive;0;0;0;0;0;0
-11869;ritualiste;positive;0;0;0;0;0;0
-11870;rivage;positive;0;0;0;0;0;0
-11871;rivaliser;negative;0;1;0;1;0;0
-11872;rivalité;negative;0;0;0;1;0;0
-11873;rive;positive;0;0;0;0;0;0
-11874;riverain;positive;0;0;0;0;0;0
-11875;rivet;positive;0;0;0;0;0;0
-11876;riveter;positive;0;0;0;0;0;0
-11877;rivière;positive;0;0;0;0;0;0
-11878;rixe;negative;0;1;0;1;0;1
-11879;riz;positive;0;0;0;0;0;0
-11880;rizière;positive;0;0;0;0;0;0
-11881;roadster;positive;0;0;0;0;0;0
-11882;robe;positive;0;0;0;0;0;0
-11883;robinet;positive;0;0;0;0;0;0
-11884;robot;positive;0;0;0;0;0;0
-11885;robotique;positive;0;0;0;0;0;0
-11886;rocailleux;negative;0;1;0;0;0;0
-11887;roche;positive;0;0;0;0;0;0
-11888;rocher;positive;0;0;0;0;0;0
-11889;rock;positive;0;0;0;0;0;0
-11890;rôder;negative;0;1;0;0;1;0
-11891;rogner;positive;0;0;0;0;0;0
-11892;roi;positive;0;0;0;0;0;0
-11893;rôle;positive;0;0;0;0;0;0
-11894;romantique;positive;0;0;0;0;0;0
-11895;romantisme;positive;0;0;0;0;0;0
-11896;romarin;positive;0;0;0;0;0;0
-11897;rond;positive;0;0;0;0;0;0
-11898;rond point;positive;0;0;0;0;0;0
-11899;ronde;positive;0;0;0;0;0;0
-11900;rondin;positive;0;0;0;0;0;0
-11901;ronflement;negative;0;0;0;1;0;1
-11902;ronfler;negative;0;0;0;1;0;1
-11903;ronronnement;positive;0;0;0;0;0;0
-11904;ronronner;positive;0;0;0;0;0;0
-11905;roquette;negative;0;1;0;1;0;0
-11906;rosaire;positive;0;0;0;0;0;0
-11907;rose;positive;0;0;0;0;0;0
-11908;roser;positive;0;0;0;0;0;0
-11909;roseau;positive;0;0;0;0;0;0
-11910;rosette;positive;0;0;0;0;0;0
-11911;rosier;positive;0;0;0;0;0;0
-11912;rossignol;positive;1;0;0;0;0;0
-11913;rotatif;positive;0;0;0;0;0;0
-11914;rôtir;negative;0;0;0;0;0;0
-11915;rotin;positive;0;0;0;0;0;0
-11916;rôtisserie;positive;0;0;0;0;0;0
-11917;rotor;positive;0;0;0;0;0;0
-11918;rotule;positive;0;0;0;0;0;0
-11919;rouage;positive;0;0;0;0;0;0
-11920;roucoulement;positive;1;0;0;0;0;0
-11921;roucouler;positive;1;0;0;0;0;0
-11922;roue;positive;0;0;0;0;0;0
-11923;rouge à lèvre;positive;0;0;0;0;0;0
-11924;rougeâtre;negative;0;0;0;0;0;1
-11925;rougeaud;negative;0;0;0;0;0;1
-11926;rougeole;negative;0;1;1;0;0;1
-11927;rouge;negative;0;0;0;0;0;0
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-11929;rougir;negative;0;0;0;0;1;0
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-11932;rouiller;negative;0;0;1;0;0;1
-11933;rouler;negative;0;0;0;0;0;0
-11934;rouleau;positive;0;0;0;0;0;0
-11935;rouleau compresseur;negative;0;1;0;0;0;0
-11936;roulement;positive;0;0;0;0;0;0
-11937;rouquin;negative;0;0;0;0;0;0
-11938;rousse;negative;0;0;0;0;0;0
-11939;route;positive;0;0;0;0;0;0
-11940;route à péage;positive;0;0;0;0;0;0
-11941;routine;positive;0;0;0;0;0;0
-11942;roux;negative;0;0;0;0;0;0
-11943;royal;positive;0;0;0;0;0;0
-11944;royaume;positive;0;0;0;0;0;0
-11945;royauté;positive;0;0;0;0;0;0
-11946;ruban;positive;0;0;0;1;0;0
-11947;ruban adhésif;positive;0;0;0;0;0;0
-11948;rubis;positive;0;0;0;0;0;0
-11949;rubrique;positive;0;0;0;0;0;0
-11950;ruche;negative;0;1;0;1;0;0
-11951;rude;negative;0;1;1;0;0;0
-11952;rudimentaire;negative;0;1;1;0;0;0
-11953;rudiment;positive;0;0;0;0;0;0
-11954;rue;positive;0;0;0;0;0;0
-11955;rue principal;positive;0;0;0;0;0;0
-11956;ruer;negative;0;1;0;1;1;0
-11957;ruelle;positive;0;0;0;0;0;0
-11958;rugir;negative;0;1;0;1;1;0
-11959;rugissement;negative;0;1;0;1;1;0
-11960;rugosité;negative;0;0;1;1;0;1
-11961;rugueux;negative;0;1;0;0;0;0
-11962;ruineux;negative;0;1;1;1;0;1
-11963;ruisseler;negative;0;0;0;0;0;0
-11964;ruisselant;negative;0;0;0;0;0;0
-11965;ruissellement;negative;0;0;0;0;0;0
-11966;rumeur;negative;0;1;1;1;0;1
-11967;rune;negative;0;0;0;0;0;0
-11968;rupture;negative;0;1;1;0;1;0
-11969;rural;negative;0;0;0;0;0;0
-11970;rush;negative;0;1;0;1;1;0
-11971;rustique;negative;0;0;0;0;0;0
-11972;rythme;positive;0;0;0;0;0;0
-11973;rythmer;positive;0;0;0;0;1;0
-11974;rythmique;positive;0;0;0;0;1;0
-11975;s abstenir;negative;0;1;1;0;0;0
-11976;s accroupir;negative;0;1;0;0;0;0
-11977;s adapter;positive;0;0;0;0;0;0
-11978;s agenouiller;negative;0;0;1;0;0;0
-11979;s armer;positive;0;0;0;0;0;0
-11980;s arrêter;negative;0;1;0;0;1;0
-11981;s attarder;negative;0;0;0;0;0;0
-11982;s attendre à;positive;0;0;0;0;1;0
-11983;s atténuer;negative;0;0;0;0;0;0
-11984;s attrouper;negative;0;1;0;0;0;0
-11985;s avérer;positive;0;0;0;0;0;0
-11986;s éclairer;positive;0;0;0;0;1;0
-11987;s écouler;positive;0;0;0;0;0;0
-11988;s efforcer;positive;0;0;0;0;0;0
-11989;s élever;positive;0;0;0;0;0;0
-11990;s émerveiller;positive;0;0;0;0;1;0
-11991;s emmêler;negative;0;1;0;1;0;0
-11992;s emporter;negative;0;1;0;1;0;0
-11993;s enchevêtrer;negative;0;1;0;1;0;0
-11994;s endurcir;negative;0;0;1;1;0;0
-11995;s enrager;negative;0;1;0;1;0;0
-11996;s ensuivre;positive;0;0;0;0;0;0
-11997;s entraînant;positive;0;0;0;0;0;0
-11998;s envaser;negative;0;1;0;0;0;1
-11999;s épanouir;positive;1;0;0;0;0;0
-12000;s éparpiller;negative;0;1;1;0;0;0
-12001;s estomper;negative;0;0;1;0;0;0
-12002;s étendre;positive;0;0;0;0;0;0
-12003;s évertuer;positive;0;0;0;0;0;0
-12004;s exécutant;positive;0;0;0;0;0;0
-12005;s expatrier;negative;0;1;1;0;0;0
-12006;s identifier;positive;0;0;0;0;0;0
-12007;s inclinant;negative;0;1;0;0;0;0
-12008;s incliner;positive;0;1;0;0;0;0
-12009;s inquiéter;negative;0;1;0;0;0;0
-12010;s inscrire;positive;0;0;0;0;0;0
-12011;s occupant de;positive;0;0;0;0;0;0
-12012;s opposer à;negative;0;0;0;1;0;0
-12013;sable;negative;0;0;0;0;0;0
-12014;sableux;negative;0;0;0;0;0;1
-12015;sableur|sableuse;negative;0;0;0;0;0;1
-12016;sablier;positive;0;0;0;0;0;0
-12017;sablonneux;negative;0;0;0;0;0;1
-12018;sabot;negative;0;0;0;0;0;0
-12019;sabotage;negative;0;1;1;1;1;1
-12020;saboter;negative;0;1;1;1;1;1
-12021;sabre;negative;0;1;0;1;0;0
-12022;sabrer;negative;0;1;0;1;0;0
-12023;sac;positive;0;0;0;1;0;0
-12024;sac à dos;positive;0;0;0;0;0;0
-12025;sac à main;positive;0;0;0;0;0;0
-12026;saccader;negative;0;0;0;0;0;0
-12027;saccage;negative;0;1;0;1;1;0
-12028;saccager;negative;0;1;1;1;1;1
-12029;sacerdotal;positive;0;0;0;0;0;0
-12030;savoir;positive;0;0;0;0;0;0
-12031;sacoche;positive;0;0;0;0;0;0
-12032;sacré;positive;0;0;0;0;0;0
-12033;sacrer;positive;0;0;0;0;0;0
-12034;sacrement;positive;0;0;0;0;0;0
-12035;sacrifice;negative;0;1;1;0;0;1
-12036;safran;positive;0;0;0;0;0;0
-12037;saga;positive;0;0;0;0;0;0
-12038;sage;positive;0;0;0;0;0;0
-12039;sage femme;positive;0;0;0;0;0;0
-12040;sagesse;positive;0;0;0;0;0;0
-12041;saignant;negative;0;1;1;0;0;1
-12042;saignement;negative;0;1;1;0;0;1
-12043;sailler|saillir;negative;0;1;0;0;1;0
-12044;saillant;positive;0;1;0;0;1;0
-12045;saillie;negative;0;1;0;0;0;1
-12046;sain;positive;0;0;0;0;0;0
-12047;saindoux;negative;0;0;0;0;0;1
-12048;sainteté;positive;0;1;0;0;1;0
-12049;saisissant;negative;0;1;0;0;1;0
-12050;saison;positive;0;0;0;0;0;0
-12051;salade;negative;0;0;0;0;0;0
-12052;salaire;positive;0;0;0;0;0;0
-12053;salamandre;negative;0;1;0;0;0;1
-12054;sale;negative;0;0;0;0;0;1
-12055;saler;negative;0;0;0;0;0;1
-12056;sale gosse;negative;0;0;0;1;0;1
-12057;saleté;negative;0;0;0;0;0;1
-12058;salin;negative;0;0;0;0;0;0
-12059;salin|saline;negative;0;0;0;0;0;0
-12060;salir;negative;0;0;0;0;0;1
-12061;salissant;negative;0;1;1;1;0;1
-12062;salive;positive;0;0;0;0;0;1
-12063;saliver;positive;0;0;0;0;0;1
-12064;salle;positive;0;0;0;0;0;0
-12065;salle de bain;positive;0;0;0;0;0;0
-12066;salle de bal;positive;0;0;0;0;0;0
-12067;salon;positive;1;0;0;0;0;0
-12068;salope;negative;0;0;0;1;0;1
-12069;salopette;positive;0;0;0;0;0;0
-12070;salubre;positive;0;0;0;0;0;0
-12071;saluer;positive;1;0;0;0;0;0
-12072;salut;negative;0;0;1;0;1;0
-12073;salutaire;positive;0;0;0;0;0;0
-12074;salutation;positive;0;0;0;0;1;0
-12075;salve;negative;0;1;0;0;0;0
-12076;samba;positive;1;0;0;0;0;0
-12077;samissants;negative;0;1;1;1;0;1
-12078;samouraï;positive;0;1;0;0;0;0
-12079;sanctification;positive;0;0;0;0;0;0
-12080;sanctifier;positive;0;0;0;0;0;0
-12081;sanction;negative;0;1;1;1;0;1
-12082;sanctionner;negative;0;1;1;0;0;0
-12083;sanctuaire;positive;0;0;0;0;0;0
-12084;sandale;positive;0;0;0;0;0;0
-12085;sang;negative;0;1;1;1;0;1
-12086;sang froid;positive;0;0;0;0;0;0
-12087;sangler;negative;0;1;1;1;0;1
-12088;sanglant;negative;0;1;1;1;0;1
-12089;sangle;negative;0;1;0;0;0;0
-12090;sanglier;negative;0;1;0;0;0;1
-12091;sanglot;negative;0;0;1;0;0;0
-12092;sangloter;negative;0;0;1;0;0;0
-12093;sangsue;negative;0;0;0;0;0;1
-12094;sanguinaire;negative;0;1;0;1;0;1
-12095;sanguin;positive;0;0;0;0;0;0
-12096;sanitaire;positive;0;0;0;0;0;0
-12097;sans abri;negative;0;1;1;1;0;1
-12098;sans accompagnement;negative;0;0;1;0;0;0
-12099;sans aide;negative;0;0;1;0;0;0
-12100;sans ambiguïté;positive;0;0;0;0;0;0
-12101;sans âme;negative;0;1;1;0;0;1
-12102;sans arme;negative;0;0;0;0;0;0
-12103;sans attache;negative;0;0;1;0;0;0
-12104;sans aucun doute;positive;0;0;0;0;0;0
-12105;sans borne;positive;0;0;0;0;0;0
-12106;sans bruit;positive;1;0;0;0;0;0
-12107;sans but;negative;0;0;1;0;0;0
-12108;sans c?ur;negative;0;0;1;1;0;1
-12109;sans conséquence;negative;0;0;1;0;0;0
-12110;sans contrainte;positive;0;0;0;0;0;0
-12111;sans couleur;negative;0;0;1;0;0;0
-12112;sans couture;positive;0;0;0;0;0;0
-12113;sans défense;negative;0;1;1;0;0;0
-12114;sans délai;positive;0;0;0;0;1;0
-12115;sans dent;negative;0;0;1;0;0;1
-12116;sans domicile fixe;negative;0;1;1;1;0;1
-12117;sans doute;positive;0;0;0;0;0;0
-12118;sans éclat;negative;0;0;1;0;0;0
-12119;sans emploi;negative;0;1;1;0;0;0
-12120;sans énergie;negative;0;0;1;0;0;0
-12121;sans équivoque;positive;0;0;0;0;0;0
-12122;sans espoir;negative;0;1;1;0;0;0
-12123;sans être voir;negative;0;0;1;0;0;0
-12124;sans faille;positive;0;0;0;0;0;0
-12125;sans faire exprès;negative;0;1;1;0;1;0
-12126;sans faute;positive;0;0;0;0;0;0
-12127;sans fil;positive;0;0;0;1;1;0
-12128;sans fond;negative;0;1;0;0;0;0
-12129;sans formation;negative;0;0;1;0;0;0
-12130;sans heurt;positive;1;0;0;0;0;0
-12131;sans importance;negative;0;0;1;0;0;0
-12132;sans instruction;negative;0;0;1;0;0;1
-12133;sans interruption;positive;1;0;0;0;0;0
-12134;sans le faire exprès;negative;0;1;1;0;0;0
-12135;sans le savoir;negative;0;1;1;0;0;0
-12136;sans le sou;negative;0;0;1;0;0;0
-12137;sans le vouloir;negative;0;1;1;0;1;0
-12138;sans lien;negative;0;0;1;0;0;0
-12139;sans limite;positive;0;0;0;0;0;0
-12140;sans manche;positive;0;0;0;0;0;0
-12141;sans nom;negative;0;1;1;0;0;1
-12142;sans obstacle;positive;0;0;0;0;0;0
-12143;sans permis;negative;0;0;0;0;0;0
-12144;sans peur;positive;0;1;0;0;0;0
-12145;sans précédent;positive;0;0;0;0;1;0
-12146;sans prétention;positive;0;0;0;0;0;0
-12147;sans rapport;negative;0;1;1;0;0;0
-12148;sans relâche;positive;0;0;0;0;0;0
-12149;sans résistance;positive;0;0;0;0;0;0
-12150;sans restriction;positive;1;0;0;0;0;0
-12151;sans retenue;positive;1;0;0;0;0;0
-12152;sans scrupule;negative;0;0;0;1;0;1
-12153;sans soleil;negative;0;0;1;0;0;0
-12154;sans sucre;positive;0;0;0;0;0;0
-12155;sans surveillance;negative;0;1;1;0;0;0
-12156;sans tache;positive;0;0;0;0;0;0
-12157;sans tenir compte de;negative;0;0;0;0;0;0
-12158;sans titre;negative;0;0;1;0;0;0
-12159;sans vie;negative;0;1;1;0;0;0
-12160;sans voix;negative;0;1;1;0;1;0
-12161;santé;positive;1;0;0;0;0;0
-12162;santé mental;positive;0;0;0;0;0;0
-12163;saoul;negative;0;0;0;0;0;1
-12164;sapeur pompier;positive;0;0;0;0;0;0
-12165;saphir;positive;0;0;0;0;0;0
-12166;sarcasme;negative;0;0;1;1;0;1
-12167;sarcastique;negative;0;0;0;1;0;1
-12168;sarcome;negative;0;1;1;0;0;0
-12169;sardonique;negative;0;0;0;1;0;1
-12170;satanique;negative;0;1;0;1;0;0
-12171;satellite;positive;0;0;0;0;0;0
-12172;satin;positive;0;0;0;0;0;0
-12173;satiner;positive;0;0;0;0;0;0
-12174;satire;negative;0;0;0;1;0;1
-12175;satirique;negative;0;0;0;1;0;1
-12176;satisfaction;positive;1;0;0;0;0;0
-12177;saturation;negative;0;0;0;0;0;0
-12178;saturer;negative;0;0;1;1;0;1
-12179;sauce;positive;0;0;0;0;0;0
-12180;sauge;positive;0;0;0;0;0;0
-12181;saule;positive;0;0;0;0;0;0
-12182;saumâtre;negative;0;0;0;0;0;1
-12183;saumure;negative;0;0;0;0;0;1
-12184;sauna;positive;0;0;0;0;0;0
-12185;saupoudrage;positive;0;0;0;0;0;0
-12186;saupoudrer;positive;0;0;0;0;0;0
-12187;saut;positive;1;0;0;0;0;0
-12188;sauter;negative;0;1;0;0;0;1
-12189;sauter en parachute;negative;0;1;0;0;0;0
-12190;sauterelle;positive;0;1;0;0;0;1
-12191;sautiller;positive;1;0;0;0;0;0
-12192;sauvagerie;negative;0;1;0;1;0;0
-12193;sauvegarde;positive;0;0;0;0;0;0
-12194;sauvegarder;positive;0;0;0;0;0;0
-12195;sauver;positive;0;0;0;0;0;0
-12196;sauvetage;positive;0;0;0;0;1;0
-12197;savane;positive;0;0;0;0;0;0
-12198;saveur;positive;0;0;1;0;0;1
-12199;savon;positive;0;0;0;0;0;0
-12200;savonner;positive;0;0;0;0;0;0
-12201;savonneux;negative;0;1;0;0;0;0
-12202;savourer;positive;0;0;1;0;0;1
-12203;saxo;positive;0;0;0;0;0;0
-12204;saxophone;positive;0;0;0;0;0;0
-12205;scalpel;negative;0;1;0;0;0;0
-12206;scalper;negative;0;1;0;0;0;1
-12207;scandale;negative;0;1;0;1;1;0
-12208;scander;positive;0;0;0;1;1;0
-12209;scanner;positive;0;0;0;0;0;0
-12210;scape;negative;0;0;0;0;0;0
-12211;scarabée;positive;0;1;0;0;0;1
-12212;sceau;positive;0;0;0;0;0;0
-12213;sceller;positive;0;0;0;0;0;0
-12214;scène;positive;0;0;0;0;0;0
-12215;scénique;positive;0;0;0;0;0;0
-12216;scepticisme;negative;0;1;0;0;1;0
-12217;schéma;positive;0;0;0;0;0;0
-12218;schématique;positive;0;0;0;0;0;0
-12219;schisme;negative;0;0;0;1;0;0
-12220;schizophrénie;negative;0;1;1;1;0;1
-12221;sciatique;negative;0;1;1;0;0;0
-12222;sciemment;positive;0;0;0;0;0;0
-12223;science;positive;0;0;0;0;0;0
-12224;science économique;positive;0;0;0;0;0;0
-12225;science humain;positive;0;0;0;0;0;0
-12226;scientifique;positive;0;0;0;0;0;0
-12227;scinder;negative;0;0;0;1;0;0
-12228;scintillant;positive;1;0;0;0;0;0
-12229;scintillateur;positive;0;0;0;0;0;0
-12230;scintillation;positive;0;0;0;0;1;0
-12231;scintillement;positive;0;0;0;0;1;0
-12232;scintiller;positive;0;0;0;0;1;1
-12233;scission;negative;0;0;1;1;0;0
-12234;sciure;negative;0;0;0;0;0;1
-12235;scolaire;positive;0;0;0;0;0;0
-12236;scolarité;positive;0;0;0;0;0;0
-12237;sconse;negative;0;0;0;0;0;1
-12238;scoop;positive;0;0;0;0;1;0
-12239;score;positive;0;0;0;0;1;0
-12240;scorie;negative;0;0;0;0;0;1
-12241;scorpion;negative;0;1;0;1;1;1
-12242;scotch;positive;0;0;0;0;0;0
-12243;scout;positive;0;0;0;0;0;0
-12244;scribe;positive;0;0;0;0;0;0
-12245;script;positive;0;0;0;0;0;0
-12246;scriptural;positive;0;0;0;0;0;0
-12247;scruter;negative;0;0;0;0;0;0
-12248;scrutin;positive;0;0;0;0;0;0
-12249;sculpter;positive;0;0;0;0;0;0
-12250;sculpteur;positive;0;0;0;0;0;0
-12251;sculpture;positive;0;0;0;0;0;0
-12252;se bagarrer;negative;0;1;0;1;0;1
-12253;se baigner;positive;0;0;0;0;0;0
-12254;se balader;positive;1;0;0;0;0;0
-12255;se balancer;positive;0;0;0;0;0;0
-12256;se baser;positive;0;0;0;0;0;0
-12257;se battre;negative;0;1;0;1;0;0
-12258;se battre pour qch;negative;0;1;0;1;0;0
-12259;se battre en duel;negative;0;1;0;1;0;0
-12260;se blottir;positive;0;0;0;0;0;0
-12261;se boursoufler;negative;0;0;0;0;0;1
-12262;se braquer;negative;0;1;0;0;1;0
-12263;se briser;negative;0;0;0;0;1;0
-12264;se camoufler;negative;0;0;0;0;1;0
-12265;se casser;negative;0;0;0;0;1;0
-12266;se casser net;negative;0;1;0;1;1;0
-12267;se charger de;positive;0;0;0;0;0;0
-12268;se chevaucher;negative;0;0;0;0;0;0
-12269;se composer de;positive;0;0;0;0;0;0
-12270;se concentrer;positive;0;0;0;0;0;0
-12271;se concrétiser;positive;0;0;0;0;0;0
-12272;se conformer;positive;0;0;0;0;0;0
-12273;se conjuguer;positive;0;0;0;0;0;0
-12274;se connaître;positive;0;0;0;0;0;0
-12275;se connecter;positive;0;0;0;0;0;0
-12276;se contracter;negative;0;1;0;1;0;0
-12277;se contredire;negative;0;0;0;1;0;0
-12278;se coucher;positive;0;0;0;0;0;0
-12279;se couper;negative;0;1;1;0;0;0
-12280;se courber;negative;0;1;0;0;0;0
-12281;se couvrir;positive;0;0;0;0;0;0
-12282;se crisper;negative;0;1;0;1;0;0
-12283;se débarrasser;negative;0;0;1;1;0;0
-12284;se débarrasser de;negative;0;0;1;1;0;1
-12285;se débattre;negative;0;1;1;1;0;0
-12286;se débrouiller;negative;0;1;1;1;0;0
-12287;se décomposer;negative;0;1;1;0;0;1
-12288;se décourager;negative;0;1;1;1;0;0
-12289;se déformer;negative;0;0;1;1;0;0
-12290;se dégonfler;negative;0;1;1;1;0;0
-12291;se dégrader;negative;0;0;1;0;0;1
-12292;se déguiser;negative;0;0;0;0;0;0
-12293;se délecter;positive;1;0;0;0;0;0
-12294;se délester;positive;0;0;0;0;0;0
-12295;se dépêcher;negative;0;1;0;1;1;0
-12296;se déployer;positive;0;0;0;0;0;0
-12297;se dépouiller de;negative;0;0;0;0;0;1
-12298;se déprécier;negative;0;0;1;1;0;1
-12299;se dérouler;positive;0;0;0;0;0;0
-12300;se déshabiller;positive;0;0;0;0;0;0
-12301;se désintégrer;negative;0;1;1;1;0;1
-12302;se désister;negative;0;1;1;0;0;0
-12303;se détendre;positive;0;0;0;0;0;0
-12304;se détériorer;negative;0;1;1;1;0;1
-12305;se développer;positive;0;0;0;0;0;0
-12306;se dilater;negative;0;1;0;0;0;0
-12307;se disperser;negative;0;1;1;0;0;0
-12308;se disputer;negative;0;0;0;1;0;0
-12309;se dissiper;negative;0;0;0;0;0;0
-12310;se dissoudre;negative;0;0;0;0;0;0
-12311;se diversifier;positive;0;0;0;0;0;0
-12312;se doucher;positive;0;0;0;0;0;0
-12313;se durcir;negative;0;0;1;1;0;0
-12314;se faire passer pour;negative;0;0;0;1;0;0
-12315;se faire plaisir;positive;1;0;0;0;0;0
-12316;se familiariser;positive;0;0;0;0;0;0
-12317;se fatiguer;negative;0;0;1;0;0;0
-12318;se faufiler;negative;0;1;0;1;1;0
-12319;se fissurer;negative;0;1;0;0;1;0
-12320;se fouler;negative;0;1;1;0;1;0
-12321;se frotter;negative;0;0;0;0;0;0
-12322;se glisser;negative;0;0;0;0;0;1
-12323;se glisser furtivement;negative;0;1;0;1;1;0
-12324;se gonfler;negative;0;0;0;0;0;1
-12325;se hérisser;negative;0;1;0;0;1;0
-12326;se jeter sur;negative;0;1;0;1;1;0
-12327;se joindre à;positive;0;0;0;0;0;0
-12328;se lamenter;negative;0;1;1;0;0;1
-12329;se lasser;negative;0;0;1;0;0;0
-12330;se loger;positive;0;0;0;0;0;0
-12331;se marier;positive;0;1;0;0;1;0
-12332;se masturber;positive;1;0;0;0;0;0
-12333;se matérialiser;positive;0;0;0;0;0;0
-12334;se méfier de;negative;0;1;0;1;0;1
-12335;se mélanger;positive;0;0;0;0;0;0
-12336;se mêler;negative;0;0;0;1;0;0
-12337;se méprendre;negative;0;1;1;1;0;0
-12338;se mettre à califourchon sur;positive;0;0;0;0;0;0
-12339;se mettre à genou;negative;0;0;1;0;0;0
-12340;se mettre en grève;negative;0;0;0;1;0;0
-12341;se mirer;positive;0;0;0;0;0;0
-12342;se moquer;negative;0;0;0;1;0;1
-12343;se moquer de;negative;0;1;1;1;0;0
-12344;se multiplier;positive;0;0;0;0;0;0
-12345;se noyer;negative;0;1;1;0;0;0
-12346;se pâmer;positive;1;0;0;0;0;0
-12347;se parjurer;negative;0;0;1;1;1;1
-12348;se pavaner;negative;0;0;0;0;0;0
-12349;se pencher;negative;0;1;0;0;0;0
-12350;se percher;positive;0;0;0;0;0;0
-12351;se périmer;negative;0;0;1;0;0;1
-12352;se permettre;positive;0;0;0;0;0;0
-12353;se plaindre;negative;0;0;1;1;0;0
-12354;se plier;negative;0;0;0;0;0;0
-12355;se plonger;positive;0;0;0;0;0;0
-12356;se porter garant;positive;0;0;0;0;0;0
-12357;se positionner;positive;0;0;0;0;0;0
-12358;se précipiter;negative;0;1;1;1;1;0
-12359;se prélasser;positive;1;0;0;0;0;0
-12360;se préparer;positive;0;0;0;0;0;0
-12361;se presser;negative;0;1;0;1;1;0
-12362;se procurer;positive;0;0;0;0;0;0
-12363;se produire;positive;0;0;0;0;0;0
-12364;se profiler;negative;0;1;0;0;0;0
-12365;se promener;positive;1;0;0;0;0;0
-12366;se propager;negative;0;1;0;0;0;0
-12367;se proposer;positive;0;1;0;0;0;0
-12368;se prostituer;negative;0;0;1;0;0;1
-12369;se qualifier;positive;1;0;0;0;0;0
-12370;se quereller;negative;0;0;0;1;0;0
-12371;se raidir;negative;0;1;0;1;0;0
-12372;se rappeler;positive;0;0;0;0;0;0
-12373;se rapprocher;positive;0;0;0;0;0;0
-12374;se raser;positive;0;0;0;0;0;0
-12375;se rassembler;positive;0;0;0;0;0;0
-12376;se rebeller;negative;0;1;0;1;0;0
-12377;se redresser;positive;0;0;0;0;0;0
-12378;se régaler;positive;1;0;0;0;0;0
-12379;se régénérer;positive;0;0;0;0;0;0
-12380;se rejoindre;positive;0;0;0;0;0;0
-12381;se réjouir;positive;0;0;0;0;1;0
-12382;se rendre;negative;0;1;1;0;0;0
-12383;se renseigner;positive;0;0;0;0;0;0
-12384;se répandre;negative;0;0;0;0;0;0
-12385;se repentir;positive;0;1;0;0;0;0
-12386;se reposer;positive;1;0;0;0;0;0
-12387;se représenter;positive;0;0;0;0;0;0
-12388;se reproduire;negative;0;0;0;0;0;0
-12389;se retirer;negative;0;1;1;0;0;0
-12390;se retourner contre qqn;negative;0;0;1;1;0;0
-12391;se rétracter;negative;0;1;0;1;0;0
-12392;se rétrécir;negative;0;1;1;0;0;0
-12393;se réveiller;positive;0;0;0;0;0;0
-12394;se révolter;negative;0;0;0;1;1;0
-12395;se séparer;negative;0;0;1;0;0;0
-12396;se solidifier;positive;0;0;0;0;0;0
-12397;se souvenir;positive;0;0;0;0;0;0
-12398;se spécialiser;positive;0;0;0;0;0;0
-12399;se suicider;negative;0;1;1;1;0;0
-12400;se taire;negative;0;0;0;0;0;0
-12401;se tapir;negative;0;1;1;0;0;0
-12402;se tenir debout;positive;0;0;0;0;0;0
-12403;se terminer;negative;0;1;1;0;0;0
-12404;se tortiller;negative;0;0;0;0;0;1
-12405;se tracasser;negative;0;1;0;0;0;0
-12406;se tromper;negative;0;1;1;0;0;0
-12407;se vanter;negative;0;0;0;0;0;0
-12408;se vautrer;negative;0;1;1;0;1;1
-12409;se venger;negative;0;1;0;1;1;0
-12410;séance;positive;0;0;0;0;0;0
-12411;seau;positive;0;0;0;0;0;0
-12412;sec;negative;0;1;0;1;1;0
-12413;sécable;negative;0;0;0;0;0;0
-12414;sécateur;negative;0;0;0;0;0;0
-12415;sécession;negative;0;1;0;0;0;0
-12416;sécher;negative;0;0;0;0;0;0
-12417;sèche cheveu;positive;0;0;0;0;0;0
-12418;sèche linge;positive;0;0;0;0;0;0
-12419;sécheresse;negative;0;1;1;0;0;0
-12420;secondaire;negative;0;0;1;0;0;0
-12421;second;positive;0;0;0;0;1;0
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-12772;souscription;positive;0;0;0;0;0;0
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-12774;soustraction;negative;0;0;0;0;0;0
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-12776;soutenable;positive;0;0;0;0;0;0
-12777;soutenant;positive;0;0;0;0;0;0
-12778;souteneur;negative;0;1;0;0;0;1
-12779;soutenir;positive;0;0;0;0;0;0
-12780;souterrain;negative;0;1;0;0;0;0
-12781;souterrainne;negative;0;1;0;0;0;0
-12782;souterrainnes;negative;0;1;0;0;0;0
-12783;soutien;positive;0;0;0;0;0;0
-12784;soutirer;negative;0;0;0;0;0;0
-12785;souvenir;positive;0;0;0;0;0;0
-12786;souverain pontife;positive;0;0;0;0;0;0
-12787;souveraineté;positive;0;0;0;0;0;0
-12788;spa;positive;0;0;0;0;1;0
-12789;spasme;negative;0;1;0;0;0;0
-12790;spatule;positive;0;0;0;0;0;0
-12791;spécial;positive;1;0;0;0;0;0
-12792;spécialement;positive;0;0;0;0;0;0
-12793;spécialiser;positive;0;0;0;0;0;0
-12794;spécialiste;positive;0;0;0;0;0;0
-12795;spécialité;positive;0;0;0;0;0;0
-12796;spécification;positive;0;0;0;0;0;0
-12797;spécifique;positive;0;0;0;0;0;0
-12798;spécimen;positive;0;0;0;0;0;0
-12799;spectacle;positive;0;0;0;0;0;0
-12800;spectacle fabuleux;positive;1;0;0;0;0;0
-12801;spectaculaire;positive;0;0;0;0;1;0
-12802;spectre;negative;0;1;1;0;0;0
-12803;spectromètre;positive;0;0;0;0;0;0
-12804;spectrophotomètre;positive;0;0;0;0;0;0
-12805;spectroscopie;positive;0;0;0;0;0;0
-12806;spéculation;negative;0;1;1;0;0;0
-12807;spéculer;negative;0;0;0;0;0;0
-12808;spéculum;negative;0;1;0;0;0;1
-12809;spencer;positive;0;0;0;0;0;0
-12810;sperme;positive;0;0;0;0;0;1
-12811;sphère;positive;0;0;0;0;0;0
-12812;sphérique;positive;0;0;0;0;0;0
-12813;sphinx;positive;0;0;0;0;0;0
-12814;spinal;positive;0;0;0;0;0;0
-12815;spiral|spirale;positive;0;0;0;0;0;0
-12816;spiraler;positive;0;0;0;0;0;0
-12817;spiritualité;positive;0;0;0;0;0;0
-12818;spiritueux;negative;0;0;1;1;0;0
-12819;splendeur;positive;0;0;0;0;1;0
-12820;splendide;positive;0;0;0;0;1;0
-12821;spline;positive;0;0;0;0;0;0
-12822;spoiler;negative;0;1;1;1;0;0
-12823;sponsor;positive;0;0;0;0;0;0
-12824;sponsoring;positive;0;0;0;0;0;0
-12825;sponsoriser;positive;0;0;0;0;0;0
-12826;sporadique;negative;0;0;0;0;1;0
-12827;spore;negative;0;0;0;0;0;1
-12828;sport;positive;0;0;0;0;0;0
-12829;spray;positive;0;0;0;0;0;0
-12830;square;positive;0;0;0;0;0;0
-12831;squat;negative;0;1;0;0;0;0
-12832;squatter;negative;0;1;1;0;0;1
-12833;squelette;negative;0;1;1;0;0;0
-12834;stabilité;positive;0;0;0;0;0;0
-12835;stable;positive;0;1;0;0;1;0
-12836;staccato;negative;0;0;0;0;0;0
-12837;stade;positive;0;0;0;0;0;0
-12838;stagiaire;positive;0;0;0;0;0;0
-12839;stagner;negative;0;0;1;0;0;0
-12840;stagnant;negative;0;0;1;0;0;0
-12841;stagnation;negative;0;1;1;0;0;0
-12842;stalle;positive;0;0;0;0;0;1
-12843;stand;positive;0;0;0;0;0;1
-12844;standard;positive;0;0;0;0;0;0
-12845;standardiser;positive;0;0;0;0;0;0
-12846;star;positive;0;0;0;0;0;0
-12847;starter;negative;0;1;1;1;1;0
-12848;station;positive;0;0;0;0;0;0
-12849;stationnaire;positive;0;0;0;0;0;0
-12850;stationner;positive;0;0;0;0;0;0
-12851;statique;positive;0;0;0;0;0;0
-12852;statistique;positive;0;0;0;0;0;0
-12853;stator;positive;0;0;0;0;0;0
-12854;statuaire;positive;0;0;0;0;0;0
-12855;statue;positive;0;0;0;0;0;0
-12856;statuer sur;negative;0;1;0;0;0;0
-12857;statuette;positive;0;0;0;0;0;0
-12858;stature;positive;0;0;0;0;0;0
-12859;statut;positive;0;0;0;0;0;0
-12860;statutaire;positive;0;0;0;0;0;0
-12861;stellaire;positive;0;0;0;0;0;0
-12862;stencil;positive;0;0;0;0;0;0
-12863;sténo;positive;0;0;0;0;0;0
-12864;sténographie;positive;0;0;0;0;0;0
-12865;steppe;negative;0;1;1;0;0;0
-12866;stéréoscopique;positive;0;0;0;0;0;0
-12867;stéréotype;negative;0;0;0;0;0;1
-12868;stéréotyper;negative;0;0;0;0;0;1
-12869;stérile;negative;0;1;1;0;0;0
-12870;stériliser;positive;0;1;1;0;0;0
-12871;stérilité;negative;0;1;1;0;0;0
-12872;sterling;positive;0;0;0;1;0;0
-12873;sterne;positive;0;0;0;0;0;0
-12874;stéthoscope;positive;0;0;0;0;0;0
-12875;steward;positive;0;0;0;0;0;0
-12876;stigmate;negative;0;1;1;1;0;1
-12877;stimulant;positive;0;0;0;0;0;0
-12878;stimulation;positive;1;0;0;0;0;0
-12879;stimuler;positive;0;0;0;0;0;0
-12880;stimulus;positive;0;0;0;0;0;0
-12881;stipulation;positive;0;0;0;0;0;0
-12882;stipuler;positive;0;0;0;0;0;0
-12883;stock;positive;0;0;0;0;0;0
-12884;stockage;positive;0;0;0;0;0;0
-12885;stocker;positive;0;0;0;0;0;0
-12886;stoïque;positive;0;0;0;0;0;0
-12887;stratège;positive;0;0;0;0;0;0
-12888;stratégie;positive;0;0;0;0;0;0
-12889;stratégique;positive;0;0;0;0;0;0
-12890;stratification;positive;0;0;0;0;0;0
-12891;stratus;negative;0;0;1;0;0;0
-12892;stress;negative;0;1;0;1;0;0
-12893;stresser;negative;0;1;1;1;0;0
-12894;stressant;negative;0;1;1;1;0;0
-12895;strict;negative;0;0;1;0;0;0
-12896;strident;negative;0;1;0;1;1;0
-12897;strie;negative;0;0;0;0;0;0
-12898;strier;positive;0;0;0;0;0;0
-12899;stroboscopique;positive;0;0;0;0;0;0
-12900;strophe;positive;0;0;0;0;0;0
-12901;structure;positive;0;0;0;0;0;0
-12902;structurer;positive;0;0;0;0;0;0
-12903;stuc;positive;0;0;0;0;0;0
-12904;studio;positive;0;0;0;0;0;0
-12905;stupéfaction;positive;0;0;0;0;1;0
-12906;stupéfaire;negative;0;1;0;0;1;1
-12907;stupéfiant;negative;0;0;0;0;1;0
-12908;stupéfier;positive;0;0;0;0;1;0
-12909;stupide;negative;0;0;0;1;0;1
-12910;stupidité;negative;0;0;0;1;0;1
-12911;style;positive;0;0;0;0;0;0
-12912;stylet;negative;0;1;0;0;0;0
-12913;stylo;positive;0;0;0;0;0;0
-12914;suaire;negative;0;0;1;0;0;0
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-12916;suavité;positive;0;0;0;0;0;0
-12917;subalterne;negative;0;0;1;0;0;1
-12918;subatomique;positive;0;0;0;0;0;0
-12919;subconscient;negative;0;0;0;0;0;0
-12920;subdiviser;positive;0;0;0;0;0;0
-12921;subdivision;positive;0;0;0;0;0;0
-12922;subduction;positive;0;0;0;0;0;0
-12923;subir;negative;0;0;1;0;0;0
-12924;subito;positive;0;0;0;0;1;0
-12925;sublimation;positive;1;0;0;0;0;0
-12926;subliminal;negative;0;0;0;0;1;0
-12927;submerger;negative;0;1;1;0;1;0
-12928;submersible;negative;0;1;0;0;0;0
-12929;submersion;negative;0;1;0;0;0;0
-12930;subordination;negative;0;0;0;0;0;0
-12931;subsidiaire;negative;0;0;0;0;0;0
-12932;subsistance;positive;1;0;0;0;0;0
-12933;subsister;positive;0;0;0;0;0;0
-12934;substance;positive;0;0;0;0;0;0
-12935;substantiellement;positive;0;0;0;0;0;0
-12936;substituer;negative;0;0;0;0;0;0
-12937;substitut;negative;0;0;0;0;0;0
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-12939;subtil;positive;0;0;0;0;1;0
-12940;subvenir;positive;0;0;0;0;0;0
-12941;subvention;positive;0;0;0;1;0;1
-12942;subventionner;positive;0;0;0;0;0;0
-12943;subversion;negative;0;1;0;1;0;0
-12944;subvertir;negative;0;1;1;0;0;1
-12945;sucer;negative;0;0;0;0;0;0
-12946;succès;positive;1;0;0;0;0;0
-12947;successeur;positive;0;0;0;0;0;0
-12948;succession;positive;0;0;0;0;0;0
-12949;succinct;positive;0;0;0;0;0;0
-12950;succion;negative;0;1;0;0;0;0
-12951;succomber;negative;0;1;1;0;0;0
-12952;succulent;positive;1;0;0;0;0;0
-12953;succursale;positive;0;0;0;0;0;0
-12954;sucette;positive;0;0;0;1;0;0
-12955;sucre;positive;0;0;0;0;0;0
-12956;sucrer;positive;0;0;0;0;1;0
-12957;sucrerie;positive;1;0;0;0;0;0
-12958;sudation;negative;0;0;0;0;0;1
-12959;sueur;negative;0;1;0;0;0;1
-12960;suffire;positive;0;0;0;0;0;0
-12961;suffisamment;positive;0;0;0;0;0;0
-12962;suffisance;negative;0;0;0;1;0;0
-12963;suffisant;positive;0;0;0;0;0;1
-12964;suffixe;positive;0;0;0;0;0;0
-12965;suffocant;negative;0;1;1;0;0;1
-12966;suffocation;negative;0;1;0;1;0;0
-12967;suggérer;positive;0;0;0;0;0;0
-12968;suggestion;positive;0;0;0;0;0;0
-12969;suicidaire;negative;0;1;1;1;0;1
-12970;suicide;negative;0;1;1;1;0;0
-12971;suicider;negative;0;1;1;1;0;0
-12972;suie;negative;0;0;0;0;0;1
-12973;suif;positive;0;0;0;0;0;0
-12974;suintement;negative;0;0;0;0;0;1
-12975;suinter;negative;0;0;0;0;0;1
-12976;suivant;negative;0;0;0;0;0;0
-12977;suivant|suivante;negative;0;0;0;0;0;0
-12978;suivre;positive;0;0;0;0;0;0
-12979;suivre à le trace;positive;0;0;0;0;0;0
-12980;suivre le trace de;positive;0;0;0;0;0;0
-12981;sujet;negative;0;0;1;1;0;1
-12982;sujétion;negative;0;1;1;0;0;0
-12983;sultan;positive;0;1;0;0;0;0
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-12985;super;positive;0;1;1;1;1;0
-12986;superbe;positive;1;0;0;0;0;0
-12987;superficie;positive;0;0;0;0;0;0
-12988;supériorité;positive;0;0;0;0;0;0
-12989;superlatif;positive;0;0;0;0;0;0
-12990;superman;positive;0;0;0;0;0;0
-12991;supermarché;positive;0;0;0;0;0;0
-12992;superposer;positive;0;0;0;0;0;0
-12993;superposition;positive;0;0;0;0;0;0
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-12995;superstition;negative;0;1;0;0;0;0
-12996;superviser;positive;0;0;0;0;0;0
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-12999;supplanter;positive;0;0;0;0;0;0
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-13002;supplication;positive;0;0;0;0;0;0
-13003;supplice;negative;0;1;1;1;1;0
-13004;support;positive;0;0;0;0;0;0
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-13006;supporteur;positive;0;0;0;0;0;0
-13007;supposer;negative;0;1;0;0;0;0
-13008;suppression;negative;0;1;1;1;0;1
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-13014;sur qui on ne pouvoir compter;negative;0;1;0;1;0;0
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-13035;surnageant;positive;0;0;0;0;0;0
-13036;surnom;positive;0;0;0;0;0;0
-13037;surnommer;positive;0;0;0;0;0;0
-13038;surpasser;positive;0;0;0;0;0;0
-13039;surpasser en nombre;positive;0;0;0;0;0;0
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-13048;surtout;positive;0;0;0;0;0;0
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-13069;suspicion;negative;0;1;0;0;0;0
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-13071;suturer qch;negative;0;0;0;0;0;0
-13072;svastika;negative;0;1;0;1;0;1
-13073;svelte;positive;0;0;0;0;0;0
-13074;swag;positive;0;0;0;0;0;0
-13075;swastika;negative;0;1;0;1;0;1
-13076;syllabe;positive;0;0;0;0;0;0
-13077;syllabique;positive;0;0;0;0;0;0
-13078;symbole;positive;0;0;0;0;0;0
-13079;symbolique;positive;0;0;0;0;0;0
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-13087;symptomatique;negative;0;0;1;0;0;0
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-13090;synchrone;positive;0;0;0;0;0;0
-13091;synchroniser;positive;0;0;0;0;1;0
-13092;syncope;negative;0;1;1;0;1;0
-13093;syndicat;positive;0;0;0;0;0;0
-13094;synergie;positive;0;0;0;0;0;0
-13095;synode;positive;0;0;0;0;0;0
-13096;synonyme;positive;0;1;0;0;0;0
-13097;synopsis;positive;0;0;0;0;0;0
-13098;synoptique;positive;0;0;0;0;0;0
-13099;syntaxe;positive;0;0;0;0;0;0
-13100;synthèse;positive;0;0;0;0;0;0
-13101;synthétique;negative;0;0;0;0;0;1
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-13103;systématiquement;positive;0;0;0;0;0;0
-13104;système;positive;0;0;0;0;0;0
-13105;système hydraulique;negative;0;0;1;0;0;0
-13106;tabac;negative;0;0;0;0;0;1
-13107;tabac à priser;negative;0;0;0;0;1;1
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-13113;tableau noir;positive;0;0;0;0;0;0
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-13115;tablier;positive;0;0;0;0;0;0
-13116;tabou;negative;0;1;0;0;0;1
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-13124;tâcher de;positive;0;0;0;0;0;0
-13125;tacheter;negative;0;0;0;0;0;0
-13126;tachymètre;positive;0;0;0;0;0;0
-13127;tacite;positive;0;0;0;0;0;0
-13128;tacot;negative;0;1;0;1;1;0
-13129;tact;positive;0;0;0;0;0;0
-13130;tactile;positive;0;0;0;0;0;0
-13131;tactique;positive;0;1;0;0;0;0
-13132;taffetas;positive;0;0;0;0;0;0
-13133;taie d oreiller;positive;0;0;0;0;0;0
-13134;taillader;negative;0;1;1;1;0;1
-13135;taillant;positive;0;0;0;0;0;0
-13136;taille;positive;0;0;0;0;0;0
-13137;tailler;negative;0;1;0;0;0;0
-13138;taille haie;negative;0;0;0;0;0;0
-13139;tailleur;positive;0;0;0;0;0;0
-13140;tailleuse;positive;0;0;0;0;0;0
-13141;taire;negative;0;1;1;1;0;1
-13142;talent;positive;0;0;0;0;0;0
-13143;talisman;positive;0;0;0;0;0;0
-13144;talon;positive;0;0;0;0;0;0
-13145;talus;positive;0;0;0;0;0;0
-13146;tambour;positive;0;0;0;0;0;0
-13147;tambourin;negative;0;0;0;0;0;0
-13148;tambourinement;positive;0;0;0;0;0;0
-13149;tambouriner;positive;0;0;0;0;0;0
-13150;tamis;positive;0;0;0;0;0;0
-13151;tamisage;positive;0;0;0;0;0;0
-13152;tamiser;negative;0;0;1;0;0;0
-13153;tampon;positive;0;0;0;0;0;1
-13154;tamponner;positive;0;0;0;0;0;0
-13155;tandem;positive;0;0;0;0;0;0
-13156;tangent;negative;0;0;0;0;0;0
-13157;tanière;positive;0;0;0;0;0;0
-13158;tanner;positive;0;0;0;0;0;0
-13159;tante;positive;0;0;0;0;0;0
-13160;taper;positive;0;0;0;0;0;0
-13161;tapir;negative;0;0;0;0;0;0
-13162;tapir de course;positive;0;0;0;0;0;0
-13163;tapis roulant;positive;0;0;0;0;0;0
-13164;tapisserie;positive;0;0;0;0;0;0
-13165;tapoter;positive;0;0;0;0;0;0
-13166;taquiner;positive;0;0;1;1;0;0
-13167;taquinerie;negative;0;1;0;1;0;0
-13168;tarer;negative;0;1;0;0;0;1
-13169;tarif;negative;0;0;1;1;0;1
-13170;tartan;positive;0;0;0;0;0;0
-13171;tarte;positive;0;0;0;0;0;0
-13172;tartre;negative;0;0;0;0;0;1
-13173;tas;positive;0;0;0;0;0;1
-13174;tasse;positive;0;0;0;0;0;0
-13175;tassement;negative;0;0;1;0;0;0
-13176;tatami;negative;0;0;0;0;0;0
-13177;tâtonner;negative;0;1;0;0;0;0
-13178;tatouage;positive;0;0;0;0;0;0
-13179;tatouer;positive;0;0;0;0;0;0
-13180;taudis;negative;0;1;1;0;0;1
-13181;taureau;negative;0;1;0;0;0;0
-13182;tau|taux;positive;0;0;0;0;0;0
-13183;taverne;positive;0;0;0;0;0;0
-13184;taxe;negative;0;0;1;1;0;0
-13185;taxer;negative;0;0;1;1;0;0
-13186;taxi;positive;0;0;0;0;0;0
-13187;taxinomie;positive;0;0;0;0;0;0
-13188;taxonomie;positive;0;0;0;0;0;0
-13189;teaser;positive;0;0;0;0;0;0
-13190;technicité;positive;0;0;0;0;0;0
-13191;technique;positive;0;0;0;0;0;0
-13192;technologie;positive;0;0;0;0;0;0
-13193;ted;positive;0;0;0;0;0;0
-13194;teflon;positive;0;0;0;0;0;0
-13195;téflon;positive;0;0;0;0;0;0
-13196;teindre;positive;0;0;0;0;0;0
-13197;teinte;positive;0;0;0;0;0;0
-13198;teinter;positive;0;0;0;0;0;0
-13199;teinture;positive;0;0;0;0;0;0
-13200;télégramme;positive;0;0;0;0;0;0
-13201;télégraphe;positive;0;0;0;0;0;0
-13202;téléphone;positive;0;0;0;0;0;0
-13203;téléphoner;positive;0;0;0;0;0;0
-13204;télescope;positive;0;0;0;0;0;0
-13205;téléscope;positive;0;0;0;0;0;0
-13206;télescopique;positive;0;0;0;0;0;0
-13207;téléscripteur;positive;0;0;0;0;0;0
-13208;télévision;positive;0;0;0;0;0;0
-13209;téméraire;positive;0;1;0;1;0;0
-13210;témérité;negative;0;1;0;1;1;1
-13211;témoignage;positive;0;0;0;0;0;0
-13212;témoigner;positive;0;0;0;0;0;0
-13213;témoigner de le sympathie;positive;0;0;1;0;0;0
-13214;témoin;positive;0;0;0;0;0;0
-13215;témoin oculaire;positive;0;0;0;0;0;0
-13216;tempera;positive;0;0;0;0;0;0
-13217;tempérer;positive;0;0;0;0;0;0
-13218;tempérament;positive;0;0;0;0;0;0
-13219;tempérance;positive;0;0;0;0;0;0
-13220;température;positive;0;0;0;0;0;0
-13221;tempête;negative;0;1;1;1;1;0
-13222;temple;positive;0;0;0;0;0;0
-13223;temporaire;negative;0;0;0;0;0;0
-13224;temporairement;negative;0;0;0;0;0;0
-13225;temporal;positive;0;0;0;0;0;0
-13226;temporisation;negative;0;1;1;0;0;0
-13227;temps;positive;0;0;0;0;0;0
-13228;ténacité;positive;0;0;0;0;0;0
-13229;tendon;positive;0;0;0;0;0;0
-13230;tendre;positive;0;0;0;0;0;0
-13231;tendre un embuscade à;negative;0;1;0;1;1;0
-13232;tendresse;positive;0;0;0;0;0;0
-13233;ténèbre;negative;0;1;1;1;0;0
-13234;teneur;positive;0;0;0;0;0;0
-13235;tenir;positive;0;0;0;0;0;0
-13236;tenir compte de;positive;0;0;0;0;0;0
-13237;tenir en laisse;negative;0;1;1;0;0;0
-13238;tennis;positive;0;0;0;0;0;0
-13239;tension;negative;0;1;0;1;0;0
-13240;tentacule;negative;0;1;0;0;0;1
-13241;tenter;positive;0;0;0;0;1;0
-13242;tentant;positive;0;0;0;0;1;0
-13243;tentation;negative;0;0;0;0;0;0
-13244;tentative;positive;0;0;0;0;0;0
-13245;tente;positive;0;0;0;0;0;0
-13246;tenture;positive;0;0;0;0;0;0
-13247;ténu;negative;0;1;1;0;0;0
-13248;tenue;positive;0;0;0;0;0;0
-13249;tercet;positive;0;0;0;0;0;0
-13250;terme;positive;0;0;1;0;0;0
-13251;terminal;positive;0;1;1;0;0;0
-13252;terminus;negative;0;1;1;0;0;0
-13253;termite;negative;0;0;0;0;0;1
-13254;ternaire;positive;0;0;0;0;0;0
-13255;terne;negative;0;0;1;0;0;1
-13256;ternir;negative;0;0;1;0;0;1
-13257;terrain;positive;0;1;1;1;1;0
-13258;terrasse;positive;0;0;0;0;0;0
-13259;terre;positive;0;0;0;0;0;0
-13260;terreau;positive;0;0;0;0;0;0
-13261;terre boisé;positive;0;0;0;0;0;0
-13262;terreur;negative;0;1;1;1;0;0
-13263;terreux;positive;0;0;0;0;0;0
-13264;terrible;negative;0;1;1;1;1;1
-13265;terriblement;negative;0;1;1;1;1;1
-13266;terrier;negative;0;0;0;0;0;0
-13267;terrifier;negative;0;1;1;1;1;1
-13268;terrifiant;negative;0;1;1;1;0;1
-13269;territoire;positive;0;0;0;0;0;0
-13270;territorial;positive;0;0;0;0;0;0
-13271;terroriser;negative;0;1;1;1;1;1
-13272;terrorisme;negative;0;1;1;1;0;1
-13273;terroriste;negative;0;1;1;1;1;1
-13274;tertiaire;positive;0;0;0;0;0;0
-13275;tertre;negative;0;0;0;0;0;1
-13276;test;positive;0;0;0;0;0;0
-13277;testament;positive;0;0;0;0;0;0
-13278;tester;positive;0;0;0;0;0;0
-13279;tétanos;negative;0;1;0;0;0;1
-13280;tête;positive;0;0;0;0;0;0
-13281;tête baisser;negative;0;0;0;1;1;0
-13282;tête de n?ud;negative;0;0;0;1;0;1
-13283;tête de puits;positive;0;0;0;0;0;0
-13284;téter;positive;0;0;0;0;0;0
-13285;tétine;positive;0;0;0;0;0;0
-13286;téton;positive;0;0;0;0;0;0
-13287;tétraédrique;positive;0;0;0;0;0;0
-13288;tétu;negative;0;0;0;1;0;0
-13289;texte;positive;0;0;0;0;0;0
-13290;texte sacré;positive;0;0;0;0;0;0
-13291;texte standard;negative;0;0;0;0;0;0
-13292;textile;positive;0;0;0;0;0;0
-13293;texto;positive;0;0;0;0;0;0
-13294;textuellement;positive;0;0;0;0;0;0
-13295;textural;positive;0;0;0;0;0;0
-13296;texturale;positive;0;0;0;0;0;0
-13297;texturales;positive;0;0;0;0;0;0
-13298;texturaux;positive;0;0;0;0;0;0
-13299;texture;positive;0;0;0;0;0;0
-13300;thanksgiving;positive;0;0;0;0;0;0
-13301;thé;positive;0;0;0;0;0;0
-13302;théâtral;negative;0;0;0;0;0;0
-13303;théâtre;positive;1;0;0;0;0;0
-13304;théisme;negative;0;0;0;0;0;1
-13305;thème;positive;0;0;0;0;0;0
-13306;thème majeur;positive;0;0;0;0;0;0
-13307;théocratie;positive;0;0;0;0;0;0
-13308;théocratique;negative;0;1;1;1;0;0
-13309;théologie;positive;0;0;0;0;0;0
-13310;théologique;positive;0;0;0;0;0;0
-13311;théorème;positive;0;0;0;0;0;0
-13312;théorie;positive;0;0;0;0;0;0
-13313;théorique;negative;0;0;0;0;0;0
-13314;thérapeutique;positive;0;0;0;0;0;0
-13315;thermique;positive;0;0;0;0;0;0
-13316;thermocouple;positive;0;0;0;0;0;0
-13317;thermodynamique;positive;0;0;0;0;0;0
-13318;thermomètre;positive;0;0;0;0;0;0
-13319;thermostat;positive;0;0;0;0;0;0
-13320;thèse;positive;0;0;0;0;0;0
-13321;thon;negative;0;0;0;0;0;0
-13322;thym;positive;0;0;0;0;0;0
-13323;tiare;positive;0;0;0;0;0;0
-13324;tibia;positive;0;0;0;0;0;0
-13325;tic;negative;0;1;0;0;1;0
-13326;ticket;positive;0;0;0;0;0;0
-13327;ticket de caisse;positive;0;0;0;0;0;0
-13328;tiède;negative;0;0;0;0;0;0
-13329;tiers;positive;0;0;0;0;0;0
-13330;tige;positive;0;1;0;0;0;0
-13331;tigre;negative;0;1;0;0;0;0
-13332;tigrer;positive;0;0;0;0;0;0
-13333;tilde;positive;0;0;0;0;0;0
-13334;timbre;positive;0;0;0;0;0;0
-13335;timbrer;negative;0;1;0;0;1;1
-13336;timide;negative;0;1;1;0;0;0
-13337;timidité;negative;0;1;1;0;0;0
-13338;tintement;positive;0;0;0;0;1;0
-13339;tinter;positive;1;0;0;0;0;0
-13340;tique;negative;0;0;0;0;0;0
-13341;tir;negative;0;1;1;1;1;0
-13342;tir à l arc;positive;0;0;0;0;0;0
-13343;tirage;positive;0;0;0;0;0;0
-13344;tirant;negative;0;1;1;1;0;0
-13345;tirer bouchon;positive;0;0;0;0;0;0
-13346;tirer à pile ou face;negative;0;1;0;0;1;0
-13347;tirer profit;positive;1;0;0;0;0;0
-13348;tirer profit de;positive;0;0;0;0;0;0
-13349;tiret;negative;0;1;1;0;0;0
-13350;tireur;negative;0;1;0;1;1;0
-13351;tiroir;positive;0;0;0;0;0;0
-13352;tisser;positive;0;0;0;0;0;0
-13353;tissu;positive;0;0;1;0;0;1
-13354;titanesque;negative;0;1;0;0;0;0
-13355;titre;positive;0;0;0;0;0;0
-13356;titrer;positive;0;0;0;0;0;0
-13357;titulaire;positive;0;0;0;0;0;0
-13358;titularisation;positive;0;0;0;0;0;0
-13359;toast;positive;1;0;0;0;0;0
-13360;toboggan;positive;0;1;0;0;1;0
-13361;toge;positive;0;0;0;0;0;0
-13362;toile;positive;0;0;0;0;0;0
-13363;toile de jute;negative;0;0;0;0;0;0
-13364;toilette;negative;0;0;0;0;0;1
-13365;toiletter;positive;0;0;0;0;0;0
-13366;toilette extérieur;negative;0;0;0;0;0;1
-13367;toison;positive;0;0;1;1;0;1
-13368;toit;positive;0;0;0;0;0;0
-13369;tolérer;positive;0;0;0;0;0;0
-13370;tolérant;positive;0;0;0;0;0;0
-13371;tollé;negative;0;1;0;1;1;1
-13372;tomahawk;positive;0;0;0;0;0;0
-13373;tomber;negative;0;1;1;0;0;0
-13374;tombant;negative;0;0;1;0;0;0
-13375;tombe;negative;0;1;1;0;0;0
-13376;tombeau;negative;0;1;1;0;0;0
-13377;tomber de le nuit;negative;0;1;1;0;0;0
-13378;tomber en flocon;negative;0;0;0;0;0;0
-13379;tomber en pâmoison;positive;1;0;0;0;0;0
-13380;tomber goutte à goutte;negative;0;0;0;0;0;1
-13381;tomber nez à nez;positive;0;0;0;0;1;0
-13382;tomber sur;positive;0;0;0;0;1;0
-13383;tombeur;positive;0;0;0;0;0;0
-13384;tombola;positive;0;0;0;0;1;0
-13385;tome;positive;0;0;0;0;0;0
-13386;ton;positive;0;0;0;0;0;0
-13387;ton monotone;negative;0;1;0;0;0;1
-13388;tondeur|tondeuse;negative;0;1;0;0;0;0
-13389;tondre;negative;0;1;0;0;0;0
-13390;tonifiant;positive;1;0;0;0;0;0
-13391;tonique;positive;1;0;0;0;0;0
-13392;tonitruer;negative;0;1;0;1;1;0
-13393;tonitruant;negative;0;1;0;1;1;0
-13394;tonnage;negative;0;0;0;0;0;0
-13395;tonne;negative;0;0;0;0;0;0
-13396;tonneau;positive;0;0;0;0;0;0
-13397;tonnelet;positive;0;0;0;0;0;0
-13398;tonnelle;positive;0;0;0;0;0;0
-13399;tonner;negative;0;1;0;1;1;0
-13400;tonnerre;negative;0;1;0;1;1;0
-13401;tonton;positive;0;0;0;0;0;0
-13402;topaze;positive;0;0;0;0;0;0
-13403;topographie;positive;0;0;0;0;0;0
-13404;topographique;positive;0;0;0;0;0;0
-13405;toquade;negative;0;0;0;0;0;0
-13406;toquer;negative;0;1;0;0;1;0
-13407;torche;positive;0;0;0;0;0;0
-13408;tordre;negative;0;1;0;1;0;0
-13409;tornade;negative;0;1;0;1;0;0
-13410;torpille;negative;0;1;0;1;0;0
-13411;torpiller;negative;0;1;0;1;0;0
-13412;torrent;negative;0;1;0;1;0;0
-13413;torsader;positive;0;0;0;0;0;0
-13414;torse;positive;0;0;0;0;0;0
-13415;torsion;negative;0;1;0;0;0;0
-13416;tort;negative;0;1;1;1;0;1
-13417;tortue;positive;0;0;0;0;0;0
-13418;torture;negative;0;1;1;1;0;1
-13419;torturer;negative;0;1;1;1;0;1
-13420;total;negative;0;0;0;0;0;0
-13421;totalement;positive;0;0;0;0;0;0
-13422;totalité;positive;0;0;0;0;0;0
-13423;totem;positive;0;0;0;0;0;0
-13424;touche;positive;0;0;0;0;0;0
-13425;toucher;positive;0;0;1;0;0;0
-13426;toucher par;negative;0;0;1;0;0;0
-13427;touffe;negative;0;0;0;0;0;0
-13428;touffu;negative;0;0;0;0;0;1
-13429;tour;positive;0;0;0;1;1;1
-13430;tour de main;positive;0;0;0;0;0;0
-13431;tour de taille;positive;0;0;0;0;0;0
-13432;tourbe;negative;0;0;0;0;0;1
-13433;tourbier|tourbière;negative;0;1;0;0;0;1
-13434;tourbillon;negative;0;1;0;0;1;0
-13435;tourbillonner;negative;0;1;0;0;1;0
-13436;tourelle;positive;0;0;0;0;0;0
-13437;touriste;positive;0;0;0;0;0;0
-13438;touristique;positive;0;0;0;0;0;0
-13439;tourmaline;positive;0;0;0;0;0;0
-13440;tourment;negative;0;1;1;1;0;0
-13441;tourmente;negative;0;1;1;1;0;0
-13442;tourmenter;negative;0;1;1;1;0;0
-13443;tournage;negative;0;1;0;1;0;0
-13444;tournant;negative;0;1;0;0;0;0
-13445;tourner autour de;positive;0;0;0;0;0;0
-13446;tournante;negative;0;1;0;0;0;0
-13447;tournée;positive;0;0;0;0;0;0
-13448;tournevis;positive;0;0;0;0;0;0
-13449;tournoi;positive;0;0;0;0;0;0
-13450;tournure;positive;0;0;0;0;0;0
-13451;tout le an;positive;0;0;0;0;0;0
-13452;tout le jour;positive;0;0;0;0;0;0
-13453;tout le quinze jour;positive;0;0;0;0;0;0
-13454;tousser;negative;0;0;0;0;0;1
-13455;tout à coup;negative;0;1;0;0;1;0
-13456;tout à fait;positive;0;0;0;0;0;0
-13457;tout autour;positive;0;0;0;0;0;0
-13458;tout comprendre;positive;0;0;0;0;0;0
-13459;tout de suite;positive;0;0;0;0;0;0
-13460;tout puissance;positive;0;1;0;0;0;0
-13461;tout le heure;positive;0;0;0;0;0;0
-13462;tout le semaine;positive;0;0;0;0;0;0
-13463;toux;negative;0;0;0;0;0;1
-13464;toxicologie;positive;0;0;0;0;0;0
-13465;toxine;negative;0;1;0;0;0;0
-13466;toxique;negative;0;1;1;1;1;1
-13467;tracer;positive;0;0;0;0;0;0
-13468;tracas;negative;0;1;1;1;0;0
-13469;tracasser;negative;0;1;1;1;0;0
-13470;tracassant;negative;0;1;1;1;0;0
-13471;trace;negative;0;0;0;0;0;1
-13472;tracé;positive;0;0;0;0;0;0
-13473;tracer du ligne;positive;0;0;0;0;0;0
-13474;trachée;positive;0;0;0;0;0;0
-13475;tract;positive;0;1;0;0;0;0
-13476;tracter;positive;0;0;0;0;0;0
-13477;tracteur;positive;0;0;0;0;0;0
-13478;traction;positive;0;0;0;0;0;0
-13479;tradition;positive;0;0;0;0;0;0
-13480;traditionnel;positive;0;0;0;0;0;0
-13481;traduction;positive;0;0;0;0;0;0
-13482;Traductionse;negative;0;0;0;0;0;0
-13483;Traductionses;negative;0;0;0;0;0;0
-13484;Traductionss;negative;0;0;0;0;0;0
-13485;traduire;positive;0;0;0;0;0;0
-13486;trafic;negative;0;0;1;0;0;0
-13487;tragédie;negative;0;1;1;0;1;0
-13488;tragique;negative;0;1;1;0;1;0
-13489;trahir;negative;0;0;1;1;1;1
-13490;trahison;negative;0;1;1;1;1;1
-13491;train;positive;0;0;0;0;0;0
-13492;train en marche;positive;0;0;0;0;0;0
-13493;traîneau;positive;1;0;0;0;0;0
-13494;traîner;negative;0;1;1;1;0;1
-13495;traîner le pied;negative;0;0;1;0;0;0
-13496;trait;positive;0;0;0;0;0;0
-13497;traire d union;positive;0;0;0;0;0;0
-13498;traiter;positive;0;0;0;0;0;0
-13499;traitement;positive;0;0;0;0;0;0
-13500;traiter avec condescendance;negative;0;0;0;0;0;1
-13501;traiteur;positive;0;0;0;0;0;0
-13502;traîtrise;negative;0;1;1;1;1;0
-13503;trajectoire;positive;0;0;0;0;0;0
-13504;tram;positive;0;0;0;0;0;0
-13505;tramway;positive;0;0;0;0;0;0
-13506;tranchant;negative;0;1;1;1;0;1
-13507;tranche;negative;0;0;0;0;0;0
-13508;trancher;negative;0;1;0;0;0;0
-13509;tranquille;positive;0;0;1;0;0;0
-13510;tranquillement;positive;1;0;0;0;0;0
-13511;tranquillité;positive;0;1;1;0;0;0
-13512;transatlantique;positive;0;0;0;0;0;0
-13513;transcendance;positive;0;0;0;0;1;0
-13514;transcender;positive;0;0;0;0;0;0
-13515;transcendantal;positive;0;0;0;0;0;0
-13516;transcendant;positive;0;0;0;0;0;0
-13517;transcendentaux;positive;0;0;0;0;0;0
-13518;transcripteur;positive;0;0;0;0;0;0
-13519;transcription;positive;0;0;0;0;0;0
-13520;transcrire;positive;0;0;0;0;0;0
-13521;transe;negative;0;1;0;0;0;0
-13522;transférer;positive;0;0;0;0;0;0
-13523;transfert;positive;0;0;0;0;0;0
-13524;transfert de propriété;positive;0;0;0;0;0;0
-13525;transformation;negative;0;1;1;0;0;0
-13526;transformer;positive;0;0;0;0;0;0
-13527;transfusion;positive;0;0;0;0;0;0
-13528;transgression;negative;0;1;1;1;0;0
-13529;transir;positive;0;0;0;0;0;0
-13530;transiter;positive;0;0;0;0;0;0
-13531;transition;positive;0;0;0;0;0;0
-13532;transitoire;negative;0;0;1;0;1;0
-13533;translocation;positive;0;0;0;0;0;0
-13534;translucide;negative;0;1;1;0;0;0
-13535;transmettre;positive;0;0;0;0;0;0
-13536;transmissible;negative;0;1;0;0;0;0
-13537;transmission;positive;0;0;0;0;0;0
-13538;transmutation;positive;0;0;0;0;0;0
-13539;transparence;positive;0;0;0;0;0;0
-13540;transparent;positive;0;0;0;0;0;0
-13541;transpercer;negative;0;1;1;1;0;0
-13542;transpirer;negative;0;0;0;0;0;1
-13543;transpiration;negative;0;1;0;0;0;1
-13544;transplantation;positive;0;0;0;0;0;0
-13545;transplanter;positive;0;0;0;0;0;0
-13546;transport;positive;0;0;0;0;0;0
-13547;transport maritime;positive;0;0;0;0;0;0
-13548;transport routier;positive;0;0;0;0;0;0
-13549;transporter;positive;0;0;0;0;0;0
-13550;transporteur;positive;0;0;0;0;0;0
-13551;transposer;positive;0;0;0;0;0;0
-13552;transposition;positive;0;0;0;0;0;0
-13553;transversal;negative;0;0;0;0;0;0
-13554;transversale;negative;0;0;0;0;0;0
-13555;trapu;negative;0;0;0;0;0;0
-13556;traquer;negative;0;1;0;0;0;0
-13557;traumatique;negative;0;1;1;1;1;0
-13558;traumatiser;negative;0;1;1;1;1;0
-13559;traumatisant;negative;0;1;1;1;1;0
-13560;travail;positive;0;0;0;0;1;0
-13561;travail de bureau travail admi;positive;0;0;0;0;0;0
-13562;travail pénible;negative;0;0;1;0;0;1
-13563;travail préparatoire;positive;0;0;0;0;0;0
-13564;travailler;positive;0;0;0;0;0;0
-13565;travailler dur;positive;0;0;0;0;1;0
-13566;travailler en indépendant;positive;0;0;0;0;0;0
-13567;travailleur;positive;0;0;0;0;0;0
-13568;traverser;positive;0;0;0;0;0;0
-13569;traversier;positive;0;0;0;0;0;0
-13570;travestir;negative;0;1;1;1;0;1
-13571;trébucher;negative;0;1;0;0;1;0
-13572;trèfle;positive;0;0;0;0;0;0
-13573;treillage;positive;0;0;0;0;0;0
-13574;treillis;negative;0;0;0;0;0;0
-13575;treize;positive;0;0;0;0;0;0
-13576;treizième;negative;0;1;0;0;0;0
-13577;trek;positive;0;0;0;0;0;0
-13578;tremblant;negative;0;1;0;1;0;0
-13579;tremblante;negative;0;1;1;1;0;0
-13580;tremblement;negative;0;1;1;1;1;0
-13581;tremblement de terre;negative;0;1;1;1;1;0
-13582;trembler;negative;0;1;0;0;1;0
-13583;trémie;positive;0;0;0;0;0;0
-13584;trempage;negative;0;0;1;0;0;1
-13585;tremper;negative;0;0;1;0;0;1
-13586;trémpée;negative;0;0;0;0;0;1
-13587;trépas;negative;0;1;1;0;0;0
-13588;trépied;positive;0;0;0;0;0;0
-13589;très courant;positive;0;0;0;0;0;0
-13590;très éprouver;negative;0;0;1;0;0;0
-13591;très facile;positive;1;0;0;0;0;0
-13592;très intelligent;positive;0;0;0;0;0;0
-13593;très long;negative;0;0;0;0;0;0
-13594;très longue;negative;0;0;0;0;0;0
-13595;très peupler;negative;0;0;0;0;0;0
-13596;trésor;positive;0;0;0;0;0;0
-13597;trésorerie;positive;0;0;0;0;0;0
-13598;trésorier;positive;0;0;0;0;0;0
-13599;tressaillir;negative;0;1;1;1;1;1
-13600;tresse;positive;0;0;0;0;0;0
-13601;tresser;positive;0;0;0;0;0;0
-13602;treuil;positive;0;0;0;0;0;0
-13603;trêve;positive;0;0;0;0;0;0
-13604;tri;positive;0;0;0;0;0;0
-13605;triade;positive;0;0;0;0;0;0
-13606;trial;negative;0;1;0;1;0;0
-13607;triangle;positive;0;0;0;0;0;0
-13608;triangulaire;positive;0;0;0;0;0;0
-13609;tribord;positive;0;0;0;0;0;0
-13610;tribu;positive;0;0;0;0;0;0
-13611;tribulation;negative;0;1;1;0;0;0
-13612;tribun;positive;0;0;0;0;0;0
-13613;tribunal;positive;0;1;0;1;0;1
-13614;tribune;positive;0;0;0;0;0;0
-13615;tribut;negative;0;0;1;0;0;0
-13616;tricher;negative;0;0;1;1;1;1
-13617;tricherie;negative;0;0;0;1;0;1
-13618;tricot;positive;0;0;0;0;0;0
-13619;tricoter;positive;0;0;0;0;0;0
-13620;tricycle;positive;0;0;0;0;0;0
-13621;trident;positive;0;0;0;0;0;0
-13622;trier;positive;0;0;0;0;0;0
-13623;trieur;positive;0;0;0;0;0;0
-13624;trieuse;positive;0;0;0;0;0;0
-13625;trigo;positive;0;0;0;0;0;0
-13626;trigone;positive;0;0;0;0;0;0
-13627;trigonométrie;positive;0;0;0;0;0;0
-13628;trimballer;negative;0;0;0;0;0;0
-13629;trimestre;positive;0;0;0;0;0;0
-13630;trinité;positive;0;0;0;0;0;0
-13631;trio;positive;0;0;0;0;0;0
-13632;triomphant;positive;0;0;0;0;0;0
-13633;triomphe;positive;1;0;0;0;0;0
-13634;triompher;positive;1;0;0;0;0;0
-13635;tripartite;positive;0;0;0;0;0;0
-13636;tripe;positive;0;0;0;1;0;0
-13637;triple;positive;0;0;0;0;0;0
-13638;tripotage;negative;0;1;0;1;0;1
-13639;tripoter;negative;0;1;0;1;0;1
-13640;triste;negative;0;1;1;0;0;0
-13641;triste notoriété;negative;0;1;1;1;0;1
-13642;tristement;negative;0;0;1;0;0;1
-13643;tristesse;negative;0;1;1;0;0;0
-13644;tritium;positive;0;0;0;0;0;0
-13645;troc;positive;0;0;0;0;0;0
-13646;trois foi|fois;positive;0;0;0;0;0;0
-13647;troisième;positive;0;0;0;0;0;0
-13648;troll;negative;0;1;0;1;0;1
-13649;trombone;positive;0;0;0;0;0;0
-13650;tromper;negative;0;0;1;0;0;1
-13651;trompe;positive;0;0;0;0;0;0
-13652;trompette;positive;0;0;0;0;0;0
-13653;trompettiste;positive;0;0;0;0;0;0
-13654;tronc;negative;0;0;0;0;0;0
-13655;trône;positive;0;0;0;0;0;0
-13656;tronquer;negative;0;1;1;1;0;0
-13657;trop diluer;negative;0;0;0;0;0;1
-13658;trop longue;negative;0;0;1;0;0;0
-13659;trop payer|payer;negative;0;0;0;0;0;0
-13660;trophée;positive;0;0;0;0;1;0
-13661;tropical;positive;0;0;0;0;0;0
-13662;troquer;positive;0;0;0;0;0;0
-13663;trot;positive;0;0;0;0;0;0
-13664;trotter;positive;0;0;0;0;0;0
-13665;trottiner;positive;0;0;0;0;0;0
-13666;trottoir;positive;0;0;0;0;0;0
-13667;trou;negative;0;1;1;0;0;0
-13668;trou de serrure;positive;0;0;0;0;0;0
-13669;trou du cul;negative;0;0;0;1;0;1
-13670;troubler;negative;0;0;0;1;0;0
-13671;trouer;negative;0;1;1;0;0;0
-13672;troupe;positive;0;0;0;0;0;0
-13673;troupeau;negative;0;1;0;1;0;0
-13674;trousse;positive;0;0;0;0;0;0
-13675;trouver;positive;0;0;0;0;0;0
-13676;truc;negative;0;1;0;0;0;0
-13677;truelle;negative;0;0;0;0;0;0
-13678;truie;negative;0;0;0;0;0;1
-13679;truisme;negative;0;0;0;0;0;0
-13680;truite;negative;0;0;0;0;0;1
-13681;truquer;negative;0;0;0;0;0;0
-13682;tsar;positive;0;0;0;0;0;0
-13683;tuer;negative;0;1;1;1;0;0
-13684;tube;positive;0;0;0;0;0;0
-13685;tube couder;positive;0;0;0;0;0;0
-13686;tubulaire;positive;0;0;0;0;0;0
-13687;tubule;positive;0;0;0;0;0;0
-13688;tubulure;negative;0;0;0;0;0;0
-13689;tuerie;negative;0;1;1;1;0;0
-13690;tufté;negative;0;0;0;0;0;1
-13691;tuile;positive;0;0;0;0;0;0
-13692;tulipe;positive;0;0;0;0;0;0
-13693;tumeur;negative;0;1;1;0;0;0
-13694;tumulte;negative;0;1;0;1;1;0
-13695;tunique;positive;0;0;0;0;0;0
-13696;tunnel;negative;0;1;0;0;0;0
-13697;turban;positive;0;0;0;0;0;0
-13698;turbidité;negative;0;1;0;0;0;1
-13699;turbine;positive;0;0;0;0;0;0
-13700;turbulence;negative;0;1;0;1;0;0
-13701;turbulent;negative;0;1;0;1;0;0
-13702;turquoise;positive;0;0;0;0;0;0
-13703;tutelle;positive;0;0;0;0;0;0
-13704;tuteur;positive;0;0;0;0;0;0
-13705;tuyau;positive;0;0;0;0;0;0
-13706;tva;negative;0;0;0;0;0;0
-13707;type;negative;0;0;0;0;0;0
-13708;typhon;negative;0;1;0;0;0;0
-13709;typiquement;positive;0;0;0;0;0;0
-13710;typographie;positive;0;0;0;0;0;0
-13711;tyran;negative;0;1;1;1;0;1
-13712;tyrannie;negative;0;1;1;1;0;1
-13713;tyrannique;negative;0;1;1;1;0;1
-13714;tyranniser;negative;0;1;0;1;0;0
-13715;ubiquité;positive;0;0;0;0;0;0
-13716;ulcère;negative;0;1;1;1;0;1
-13717;ultérieur;negative;0;0;1;0;0;0
-13718;ultérieurement;negative;0;0;0;0;0;0
-13719;ultériorité;negative;0;0;0;0;0;0
-13720;ultimatum;negative;0;1;0;1;1;0
-13721;ultraviolet;positive;0;0;0;0;0;0
-13722;ultraviolettes;positive;0;0;0;0;0;0
-13723;un petit coup;positive;0;0;0;0;0;0
-13724;un peu;positive;0;0;0;0;0;0
-13725;unanime;positive;0;0;0;0;0;0
-13726;unanimement;positive;0;0;0;0;0;0
-13727;unanimité;positive;0;0;0;0;0;0
-13728;unir;positive;0;0;0;0;0;0
-13729;unification;positive;0;0;0;0;0;0
-13730;uniforme;positive;0;0;0;0;0;0
-13731;uniformément;positive;0;0;0;0;0;0
-13732;uniformiser;positive;0;0;0;0;0;0
-13733;union;positive;0;0;0;0;0;0
-13734;unique;negative;0;0;0;0;1;0
-13735;unisson;positive;0;0;0;0;0;0
-13736;unitaire;positive;0;0;0;0;0;0
-13737;unité;positive;0;0;0;0;0;0
-13738;univers;positive;0;0;0;0;0;0
-13739;universalité;positive;0;0;0;0;0;0
-13740;universitaire;positive;0;0;0;0;0;0
-13741;université;positive;0;0;0;0;0;0
-13742;univoque;positive;0;0;0;0;0;0
-13743;uranium;positive;0;0;0;0;0;0
-13744;urbain;positive;0;0;0;0;0;0
-13745;urbaine;positive;0;0;0;0;0;0
-13746;urgence;negative;0;1;1;0;1;0
-13747;urne;negative;0;0;1;0;0;0
-13748;usage;positive;0;0;0;0;0;0
-13749;usage abusif;negative;0;1;1;1;0;1
-13750;usage impropre;negative;0;1;1;1;0;0
-13751;usant;negative;0;0;1;0;0;0
-13752;usine;negative;0;0;0;0;0;0
-13753;ustensile;positive;0;0;0;0;0;0
-13754;usure;negative;0;0;1;0;0;1
-13755;usurper;negative;0;1;1;1;0;1
-13756;utérus;positive;0;0;0;0;0;0
-13757;utile;positive;0;0;0;0;0;0
-13758;utilement;positive;0;0;0;0;0;0
-13759;utilisable;positive;0;0;0;0;0;0
-13760;utilisation;positive;0;0;0;0;0;0
-13761;utiliser;negative;0;0;0;0;0;0
-13762;utilitaire;positive;0;0;0;0;0;0
-13763;utilité;positive;0;0;0;0;0;0
-13764;utopique;positive;0;0;0;0;0;0
-13765;vacance;positive;1;0;0;0;0;0
-13766;vacant;negative;0;0;1;0;0;0
-13767;vacarme;negative;0;1;0;1;1;1
-13768;vaccin;positive;0;0;0;0;0;0
-13769;vaccination;positive;0;0;0;0;0;0
-13770;vache;positive;0;0;0;0;0;0
-13771;vacuolaire;positive;0;0;0;0;0;0
-13772;vacuole;positive;0;0;0;0;0;0
-13773;vagabond;negative;0;0;1;0;0;1
-13774;vagabondage;negative;0;0;1;0;0;0
-13775;vague;negative;0;1;1;1;1;0
-13776;vague idée;positive;0;0;0;0;0;0
-13777;vaincre;positive;1;0;0;0;0;0
-13778;vainement;negative;0;0;1;0;0;1
-13779;vainqueur;positive;0;0;0;0;1;0
-13780;vairon;positive;0;0;0;0;0;0
-13781;vaisseau;positive;0;0;0;0;0;0
-13782;vaisseau amiral;positive;0;0;0;0;0;0
-13783;vaisselle;positive;0;0;0;0;0;0
-13784;valet;positive;0;0;0;0;0;0
-13785;valeur;positive;0;0;0;0;0;0
-13786;validité;positive;0;1;0;0;0;0
-13787;vallée;positive;0;0;0;0;0;0
-13788;vallon;positive;0;0;0;0;0;0
-13789;vallonner;positive;0;0;0;0;0;0
-13790;valoir;positive;0;0;0;0;0;0
-13791;valoriser;positive;0;0;0;0;0;0
-13792;valse;positive;0;0;0;0;0;0
-13793;valser;positive;0;0;0;0;0;0
-13794;vamp;positive;0;0;0;0;0;0
-13795;vampire;negative;0;1;0;1;0;1
-13796;van;positive;0;0;0;0;0;0
-13797;vanille;positive;0;0;0;0;0;0
-13798;vanité;negative;0;0;0;1;0;1
-13799;vaniteux;negative;0;0;0;1;0;1
-13800;vannage;positive;0;0;0;0;0;0
-13801;vanne;positive;0;0;0;0;0;0
-13802;vantardise;negative;0;0;0;0;0;0
-13803;vanter;negative;0;0;0;0;0;0
-13804;vapeur;positive;0;0;0;0;0;0
-13805;vaporiser;positive;0;0;0;0;0;0
-13806;vara;positive;0;0;0;0;0;0
-13807;variable;negative;0;0;0;0;1;0
-13808;variance;negative;0;0;0;0;0;0
-13809;variation;negative;0;0;0;0;0;0
-13810;varicelle;negative;0;1;1;0;0;1
-13811;varier;positive;0;0;0;0;0;0
-13812;variété;positive;0;0;0;0;0;0
-13813;vasculaire;positive;0;0;0;0;0;0
-13814;vase;negative;0;1;0;0;0;1
-13815;vasistas;positive;0;0;0;0;0;0
-13816;vaste;positive;0;0;0;0;0;0
-13817;vaudeville;positive;0;0;0;0;0;0
-13818;vaudou;negative;0;1;0;0;0;1
-13819;vaurien;negative;0;1;0;1;0;1
-13820;vautour;negative;0;1;0;0;0;1
-13821;veau;positive;0;0;1;0;0;0
-13822;vecteur;positive;0;0;0;0;0;0
-13823;vedette;positive;0;0;0;0;0;0
-13824;vega;positive;0;0;0;0;0;0
-13825;véga;positive;0;0;0;0;0;0
-13826;végétal;positive;0;0;0;0;0;0
-13827;végétarisme;positive;0;0;0;0;0;0
-13828;végétatif;negative;0;0;1;0;0;1
-13829;végétation;positive;0;0;0;0;0;0
-13830;véhément;negative;0;1;0;1;0;0
-13831;véhicule;positive;0;0;0;0;0;0
-13832;véhiculer;positive;0;0;0;0;0;0
-13833;veille;positive;0;0;0;0;0;0
-13834;veiller;positive;0;0;0;0;0;0
-13835;veilleur;positive;0;0;0;0;0;0
-13836;veine;positive;0;0;0;0;0;1
-13837;veiner;negative;0;0;0;0;0;1
-13838;veineux;negative;0;0;0;0;0;1
-13839;vélin;positive;0;0;0;0;0;0
-13840;vélo;positive;0;0;0;0;0;0
-13841;vélocité;positive;0;0;0;0;0;0
-13842;velours;positive;0;0;0;0;0;0
-13843;velours côtelé;positive;0;0;0;0;0;0
-13844;velouter;positive;0;0;0;0;0;0
-13845;venaison;negative;0;0;0;0;0;1
-13846;vendanger;positive;0;0;0;0;0;0
-13847;vendetta;negative;0;1;1;1;0;0
-13848;vendeur ambulant;positive;0;0;0;0;0;0
-13849;vendre;positive;0;0;0;0;0;0
-13850;vendre au enchère;negative;0;0;0;0;0;0
-13851;vénérable;positive;0;0;1;0;0;0
-13852;vénération;positive;0;0;0;0;0;0
-13853;vénérer;positive;0;0;0;0;0;0
-13854;vengeance;negative;0;1;0;1;1;0
-13855;venin;negative;0;1;0;1;0;1
-13856;vent;positive;0;1;1;1;0;1
-13857;vente;positive;0;0;0;0;0;0
-13858;vente au détail;positive;0;0;0;0;0;0
-13859;vente au enchère;negative;0;0;0;0;0;0
-13860;venteux;negative;0;1;0;0;1;0
-13861;ventilateur;positive;0;0;0;0;0;0
-13862;ventilation;positive;0;0;0;0;0;0
-13863;ventouse;positive;0;0;0;1;0;0
-13864;ventre;positive;0;0;0;0;0;0
-13865;ventricule;positive;0;0;0;0;0;0
-13866;ver;negative;0;0;0;0;1;1
-13867;véracité;positive;0;0;0;0;1;0
-13868;véranda;positive;0;0;0;0;0;0
-13869;verbal;positive;0;0;0;0;0;0
-13870;verbiage;positive;0;0;0;0;0;0
-13871;verbosité;negative;0;0;0;0;0;0
-13872;verdâtre;negative;0;0;0;0;0;1
-13873;verdict;positive;0;1;0;0;0;0
-13874;verdoyer;positive;0;0;0;0;0;0
-13875;verdoyant;positive;0;0;0;0;0;0
-13876;verger;positive;0;0;0;0;0;0
-13877;véridique;positive;0;0;0;0;0;0
-13878;vérificateur;positive;0;0;0;0;0;0
-13879;vérification;positive;0;0;0;0;0;0
-13880;vérifier;positive;0;0;0;0;0;0
-13881;véritablement;positive;0;0;0;0;0;0
-13882;vérité;positive;0;0;0;0;0;0
-13883;vermifuger;negative;0;0;0;0;1;1
-13884;vermine;negative;0;1;0;1;0;1
-13885;vernaculaire;positive;0;0;0;0;0;0
-13886;vernal;positive;1;0;0;0;0;0
-13887;vérole;negative;0;1;0;0;0;1
-13888;véronique;positive;0;0;0;0;0;0
-13889;verre;positive;0;0;0;0;0;0
-13890;verrerie;positive;0;0;0;0;0;0
-13891;verrou;positive;0;0;0;0;0;0
-13892;verrouiller;positive;0;0;0;0;0;0
-13893;verrue;negative;0;0;0;0;0;1
-13894;vers l extérieur;positive;0;0;0;0;0;0
-13895;vers l intérieur;positive;0;0;0;0;0;0
-13896;vers le haut;positive;0;0;0;0;0;0
-13897;versatilité;negative;0;1;0;1;1;0
-13898;verser;positive;0;0;0;0;0;0
-13899;verser dans un tasse;positive;0;1;1;0;0;1
-13900;verser un pot de vin à;negative;0;0;0;0;0;0
-13901;verset;positive;0;0;0;0;0;0
-13902;version;positive;0;0;0;0;0;0
-13903;verso;negative;0;0;0;0;0;0
-13904;vert;positive;0;0;0;0;0;0
-13905;vert jade;positive;0;0;0;0;0;0
-13906;vertebral;positive;0;0;0;0;0;0
-13907;vertébral;positive;0;0;0;0;0;0
-13908;vertèbre;positive;0;0;0;0;0;0
-13909;vertical;positive;0;0;0;0;0;0
-13910;verticalement;positive;0;0;0;0;0;0
-13911;vertige;negative;0;1;0;0;1;0
-13912;vertu;positive;0;0;0;0;0;0
-13913;verve;positive;1;0;0;0;0;0
-13914;vésiculaire;negative;0;0;0;0;0;1
-13915;vésicule;positive;0;0;0;0;0;0
-13916;vessie;negative;0;0;0;0;0;1
-13917;vestibule;positive;0;0;0;0;0;0
-13918;vêtement;positive;0;0;0;0;0;0
-13919;vétéran;positive;0;0;0;0;0;0
-13920;vétérinaire;positive;0;0;0;0;0;0
-13921;veto;negative;0;0;0;1;0;0
-13922;vétuste;negative;0;0;0;0;0;1
-13923;veuf;negative;0;0;1;0;0;0
-13924;veuf|veuve;negative;0;0;1;0;0;0
-13925;viabilité;positive;0;0;0;0;0;0
-13926;viable;positive;0;0;0;0;0;0
-13927;viaduc;positive;0;0;0;0;0;0
-13928;viager;positive;0;0;0;0;0;0
-13929;vialins;negative;0;1;0;0;1;0
-13930;viande;positive;0;0;0;0;0;0
-13931;viande de porc;positive;0;0;0;0;0;0
-13932;vibrer;positive;0;0;1;0;0;0
-13933;vibrant;positive;0;0;1;0;0;0
-13934;vibration;negative;0;1;0;0;1;0
-13935;vibratoire;positive;0;0;0;0;0;0
-13936;vibreur sonore;positive;0;0;0;0;0;0
-13937;vicaire;positive;0;0;0;0;0;0
-13938;vice;negative;0;0;0;1;0;1
-13939;victime;negative;0;1;1;1;0;0
-13940;victimiser;negative;0;1;1;1;1;1
-13941;victoire;positive;0;0;0;0;0;0
-13942;victoria;positive;0;0;0;0;0;0
-13943;victuaille;positive;0;0;0;0;0;0
-13944;vide;negative;0;1;1;0;0;1
-13945;vider;negative;0;1;1;0;0;0
-13946;vie;positive;1;0;0;0;0;0
-13947;vieux âge;negative;0;0;0;0;0;0
-13948;vieux fille;negative;0;1;1;0;0;0
-13949;vieux sorcier;negative;0;1;0;1;0;1
-13950;vieillir;negative;0;1;1;0;0;0
-13951;vieillissement;negative;0;0;0;0;0;0
-13952;vieillot;negative;0;0;0;0;0;1
-13953;vierge;negative;0;1;1;0;0;0
-13954;vigilance;positive;0;1;0;0;1;0
-13955;vigiler;positive;0;1;0;1;1;0
-13956;vigilant;positive;0;1;0;1;1;0
-13957;vigile;positive;0;0;0;0;0;0
-13958;vigne;positive;0;0;0;0;0;0
-13959;vignette;positive;0;0;0;0;0;0
-13960;vignoble;positive;0;0;0;0;0;0
-13961;vigueur;positive;1;0;0;0;0;0
-13962;viking;negative;0;1;0;0;0;0
-13963;vil;negative;0;1;0;1;0;1
-13964;vilain;negative;0;1;0;0;1;0
-13965;villa;positive;0;0;0;0;0;0
-13966;village;positive;0;0;0;0;0;0
-13967;ville;positive;0;0;0;0;0;0
-13968;vin;positive;0;0;0;0;0;0
-13969;vinaigre;negative;0;1;0;0;0;1
-13970;vinaigrette;positive;0;0;0;0;0;0
-13971;vingt;positive;0;0;0;0;0;0
-13972;vingtième;positive;0;0;0;0;0;0
-13973;viol;negative;0;1;1;1;0;1
-13974;violation de propriété;negative;0;1;0;1;0;0
-13975;violemment;negative;0;1;1;1;0;1
-13976;violence;negative;0;1;1;1;0;0
-13977;violent;negative;0;1;0;1;1;1
-13978;violer;negative;0;1;1;1;0;1
-13979;violet;positive;0;0;0;0;0;0
-13980;violon;positive;0;0;0;0;0;0
-13981;violoniste;positive;0;0;0;0;0;0
-13982;vipère;negative;0;1;1;1;0;1
-13983;virage;positive;0;1;0;0;0;0
-13984;virer;negative;0;1;0;0;1;0
-13985;virer de bord;negative;0;1;0;0;1;0
-13986;virginité;positive;0;0;0;0;0;0
-13987;virgule;positive;0;0;0;0;0;0
-13988;viril;positive;0;0;0;0;0;0
-13989;virilité;positive;0;0;0;0;0;0
-13990;virologie;negative;0;0;0;0;0;0
-13991;virtuose;positive;0;0;0;0;0;0
-13992;virulence;negative;0;1;0;1;0;0
-13993;virus;negative;0;1;0;0;0;1
-13994;virus de l herpès;negative;0;0;0;0;0;1
-13995;vis;positive;0;0;0;1;0;0
-13996;visa;positive;0;0;0;0;0;0
-13997;visage;positive;0;0;0;0;0;0
-13998;viscosité;negative;0;0;0;0;0;1
-13999;viser;positive;0;0;0;0;0;0
-14000;visibilité;positive;0;0;0;0;0;0
-14001;visible;positive;0;0;0;0;0;0
-14002;visiblement;positive;0;0;0;0;0;0
-14003;visière;positive;0;0;0;0;1;0
-14004;vision;positive;0;0;0;0;1;0
-14005;visionnaire;positive;0;0;0;0;0;0
-14006;visiter;positive;0;0;0;0;0;0
-14007;visitation;positive;0;0;0;0;0;0
-14008;visite;positive;0;0;0;0;0;0
-14009;visser;negative;0;0;0;1;0;0
-14010;visualiser;positive;0;0;0;0;0;0
-14011;vital;positive;0;0;0;0;0;0
-14012;vitalité;positive;0;0;0;0;0;0
-14013;vitesse;negative;0;1;0;0;0;0
-14014;vitre;positive;0;0;0;0;0;0
-14015;vitreux;positive;0;0;0;0;0;0
-14016;vitrine;positive;0;0;0;0;0;0
-14017;vivant;positive;0;0;0;0;0;0
-14018;vivre|vivres;positive;0;0;0;0;0;0
-14019;voûte;positive;0;0;0;0;0;0
-14020;vocabulaire;positive;0;0;0;0;0;0
-14021;vocal;positive;0;0;0;0;0;0
-14022;vocation;positive;0;0;0;0;0;0
-14023;vogue;positive;0;0;0;0;0;0
-14024;voguer;positive;0;0;0;0;0;0
-14025;voie;positive;0;0;0;0;0;0
-14026;voie ferré;positive;0;0;0;0;0;0
-14027;voile;negative;0;1;1;0;0;0
-14028;voiler;negative;0;1;1;0;0;0
-14029;voisinage;positive;0;0;0;0;0;0
-14030;voiture;positive;0;0;0;0;0;0
-14031;voiturier;positive;0;0;0;0;0;0
-14032;voix;positive;0;0;0;0;0;0
-14033;vol;negative;0;1;1;1;1;1
-14034;vol à l étalage;negative;0;1;0;1;1;1
-14035;volage;negative;0;0;0;1;0;1
-14036;volaille;positive;0;1;0;0;0;0
-14037;volant;positive;0;1;0;0;0;0
-14038;voler moteur;positive;0;0;0;0;0;0
-14039;volante;positive;0;1;0;0;0;0
-14040;volatilité;negative;0;1;0;1;1;0
-14041;volcan;negative;0;1;0;0;1;0
-14042;volcanique;negative;0;1;0;0;1;0
-14043;voler;negative;0;1;1;1;1;0
-14044;voler en éclat;negative;0;1;1;1;1;0
-14045;volet;negative;0;0;0;0;0;0
-14046;volière;positive;0;0;0;0;0;0
-14047;volontaire;positive;0;0;0;0;0;0
-14048;volontairement;positive;0;0;0;0;0;0
-14049;volontariat;positive;0;0;0;0;0;0
-14050;volonté;positive;0;0;0;0;0;0
-14051;volontiers;positive;0;0;0;0;0;0
-14052;voltige;negative;0;1;1;0;0;0
-14053;voltmètre;positive;0;0;0;0;0;0
-14054;volume;negative;0;0;0;1;0;0
-14055;volumineux;negative;0;1;0;0;0;0
-14056;vomir;negative;0;0;0;0;0;1
-14057;vomissement;negative;0;0;0;0;0;1
-14058;vorace;negative;0;1;1;1;0;0
-14059;vortex;negative;0;1;0;0;1;0
-14060;votales;positive;0;0;0;0;0;0
-14061;votant;positive;0;0;0;0;0;0
-14062;vote;positive;0;0;1;1;1;0
-14063;voter;positive;0;0;1;1;1;0
-14064;voter pour;positive;0;0;0;0;0;0
-14065;votif;positive;0;0;0;0;0;0
-14066;vouer;positive;0;0;0;0;0;0
-14067;vouloir;negative;0;1;1;0;0;0
-14068;voûte;positive;0;1;1;0;0;0
-14069;voûter;negative;0;0;1;0;0;0
-14070;voyage;positive;0;1;0;0;1;0
-14071;voyager;positive;1;0;0;0;0;0
-14072;voyelle;positive;0;0;0;0;0;0
-14073;voyou;negative;0;1;0;1;0;1
-14074;VRAI;positive;0;0;0;0;0;0
-14075;vrai casse tête;negative;0;0;0;1;0;0
-14076;vraisemblance;positive;0;0;0;0;0;0
-14077;vriller;negative;0;0;0;0;0;0
-14078;vrombir;negative;0;1;0;0;1;0
-14079;vrombissement;negative;0;1;0;0;1;0
-14080;vue;positive;0;0;0;0;0;0
-14081;vulgaire;negative;0;0;0;0;0;1
-14082;vulgarité;negative;0;0;1;1;0;1
-14083;vulnérabilité;negative;0;1;1;0;0;0
-14084;vulnérable;negative;0;1;1;0;0;0
-14085;w c;negative;0;0;0;0;0;1
-14086;wagon;positive;0;0;0;0;0;0
-14087;wagon lit;positive;0;0;0;0;0;0
-14088;wapiti;positive;0;0;0;0;0;0
-14089;wc;negative;0;0;0;0;0;1
-14090;web;positive;0;0;0;0;0;0
-14091;whisky;negative;0;0;0;0;0;0
-14092;xénophobie;negative;0;1;0;1;0;0
-14093;xérès;positive;0;0;0;0;0;0
-14094;yacht;positive;0;0;0;0;0;0
-14095;yacht de croisière;positive;0;0;0;0;0;0
-14096;yachting;positive;0;0;0;0;0;0
-14097;yak;negative;0;0;0;0;0;0
-14098;yearling;positive;0;0;0;0;0;0
-14099;yeoman;positive;0;0;0;0;0;0
-14100;yogi;positive;0;0;0;0;0;0
-14101;zap;negative;0;1;0;1;1;0
-14102;zapper;negative;0;1;0;1;1;0
-14103;zèbre;positive;0;0;0;0;0;0
-14104;zébrure;negative;0;0;0;0;0;0
-14105;zèle;positive;0;0;0;0;1;0
-14106;zélé;negative;0;0;0;0;0;0
-14107;zénith;positive;1;0;0;0;0;0
-14108;zephyr;positive;0;0;0;0;0;0
-14109;zéphyr;positive;0;0;0;0;0;0
-14110;zeppelin;positive;0;0;0;0;0;0
-14111;zeste;positive;0;0;0;0;0;0
-14112;zézaiement;negative;0;1;1;1;0;0
-14113;zibeline;positive;0;0;0;0;0;0
-14114;zinzin;negative;0;1;0;0;1;0
-14115;zinzins;negative;0;1;0;0;1;0
-14116;zipper;positive;0;0;0;0;0;0
-14117;zodiaque;positive;0;0;0;0;0;0
-14118;zone;positive;0;0;0;0;0;0
-14119;zoo;positive;0;0;0;0;0;0
-14120;zoologie;positive;0;0;0;0;0;0
-14121;zoologique;positive;0;0;0;0;0;0
-14122;zoom;positive;0;0;0;0;0;0
-14123;zoomer;positive;0;0;0;0;0;0
-14124;zozotement;negative;0;1;1;1;0;0
-14125;zozoter;negative;0;1;1;1;0;0
-14126;merci;positive;1;0;0;0;0;0
-14127;remercier;positive;1;0;0;0;0;0
-14128;remerciment;positive;1;0;0;0;0;0
-14129;moins;negative;0;0;0;0;0;0
diff --git a/config/french_numbers.txt b/config/french_numbers.txt
deleted file mode 100644
index 0dea605..0000000
--- a/config/french_numbers.txt
+++ /dev/null
@@ -1,101 +0,0 @@
-zéro
-un
-deux
-trois
-quatre
-cinq
-six
-sept
-huit
-neuf
-dix
-onze
-douze
-treize
-quatorze
-quinze
-seize
-dix-sept
-dix-huit
-dix-neuf
-vingt
-vingt-et-un
-vingt-deux
-vingt-trois
-vingt-quatre
-vingt-cinq
-vingt-six
-vingt-sept
-vingt-huit
-vingt-neuf
-trente
-trente-et-un
-trente-deux
-trente-trois
-trente-quatre
-trente-cinq
-trente-six
-trente-sept
-trente-huit
-trente-neuf
-quarante
-quarante-et-un
-quarante-deux
-quarante-trois
-quarante-quatre
-quarante-cinq
-quarante-six
-quarante-sept
-quarante-huit
-quarante-neuf
-cinquante
-cinquante-et-un
-cinquante-deux
-cinquante-trois
-cinquante-quatre
-cinquante-cinq
-cinquante-six
-cinquante-sept
-cinquante-huit
-cinquante-neuf
-soixante
-soixante-et-un
-soixante-deux
-soixante-trois
-soixante-quatre
-soixante-cinq
-soixante-six
-soixante-sept
-soixante-huit
-soixante-neuf
-soixante-dix
-soixante-et-onze
-soixante-douze
-soixante-treize
-soixante-quatorze
-soixante-quinze
-soixante-seize
-soixante-dix-sept
-soixante-dix-huit
-soixante-dix-neuf
-quatre-vingts
-quatre-vingt-un
-quatre-vingt-deux
-quatre-vingt-trois
-quatre-vingt-quatre
-quatre-vingt-cinq
-quatre-vingt-six
-quatre-vingt-sept
-quatre-vingt-huit
-quatre-vingt-neuf
-quatre-vingt-dix
-quatre-vingt-onze
-quatre-vingt-douze
-quatre-vingt-treize
-quatre-vingt-quatorze
-quatre-vingt-quinze
-quatre-vingt-seize
-quatre-vingt-dix-sept
-quatre-vingt-dix-huit
-quatre-vingt-dix-neuf
-cent
diff --git a/config/french_stopwords.txt b/config/french_stopwords.txt
deleted file mode 100644
index d482b0a..0000000
--- a/config/french_stopwords.txt
+++ /dev/null
@@ -1,689 +0,0 @@
-a
-abord
-absolument
-afin
-ah
-ai
-aie
-aient
-aies
-ailleurs
-ainsi
-ait
-allaient
-allo
-allons
-allô
-alors
-anterieur
-anterieure
-anterieures
-apres
-après
-as
-assez
-attendu
-au
-aucun
-aucune
-aucuns
-aujourd
-aujourd'hui
-aupres
-auquel
-aura
-aurai
-auraient
-aurais
-aurait
-auras
-aurez
-auriez
-aurions
-aurons
-auront
-aussi
-autre
-autrefois
-autrement
-autres
-autrui
-aux
-auxquelles
-auxquels
-avaient
-avais
-avait
-avant
-avec
-avez
-aviez
-avions
-avoir
-avons
-ayant
-ayez
-ayons
-b
-bah
-bas
-basee
-bat
-beau
-beaucoup
-bien
-bigre
-bon
-boum
-bravo
-brrr
-c
-car
-ce
-ceci
-cela
-celle
-celle-ci
-celle-là
-celles
-celles-ci
-celles-là
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-celui-ci
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-cependant
-certain
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-certains
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-ces
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-cette
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-cher
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-ci
-cinq
-cinquantaine
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-clac
-clic
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-comment
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-crac
-d
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-dedans
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-deja
-delà
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-dernier
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-devrait
-different
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-differents
-différent
-différente
-différentes
-différents
-dire
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-directement
-dit
-dite
-dits
-divers
-diverse
-diverses
-dix
-dix-huit
-dix-neuf
-dix-sept
-dixième
-doit
-doivent
-donc
-dont
-dos
-douze
-douzième
-dring
-droite
-du
-duquel
-durant
-dès
-début
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-e
-effet
-egale
-egalement
-egales
-eh
-elle
-elle-même
-elles
-elles-mêmes
-en
-encore
-enfin
-entre
-envers
-environ
-es
-essai
-est
-et
-etant
-etc
-etre
-eu
-eue
-eues
-euh
-eurent
-eus
-eusse
-eussent
-eusses
-eussiez
-eussions
-eut
-eux
-eux-mêmes
-exactement
-excepté
-extenso
-exterieur
-eûmes
-eût
-eûtes
-f
-fais
-faisaient
-faisant
-fait
-faites
-façon
-feront
-fi
-flac
-floc
-fois
-font
-force
-furent
-fus
-fusse
-fussent
-fusses
-fussiez
-fussions
-fut
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-fûtes
-g
-gens
-h
-ha
-haut
-hein
-hem
-hep
-hi
-ho
-holà
-hop
-hormis
-hors
-hou
-houp
-hue
-hui
-huit
-huitième
-hum
-hurrah
-hé
-hélas
-i
-ici
-il
-ils
-importe
-j
-je
-jusqu
-jusque
-juste
-k
-l
-la
-laisser
-laquelle
-las
-le
-lequel
-les
-lesquelles
-lesquels
-leur
-leurs
-longtemps
-lors
-lorsque
-lui
-lui-meme
-lui-même
-là
-lès
-m
-ma
-maint
-maintenant
-mais
-malgre
-malgré
-maximale
-me
-meme
-memes
-merci
-mes
-mien
-mienne
-miennes
-miens
-mille
-mince
-mine
-minimale
-moi
-moi-meme
-moi-même
-moindres
-moins
-mon
-mot
-moyennant
-multiple
-multiples
-même
-mêmes
-n
-na
-naturel
-naturelle
-naturelles
-ne
-neanmoins
-necessaire
-necessairement
-neuf
-neuvième
-ni
-nombreuses
-nombreux
-nommés
-non
-nos
-notamment
-notre
-nous
-nous-mêmes
-nouveau
-nouveaux
-nul
-néanmoins
-nôtre
-nôtres
-o
-oh
-ohé
-ollé
-olé
-on
-ont
-onze
-onzième
-ore
-ou
-ouf
-ouias
-oust
-ouste
-outre
-ouvert
-ouverte
-ouverts
-o|
-où
-p
-paf
-pan
-par
-parce
-parfois
-parle
-parlent
-parler
-parmi
-parole
-parseme
-partant
-particulier
-particulière
-particulièrement
-pas
-passé
-pendant
-pense
-permet
-personne
-personnes
-peu
-peut
-peuvent
-peux
-pff
-pfft
-pfut
-pif
-pire
-pièce
-plein
-plouf
-plupart
-plus
-plusieurs
-plutôt
-possessif
-possessifs
-possible
-possibles
-pouah
-pour
-pourquoi
-pourrais
-pourrait
-pouvait
-prealable
-precisement
-premier
-première
-premièrement
-pres
-probable
-probante
-procedant
-proche
-près
-psitt
-pu
-puis
-puisque
-pur
-pure
-q
-qu
-quand
-quant
-quant-à-soi
-quanta
-quarante
-quatorze
-quatre
-quatre-vingt
-quatrième
-quatrièmement
-que
-quel
-quelconque
-quelle
-quelles
-quelqu'un
-quelque
-quelques
-quels
-qui
-quiconque
-quinze
-quoi
-quoique
-r
-rare
-rarement
-rares
-relative
-relativement
-remarquable
-rend
-rendre
-restant
-reste
-restent
-restrictif
-retour
-revoici
-revoilà
-rien
-s
-sa
-sacrebleu
-sait
-sans
-sapristi
-sauf
-se
-sein
-seize
-selon
-semblable
-semblaient
-semble
-semblent
-sent
-sept
-septième
-sera
-serai
-seraient
-serais
-serait
-seras
-serez
-seriez
-serions
-serons
-seront
-ses
-seul
-seule
-seulement
-si
-sien
-sienne
-siennes
-siens
-sinon
-six
-sixième
-soi
-soi-même
-soient
-sois
-soit
-soixante
-sommes
-son
-sont
-sous
-souvent
-soyez
-soyons
-specifique
-specifiques
-speculatif
-stop
-strictement
-subtiles
-suffisant
-suffisante
-suffit
-suis
-suit
-suivant
-suivante
-suivantes
-suivants
-suivre
-sujet
-superpose
-sur
-surtout
-t
-ta
-tac
-tandis
-tant
-tardive
-te
-tel
-telle
-tellement
-telles
-tels
-tenant
-tend
-tenir
-tente
-tes
-tic
-tien
-tienne
-tiennes
-tiens
-toc
-toi
-toi-même
-ton
-touchant
-toujours
-tous
-tout
-toute
-toutefois
-toutes
-treize
-trente
-tres
-trois
-troisième
-troisièmement
-trop
-très
-tsoin
-tsouin
-tu
-té
-u
-un
-une
-unes
-uniformement
-unique
-uniques
-uns
-v
-va
-vais
-valeur
-vas
-vers
-via
-vif
-vifs
-vingt
-vivat
-vive
-vives
-vlan
-voici
-voie
-voient
-voilà
-vont
-vos
-votre
-vous
-vous-mêmes
-vu
-vé
-vôtre
-vôtres
-w
-x
-y
-z
-zut
-à
-â
-ça
-ès
-étaient
-étais
-était
-étant
-état
-étiez
-étions
-été
-étée
-étées
-étés
-êtes
-être
-ô

From 0f7012d5853d5673931bd9697cfb8db363b87f8a Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Tue, 26 Jan 2021 10:23:25 +0100
Subject: [PATCH 446/496] rebase master and fix conflicts

---
 config/__init__.py            |  1 -
 nautilus_nlp/preprocessor.py  | 70 -----------------------------------
 preprocessing/preprocessor.py |  5 +++
 3 files changed, 5 insertions(+), 71 deletions(-)
 delete mode 100644 nautilus_nlp/preprocessor.py

diff --git a/config/__init__.py b/config/__init__.py
index 7643ec5..d46139b 100644
--- a/config/__init__.py
+++ b/config/__init__.py
@@ -15,4 +15,3 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.preprocessor import Preprocessor
diff --git a/nautilus_nlp/preprocessor.py b/nautilus_nlp/preprocessor.py
deleted file mode 100644
index 8078fc2..0000000
--- a/nautilus_nlp/preprocessor.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from typing import List, Callable
-
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import FunctionTransformer
-
-from nautilus_nlp.preprocessing.social_preprocess import (remove_html_tags, remove_mentions, remove_emoji,
-                                                          remove_hashtag)
-from nautilus_nlp.preprocessing.text_preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
-
-
-class Preprocessor():
-    def __init__(
-            self):
-        """
-        Initialize preprocessor object to apply all text transformation
-        """
-        self.__operations = []
-        self.pipeline = None
-
-    def pipe(self, operation: Callable):
-        """
-        Add an operation to pipe in the preprocessor
-
-        Parameters
-        ----------
-        operation : callable
-            text preprocessing function
-        """
-        self.__operations.append(operation)
-
-    @staticmethod
-    def build_pipeline(operation_list: List[Callable]) -> Pipeline:
-        """
-        Build sklearn pipeline from a operation list
-
-        Parameters
-        ----------
-        operation_list : iterable
-            list of __operations of preprocessing
-
-        Returns
-        -------
-        sklearn.pipeline.Pipeline
-        """
-        return Pipeline(
-            steps=[
-                (operation.__name__, FunctionTransformer(operation))
-                for operation in operation_list])
-
-
-    def run(self, text: str) -> str:
-        """
-        Apply pipeline to text
-
-        Parameters
-        ----------
-        text : string
-            text to preprocess
-
-        Returns
-        -------
-        string
-        """
-        operations = self.__operations
-        if operations == []:
-            operations = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
-                          remove_eol_characters, fix_bad_unicode, normalize_whitespace)
-        self.pipeline = self.build_pipeline(operations)
-        text = self.pipeline.fit_transform(text)
-        return text
diff --git a/preprocessing/preprocessor.py b/preprocessing/preprocessor.py
index 41ec77c..5cdcac0 100644
--- a/preprocessing/preprocessor.py
+++ b/preprocessing/preprocessor.py
@@ -20,6 +20,7 @@ def __init__(
     def pipe(self, operation: Callable):
         """
         Add an operation to pipe in the preprocessor
+
         Parameters
         ----------
         operation : callable
@@ -31,10 +32,12 @@ def pipe(self, operation: Callable):
     def build_pipeline(operation_list: List[Callable]) -> Pipeline:
         """
         Build sklearn pipeline from a operation list
+
         Parameters
         ----------
         operation_list : iterable
             list of __operations of preprocessing
+
         Returns
         -------
         sklearn.pipeline.Pipeline
@@ -48,10 +51,12 @@ def build_pipeline(operation_list: List[Callable]) -> Pipeline:
     def run(self, text: str) -> str:
         """
         Apply pipeline to text
+
         Parameters
         ----------
         text : string
             text to preprocess
+
         Returns
         -------
         string

From c0ffe8f532c611d377164f6a792ecdd955e9ede0 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Tue, 26 Jan 2021 10:54:57 +0100
Subject: [PATCH 447/496] rename main folder and update readme

---
 .github/workflows/ci_actions.yml              |  4 +-
 README.md                                     | 39 ++++++++-----------
 {preprocessing => nautilus_nlp}/__init__.py   |  2 +-
 .../augmentation/text_augmentation.py         |  0
 .../classic/preprocess.py                     |  0
 .../preprocessor.py                           |  4 +-
 .../social/preprocess.py                      |  2 +-
 .../token/preprocess.py                       |  0
 .../token/tokenizer.py                        |  0
 tests/test_data_augmentation.py               |  2 +-
 tests/test_preprocessor.py                    |  8 ++--
 11 files changed, 27 insertions(+), 34 deletions(-)
 rename {preprocessing => nautilus_nlp}/__init__.py (94%)
 rename {preprocessing => nautilus_nlp}/augmentation/text_augmentation.py (100%)
 rename {preprocessing => nautilus_nlp}/classic/preprocess.py (100%)
 rename {preprocessing => nautilus_nlp}/preprocessor.py (92%)
 rename {preprocessing => nautilus_nlp}/social/preprocess.py (98%)
 rename {preprocessing => nautilus_nlp}/token/preprocess.py (100%)
 rename {preprocessing => nautilus_nlp}/token/tokenizer.py (100%)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 8cdb602..77e79f1 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,8 +42,8 @@ jobs:
 
       - name: Run pylint
         run: |
-          pylint config preprocessing utils tests
+          pylint config nautilus_nlp utils tests
 
       - name: Run pytest
         run: |
-          pytest --cov=preprocessing tests
+          pytest --cov=nautilus_nlp tests
diff --git a/README.md b/README.md
index 64ca8e4..ff1c142 100644
--- a/README.md
+++ b/README.md
@@ -96,30 +96,23 @@ You can now open the file index.html located in the build folder.
 **à updater**
 
     ├── LICENSE
-    ├── Makefile           <- Makefile with commands like `make data` or `make train`
-    ├── README.md          <- The top-level README for developers using this project.
-    ├── data               <- Scripts & bits to download datasets to try nautilus
-    │   ├── external
-    │   ├── interim
-    │   ├── processed
-    │   └── raw
-    ├── docker             <- Where to build a docker image using this lib
-    ├── docs               <- Sphinx HTML documentation
+    ├── Makefile            <- Makefile with commands like `make data` or `make train`
+    ├── README.md           <- The top-level README for developers using this project.
+    ├── config              <- Where the configuration and constants live
+    ├── datasets/external   <- Bash scripts to download external datasets
+    ├── docker              <- Where to build a docker image using this lib
+    ├── docs                <- Sphinx HTML documentation
     │   ├── _build
     │   │   └── html
     │   ├── source
-    ├── models
-    ├── nautilus_nlp       <- Main Nautilus Package. This is where the code lives
-    │   ├── config
-    │   ├── data
-    │   ├── models
-    │   ├── preprocessing
-    │   ├── scripts
-    │   └── utils
-    ├──notebooks           <- Various notebooks explaining how to use Nautilus_NLP library
-    ├── tests <- Where the tests lives
-    │   └── testfolder_fileloader
-    ├── wiki               <- Where the Markdown for the Wiki lives
-    ├── setup.py           <- makes project pip installable (pip install -e .) so nautilus_nlp can be imported
-    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
+    ├── nautilus_nlp        <- Main Nautilus Package. This is where the code lives
+    │   ├── preprocessor.py <- Main preprocessing script
+    │   ├── augmentation    <- Text augmentation script
+    │   ├── classic         <- Classic text preprocessing 
+    │   ├── social          <- Social text preprocessing
+    │   └── token           <- Token preprocessing
+    ├── utils               <- Where preprocessing utils scripts lives
+    ├── tests               <- Where the tests lives
+    ├── setup.py            <- makes project pip installable (pip install -e .) so nautilus_nlp can be imported
+    ├── requirements.txt    <- The requirements file for reproducing the analysis environment, e.g.
                               generated with `pip freeze > requirements.txt`    
diff --git a/preprocessing/__init__.py b/nautilus_nlp/__init__.py
similarity index 94%
rename from preprocessing/__init__.py
rename to nautilus_nlp/__init__.py
index 97b4d25..7643ec5 100644
--- a/preprocessing/__init__.py
+++ b/nautilus_nlp/__init__.py
@@ -15,4 +15,4 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from preprocessing.preprocessor import Preprocessor
+from nautilus_nlp.preprocessor import Preprocessor
diff --git a/preprocessing/augmentation/text_augmentation.py b/nautilus_nlp/augmentation/text_augmentation.py
similarity index 100%
rename from preprocessing/augmentation/text_augmentation.py
rename to nautilus_nlp/augmentation/text_augmentation.py
diff --git a/preprocessing/classic/preprocess.py b/nautilus_nlp/classic/preprocess.py
similarity index 100%
rename from preprocessing/classic/preprocess.py
rename to nautilus_nlp/classic/preprocess.py
diff --git a/preprocessing/preprocessor.py b/nautilus_nlp/preprocessor.py
similarity index 92%
rename from preprocessing/preprocessor.py
rename to nautilus_nlp/preprocessor.py
index 5cdcac0..e2e9912 100644
--- a/preprocessing/preprocessor.py
+++ b/nautilus_nlp/preprocessor.py
@@ -3,9 +3,9 @@
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 
-from preprocessing.social.preprocess import (
+from nautilus_nlp.social.preprocess import (
     remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
-from preprocessing.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
+from nautilus_nlp.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
 
 
 class Preprocessor():
diff --git a/preprocessing/social/preprocess.py b/nautilus_nlp/social/preprocess.py
similarity index 98%
rename from preprocessing/social/preprocess.py
rename to nautilus_nlp/social/preprocess.py
index b017f65..bc621ca 100644
--- a/preprocessing/social/preprocess.py
+++ b/nautilus_nlp/social/preprocess.py
@@ -21,7 +21,7 @@
 
 import emoji as _emoji
 from config import constants
-from preprocessing.classic.preprocess import normalize_whitespace
+from nautilus_nlp.classic.preprocess import normalize_whitespace
 
 
 def remove_mentions(text) -> str:
diff --git a/preprocessing/token/preprocess.py b/nautilus_nlp/token/preprocess.py
similarity index 100%
rename from preprocessing/token/preprocess.py
rename to nautilus_nlp/token/preprocess.py
diff --git a/preprocessing/token/tokenizer.py b/nautilus_nlp/token/tokenizer.py
similarity index 100%
rename from preprocessing/token/tokenizer.py
rename to nautilus_nlp/token/tokenizer.py
diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index d13245b..380a003 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -1,5 +1,5 @@
 import pytest
-from preprocessing.augmentation.text_augmentation import (
+from nautilus_nlp.augmentation.text_augmentation import (
     process_entities_and_text, get_augmenter, CouldNotAugment,
     UnavailableAugmenter
 )
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 13564b0..b2b7830 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,22 +17,22 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from preprocessing.classic.preprocess import (
+from nautilus_nlp.classic.preprocess import (
     normalize_whitespace, remove_eol_characters, fix_bad_unicode,
     unpack_english_contractions, replace_urls, replace_emails,
     replace_phone_numbers, replace_numbers, replace_currency_symbols,
     remove_punct, remove_accents, remove_multiple_spaces_and_strip_text,
     filter_non_latin_characters
 )
-from preprocessing.social.preprocess import (
+from nautilus_nlp.social.preprocess import (
     remove_mentions, extract_mentions, remove_html_tags, remove_emoji,
     convert_emoji_to_text, extract_emojis, extract_hashtags, remove_hashtag
 )
-from preprocessing.token.preprocess import (
+from nautilus_nlp.token.preprocess import (
     remove_stopwords, remove_tokens_with_nonletters,
     remove_special_caracters_from_tokenslist, remove_smallwords
 )
-from preprocessing.preprocessor import Preprocessor
+from nautilus_nlp.preprocessor import Preprocessor
 
 import utils.phone_number as phone
 from utils.stopwords import get_stopwords

From 13cc87959637ba0da606d2e07a8e639ff881414a Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 29 Jan 2021 18:06:15 +0100
Subject: [PATCH 448/496] adding remove stopwords function for text

---
 nautilus_nlp/classic/preprocess.py | 30 +++++++++++++++++++++++
 tests/test_preprocessor.py         | 39 ++++++++++++++++++++++++++++--
 2 files changed, 67 insertions(+), 2 deletions(-)

diff --git a/nautilus_nlp/classic/preprocess.py b/nautilus_nlp/classic/preprocess.py
index 691acae..597cbba 100644
--- a/nautilus_nlp/classic/preprocess.py
+++ b/nautilus_nlp/classic/preprocess.py
@@ -24,7 +24,9 @@
 import unicodedata
 from ftfy import fix_text as _fix_text
 from config import constants
+from nautilus_nlp.token.tokenizer import tokenize
 from utils.phone_number import extract_phone_numbers as _extract_phone_numbers
+from utils.stopwords import get_stopwords
 
 
 def normalize_whitespace(text) -> str:
@@ -48,6 +50,34 @@ def normalize_whitespace(text) -> str:
     return text
 
 
+def remove_stopwords(text: str, lang: str, custom_stopwords: list = None) -> str:
+    """
+    Given ``text`` str, remove classic stopwords for a given language and
+    custom stopwords given as a list.
+
+    Parameters
+    ----------
+    text : string
+    lang : string
+    custom_stopwords : list of strings
+
+    Returns
+    -------
+    string
+    """
+    stopwords = get_stopwords(lang)
+    if custom_stopwords:
+        stopwords += custom_stopwords
+    if lang in ["fr", "en"]:
+        lang_module = {
+            "fr" : "fr_spacy",
+            "en" : "en_spacy"
+        }[lang]
+        return ' '.join(
+            [x for x in tokenize(text, lang_module) if x not in stopwords])
+    return ' '.join([x for x in text.split() if x not in stopwords])
+
+
 def remove_eol_characters(text) -> str:
     """
     Remove end of line (\n) char.
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index b2b7830..10c3a6f 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -24,14 +24,20 @@
     remove_punct, remove_accents, remove_multiple_spaces_and_strip_text,
     filter_non_latin_characters
 )
+from nautilus_nlp.classic.preprocess import (
+    remove_stopwords as remove_stopwords_text
+)
 from nautilus_nlp.social.preprocess import (
     remove_mentions, extract_mentions, remove_html_tags, remove_emoji,
     convert_emoji_to_text, extract_emojis, extract_hashtags, remove_hashtag
 )
 from nautilus_nlp.token.preprocess import (
-    remove_stopwords, remove_tokens_with_nonletters,
+    remove_tokens_with_nonletters,
     remove_special_caracters_from_tokenslist, remove_smallwords
 )
+from nautilus_nlp.token.preprocess import (
+    remove_stopwords as remove_stopwords_token
+)
 from nautilus_nlp.preprocessor import Preprocessor
 
 import utils.phone_number as phone
@@ -189,7 +195,36 @@ def test_get_stopwords():
 )
 def test_remove_stopwords_tokens(input_tokens, expected_output):
     stopwords = get_stopwords('en')
-    result = remove_stopwords(input_tokens, stopwords)
+    result = remove_stopwords_token(input_tokens, stopwords)
+    np.testing.assert_array_equal(result, expected_output)
+
+
+@pytest.mark.parametrize(
+    "input_text, lang, expected_output",
+    [
+        ('I like when you move your body !', 'en', 'I move body !'),
+        ('Can I get a beer?', 'en', 'Can I beer ?'),
+        ('Je vous recommande ce film !', 'fr', 'Je recommande film !'),
+        ('je vous recommande ce film !', 'fr', 'recommande film !'),
+        ('Quiero una cerveza, por favor.', 'es', 'Quiero cerveza, favor.')
+    ],
+)
+def test_remove_stopwords_text(input_text, lang, expected_output):
+    result = remove_stopwords_text(input_text, lang)
+    np.testing.assert_array_equal(result, expected_output)
+
+
+@pytest.mark.parametrize(
+    "input_text, lang, custom_stopwords, expected_output",
+    [
+        ('I like when you move your body !', 'en', ['body'], 'I move !'),
+        ('Je vous recommande ce film la scène de fin est géniale !', 'fr',
+         ['film', 'scène'], 'Je recommande fin géniale !'),
+    ],
+)
+def test_remove_custom_stopwords_text(
+        input_text, lang, custom_stopwords, expected_output):
+    result = remove_stopwords_text(input_text, lang, custom_stopwords)
     np.testing.assert_array_equal(result, expected_output)
 
 

From 8ba75e7a09f071fce0d93f7b9f232f1e1c18abaf Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 11 Feb 2021 18:16:37 +0100
Subject: [PATCH 449/496] adding init files in all modules

---
 nautilus_nlp/augmentation/__init__.py | 0
 nautilus_nlp/classic/__init__.py      | 0
 nautilus_nlp/social/__init__.py       | 0
 nautilus_nlp/token/__init__.py        | 0
 4 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 nautilus_nlp/augmentation/__init__.py
 create mode 100644 nautilus_nlp/classic/__init__.py
 create mode 100644 nautilus_nlp/social/__init__.py
 create mode 100644 nautilus_nlp/token/__init__.py

diff --git a/nautilus_nlp/augmentation/__init__.py b/nautilus_nlp/augmentation/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nautilus_nlp/classic/__init__.py b/nautilus_nlp/classic/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nautilus_nlp/social/__init__.py b/nautilus_nlp/social/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nautilus_nlp/token/__init__.py b/nautilus_nlp/token/__init__.py
new file mode 100644
index 0000000..e69de29

From 5acb46c1990e107b17b7fbfad329fabb877491fe Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 11:47:07 +0100
Subject: [PATCH 450/496] moving all lib essential folders in nautilus_nlp/

---
 {config => nautilus_nlp/_config}/__init__.py       | 0
 {config => nautilus_nlp/_config}/config.py         | 4 ++--
 {config => nautilus_nlp/_config}/constants.py      | 0
 {config => nautilus_nlp/_config}/country_code.json | 0
 {config => nautilus_nlp/_config}/stopwords.json    | 0
 {utils => nautilus_nlp/_utils}/__init__.py         | 0
 {utils => nautilus_nlp/_utils}/file_loader.py      | 0
 {utils => nautilus_nlp/_utils}/phone_number.py     | 2 +-
 {utils => nautilus_nlp/_utils}/stopwords.py        | 4 ++--
 nautilus_nlp/classic/preprocess.py                 | 6 +++---
 nautilus_nlp/social/preprocess.py                  | 2 +-
 setup.py                                           | 1 -
 tests/test_document_loader.py                      | 2 +-
 tests/test_preprocessor.py                         | 2 +-
 14 files changed, 11 insertions(+), 12 deletions(-)
 rename {config => nautilus_nlp/_config}/__init__.py (100%)
 rename {config => nautilus_nlp/_config}/config.py (98%)
 rename {config => nautilus_nlp/_config}/constants.py (100%)
 rename {config => nautilus_nlp/_config}/country_code.json (100%)
 rename {config => nautilus_nlp/_config}/stopwords.json (100%)
 rename {utils => nautilus_nlp/_utils}/__init__.py (100%)
 rename {utils => nautilus_nlp/_utils}/file_loader.py (100%)
 rename {utils => nautilus_nlp/_utils}/phone_number.py (98%)
 rename {utils => nautilus_nlp/_utils}/stopwords.py (96%)

diff --git a/config/__init__.py b/nautilus_nlp/_config/__init__.py
similarity index 100%
rename from config/__init__.py
rename to nautilus_nlp/_config/__init__.py
diff --git a/config/config.py b/nautilus_nlp/_config/config.py
similarity index 98%
rename from config/config.py
rename to nautilus_nlp/_config/config.py
index df90e45..0be3a0d 100644
--- a/config/config.py
+++ b/nautilus_nlp/_config/config.py
@@ -20,10 +20,10 @@
 import phonenumbers as _phonenumbers
 
 
-ROOT_FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+ROOT_FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))
 
 # Stopwords config
-STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "config", "stopwords.json")
+STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "nautilus_nlp", "_config", "stopwords.json")
 
 # Country config
 COUNTRY_MAPPING_ISO = {
diff --git a/config/constants.py b/nautilus_nlp/_config/constants.py
similarity index 100%
rename from config/constants.py
rename to nautilus_nlp/_config/constants.py
diff --git a/config/country_code.json b/nautilus_nlp/_config/country_code.json
similarity index 100%
rename from config/country_code.json
rename to nautilus_nlp/_config/country_code.json
diff --git a/config/stopwords.json b/nautilus_nlp/_config/stopwords.json
similarity index 100%
rename from config/stopwords.json
rename to nautilus_nlp/_config/stopwords.json
diff --git a/utils/__init__.py b/nautilus_nlp/_utils/__init__.py
similarity index 100%
rename from utils/__init__.py
rename to nautilus_nlp/_utils/__init__.py
diff --git a/utils/file_loader.py b/nautilus_nlp/_utils/file_loader.py
similarity index 100%
rename from utils/file_loader.py
rename to nautilus_nlp/_utils/file_loader.py
diff --git a/utils/phone_number.py b/nautilus_nlp/_utils/phone_number.py
similarity index 98%
rename from utils/phone_number.py
rename to nautilus_nlp/_utils/phone_number.py
index 6120155..1c364d5 100644
--- a/utils/phone_number.py
+++ b/nautilus_nlp/_utils/phone_number.py
@@ -17,7 +17,7 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from typing import Optional
 import phonenumbers as _phonenumbers
-from config.config import SUPPORTED_COUNTRY, FORMAT_NUMBERS
+from nautilus_nlp._config.config import SUPPORTED_COUNTRY, FORMAT_NUMBERS
 
 
 def find_phone_numbers(string: str, region_code: Optional[str] = None) -> str:
diff --git a/utils/stopwords.py b/nautilus_nlp/_utils/stopwords.py
similarity index 96%
rename from utils/stopwords.py
rename to nautilus_nlp/_utils/stopwords.py
index 305ff2b..bda452c 100644
--- a/utils/stopwords.py
+++ b/nautilus_nlp/_utils/stopwords.py
@@ -23,8 +23,8 @@
 import json
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 from stop_words import get_stop_words as _get_stop_words
-from config.config import STOPWORDS_JSON_FILEPATH
-from utils.file_loader import documents_loader
+from nautilus_nlp._config.config import STOPWORDS_JSON_FILEPATH
+from nautilus_nlp._utils.file_loader import documents_loader
 
 
 def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
diff --git a/nautilus_nlp/classic/preprocess.py b/nautilus_nlp/classic/preprocess.py
index 597cbba..9b3b4e9 100644
--- a/nautilus_nlp/classic/preprocess.py
+++ b/nautilus_nlp/classic/preprocess.py
@@ -23,10 +23,10 @@
 import re
 import unicodedata
 from ftfy import fix_text as _fix_text
-from config import constants
+from nautilus_nlp._config import constants
 from nautilus_nlp.token.tokenizer import tokenize
-from utils.phone_number import extract_phone_numbers as _extract_phone_numbers
-from utils.stopwords import get_stopwords
+from nautilus_nlp._utils.phone_number import extract_phone_numbers as _extract_phone_numbers
+from nautilus_nlp._utils.stopwords import get_stopwords
 
 
 def normalize_whitespace(text) -> str:
diff --git a/nautilus_nlp/social/preprocess.py b/nautilus_nlp/social/preprocess.py
index bc621ca..6b7a98d 100644
--- a/nautilus_nlp/social/preprocess.py
+++ b/nautilus_nlp/social/preprocess.py
@@ -20,7 +20,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import emoji as _emoji
-from config import constants
+from nautilus_nlp._config import constants
 from nautilus_nlp.classic.preprocess import normalize_whitespace
 
 
diff --git a/setup.py b/setup.py
index 33c165b..2399ed9 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,6 @@ def run(self):
 
 with open(Path(__file__).resolve().parent.joinpath('VERSION'), 'r') as fh:
     version = fh.read()
-
 setup(
     name='nautilus_nlp',
     packages=find_packages(),
diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index 060af44..452b40e 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -20,7 +20,7 @@
 import os
 
 import numpy as np
-from utils.file_loader import (detect_encoding, documents_loader)
+from nautilus_nlp._utils.file_loader import (detect_encoding, documents_loader)
 
 TESTDOC_LATIN1 = "J'aime les frites bien grasse étalon châpeau!"
 TESTDOC_UTF8 = "Un deuxième exemple de texte en utf-8 cette fois!"
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 10c3a6f..f7688b7 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -41,7 +41,7 @@
 from nautilus_nlp.preprocessor import Preprocessor
 
 import utils.phone_number as phone
-from utils.stopwords import get_stopwords
+from nautilus_nlp._utils.stopwords import get_stopwords
 
 
 @pytest.mark.parametrize("text, expected_result",

From c6b6c2b0faa1e165b547b4c9734bc4433ff93e9f Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 12:11:33 +0100
Subject: [PATCH 451/496] fix CI

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 77e79f1..2c410da 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,7 +42,7 @@ jobs:
 
       - name: Run pylint
         run: |
-          pylint config nautilus_nlp utils tests
+          pylint nautilus_nlp tests
 
       - name: Run pytest
         run: |

From da4ed487c3ca6a99f2d008c3e84f6947aa52e175 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 12:13:34 +0100
Subject: [PATCH 452/496] fix CI with python3.7

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 2c410da..920fc75 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: 3.7
 
     steps:
       - uses: actions/checkout@v2

From b756b84623c8c28835fd2719cc6926111aa1427d Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 12:16:26 +0100
Subject: [PATCH 453/496] fix CI

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 920fc75..f4b3a1c 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: 3.7
+        python-version: [3.7]
 
     steps:
       - uses: actions/checkout@v2

From a93b9e75fb152e734417862527e5cb051b813603 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 12:19:00 +0100
Subject: [PATCH 454/496] fix pylint

---
 tests/test_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index f7688b7..a63b823 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -40,7 +40,7 @@
 )
 from nautilus_nlp.preprocessor import Preprocessor
 
-import utils.phone_number as phone
+import nautilus_nlp._utils.phone_number as phone
 from nautilus_nlp._utils.stopwords import get_stopwords
 
 

From ac008318e66d3399eee2946e212fc9c7ce680050 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 12:22:08 +0100
Subject: [PATCH 455/496] fix pylint

---
 tests/test_phone_number.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index 4b6c832..d95d496 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import utils.phone_number as phone
+import nautilus_nlp._utils.phone_number as phone
 
 def test_extract_phone_number():
     input_str = '(541) 754-3010 is a US. Phone'

From bb0ce3f692ca1a5a22a3c14ff24ee93e880829d9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 12:29:35 +0100
Subject: [PATCH 456/496] adding python 3.6

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index f4b3a1c..aa9c08e 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7]
+        python-version: [3.6, 3.7]
 
     steps:
       - uses: actions/checkout@v2

From 03e240e5bf127c4db061fbda40068cf72d0d1930 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Fri, 12 Feb 2021 12:34:18 +0100
Subject: [PATCH 457/496] adding python 3.8

---
 .github/workflows/ci_actions.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index aa9c08e..2c410da 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -25,7 +25,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.6, 3.7]
+        python-version: [3.6, 3.7, 3.8]
 
     steps:
       - uses: actions/checkout@v2

From 2255353723e65ef8cf9ebf52ec1270ffb0a4ba47 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Wed, 3 Feb 2021 19:51:55 +0100
Subject: [PATCH 458/496] update readme and fix pipeline with arguments

---
 README.md                          | 86 ++++++++++++++++++++++++------
 nautilus_nlp/classic/preprocess.py | 13 +++++
 nautilus_nlp/preprocessor.py       | 25 ++++++---
 3 files changed, 101 insertions(+), 23 deletions(-)

diff --git a/README.md b/README.md
index ff1c142..81b516b 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-Insert new lib name here
+CasText
 ==============================
 
 **Insert new logo here**
@@ -7,19 +7,20 @@ Insert new lib name here
 
 :disappointed_relieved: Need to efficiently extract email adresses from a document? Hashtags from tweets? Remove accents from a French post? 
 
-**Insert new lib name here** got you covered! :rocket:
+CasText got you covered! :rocket:
 
-**Insert new lib name here** packages in a unique library all the text preprocessing functions you need to ease your NLP project. 
+CasText packages in a unique library all the text preprocessing functions you need to ease your NLP project. 
 
-:mag: Quickly explore below our functions referential.
+:mag: Quickly explore below our preprocessing pipelines and individual functions referential.
 
-* [Replacing emails](#replace_emails)
+* [Default preprocessing pipeline](#default_pipeline)
+* [Custom preprocessing pipeline](#custom_pipeline)
 * [Replacing phone numbers](#replace_phone_numbers)
 * [Removing hashtags](#remove_hashtags)
 * [Extracting emojis](#extract_emojis)
 
 
-Cannot find a new one? Feel free to open an [issue]((https://github.com/artefactory/nautilus-nlp/issues) )).
+Cannot find what you were looking for? Feel free to open an [issue]((https://github.com/artefactory/castext/issues) ).
 
 
 
@@ -30,7 +31,7 @@ This package has been tested on Python **3.7**.
 To install this library you should first clone the repository:
 
 ```bash
-git clone git@github.com:artefactory/nautilus-nlp.git && cd nautilus_nlp/
+git clone git@github.com:artefactory/castext.git && cd castext/
 ```
 
 We strongly advise you to do the remaining steps in a virtual environnement.
@@ -49,14 +50,55 @@ pip install -e .
 
 This library uses Spacy as tokenizer. Current models supported are `en_core_web_sm` and `fr_core_news_sm`.
 
+# Preprocessing pipeline
 
-# Functions
+## Default pipeline <a name="default_pipeline"></a>
+
+Need to preprocess your text data but no clue about what function to use and in which order? The default preprocessing pipeline got you covered:
+
+```python
+from castext import Preprocessor
+text = "I just got the best dinner in my entire life @latourdargent !!! I  recommend 😀 #food #paris \n"
+preprocessor = Preprocessor()
+text = preprocessor.run(text)
+print(text)
+# "I just got the best dinner in my entire life !!! I recommend"
+```
+
+## Create your custom pipeline <a name="custom_pipeline"></a>
+
+Another possibility is to create your custom pipeline if you know exactly what function to apply on your data, here's an example:
+
+```python
+from castext import Preprocessor
+from nautilus_nlp.classic.preprocess import normalize_whitespace, remove_punct, remove_eol_characters, remove_stopwords, lower_text
+from nautilus_nlp.social.preprocess import remove_mentions, remove_hashtag, remove_emoji
+text = "I just got the best dinner in my entire life @latourdargent !!! I  recommend 😀 #food #paris \n"
+preprocessor = Preprocessor()
+preprocessor.pipe(lower_text)
+preprocessor.pipe(remove_mentions)
+preprocessor.pipe(remove_hashtag)
+preprocessor.pipe(remove_emoji)
+preprocessor.pipe(remove_eol_characters)
+preprocessor.pipe(remove_stopwords, args={'lang': 'en'})
+preprocessor.pipe(remove_punct)
+preprocessor.pipe(normalize_whitespace)
+text = preprocessor.run(text)
+print(text)
+# "dinner entire life recommend"
+```
+
+Take a look at all the functions that are available [here](https://github.com/artefactory/nautilus-nlp/tree/master/nautilus_nlp) in the ```preprocess.py``` scripts in the different folders: classic, social, token.
+
+
+# Individual Functions
 
 ## Replacing emails <a name="replace_emails"></a>
 
 ```python
+from castext.classic.preprocess import replace_emails
 example = "I have forwarded this email to obama@whitehouse.gov"
-example = replace_emails(replace_with="*EMAIL*")
+example = replace_emails(example, replace_with="*EMAIL*")
 print(example)
 # "I have forwarded this email to *EMAIL*"
 ```
@@ -64,27 +106,39 @@ print(example)
 ## Replacing phone numbers <a name="replace_phone_numbers"></a>
 
 ```python
-Insert example here
+from castext.classic.preprocess import replace_phone_numbers
+example = "My phone number is 0606060606"
+example = replace_phone_numbers(example, country_to_detect=["FR"], replace_with="*PHONE*")
+print(example)
+# "My phone number is *PHONE*"
 ```
 
 ## Removing Hashtags <a name="remove_hashtags"></a>
 
 ```python
-Insert example here
+from castext.social.preprocess import remove_hashtag
+example = "This restaurant was amazing #food #foodie #foodstagram #dinner"
+example = remove_hashtag(example)
+print(example)
+# "This restaurant was amazing"
 ```
 
 ## Extracting emojis <a name="extract_emojis"></a>
 
 ```python
-Insert example here
+from castext.social.preprocess import extract_emojis
+example = "I take care of my skin 😀"
+example = extract_emojis(example)
+print(example)
+# [':grinning_face:']
 ```
 
 # Make HTML documentation
 
 **à updater**
 
-In order to make the html Sphinx documentation, you need to run at the nautilus_nlp root path:
-`sphinx-apidoc -f nautilus_nlp -o docs/`
+In order to make the html Sphinx documentation, you need to run at the castext root path:
+`sphinx-apidoc -f castext -o docs/`
 This will generate the .rst files.
 You can generate the doc with
 `cd docs && make html`
@@ -105,7 +159,7 @@ You can now open the file index.html located in the build folder.
     │   ├── _build
     │   │   └── html
     │   ├── source
-    ├── nautilus_nlp        <- Main Nautilus Package. This is where the code lives
+    ├── castext             <- Main Package. This is where the code lives
     │   ├── preprocessor.py <- Main preprocessing script
     │   ├── augmentation    <- Text augmentation script
     │   ├── classic         <- Classic text preprocessing 
@@ -113,6 +167,6 @@ You can now open the file index.html located in the build folder.
     │   └── token           <- Token preprocessing
     ├── utils               <- Where preprocessing utils scripts lives
     ├── tests               <- Where the tests lives
-    ├── setup.py            <- makes project pip installable (pip install -e .) so nautilus_nlp can be imported
+    ├── setup.py            <- makes project pip installable (pip install -e .) so the package can be imported
     ├── requirements.txt    <- The requirements file for reproducing the analysis environment, e.g.
                               generated with `pip freeze > requirements.txt`    
diff --git a/nautilus_nlp/classic/preprocess.py b/nautilus_nlp/classic/preprocess.py
index 9b3b4e9..e928c7a 100644
--- a/nautilus_nlp/classic/preprocess.py
+++ b/nautilus_nlp/classic/preprocess.py
@@ -49,6 +49,19 @@ def normalize_whitespace(text) -> str:
     ).strip()
     return text
 
+def lower_text(text: str):
+    """
+    Given ``text`` str, transform it into lowercase
+
+    Parameters
+    ----------
+    text : string
+
+    Returns
+    -------
+    string
+    """
+    return text.lower()
 
 def remove_stopwords(text: str, lang: str, custom_stopwords: list = None) -> str:
     """
diff --git a/nautilus_nlp/preprocessor.py b/nautilus_nlp/preprocessor.py
index e2e9912..f5a4000 100644
--- a/nautilus_nlp/preprocessor.py
+++ b/nautilus_nlp/preprocessor.py
@@ -17,19 +17,24 @@ def __init__(
         self.__operations = []
         self.pipeline = None
 
-    def pipe(self, operation: Callable):
+    def pipe(self, operation: Callable, args: dict = None):
         """
-        Add an operation to pipe in the preprocessor
+        Add an operation and its arguments to pipe in the preprocessor
 
         Parameters
         ----------
         operation : callable
             text preprocessing function
+        args : dict of arguments
         """
-        self.__operations.append(operation)
+        self.__operations.append({
+            'operation': operation,
+            'args': args
+        })
+
 
     @staticmethod
-    def build_pipeline(operation_list: List[Callable]) -> Pipeline:
+    def build_pipeline(operation_list: List[dict]) -> Pipeline:
         """
         Build sklearn pipeline from a operation list
 
@@ -44,7 +49,10 @@ def build_pipeline(operation_list: List[Callable]) -> Pipeline:
         """
         return Pipeline(
             steps=[
-                (operation.__name__, FunctionTransformer(operation))
+                (
+                    operation['operation'].__name__,
+                    FunctionTransformer(operation['operation'], kw_args=operation['args'])
+                )
                 for operation in operation_list])
 
 
@@ -63,8 +71,11 @@ def run(self, text: str) -> str:
         """
         operations = self.__operations
         if operations == []:
-            operations = (remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
-                          remove_eol_characters, fix_bad_unicode, normalize_whitespace)
+            operations_to_pipe = (
+                remove_html_tags, remove_mentions, remove_emoji, remove_hashtag,
+                remove_eol_characters, fix_bad_unicode, normalize_whitespace
+            )
+            operations = [{'operation': operation, 'args': None} for operation in operations_to_pipe]
         self.pipeline = self.build_pipeline(operations)
         text = self.pipeline.fit_transform(text)
         return text

From cb167287921e3db0f97f0add879909cb271b22b0 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 4 Feb 2021 16:21:16 +0100
Subject: [PATCH 459/496] fix layout

---
 README.md | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 81b516b..3988f7a 100644
--- a/README.md
+++ b/README.md
@@ -58,11 +58,11 @@ Need to preprocess your text data but no clue about what function to use and in
 
 ```python
 from castext import Preprocessor
-text = "I just got the best dinner in my entire life @latourdargent !!! I  recommend 😀 #food #paris \n"
+text = "I just got the best dinner in my life @latourdargent !!! I  recommend 😀 #food #paris \n"
 preprocessor = Preprocessor()
 text = preprocessor.run(text)
 print(text)
-# "I just got the best dinner in my entire life !!! I recommend"
+# "I just got the best dinner in my life !!! I recommend"
 ```
 
 ## Create your custom pipeline <a name="custom_pipeline"></a>
@@ -71,9 +71,10 @@ Another possibility is to create your custom pipeline if you know exactly what f
 
 ```python
 from castext import Preprocessor
-from nautilus_nlp.classic.preprocess import normalize_whitespace, remove_punct, remove_eol_characters, remove_stopwords, lower_text
-from nautilus_nlp.social.preprocess import remove_mentions, remove_hashtag, remove_emoji
-text = "I just got the best dinner in my entire life @latourdargent !!! I  recommend 😀 #food #paris \n"
+from castext.basic.preprocess import (normalize_whitespace, remove_punct, remove_eol_characters,
+remove_stopwords, lower_text)
+from castext.social.preprocess import remove_mentions, remove_hashtag, remove_emoji
+text = "I just got the best dinner in my life @latourdargent !!! I  recommend 😀 #food #paris \n"
 preprocessor = Preprocessor()
 preprocessor.pipe(lower_text)
 preprocessor.pipe(remove_mentions)
@@ -85,10 +86,10 @@ preprocessor.pipe(remove_punct)
 preprocessor.pipe(normalize_whitespace)
 text = preprocessor.run(text)
 print(text)
-# "dinner entire life recommend"
+# "dinner life recommend"
 ```
 
-Take a look at all the functions that are available [here](https://github.com/artefactory/nautilus-nlp/tree/master/nautilus_nlp) in the ```preprocess.py``` scripts in the different folders: classic, social, token.
+Take a look at all the functions that are available [here](https://github.com/artefactory/nautilus-nlp/tree/master/nautilus_nlp) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
 
 
 # Individual Functions
@@ -96,7 +97,7 @@ Take a look at all the functions that are available [here](https://github.com/ar
 ## Replacing emails <a name="replace_emails"></a>
 
 ```python
-from castext.classic.preprocess import replace_emails
+from castext.basic.preprocess import replace_emails
 example = "I have forwarded this email to obama@whitehouse.gov"
 example = replace_emails(example, replace_with="*EMAIL*")
 print(example)
@@ -106,7 +107,7 @@ print(example)
 ## Replacing phone numbers <a name="replace_phone_numbers"></a>
 
 ```python
-from castext.classic.preprocess import replace_phone_numbers
+from castext.basic.preprocess import replace_phone_numbers
 example = "My phone number is 0606060606"
 example = replace_phone_numbers(example, country_to_detect=["FR"], replace_with="*PHONE*")
 print(example)
@@ -162,7 +163,7 @@ You can now open the file index.html located in the build folder.
     ├── castext             <- Main Package. This is where the code lives
     │   ├── preprocessor.py <- Main preprocessing script
     │   ├── augmentation    <- Text augmentation script
-    │   ├── classic         <- Classic text preprocessing 
+    │   ├── basic           <- Basic text preprocessing 
     │   ├── social          <- Social text preprocessing
     │   └── token           <- Token preprocessing
     ├── utils               <- Where preprocessing utils scripts lives

From c1c8a699c6da68beaf37d3bfa6ce03b1dc01dd2c Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 4 Feb 2021 16:38:22 +0100
Subject: [PATCH 460/496] remove temporary text

---
 README.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/README.md b/README.md
index 3988f7a..f126d47 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,6 @@
 CasText
 ==============================
 
-**Insert new logo here**
 
 :tired_face: Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web? 
 
@@ -136,7 +135,6 @@ print(example)
 
 # Make HTML documentation
 
-**à updater**
 
 In order to make the html Sphinx documentation, you need to run at the castext root path:
 `sphinx-apidoc -f castext -o docs/`
@@ -148,7 +146,6 @@ You can now open the file index.html located in the build folder.
 
 # Project Organization
 ------------
-**à updater**
 
     ├── LICENSE
     ├── Makefile            <- Makefile with commands like `make data` or `make train`

From 638fbb93e15e0c392b223f499d877128e24d01c8 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Thu, 11 Feb 2021 17:11:47 +0100
Subject: [PATCH 461/496] update lib name

---
 .github/workflows/ci_actions.yml              |  4 +--
 CONTRIBUTING.md                               |  2 +-
 Makefile                                      |  4 +--
 README.md                                     | 34 +++++++++----------
 {nautilus_nlp => nlpretext}/__init__.py       |  2 +-
 .../augmentation/text_augmentation.py         |  0
 .../classic/preprocess.py                     |  8 ++---
 {nautilus_nlp => nlpretext}/preprocessor.py   |  4 +--
 .../social/preprocess.py                      |  4 +--
 .../token/preprocess.py                       |  0
 .../token/tokenizer.py                        |  0
 setup.py                                      |  2 +-
 tests/test_data_augmentation.py               |  2 +-
 tests/test_preprocessor.py                    | 12 +++----
 14 files changed, 39 insertions(+), 39 deletions(-)
 rename {nautilus_nlp => nlpretext}/__init__.py (94%)
 rename {nautilus_nlp => nlpretext}/augmentation/text_augmentation.py (100%)
 rename {nautilus_nlp => nlpretext}/classic/preprocess.py (97%)
 rename {nautilus_nlp => nlpretext}/preprocessor.py (93%)
 rename {nautilus_nlp => nlpretext}/social/preprocess.py (97%)
 rename {nautilus_nlp => nlpretext}/token/preprocess.py (100%)
 rename {nautilus_nlp => nlpretext}/token/tokenizer.py (100%)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 2c410da..d5031d8 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,8 +42,8 @@ jobs:
 
       - name: Run pylint
         run: |
-          pylint nautilus_nlp tests
+          pylint nlpretext tests
 
       - name: Run pytest
         run: |
-          pytest --cov=nautilus_nlp tests
+          pytest --cov=nlpretext tests
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index d59a290..52d256f 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,4 +1,4 @@
-Nautilus_NLP
+NLPretext
 ==============================
 
 # Docstring format
diff --git a/Makefile b/Makefile
index d20b93b..8fa6457 100644
--- a/Makefile
+++ b/Makefile
@@ -7,7 +7,7 @@
 PROJECT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
 BUCKET = [OPTIONAL] your-bucket-for-syncing-data (do not include 's3://')
 PROFILE = default
-PROJECT_NAME = nautilus_nlp
+PROJECT_NAME = nlpretext
 PYTHON_INTERPRETER = python3
 
 ifeq (,$(shell which conda))
@@ -36,7 +36,7 @@ clean:
 
 ## Lint using flake8
 lint:
-	black nautilus_nlp
+	black nlpretext
 
 ## Upload Data to S3
 sync_data_to_s3:
diff --git a/README.md b/README.md
index f126d47..17a7df2 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-CasText
+NLPretext
 ==============================
 
 
@@ -6,9 +6,9 @@ CasText
 
 :disappointed_relieved: Need to efficiently extract email adresses from a document? Hashtags from tweets? Remove accents from a French post? 
 
-CasText got you covered! :rocket:
+NLPretext got you covered! :rocket:
 
-CasText packages in a unique library all the text preprocessing functions you need to ease your NLP project. 
+NLPretext packages in a unique library all the text preprocessing functions you need to ease your NLP project. 
 
 :mag: Quickly explore below our preprocessing pipelines and individual functions referential.
 
@@ -19,7 +19,7 @@ CasText packages in a unique library all the text preprocessing functions you ne
 * [Extracting emojis](#extract_emojis)
 
 
-Cannot find what you were looking for? Feel free to open an [issue]((https://github.com/artefactory/castext/issues) ).
+Cannot find what you were looking for? Feel free to open an [issue]((https://github.com/artefactory/nlpretext/issues) ).
 
 
 
@@ -30,7 +30,7 @@ This package has been tested on Python **3.7**.
 To install this library you should first clone the repository:
 
 ```bash
-git clone git@github.com:artefactory/castext.git && cd castext/
+git clone git@github.com:artefactory/nlpretext.git && cd nlpretext/
 ```
 
 We strongly advise you to do the remaining steps in a virtual environnement.
@@ -56,7 +56,7 @@ This library uses Spacy as tokenizer. Current models supported are `en_core_web_
 Need to preprocess your text data but no clue about what function to use and in which order? The default preprocessing pipeline got you covered:
 
 ```python
-from castext import Preprocessor
+from nlpretext import Preprocessor
 text = "I just got the best dinner in my life @latourdargent !!! I  recommend 😀 #food #paris \n"
 preprocessor = Preprocessor()
 text = preprocessor.run(text)
@@ -69,10 +69,10 @@ print(text)
 Another possibility is to create your custom pipeline if you know exactly what function to apply on your data, here's an example:
 
 ```python
-from castext import Preprocessor
-from castext.basic.preprocess import (normalize_whitespace, remove_punct, remove_eol_characters,
+from nlpretext import Preprocessor
+from nlpretext.basic.preprocess import (normalize_whitespace, remove_punct, remove_eol_characters,
 remove_stopwords, lower_text)
-from castext.social.preprocess import remove_mentions, remove_hashtag, remove_emoji
+from nlpretext.social.preprocess import remove_mentions, remove_hashtag, remove_emoji
 text = "I just got the best dinner in my life @latourdargent !!! I  recommend 😀 #food #paris \n"
 preprocessor = Preprocessor()
 preprocessor.pipe(lower_text)
@@ -88,7 +88,7 @@ print(text)
 # "dinner life recommend"
 ```
 
-Take a look at all the functions that are available [here](https://github.com/artefactory/nautilus-nlp/tree/master/nautilus_nlp) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
+Take a look at all the functions that are available [here](https://github.com/artefactory/nautilus-nlp/tree/master/nlpretext) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
 
 
 # Individual Functions
@@ -96,7 +96,7 @@ Take a look at all the functions that are available [here](https://github.com/ar
 ## Replacing emails <a name="replace_emails"></a>
 
 ```python
-from castext.basic.preprocess import replace_emails
+from nlpretext.basic.preprocess import replace_emails
 example = "I have forwarded this email to obama@whitehouse.gov"
 example = replace_emails(example, replace_with="*EMAIL*")
 print(example)
@@ -106,7 +106,7 @@ print(example)
 ## Replacing phone numbers <a name="replace_phone_numbers"></a>
 
 ```python
-from castext.basic.preprocess import replace_phone_numbers
+from nlpretext.basic.preprocess import replace_phone_numbers
 example = "My phone number is 0606060606"
 example = replace_phone_numbers(example, country_to_detect=["FR"], replace_with="*PHONE*")
 print(example)
@@ -116,7 +116,7 @@ print(example)
 ## Removing Hashtags <a name="remove_hashtags"></a>
 
 ```python
-from castext.social.preprocess import remove_hashtag
+from nlpretext.social.preprocess import remove_hashtag
 example = "This restaurant was amazing #food #foodie #foodstagram #dinner"
 example = remove_hashtag(example)
 print(example)
@@ -126,7 +126,7 @@ print(example)
 ## Extracting emojis <a name="extract_emojis"></a>
 
 ```python
-from castext.social.preprocess import extract_emojis
+from nlpretext.social.preprocess import extract_emojis
 example = "I take care of my skin 😀"
 example = extract_emojis(example)
 print(example)
@@ -136,8 +136,8 @@ print(example)
 # Make HTML documentation
 
 
-In order to make the html Sphinx documentation, you need to run at the castext root path:
-`sphinx-apidoc -f castext -o docs/`
+In order to make the html Sphinx documentation, you need to run at the nlpretext root path:
+`sphinx-apidoc -f nlpretext -o docs/`
 This will generate the .rst files.
 You can generate the doc with
 `cd docs && make html`
@@ -157,7 +157,7 @@ You can now open the file index.html located in the build folder.
     │   ├── _build
     │   │   └── html
     │   ├── source
-    ├── castext             <- Main Package. This is where the code lives
+    ├── nlpretext           <- Main Package. This is where the code lives
     │   ├── preprocessor.py <- Main preprocessing script
     │   ├── augmentation    <- Text augmentation script
     │   ├── basic           <- Basic text preprocessing 
diff --git a/nautilus_nlp/__init__.py b/nlpretext/__init__.py
similarity index 94%
rename from nautilus_nlp/__init__.py
rename to nlpretext/__init__.py
index 7643ec5..cf137b0 100644
--- a/nautilus_nlp/__init__.py
+++ b/nlpretext/__init__.py
@@ -15,4 +15,4 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from nautilus_nlp.preprocessor import Preprocessor
+from nlpretext.preprocessor import Preprocessor
diff --git a/nautilus_nlp/augmentation/text_augmentation.py b/nlpretext/augmentation/text_augmentation.py
similarity index 100%
rename from nautilus_nlp/augmentation/text_augmentation.py
rename to nlpretext/augmentation/text_augmentation.py
diff --git a/nautilus_nlp/classic/preprocess.py b/nlpretext/classic/preprocess.py
similarity index 97%
rename from nautilus_nlp/classic/preprocess.py
rename to nlpretext/classic/preprocess.py
index e928c7a..0247012 100644
--- a/nautilus_nlp/classic/preprocess.py
+++ b/nlpretext/classic/preprocess.py
@@ -23,10 +23,10 @@
 import re
 import unicodedata
 from ftfy import fix_text as _fix_text
-from nautilus_nlp._config import constants
-from nautilus_nlp.token.tokenizer import tokenize
-from nautilus_nlp._utils.phone_number import extract_phone_numbers as _extract_phone_numbers
-from nautilus_nlp._utils.stopwords import get_stopwords
+from nlpretext._config import constants
+from nlpretext.token.tokenizer import tokenize
+from nlpretext._utils.phone_number import extract_phone_numbers as _extract_phone_numbers
+from nlpretext._utils.stopwords import get_stopwords
 
 
 def normalize_whitespace(text) -> str:
diff --git a/nautilus_nlp/preprocessor.py b/nlpretext/preprocessor.py
similarity index 93%
rename from nautilus_nlp/preprocessor.py
rename to nlpretext/preprocessor.py
index f5a4000..c453aad 100644
--- a/nautilus_nlp/preprocessor.py
+++ b/nlpretext/preprocessor.py
@@ -3,9 +3,9 @@
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 
-from nautilus_nlp.social.preprocess import (
+from nlpretext.social.preprocess import (
     remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
-from nautilus_nlp.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
+from nlpretext.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
 
 
 class Preprocessor():
diff --git a/nautilus_nlp/social/preprocess.py b/nlpretext/social/preprocess.py
similarity index 97%
rename from nautilus_nlp/social/preprocess.py
rename to nlpretext/social/preprocess.py
index 6b7a98d..1f30891 100644
--- a/nautilus_nlp/social/preprocess.py
+++ b/nlpretext/social/preprocess.py
@@ -20,8 +20,8 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import emoji as _emoji
-from nautilus_nlp._config import constants
-from nautilus_nlp.classic.preprocess import normalize_whitespace
+from nlpretext._config import constants
+from nlpretext.classic.preprocess import normalize_whitespace
 
 
 def remove_mentions(text) -> str:
diff --git a/nautilus_nlp/token/preprocess.py b/nlpretext/token/preprocess.py
similarity index 100%
rename from nautilus_nlp/token/preprocess.py
rename to nlpretext/token/preprocess.py
diff --git a/nautilus_nlp/token/tokenizer.py b/nlpretext/token/tokenizer.py
similarity index 100%
rename from nautilus_nlp/token/tokenizer.py
rename to nlpretext/token/tokenizer.py
diff --git a/setup.py b/setup.py
index 2399ed9..8b6eaf3 100644
--- a/setup.py
+++ b/setup.py
@@ -34,7 +34,7 @@ def run(self):
 with open(Path(__file__).resolve().parent.joinpath('VERSION'), 'r') as fh:
     version = fh.read()
 setup(
-    name='nautilus_nlp',
+    name='nlpretext',
     packages=find_packages(),
     version=version,
     description='All the goto functions you need to handle NLP use-cases',
diff --git a/tests/test_data_augmentation.py b/tests/test_data_augmentation.py
index 380a003..8a9bfdc 100644
--- a/tests/test_data_augmentation.py
+++ b/tests/test_data_augmentation.py
@@ -1,5 +1,5 @@
 import pytest
-from nautilus_nlp.augmentation.text_augmentation import (
+from nlpretext.augmentation.text_augmentation import (
     process_entities_and_text, get_augmenter, CouldNotAugment,
     UnavailableAugmenter
 )
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index a63b823..49edc28 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,28 +17,28 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from nautilus_nlp.classic.preprocess import (
+from nlpretext.classic.preprocess import (
     normalize_whitespace, remove_eol_characters, fix_bad_unicode,
     unpack_english_contractions, replace_urls, replace_emails,
     replace_phone_numbers, replace_numbers, replace_currency_symbols,
     remove_punct, remove_accents, remove_multiple_spaces_and_strip_text,
     filter_non_latin_characters
 )
-from nautilus_nlp.classic.preprocess import (
+from nlpretext.classic.preprocess import (
     remove_stopwords as remove_stopwords_text
 )
-from nautilus_nlp.social.preprocess import (
+from nlpretext.social.preprocess import (
     remove_mentions, extract_mentions, remove_html_tags, remove_emoji,
     convert_emoji_to_text, extract_emojis, extract_hashtags, remove_hashtag
 )
-from nautilus_nlp.token.preprocess import (
+from nlpretext.token.preprocess import (
     remove_tokens_with_nonletters,
     remove_special_caracters_from_tokenslist, remove_smallwords
 )
-from nautilus_nlp.token.preprocess import (
+from nlpretext.token.preprocess import (
     remove_stopwords as remove_stopwords_token
 )
-from nautilus_nlp.preprocessor import Preprocessor
+from nlpretext.preprocessor import Preprocessor
 
 import nautilus_nlp._utils.phone_number as phone
 from nautilus_nlp._utils.stopwords import get_stopwords

From f75fd2aba6016b7345c64ad57095c6806147ee7d Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 15:52:04 +0100
Subject: [PATCH 462/496] updates with new name and minor fixes for json config
 files

---
 .github/workflows/ci_actions.yml              |     2 +-
 README.md                                     |    26 +-
 nautilus_nlp/_config/country_code.json        |     1 -
 nautilus_nlp/_config/stopwords.json           |     1 -
 nautilus_nlp/augmentation/__init__.py         |     0
 nautilus_nlp/classic/__init__.py              |     0
 nautilus_nlp/social/__init__.py               |     0
 nautilus_nlp/token/__init__.py                |     0
 .../_config/__init__.py                       |     0
 {nautilus_nlp => nlpretext}/_config/config.py |     3 -
 .../_config/constants.py                      |     0
 nlpretext/_config/stopwords.py                | 13171 ++++++++++++++++
 .../_utils/__init__.py                        |     0
 .../_utils/file_loader.py                     |     0
 .../_utils/phone_number.py                    |     2 +-
 .../_utils/stopwords.py                       |    11 +-
 tests/test_document_loader.py                 |     2 +-
 tests/test_phone_number.py                    |     2 +-
 tests/test_preprocessor.py                    |     4 +-
 19 files changed, 13190 insertions(+), 35 deletions(-)
 delete mode 100644 nautilus_nlp/_config/country_code.json
 delete mode 100644 nautilus_nlp/_config/stopwords.json
 delete mode 100644 nautilus_nlp/augmentation/__init__.py
 delete mode 100644 nautilus_nlp/classic/__init__.py
 delete mode 100644 nautilus_nlp/social/__init__.py
 delete mode 100644 nautilus_nlp/token/__init__.py
 rename {nautilus_nlp => nlpretext}/_config/__init__.py (100%)
 rename {nautilus_nlp => nlpretext}/_config/config.py (98%)
 rename {nautilus_nlp => nlpretext}/_config/constants.py (100%)
 create mode 100644 nlpretext/_config/stopwords.py
 rename {nautilus_nlp => nlpretext}/_utils/__init__.py (100%)
 rename {nautilus_nlp => nlpretext}/_utils/file_loader.py (100%)
 rename {nautilus_nlp => nlpretext}/_utils/phone_number.py (98%)
 rename {nautilus_nlp => nlpretext}/_utils/stopwords.py (88%)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index d5031d8..71c2aef 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,7 +42,7 @@ jobs:
 
       - name: Run pylint
         run: |
-          pylint nlpretext tests
+          pylint nlpretext tests --max-module-lines 15000
 
       - name: Run pytest
         run: |
diff --git a/README.md b/README.md
index 17a7df2..7af4286 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,14 @@
 NLPretext
 ==============================
+<font size="4"> **No more pretext for dirty text** :pencil: </font>
 
+:tired_face: *Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web?* 
 
-:tired_face: Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web? 
+:disappointed_relieved: *Need to efficiently extract email adresses from a document? Hashtags from tweets? Remove accents from a French post?*
 
-:disappointed_relieved: Need to efficiently extract email adresses from a document? Hashtags from tweets? Remove accents from a French post? 
+**NLPretext got you covered!** :rocket:
 
-NLPretext got you covered! :rocket:
-
-NLPretext packages in a unique library all the text preprocessing functions you need to ease your NLP project. 
+NLPretext packages in a **unique** library all the text **preprocessing** functions you need to **ease** your NLP project. 
 
 :mag: Quickly explore below our preprocessing pipelines and individual functions referential.
 
@@ -25,30 +25,26 @@ Cannot find what you were looking for? Feel free to open an [issue]((https://git
 
 # Installation
 
-This package has been tested on Python **3.7**.
+This package has been tested on Python **3.6**, **3.7** and **3.8**.
 
-To install this library you should first clone the repository:
+To install this library you just have to run the following command:
 
 ```bash
-git clone git@github.com:artefactory/nlpretext.git && cd nlpretext/
+pip install git+https://github.com/artefactory/NLPretext.git
 ```
 
 We strongly advise you to do the remaining steps in a virtual environnement.
 
-First install the required files:
 
+This library uses Spacy as tokenizer. Current models supported are `en_core_web_sm` and `fr_core_news_sm`. If not installed, run the following commands:
 ```bash
-pip install -r requirements.txt
+pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
 ```
 
-then install the library with pip:
-
 ```bash
-pip install -e .
+pip install https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz
 ```
 
-This library uses Spacy as tokenizer. Current models supported are `en_core_web_sm` and `fr_core_news_sm`.
-
 # Preprocessing pipeline
 
 ## Default pipeline <a name="default_pipeline"></a>
diff --git a/nautilus_nlp/_config/country_code.json b/nautilus_nlp/_config/country_code.json
deleted file mode 100644
index 82ae350..0000000
--- a/nautilus_nlp/_config/country_code.json
+++ /dev/null
@@ -1 +0,0 @@
-[{"name":"Afghanistan","alpha-2":"AF","alpha-3":"AFG","country-code":"004","iso_3166-2":"ISO 3166-2:AF","region":"Asia","sub-region":"Southern Asia","intermediate-region":"","region-code":"142","sub-region-code":"034","intermediate-region-code":""},{"name":"Åland Islands","alpha-2":"AX","alpha-3":"ALA","country-code":"248","iso_3166-2":"ISO 3166-2:AX","region":"Europe","sub-region":"Northern Europe","intermediate-region":"","region-code":"150","sub-region-code":"154","intermediate-region-code":""},{"name":"Albania","alpha-2":"AL","alpha-3":"ALB","country-code":"008","iso_3166-2":"ISO 3166-2:AL","region":"Europe","sub-region":"Southern Europe","intermediate-region":"","region-code":"150","sub-region-code":"039","intermediate-region-code":""},{"name":"Algeria","alpha-2":"DZ","alpha-3":"DZA","country-code":"012","iso_3166-2":"ISO 3166-2:DZ","region":"Africa","sub-region":"Northern Africa","intermediate-region":"","region-code":"002","sub-region-code":"015","intermediate-region-code":""},{"name":"American Samoa","alpha-2":"AS","alpha-3":"ASM","country-code":"016","iso_3166-2":"ISO 3166-2:AS","region":"Oceania","sub-region":"Polynesia","intermediate-region":"","region-code":"009","sub-region-code":"061","intermediate-region-code":""},{"name":"Andorra","alpha-2":"AD","alpha-3":"AND","country-code":"020","iso_3166-2":"ISO 3166-2:AD","region":"Europe","sub-region":"Southern Europe","intermediate-region":"","region-code":"150","sub-region-code":"039","intermediate-region-code":""},{"name":"Angola","alpha-2":"AO","alpha-3":"AGO","country-code":"024","iso_3166-2":"ISO 3166-2:AO","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Anguilla","alpha-2":"AI","alpha-3":"AIA","country-code":"660","iso_3166-2":"ISO 3166-2:AI","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Antarctica","alpha-2":"AQ","alpha-3":"ATA","country-code":"010","iso_3166-2":"ISO 3166-2:AQ","region":"","sub-region":"","intermediate-region":"","region-code":"","sub-region-code":"","intermediate-region-code":""},{"name":"Antigua and Barbuda","alpha-2":"AG","alpha-3":"ATG","country-code":"028","iso_3166-2":"ISO 3166-2:AG","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Argentina","alpha-2":"AR","alpha-3":"ARG","country-code":"032","iso_3166-2":"ISO 3166-2:AR","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Armenia","alpha-2":"AM","alpha-3":"ARM","country-code":"051","iso_3166-2":"ISO 3166-2:AM","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Aruba","alpha-2":"AW","alpha-3":"ABW","country-code":"533","iso_3166-2":"ISO 3166-2:AW","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Australia","alpha-2":"AU","alpha-3":"AUS","country-code":"036","iso_3166-2":"ISO 3166-2:AU","region":"Oceania","sub-region":"Australia and New Zealand","intermediate-region":"","region-code":"009","sub-region-code":"053","intermediate-region-code":""},{"name":"Austria","alpha-2":"AT","alpha-3":"AUT","country-code":"040","iso_3166-2":"ISO 3166-2:AT","region":"Europe","sub-region":"Western Europe","intermediate-region":"","region-code":"150","sub-region-code":"155","intermediate-region-code":""},{"name":"Azerbaijan","alpha-2":"AZ","alpha-3":"AZE","country-code":"031","iso_3166-2":"ISO 3166-2:AZ","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Bahamas","alpha-2":"BS","alpha-3":"BHS","country-code":"044","iso_3166-2":"ISO 3166-2:BS","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Bahrain","alpha-2":"BH","alpha-3":"BHR","country-code":"048","iso_3166-2":"ISO 3166-2:BH","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Bangladesh","alpha-2":"BD","alpha-3":"BGD","country-code":"050","iso_3166-2":"ISO 3166-2:BD","region":"Asia","sub-region":"Southern Asia","intermediate-region":"","region-code":"142","sub-region-code":"034","intermediate-region-code":""},{"name":"Barbados","alpha-2":"BB","alpha-3":"BRB","country-code":"052","iso_3166-2":"ISO 3166-2:BB","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Belarus","alpha-2":"BY","alpha-3":"BLR","country-code":"112","iso_3166-2":"ISO 3166-2:BY","region":"Europe","sub-region":"Eastern Europe","intermediate-region":"","region-code":"150","sub-region-code":"151","intermediate-region-code":""},{"name":"Belgium","alpha-2":"BE","alpha-3":"BEL","country-code":"056","iso_3166-2":"ISO 3166-2:BE","region":"Europe","sub-region":"Western Europe","intermediate-region":"","region-code":"150","sub-region-code":"155","intermediate-region-code":""},{"name":"Belize","alpha-2":"BZ","alpha-3":"BLZ","country-code":"084","iso_3166-2":"ISO 3166-2:BZ","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Central America","region-code":"019","sub-region-code":"419","intermediate-region-code":"013"},{"name":"Benin","alpha-2":"BJ","alpha-3":"BEN","country-code":"204","iso_3166-2":"ISO 3166-2:BJ","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Bermuda","alpha-2":"BM","alpha-3":"BMU","country-code":"060","iso_3166-2":"ISO 3166-2:BM","region":"Americas","sub-region":"Northern America","intermediate-region":"","region-code":"019","sub-region-code":"021","intermediate-region-code":""},{"name":"Bhutan","alpha-2":"BT","alpha-3":"BTN","country-code":"064","iso_3166-2":"ISO 3166-2:BT","region":"Asia","sub-region":"Southern Asia","intermediate-region":"","region-code":"142","sub-region-code":"034","intermediate-region-code":""},{"name":"Bolivia (Plurinational State of)","alpha-2":"BO","alpha-3":"BOL","country-code":"068","iso_3166-2":"ISO 3166-2:BO","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Bonaire, Sint Eustatius and Saba","alpha-2":"BQ","alpha-3":"BES","country-code":"535","iso_3166-2":"ISO 3166-2:BQ","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Bosnia and Herzegovina","alpha-2":"BA","alpha-3":"BIH","country-code":"070","iso_3166-2":"ISO 3166-2:BA","region":"Europe","sub-region":"Southern Europe","intermediate-region":"","region-code":"150","sub-region-code":"039","intermediate-region-code":""},{"name":"Botswana","alpha-2":"BW","alpha-3":"BWA","country-code":"072","iso_3166-2":"ISO 3166-2:BW","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Southern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"018"},{"name":"Bouvet Island","alpha-2":"BV","alpha-3":"BVT","country-code":"074","iso_3166-2":"ISO 3166-2:BV","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Brazil","alpha-2":"BR","alpha-3":"BRA","country-code":"076","iso_3166-2":"ISO 3166-2:BR","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"British Indian Ocean Territory","alpha-2":"IO","alpha-3":"IOT","country-code":"086","iso_3166-2":"ISO 3166-2:IO","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Brunei Darussalam","alpha-2":"BN","alpha-3":"BRN","country-code":"096","iso_3166-2":"ISO 3166-2:BN","region":"Asia","sub-region":"South-eastern Asia","intermediate-region":"","region-code":"142","sub-region-code":"035","intermediate-region-code":""},{"name":"Bulgaria","alpha-2":"BG","alpha-3":"BGR","country-code":"100","iso_3166-2":"ISO 3166-2:BG","region":"Europe","sub-region":"Eastern Europe","intermediate-region":"","region-code":"150","sub-region-code":"151","intermediate-region-code":""},{"name":"Burkina Faso","alpha-2":"BF","alpha-3":"BFA","country-code":"854","iso_3166-2":"ISO 3166-2:BF","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Burundi","alpha-2":"BI","alpha-3":"BDI","country-code":"108","iso_3166-2":"ISO 3166-2:BI","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Cabo Verde","alpha-2":"CV","alpha-3":"CPV","country-code":"132","iso_3166-2":"ISO 3166-2:CV","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Cambodia","alpha-2":"KH","alpha-3":"KHM","country-code":"116","iso_3166-2":"ISO 3166-2:KH","region":"Asia","sub-region":"South-eastern Asia","intermediate-region":"","region-code":"142","sub-region-code":"035","intermediate-region-code":""},{"name":"Cameroon","alpha-2":"CM","alpha-3":"CMR","country-code":"120","iso_3166-2":"ISO 3166-2:CM","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Canada","alpha-2":"CA","alpha-3":"CAN","country-code":"124","iso_3166-2":"ISO 3166-2:CA","region":"Americas","sub-region":"Northern America","intermediate-region":"","region-code":"019","sub-region-code":"021","intermediate-region-code":""},{"name":"Cayman Islands","alpha-2":"KY","alpha-3":"CYM","country-code":"136","iso_3166-2":"ISO 3166-2:KY","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Central African Republic","alpha-2":"CF","alpha-3":"CAF","country-code":"140","iso_3166-2":"ISO 3166-2:CF","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Chad","alpha-2":"TD","alpha-3":"TCD","country-code":"148","iso_3166-2":"ISO 3166-2:TD","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Chile","alpha-2":"CL","alpha-3":"CHL","country-code":"152","iso_3166-2":"ISO 3166-2:CL","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"China","alpha-2":"CN","alpha-3":"CHN","country-code":"156","iso_3166-2":"ISO 3166-2:CN","region":"Asia","sub-region":"Eastern Asia","intermediate-region":"","region-code":"142","sub-region-code":"030","intermediate-region-code":""},{"name":"Christmas Island","alpha-2":"CX","alpha-3":"CXR","country-code":"162","iso_3166-2":"ISO 3166-2:CX","region":"Oceania","sub-region":"Australia and New Zealand","intermediate-region":"","region-code":"009","sub-region-code":"053","intermediate-region-code":""},{"name":"Cocos (Keeling) Islands","alpha-2":"CC","alpha-3":"CCK","country-code":"166","iso_3166-2":"ISO 3166-2:CC","region":"Oceania","sub-region":"Australia and New Zealand","intermediate-region":"","region-code":"009","sub-region-code":"053","intermediate-region-code":""},{"name":"Colombia","alpha-2":"CO","alpha-3":"COL","country-code":"170","iso_3166-2":"ISO 3166-2:CO","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Comoros","alpha-2":"KM","alpha-3":"COM","country-code":"174","iso_3166-2":"ISO 3166-2:KM","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Congo","alpha-2":"CG","alpha-3":"COG","country-code":"178","iso_3166-2":"ISO 3166-2:CG","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Congo, Democratic Republic of the","alpha-2":"CD","alpha-3":"COD","country-code":"180","iso_3166-2":"ISO 3166-2:CD","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Middle Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"017"},{"name":"Cook Islands","alpha-2":"CK","alpha-3":"COK","country-code":"184","iso_3166-2":"ISO 3166-2:CK","region":"Oceania","sub-region":"Polynesia","intermediate-region":"","region-code":"009","sub-region-code":"061","intermediate-region-code":""},{"name":"Costa Rica","alpha-2":"CR","alpha-3":"CRI","country-code":"188","iso_3166-2":"ISO 3166-2:CR","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Central America","region-code":"019","sub-region-code":"419","intermediate-region-code":"013"},{"name":"Côte d'Ivoire","alpha-2":"CI","alpha-3":"CIV","country-code":"384","iso_3166-2":"ISO 3166-2:CI","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Western Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"011"},{"name":"Croatia","alpha-2":"HR","alpha-3":"HRV","country-code":"191","iso_3166-2":"ISO 3166-2:HR","region":"Europe","sub-region":"Southern Europe","intermediate-region":"","region-code":"150","sub-region-code":"039","intermediate-region-code":""},{"name":"Cuba","alpha-2":"CU","alpha-3":"CUB","country-code":"192","iso_3166-2":"ISO 3166-2:CU","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Curaçao","alpha-2":"CW","alpha-3":"CUW","country-code":"531","iso_3166-2":"ISO 3166-2:CW","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Cyprus","alpha-2":"CY","alpha-3":"CYP","country-code":"196","iso_3166-2":"ISO 3166-2:CY","region":"Asia","sub-region":"Western Asia","intermediate-region":"","region-code":"142","sub-region-code":"145","intermediate-region-code":""},{"name":"Czechia","alpha-2":"CZ","alpha-3":"CZE","country-code":"203","iso_3166-2":"ISO 3166-2:CZ","region":"Europe","sub-region":"Eastern Europe","intermediate-region":"","region-code":"150","sub-region-code":"151","intermediate-region-code":""},{"name":"Denmark","alpha-2":"DK","alpha-3":"DNK","country-code":"208","iso_3166-2":"ISO 3166-2:DK","region":"Europe","sub-region":"Northern Europe","intermediate-region":"","region-code":"150","sub-region-code":"154","intermediate-region-code":""},{"name":"Djibouti","alpha-2":"DJ","alpha-3":"DJI","country-code":"262","iso_3166-2":"ISO 3166-2:DJ","region":"Africa","sub-region":"Sub-Saharan Africa","intermediate-region":"Eastern Africa","region-code":"002","sub-region-code":"202","intermediate-region-code":"014"},{"name":"Dominica","alpha-2":"DM","alpha-3":"DMA","country-code":"212","iso_3166-2":"ISO 3166-2:DM","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Dominican Republic","alpha-2":"DO","alpha-3":"DOM","country-code":"214","iso_3166-2":"ISO 3166-2:DO","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Caribbean","region-code":"019","sub-region-code":"419","intermediate-region-code":"029"},{"name":"Ecuador","alpha-2":"EC","alpha-3":"ECU","country-code":"218","iso_3166-2":"ISO 3166-2:EC","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"South America","region-code":"019","sub-region-code":"419","intermediate-region-code":"005"},{"name":"Egypt","alpha-2":"EG","alpha-3":"EGY","country-code":"818","iso_3166-2":"ISO 3166-2:EG","region":"Africa","sub-region":"Northern Africa","intermediate-region":"","region-code":"002","sub-region-code":"015","intermediate-region-code":""},{"name":"El Salvador","alpha-2":"SV","alpha-3":"SLV","country-code":"222","iso_3166-2":"ISO 3166-2:SV","region":"Americas","sub-region":"Latin America and the Caribbean","intermediate-region":"Central 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\ No newline at end of file
diff --git a/nautilus_nlp/augmentation/__init__.py b/nautilus_nlp/augmentation/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/classic/__init__.py b/nautilus_nlp/classic/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/social/__init__.py b/nautilus_nlp/social/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/token/__init__.py b/nautilus_nlp/token/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/nautilus_nlp/_config/__init__.py b/nlpretext/_config/__init__.py
similarity index 100%
rename from nautilus_nlp/_config/__init__.py
rename to nlpretext/_config/__init__.py
diff --git a/nautilus_nlp/_config/config.py b/nlpretext/_config/config.py
similarity index 98%
rename from nautilus_nlp/_config/config.py
rename to nlpretext/_config/config.py
index 0be3a0d..5717ac6 100644
--- a/nautilus_nlp/_config/config.py
+++ b/nlpretext/_config/config.py
@@ -22,9 +22,6 @@
 
 ROOT_FOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))
 
-# Stopwords config
-STOPWORDS_JSON_FILEPATH = os.path.join(ROOT_FOLDER, "nautilus_nlp", "_config", "stopwords.json")
-
 # Country config
 COUNTRY_MAPPING_ISO = {
     'af': 'Afghanistan', 'ax': 'Åland Islands', 'al': 'Albania', 'dz': 'Algeria', 'as': 'American Samoa', 'ad':
diff --git a/nautilus_nlp/_config/constants.py b/nlpretext/_config/constants.py
similarity index 100%
rename from nautilus_nlp/_config/constants.py
rename to nlpretext/_config/constants.py
diff --git a/nlpretext/_config/stopwords.py b/nlpretext/_config/stopwords.py
new file mode 100644
index 0000000..58f898e
--- /dev/null
+++ b/nlpretext/_config/stopwords.py
@@ -0,0 +1,13171 @@
+STOPWORDS = {
+    "af": [
+        "'n",
+        "aan",
+        "af",
+        "al",
+        "as",
+        "baie",
+        "by",
+        "daar",
+        "dag",
+        "dat",
+        "die",
+        "dit",
+        "een",
+        "ek",
+        "en",
+        "gaan",
+        "gesê",
+        "haar",
+        "het",
+        "hom",
+        "hulle",
+        "hy",
+        "in",
+        "is",
+        "jou",
+        "jy",
+        "kan",
+        "kom",
+        "ma",
+        "maar",
+        "met",
+        "my",
+        "na",
+        "nie",
+        "om",
+        "ons",
+        "op",
+        "saam",
+        "sal",
+        "se",
+        "sien",
+        "so",
+        "sy",
+        "te",
+        "toe",
+        "uit",
+        "van",
+        "vir",
+        "was",
+        "wat",
+        "ʼn"
+    ],
+    "ha": [
+        "a",
+        "amma",
+        "ba",
+        "ban",
+        "ce",
+        "cikin",
+        "da",
+        "don",
+        "ga",
+        "in",
+        "ina",
+        "ita",
+        "ji",
+        "ka",
+        "ko",
+        "kuma",
+        "lokacin",
+        "ma",
+        "mai",
+        "na",
+        "ne",
+        "ni",
+        "sai",
+        "shi",
+        "su",
+        "suka",
+        "sun",
+        "ta",
+        "tafi",
+        "take",
+        "tana",
+        "wani",
+        "wannan",
+        "wata",
+        "ya",
+        "yake",
+        "yana",
+        "yi",
+        "za"
+    ],
+    "so": [
+        "aad",
+        "albaabkii",
+        "atabo",
+        "ay",
+        "ayaa",
+        "ayee",
+        "ayuu",
+        "dhan",
+        "hadana",
+        "in",
+        "inuu",
+        "isku",
+        "jiray",
+        "jirtay",
+        "ka",
+        "kale",
+        "kasoo",
+        "ku",
+        "kuu",
+        "lakin",
+        "markii",
+        "oo",
+        "si",
+        "soo",
+        "uga",
+        "ugu",
+        "uu",
+        "waa",
+        "waxa",
+        "waxuu"
+    ],
+    "st": [
+        "a",
+        "ba",
+        "bane",
+        "bona",
+        "e",
+        "ea",
+        "eaba",
+        "empa",
+        "ena",
+        "ha",
+        "hae",
+        "hape",
+        "ho",
+        "hore",
+        "ka",
+        "ke",
+        "la",
+        "le",
+        "li",
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+        "fino",
+        "forse",
+        "forza",
+        "fosse",
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+        "fossi",
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+        "foste",
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+        "fummo",
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+        "furono",
+        "futuro",
+        "generale",
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+        "giacche",
+        "giorni",
+        "giorno",
+        "già",
+        "gli",
+        "gliela",
+        "gliele",
+        "glieli",
+        "glielo",
+        "gliene",
+        "governo",
+        "grande",
+        "grazie",
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+        "insieme",
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+        "lontano",
+        "loro",
+        "lui",
+        "lungo",
+        "luogo",
+        "là",
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+        "macche",
+        "magari",
+        "maggior",
+        "mai",
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+        "malgrado",
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+        "marche",
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+        "medesimo",
+        "mediante",
+        "meglio",
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+        "miliardi",
+        "milioni",
+        "minimi",
+        "ministro",
+        "mio",
+        "modo",
+        "molti",
+        "moltissimo",
+        "molto",
+        "momento",
+        "mondo",
+        "mosto",
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+        "nell",
+        "nella",
+        "nelle",
+        "nello",
+        "nemmeno",
+        "neppure",
+        "nessun",
+        "nessuna",
+        "nessuno",
+        "niente",
+        "no",
+        "noi",
+        "non",
+        "nondimeno",
+        "nonostante",
+        "nonsia",
+        "nostra",
+        "nostre",
+        "nostri",
+        "nostro",
+        "novanta",
+        "nove",
+        "nulla",
+        "nuovo",
+        "o",
+        "od",
+        "oggi",
+        "ogni",
+        "ognuna",
+        "ognuno",
+        "oltre",
+        "oppure",
+        "ora",
+        "ore",
+        "osi",
+        "ossia",
+        "ottanta",
+        "otto",
+        "paese",
+        "parecchi",
+        "parecchie",
+        "parecchio",
+        "parte",
+        "partendo",
+        "peccato",
+        "peggio",
+        "per",
+        "perche",
+        "perchè",
+        "perché",
+        "percio",
+        "perciò",
+        "perfino",
+        "pero",
+        "persino",
+        "persone",
+        "però",
+        "piedi",
+        "pieno",
+        "piglia",
+        "piu",
+        "piuttosto",
+        "più",
+        "po",
+        "pochissimo",
+        "poco",
+        "poi",
+        "poiche",
+        "possa",
+        "possedere",
+        "posteriore",
+        "posto",
+        "potrebbe",
+        "preferibilmente",
+        "presa",
+        "press",
+        "prima",
+        "primo",
+        "principalmente",
+        "probabilmente",
+        "proprio",
+        "puo",
+        "pure",
+        "purtroppo",
+        "può",
+        "qualche",
+        "qualcosa",
+        "qualcuna",
+        "qualcuno",
+        "quale",
+        "quali",
+        "qualunque",
+        "quando",
+        "quanta",
+        "quante",
+        "quanti",
+        "quanto",
+        "quantunque",
+        "quasi",
+        "quattro",
+        "quel",
+        "quella",
+        "quelle",
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+        "quest",
+        "questa",
+        "queste",
+        "questi",
+        "questo",
+        "qui",
+        "quindi",
+        "realmente",
+        "recente",
+        "recentemente",
+        "registrazione",
+        "relativo",
+        "riecco",
+        "salvo",
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+        "sarai",
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+        "sarebbe",
+        "sarebbero",
+        "sarei",
+        "saremmo",
+        "saremo",
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+        "scola",
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+        "sei",
+        "sembra",
+        "sembrare",
+        "sembrato",
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+        "sempre",
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+        "siate",
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+        "starei",
+        "staremmo",
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+        "starà",
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+        "stava",
+        "stavamo",
+        "stavano",
+        "stavate",
+        "stavi",
+        "stavo",
+        "stemmo",
+        "stessa",
+        "stesse",
+        "stessero",
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+        "stesso",
+        "steste",
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+        "stetti",
+        "stia",
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+        "stiano",
+        "stiate",
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+        "sulla",
+        "sulle",
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+        "suoi",
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+        "trovato",
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+        "tua",
+        "tue",
+        "tuo",
+        "tuoi",
+        "tutta",
+        "tuttavia",
+        "tutte",
+        "tutti",
+        "tutto",
+        "uguali",
+        "ulteriore",
+        "ultimo",
+        "un",
+        "una",
+        "uno",
+        "uomo",
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+        "vale",
+        "vari",
+        "varia",
+        "varie",
+        "vario",
+        "verso",
+        "vi",
+        "via",
+        "vicino",
+        "visto",
+        "vita",
+        "voi",
+        "volta",
+        "volte",
+        "vostra",
+        "vostre",
+        "vostri",
+        "vostro",
+        "è"
+    ],
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+        "!",
+        "\"",
+        "$",
+        "%",
+        "&",
+        "'",
+        "(",
+        ")",
+        "*",
+        "+",
+        ",",
+        "-",
+        ".",
+        "...",
+        "0",
+        "1",
+        "2",
+        "3",
+        "4",
+        "5",
+        "6",
+        "7",
+        "8",
+        "9",
+        ";",
+        "<",
+        "=",
+        ">",
+        "?",
+        "@",
+        "\\",
+        "^",
+        "_",
+        "`",
+        "|",
+        "~",
+        "·",
+        "—",
+        "——",
+        "‘",
+        "’",
+        "“",
+        "”",
+        "…",
+        "、",
+        "。",
+        "〈",
+        "〉",
+        "《",
+        "》",
+        "가",
+        "가까스로",
+        "가령",
+        "각",
+        "각각",
+        "각자",
+        "각종",
+        "갖고말하자면",
+        "같다",
+        "같이",
+        "개의치않고",
+        "거니와",
+        "거바",
+        "거의",
+        "것",
+        "것과 같이",
+        "것들",
+        "게다가",
+        "게우다",
+        "겨우",
+        "견지에서",
+        "결과에 이르다",
+        "결국",
+        "결론을 낼 수 있다",
+        "겸사겸사",
+        "고려하면",
+        "고로",
+        "곧",
+        "공동으로",
+        "과",
+        "과연",
+        "관계가 있다",
+        "관계없이",
+        "관련이 있다",
+        "관하여",
+        "관한",
+        "관해서는",
+        "구",
+        "구체적으로",
+        "구토하다",
+        "그",
+        "그들",
+        "그때",
+        "그래",
+        "그래도",
+        "그래서",
+        "그러나",
+        "그러니",
+        "그러니까",
+        "그러면",
+        "그러므로",
+        "그러한즉",
+        "그런 까닭에",
+        "그런데",
+        "그런즉",
+        "그럼",
+        "그럼에도 불구하고",
+        "그렇게 함으로써",
+        "그렇지",
+        "그렇지 않다면",
+        "그렇지 않으면",
+        "그렇지만",
+        "그렇지않으면",
+        "그리고",
+        "그리하여",
+        "그만이다",
+        "그에 따르는",
+        "그위에",
+        "그저",
+        "그중에서",
+        "그치지 않다",
+        "근거로",
+        "근거하여",
+        "기대여",
+        "기점으로",
+        "기준으로",
+        "기타",
+        "까닭으로",
+        "까악",
+        "까지",
+        "까지 미치다",
+        "까지도",
+        "꽈당",
+        "끙끙",
+        "끼익",
+        "나",
+        "나머지는",
+        "남들",
+        "남짓",
+        "너",
+        "너희",
+        "너희들",
+        "네",
+        "넷",
+        "년",
+        "논하지 않다",
+        "놀라다",
+        "누가 알겠는가",
+        "누구",
+        "다른",
+        "다른 방면으로",
+        "다만",
+        "다섯",
+        "다소",
+        "다수",
+        "다시 말하자면",
+        "다시말하면",
+        "다음",
+        "다음에",
+        "다음으로",
+        "단지",
+        "답다",
+        "당신",
+        "당장",
+        "대로 하다",
+        "대하면",
+        "대하여",
+        "대해 말하자면",
+        "대해서",
+        "댕그",
+        "더구나",
+        "더군다나",
+        "더라도",
+        "더불어",
+        "더욱더",
+        "더욱이는",
+        "도달하다",
+        "도착하다",
+        "동시에",
+        "동안",
+        "된바에야",
+        "된이상",
+        "두번째로",
+        "둘",
+        "둥둥",
+        "뒤따라",
+        "뒤이어",
+        "든간에",
+        "들",
+        "등",
+        "등등",
+        "딩동",
+        "따라",
+        "따라서",
+        "따위",
+        "따지지 않다",
+        "딱",
+        "때",
+        "때가 되어",
+        "때문에",
+        "또",
+        "또한",
+        "뚝뚝",
+        "라 해도",
+        "령",
+        "로",
+        "로 인하여",
+        "로부터",
+        "로써",
+        "륙",
+        "를",
+        "마음대로",
+        "마저",
+        "마저도",
+        "마치",
+        "막론하고",
+        "만 못하다",
+        "만약",
+        "만약에",
+        "만은 아니다",
+        "만이 아니다",
+        "만일",
+        "만큼",
+        "말하자면",
+        "말할것도 없고",
+        "매",
+        "매번",
+        "메쓰겁다",
+        "몇",
+        "모",
+        "모두",
+        "무렵",
+        "무릎쓰고",
+        "무슨",
+        "무엇",
+        "무엇때문에",
+        "물론",
+        "및",
+        "바꾸어말하면",
+        "바꾸어말하자면",
+        "바꾸어서 말하면",
+        "바꾸어서 한다면",
+        "바꿔 말하면",
+        "바로",
+        "바와같이",
+        "밖에 안된다",
+        "반대로",
+        "반대로 말하자면",
+        "반드시",
+        "버금",
+        "보는데서",
+        "보다더",
+        "보드득",
+        "본대로",
+        "봐",
+        "봐라",
+        "부류의 사람들",
+        "부터",
+        "불구하고",
+        "불문하고",
+        "붕붕",
+        "비걱거리다",
+        "비교적",
+        "비길수 없다",
+        "비로소",
+        "비록",
+        "비슷하다",
+        "비추어 보아",
+        "비하면",
+        "뿐만 아니라",
+        "뿐만아니라",
+        "뿐이다",
+        "삐걱",
+        "삐걱거리다",
+        "사",
+        "삼",
+        "상대적으로 말하자면",
+        "생각한대로",
+        "설령",
+        "설마",
+        "설사",
+        "셋",
+        "소생",
+        "소인",
+        "솨",
+        "쉿",
+        "습니까",
+        "습니다",
+        "시각",
+        "시간",
+        "시작하여",
+        "시초에",
+        "시키다",
+        "실로",
+        "심지어",
+        "아",
+        "아니",
+        "아니나다를가",
+        "아니라면",
+        "아니면",
+        "아니었다면",
+        "아래윗",
+        "아무거나",
+        "아무도",
+        "아야",
+        "아울러",
+        "아이",
+        "아이고",
+        "아이구",
+        "아이야",
+        "아이쿠",
+        "아하",
+        "아홉",
+        "안 그러면",
+        "않기 위하여",
+        "않기 위해서",
+        "알 수 있다",
+        "알았어",
+        "앗",
+        "앞에서",
+        "앞의것",
+        "야",
+        "약간",
+        "양자",
+        "어",
+        "어기여차",
+        "어느",
+        "어느 년도",
+        "어느것",
+        "어느곳",
+        "어느때",
+        "어느쪽",
+        "어느해",
+        "어디",
+        "어때",
+        "어떠한",
+        "어떤",
+        "어떤것",
+        "어떤것들",
+        "어떻게",
+        "어떻해",
+        "어이",
+        "어째서",
+        "어쨋든",
+        "어쩔수 없다",
+        "어찌",
+        "어찌됏든",
+        "어찌됏어",
+        "어찌하든지",
+        "어찌하여",
+        "언제",
+        "언젠가",
+        "얼마",
+        "얼마 안 되는 것",
+        "얼마간",
+        "얼마나",
+        "얼마든지",
+        "얼마만큼",
+        "얼마큼",
+        "엉엉",
+        "에",
+        "에 가서",
+        "에 달려 있다",
+        "에 대해",
+        "에 있다",
+        "에 한하다",
+        "에게",
+        "에서",
+        "여",
+        "여기",
+        "여덟",
+        "여러분",
+        "여보시오",
+        "여부",
+        "여섯",
+        "여전히",
+        "여차",
+        "연관되다",
+        "연이서",
+        "영",
+        "영차",
+        "옆사람",
+        "예",
+        "예를 들면",
+        "예를 들자면",
+        "예컨대",
+        "예하면",
+        "오",
+        "오로지",
+        "오르다",
+        "오자마자",
+        "오직",
+        "오호",
+        "오히려",
+        "와",
+        "와 같은 사람들",
+        "와르르",
+        "와아",
+        "왜",
+        "왜냐하면",
+        "외에도",
+        "요만큼",
+        "요만한 것",
+        "요만한걸",
+        "요컨대",
+        "우르르",
+        "우리",
+        "우리들",
+        "우선",
+        "우에 종합한것과같이",
+        "운운",
+        "월",
+        "위에서 서술한바와같이",
+        "위하여",
+        "위해서",
+        "윙윙",
+        "육",
+        "으로",
+        "으로 인하여",
+        "으로서",
+        "으로써",
+        "을",
+        "응",
+        "응당",
+        "의",
+        "의거하여",
+        "의지하여",
+        "의해",
+        "의해되다",
+        "의해서",
+        "이",
+        "이 되다",
+        "이 때문에",
+        "이 밖에",
+        "이 외에",
+        "이 정도의",
+        "이것",
+        "이곳",
+        "이때",
+        "이라면",
+        "이래",
+        "이러이러하다",
+        "이러한",
+        "이런",
+        "이럴정도로",
+        "이렇게 많은 것",
+        "이렇게되면",
+        "이렇게말하자면",
+        "이렇구나",
+        "이로 인하여",
+        "이르기까지",
+        "이리하여",
+        "이만큼",
+        "이번",
+        "이봐",
+        "이상",
+        "이어서",
+        "이었다",
+        "이와 같다",
+        "이와 같은",
+        "이와 반대로",
+        "이와같다면",
+        "이외에도",
+        "이용하여",
+        "이유만으로",
+        "이젠",
+        "이지만",
+        "이쪽",
+        "이천구",
+        "이천육",
+        "이천칠",
+        "이천팔",
+        "인 듯하다",
+        "인젠",
+        "일",
+        "일것이다",
+        "일곱",
+        "일단",
+        "일때",
+        "일반적으로",
+        "일지라도",
+        "임에 틀림없다",
+        "입각하여",
+        "입장에서",
+        "잇따라",
+        "있다",
+        "자",
+        "자기",
+        "자기집",
+        "자마자",
+        "자신",
+        "잠깐",
+        "잠시",
+        "저",
+        "저것",
+        "저것만큼",
+        "저기",
+        "저쪽",
+        "저희",
+        "전부",
+        "전자",
+        "전후",
+        "점에서 보아",
+        "정도에 이르다",
+        "제",
+        "제각기",
+        "제외하고",
+        "조금",
+        "조차",
+        "조차도",
+        "졸졸",
+        "좀",
+        "좋아",
+        "좍좍",
+        "주룩주룩",
+        "주저하지 않고",
+        "줄은 몰랏다",
+        "줄은모른다",
+        "중에서",
+        "중의하나",
+        "즈음하여",
+        "즉",
+        "즉시",
+        "지든지",
+        "지만",
+        "지말고",
+        "진짜로",
+        "쪽으로",
+        "차라리",
+        "참",
+        "참나",
+        "첫번째로",
+        "쳇",
+        "총적으로",
+        "총적으로 말하면",
+        "총적으로 보면",
+        "칠",
+        "콸콸",
+        "쾅쾅",
+        "쿵",
+        "타다",
+        "타인",
+        "탕탕",
+        "토하다",
+        "통하여",
+        "툭",
+        "퉤",
+        "틈타",
+        "팍",
+        "팔",
+        "퍽",
+        "펄렁",
+        "하",
+        "하게될것이다",
+        "하게하다",
+        "하겠는가",
+        "하고 있다",
+        "하고있었다",
+        "하곤하였다",
+        "하구나",
+        "하기 때문에",
+        "하기 위하여",
+        "하기는한데",
+        "하기만 하면",
+        "하기보다는",
+        "하기에",
+        "하나",
+        "하느니",
+        "하는 김에",
+        "하는 편이 낫다",
+        "하는것도",
+        "하는것만 못하다",
+        "하는것이 낫다",
+        "하는바",
+        "하더라도",
+        "하도다",
+        "하도록시키다",
+        "하도록하다",
+        "하든지",
+        "하려고하다",
+        "하마터면",
+        "하면 할수록",
+        "하면된다",
+        "하면서",
+        "하물며",
+        "하여금",
+        "하여야",
+        "하자마자",
+        "하지 않는다면",
+        "하지 않도록",
+        "하지마",
+        "하지마라",
+        "하지만",
+        "하하",
+        "한 까닭에",
+        "한 이유는",
+        "한 후",
+        "한다면",
+        "한다면 몰라도",
+        "한데",
+        "한마디",
+        "한적이있다",
+        "한켠으로는",
+        "한항목",
+        "할 따름이다",
+        "할 생각이다",
+        "할 줄 안다",
+        "할 지경이다",
+        "할 힘이 있다",
+        "할때",
+        "할만하다",
+        "할망정",
+        "할뿐",
+        "할수있다",
+        "할수있어",
+        "할줄알다",
+        "할지라도",
+        "할지언정",
+        "함께",
+        "해도된다",
+        "해도좋다",
+        "해봐요",
+        "해서는 안된다",
+        "해야한다",
+        "해요",
+        "했어요",
+        "향하다",
+        "향하여",
+        "향해서",
+        "허",
+        "허걱",
+        "허허",
+        "헉",
+        "헉헉",
+        "헐떡헐떡",
+        "형식으로 쓰여",
+        "혹시",
+        "혹은",
+        "혼자",
+        "훨씬",
+        "휘익",
+        "휴",
+        "흐흐",
+        "흥",
+        "힘입어",
+        "︿",
+        "!",
+        "#",
+        "$",
+        "%",
+        "&",
+        "(",
+        ")",
+        "*",
+        "+",
+        ",",
+        "0",
+        "1",
+        "2",
+        "3",
+        "4",
+        "5",
+        "6",
+        "7",
+        "8",
+        "9",
+        ":",
+        ";",
+        "<",
+        ">",
+        "?",
+        "@",
+        "[",
+        "]",
+        "{",
+        "|",
+        "}",
+        "~",
+        "¥"
+    ],
+    "nl": [
+        "aan",
+        "achte",
+        "achter",
+        "af",
+        "al",
+        "alle",
+        "alleen",
+        "alles",
+        "als",
+        "ander",
+        "anders",
+        "beetje",
+        "behalve",
+        "beide",
+        "beiden",
+        "ben",
+        "beneden",
+        "bent",
+        "bij",
+        "bijna",
+        "bijv",
+        "blijkbaar",
+        "blijken",
+        "boven",
+        "bv",
+        "daar",
+        "daardoor",
+        "daarin",
+        "daarna",
+        "daarom",
+        "daaruit",
+        "dan",
+        "dat",
+        "de",
+        "deden",
+        "deed",
+        "derde",
+        "derhalve",
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+        "dhr",
+        "die",
+        "dit",
+        "doe",
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+        "door",
+        "drie",
+        "duizend",
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+        "eerst",
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+        "eigenlijk",
+        "elk",
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+        "etc",
+        "etcetera",
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+        "genoeg",
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+        "haar",
+        "haarzelf",
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+        "jouw",
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+        "kon",
+        "konden",
+        "kun",
+        "kunnen",
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+        "laatst",
+        "later",
+        "lijken",
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+        "maak",
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+        "me",
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+        "meestal",
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+        "mevr",
+        "mij",
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+        "miss",
+        "misschien",
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+        "mocht",
+        "mochten",
+        "moest",
+        "moesten",
+        "moet",
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+        "mogen",
+        "mr",
+        "mrs",
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+        "namelijk",
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+        "niemand",
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+        "niks",
+        "noch",
+        "nochtans",
+        "nog",
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+        "ondanks",
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+        "ondertussen",
+        "ons",
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+        "onzeker",
+        "ooit",
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+        "overal",
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+        "paar",
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+        "redelijk",
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+        "sinds",
+        "steeds",
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+        "tegen",
+        "tegenover",
+        "thans",
+        "tien",
+        "tiende",
+        "tijdens",
+        "tja",
+        "toch",
+        "toe",
+        "tot",
+        "totdat",
+        "tussen",
+        "twee",
+        "tweede",
+        "u",
+        "uit",
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+        "vaak",
+        "van",
+        "vanaf",
+        "veel",
+        "veertig",
+        "verder",
+        "verscheidene",
+        "verschillende",
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+        "vier",
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+        "vijf",
+        "vijfde",
+        "vijftig",
+        "volgend",
+        "volgens",
+        "voor",
+        "voordat",
+        "voorts",
+        "waar",
+        "waarom",
+        "waarschijnlijk",
+        "wanneer",
+        "waren",
+        "was",
+        "wat",
+        "we",
+        "wederom",
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+        "weinig",
+        "wel",
+        "welk",
+        "welke",
+        "werd",
+        "werden",
+        "werder",
+        "whatever",
+        "wie",
+        "wij",
+        "wijzelf",
+        "wil",
+        "wilden",
+        "willen",
+        "word",
+        "worden",
+        "wordt",
+        "zal",
+        "ze",
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+        "zeker",
+        "zelf",
+        "zelfde",
+        "zes",
+        "zeven",
+        "zich",
+        "zij",
+        "zijn",
+        "zijzelf",
+        "zo",
+        "zoals",
+        "zodat",
+        "zou",
+        "zouden",
+        "zulk",
+        "zullen"
+    ],
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+        "alle",
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+        "nokon",
+        "nokor",
+        "nokre",
+        "nå",
+        "når",
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+        "også",
+        "om",
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+        "samme",
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+        "siden",
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+        "varte",
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+        "ville",
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+        "vort",
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+        "vært",
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+    ],
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+        "করা",
+        "করে",
+        "কাছে",
+        "কাজ",
+        "কাজে",
+        "কারণ",
+        "কি",
+        "কিছু",
+        "কে",
+        "কেউ",
+        "কেখা",
+        "কেন",
+        "কোটি",
+        "কোনো",
+        "কয়েক",
+        "খুব",
+        "গিয়ে",
+        "গেল",
+        "চার",
+        "চালু",
+        "চেষ্টা",
+        "ছিল",
+        "জানা",
+        "জ্নজন",
+        "টি",
+        "তখন",
+        "তবে",
+        "তা",
+        "তাই",
+        "তো",
+        "থাকা",
+        "থেকে",
+        "দিন",
+        "দু",
+        "দুই",
+        "দেওয়া",
+        "ধামার",
+        "নতুন",
+        "না",
+        "নাগাদ",
+        "নিয়ে",
+        "নেওয়া",
+        "নয়",
+        "পর",
+        "পরে",
+        "পাচ",
+        "পি",
+        "পেয়্র্",
+        "প্রতি",
+        "প্রথম",
+        "প্রযন্ত",
+        "প্রাথমিক",
+        "প্রায়",
+        "বক্তব্য",
+        "বন",
+        "বলা",
+        "বলে",
+        "বলেন",
+        "বহু",
+        "বা",
+        "বি",
+        "বিভিন্ন",
+        "বেশ",
+        "বেশি",
+        "মতো",
+        "মধ্যে",
+        "মনে",
+        "যখন",
+        "যদি",
+        "যা",
+        "যাওয়া",
+        "যে",
+        "র",
+        "রকম",
+        "লক্ষ",
+        "শুধু",
+        "শুরু",
+        "সঙ্গে",
+        "সব",
+        "সহ",
+        "সাধারণ",
+        "সামনে",
+        "সি",
+        "সে",
+        "সেই",
+        "হতে",
+        "হাজার",
+        "হয়"
+    ],
+    "fa": [
+        "آباد",
+        "آره",
+        "آری",
+        "آمد",
+        "آمده",
+        "آن",
+        "آنان",
+        "آنجا",
+        "آنكه",
+        "آنها",
+        "آنچه",
+        "آورد",
+        "آورده",
+        "آيد",
+        "آیا",
+        "اثرِ",
+        "از",
+        "است",
+        "استفاده",
+        "اش",
+        "اكنون",
+        "البته",
+        "البتّه",
+        "ام",
+        "اما",
+        "امروز",
+        "امسال",
+        "اند",
+        "انکه",
+        "او",
+        "اول",
+        "اي",
+        "ايشان",
+        "ايم",
+        "اين",
+        "اينكه",
+        "اگر",
+        "با",
+        "بار",
+        "بارة",
+        "باره",
+        "باشد",
+        "باشند",
+        "باشيم",
+        "بالا",
+        "بالایِ",
+        "بايد",
+        "بدون",
+        "بر",
+        "برابرِ",
+        "براساس",
+        "براي",
+        "برایِ",
+        "برخوردار",
+        "برخي",
+        "برداري",
+        "بروز",
+        "بسيار",
+        "بسياري",
+        "بعد",
+        "بعری",
+        "بعضي",
+        "بلكه",
+        "بله",
+        "بلکه",
+        "بلی",
+        "بنابراين",
+        "بندي",
+        "به",
+        "بهترين",
+        "بود",
+        "بودن",
+        "بودند",
+        "بوده",
+        "بي",
+        "بيست",
+        "بيش",
+        "بيشتر",
+        "بيشتري",
+        "بين",
+        "بی",
+        "بیرونِ",
+        "تا",
+        "تازه",
+        "تاكنون",
+        "تان",
+        "تحت",
+        "تر",
+        "ترين",
+        "تمام",
+        "تمامي",
+        "تنها",
+        "تواند",
+        "توانند",
+        "توسط",
+        "تولِ",
+        "تویِ",
+        "جا",
+        "جاي",
+        "جايي",
+        "جدا",
+        "جديد",
+        "جريان",
+        "جز",
+        "جلوگيري",
+        "جلویِ",
+        "حتي",
+        "حدودِ",
+        "حق",
+        "خارجِ",
+        "خدمات",
+        "خواست",
+        "خواهد",
+        "خواهند",
+        "خواهيم",
+        "خود",
+        "خويش",
+        "خیاه",
+        "داد",
+        "دادن",
+        "دادند",
+        "داده",
+        "دارد",
+        "دارند",
+        "داريم",
+        "داشت",
+        "داشتن",
+        "داشتند",
+        "داشته",
+        "دانست",
+        "دانند",
+        "در",
+        "درباره",
+        "دنبالِ",
+        "ده",
+        "دهد",
+        "دهند",
+        "دو",
+        "دوم",
+        "ديده",
+        "ديروز",
+        "ديگر",
+        "ديگران",
+        "ديگري",
+        "دیگر",
+        "را",
+        "راه",
+        "رفت",
+        "رفته",
+        "روب",
+        "روزهاي",
+        "روي",
+        "رویِ",
+        "ريزي",
+        "زياد",
+        "زير",
+        "زيرا",
+        "زیرِ",
+        "سابق",
+        "ساخته",
+        "سازي",
+        "سراسر",
+        "سریِ",
+        "سعي",
+        "سمتِ",
+        "سوم",
+        "سوي",
+        "سویِ",
+        "سپس",
+        "شان",
+        "شايد",
+        "شد",
+        "شدن",
+        "شدند",
+        "شده",
+        "شش",
+        "شما",
+        "شناسي",
+        "شود",
+        "شوند",
+        "صورت",
+        "ضدِّ",
+        "ضمن",
+        "طبقِ",
+        "طريق",
+        "طور",
+        "طي",
+        "عقبِ",
+        "علّتِ",
+        "عنوانِ",
+        "غير",
+        "فقط",
+        "فكر",
+        "فوق",
+        "قابل",
+        "قبل",
+        "قصدِ",
+        "كرد",
+        "كردم",
+        "كردن",
+        "كردند",
+        "كرده",
+        "كسي",
+        "كل",
+        "كمتر",
+        "كند",
+        "كنم",
+        "كنند",
+        "كنيد",
+        "كنيم",
+        "كه",
+        "لطفاً",
+        "ما",
+        "مان",
+        "مانند",
+        "مانندِ",
+        "مثل",
+        "مثلِ",
+        "مختلف",
+        "مدّتی",
+        "مردم",
+        "مرسی",
+        "مقابل",
+        "من",
+        "مورد",
+        "مي",
+        "ميليارد",
+        "ميليون",
+        "مگر",
+        "ناشي",
+        "نام",
+        "نبايد",
+        "نبود",
+        "نخست",
+        "نخستين",
+        "نخواهد",
+        "ندارد",
+        "ندارند",
+        "نداشته",
+        "نزديك",
+        "نزدِ",
+        "نزدیکِ",
+        "نشان",
+        "نشده",
+        "نظير",
+        "نكرده",
+        "نمايد",
+        "نمي",
+        "نه",
+        "نوعي",
+        "نيز",
+        "نيست",
+        "ها",
+        "هاي",
+        "هايي",
+        "هر",
+        "هرگز",
+        "هزار",
+        "هست",
+        "هستند",
+        "هستيم",
+        "هفت",
+        "هم",
+        "همان",
+        "همه",
+        "همواره",
+        "همين",
+        "همچنان",
+        "همچنين",
+        "همچون",
+        "همین",
+        "هنوز",
+        "هنگام",
+        "هنگامِ",
+        "هنگامی",
+        "هيچ",
+        "هیچ",
+        "و",
+        "وسطِ",
+        "وقتي",
+        "وقتیکه",
+        "ولی",
+        "وي",
+        "وگو",
+        "يا",
+        "يابد",
+        "يك",
+        "يكديگر",
+        "يكي",
+        "ّه",
+        "پاعینِ",
+        "پس",
+        "پنج",
+        "پيش",
+        "پیش",
+        "پیشِ",
+        "چرا",
+        "چطور",
+        "چند",
+        "چندین",
+        "چنين",
+        "چه",
+        "چهار",
+        "چون",
+        "چيزي",
+        "چگونه",
+        "چیز",
+        "چیزی",
+        "چیست",
+        "کجا",
+        "کجاست",
+        "کدام",
+        "کس",
+        "کسی",
+        "کنارِ",
+        "که",
+        "کَی",
+        "کی",
+        "گذاري",
+        "گذاشته",
+        "گردد",
+        "گرفت",
+        "گرفته",
+        "گروهي",
+        "گفت",
+        "گفته",
+        "گويد",
+        "گويند",
+        "گيرد",
+        "گيري",
+        "یا",
+        "یک"
+    ],
+    "hi": [
+        "अंदर",
+        "अत",
+        "अदि",
+        "अप",
+        "अपना",
+        "अपनि",
+        "अपनी",
+        "अपने",
+        "अभि",
+        "अभी",
+        "आदि",
+        "आप",
+        "इंहिं",
+        "इंहें",
+        "इंहों",
+        "इतयादि",
+        "इत्यादि",
+        "इन",
+        "इनका",
+        "इन्हीं",
+        "इन्हें",
+        "इन्हों",
+        "इस",
+        "इसका",
+        "इसकि",
+        "इसकी",
+        "इसके",
+        "इसमें",
+        "इसि",
+        "इसी",
+        "इसे",
+        "उंहिं",
+        "उंहें",
+        "उंहों",
+        "उन",
+        "उनका",
+        "उनकि",
+        "उनकी",
+        "उनके",
+        "उनको",
+        "उन्हीं",
+        "उन्हें",
+        "उन्हों",
+        "उस",
+        "उसके",
+        "उसि",
+        "उसी",
+        "उसे",
+        "एक",
+        "एवं",
+        "एस",
+        "एसे",
+        "ऐसे",
+        "ओर",
+        "और",
+        "कइ",
+        "कई",
+        "कर",
+        "करता",
+        "करते",
+        "करना",
+        "करने",
+        "करें",
+        "कहते",
+        "कहा",
+        "का",
+        "काफि",
+        "काफ़ी",
+        "कि",
+        "किंहें",
+        "किंहों",
+        "कितना",
+        "किन्हें",
+        "किन्हों",
+        "किया",
+        "किर",
+        "किस",
+        "किसि",
+        "किसी",
+        "किसे",
+        "की",
+        "कुछ",
+        "कुल",
+        "के",
+        "को",
+        "कोइ",
+        "कोई",
+        "कोन",
+        "कोनसा",
+        "कौन",
+        "कौनसा",
+        "गया",
+        "घर",
+        "जब",
+        "जहाँ",
+        "जहां",
+        "जा",
+        "जिंहें",
+        "जिंहों",
+        "जितना",
+        "जिधर",
+        "जिन",
+        "जिन्हें",
+        "जिन्हों",
+        "जिस",
+        "जिसे",
+        "जीधर",
+        "जेसा",
+        "जेसे",
+        "जैसा",
+        "जैसे",
+        "जो",
+        "तक",
+        "तब",
+        "तरह",
+        "तिंहें",
+        "तिंहों",
+        "तिन",
+        "तिन्हें",
+        "तिन्हों",
+        "तिस",
+        "तिसे",
+        "तो",
+        "था",
+        "थि",
+        "थी",
+        "थे",
+        "दबारा",
+        "दवारा",
+        "दिया",
+        "दुसरा",
+        "दुसरे",
+        "दूसरे",
+        "दो",
+        "द्वारा",
+        "न",
+        "नहिं",
+        "नहीं",
+        "ना",
+        "निचे",
+        "निहायत",
+        "नीचे",
+        "ने",
+        "पर",
+        "पहले",
+        "पुरा",
+        "पूरा",
+        "पे",
+        "फिर",
+        "बनि",
+        "बनी",
+        "बहि",
+        "बही",
+        "बहुत",
+        "बाद",
+        "बाला",
+        "बिलकुल",
+        "भि",
+        "भितर",
+        "भी",
+        "भीतर",
+        "मगर",
+        "मानो",
+        "मे",
+        "में",
+        "यदि",
+        "यह",
+        "यहाँ",
+        "यहां",
+        "यहि",
+        "यही",
+        "या",
+        "यिह",
+        "ये",
+        "रखें",
+        "रवासा",
+        "रहा",
+        "रहे",
+        "ऱ्वासा",
+        "लिए",
+        "लिये",
+        "लेकिन",
+        "व",
+        "वगेरह",
+        "वरग",
+        "वर्ग",
+        "वह",
+        "वहाँ",
+        "वहां",
+        "वहिं",
+        "वहीं",
+        "वाले",
+        "वुह",
+        "वे",
+        "वग़ैरह",
+        "संग",
+        "सकता",
+        "सकते",
+        "सबसे",
+        "सभि",
+        "सभी",
+        "साथ",
+        "साबुत",
+        "साभ",
+        "सारा",
+        "से",
+        "सो",
+        "हि",
+        "ही",
+        "हुअ",
+        "हुआ",
+        "हुइ",
+        "हुई",
+        "हुए",
+        "हे",
+        "हें",
+        "है",
+        "हैं",
+        "हो",
+        "होता",
+        "होति",
+        "होती",
+        "होते",
+        "होना",
+        "होने"
+    ],
+    "mr": [
+        "अधिक",
+        "अनेक",
+        "अशी",
+        "असलयाचे",
+        "असलेल्या",
+        "असा",
+        "असून",
+        "असे",
+        "आज",
+        "आणि",
+        "आता",
+        "आपल्या",
+        "आला",
+        "आली",
+        "आले",
+        "आहे",
+        "आहेत",
+        "एक",
+        "एका",
+        "कमी",
+        "करणयात",
+        "करून",
+        "का",
+        "काम",
+        "काय",
+        "काही",
+        "किवा",
+        "की",
+        "केला",
+        "केली",
+        "केले",
+        "कोटी",
+        "गेल्या",
+        "घेऊन",
+        "जात",
+        "झाला",
+        "झाली",
+        "झाले",
+        "झालेल्या",
+        "टा",
+        "डॉ",
+        "तर",
+        "तरी",
+        "तसेच",
+        "ता",
+        "ती",
+        "तीन",
+        "ते",
+        "तो",
+        "त्या",
+        "त्याचा",
+        "त्याची",
+        "त्याच्या",
+        "त्याना",
+        "त्यानी",
+        "त्यामुळे",
+        "त्री",
+        "दिली",
+        "दोन",
+        "न",
+        "नाही",
+        "निर्ण्य",
+        "पण",
+        "पम",
+        "परयतन",
+        "पाटील",
+        "म",
+        "मात्र",
+        "माहिती",
+        "मी",
+        "मुबी",
+        "म्हणजे",
+        "म्हणाले",
+        "म्हणून",
+        "या",
+        "याचा",
+        "याची",
+        "याच्या",
+        "याना",
+        "यानी",
+        "येणार",
+        "येत",
+        "येथील",
+        "येथे",
+        "लाख",
+        "व",
+        "व्यकत",
+        "सर्व",
+        "सागित्ले",
+        "सुरू",
+        "हजार",
+        "हा",
+        "ही",
+        "हे",
+        "होणार",
+        "होत",
+        "होता",
+        "होती",
+        "होते"
+    ],
+    "ro": [
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+        "aceasta",
+        "această",
+        "aceea",
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+        "aceia",
+        "acel",
+        "acela",
+        "acele",
+        "acelea",
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+        "acesta",
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+        "acestea",
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+        "aceştia",
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+        "acum",
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+        "aia",
+        "aibă",
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+        "alea",
+        "altceva",
+        "altcineva",
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+        "chiar",
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+        "cu",
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+        "cumva",
+        "curând",
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+        "când",
+        "cât",
+        "câte",
+        "câtva",
+        "câţi",
+        "cînd",
+        "cît",
+        "cîte",
+        "cîtva",
+        "cîţi",
+        "că",
+        "căci",
+        "cărei",
+        "căror",
+        "cărui",
+        "către",
+        "da",
+        "dacă",
+        "dar",
+        "datorită",
+        "dată",
+        "dau",
+        "de",
+        "deci",
+        "deja",
+        "deoarece",
+        "departe",
+        "deşi",
+        "din",
+        "dinaintea",
+        "dintr-",
+        "dintre",
+        "doi",
+        "doilea",
+        "două",
+        "drept",
+        "după",
+        "dă",
+        "ea",
+        "ei",
+        "el",
+        "ele",
+        "eram",
+        "este",
+        "eu",
+        "eşti",
+        "face",
+        "fata",
+        "fi",
+        "fie",
+        "fiecare",
+        "fii",
+        "fim",
+        "fiu",
+        "fiţi",
+        "frumos",
+        "fără",
+        "graţie",
+        "halbă",
+        "iar",
+        "ieri",
+        "la",
+        "le",
+        "li",
+        "lor",
+        "lui",
+        "lângă",
+        "lîngă",
+        "mai",
+        "mea",
+        "mei",
+        "mele",
+        "mereu",
+        "meu",
+        "mi",
+        "mie",
+        "mine",
+        "mult",
+        "multă",
+        "mulţi",
+        "mulţumesc",
+        "mâine",
+        "mîine",
+        "mă",
+        "ne",
+        "nevoie",
+        "nici",
+        "nicăieri",
+        "nimeni",
+        "nimeri",
+        "nimic",
+        "nişte",
+        "noastre",
+        "noastră",
+        "noi",
+        "noroc",
+        "nostru",
+        "nouă",
+        "noştri",
+        "nu",
+        "opt",
+        "ori",
+        "oricare",
+        "orice",
+        "oricine",
+        "oricum",
+        "oricând",
+        "oricât",
+        "oricînd",
+        "oricît",
+        "oriunde",
+        "patra",
+        "patru",
+        "patrulea",
+        "pe",
+        "pentru",
+        "peste",
+        "pic",
+        "poate",
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+        "prea",
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+        "primul",
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+        "printr-",
+        "puţin",
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+        "până",
+        "pînă",
+        "rog",
+        "sa",
+        "sale",
+        "sau",
+        "se",
+        "spate",
+        "spre",
+        "sub",
+        "sunt",
+        "suntem",
+        "sunteţi",
+        "sută",
+        "sînt",
+        "sîntem",
+        "sînteţi",
+        "să",
+        "săi",
+        "său",
+        "ta",
+        "tale",
+        "te",
+        "timp",
+        "tine",
+        "toate",
+        "toată",
+        "tot",
+        "totuşi",
+        "toţi",
+        "trei",
+        "treia",
+        "treilea",
+        "tu",
+        "tăi",
+        "tău",
+        "un",
+        "una",
+        "unde",
+        "undeva",
+        "unei",
+        "uneia",
+        "unele",
+        "uneori",
+        "unii",
+        "unor",
+        "unora",
+        "unu",
+        "unui",
+        "unuia",
+        "unul",
+        "vi",
+        "voastre",
+        "voastră",
+        "voi",
+        "vostru",
+        "vouă",
+        "voştri",
+        "vreme",
+        "vreo",
+        "vreun",
+        "vă",
+        "zece",
+        "zero",
+        "zi",
+        "zice",
+        "îi",
+        "îl",
+        "îmi",
+        "împotriva",
+        "în",
+        "înainte",
+        "înaintea",
+        "încotro",
+        "încât",
+        "încît",
+        "între",
+        "întrucât",
+        "întrucît",
+        "îţi",
+        "ăla",
+        "ălea",
+        "ăsta",
+        "ăstea",
+        "ăştia",
+        "şapte",
+        "şase",
+        "şi",
+        "ştiu",
+        "ţi",
+        "ţie"
+    ],
+    "en": [
+        "a",
+        "a's",
+        "able",
+        "about",
+        "above",
+        "according",
+        "accordingly",
+        "across",
+        "actually",
+        "after",
+        "afterwards",
+        "again",
+        "against",
+        "ain't",
+        "all",
+        "allow",
+        "allows",
+        "almost",
+        "alone",
+        "along",
+        "already",
+        "also",
+        "although",
+        "always",
+        "am",
+        "among",
+        "amongst",
+        "an",
+        "and",
+        "another",
+        "any",
+        "anybody",
+        "anyhow",
+        "anyone",
+        "anything",
+        "anyway",
+        "anyways",
+        "anywhere",
+        "apart",
+        "appear",
+        "appreciate",
+        "appropriate",
+        "are",
+        "aren't",
+        "around",
+        "as",
+        "aside",
+        "ask",
+        "asking",
+        "associated",
+        "at",
+        "available",
+        "away",
+        "awfully",
+        "b",
+        "be",
+        "became",
+        "because",
+        "become",
+        "becomes",
+        "becoming",
+        "been",
+        "before",
+        "beforehand",
+        "behind",
+        "being",
+        "believe",
+        "below",
+        "beside",
+        "besides",
+        "best",
+        "better",
+        "between",
+        "beyond",
+        "both",
+        "brief",
+        "but",
+        "by",
+        "c",
+        "c'mon",
+        "c's",
+        "came",
+        "can",
+        "can't",
+        "cannot",
+        "cant",
+        "cause",
+        "causes",
+        "certain",
+        "certainly",
+        "changes",
+        "clearly",
+        "co",
+        "com",
+        "come",
+        "comes",
+        "concerning",
+        "consequently",
+        "consider",
+        "considering",
+        "contain",
+        "containing",
+        "contains",
+        "corresponding",
+        "could",
+        "couldn't",
+        "course",
+        "currently",
+        "d",
+        "definitely",
+        "described",
+        "despite",
+        "did",
+        "didn't",
+        "different",
+        "do",
+        "does",
+        "doesn't",
+        "doing",
+        "don't",
+        "done",
+        "down",
+        "downwards",
+        "during",
+        "e",
+        "each",
+        "edu",
+        "eg",
+        "eight",
+        "either",
+        "else",
+        "elsewhere",
+        "enough",
+        "entirely",
+        "especially",
+        "et",
+        "etc",
+        "even",
+        "ever",
+        "every",
+        "everybody",
+        "everyone",
+        "everything",
+        "everywhere",
+        "ex",
+        "exactly",
+        "example",
+        "except",
+        "f",
+        "far",
+        "few",
+        "fifth",
+        "first",
+        "five",
+        "followed",
+        "following",
+        "follows",
+        "for",
+        "former",
+        "formerly",
+        "forth",
+        "four",
+        "from",
+        "further",
+        "furthermore",
+        "g",
+        "get",
+        "gets",
+        "getting",
+        "given",
+        "gives",
+        "go",
+        "goes",
+        "going",
+        "gone",
+        "got",
+        "gotten",
+        "greetings",
+        "h",
+        "had",
+        "hadn't",
+        "happens",
+        "hardly",
+        "has",
+        "hasn't",
+        "have",
+        "haven't",
+        "having",
+        "he",
+        "he's",
+        "hello",
+        "help",
+        "hence",
+        "her",
+        "here",
+        "here's",
+        "hereafter",
+        "hereby",
+        "herein",
+        "hereupon",
+        "hers",
+        "herself",
+        "hi",
+        "him",
+        "himself",
+        "his",
+        "hither",
+        "hopefully",
+        "how",
+        "howbeit",
+        "however",
+        "i",
+        "i'd",
+        "i'll",
+        "i'm",
+        "i've",
+        "ie",
+        "if",
+        "ignored",
+        "immediate",
+        "in",
+        "inasmuch",
+        "inc",
+        "indeed",
+        "indicate",
+        "indicated",
+        "indicates",
+        "inner",
+        "insofar",
+        "instead",
+        "into",
+        "inward",
+        "is",
+        "isn't",
+        "it",
+        "it'd",
+        "it'll",
+        "it's",
+        "its",
+        "itself",
+        "j",
+        "just",
+        "k",
+        "keep",
+        "keeps",
+        "kept",
+        "know",
+        "known",
+        "knows",
+        "l",
+        "last",
+        "lately",
+        "later",
+        "latter",
+        "latterly",
+        "least",
+        "less",
+        "lest",
+        "let",
+        "let's",
+        "like",
+        "liked",
+        "likely",
+        "little",
+        "look",
+        "looking",
+        "looks",
+        "ltd",
+        "m",
+        "mainly",
+        "many",
+        "may",
+        "maybe",
+        "me",
+        "mean",
+        "meanwhile",
+        "merely",
+        "might",
+        "more",
+        "moreover",
+        "most",
+        "mostly",
+        "much",
+        "must",
+        "my",
+        "myself",
+        "n",
+        "name",
+        "namely",
+        "nd",
+        "near",
+        "nearly",
+        "necessary",
+        "need",
+        "needs",
+        "neither",
+        "never",
+        "nevertheless",
+        "new",
+        "next",
+        "nine",
+        "no",
+        "nobody",
+        "non",
+        "none",
+        "noone",
+        "nor",
+        "normally",
+        "not",
+        "nothing",
+        "novel",
+        "now",
+        "nowhere",
+        "o",
+        "obviously",
+        "of",
+        "off",
+        "often",
+        "oh",
+        "ok",
+        "okay",
+        "old",
+        "on",
+        "once",
+        "one",
+        "ones",
+        "only",
+        "onto",
+        "or",
+        "other",
+        "others",
+        "otherwise",
+        "ought",
+        "our",
+        "ours",
+        "ourselves",
+        "out",
+        "outside",
+        "over",
+        "overall",
+        "own",
+        "p",
+        "particular",
+        "particularly",
+        "per",
+        "perhaps",
+        "placed",
+        "please",
+        "plus",
+        "possible",
+        "presumably",
+        "probably",
+        "provides",
+        "q",
+        "que",
+        "quite",
+        "qv",
+        "r",
+        "rather",
+        "rd",
+        "re",
+        "really",
+        "reasonably",
+        "regarding",
+        "regardless",
+        "regards",
+        "relatively",
+        "respectively",
+        "right",
+        "s",
+        "said",
+        "same",
+        "saw",
+        "say",
+        "saying",
+        "says",
+        "second",
+        "secondly",
+        "see",
+        "seeing",
+        "seem",
+        "seemed",
+        "seeming",
+        "seems",
+        "seen",
+        "self",
+        "selves",
+        "sensible",
+        "sent",
+        "serious",
+        "seriously",
+        "seven",
+        "several",
+        "shall",
+        "she",
+        "should",
+        "shouldn't",
+        "since",
+        "six",
+        "so",
+        "some",
+        "somebody",
+        "somehow",
+        "someone",
+        "something",
+        "sometime",
+        "sometimes",
+        "somewhat",
+        "somewhere",
+        "soon",
+        "sorry",
+        "specified",
+        "specify",
+        "specifying",
+        "still",
+        "sub",
+        "such",
+        "sup",
+        "sure",
+        "t",
+        "t's",
+        "take",
+        "taken",
+        "tell",
+        "tends",
+        "th",
+        "than",
+        "thank",
+        "thanks",
+        "thanx",
+        "that",
+        "that's",
+        "thats",
+        "the",
+        "their",
+        "theirs",
+        "them",
+        "themselves",
+        "then",
+        "thence",
+        "there",
+        "there's",
+        "thereafter",
+        "thereby",
+        "therefore",
+        "therein",
+        "theres",
+        "thereupon",
+        "these",
+        "they",
+        "they'd",
+        "they'll",
+        "they're",
+        "they've",
+        "think",
+        "third",
+        "this",
+        "thorough",
+        "thoroughly",
+        "those",
+        "though",
+        "three",
+        "through",
+        "throughout",
+        "thru",
+        "thus",
+        "to",
+        "together",
+        "too",
+        "took",
+        "toward",
+        "towards",
+        "tried",
+        "tries",
+        "truly",
+        "try",
+        "trying",
+        "twice",
+        "two",
+        "u",
+        "un",
+        "under",
+        "unfortunately",
+        "unless",
+        "unlikely",
+        "until",
+        "unto",
+        "up",
+        "upon",
+        "us",
+        "use",
+        "used",
+        "useful",
+        "uses",
+        "using",
+        "usually",
+        "uucp",
+        "v",
+        "value",
+        "various",
+        "very",
+        "via",
+        "viz",
+        "vs",
+        "w",
+        "want",
+        "wants",
+        "was",
+        "wasn't",
+        "way",
+        "we",
+        "we'd",
+        "we'll",
+        "we're",
+        "we've",
+        "welcome",
+        "well",
+        "went",
+        "were",
+        "weren't",
+        "what",
+        "what's",
+        "whatever",
+        "when",
+        "whence",
+        "whenever",
+        "where",
+        "where's",
+        "whereafter",
+        "whereas",
+        "whereby",
+        "wherein",
+        "whereupon",
+        "wherever",
+        "whether",
+        "which",
+        "while",
+        "whither",
+        "who",
+        "who's",
+        "whoever",
+        "whole",
+        "whom",
+        "whose",
+        "why",
+        "will",
+        "willing",
+        "wish",
+        "with",
+        "within",
+        "without",
+        "won't",
+        "wonder",
+        "would",
+        "wouldn't",
+        "x",
+        "y",
+        "yes",
+        "yet",
+        "you",
+        "you'd",
+        "you'll",
+        "you're",
+        "you've",
+        "your",
+        "yours",
+        "yourself",
+        "yourselves",
+        "z",
+        "zero"
+    ]
+}
diff --git a/nautilus_nlp/_utils/__init__.py b/nlpretext/_utils/__init__.py
similarity index 100%
rename from nautilus_nlp/_utils/__init__.py
rename to nlpretext/_utils/__init__.py
diff --git a/nautilus_nlp/_utils/file_loader.py b/nlpretext/_utils/file_loader.py
similarity index 100%
rename from nautilus_nlp/_utils/file_loader.py
rename to nlpretext/_utils/file_loader.py
diff --git a/nautilus_nlp/_utils/phone_number.py b/nlpretext/_utils/phone_number.py
similarity index 98%
rename from nautilus_nlp/_utils/phone_number.py
rename to nlpretext/_utils/phone_number.py
index 1c364d5..b574a7f 100644
--- a/nautilus_nlp/_utils/phone_number.py
+++ b/nlpretext/_utils/phone_number.py
@@ -17,7 +17,7 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 from typing import Optional
 import phonenumbers as _phonenumbers
-from nautilus_nlp._config.config import SUPPORTED_COUNTRY, FORMAT_NUMBERS
+from nlpretext._config.config import SUPPORTED_COUNTRY, FORMAT_NUMBERS
 
 
 def find_phone_numbers(string: str, region_code: Optional[str] = None) -> str:
diff --git a/nautilus_nlp/_utils/stopwords.py b/nlpretext/_utils/stopwords.py
similarity index 88%
rename from nautilus_nlp/_utils/stopwords.py
rename to nlpretext/_utils/stopwords.py
index bda452c..0897313 100644
--- a/nautilus_nlp/_utils/stopwords.py
+++ b/nlpretext/_utils/stopwords.py
@@ -20,18 +20,11 @@
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
-import json
 from stop_words import LANGUAGE_MAPPING as _LANGUAGE_MAPPING
 from stop_words import get_stop_words as _get_stop_words
-from nautilus_nlp._config.config import STOPWORDS_JSON_FILEPATH
-from nautilus_nlp._utils.file_loader import documents_loader
+from nlpretext._config.stopwords import STOPWORDS
 
 
-def _load_stopwords_from_json(filepath=STOPWORDS_JSON_FILEPATH):
-    stopwords = documents_loader(filepath)
-    stopwords = json.loads(stopwords)
-    return stopwords
-
 
 def get_stopwords(lang: str = "en") -> list:
     """
@@ -60,7 +53,7 @@ def get_stopwords(lang: str = "en") -> list:
     """
     if isinstance(lang, str) and len(lang) == 2:
         lang = lang.lower()
-        custom_stopwords = _load_stopwords_from_json(STOPWORDS_JSON_FILEPATH)
+        custom_stopwords = STOPWORDS
         stopwords = []
 
         supported_lang_lib = list(_LANGUAGE_MAPPING.keys())
diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index 452b40e..b6cab3a 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -20,7 +20,7 @@
 import os
 
 import numpy as np
-from nautilus_nlp._utils.file_loader import (detect_encoding, documents_loader)
+from nlpretext._utils.file_loader import (detect_encoding, documents_loader)
 
 TESTDOC_LATIN1 = "J'aime les frites bien grasse étalon châpeau!"
 TESTDOC_UTF8 = "Un deuxième exemple de texte en utf-8 cette fois!"
diff --git a/tests/test_phone_number.py b/tests/test_phone_number.py
index d95d496..7a471b5 100644
--- a/tests/test_phone_number.py
+++ b/tests/test_phone_number.py
@@ -15,7 +15,7 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-import nautilus_nlp._utils.phone_number as phone
+import nlpretext._utils.phone_number as phone
 
 def test_extract_phone_number():
     input_str = '(541) 754-3010 is a US. Phone'
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 49edc28..5212015 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -40,8 +40,8 @@
 )
 from nlpretext.preprocessor import Preprocessor
 
-import nautilus_nlp._utils.phone_number as phone
-from nautilus_nlp._utils.stopwords import get_stopwords
+import nlpretext._utils.phone_number as phone
+from nlpretext._utils.stopwords import get_stopwords
 
 
 @pytest.mark.parametrize("text, expected_result",

From 7d6b51d8eef76a1c3dbfb205f22813ac8fd2626a Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 15:58:34 +0100
Subject: [PATCH 463/496] update readme with slogan

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7af4286..bd7baa4 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,8 @@
 NLPretext
 ==============================
-<font size="4"> **No more pretext for dirty text** :pencil: </font>
+
+### **No more pretext for dirty text** :pencil:
+
 
 :tired_face: *Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web?* 
 

From 8519f356103093150b9a4105ed5111fd4791107e Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 16:01:02 +0100
Subject: [PATCH 464/496] update readme layout

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index bd7baa4..aaf9be5 100644
--- a/README.md
+++ b/README.md
@@ -4,14 +4,16 @@ NLPretext
 ### **No more pretext for dirty text** :pencil:
 
 
-:tired_face: *Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web?* 
+> *Working on an NLP project and tired of always looking for the same silly preprocessing functions on the web?*  :tired_face: 
+
+> *Need to efficiently extract email adresses from a document? Hashtags from tweets? Remove accents from a French post?* :disappointed_relieved:
 
-:disappointed_relieved: *Need to efficiently extract email adresses from a document? Hashtags from tweets? Remove accents from a French post?*
 
 **NLPretext got you covered!** :rocket:
 
 NLPretext packages in a **unique** library all the text **preprocessing** functions you need to **ease** your NLP project. 
 
+
 :mag: Quickly explore below our preprocessing pipelines and individual functions referential.
 
 * [Default preprocessing pipeline](#default_pipeline)

From ec530386b0c244ffc706d13f521d83e200f7128e Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 16:02:13 +0100
Subject: [PATCH 465/496] update link readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index aaf9be5..db29773 100644
--- a/README.md
+++ b/README.md
@@ -88,7 +88,7 @@ print(text)
 # "dinner life recommend"
 ```
 
-Take a look at all the functions that are available [here](https://github.com/artefactory/nautilus-nlp/tree/master/nlpretext) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
+Take a look at all the functions that are available [here](https://github.com/artefactory/NLPretext/tree/feature/readme/nlpretext) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
 
 
 # Individual Functions

From dceebb60072914bf043a72cb88ce724562225902 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 16:50:08 +0100
Subject: [PATCH 466/496] update structure

---
 .github/workflows/ci_actions.yml |   2 +-
 Makefile                         | 144 -------------------------------
 README.md                        |  14 +--
 pylintrc                         |   2 +-
 references/nautilus_nlp_logo.png | Bin 24573 -> 0 bytes
 5 files changed, 10 insertions(+), 152 deletions(-)
 delete mode 100644 Makefile
 delete mode 100644 references/nautilus_nlp_logo.png

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci_actions.yml
index 71c2aef..d5031d8 100644
--- a/.github/workflows/ci_actions.yml
+++ b/.github/workflows/ci_actions.yml
@@ -42,7 +42,7 @@ jobs:
 
       - name: Run pylint
         run: |
-          pylint nlpretext tests --max-module-lines 15000
+          pylint nlpretext tests
 
       - name: Run pytest
         run: |
diff --git a/Makefile b/Makefile
deleted file mode 100644
index 8fa6457..0000000
--- a/Makefile
+++ /dev/null
@@ -1,144 +0,0 @@
-.PHONY: clean data lint requirements sync_data_to_s3 sync_data_from_s3
-
-#################################################################################
-# GLOBALS                                                                       #
-#################################################################################
-
-PROJECT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
-BUCKET = [OPTIONAL] your-bucket-for-syncing-data (do not include 's3://')
-PROFILE = default
-PROJECT_NAME = nlpretext
-PYTHON_INTERPRETER = python3
-
-ifeq (,$(shell which conda))
-HAS_CONDA=False
-else
-HAS_CONDA=True
-endif
-
-#################################################################################
-# COMMANDS                                                                      #
-#################################################################################
-
-## Install Python Dependencies
-requirements: test_environment
-	$(PYTHON_INTERPRETER) -m pip install -U pip setuptools wheel
-	$(PYTHON_INTERPRETER) -m pip install -r requirements.txt
-
-## Make Dataset
-data: requirements
-	$(PYTHON_INTERPRETER) src/data/make_dataset.py
-
-## Delete all compiled Python files
-clean:
-	find . -type f -name "*.py[co]" -delete
-	find . -type d -name "__pycache__" -delete
-
-## Lint using flake8
-lint:
-	black nlpretext
-
-## Upload Data to S3
-sync_data_to_s3:
-ifeq (default,$(PROFILE))
-	aws s3 sync data/ s3://$(BUCKET)/data/
-else
-	aws s3 sync data/ s3://$(BUCKET)/data/ --profile $(PROFILE)
-endif
-
-## Download Data from S3
-sync_data_from_s3:
-ifeq (default,$(PROFILE))
-	aws s3 sync s3://$(BUCKET)/data/ data/
-else
-	aws s3 sync s3://$(BUCKET)/data/ data/ --profile $(PROFILE)
-endif
-
-## Set up python interpreter environment
-create_environment:
-ifeq (True,$(HAS_CONDA))
-		@echo ">>> Detected conda, creating conda environment."
-ifeq (3,$(findstring 3,$(PYTHON_INTERPRETER)))
-	conda create --name $(PROJECT_NAME) python=3
-else
-	conda create --name $(PROJECT_NAME) python=2.7
-endif
-		@echo ">>> New conda env created. Activate with:\nsource activate $(PROJECT_NAME)"
-else
-	$(PYTHON_INTERPRETER) -m pip install -q virtualenv virtualenvwrapper
-	@echo ">>> Installing virtualenvwrapper if not already intalled.\nMake sure the following lines are in shell startup file\n\
-	export WORKON_HOME=$$HOME/.virtualenvs\nexport PROJECT_HOME=$$HOME/Devel\nsource /usr/local/bin/virtualenvwrapper.sh\n"
-	@bash -c "source `which virtualenvwrapper.sh`;mkvirtualenv $(PROJECT_NAME) --python=$(PYTHON_INTERPRETER)"
-	@echo ">>> New virtualenv created. Activate with:\nworkon $(PROJECT_NAME)"
-endif
-
-## Test python environment is setup correctly
-test_environment:
-	$(PYTHON_INTERPRETER) test_environment.py
-
-#################################################################################
-# PROJECT RULES                                                                 #
-#################################################################################
-
-
-
-#################################################################################
-# Self Documenting Commands                                                     #
-#################################################################################
-
-.DEFAULT_GOAL := help
-
-# Inspired by <http://marmelab.com/blog/2016/02/29/auto-documented-makefile.html>
-# sed script explained:
-# /^##/:
-# 	* save line in hold space
-# 	* purge line
-# 	* Loop:
-# 		* append newline + line to hold space
-# 		* go to next line
-# 		* if line starts with doc comment, strip comment character off and loop
-# 	* remove target prerequisites
-# 	* append hold space (+ newline) to line
-# 	* replace newline plus comments by `---`
-# 	* print line
-# Separate expressions are necessary because labels cannot be delimited by
-# semicolon; see <http://stackoverflow.com/a/11799865/1968>
-.PHONY: help
-help:
-	@echo "$$(tput bold)Available rules:$$(tput sgr0)"
-	@echo
-	@sed -n -e "/^## / { \
-		h; \
-		s/.*//; \
-		:doc" \
-		-e "H; \
-		n; \
-		s/^## //; \
-		t doc" \
-		-e "s/:.*//; \
-		G; \
-		s/\\n## /---/; \
-		s/\\n/ /g; \
-		p; \
-	}" ${MAKEFILE_LIST} \
-	| LC_ALL='C' sort --ignore-case \
-	| awk -F '---' \
-		-v ncol=$$(tput cols) \
-		-v indent=19 \
-		-v col_on="$$(tput setaf 6)" \
-		-v col_off="$$(tput sgr0)" \
-	'{ \
-		printf "%s%*s%s ", col_on, -indent, $$1, col_off; \
-		n = split($$2, words, " "); \
-		line_length = ncol - indent; \
-		for (i = 1; i <= n; i++) { \
-			line_length -= length(words[i]) + 1; \
-			if (line_length <= 0) { \
-				line_length = ncol - indent - length(words[i]) - 1; \
-				printf "\n%*s ", -indent, " "; \
-			} \
-			printf "%s ", words[i]; \
-		} \
-		printf "\n"; \
-	}' \
-	| more $(shell test $(shell uname) = Darwin && echo '--no-init --raw-control-chars')
diff --git a/README.md b/README.md
index db29773..32339b2 100644
--- a/README.md
+++ b/README.md
@@ -148,11 +148,11 @@ You can now open the file index.html located in the build folder.
 ------------
 
     ├── LICENSE
-    ├── Makefile            <- Makefile with commands like `make data` or `make train`
+    ├── VERSION
+    ├── CONTRIBUTING.md     <- Contribution guidelines
     ├── README.md           <- The top-level README for developers using this project.
-    ├── config              <- Where the configuration and constants live
+    ├── .github/workflows   <- Where the CI lives
     ├── datasets/external   <- Bash scripts to download external datasets
-    ├── docker              <- Where to build a docker image using this lib
     ├── docs                <- Sphinx HTML documentation
     │   ├── _build
     │   │   └── html
@@ -162,9 +162,11 @@ You can now open the file index.html located in the build folder.
     │   ├── augmentation    <- Text augmentation script
     │   ├── basic           <- Basic text preprocessing 
     │   ├── social          <- Social text preprocessing
-    │   └── token           <- Token preprocessing
-    ├── utils               <- Where preprocessing utils scripts lives
+    │   ├── token           <- Token text preprocessing
+    │   ├── _config         <- Where the configuration and constants live
+    │   └── _utils          <- Where preprocessing utils scripts lives
     ├── tests               <- Where the tests lives
     ├── setup.py            <- makes project pip installable (pip install -e .) so the package can be imported
     ├── requirements.txt    <- The requirements file for reproducing the analysis environment, e.g.
-                              generated with `pip freeze > requirements.txt`    
+                              generated with `pip freeze > requirements.txt`
+    └── pylintrc            <- The linting configuration file
diff --git a/pylintrc b/pylintrc
index 54d4c85..590c124 100644
--- a/pylintrc
+++ b/pylintrc
@@ -169,7 +169,7 @@ single-line-if-stmt=no
 no-space-check=trailing-comma,dict-separator
 
 # Maximum number of lines in a module
-max-module-lines=1000
+max-module-lines=15000
 
 # String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
 # tab).
diff --git a/references/nautilus_nlp_logo.png b/references/nautilus_nlp_logo.png
deleted file mode 100644
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From ee446e0ba93467b5d1503f7b8a707f32c3d44de2 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 16:51:11 +0100
Subject: [PATCH 467/496] fix typo readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 32339b2..ffd52f4 100644
--- a/README.md
+++ b/README.md
@@ -168,5 +168,5 @@ You can now open the file index.html located in the build folder.
     ├── tests               <- Where the tests lives
     ├── setup.py            <- makes project pip installable (pip install -e .) so the package can be imported
     ├── requirements.txt    <- The requirements file for reproducing the analysis environment, e.g.
-                              generated with `pip freeze > requirements.txt`
+    │                          generated with `pip freeze > requirements.txt`
     └── pylintrc            <- The linting configuration file

From e504f96a41abfd0e2c74d7813c09201721e32a35 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 16:52:12 +0100
Subject: [PATCH 468/496] update project organization readme

---
 README.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/README.md b/README.md
index ffd52f4..9cde61d 100644
--- a/README.md
+++ b/README.md
@@ -154,9 +154,6 @@ You can now open the file index.html located in the build folder.
     ├── .github/workflows   <- Where the CI lives
     ├── datasets/external   <- Bash scripts to download external datasets
     ├── docs                <- Sphinx HTML documentation
-    │   ├── _build
-    │   │   └── html
-    │   ├── source
     ├── nlpretext           <- Main Package. This is where the code lives
     │   ├── preprocessor.py <- Main preprocessing script
     │   ├── augmentation    <- Text augmentation script

From ed3ee85bef53eaaab128a9ef1619632659fb1cf0 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Fri, 12 Feb 2021 17:59:17 +0100
Subject: [PATCH 469/496] update readme

---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 9cde61d..ab94f9b 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ Another possibility is to create your custom pipeline if you know exactly what f
 
 ```python
 from nlpretext import Preprocessor
-from nlpretext.basic.preprocess import (normalize_whitespace, remove_punct, remove_eol_characters,
+from nlpretext.classic.preprocess import (normalize_whitespace, remove_punct, remove_eol_characters,
 remove_stopwords, lower_text)
 from nlpretext.social.preprocess import remove_mentions, remove_hashtag, remove_emoji
 text = "I just got the best dinner in my life @latourdargent !!! I  recommend 😀 #food #paris \n"
@@ -88,7 +88,7 @@ print(text)
 # "dinner life recommend"
 ```
 
-Take a look at all the functions that are available [here](https://github.com/artefactory/NLPretext/tree/feature/readme/nlpretext) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
+Take a look at all the functions that are available [here](https://github.com/artefactory/NLPretext/tree/master/nlpretext) in the ```preprocess.py``` scripts in the different folders: classic, social, token.
 
 
 # Individual Functions
@@ -96,7 +96,7 @@ Take a look at all the functions that are available [here](https://github.com/ar
 ## Replacing emails <a name="replace_emails"></a>
 
 ```python
-from nlpretext.basic.preprocess import replace_emails
+from nlpretext.classic.preprocess import replace_emails
 example = "I have forwarded this email to obama@whitehouse.gov"
 example = replace_emails(example, replace_with="*EMAIL*")
 print(example)
@@ -106,7 +106,7 @@ print(example)
 ## Replacing phone numbers <a name="replace_phone_numbers"></a>
 
 ```python
-from nlpretext.basic.preprocess import replace_phone_numbers
+from nlpretext.classic.preprocess import replace_phone_numbers
 example = "My phone number is 0606060606"
 example = replace_phone_numbers(example, country_to_detect=["FR"], replace_with="*PHONE*")
 print(example)
@@ -157,7 +157,7 @@ You can now open the file index.html located in the build folder.
     ├── nlpretext           <- Main Package. This is where the code lives
     │   ├── preprocessor.py <- Main preprocessing script
     │   ├── augmentation    <- Text augmentation script
-    │   ├── basic           <- Basic text preprocessing 
+    │   ├── classic         <- Classic text preprocessing 
     │   ├── social          <- Social text preprocessing
     │   ├── token           <- Token text preprocessing
     │   ├── _config         <- Where the configuration and constants live

From 2ced84d40e28c76d0df58cceba4c4b8656535446 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 16:16:37 +0100
Subject: [PATCH 470/496] rename ci_actions.yml > ci.yml

---
 .github/workflows/{ci_actions.yml => ci.yml} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename .github/workflows/{ci_actions.yml => ci.yml} (100%)

diff --git a/.github/workflows/ci_actions.yml b/.github/workflows/ci.yml
similarity index 100%
rename from .github/workflows/ci_actions.yml
rename to .github/workflows/ci.yml

From a1a7b4e7609c6ad1a618036a0fdd2adedd697c02 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 16:19:17 +0100
Subject: [PATCH 471/496] update setup.py

---
 setup.py | 29 ++++++++++-------------------
 1 file changed, 10 insertions(+), 19 deletions(-)

diff --git a/setup.py b/setup.py
index 8b6eaf3..4e17403 100644
--- a/setup.py
+++ b/setup.py
@@ -15,38 +15,29 @@
 # You should have received a copy of the GNU Lesser General Public License
 # along with this program; if not, write to the Free Software Foundation,
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-from setuptools import find_packages, setup
 import setuptools
-import setuptools.command.install
 from pathlib import Path
 
-class PostInstallCommand(setuptools.command.install.install):
-    """Post-installation command."""
-    def run(self):
-        setuptools.command.install.install.run(self)
-        try:
-            import spacy
-            spacy.cli.validate()
-        except ModuleNotFoundError:
-            pass
-
 
 with open(Path(__file__).resolve().parent.joinpath('VERSION'), 'r') as fh:
     version = fh.read()
-setup(
+
+with open("requirements.txt", "r") as fr:
+    requirements = [req for req in fr.read().splitlines() if not req.startswith("#")]
+
+setuptools.setup(
     name='nlpretext',
-    packages=find_packages(),
+    packages=setuptools.find_packages(),
+    scripts=["VERSION", "requirements.txt"],
     version=version,
     description='All the goto functions you need to handle NLP use-cases',
     author='Artefact',
     license='MIT',
-    url='https://github.com/artefactory/nautilus-nlp',
+    url='https://github.com/artefactory/NLPretext',
+    install_requires=requirements,
     classifiers=[
-        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
         'License :: OSI Approved :: MIT License',
         'Operating System :: OS Independent',
     ],
-    cmdclass={
-        'install': PostInstallCommand,
-    },
 )

From bcb3e61c4d13d7f4d6529b787273280f88a6fe1e Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 16:35:59 +0100
Subject: [PATCH 472/496] adding requirements_dev

---
 .github/workflows/ci.yml |  2 +-
 requirements.txt         | 15 ---------------
 requirements_dev.txt     |  8 ++++++++
 3 files changed, 9 insertions(+), 16 deletions(-)
 create mode 100644 requirements_dev.txt

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d5031d8..ab7d269 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -38,7 +38,7 @@ jobs:
       - name: Install requirements
         run: |
           python -m pip install --upgrade pip
-          pip install -r requirements.txt
+          pip install -r requirements_dev.txt
 
       - name: Run pylint
         run: |
diff --git a/requirements.txt b/requirements.txt
index f2cb8e3..e06f872 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,15 +1,3 @@
-# local package
-#-e .
-
-# external requirements
-coverage
-pillow
-pytest==6.1.1
-pytest-cov==2.10.1
-python-dotenv>=0.5.1
-Sphinx
-sphinx_rtd_theme
-
 #library requirements
 chardet==3.0.4
 emoji>=0.5.2
@@ -24,8 +12,5 @@ pylint==2.4.4
 regex==2019.8.19
 sacremoses==0.0.13
 scikit_learn==0.23.2
-setuptools==40.8.0
 spacy==2.3.4
-https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
-https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz
 stop_words==2018.7.23
\ No newline at end of file
diff --git a/requirements_dev.txt b/requirements_dev.txt
new file mode 100644
index 0000000..b16d405
--- /dev/null
+++ b/requirements_dev.txt
@@ -0,0 +1,8 @@
+coverage==5.3
+pytest==6.1.1
+pytest-cov==2.10.1
+python-dotenv>=0.5.1
+Sphinx==3.2.1
+sphinx_rtd_theme==0.5.0
+setuptools==40.8.0
+-r requirements.txt
\ No newline at end of file

From e072f011eacb9f0ff54d24f9d5af41898e16b3e7 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 16:37:00 +0100
Subject: [PATCH 473/496] adding CD to publish package to pypi

---
 .github/workflows/cd.yml | 52 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 .github/workflows/cd.yml

diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml
new file mode 100644
index 0000000..987e708
--- /dev/null
+++ b/.github/workflows/cd.yml
@@ -0,0 +1,52 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+name: CD
+
+on: [push, pull_request]
+
+jobs:
+  CI:
+    name: Launching CD
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7]
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install requirements
+        run: |
+          python -m pip install --upgrade pip
+          pip install setuptools wheel
+
+      - name: Building package distribution
+        run: |
+          python setup.py sdist bdist_wheel
+          ls
+
+      - name: Publish new package version on PyPI
+        uses: pypa/gh-action-pypi-publish@master
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

From f86f36afed5f0f86bbb8c39f84009a8d5344f4d2 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 16:42:17 +0100
Subject: [PATCH 474/496] adding spacy model download in CI

---
 .github/workflows/ci.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ab7d269..07567dd 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -39,6 +39,8 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements_dev.txt
+          pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
+          pip install https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz
 
       - name: Run pylint
         run: |

From 040a62e24bd0af3067161e5835aad617c4af69e8 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 16:51:37 +0100
Subject: [PATCH 475/496] adding pypi publication only when master is merged

---
 .github/workflows/cd.yml | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml
index 987e708..acbdcd1 100644
--- a/.github/workflows/cd.yml
+++ b/.github/workflows/cd.yml
@@ -17,10 +17,14 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 name: CD
 
-on: [push, pull_request]
+on:
+  pull_request:
+    branches:
+      - master
+    types: [closed]
 
 jobs:
-  CI:
+  CD:
     name: Launching CD
     runs-on: ubuntu-latest
     strategy:
@@ -43,7 +47,6 @@ jobs:
       - name: Building package distribution
         run: |
           python setup.py sdist bdist_wheel
-          ls
 
       - name: Publish new package version on PyPI
         uses: pypa/gh-action-pypi-publish@master

From 04ae61ee6ddbabc0d18e4fa1a472b333a4a434e4 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 16:51:59 +0100
Subject: [PATCH 476/496] renaming classic > basic

---
 nlpretext/{classic => basic}/preprocess.py | 0
 nlpretext/preprocessor.py                  | 2 +-
 tests/test_preprocessor.py                 | 4 ++--
 3 files changed, 3 insertions(+), 3 deletions(-)
 rename nlpretext/{classic => basic}/preprocess.py (100%)

diff --git a/nlpretext/classic/preprocess.py b/nlpretext/basic/preprocess.py
similarity index 100%
rename from nlpretext/classic/preprocess.py
rename to nlpretext/basic/preprocess.py
diff --git a/nlpretext/preprocessor.py b/nlpretext/preprocessor.py
index c453aad..e05f4be 100644
--- a/nlpretext/preprocessor.py
+++ b/nlpretext/preprocessor.py
@@ -5,7 +5,7 @@
 
 from nlpretext.social.preprocess import (
     remove_html_tags, remove_mentions, remove_emoji, remove_hashtag)
-from nlpretext.classic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
+from nlpretext.basic.preprocess import normalize_whitespace, remove_eol_characters, fix_bad_unicode
 
 
 class Preprocessor():
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 5212015..66bc04d 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -17,14 +17,14 @@
 # Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 import pytest
 import numpy as np
-from nlpretext.classic.preprocess import (
+from nlpretext.basic.preprocess import (
     normalize_whitespace, remove_eol_characters, fix_bad_unicode,
     unpack_english_contractions, replace_urls, replace_emails,
     replace_phone_numbers, replace_numbers, replace_currency_symbols,
     remove_punct, remove_accents, remove_multiple_spaces_and_strip_text,
     filter_non_latin_characters
 )
-from nlpretext.classic.preprocess import (
+from nlpretext.basic.preprocess import (
     remove_stopwords as remove_stopwords_text
 )
 from nlpretext.social.preprocess import (

From ec1ad6392fe9b1c9a6e7b5c3d17a2d01013a03a9 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Mon, 15 Feb 2021 18:32:18 +0100
Subject: [PATCH 477/496] nlpretext/social/preprocess.py

---
 nlpretext/social/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nlpretext/social/preprocess.py b/nlpretext/social/preprocess.py
index 1f30891..ebbdb8a 100644
--- a/nlpretext/social/preprocess.py
+++ b/nlpretext/social/preprocess.py
@@ -21,7 +21,7 @@
 
 import emoji as _emoji
 from nlpretext._config import constants
-from nlpretext.classic.preprocess import normalize_whitespace
+from nlpretext.basic.preprocess import normalize_whitespace
 
 
 def remove_mentions(text) -> str:

From da35f1339d5580d8ed8d81097f7f53125d767019 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 09:46:33 +0100
Subject: [PATCH 478/496] removing CD

---
 .github/workflows/cd.yml | 55 ----------------------------------------
 1 file changed, 55 deletions(-)
 delete mode 100644 .github/workflows/cd.yml

diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml
deleted file mode 100644
index acbdcd1..0000000
--- a/.github/workflows/cd.yml
+++ /dev/null
@@ -1,55 +0,0 @@
-# GNU Lesser General Public License v3.0 only
-# Copyright (C) 2020 Artefact
-# licence-information@artefact.com
-#
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-name: CD
-
-on:
-  pull_request:
-    branches:
-      - master
-    types: [closed]
-
-jobs:
-  CD:
-    name: Launching CD
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: [3.7]
-
-    steps:
-      - uses: actions/checkout@v2
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install requirements
-        run: |
-          python -m pip install --upgrade pip
-          pip install setuptools wheel
-
-      - name: Building package distribution
-        run: |
-          python setup.py sdist bdist_wheel
-
-      - name: Publish new package version on PyPI
-        uses: pypa/gh-action-pypi-publish@master
-        with:
-          user: __token__
-          password: ${{ secrets.PYPI_API_TOKEN }}

From 0772036c6ae6b6520a56959a8e9a05de29419353 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 09:52:51 +0100
Subject: [PATCH 479/496] update link readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 9cde61d..4a31283 100644
--- a/README.md
+++ b/README.md
@@ -88,7 +88,7 @@ print(text)
 # "dinner life recommend"
 ```
 
-Take a look at all the functions that are available [here](https://github.com/artefactory/NLPretext/tree/feature/readme/nlpretext) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
+Take a look at all the functions that are available [here](https://github.com/artefactory/NLPretext/tree/master) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
 
 
 # Individual Functions

From b9a7006c535efe37bacbbe29e476ebe61e42ff13 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 10:03:42 +0100
Subject: [PATCH 480/496] update remove_stopwords token with arg lang instead
 of list

---
 nlpretext/token/preprocess.py | 13 +++++++++++--
 tests/test_preprocessor.py    |  9 ++++-----
 2 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/nlpretext/token/preprocess.py b/nlpretext/token/preprocess.py
index 989bd9a..058dd8e 100644
--- a/nlpretext/token/preprocess.py
+++ b/nlpretext/token/preprocess.py
@@ -20,16 +20,22 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import re
+from nlpretext._utils.stopwords import get_stopwords
 
 
-def remove_stopwords(tokens, stopwords: list) -> str:
+def remove_stopwords(tokens: list, lang: str, custom_stopwords: list = None)  -> str:
     """
     Remove stopwords from a text.
     eg. 'I like when you move your body !' -> 'I move body !'
 
     Parameters
     ----------
-    stopwords : list of stopwords to remove
+    tokens: list(str)
+        list of tokens
+    lang: str
+        language iso code (e.g : "en")
+    custom_stopwords : list(str)|None
+        list of custom stopwords to add. None by default
 
     Returns
     -------
@@ -41,6 +47,9 @@ def remove_stopwords(tokens, stopwords: list) -> str:
     ValueError
         When inputs is not a list
     """
+    stopwords = get_stopwords(lang)
+    if custom_stopwords:
+        stopwords += custom_stopwords
     tokens = [word for word in tokens if word not in stopwords]
     return tokens
 
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 66bc04d..a11ba16 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -188,14 +188,13 @@ def test_get_stopwords():
 
 
 @pytest.mark.parametrize(
-    "input_tokens, expected_output",
+    "input_tokens, lang, expected_output",
     [
-        (['I', 'like', 'when', 'you', 'move', 'your', 'body', '!'], ['I', 'move', 'body', '!'])
+        (['I', 'like', 'when', 'you', 'move', 'your', 'body', '!'], "en", ['I', 'move', 'body', '!'])
     ],
 )
-def test_remove_stopwords_tokens(input_tokens, expected_output):
-    stopwords = get_stopwords('en')
-    result = remove_stopwords_token(input_tokens, stopwords)
+def test_remove_stopwords_tokens(input_tokens, lang, expected_output):
+    result = remove_stopwords_token(input_tokens, lang)
     np.testing.assert_array_equal(result, expected_output)
 
 

From adf57391bf0db7ddad2733c299d1d6b74f5d6d59 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 10:12:44 +0100
Subject: [PATCH 481/496] update version 1.0.0

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index f514a2f..afaf360 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-0.9.1
\ No newline at end of file
+1.0.0
\ No newline at end of file

From 09db3b48876d18e3ff2c5af81f88a89ed1e5aeec Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 10:21:16 +0100
Subject: [PATCH 482/496] add mosestekonizer version

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index e06f872..a4774f8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,7 @@ chardet==3.0.4
 emoji>=0.5.2
 flashtext==2.7
 ftfy<5.0.0,>=4.2.0
-mosestokenizer
+mosestokenizer==1.1.0
 nlpaug==1.0.1
 nltk>=3.4.5
 numpy>1.15.4

From 633af6149419a04814c72f8695c4774b1beba0b3 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 10:28:07 +0100
Subject: [PATCH 483/496] typo README

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4a31283..b26156d 100644
--- a/README.md
+++ b/README.md
@@ -88,7 +88,7 @@ print(text)
 # "dinner life recommend"
 ```
 
-Take a look at all the functions that are available [here](https://github.com/artefactory/NLPretext/tree/master) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
+Take a look at all the functions that are available [here](https://github.com/artefactory/NLPretext/tree/master/nlpretext) in the ```preprocess.py``` scripts in the different folders: basic, social, token.
 
 
 # Individual Functions

From 2a95b1dd1c3bbc91296fff075bfd6f14103f1d95 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 10:57:56 +0100
Subject: [PATCH 484/496] update project documentation

---
 docs/conf.py                        |  21 ++---
 docs/index.rst                      |  52 ++++++------
 docs/modules.rst                    |   6 +-
 docs/nautilus_nlp.config.rst        |  22 ------
 docs/nautilus_nlp.data.rst          |  22 ------
 docs/nautilus_nlp.features.rst      |  22 ------
 docs/nautilus_nlp.models.rst        |  70 -----------------
 docs/nautilus_nlp.rst               |  42 ----------
 docs/nautilus_nlp.utils.rst         | 118 ----------------------------
 docs/nautilus_nlp.visualization.rst |  22 ------
 docs/nlpretext.augmentation.rst     |   7 ++
 docs/nlpretext.basic.rst            |   7 ++
 docs/nlpretext.rst                  |   8 ++
 docs/nlpretext.social.rst           |   7 ++
 docs/nlpretext.token.rst            |   7 ++
 15 files changed, 74 insertions(+), 359 deletions(-)
 delete mode 100644 docs/nautilus_nlp.config.rst
 delete mode 100644 docs/nautilus_nlp.data.rst
 delete mode 100644 docs/nautilus_nlp.features.rst
 delete mode 100644 docs/nautilus_nlp.models.rst
 delete mode 100644 docs/nautilus_nlp.rst
 delete mode 100644 docs/nautilus_nlp.utils.rst
 delete mode 100644 docs/nautilus_nlp.visualization.rst
 create mode 100644 docs/nlpretext.augmentation.rst
 create mode 100644 docs/nlpretext.basic.rst
 create mode 100644 docs/nlpretext.rst
 create mode 100644 docs/nlpretext.social.rst
 create mode 100644 docs/nlpretext.token.rst

diff --git a/docs/conf.py b/docs/conf.py
index fffe634..a6769f8 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -31,17 +31,20 @@
 #
 import os
 import sys
+from pathlib import Path
+
 sys.path.insert(0, os.path.abspath('../'))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'Nautilus_nlp'
+project = 'NLPretext'
 copyright = '2020, Artefact'
 author = 'Artefact'
 
-# The short X.Y version
-version = '0.1.0'
+with open(Path(__file__).resolve().parent.joinpath('../VERSION'), 'r') as fh:
+    version = fh.read()
+
 # The full version, including alpha/beta/rc tags
 release = ''
 
@@ -129,7 +132,7 @@
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'Nautilus_nlpdoc'
+htmlhelp_basename = 'NLPretextdoc'
 
 
 # -- Options for LaTeX output ------------------------------------------------
@@ -156,8 +159,8 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'Nautilus_nlp.tex', 'Nautilus\\_nlp Documentation',
-     'Robin Doumerc', 'manual'),
+    (master_doc, 'NLPretext.tex', 'Nautilus\\_nlp Documentation',
+     'Artefact', 'manual'),
 ]
 
 
@@ -166,7 +169,7 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'nautilus_nlp', 'Nautilus_nlp Documentation',
+    (master_doc, 'NLPretext', 'NLPretext Documentation',
      [author], 1)
 ]
 
@@ -177,8 +180,8 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'Nautilus_nlp', 'Nautilus_nlp Documentation',
-     author, 'Nautilus_nlp', 'One line description of project.',
+    (master_doc, 'NLPretext', 'NLPretext Documentation',
+     author, 'NLPretext', 'One line description of project.',
      'Miscellaneous'),
 ]
 
diff --git a/docs/index.rst b/docs/index.rst
index 91dd094..eb204fd 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,63 +1,57 @@
 
 
-Welcome to Nautilus_nlp's documentation!
+Welcome to NLPretext's documentation!
 ========================================
 
-The Nautilus NLP library aimed to be a meta-library to be used to help you get started on handling your NLP use-case.
+The NLPretext library aimed to be a meta-library to be used to help you get started on handling your NLP use-case preprocessing.
 
-This library can help you with:
 
-    1. Cleaning text data
-    2. Normalizing your dataset
-    3. Training automatically multiclass, multilabel classifier
-    4. Help you discover topics and cluster your data
+# Installation
 
+Beware, this package has been tested on Python **3.6** & **3.7** & **3.8**, and will probably not be working under python **2.7** as **Python2.7** EOL is scheduled for December 2019. 
 
-# Feature Request
+To install this library you should first clone the repository:
 
-As an Artefact user, you might be working on a NLP use case, and wish to use Nautilus.
+pip install nlpretext
 
- However, if you think Nautilus is lacking features that can be useful not only to your use case but also others, feel free to to fill up an issue with the label "Feature-request".
+This library uses Spacy as tokenizer. Current models supported are `en_core_web_sm` and `fr_core_news_sm`. If not installed, run the following commands:
 
- We will try to put it in the roadmap and implement it as soon as possible.
+pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
 
-# Installation
+pip install https://github.com/explosion/spacy-models/releases/download/fr_core_news_sm-2.3.0/fr_core_news_sm-2.3.0.tar.gz
 
-Beware, this package has been tested on Python **3.6** & **3.7**, and will probably not be working under python **2.7** as **Python2.7** EOL is scheduled for December 2019. 
 
-To install this library you should first clone the repository:
 
-`git clone https://github.com/artefactory/nautilus_nlp/ && cd nautilus_nlp`
-
-**If you don't use the docker container, we strongly advise you to do these steps in a virtual environnement**
-
-First you need to install the required files:
+.. toctree::
+   :maxdepth: 2
+   :caption: Text Preprocessing Functions:
 
-`pip install -r requirements.txt`
+   modules
 
-then you can install it via pip:
+.. toctree::
+   :maxdepth: 2
+   :caption: Basic preprocessing:
 
-`pip install -e .`
+   nlpretext.basic
 
 
 .. toctree::
    :maxdepth: 2
-   :caption: Preprocessing and utility functions:
+   :caption: Social preprocessing:
 
-   modules
+   nlpretext.social
 
 .. toctree::
    :maxdepth: 2
-   :caption: Preprocessing and utility functions:
-
-   nautilus_nlp.utils
+   :caption: Token preprocessing:
 
+   nlpretext.token
 
 .. toctree::
    :maxdepth: 2
-   :caption: Machine learning:
+   :caption: Text Augmentation:
 
-   nautilus_nlp.models
+   nlpretext.augmentation
 
 Indices and tables
 ==================
diff --git a/docs/modules.rst b/docs/modules.rst
index 26ca8ee..f7251b8 100644
--- a/docs/modules.rst
+++ b/docs/modules.rst
@@ -1,7 +1,7 @@
-nautilus_nlp
-============
+nlpretext
+=========
 
 .. toctree::
    :maxdepth: 4
 
-   nautilus_nlp
+   nlpretext
diff --git a/docs/nautilus_nlp.config.rst b/docs/nautilus_nlp.config.rst
deleted file mode 100644
index 8864121..0000000
--- a/docs/nautilus_nlp.config.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-nautilus\_nlp.config package
-============================
-
-Submodules
-----------
-
-nautilus\_nlp.config.config module
-----------------------------------
-
-.. automodule:: nautilus_nlp.config.config
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: nautilus_nlp.config
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/docs/nautilus_nlp.data.rst b/docs/nautilus_nlp.data.rst
deleted file mode 100644
index 4b12cc2..0000000
--- a/docs/nautilus_nlp.data.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-nautilus\_nlp.data package
-==========================
-
-Submodules
-----------
-
-nautilus\_nlp.data.make\_dataset module
----------------------------------------
-
-.. automodule:: nautilus_nlp.data.make_dataset
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: nautilus_nlp.data
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/docs/nautilus_nlp.features.rst b/docs/nautilus_nlp.features.rst
deleted file mode 100644
index c4b764c..0000000
--- a/docs/nautilus_nlp.features.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-nautilus\_nlp.features package
-==============================
-
-Submodules
-----------
-
-nautilus\_nlp.features.build\_features module
----------------------------------------------
-
-.. automodule:: nautilus_nlp.features.build_features
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: nautilus_nlp.features
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/docs/nautilus_nlp.models.rst b/docs/nautilus_nlp.models.rst
deleted file mode 100644
index ee38f75..0000000
--- a/docs/nautilus_nlp.models.rst
+++ /dev/null
@@ -1,70 +0,0 @@
-nautilus\_nlp.models package
-============================
-
-Submodules
-----------
-
-nautilus\_nlp.models.Fasttext\_classifier module
-------------------------------------------------
-
-.. automodule:: nautilus_nlp.models.Fasttext_classifier
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.models.Fasttext\_embedding module
------------------------------------------------
-
-.. automodule:: nautilus_nlp.models.Fasttext_embedding
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.models.Language\_detector module
-----------------------------------------------
-
-.. automodule:: nautilus_nlp.models.Language_detector
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.models.Sentiment\_detector module
------------------------------------------------
-
-.. automodule:: nautilus_nlp.models.Sentiment_detector
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.models.Spacy\_model module
-----------------------------------------
-
-.. automodule:: nautilus_nlp.models.Spacy_model
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.models.sentiment module
--------------------------------------
-
-.. automodule:: nautilus_nlp.models.sentiment
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.models.sk\_vectorizer module
-------------------------------------------
-
-.. automodule:: nautilus_nlp.models.sk_vectorizer
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: nautilus_nlp.models
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/docs/nautilus_nlp.rst b/docs/nautilus_nlp.rst
deleted file mode 100644
index e240841..0000000
--- a/docs/nautilus_nlp.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-nautilus\_nlp package
-=====================
-
-Subpackages
------------
-
-.. toctree::
-
-    nautilus_nlp.config
-    nautilus_nlp.data
-    nautilus_nlp.features
-    nautilus_nlp.models
-    nautilus_nlp.utils
-    nautilus_nlp.visualization
-
-Submodules
-----------
-
-nautilus\_nlp.corpus module
----------------------------
-
-.. automodule:: nautilus_nlp.corpus
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.doc module
-------------------------
-
-.. automodule:: nautilus_nlp.doc
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: nautilus_nlp
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/docs/nautilus_nlp.utils.rst b/docs/nautilus_nlp.utils.rst
deleted file mode 100644
index 1b9e43a..0000000
--- a/docs/nautilus_nlp.utils.rst
+++ /dev/null
@@ -1,118 +0,0 @@
-nautilus\_nlp.utils package
-===========================
-
-Submodules
-----------
-
-nautilus\_nlp.utils.Text\_processor module
-------------------------------------------
-
-.. automodule:: nautilus_nlp.utils.Text_processor
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.compat module
----------------------------------
-
-.. automodule:: nautilus_nlp.utils.compat
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.constants module
-------------------------------------
-
-.. automodule:: nautilus_nlp.utils.constants
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.emoji module
---------------------------------
-
-.. automodule:: nautilus_nlp.utils.emoji
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.encoding module
------------------------------------
-
-.. automodule:: nautilus_nlp.utils.encoding
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.export module
----------------------------------
-
-.. automodule:: nautilus_nlp.utils.export
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.file\_loader module
----------------------------------------
-
-.. automodule:: nautilus_nlp.utils.file_loader
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.lemmatizer module
--------------------------------------
-
-.. automodule:: nautilus_nlp.utils.lemmatizer
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.preprocess module
--------------------------------------
-
-.. automodule:: nautilus_nlp.utils.preprocess
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.stemmer module
-----------------------------------
-
-.. automodule:: nautilus_nlp.utils.stemmer
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.text\_vectorizer module
--------------------------------------------
-
-.. automodule:: nautilus_nlp.utils.text_vectorizer
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.tokenizer module
-------------------------------------
-
-.. automodule:: nautilus_nlp.utils.tokenizer
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-nautilus\_nlp.utils.vector\_similarity module
----------------------------------------------
-
-.. automodule:: nautilus_nlp.utils.vector_similarity
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: nautilus_nlp.utils
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/docs/nautilus_nlp.visualization.rst b/docs/nautilus_nlp.visualization.rst
deleted file mode 100644
index 3f312ce..0000000
--- a/docs/nautilus_nlp.visualization.rst
+++ /dev/null
@@ -1,22 +0,0 @@
-nautilus\_nlp.visualization package
-===================================
-
-Submodules
-----------
-
-nautilus\_nlp.visualization.visualize module
---------------------------------------------
-
-.. automodule:: nautilus_nlp.visualization.visualize
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: nautilus_nlp.visualization
-    :members:
-    :undoc-members:
-    :show-inheritance:
diff --git a/docs/nlpretext.augmentation.rst b/docs/nlpretext.augmentation.rst
new file mode 100644
index 0000000..11afca2
--- /dev/null
+++ b/docs/nlpretext.augmentation.rst
@@ -0,0 +1,7 @@
+nlpretext.augmentation module
+-----------------------------
+
+.. automodule:: nlpretext.augmentation.preprocess
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/nlpretext.basic.rst b/docs/nlpretext.basic.rst
new file mode 100644
index 0000000..ca40843
--- /dev/null
+++ b/docs/nlpretext.basic.rst
@@ -0,0 +1,7 @@
+nlpretext.basic module
+-----------------------------
+
+.. automodule:: nlpretext.basic.preprocess
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/nlpretext.rst b/docs/nlpretext.rst
new file mode 100644
index 0000000..7fa285f
--- /dev/null
+++ b/docs/nlpretext.rst
@@ -0,0 +1,8 @@
+nlpretext.preprocessor module
+-----------------------------
+
+.. automodule:: nlpretext.preprocessor
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
diff --git a/docs/nlpretext.social.rst b/docs/nlpretext.social.rst
new file mode 100644
index 0000000..06a10a0
--- /dev/null
+++ b/docs/nlpretext.social.rst
@@ -0,0 +1,7 @@
+nlpretext.social module
+-----------------------------
+
+.. automodule:: nlpretext.social.preprocess
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/nlpretext.token.rst b/docs/nlpretext.token.rst
new file mode 100644
index 0000000..28adcb7
--- /dev/null
+++ b/docs/nlpretext.token.rst
@@ -0,0 +1,7 @@
+nlpretext.token module
+-----------------------------
+
+.. automodule:: nlpretext.token.preprocess
+   :members:
+   :undoc-members:
+   :show-inheritance:

From 7112402bd1443be4a447235b92aa2fd63cf639bb Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Tue, 16 Feb 2021 11:01:55 +0100
Subject: [PATCH 485/496] fix Nautilus > NLPretext

---
 docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index a6769f8..ef88628 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -159,7 +159,7 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'NLPretext.tex', 'Nautilus\\_nlp Documentation',
+    (master_doc, 'NLPretext.tex', 'NLPretext Documentation',
      'Artefact', 'manual'),
 ]
 

From 4fbb35093b9b93ed1b72732f6e466e19ef4d93da Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Tue, 16 Feb 2021 17:33:37 +0100
Subject: [PATCH 486/496] add init files

---
 nlpretext/augmentation/__init__.py | 17 +++++++++++++++++
 nlpretext/basic/__init__.py        | 17 +++++++++++++++++
 nlpretext/social/__init__.py       | 17 +++++++++++++++++
 nlpretext/token/__init__.py        | 17 +++++++++++++++++
 4 files changed, 68 insertions(+)
 create mode 100644 nlpretext/augmentation/__init__.py
 create mode 100644 nlpretext/basic/__init__.py
 create mode 100644 nlpretext/social/__init__.py
 create mode 100644 nlpretext/token/__init__.py

diff --git a/nlpretext/augmentation/__init__.py b/nlpretext/augmentation/__init__.py
new file mode 100644
index 0000000..d46139b
--- /dev/null
+++ b/nlpretext/augmentation/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nlpretext/basic/__init__.py b/nlpretext/basic/__init__.py
new file mode 100644
index 0000000..d46139b
--- /dev/null
+++ b/nlpretext/basic/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nlpretext/social/__init__.py b/nlpretext/social/__init__.py
new file mode 100644
index 0000000..d46139b
--- /dev/null
+++ b/nlpretext/social/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
diff --git a/nlpretext/token/__init__.py b/nlpretext/token/__init__.py
new file mode 100644
index 0000000..d46139b
--- /dev/null
+++ b/nlpretext/token/__init__.py
@@ -0,0 +1,17 @@
+# GNU Lesser General Public License v3.0 only
+# Copyright (C) 2020 Artefact
+# licence-information@artefact.com
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 3 of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with this program; if not, write to the Free Software Foundation,
+# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

From 3cbf230ac60ffbefdf0977dd8e7db2c7ac2d88a9 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Tue, 16 Feb 2021 17:35:02 +0100
Subject: [PATCH 487/496] update readme classic changed to basic

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index bb1480f..b26156d 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ Another possibility is to create your custom pipeline if you know exactly what f
 
 ```python
 from nlpretext import Preprocessor
-from nlpretext.classic.preprocess import (normalize_whitespace, remove_punct, remove_eol_characters,
+from nlpretext.basic.preprocess import (normalize_whitespace, remove_punct, remove_eol_characters,
 remove_stopwords, lower_text)
 from nlpretext.social.preprocess import remove_mentions, remove_hashtag, remove_emoji
 text = "I just got the best dinner in my life @latourdargent !!! I  recommend 😀 #food #paris \n"
@@ -96,7 +96,7 @@ Take a look at all the functions that are available [here](https://github.com/ar
 ## Replacing emails <a name="replace_emails"></a>
 
 ```python
-from nlpretext.classic.preprocess import replace_emails
+from nlpretext.basic.preprocess import replace_emails
 example = "I have forwarded this email to obama@whitehouse.gov"
 example = replace_emails(example, replace_with="*EMAIL*")
 print(example)
@@ -106,7 +106,7 @@ print(example)
 ## Replacing phone numbers <a name="replace_phone_numbers"></a>
 
 ```python
-from nlpretext.classic.preprocess import replace_phone_numbers
+from nlpretext.basic.preprocess import replace_phone_numbers
 example = "My phone number is 0606060606"
 example = replace_phone_numbers(example, country_to_detect=["FR"], replace_with="*PHONE*")
 print(example)
@@ -157,7 +157,7 @@ You can now open the file index.html located in the build folder.
     ├── nlpretext           <- Main Package. This is where the code lives
     │   ├── preprocessor.py <- Main preprocessing script
     │   ├── augmentation    <- Text augmentation script
-    │   ├── classic         <- Classic text preprocessing 
+    │   ├── basic           <- Basic text preprocessing 
     │   ├── social          <- Social text preprocessing
     │   ├── token           <- Token text preprocessing
     │   ├── _config         <- Where the configuration and constants live

From b2d22ea1e88ae524e29cb149159245081979c7f1 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Wed, 17 Feb 2021 14:03:01 +0100
Subject: [PATCH 488/496] change Licence to Apache

---
 LICENSE                        | 432 ++++++++++++++++++++-------------
 docs/conf.py                   |  23 +-
 nlpretext/_config/constants.py |  23 +-
 nlpretext/basic/preprocess.py  |  23 +-
 nlpretext/social/preprocess.py |  23 +-
 nlpretext/token/preprocess.py  |  23 +-
 tests/test_document_loader.py  |  23 +-
 7 files changed, 327 insertions(+), 243 deletions(-)

diff --git a/LICENSE b/LICENSE
index 153d416..751384f 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,165 +1,267 @@
-                   GNU LESSER GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-
-  This version of the GNU Lesser General Public License incorporates
-the terms and conditions of version 3 of the GNU General Public
-License, supplemented by the additional permissions listed below.
-
-  0. Additional Definitions.
-
-  As used herein, "this License" refers to version 3 of the GNU Lesser
-General Public License, and the "GNU GPL" refers to version 3 of the GNU
-General Public License.
-
-  "The Library" refers to a covered work governed by this License,
-other than an Application or a Combined Work as defined below.
-
-  An "Application" is any work that makes use of an interface provided
-by the Library, but which is not otherwise based on the Library.
-Defining a subclass of a class defined by the Library is deemed a mode
-of using an interface provided by the Library.
-
-  A "Combined Work" is a work produced by combining or linking an
-Application with the Library.  The particular version of the Library
-with which the Combined Work was made is also called the "Linked
-Version".
-
-  The "Minimal Corresponding Source" for a Combined Work means the
-Corresponding Source for the Combined Work, excluding any source code
-for portions of the Combined Work that, considered in isolation, are
-based on the Application, and not on the Linked Version.
-
-  The "Corresponding Application Code" for a Combined Work means the
-object code and/or source code for the Application, including any data
-and utility programs needed for reproducing the Combined Work from the
-Application, but excluding the System Libraries of the Combined Work.
-
-  1. Exception to Section 3 of the GNU GPL.
-
-  You may convey a covered work under sections 3 and 4 of this License
-without being bound by section 3 of the GNU GPL.
-
-  2. Conveying Modified Versions.
-
-  If you modify a copy of the Library, and, in your modifications, a
-facility refers to a function or data to be supplied by an Application
-that uses the facility (other than as an argument passed when the
-facility is invoked), then you may convey a copy of the modified
-version:
-
-   a) under this License, provided that you make a good faith effort to
-   ensure that, in the event an Application does not supply the
-   function or data, the facility still operates, and performs
-   whatever part of its purpose remains meaningful, or
-
-   b) under the GNU GPL, with none of the additional permissions of
-   this License applicable to that copy.
-
-  3. Object Code Incorporating Material from Library Header Files.
-
-  The object code form of an Application may incorporate material from
-a header file that is part of the Library.  You may convey such object
-code under terms of your choice, provided that, if the incorporated
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-layouts and accessors, or small macros, inline functions and templates
-(ten or fewer lines in length), you do both of the following:
-
-   a) Give prominent notice with each copy of the object code that the
-   Library is used in it and that the Library and its use are
-   covered by this License.
-
-   b) Accompany the object code with a copy of the GNU GPL and this license
-   document.
-
-  4. Combined Works.
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-  You may convey a Combined Work under terms of your choice that,
-taken together, effectively do not restrict modification of the
-portions of the Library contained in the Combined Work and reverse
-engineering for debugging such modifications, if you also do each of
-the following:
-
-   a) Give prominent notice with each copy of the Combined Work that
-   the Library is used in it and that the Library and its use are
-   covered by this License.
-
-   b) Accompany the Combined Work with a copy of the GNU GPL and this license
-   document.
-
-   c) For a Combined Work that displays copyright notices during
-   execution, include the copyright notice for the Library among
-   these notices, as well as a reference directing the user to the
-   copies of the GNU GPL and this license document.
-
-   d) Do one of the following:
-
-       0) Convey the Minimal Corresponding Source under the terms of this
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-       manner specified by section 6 of the GNU GPL for conveying
-       Corresponding Source.
-
-       1) Use a suitable shared library mechanism for linking with the
-       Library.  A suitable mechanism is one that (a) uses at run time
-       a copy of the Library already present on the user's computer
-       system, and (b) will operate properly with a modified version
-       of the Library that is interface-compatible with the Linked
-       Version.
-
-   e) Provide Installation Information, but only if you would otherwise
-   be required to provide such information under section 6 of the
-   GNU GPL, and only to the extent that such information is
-   necessary to install and execute a modified version of the
-   Combined Work produced by recombining or relinking the
-   Application with a modified version of the Linked Version. (If
-   you use option 4d0, the Installation Information must accompany
-   the Minimal Corresponding Source and Corresponding Application
-   Code. If you use option 4d1, you must provide the Installation
-   Information in the manner specified by section 6 of the GNU GPL
-   for conveying Corresponding Source.)
-
-  5. Combined Libraries.
-
-  You may place library facilities that are a work based on the
-Library side by side in a single library together with other library
-facilities that are not Applications and are not covered by this
-License, and convey such a combined library under terms of your
-choice, if you do both of the following:
-
-   a) Accompany the combined library with a copy of the same work based
-   on the Library, uncombined with any other library facilities,
-   conveyed under the terms of this License.
-
-   b) Give prominent notice with the combined library that part of it
-   is a work based on the Library, and explaining where to find the
-   accompanying uncombined form of the same work.
-
-  6. Revised Versions of the GNU Lesser General Public License.
-
-  The Free Software Foundation may publish revised and/or new versions
-of the GNU Lesser General Public License from time to time. Such new
-versions will be similar in spirit to the present version, but may
-differ in detail to address new problems or concerns.
-
-  Each version is given a distinguishing version number. If the
-Library as you received it specifies that a certain numbered version
-of the GNU Lesser General Public License "or any later version"
-applies to it, you have the option of following the terms and
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-published by the Free Software Foundation. If the Library as you
-received it does not specify a version number of the GNU Lesser
-General Public License, you may choose any version of the GNU Lesser
-General Public License ever published by the Free Software Foundation.
-
-  If the Library as you received it specifies that a proxy can decide
-whether future versions of the GNU Lesser General Public License shall
-apply, that proxy's public statement of acceptance of any version is
-permanent authorization for you to choose that version for the
-Library.
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+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
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+
+      To apply the Apache License to your work, attach the following
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+      file or class name and description of purpose be included on the
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+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+© 2021 GitHub, Inc.
+Terms
+Privacy
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diff --git a/docs/conf.py b/docs/conf.py
index ef88628..ae7ccbc 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,21 +1,18 @@
-# GNU Lesser General Public License v3.0 only
+# coding=utf-8
 # Copyright (C) 2020 Artefact
 # licence-information@artefact.com
 #
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
 #
 # Configuration file for the Sphinx documentation builder.
 #
diff --git a/nlpretext/_config/constants.py b/nlpretext/_config/constants.py
index 401811b..d7c4b20 100644
--- a/nlpretext/_config/constants.py
+++ b/nlpretext/_config/constants.py
@@ -1,21 +1,18 @@
-# GNU Lesser General Public License v3.0 only
+# coding=utf-8
 # Copyright (C) 2020 Artefact
 # licence-information@artefact.com
 #
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
 """
 Collection of regular expressions and other (small, generally useful) constants.
 """
diff --git a/nlpretext/basic/preprocess.py b/nlpretext/basic/preprocess.py
index 0247012..f7eedf2 100644
--- a/nlpretext/basic/preprocess.py
+++ b/nlpretext/basic/preprocess.py
@@ -1,21 +1,18 @@
-# GNU Lesser General Public License v3.0 only
+# coding=utf-8
 # Copyright (C) 2020 Artefact
 # licence-information@artefact.com
 #
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
diff --git a/nlpretext/social/preprocess.py b/nlpretext/social/preprocess.py
index ebbdb8a..4fa0247 100644
--- a/nlpretext/social/preprocess.py
+++ b/nlpretext/social/preprocess.py
@@ -1,21 +1,18 @@
-# GNU Lesser General Public License v3.0 only
+# coding=utf-8
 # Copyright (C) 2020 Artefact
 # licence-information@artefact.com
 #
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
 
 from __future__ import absolute_import, division, print_function, unicode_literals
 
diff --git a/nlpretext/token/preprocess.py b/nlpretext/token/preprocess.py
index 058dd8e..682aae1 100644
--- a/nlpretext/token/preprocess.py
+++ b/nlpretext/token/preprocess.py
@@ -1,21 +1,18 @@
-# GNU Lesser General Public License v3.0 only
+# coding=utf-8
 # Copyright (C) 2020 Artefact
 # licence-information@artefact.com
 #
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
 
 from __future__ import absolute_import, division, print_function, unicode_literals
 
diff --git a/tests/test_document_loader.py b/tests/test_document_loader.py
index b6cab3a..b4caf2d 100644
--- a/tests/test_document_loader.py
+++ b/tests/test_document_loader.py
@@ -1,21 +1,18 @@
-# GNU Lesser General Public License v3.0 only
+# coding=utf-8
 # Copyright (C) 2020 Artefact
 # licence-information@artefact.com
 #
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
 
 import os
 

From 789438b64abe00afc7c0ab123a6b5883c0dbc081 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 18 Feb 2021 09:31:11 +0100
Subject: [PATCH 489/496] correct license

---
 LICENSE                       | 30 ------------------------------
 nlpretext/_utils/stopwords.py | 23 ++++++++++-------------
 2 files changed, 10 insertions(+), 43 deletions(-)

diff --git a/LICENSE b/LICENSE
index 751384f..1607e15 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,33 +1,3 @@
-Skip to content
-Search or jump to…
-
-Pull requests
-Issues
-Marketplace
-Explore
- 
-@amaleelhamri 
-fastai
-/
-fastai
-630
-20.5k
-6.9k
-Code
-Issues
-56
-Pull requests
-12
-Discussions
-Actions
-Projects
-1
-Wiki
-Security
-Insights
-fastai/LICENSE
-fastai/fastai is licensed under the
-
 Apache License 2.0
 A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
 
diff --git a/nlpretext/_utils/stopwords.py b/nlpretext/_utils/stopwords.py
index 0897313..e0f0013 100644
--- a/nlpretext/_utils/stopwords.py
+++ b/nlpretext/_utils/stopwords.py
@@ -1,21 +1,18 @@
-# GNU Lesser General Public License v3.0 only
+# coding=utf-8
 # Copyright (C) 2020 Artefact
 # licence-information@artefact.com
 #
-# This program is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 3 of the License, or (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program; if not, write to the Free Software Foundation,
-# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
-# -*- coding: utf-8 -*-
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)

From 29090447a7db5cc58796d2199a05d66a5c1484d2 Mon Sep 17 00:00:00 2001
From: Amale EL HAMRI <amale.elhamri@artefact.com>
Date: Thu, 18 Feb 2021 09:36:21 +0100
Subject: [PATCH 490/496] correct license

---
 LICENSE | 26 +-------------------------
 1 file changed, 1 insertion(+), 25 deletions(-)

diff --git a/LICENSE b/LICENSE
index 1607e15..aa7be8d 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,28 +1,4 @@
-Apache License 2.0
-A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
-
-Permissions
- Commercial use
- Modification
- Distribution
- Patent use
- Private use
-Limitations
- Trademark use
- Liability
- Warranty
-Conditions
- License and copyright notice
- State changes
-This is not legal advice. Learn more about repository licenses.
-@jph00
-jph00 Initial commit
-Latest commit ff0dff1 on 23 Nov 2019
- History
- 1 contributor
- 201 lines (169 sloc)  11.1 KB
-  
-                                 Apache License
+                                   Apache License
                            Version 2.0, January 2004
                         http://www.apache.org/licenses/
 

From 84dec97879cc45777f1fd23954bc3a39229fae8b Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 10 May 2021 11:40:03 +0200
Subject: [PATCH 491/496] add credits

---
 README.md                      | 16 +++++++++++
 nlpretext/_config/constants.py |  1 +
 nlpretext/basic/preprocess.py  | 50 ++++++++++++++++++++++++++++++++++
 3 files changed, 67 insertions(+)

diff --git a/README.md b/README.md
index 4ec57a9..5e389b8 100644
--- a/README.md
+++ b/README.md
@@ -183,3 +183,19 @@ You can now open the file index.html located in the build folder.
     ├── requirements.txt    <- The requirements file for reproducing the analysis environment, e.g.
     │                          generated with `pip freeze > requirements.txt`
     └── pylintrc            <- The linting configuration file
+
+
+# Credits
+
+- [textacy](https://github.com/chartbeat-labs/textacy) for the following basic preprocessing functions:
+    - `fix_bad_unicode`
+    - `normalize_whitespace`
+    - `unpack_english_contractions`
+    - `replace_urls`
+    - `replace_emails`
+    - `replace_numbers`
+    - `replace_currency_symbols`
+    - `remove_punct`
+    - `remove_accents`
+    - `replace_phone_numbers` *(with some modifications of our own)*
+
diff --git a/nlpretext/_config/constants.py b/nlpretext/_config/constants.py
index d7c4b20..218cc17 100644
--- a/nlpretext/_config/constants.py
+++ b/nlpretext/_config/constants.py
@@ -15,6 +15,7 @@
 # limitations under the License
 """
 Collection of regular expressions and other (small, generally useful) constants.
+Credits to textacy for some of them: https://github.com/chartbeat-labs/textacy
 """
 from __future__ import unicode_literals
 
diff --git a/nlpretext/basic/preprocess.py b/nlpretext/basic/preprocess.py
index f7eedf2..3aa942a 100644
--- a/nlpretext/basic/preprocess.py
+++ b/nlpretext/basic/preprocess.py
@@ -28,6 +28,11 @@
 
 def normalize_whitespace(text) -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Given ``text`` str, replace one or more spacings with a single space, and
     one or more linebreaks with a single newline. Also strip leading/trailing
     whitespace.
@@ -106,6 +111,11 @@ def remove_eol_characters(text) -> str:
 
 def fix_bad_unicode(text, normalization: str = "NFC") -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Fix unicode text that's "broken" using `ftfy
     <http://ftfy.readthedocs.org/>`_;
     this includes mojibake, HTML entities and other code cruft,
@@ -133,6 +143,11 @@ def fix_bad_unicode(text, normalization: str = "NFC") -> str:
 
 def unpack_english_contractions(text) -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Replace *English* contractions in ``text`` str with their unshortened
     forms.
     N.B. The "'d" and "'s" forms are ambiguous (had/would, is/has/possessive),
@@ -173,6 +188,11 @@ def unpack_english_contractions(text) -> str:
 
 def replace_urls(text, replace_with: str = "*URL*") -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Replace all URLs in ``text`` str with ``replace_with`` str.
 
     Parameters
@@ -193,6 +213,11 @@ def replace_urls(text, replace_with: str = "*URL*") -> str:
 
 def replace_emails(text, replace_with="*EMAIL*") -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Replace all emails in ``text`` str with ``replace_with`` str
 
     Parameters
@@ -213,6 +238,11 @@ def replace_phone_numbers(text, country_to_detect: list,
                           replace_with: str = "*PHONE*",
                           method: str = "regex") -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Inspired code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Replace all phone numbers in ``text`` str with ``replace_with`` str
 
     Parameters
@@ -249,6 +279,11 @@ def replace_phone_numbers(text, country_to_detect: list,
 
 def replace_numbers(text, replace_with="*NUMBER*") -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Replace all numbers in ``text`` str with ``replace_with`` str.
 
     Parameters
@@ -267,6 +302,11 @@ def replace_numbers(text, replace_with="*NUMBER*") -> str:
 
 def replace_currency_symbols(text, replace_with=None) -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Replace all currency symbols in ``text`` str with string specified by
     ``replace_with`` str.
 
@@ -294,6 +334,11 @@ def replace_currency_symbols(text, replace_with=None) -> str:
 
 def remove_punct(text, marks=None) -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Remove punctuation from ``text`` by replacing all instances of ``marks``
     with whitespace.
 
@@ -327,6 +372,11 @@ def remove_punct(text, marks=None) -> str:
 
 def remove_accents(text, method: str = "unicode") -> str:
     """
+    ----
+    Copyright 2016 Chartbeat, Inc.
+    Code from textacy: https://github.com/chartbeat-labs/textacy
+    ----
+
     Remove accents from any accented unicode characters in ``text`` str,
     either by transforming them into ascii equivalents or removing them
     entirely.

From aaea0e0610979500f4b84f1d449cdb45d0844196 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 10 May 2021 11:40:18 +0200
Subject: [PATCH 492/496] clean test

---
 tests/test_preprocessor.py | 49 ++++++++++++++++++--------------------
 1 file changed, 23 insertions(+), 26 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index a11ba16..1003001 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -249,7 +249,6 @@ def test_remove_accents():
      ('proportienelle', 'proportienelle'),
      ('Pour plus de démocratie participative', 'Pour plus de démocratie participative'),
      ('Transparence de la vie public', 'Transparence de la vie public'),
-     ('18 mois de trop....ca suffit macron', '18 mois de trop....ca suffit macron'),
      ('Egalité devant les infractions routières', 'Egalité devant les infractions routières')],)
 def test_fix_bad_unicode(input_str, expected_str):
     result = fix_bad_unicode(input_str)
@@ -287,14 +286,14 @@ def test_unpack_english_contractions(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [(
-        "Wan't to contribute to Nautilus? read https://github.com/artefactory/nautilus-nlp/blob/docs/CONTRIBUTING.md"\
+        "Wan't to contribute to NLPretext? read https://github.com/artefactory/NLPretext/blob/master/CONTRIBUTING.md"\
             " first",
-        "Wan't to contribute to Nautilus? read *URL* first"),
-     ("The ip address of my VM is http://34.76.182.5:8888", "The ip address of my VM is *URL*"),
+        "Wan't to contribute to NLPretext? read *URL* first"),
+     ("The ip address of my VM is http://00.00.000.0:8888", "The ip address of my VM is *URL*"),
      ("If you go to http://internet.org, you will find a website hosted by FB.",
       "If you go to *URL*, you will find a website hosted by FB."),
      ("Ishttps://waaaou.com/ available?", 'Is*URL* available?'),
-     ("mailto:hugo.vasselin@artefact.com", '*URL*')])
+     ("mailto:hugo.dupont@artefact.com", '*URL*')])
 def test_replace_urls(input_str, expected_str):
     result = replace_urls(input_str)
     np.testing.assert_equal(result, expected_str)
@@ -303,10 +302,9 @@ def test_replace_urls(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("my email:hugo.vasselin@artefact.com", "my email:*EMAIL*"),
+        ("my email:hugo.dupont@artefact.com", "my email:*EMAIL*"),
         ("v543143@nwytg.net is a temporary email", "*EMAIL* is a temporary email"),
-        ("our emails used to be name.surname@artefact.is", "our emails used to be *EMAIL*"),
-        ("chaudasse_du_13@hotmail.fr,C ton email bb?", '*EMAIL*,C ton email bb?')
+        ("our emails used to be name.surname@artefact.is", "our emails used to be *EMAIL*")
     ]
 )
 def test_replace_emails(input_str, expected_str):
@@ -317,17 +315,17 @@ def test_replace_emails(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("mon 06 bb: 0625093267", "mon 06 bb: *PHONE*"),
-        ("mon 06 bb: 06.25.09.32.67", "mon 06 bb: *PHONE*"),
-        ("call me at +33625093267", "call me at *PHONE*"),
-        ("call me at +33 6 25 09 32 67", "call me at *PHONE*"),
-        ("call me at +33 625 093 267", "call me at *PHONE*"),
+        ("mon 06: 0601020304", "mon 06: *PHONE*"),
+        ("mon 06: 06.01.02.03.04", "mon 06: *PHONE*"),
+        ("call me at +33601020304", "call me at *PHONE*"),
+        ("call me at +33 6 01 02 03 04", "call me at *PHONE*"),
+        ("call me at +33 601 020 304", "call me at *PHONE*"),
         ("if this unit test doesn't work, call 3615 and says 'ROBIN'",
          "if this unit test doesn't work, call *PHONE* and says 'ROBIN'"),
-        ('(541) 754-3010 is a US. Phone', '*PHONE* is a US. Phone'),
-        ('+1-541-754-3010 is an international Phone', '*PHONE* is an international Phone'),
-        ('+1-541-754-3010 Dialed in the US', '*PHONE* Dialed in the US'),
-        ('+1-541-754-3010 Dialed from Germany', '*PHONE* Dialed from Germany')
+        ('(541) 754-0000 is a US. Phone', '*PHONE* is a US. Phone'),
+        ('+1-541-754-0000 is an international Phone', '*PHONE* is an international Phone'),
+        ('+1-541-754-0000 Dialed in the US', '*PHONE* Dialed in the US'),
+        ('+1-541-754-0000 Dialed from Germany', '*PHONE* Dialed from Germany')
     ]
 )
 def test_replace_phone_numbers(input_str, expected_str):
@@ -343,9 +341,8 @@ def test_replace_phone_numbers(input_str, expected_str):
     "input_str, expected_str",
     [
         ("123, 3 petits chats", "*NUMBER*, *NUMBER* petits chats"),
-        ("l0ve 2 twa <3", "l0ve *NUMBER* twa <*NUMBER*"),
         ("Give me 45bucks!", "Give me *NUMBER*bucks!"),
-        ("call me at +33625093267", "call me at *NUMBER*")
+        ("call me at +33601020304", "call me at *NUMBER*")
     ]
 )
 def test_replace_numbers(input_str, expected_str):
@@ -384,9 +381,9 @@ def test_replace_currency_symbols(input_str, param, expected_str):
         ("Seriously.,.", '.,;', "Seriously "),
         ("Seriously...", '.,;', "Seriously "),
         ("Seriously.!.", '.,;', "Seriously ! "),
-        ("hugo.vasselin@artefact.com", '.,;', "hugo vasselin@artefact com"),
-        ("hugo.vasselin@artefact.com", None, "hugo vasselin artefact com"),
-        ("hugo-vasselin@artefact.com", None, "hugo vasselin artefact com")
+        ("hugo.dupont@artefact.com", '.,;', "hugo dupont@artefact com"),
+        ("hugo.dupont@artefact.com", None, "hugo dupont artefact com"),
+        ("hugo-dupont@artefact.com", None, "hugo dupont artefact com")
     ]
 )
 def test_remove_punct(input_str, param, expected_str):
@@ -401,10 +398,10 @@ def test_remove_punct(input_str, param, expected_str):
         ("🎅🏿⌚", ""),
         ("🥖✊💦", ""),
         ("✊", ""),
-        ("J'espère que les 🚓 vont pas lire ce test",
-         "J'espère que les  vont pas lire ce test"),
-        ("J'espère que les vont pas lire ce test🚓",
-         "J'espère que les vont pas lire ce test")
+        ("J'espère que les 🚓 vont bien",
+         "J'espère que les  vont bien"),
+        ("J'espère que les vont bien🚓",
+         "J'espère que les vont bien")
     ]
 )
 def test_remove_emoji(input_str, expected_str):

From 4a470f99efddf2e447ffefcd38226d1b82327390 Mon Sep 17 00:00:00 2001
From: Rafaelle AYGALENQ <rafaelle.aygalenq@artefact.com>
Date: Mon, 10 May 2021 11:45:10 +0200
Subject: [PATCH 493/496] fix test replace url

---
 tests/test_preprocessor.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 1003001..7e1e5a3 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -289,7 +289,6 @@ def test_unpack_english_contractions(input_str, expected_str):
         "Wan't to contribute to NLPretext? read https://github.com/artefactory/NLPretext/blob/master/CONTRIBUTING.md"\
             " first",
         "Wan't to contribute to NLPretext? read *URL* first"),
-     ("The ip address of my VM is http://00.00.000.0:8888", "The ip address of my VM is *URL*"),
      ("If you go to http://internet.org, you will find a website hosted by FB.",
       "If you go to *URL*, you will find a website hosted by FB."),
      ("Ishttps://waaaou.com/ available?", 'Is*URL* available?'),

From 7545f26d250da0996f128f4ba407b34706ef78b0 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Mon, 10 May 2021 12:18:34 +0200
Subject: [PATCH 494/496] fix: englify tests

---
 tests/test_preprocessor.py | 36 +++++++++++++++++-------------------
 1 file changed, 17 insertions(+), 19 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 7e1e5a3..3909113 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -190,7 +190,7 @@ def test_get_stopwords():
 @pytest.mark.parametrize(
     "input_tokens, lang, expected_output",
     [
-        (['I', 'like', 'when', 'you', 'move', 'your', 'body', '!'], "en", ['I', 'move', 'body', '!'])
+        (['I', 'like', 'this', 'song', 'very', 'much', '!'], "en", ['I', 'song', '!'])
     ],
 )
 def test_remove_stopwords_tokens(input_tokens, lang, expected_output):
@@ -201,7 +201,7 @@ def test_remove_stopwords_tokens(input_tokens, lang, expected_output):
 @pytest.mark.parametrize(
     "input_text, lang, expected_output",
     [
-        ('I like when you move your body !', 'en', 'I move body !'),
+        ('I like this song very much !', 'en', 'I song !'),
         ('Can I get a beer?', 'en', 'Can I beer ?'),
         ('Je vous recommande ce film !', 'fr', 'Je recommande film !'),
         ('je vous recommande ce film !', 'fr', 'recommande film !'),
@@ -216,7 +216,7 @@ def test_remove_stopwords_text(input_text, lang, expected_output):
 @pytest.mark.parametrize(
     "input_text, lang, custom_stopwords, expected_output",
     [
-        ('I like when you move your body !', 'en', ['body'], 'I move !'),
+        ('I like this song very much !', 'en', ['song'], 'I !'),
         ('Je vous recommande ce film la scène de fin est géniale !', 'fr',
          ['film', 'scène'], 'Je recommande fin géniale !'),
     ],
@@ -291,8 +291,8 @@ def test_unpack_english_contractions(input_str, expected_str):
         "Wan't to contribute to NLPretext? read *URL* first"),
      ("If you go to http://internet.org, you will find a website hosted by FB.",
       "If you go to *URL*, you will find a website hosted by FB."),
-     ("Ishttps://waaaou.com/ available?", 'Is*URL* available?'),
-     ("mailto:hugo.dupont@artefact.com", '*URL*')])
+     ("Ishttps://internet.org/ available?", 'Is*URL* available?'),
+     ("mailto:john.doe@artefact.com", '*URL*')])
 def test_replace_urls(input_str, expected_str):
     result = replace_urls(input_str)
     np.testing.assert_equal(result, expected_str)
@@ -301,7 +301,7 @@ def test_replace_urls(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("my email:hugo.dupont@artefact.com", "my email:*EMAIL*"),
+        ("my email:john.doe@artefact.com", "my email:*EMAIL*"),
         ("v543143@nwytg.net is a temporary email", "*EMAIL* is a temporary email"),
         ("our emails used to be name.surname@artefact.is", "our emails used to be *EMAIL*")
     ]
@@ -319,8 +319,8 @@ def test_replace_emails(input_str, expected_str):
         ("call me at +33601020304", "call me at *PHONE*"),
         ("call me at +33 6 01 02 03 04", "call me at *PHONE*"),
         ("call me at +33 601 020 304", "call me at *PHONE*"),
-        ("if this unit test doesn't work, call 3615 and says 'ROBIN'",
-         "if this unit test doesn't work, call *PHONE* and says 'ROBIN'"),
+        ("if this unit test doesn't work, call 3615 and says 'HELP'",
+         "if this unit test doesn't work, call *PHONE* and says 'HELP'"),
         ('(541) 754-0000 is a US. Phone', '*PHONE* is a US. Phone'),
         ('+1-541-754-0000 is an international Phone', '*PHONE* is an international Phone'),
         ('+1-541-754-0000 Dialed in the US', '*PHONE* Dialed in the US'),
@@ -380,9 +380,9 @@ def test_replace_currency_symbols(input_str, param, expected_str):
         ("Seriously.,.", '.,;', "Seriously "),
         ("Seriously...", '.,;', "Seriously "),
         ("Seriously.!.", '.,;', "Seriously ! "),
-        ("hugo.dupont@artefact.com", '.,;', "hugo dupont@artefact com"),
-        ("hugo.dupont@artefact.com", None, "hugo dupont artefact com"),
-        ("hugo-dupont@artefact.com", None, "hugo dupont artefact com")
+        ("john.doe@artefact.com", '.,;', "john doe@artefact com"),
+        ("john.doe@artefact.com", None, "john doe artefact com"),
+        ("john-doe@artefact.com", None, "john doe artefact com")
     ]
 )
 def test_remove_punct(input_str, param, expected_str):
@@ -393,14 +393,12 @@ def test_remove_punct(input_str, param, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("👉👌", ""),
+        ("⚽️👌", ""),
         ("🎅🏿⌚", ""),
-        ("🥖✊💦", ""),
+        ("🥖🍷🇫🇷", ""),
         ("✊", ""),
-        ("J'espère que les 🚓 vont bien",
-         "J'espère que les  vont bien"),
-        ("J'espère que les vont bien🚓",
-         "J'espère que les vont bien")
+        ("Save 🐼 and 🐟",
+         "Save  and "),
     ]
 )
 def test_remove_emoji(input_str, expected_str):
@@ -411,9 +409,9 @@ def test_remove_emoji(input_str, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("👉👌", ":backhand_index_pointing_right::OK_hand:"),
+        ("⚽️👌", ":soccer_ball::OK_hand:"),
         ("🎅🏿⌚", ":Santa_Claus_dark_skin_tone::watch:"),
-        ("🥖✊💦", ":baguette_bread::raised_fist::sweat_droplets:"),
+        ("🥖🍷🇫🇷", ":baguette_bread::wine_glass::France:"),
         ("✊", ":raised_fist:")
     ]
 )

From 4ca5127453f87910391b039ea2940e861b967a83 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Mon, 10 May 2021 12:30:27 +0200
Subject: [PATCH 495/496] fix: emoji test

---
 tests/test_preprocessor.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 3909113..6c9db84 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -393,7 +393,7 @@ def test_remove_punct(input_str, param, expected_str):
 @pytest.mark.parametrize(
     "input_str, expected_str",
     [
-        ("⚽️👌", ""),
+        ("⚽👌", ""),
         ("🎅🏿⌚", ""),
         ("🥖🍷🇫🇷", ""),
         ("✊", ""),
@@ -403,7 +403,8 @@ def test_remove_punct(input_str, param, expected_str):
 )
 def test_remove_emoji(input_str, expected_str):
     result = remove_emoji(input_str)
-    np.testing.assert_equal(result, expected_str)
+    assert len(result) == len(expected_str)
+    assert result == expected_str
 
 
 @pytest.mark.parametrize(

From 45f00dc8976c34eb40bfe9890759cc68d4e0e486 Mon Sep 17 00:00:00 2001
From: hugovasselin <waaaouu@gmail.com>
Date: Mon, 10 May 2021 12:35:38 +0200
Subject: [PATCH 496/496] fix: update version number

---
 VERSION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/VERSION b/VERSION
index 6d7de6e..21e8796 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-1.0.2
+1.0.3