POS tagging using transformers.ipynb

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 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T4bQdyZ_u7It",
    "outputId": "249f2720-8cd4-4373-b1b5-f43c176c625e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting datasets\n",
      "  Downloading datasets-2.4.0-py3-none-any.whl (365 kB)\n",
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      "Collecting xxhash\n",
      "  Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
      "\u001B[K     |████████████████████████████████| 212 kB 55.5 MB/s \n",
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      "  Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n",
      "\u001B[K     |████████████████████████████████| 101 kB 11.4 MB/s \n",
      "\u001B[?25hCollecting fsspec[http]>=2021.11.1\n",
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      "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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      "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
      "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
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      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.1)\n",
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      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
      "Installing collected packages: urllib3, pyyaml, fsspec, xxhash, responses, multiprocess, huggingface-hub, datasets\n",
      "  Attempting uninstall: urllib3\n",
      "    Found existing installation: urllib3 1.24.3\n",
      "    Uninstalling urllib3-1.24.3:\n",
      "      Successfully uninstalled urllib3-1.24.3\n",
      "  Attempting uninstall: pyyaml\n",
      "    Found existing installation: PyYAML 3.13\n",
      "    Uninstalling PyYAML-3.13:\n",
      "      Successfully uninstalled PyYAML-3.13\n",
      "Successfully installed datasets-2.4.0 fsspec-2022.7.1 huggingface-hub-0.8.1 multiprocess-0.70.13 pyyaml-6.0 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting transformers\n",
      "  Downloading transformers-4.21.1-py3-none-any.whl (4.7 MB)\n",
      "\u001B[K     |████████████████████████████████| 4.7 MB 8.1 MB/s \n",
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      "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
      "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
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      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
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      "Installing collected packages: tokenizers, transformers\n",
      "Successfully installed tokenizers-0.12.1 transformers-4.21.1\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting seqeval\n",
      "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
      "\u001B[K     |████████████████████████████████| 43 kB 1.3 MB/s \n",
      "\u001B[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from seqeval) (1.21.6)\n",
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      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.1.0)\n",
      "Building wheels for collected packages: seqeval\n",
      "  Building wheel for seqeval (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=072ca6243641ea4d22f8c1e020568bc6dbcee72db9aa0499d37be4d9c0864d0c\n",
      "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
      "Successfully built seqeval\n",
      "Installing collected packages: seqeval\n",
      "Successfully installed seqeval-1.2.2\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets\n",
    "!pip install  transformers\n",
    "!pip install seqeval\n"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hoAJak6RvBVL",
    "outputId": "7d6468bc-08ea-47a7-c73c-cb7efee452e2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from datasets import load_dataset\n",
    "from datasets import load_metric\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from transformers import AutoTokenizer\n",
    "from datasets import Dataset, DatasetDict, Features, Sequence, ClassLabel, Value\n",
    "from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer\n",
    "from transformers import DataCollatorForTokenClassification\n"
   ],
   "metadata": {
    "id": "SABnRV-QvKa6",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train_p = '/content/drive/My Drive/Train.txt'\n",
    "test_p='/content/drive/My Drive/Test.txt'"
   ],
   "metadata": {
    "id": "OgEp8uG5vUr4",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "f=open(train_p,encoding=\"utf8\")\n",
    "train = f.readlines()\n",
    "f.close()\n",
    "f=open(test_p,encoding=\"utf8\")\n",
    "test = f.readlines()\n",
    "f.close()"
   ],
   "metadata": {
    "id": "cKPz4ngcva40",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def create_df(corpus):\n",
    "  text=[]\n",
    "  tag=[]\n",
    "  for line in corpus:\n",
    "    if line =='\\n':\n",
    "      continue\n",
    "    else :\n",
    "      tup = line.split()\n",
    "      text.append(tup[0])\n",
    "      tag.append(tup[1])\n",
    "  text_tags=[]\n",
    "  for i in range(len(text)):\n",
    "    text_tags.append(np.hstack((text[i],tag[i])))\n",
    "  df = []\n",
    "  row_tokens = []\n",
    "  row_tags = []\n",
    "  tags = []\n",
    "  for [token, pos_tag] in text_tags:\n",
    "      tag = pos_tag.split(\"_\")[0]\n",
    "      if tag not in tags:\n",
    "          tags.append(tag)\n",
    "      elif token == \".\":\n",
    "          row_tokens.append(token)\n",
    "          row_tags.append(tags.index(tag))\n",
    "          df.append([row_tokens, row_tags])\n",
    "          row_tokens = []\n",
    "          row_tags = []\n",
    "      else:\n",
    "          row_tokens.append(token)\n",
    "          row_tags.append(tags.index(tag))\n",
    "  df=pd.DataFrame(df)\n",
    "  df.columns = [\"tokens\", \"pos_tags\"]       \n",
    "  return df,tags     "
   ],
   "metadata": {
    "id": "G9s_pxSEvgFK",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df_train,tags=create_df(train)\n",
    "df_test,test_tags=create_df(test)"
   ],
   "metadata": {
    "id": "E5ne3VQqwF1W",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df_test.head(5)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "Zad2K1wYwK6Q",
    "outputId": "c5024760-1972-4ea3-c49d-9b3b9a962024",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                              tokens  \\\n",
       "0      [هجري, شمسي, فوت, كرد, و, در, قم, دفن, شد, .]   \n",
       "1  [پروين, اعتصامي, ،, از, داستان, \", ويلانالدوله...   \n",
       "2  [محض, خالي, نبودن, عريضه, با, چايي, مقدار, معت...   \n",
       "3  [بعد, معلوم, ميشود, وقتيكه, ويلانالدوله, خواب,...   \n",
       "4  [ويلانالدوله, خدا, را, شكر, ميكند, كه, آخرش, پ...   \n",
       "\n",
       "                                            pos_tags  \n",
       "0                     [0, 0, 1, 2, 3, 4, 1, 1, 2, 5]  \n",
       "1  [1, 1, 5, 4, 1, 5, 1, 5, 1, 5, 6, 2, 6, 2, 5, ...  \n",
       "2  [4, 0, 1, 1, 4, 1, 1, 0, 1, 0, 0, 2, 5, 4, 6, ...  \n",
       "3  [3, 0, 2, 3, 1, 1, 2, 5, 1, 4, 1, 5, 1, 0, 0, ...  \n",
       "4  [1, 1, 4, 1, 2, 3, 1, 1, 4, 1, 1, 3, 1, 1, 2, ...  "
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tokens</th>\n",
       "      <th>pos_tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[هجري, شمسي, فوت, كرد, و, در, قم, دفن, شد, .]</td>\n",
       "      <td>[0, 0, 1, 2, 3, 4, 1, 1, 2, 5]</td>\n",
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       "      <th>1</th>\n",
       "      <td>[پروين, اعتصامي, ،, از, داستان, \", ويلانالدوله...</td>\n",
       "      <td>[1, 1, 5, 4, 1, 5, 1, 5, 1, 5, 6, 2, 6, 2, 5, ...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[محض, خالي, نبودن, عريضه, با, چايي, مقدار, معت...</td>\n",
       "      <td>[4, 0, 1, 1, 4, 1, 1, 0, 1, 0, 0, 2, 5, 4, 6, ...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[بعد, معلوم, ميشود, وقتيكه, ويلانالدوله, خواب,...</td>\n",
       "      <td>[3, 0, 2, 3, 1, 1, 2, 5, 1, 4, 1, 5, 1, 0, 0, ...</td>\n",
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       "      <td>[ويلانالدوله, خدا, را, شكر, ميكند, كه, آخرش, پ...</td>\n",
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     "metadata": {},
     "execution_count": 9
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  {
   "cell_type": "code",
   "source": [
    "\n",
    "ds_all = Dataset.from_pandas(df_train, features=Features({\n",
    "                \"tokens\": Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
    "                \"pos_tags\": Sequence(feature=ClassLabel(num_classes=len(tags), names=tags, names_file=None, id=None), length=-1, id=None)\n",
    "            })).train_test_split(test_size=0.2)\n",
    "ds_test = Dataset.from_pandas(df_test, features=Features({\n",
    "                \"tokens\": Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
    "                \"pos_tags\": Sequence(feature=ClassLabel(num_classes=len(tags), names=tags, names_file=None, id=None), length=-1, id=None)\n",
    "            }))\n",
    "dataset = DatasetDict({\n",
    "    \"train\": ds_all[\"train\"],\n",
    "    \"test\": ds_test,\n",
    "    \"validation\": ds_all[\"test\"]})\n"
   ],
   "metadata": {
    "id": "sCsJEoZSwugR",
    "pycharm": {
     "name": "#%%\n"
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   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "dataset[\"train\"].features"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2G4-AXwUzNYC",
    "outputId": "f0eb7afb-602e-4fb7-c261-f25d7631f02b",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'pos_tags': Sequence(feature=ClassLabel(num_classes=23, names=['V', 'DELM', 'N', 'ADJ', 'PRO', 'CON', 'P', 'QUA', 'ADV', 'DET', 'SPEC', 'IF', 'DEFAULT', 'MQUA', 'PP', 'INT', 'MORP', 'PS', 'MS', 'AR', 'NP', 'OHH', 'OH'], id=None), length=-1, id=None),\n",
       " 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def train_model(model_checkpoint):\n",
    "  tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
    "  label_all_tokens = True\n",
    "  def tokenize_and_align_labels(examples):\n",
    "      tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True)\n",
    "\n",
    "      labels = []\n",
    "      for i, label in enumerate(examples[\"pos_tags\"]):\n",
    "          word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
    "          previous_word_idx = None\n",
    "          label_ids = []\n",
    "          for word_idx in word_ids:\n",
    "              if word_idx is None:\n",
    "                  label_ids.append(-100)\n",
    "              elif word_idx != previous_word_idx:\n",
    "                  label_ids.append(label[word_idx])\n",
    "              else:\n",
    "                  label_ids.append(label[word_idx] if label_all_tokens else -100)\n",
    "              previous_word_idx = word_idx\n",
    "\n",
    "          labels.append(label_ids)\n",
    "\n",
    "      tokenized_inputs[\"labels\"] = labels\n",
    "      return tokenized_inputs\n",
    "  tokenized_dataset = dataset.map(tokenize_and_align_labels, batched=True)  \n",
    "  model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=len(tags))  \n",
    "\n",
    "  data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)\n",
    "  metric = load_metric(\"seqeval\")\n",
    "  return model,tokenized_dataset,tokenizer,data_collator"
   ],
   "metadata": {
    "id": "p9SCPRn25PG9",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model1,tokenized_dataset1,tokenizer1,data_collator1=train_model(\"HooshvareLab/bert-base-parsbert-uncased\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244,
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    "id": "JOT6ln2q7FPw",
    "outputId": "5a40720d-a5dc-4d9a-f27b-06738998664b",
    "pycharm": {
     "name": "#%%\n"
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   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  0%|          | 0/7 [00:00<?, ?ba/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "426ec6e75b034a70864d0cf4b029f4c1"
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?ba/s]"
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      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?ba/s]"
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      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of the model checkpoint at HooshvareLab/bert-base-parsbert-uncased were not used when initializing BertForTokenClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n",
      "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForTokenClassification were not initialized from the model checkpoint at HooshvareLab/bert-base-parsbert-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "metric = load_metric(\"seqeval\")\n",
    "def compute_metrics(p):\n",
    "    predictions, labels = p\n",
    "    predictions = np.argmax(predictions, axis=2)\n",
    "\n",
    "    true_predictions = [\n",
    "        [tags[p] for (p, l) in zip(prediction, label) if l != -100]\n",
    "        for prediction, label in zip(predictions, labels)\n",
    "    ]\n",
    "    true_labels = [\n",
    "        [tags[l] for (p, l) in zip(prediction, label) if l != -100]\n",
    "        for prediction, label in zip(predictions, labels)\n",
    "    ]\n",
    "\n",
    "    results = metric.compute(predictions=true_predictions, references=true_labels)\n",
    "    return {\n",
    "        \"accuracy\": results[\"overall_accuracy\"],\n",
    "    }"
   ],
   "metadata": {
    "id": "6erOOndM4Hi4",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    num_train_epochs=3,\n",
    "    weight_decay=0.01,\n",
    ")\n",
    "trainer = Trainer(\n",
    "    model=model1,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_dataset1[\"train\"],\n",
    "    eval_dataset=tokenized_dataset1[\"validation\"],\n",
    "    tokenizer=tokenizer1,\n",
    "    data_collator=data_collator1,\n",
    "    compute_metrics=compute_metrics\n",
    ")\n"
   ],
   "metadata": {
    "id": "LNvstOB61aeD",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "trainer.train()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "bA2pie7E10fw",
    "outputId": "60b9d1f6-97bd-4045-bf1f-241da0dc7d15",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 6977\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 16\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1311\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1311' max='1311' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1311/1311 05:19, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.188032</td>\n",
       "      <td>0.941397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.381900</td>\n",
       "      <td>0.159424</td>\n",
       "      <td>0.951663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.149200</td>\n",
       "      <td>0.150155</td>\n",
       "      <td>0.956461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 16\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to ./results/checkpoint-500\n",
      "Configuration saved in ./results/checkpoint-500/config.json\n",
      "Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 16\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to ./results/checkpoint-1000\n",
      "Configuration saved in ./results/checkpoint-1000/config.json\n",
      "Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-1000/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-1000/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 16\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1311, training_loss=0.23006468888551201, metrics={'train_runtime': 321.4897, 'train_samples_per_second': 65.106, 'train_steps_per_second': 4.078, 'total_flos': 1225443413949378.0, 'train_loss': 0.23006468888551201, 'epoch': 3.0})"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer.evaluate()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "SmxPAkyr17ox",
    "outputId": "a869082c-1226-4edb-da36-42a19b0e0801",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 16\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='110' max='110' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [110/110 00:06]\n",
       "    </div>\n",
       "    "
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'epoch': 3.0,\n",
       " 'eval_accuracy': 0.9564611157437152,\n",
       " 'eval_loss': 0.1501549780368805,\n",
       " 'eval_runtime': 9.6466,\n",
       " 'eval_samples_per_second': 180.893,\n",
       " 'eval_steps_per_second': 11.403}"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model2,tokenized_dataset2,tokenizer2,data_collator2=train_model(\"xlm-roberta-base\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    "outputId": "c07fa131-2693-408e-b7b2-46d6b159cc90",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
      "https://huggingface.co/xlm-roberta-base/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpldcej2zy\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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      }
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     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/xlm-roberta-base/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a\n",
      "creating metadata file for /root/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a\n",
      "loading configuration file https://huggingface.co/xlm-roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a\n",
      "Model config XLMRobertaConfig {\n",
      "  \"_name_or_path\": \"xlm-roberta-base\",\n",
      "  \"architectures\": [\n",
      "    \"XLMRobertaForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"xlm-roberta\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"output_past\": true,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.21.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 250002\n",
      "}\n",
      "\n",
      "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp6dupoyyw\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading sentencepiece.bpe.model:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
      ],
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       "version_major": 2,
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     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model in cache at /root/.cache/huggingface/transformers/9df9ae4442348b73950203b63d1b8ed2d18eba68921872aee0c3a9d05b9673c6.00628a9eeb8baf4080d44a0abe9fe8057893de20c7cb6e6423cddbf452f7d4d8\n",
      "creating metadata file for /root/.cache/huggingface/transformers/9df9ae4442348b73950203b63d1b8ed2d18eba68921872aee0c3a9d05b9673c6.00628a9eeb8baf4080d44a0abe9fe8057893de20c7cb6e6423cddbf452f7d4d8\n",
      "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpx_0vjami\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading tokenizer.json:   0%|          | 0.00/8.68M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "31d8298fac45479b9db22ea8b51c2fae"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json in cache at /root/.cache/huggingface/transformers/daeda8d936162ca65fe6dd158ecce1d8cb56c17d89b78ab86be1558eaef1d76a.a984cf52fc87644bd4a2165f1e07e0ac880272c1e82d648b4674907056912bd7\n",
      "creating metadata file for /root/.cache/huggingface/transformers/daeda8d936162ca65fe6dd158ecce1d8cb56c17d89b78ab86be1558eaef1d76a.a984cf52fc87644bd4a2165f1e07e0ac880272c1e82d648b4674907056912bd7\n",
      "loading file https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model from cache at /root/.cache/huggingface/transformers/9df9ae4442348b73950203b63d1b8ed2d18eba68921872aee0c3a9d05b9673c6.00628a9eeb8baf4080d44a0abe9fe8057893de20c7cb6e6423cddbf452f7d4d8\n",
      "loading file https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/daeda8d936162ca65fe6dd158ecce1d8cb56c17d89b78ab86be1558eaef1d76a.a984cf52fc87644bd4a2165f1e07e0ac880272c1e82d648b4674907056912bd7\n",
      "loading file https://huggingface.co/xlm-roberta-base/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/xlm-roberta-base/resolve/main/special_tokens_map.json from cache at None\n",
      "loading file https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer_config.json from cache at None\n",
      "loading configuration file https://huggingface.co/xlm-roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a\n",
      "Model config XLMRobertaConfig {\n",
      "  \"_name_or_path\": \"xlm-roberta-base\",\n",
      "  \"architectures\": [\n",
      "    \"XLMRobertaForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"xlm-roberta\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"output_past\": true,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.21.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 250002\n",
      "}\n",
      "\n"
     ]
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "loading configuration file https://huggingface.co/xlm-roberta-base/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/87683eb92ea383b0475fecf99970e950a03c9ff5e51648d6eee56fb754612465.dfaaaedc7c1c475302398f09706cbb21e23951b73c6e2b3162c1c8a99bb3b62a\n",
      "Model config XLMRobertaConfig {\n",
      "  \"_name_or_path\": \"xlm-roberta-base\",\n",
      "  \"architectures\": [\n",
      "    \"XLMRobertaForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"LABEL_0\",\n",
      "    \"1\": \"LABEL_1\",\n",
      "    \"2\": \"LABEL_2\",\n",
      "    \"3\": \"LABEL_3\",\n",
      "    \"4\": \"LABEL_4\",\n",
      "    \"5\": \"LABEL_5\",\n",
      "    \"6\": \"LABEL_6\",\n",
      "    \"7\": \"LABEL_7\",\n",
      "    \"8\": \"LABEL_8\",\n",
      "    \"9\": \"LABEL_9\",\n",
      "    \"10\": \"LABEL_10\",\n",
      "    \"11\": \"LABEL_11\",\n",
      "    \"12\": \"LABEL_12\",\n",
      "    \"13\": \"LABEL_13\",\n",
      "    \"14\": \"LABEL_14\",\n",
      "    \"15\": \"LABEL_15\",\n",
      "    \"16\": \"LABEL_16\",\n",
      "    \"17\": \"LABEL_17\",\n",
      "    \"18\": \"LABEL_18\",\n",
      "    \"19\": \"LABEL_19\",\n",
      "    \"20\": \"LABEL_20\",\n",
      "    \"21\": \"LABEL_21\",\n",
      "    \"22\": \"LABEL_22\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"LABEL_0\": 0,\n",
      "    \"LABEL_1\": 1,\n",
      "    \"LABEL_10\": 10,\n",
      "    \"LABEL_11\": 11,\n",
      "    \"LABEL_12\": 12,\n",
      "    \"LABEL_13\": 13,\n",
      "    \"LABEL_14\": 14,\n",
      "    \"LABEL_15\": 15,\n",
      "    \"LABEL_16\": 16,\n",
      "    \"LABEL_17\": 17,\n",
      "    \"LABEL_18\": 18,\n",
      "    \"LABEL_19\": 19,\n",
      "    \"LABEL_2\": 2,\n",
      "    \"LABEL_20\": 20,\n",
      "    \"LABEL_21\": 21,\n",
      "    \"LABEL_22\": 22,\n",
      "    \"LABEL_3\": 3,\n",
      "    \"LABEL_4\": 4,\n",
      "    \"LABEL_5\": 5,\n",
      "    \"LABEL_6\": 6,\n",
      "    \"LABEL_7\": 7,\n",
      "    \"LABEL_8\": 8,\n",
      "    \"LABEL_9\": 9\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"xlm-roberta\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"output_past\": true,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.21.1\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 250002\n",
      "}\n",
      "\n",
      "https://huggingface.co/xlm-roberta-base/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmplfef9t3q\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/xlm-roberta-base/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/97d0ea09f8074264957d062ec20ccb79af7b917d091add8261b26874daf51b5d.f42212747c1c27fcebaa0a89e2a83c38c6d3d4340f21922f892b88d882146ac2\n",
      "creating metadata file for /root/.cache/huggingface/transformers/97d0ea09f8074264957d062ec20ccb79af7b917d091add8261b26874daf51b5d.f42212747c1c27fcebaa0a89e2a83c38c6d3d4340f21922f892b88d882146ac2\n",
      "loading weights file https://huggingface.co/xlm-roberta-base/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/97d0ea09f8074264957d062ec20ccb79af7b917d091add8261b26874daf51b5d.f42212747c1c27fcebaa0a89e2a83c38c6d3d4340f21922f892b88d882146ac2\n",
      "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForTokenClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']\n",
      "- This IS expected if you are initializing XLMRobertaForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing XLMRobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of XLMRobertaForTokenClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "training_args2 = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    num_train_epochs=3,\n",
    "    weight_decay=0.01,\n",
    ")\n",
    "trainer2 = Trainer(\n",
    "    model=model2,\n",
    "    args=training_args2,\n",
    "    train_dataset=tokenized_dataset2[\"train\"],\n",
    "    eval_dataset=tokenized_dataset2[\"validation\"],\n",
    "    tokenizer=tokenizer2,\n",
    "    data_collator=data_collator2,\n",
    "    compute_metrics=compute_metrics\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ecMZ9FVR-jNn",
    "outputId": "0b61c402-2c9c-4db6-e9de-4dcb7419f6fd",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer2.train()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_jRirVzZ-woD",
    "outputId": "a80a99cd-ecff-4634-f10e-95e8427d524c",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The following columns in the training set don't have a corresponding argument in `XLMRobertaForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `XLMRobertaForTokenClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 6977\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 2619\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='2619' max='2619' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [2619/2619 07:44, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.325300</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.971574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.085900</td>\n",
       "      <td>0.078606</td>\n",
       "      <td>0.977512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.059100</td>\n",
       "      <td>0.076339</td>\n",
       "      <td>0.978700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Saving model checkpoint to ./results/checkpoint-500\n",
      "Configuration saved in ./results/checkpoint-500/config.json\n",
      "Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `XLMRobertaForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `XLMRobertaForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to ./results/checkpoint-1000\n",
      "Configuration saved in ./results/checkpoint-1000/config.json\n",
      "Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-1000/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-1000/special_tokens_map.json\n",
      "Saving model checkpoint to ./results/checkpoint-1500\n",
      "Configuration saved in ./results/checkpoint-1500/config.json\n",
      "Model weights saved in ./results/checkpoint-1500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-1500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-1500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `XLMRobertaForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `XLMRobertaForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to ./results/checkpoint-2000\n",
      "Configuration saved in ./results/checkpoint-2000/config.json\n",
      "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-2000/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-2000/special_tokens_map.json\n",
      "Saving model checkpoint to ./results/checkpoint-2500\n",
      "Configuration saved in ./results/checkpoint-2500/config.json\n",
      "Model weights saved in ./results/checkpoint-2500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-2500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-2500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `XLMRobertaForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `XLMRobertaForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TrainOutput(global_step=2619, training_loss=0.12874314620231753, metrics={'train_runtime': 465.0918, 'train_samples_per_second': 45.004, 'train_steps_per_second': 5.631, 'total_flos': 1035378758496312.0, 'train_loss': 0.12874314620231753, 'epoch': 3.0})"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer2.evaluate()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "sl9CwMZz-47B",
    "outputId": "565126d0-5119-46c4-8539-2e08845cd19a",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `XLMRobertaForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `XLMRobertaForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='219' max='219' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [219/219 00:06]\n",
       "    </div>\n",
       "    "
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'epoch': 3.0,\n",
       " 'eval_accuracy': 0.9786997831250646,\n",
       " 'eval_loss': 0.07633917033672333,\n",
       " 'eval_runtime': 9.6144,\n",
       " 'eval_samples_per_second': 181.499,\n",
       " 'eval_steps_per_second': 22.778}"
      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model3,tokenized_dataset3,tokenizer3,data_collator3=train_model(\"bert-base-cased\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    },
    "id": "5abu_yDA-_aM",
    "outputId": "317b96f3-257c-472b-cba0-04a3c0f98110",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "https://huggingface.co/bert-base-cased/resolve/main/tokenizer_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpultx2lin\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8fb5f33513b74b76b53de8351b8e0990"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/bert-base-cased/resolve/main/tokenizer_config.json in cache at /root/.cache/huggingface/transformers/ec84e86ee39bfe112543192cf981deebf7e6cbe8c91b8f7f8f63c9be44366158.ec5c189f89475aac7d8cbd243960a0655cfadc3d0474da8ff2ed0bf1699c2a5f\n",
      "creating metadata file for /root/.cache/huggingface/transformers/ec84e86ee39bfe112543192cf981deebf7e6cbe8c91b8f7f8f63c9be44366158.ec5c189f89475aac7d8cbd243960a0655cfadc3d0474da8ff2ed0bf1699c2a5f\n",
      "https://huggingface.co/bert-base-cased/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmp2091qyif\n"
     ]
    },
    {
     "output_type": "display_data",
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       "Downloading config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1942b2f958dc4f54bd29c6203d17f2e7"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/bert-base-cased/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/a803e0468a8fe090683bdc453f4fac622804f49de86d7cecaee92365d4a0f829.a64a22196690e0e82ead56f388a3ef3a50de93335926ccfa20610217db589307\n",
      "creating metadata file for /root/.cache/huggingface/transformers/a803e0468a8fe090683bdc453f4fac622804f49de86d7cecaee92365d4a0f829.a64a22196690e0e82ead56f388a3ef3a50de93335926ccfa20610217db589307\n",
      "loading configuration file https://huggingface.co/bert-base-cased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/a803e0468a8fe090683bdc453f4fac622804f49de86d7cecaee92365d4a0f829.a64a22196690e0e82ead56f388a3ef3a50de93335926ccfa20610217db589307\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-cased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.21.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 28996\n",
      "}\n",
      "\n",
      "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpy5wli91k\n"
     ]
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9eb88ab60cc84cb6b56aba66635c7aae"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/bert-base-cased/resolve/main/vocab.txt in cache at /root/.cache/huggingface/transformers/6508e60ab3c1200bffa26c95f4b58ac6b6d95fba4db1f195f632fa3cd7bc64cc.437aa611e89f6fc6675a049d2b5545390adbc617e7d655286421c191d2be2791\n",
      "creating metadata file for /root/.cache/huggingface/transformers/6508e60ab3c1200bffa26c95f4b58ac6b6d95fba4db1f195f632fa3cd7bc64cc.437aa611e89f6fc6675a049d2b5545390adbc617e7d655286421c191d2be2791\n",
      "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpl2yi2b9w\n"
     ]
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "75d2a6760e124228b6f40283f2c89c7e"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json in cache at /root/.cache/huggingface/transformers/226a307193a9f4344264cdc76a12988448a25345ba172f2c7421f3b6810fddad.3dab63143af66769bbb35e3811f75f7e16b2320e12b7935e216bd6159ce6d9a6\n",
      "creating metadata file for /root/.cache/huggingface/transformers/226a307193a9f4344264cdc76a12988448a25345ba172f2c7421f3b6810fddad.3dab63143af66769bbb35e3811f75f7e16b2320e12b7935e216bd6159ce6d9a6\n",
      "loading file https://huggingface.co/bert-base-cased/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/6508e60ab3c1200bffa26c95f4b58ac6b6d95fba4db1f195f632fa3cd7bc64cc.437aa611e89f6fc6675a049d2b5545390adbc617e7d655286421c191d2be2791\n",
      "loading file https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/226a307193a9f4344264cdc76a12988448a25345ba172f2c7421f3b6810fddad.3dab63143af66769bbb35e3811f75f7e16b2320e12b7935e216bd6159ce6d9a6\n",
      "loading file https://huggingface.co/bert-base-cased/resolve/main/added_tokens.json from cache at None\n",
      "loading file https://huggingface.co/bert-base-cased/resolve/main/special_tokens_map.json from cache at None\n",
      "loading file https://huggingface.co/bert-base-cased/resolve/main/tokenizer_config.json from cache at /root/.cache/huggingface/transformers/ec84e86ee39bfe112543192cf981deebf7e6cbe8c91b8f7f8f63c9be44366158.ec5c189f89475aac7d8cbd243960a0655cfadc3d0474da8ff2ed0bf1699c2a5f\n",
      "loading configuration file https://huggingface.co/bert-base-cased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/a803e0468a8fe090683bdc453f4fac622804f49de86d7cecaee92365d4a0f829.a64a22196690e0e82ead56f388a3ef3a50de93335926ccfa20610217db589307\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-cased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.21.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 28996\n",
      "}\n",
      "\n"
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "loading configuration file https://huggingface.co/bert-base-cased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/a803e0468a8fe090683bdc453f4fac622804f49de86d7cecaee92365d4a0f829.a64a22196690e0e82ead56f388a3ef3a50de93335926ccfa20610217db589307\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-cased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"LABEL_0\",\n",
      "    \"1\": \"LABEL_1\",\n",
      "    \"2\": \"LABEL_2\",\n",
      "    \"3\": \"LABEL_3\",\n",
      "    \"4\": \"LABEL_4\",\n",
      "    \"5\": \"LABEL_5\",\n",
      "    \"6\": \"LABEL_6\",\n",
      "    \"7\": \"LABEL_7\",\n",
      "    \"8\": \"LABEL_8\",\n",
      "    \"9\": \"LABEL_9\",\n",
      "    \"10\": \"LABEL_10\",\n",
      "    \"11\": \"LABEL_11\",\n",
      "    \"12\": \"LABEL_12\",\n",
      "    \"13\": \"LABEL_13\",\n",
      "    \"14\": \"LABEL_14\",\n",
      "    \"15\": \"LABEL_15\",\n",
      "    \"16\": \"LABEL_16\",\n",
      "    \"17\": \"LABEL_17\",\n",
      "    \"18\": \"LABEL_18\",\n",
      "    \"19\": \"LABEL_19\",\n",
      "    \"20\": \"LABEL_20\",\n",
      "    \"21\": \"LABEL_21\",\n",
      "    \"22\": \"LABEL_22\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"LABEL_0\": 0,\n",
      "    \"LABEL_1\": 1,\n",
      "    \"LABEL_10\": 10,\n",
      "    \"LABEL_11\": 11,\n",
      "    \"LABEL_12\": 12,\n",
      "    \"LABEL_13\": 13,\n",
      "    \"LABEL_14\": 14,\n",
      "    \"LABEL_15\": 15,\n",
      "    \"LABEL_16\": 16,\n",
      "    \"LABEL_17\": 17,\n",
      "    \"LABEL_18\": 18,\n",
      "    \"LABEL_19\": 19,\n",
      "    \"LABEL_2\": 2,\n",
      "    \"LABEL_20\": 20,\n",
      "    \"LABEL_21\": 21,\n",
      "    \"LABEL_22\": 22,\n",
      "    \"LABEL_3\": 3,\n",
      "    \"LABEL_4\": 4,\n",
      "    \"LABEL_5\": 5,\n",
      "    \"LABEL_6\": 6,\n",
      "    \"LABEL_7\": 7,\n",
      "    \"LABEL_8\": 8,\n",
      "    \"LABEL_9\": 9\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.21.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 28996\n",
      "}\n",
      "\n",
      "https://huggingface.co/bert-base-cased/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpj4l561gc\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/416M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5df9d61c1767465f9eb6401de504b98d"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "storing https://huggingface.co/bert-base-cased/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/092cc582560fc3833e556b3f833695c26343cb54b7e88cd02d40821462a74999.1f48cab6c959fc6c360d22bea39d06959e90f5b002e77e836d2da45464875cda\n",
      "creating metadata file for /root/.cache/huggingface/transformers/092cc582560fc3833e556b3f833695c26343cb54b7e88cd02d40821462a74999.1f48cab6c959fc6c360d22bea39d06959e90f5b002e77e836d2da45464875cda\n",
      "loading weights file https://huggingface.co/bert-base-cased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/092cc582560fc3833e556b3f833695c26343cb54b7e88cd02d40821462a74999.1f48cab6c959fc6c360d22bea39d06959e90f5b002e77e836d2da45464875cda\n",
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
      "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "training_args3 = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    num_train_epochs=3,\n",
    "    weight_decay=0.01,\n",
    ")\n",
    "trainer3 = Trainer(\n",
    "    model=model3,\n",
    "    args=training_args3,\n",
    "    train_dataset=tokenized_dataset3[\"train\"],\n",
    "    eval_dataset=tokenized_dataset3[\"validation\"],\n",
    "    tokenizer=tokenizer3,\n",
    "    data_collator=data_collator3,\n",
    "    compute_metrics=compute_metrics\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t4mB35ZtBahN",
    "outputId": "385e6800-24f6-47dc-e6d0-f51a4cbc02ce",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer3.train()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Em_tsSTsBqh1",
    "outputId": "c71b8425-a344-4bc4-d379-c3e6671891d5",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 6977\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 2619\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='2619' max='2619' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [2619/2619 10:43, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.268479</td>\n",
       "      <td>0.911530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.242800</td>\n",
       "      <td>0.203992</td>\n",
       "      <td>0.933764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.182300</td>\n",
       "      <td>0.187085</td>\n",
       "      <td>0.941973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Saving model checkpoint to ./results/checkpoint-500\n",
      "Configuration saved in ./results/checkpoint-500/config.json\n",
      "Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to ./results/checkpoint-1000\n",
      "Configuration saved in ./results/checkpoint-1000/config.json\n",
      "Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-1000/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-1000/special_tokens_map.json\n",
      "Saving model checkpoint to ./results/checkpoint-1500\n",
      "Configuration saved in ./results/checkpoint-1500/config.json\n",
      "Model weights saved in ./results/checkpoint-1500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-1500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-1500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "Saving model checkpoint to ./results/checkpoint-2000\n",
      "Configuration saved in ./results/checkpoint-2000/config.json\n",
      "Model weights saved in ./results/checkpoint-2000/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-2000/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-2000/special_tokens_map.json\n",
      "Saving model checkpoint to ./results/checkpoint-2500\n",
      "Configuration saved in ./results/checkpoint-2500/config.json\n",
      "Model weights saved in ./results/checkpoint-2500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-2500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-2500/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TrainOutput(global_step=2619, training_loss=0.31185966031430284, metrics={'train_runtime': 643.7981, 'train_samples_per_second': 32.512, 'train_steps_per_second': 4.068, 'total_flos': 2473627024433856.0, 'train_loss': 0.31185966031430284, 'epoch': 3.0})"
      ]
     },
     "metadata": {},
     "execution_count": 44
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "trainer3.evaluate()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "3mFN82paBvTs",
    "outputId": "cb2923a9-12a6-4e0f-9df0-be677febcb53",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, pos_tags. If tokens, pos_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1745\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='219' max='219' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [219/219 00:14]\n",
       "    </div>\n",
       "    "
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: N seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DELM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADJ seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: CON seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: P seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DET seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: QUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PRO seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MQUA seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: IF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MORP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: MS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: SPEC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: NP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: DEFAULT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: INT seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: OH seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PS seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/usr/local/lib/python3.7/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'epoch': 3.0,\n",
       " 'eval_accuracy': 0.9419725273843224,\n",
       " 'eval_loss': 0.1870846301317215,\n",
       " 'eval_runtime': 21.3843,\n",
       " 'eval_samples_per_second': 81.602,\n",
       " 'eval_steps_per_second': 10.241}"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "CDWdJ80qBy_g",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}