nlp-disaster-architecture-roberta.ipynb

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   "source": [
    "# Imports"
   ]
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      "2024-07-04 09:39:42.912464: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-07-04 09:39:42.912582: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-07-04 09:39:43.044339: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
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   "source": [
    "import os\n",
    "\n",
    "import warnings\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "## General imports \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import string\n",
    "\n",
    "## Torch library\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "## Sklearn ilbrary\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\n",
    "\n",
    "## Transformers library\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "from transformers import DataCollatorWithPadding\n",
    "from transformers import AdamW, get_scheduler\n",
    "\n",
    "\n",
    "import re\n",
    "from collections import Counter\n",
    "\n",
    "from tqdm.auto import tqdm\n"
   ]
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   id keyword location                                               text  \\\n",
       "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
       "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
       "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
       "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
       "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
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   "source": [
    "# Loading the data\n",
    "\n",
    "df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')\n",
    "test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')\n",
    "sample_sub = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')\n",
    "df.head()"
   ]
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   },
   "source": [
    "# Data Cleaning "
   ]
  },
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   "source": [
    "Now we need to remove certain text parts. Those parts does not contribute to the overall score and also might confuse the text\n",
    "\n",
    " - Removing urls\n",
    " - Removing HTML tags\n",
    " - Removing Emojis\n",
    " - Removing punctuations"
   ]
  },
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    "####### Removing urls\n",
    "def remove_URL(text):\n",
    "    url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
    "    return url.sub(r'',text)\n",
    "\n",
    "####### Removing HTML tags\n",
    "def remove_html(text):\n",
    "    html=re.compile(r'<.*?>')\n",
    "    return html.sub(r'',text)\n",
    "\n",
    "####### Removing Emojis\n",
    "def remove_emoji(text):\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           u\"\\U00002702-\\U000027B0\"\n",
    "                           u\"\\U000024C2-\\U0001F251\"\n",
    "                           \"]+\", flags=re.UNICODE)\n",
    "    return emoji_pattern.sub(r'', text)\n",
    "\n",
    "\n",
    "####### Removing punctuations\n",
    "def remove_punct(text):\n",
    "    table=str.maketrans('','',string.punctuation)\n",
    "    return text.translate(table)"
   ]
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   "source": [
    "##### Performing the function will built on all the text using lambda principal #####\n",
    "df['text']=df['text'].apply(lambda x : remove_URL(x))\n",
    "df['text']=df['text'].apply(lambda x : remove_html(x))\n",
    "df['text']=df['text'].apply(lambda x: remove_emoji(x))\n",
    "df['text']=df['text'].apply(lambda x : remove_punct(x))\n",
    "\n",
    "test['text']=test['text'].apply(lambda x : remove_URL(x))\n",
    "test['text']=test['text'].apply(lambda x : remove_html(x))\n",
    "test['text']=test['text'].apply(lambda x: remove_emoji(x))\n",
    "test['text']=test['text'].apply(lambda x : remove_punct(x))"
   ]
  },
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   },
   "source": [
    "# Modeling - Architcture "
   ]
  },
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      "/opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n",
      "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "# Load model directly\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"cardiffnlp/twitter-roberta-base-sentiment-latest\")"
   ]
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   "source": [
    "# 1. Tokenize\n",
    "The first step after the preprocessing is to tokenize the data. Tokenization is the process where words are converted into numbers and ids. Each word or part of a word (depend on the tokenizer) are converted into numbers that are readable for the machine\n",
    "\n",
    "Later we splitting the data into train and validation sets "
   ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "# Tokenize the train dataset and add target column \n",
    "def tokenize_train(df):\n",
    "    \n",
    "    tokenized_texts = []\n",
    "    \n",
    "    # For each text & label we tokenize the text and add the label target into the dictonary\n",
    "    for text, label in zip(df[\"text\"], df[\"target\"]):\n",
    "        tokenized_text = tokenizer(text, truncation=True)\n",
    "        tokenized_text[\"labels\"] = label\n",
    "        tokenized_texts.append(tokenized_text)\n",
    "        \n",
    "    return tokenized_texts\n",
    "\n",
    "# Tokenize the test dataset \n",
    "def tokenize_test(test):\n",
    "    tokenized_texts = []\n",
    "    for text in test[\"text\"]:\n",
    "          tokenized_texts.append(tokenizer(text, truncation=True))\n",
    "    return tokenized_texts\n",
    "            \n",
    "tokenized_texts = tokenize_train(df)\n",
    "test_texts = tokenize_test(test)"
   ]
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     "text": [
      "6090\n",
      "1523\n"
     ]
    }
   ],
   "source": [
    "# Split the whole text set into train and validation\n",
    "train_texts, val_texts = train_test_split(tokenized_texts, test_size=0.2, random_state=42)  # Set a seed for reproducibility\n",
    "\n",
    "print(len(train_texts))\n",
    "print(len(val_texts))"
   ]
  },
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   "source": [
    "# 2. Split to batches "
   ]
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    "papermill": {
     "duration": 0.015802,
     "end_time": "2024-07-04T09:40:00.990286",
     "exception": false,
     "start_time": "2024-07-04T09:40:00.974484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
    "\n",
    "## Set train texts DataLoader\n",
    "train_dataloader = DataLoader(\n",
    "    train_texts, shuffle=True, batch_size=8, collate_fn=data_collator\n",
    ")\n",
    "\n",
    "## Set val texts DataLoader\n",
    "eval_dataloader = DataLoader(\n",
    "    val_texts, batch_size=8, collate_fn=data_collator\n",
    ")\n",
    "\n",
    "## Set test texts DataLoader\n",
    "test_dataloader = DataLoader(\n",
    "    test_texts, batch_size=8, collate_fn=data_collator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "94046acc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-04T09:40:01.005847Z",
     "iopub.status.busy": "2024-07-04T09:40:01.005587Z",
     "iopub.status.idle": "2024-07-04T09:40:01.023242Z",
     "shell.execute_reply": "2024-07-04T09:40:01.022415Z"
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     "duration": 0.027762,
     "end_time": "2024-07-04T09:40:01.025240",
     "exception": false,
     "start_time": "2024-07-04T09:40:00.997478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': torch.Size([8, 37]),\n",
       " 'attention_mask': torch.Size([8, 37]),\n",
       " 'labels': torch.Size([8])}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Small test to see that everything went well with the Dataloader method\n",
    "for batch in train_dataloader:\n",
    "    break\n",
    "{k: v.shape for k, v in batch.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2a775e",
   "metadata": {
    "papermill": {
     "duration": 0.007337,
     "end_time": "2024-07-04T09:40:01.039974",
     "exception": false,
     "start_time": "2024-07-04T09:40:01.032637",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db3f37e8",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:588: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cdfd09c3a9c44fc929e1d12ee3b2e14",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2286 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create optimizer\n",
    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
    "\n",
    "# Move to GPU to train\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "# Set epochs numbers, number of training steps and learning rate\n",
    "num_epochs = 3\n",
    "num_training_steps = num_epochs * len(train_dataloader)\n",
    "lr_scheduler = get_scheduler(\n",
    "    \"linear\",\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0,\n",
    "    num_training_steps=num_training_steps,\n",
    ")\n",
    "\n",
    "progress_bar = tqdm(range(num_training_steps))\n",
    "\n",
    "# Train the model\n",
    "model.train()\n",
    "for epoch in range(num_epochs):\n",
    "    # In each batch of the train_dataloader compute \n",
    "    for batch in train_dataloader:\n",
    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
    "        outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "        progress_bar.update(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68dfc5d7",
   "metadata": {
    "papermill": {
     "duration": 0.007658,
     "end_time": "2024-07-04T09:42:01.885754",
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     "start_time": "2024-07-04T09:42:01.878096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Evaluate the model using the validation set\n",
    "use metrics from evaluate library in order to give an estimation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "500a6ad1",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "  pid, fd = os.forkpty()\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting evaluate\r\n",
      "  Downloading evaluate-0.4.2-py3-none-any.whl.metadata (9.3 kB)\r\n",
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      "Downloading evaluate-0.4.2-py3-none-any.whl (84 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: evaluate\r\n",
      "Successfully installed evaluate-0.4.2\r\n"
     ]
    },
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       "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
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    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8240315167432699, 'f1': 0.7947932618683}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install evaluate\n",
    "import evaluate\n",
    "\n",
    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
    "model.eval()\n",
    "for batch in eval_dataloader:\n",
    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**batch)\n",
    "\n",
    "    logits = outputs.logits\n",
    "    predictions = torch.argmax(logits, dim=-1)\n",
    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
    "\n",
    "metric.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e8edf8",
   "metadata": {
    "papermill": {
     "duration": 0.009081,
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     "start_time": "2024-07-04T09:42:19.926873",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Making the predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "56f7400a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-04T09:42:19.956834Z",
     "iopub.status.busy": "2024-07-04T09:42:19.955644Z",
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   "outputs": [],
   "source": [
    "model.eval()\n",
    "    \n",
    "predictions = []  # List to store predictions\n",
    "\n",
    "for batch in test_dataloader:  \n",
    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**batch)\n",
    "        logits = outputs.logits\n",
    "        predictions.extend(torch.argmax(logits, dim=-1).cpu().tolist())  # Append predictions to list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d0cd671",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-04T09:42:25.408491Z",
     "iopub.status.busy": "2024-07-04T09:42:25.407922Z",
     "iopub.status.idle": "2024-07-04T09:42:25.421698Z",
     "shell.execute_reply": "2024-07-04T09:42:25.420969Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Insert the predictions into the submission file\n",
    "sample_sub[\"target\"] = predictions\n",
    "sample_sub.to_csv('submission.csv',index=False)"
   ]
  }
 ],
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