bert-ner-bhavneek-v5.ipynb

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   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
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     "text": [
      "Requirement already satisfied: transformers in /opt/conda/lib/python3.7/site-packages (4.27.4)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (2021.11.10)\r\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers) (4.11.4)\r\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers) (0.13.2)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers) (2.28.2)\r\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /opt/conda/lib/python3.7/site-packages (from transformers) (0.13.3)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers) (3.9.0)\r\n",
      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (1.21.6)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from transformers) (6.0)\r\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers) (4.64.1)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from transformers) (23.0)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.4.0)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers) (3.11.0)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (1.26.14)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2022.12.7)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (3.4)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2.1.1)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "from transformers import BertTokenizerFast\n",
    "from transformers import BertForTokenClassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c53f094",
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   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/ner-data/ner.csv\")"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thousands of demonstrators have marched throug...</td>\n",
       "      <td>O O O O O O B-geo O O O O O B-geo O O O O O B-...</td>\n",
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       "      <td>Iranian officials say they expect to get acces...</td>\n",
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       "      <td>Helicopter gunships Saturday pounded militant ...</td>\n",
       "      <td>O O B-tim O O O O O B-geo O O O O O B-org O O ...</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>U.N. relief coordinator Jan Egeland said Sunda...</td>\n",
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       "                                                text  \\\n",
       "0  Thousands of demonstrators have marched throug...   \n",
       "1  Iranian officials say they expect to get acces...   \n",
       "2  Helicopter gunships Saturday pounded militant ...   \n",
       "3  They left after a tense hour-long standoff wit...   \n",
       "4  U.N. relief coordinator Jan Egeland said Sunda...   \n",
       "\n",
       "                                              labels  \n",
       "0  O O O O O O B-geo O O O O O B-geo O O O O O B-...  \n",
       "1  B-gpe O O O O O O O O O O O O O O B-tim O O O ...  \n",
       "2  O O B-tim O O O O O B-geo O O O O O B-org O O ...  \n",
       "3                              O O O O O O O O O O O  \n",
       "4  B-geo O O B-per I-per O B-tim O B-geo O B-gpe ...  "
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    "df.head()"
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    {
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       "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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   "source": [
    "tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')"
   ]
  },
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   "outputs": [],
   "source": [
    "maxlen = 0\n",
    "\n",
    "labels = []\n",
    "for i in df['labels'].values.tolist():\n",
    "    if isinstance(i, str):\n",
    "        labels.append(i.split())\n",
    "    else:\n",
    "        labels.append([])\n",
    "\n",
    "unique_labels = set()\n",
    "for lb in labels:\n",
    "  maxlen = max(maxlen, len(lb))\n",
    "  [unique_labels.add(i) for i in lb if i not in unique_labels]\n",
    "\n",
    "\n",
    "sentences = df['text'].apply(tokenizer).values\n",
    "for sts in sentences:\n",
    "  maxlen = max(maxlen, len(sts['input_ids']))"
   ]
  },
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'I-eve', 'I-nat', 'B-gpe', 'B-art', 'B-eve', 'B-org', 'I-geo', 'I-tim', 'B-geo', 'I-art', 'I-per', 'B-nat', 'I-org', 'B-tim', 'O', 'I-gpe', 'B-per'}\n",
      "156\n"
     ]
    }
   ],
   "source": [
    "print(unique_labels)\n",
    "print(maxlen)"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "df_hold, df_train, df_val, df_test = np.split(df.sample(frac=1,random_state=42), [int(0 * len(df)), int(.96 * len(df)), int(.98 * len(df))])"
   ]
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     "name": "stdout",
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     "text": [
      "0 46040 959 960\n"
     ]
    }
   ],
   "source": [
    "print(len(df_hold), len(df_train), len(df_val), len(df_test))"
   ]
  },
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   "source": [
    "def my_tokenizer(sentence):\n",
    "  tokens = tokenizer.tokenize(sentence)\n",
    "  tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
    "\n",
    "  padded_tokens = tokens + ['[PAD]' for _ in range(maxlen - len(tokens))]\n",
    "\n",
    "  inp = torch.tensor(tokenizer.convert_tokens_to_ids(padded_tokens), dtype = torch.int32)\n",
    "\n",
    "  attn = torch.tensor([1 if token != '[PAD]' else 0 for token in padded_tokens], dtype = torch.int32)\n",
    "  seg = torch.tensor([0 for _ in range(len(padded_tokens))], dtype = torch.int32)\n",
    "\n",
    "  return inp, attn, seg"
   ]
  },
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   "source": [
    "def tokenize_column(column):\n",
    "    \"\"\"Preprocesses a Pandas column and returns input ids, attention masks, and token type ids as 2D arrays.\"\"\"\n",
    "    input_ids = torch.empty(0, maxlen)\n",
    "    attention_masks = torch.empty(0, maxlen)\n",
    "    token_type_ids = torch.empty(0, maxlen)\n",
    "    \n",
    "    for text in column:\n",
    "        inp, attn, seg = my_tokenizer(text)\n",
    "        input_ids = torch.cat((input_ids, inp.unsqueeze(0)), dim=0)\n",
    "        attention_masks = torch.cat((attention_masks, attn.unsqueeze(0)), dim=0)\n",
    "        token_type_ids = torch.cat((token_type_ids, seg.unsqueeze(0)), dim=0)\n",
    "\n",
    "    input_ids = input_ids.long()\n",
    "    attention_masks = attention_masks.long()\n",
    "    token_type_ids = token_type_ids.long()\n",
    "    return input_ids, attention_masks, token_type_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3888eb98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:13:53.638743Z",
     "iopub.status.busy": "2023-04-27T17:13:53.638446Z",
     "iopub.status.idle": "2023-04-27T17:13:53.645952Z",
     "shell.execute_reply": "2023-04-27T17:13:53.644967Z"
    },
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    "outputId": "7935f7b2-4826-41d9-8bde-9fb027368e29",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I-eve': 1,\n",
       " 'I-nat': 2,\n",
       " 'B-gpe': 3,\n",
       " 'B-art': 4,\n",
       " 'B-eve': 5,\n",
       " 'B-org': 6,\n",
       " 'I-geo': 7,\n",
       " 'I-tim': 8,\n",
       " 'B-geo': 9,\n",
       " 'I-art': 10,\n",
       " 'I-per': 11,\n",
       " 'B-nat': 12,\n",
       " 'I-org': 13,\n",
       " 'B-tim': 14,\n",
       " 'O': 15,\n",
       " 'I-gpe': 16,\n",
       " 'B-per': 17}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,18) # 1-17 because we will have a 0 class for empty slots, i.e no word there\n",
    "dict = {}\n",
    "for A, B in zip(unique_labels, arr):\n",
    "    dict[A] = B\n",
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a14dd551",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:13:53.664561Z",
     "iopub.status.busy": "2023-04-27T17:13:53.664295Z",
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     "shell.execute_reply": "2023-04-27T17:13:58.857025Z"
    },
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    "papermill": {
     "duration": 5.205068,
     "end_time": "2023-04-27T17:13:58.860884",
     "exception": false,
     "start_time": "2023-04-27T17:13:53.655816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "def cat_labelize(column):\n",
    "  labels = [i.split() for i in column.values.tolist()]\n",
    "  Y_labels = []\n",
    "  for i in range(len(labels)):\n",
    "    arr = []\n",
    "    for j in range(len(labels[i])):\n",
    "      arr.append(int(dict[labels[i][j]]))\n",
    "    arr = np.array(arr, dtype = 'int32').astype('int32')\n",
    "    Y_labels.append(arr)\n",
    "  Y_labels = np.array(Y_labels)\n",
    "  Y_cat = pad_sequences(Y_labels, maxlen = maxlen,  padding = 'post', truncating = 'post')\n",
    "  Y_cat = to_categorical(Y_cat, num_classes=18)\n",
    "  return Y_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "410ba7c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:13:58.881199Z",
     "iopub.status.busy": "2023-04-27T17:13:58.880249Z",
     "iopub.status.idle": "2023-04-27T17:26:40.616609Z",
     "shell.execute_reply": "2023-04-27T17:26:40.615479Z"
    },
    "id": "qEdVpQBTVuqM",
    "papermill": {
     "duration": 761.748673,
     "end_time": "2023-04-27T17:26:40.619406",
     "exception": false,
     "start_time": "2023-04-27T17:13:58.870733",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "inp_train, attn_train, seg_train = tokenize_column(df_train['text'])\n",
    "inp_val, attn_val, seg_val = tokenize_column(df_val['text'])\n",
    "inp_test, attn_test, seg_test = tokenize_column(df_test['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cded963d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:40.637092Z",
     "iopub.status.busy": "2023-04-27T17:26:40.636762Z",
     "iopub.status.idle": "2023-04-27T17:26:42.089644Z",
     "shell.execute_reply": "2023-04-27T17:26:42.088263Z"
    },
    "id": "2SFW4U1fO7Rm",
    "outputId": "974a1f0d-c670-4e01-cffc-5726b5d23bc4",
    "papermill": {
     "duration": 1.464712,
     "end_time": "2023-04-27T17:26:42.092563",
     "exception": false,
     "start_time": "2023-04-27T17:26:40.627851",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "ycat_train = cat_labelize(df_train['labels'])\n",
    "ycat_val = cat_labelize(df_val['labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "247df941",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.110819Z",
     "iopub.status.busy": "2023-04-27T17:26:42.110490Z",
     "iopub.status.idle": "2023-04-27T17:26:42.117637Z",
     "shell.execute_reply": "2023-04-27T17:26:42.116682Z"
    },
    "papermill": {
     "duration": 0.018349,
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     "exception": false,
     "start_time": "2023-04-27T17:26:42.101337",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def labelize_test(column):\n",
    "  labels = [i.split() for i in column.values.tolist()]\n",
    "  Y_labels = []\n",
    "  for i in range(len(labels)):\n",
    "    arr = []\n",
    "    for j in range(len(labels[i])):\n",
    "      arr.append(int(dict[labels[i][j]]))\n",
    "    arr = np.array(arr, dtype = 'int32').astype('int32')\n",
    "    Y_labels.append(arr)\n",
    "  Y_labels = np.array(Y_labels)\n",
    "  Y_cat = pad_sequences(Y_labels, maxlen = maxlen,  padding = 'post', truncating = 'post')\n",
    "  return Y_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf2c29f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.137448Z",
     "iopub.status.busy": "2023-04-27T17:26:42.136713Z",
     "iopub.status.idle": "2023-04-27T17:26:42.168827Z",
     "shell.execute_reply": "2023-04-27T17:26:42.167333Z"
    },
    "papermill": {
     "duration": 0.043323,
     "end_time": "2023-04-27T17:26:42.171061",
     "exception": false,
     "start_time": "2023-04-27T17:26:42.127738",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:10: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "y_test = labelize_test(df_test['labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f35d53b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.187782Z",
     "iopub.status.busy": "2023-04-27T17:26:42.187471Z",
     "iopub.status.idle": "2023-04-27T17:26:42.191968Z",
     "shell.execute_reply": "2023-04-27T17:26:42.191176Z"
    },
    "papermill": {
     "duration": 0.015947,
     "end_time": "2023-04-27T17:26:42.194820",
     "exception": false,
     "start_time": "2023-04-27T17:26:42.178873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46040, 156, 18)\n",
      "(960, 156)\n"
     ]
    }
   ],
   "source": [
    "print(ycat_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6ef9af5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.211567Z",
     "iopub.status.busy": "2023-04-27T17:26:42.211302Z",
     "iopub.status.idle": "2023-04-27T17:26:42.215534Z",
     "shell.execute_reply": "2023-04-27T17:26:42.214774Z"
    },
    "id": "lctw5lL0-MgE",
    "outputId": "5f81602b-0869-465c-c6d2-1828fa970634",
    "papermill": {
     "duration": 0.015773,
     "end_time": "2023-04-27T17:26:42.218363",
     "exception": false,
     "start_time": "2023-04-27T17:26:42.202590",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([46040, 156]) torch.Size([46040, 156]) torch.Size([46040, 156]) (46040, 156, 18)\n"
     ]
    }
   ],
   "source": [
    "print(inp_train.shape, attn_train.shape, seg_train.shape, ycat_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29458e68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.235032Z",
     "iopub.status.busy": "2023-04-27T17:26:42.234772Z",
     "iopub.status.idle": "2023-04-27T17:26:42.240822Z",
     "shell.execute_reply": "2023-04-27T17:26:42.239811Z"
    },
    "id": "uG9VdJhcXcbY",
    "outputId": "70cf55f6-8cc3-4003-bf83-c30bec942271",
    "papermill": {
     "duration": 0.016775,
     "end_time": "2023-04-27T17:26:42.242853",
     "exception": false,
     "start_time": "2023-04-27T17:26:42.226078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 18)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ycat_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "28c2c26e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.259812Z",
     "iopub.status.busy": "2023-04-27T17:26:42.259532Z",
     "iopub.status.idle": "2023-04-27T17:26:42.269316Z",
     "shell.execute_reply": "2023-04-27T17:26:42.267957Z"
    },
    "id": "1fiKwxTXSYQm",
    "outputId": "7cf9f1b3-16aa-4c09-8eb0-5b12788cedbd",
    "papermill": {
     "duration": 0.020497,
     "end_time": "2023-04-27T17:26:42.271336",
     "exception": false,
     "start_time": "2023-04-27T17:26:42.250839",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  cpuset_checked))\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class bhavneek_dataset(Dataset):\n",
    "    def __init__(self, inp, attn, seg, ycat):\n",
    "        self.inp = inp\n",
    "        self.attn = attn\n",
    "        self.seg = seg\n",
    "        self.ycat = ycat\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.inp)\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        return self.inp[index], self.attn[index], self.seg[index], self.ycat[index]\n",
    "\n",
    "\n",
    "train_data = bhavneek_dataset(inp=inp_train, attn=attn_train, seg=seg_train, ycat=ycat_train)\n",
    "\n",
    "batch_size = 32\n",
    "num_workers = 4\n",
    "\n",
    "dataloader = DataLoader(train_data, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "994e7870",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.288514Z",
     "iopub.status.busy": "2023-04-27T17:26:42.288248Z",
     "iopub.status.idle": "2023-04-27T17:26:42.294207Z",
     "shell.execute_reply": "2023-04-27T17:26:42.293168Z"
    },
    "id": "SFHbs553hp-5",
    "papermill": {
     "duration": 0.016967,
     "end_time": "2023-04-27T17:26:42.296166",
     "exception": false,
     "start_time": "2023-04-27T17:26:42.279199",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class BertModel(nn.Module):\n",
    "\n",
    "  def __init__(self):\n",
    "    super(BertModel, self).__init__()\n",
    "    self.bert = BertForTokenClassification.from_pretrained('bert-base-cased', num_labels=len(unique_labels)+1)\n",
    "\n",
    "  def forward(self, inp, attn, lab):\n",
    "    softmax_layer = nn.Softmax(dim=1)\n",
    "    output = softmax_layer(self.bert(input_ids=inp, attention_mask=attn, labels=lab, return_dict=False)[1])\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6fd57d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:42.314078Z",
     "iopub.status.busy": "2023-04-27T17:26:42.313279Z",
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     "shell.execute_reply": "2023-04-27T17:26:46.240098Z"
    },
    "id": "0apNPg1RhyME",
    "outputId": "6eb44fb7-36a1-41d4-f211-c3d51fdd3b4a",
    "papermill": {
     "duration": 3.939406,
     "end_time": "2023-04-27T17:26:46.243669",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "beceed485c024d91aafeef3bda38f0b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/436M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']\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"
     ]
    }
   ],
   "source": [
    "model = BertModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2cd41cbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:46.263153Z",
     "iopub.status.busy": "2023-04-27T17:26:46.262860Z",
     "iopub.status.idle": "2023-04-27T17:26:46.268946Z",
     "shell.execute_reply": "2023-04-27T17:26:46.267852Z"
    },
    "id": "M9gxpORmXzA5",
    "papermill": {
     "duration": 0.018672,
     "end_time": "2023-04-27T17:26:46.271412",
     "exception": false,
     "start_time": "2023-04-27T17:26:46.252740",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "criterion.to(device)\n",
    "opti = torch.optim.Adam(model.parameters(), lr = 1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6ce92496",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T17:26:46.289415Z",
     "iopub.status.busy": "2023-04-27T17:26:46.289150Z",
     "iopub.status.idle": "2023-04-27T20:57:50.137178Z",
     "shell.execute_reply": "2023-04-27T20:57:50.135909Z"
    },
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    "papermill": {
     "duration": 12663.860472,
     "end_time": "2023-04-27T20:57:50.140234",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "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",
      "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",
      "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",
      "Epoch 1, Batch 0, Training Loss 43.7654, Val Loss 43.7646\n",
      "Epoch 1, Batch 50, Training Loss 43.7175, Val Loss 43.7163\n",
      "Epoch 1, Batch 100, Training Loss 43.7059, Val Loss 43.7051\n",
      "Epoch 1, Batch 150, Training Loss 43.6825, Val Loss 43.6773\n",
      "Epoch 1, Batch 200, Training Loss 43.6607, Val Loss 43.6592\n",
      "Epoch 1, Batch 250, Training Loss 43.6566, Val Loss 43.6529\n",
      "Epoch 1, Batch 300, Training Loss 43.6591, Val Loss 43.6494\n",
      "Epoch 1, Batch 350, Training Loss 43.6495, Val Loss 43.6469\n",
      "Epoch 1, Batch 400, Training Loss 43.6438, Val Loss 43.6456\n",
      "Epoch 1, Batch 450, Training Loss 43.6581, Val Loss 43.6441\n",
      "Epoch 1, Batch 500, Training Loss 43.6423, Val Loss 43.6436\n",
      "Epoch 1, Batch 550, Training Loss 43.6205, Val Loss 43.6433\n",
      "Epoch 1, Batch 600, Training Loss 43.6392, Val Loss 43.6424\n",
      "Epoch 1, Batch 650, Training Loss 43.6513, Val Loss 43.6414\n",
      "Epoch 1, Batch 700, Training Loss 43.6659, Val Loss 43.6411\n",
      "Epoch 1, Batch 750, Training Loss 43.6448, Val Loss 43.6404\n",
      "Epoch 1, Batch 800, Training Loss 43.6195, Val Loss 43.6394\n",
      "Epoch 1, Batch 850, Training Loss 43.6460, Val Loss 43.6374\n",
      "Epoch 1, Batch 900, Training Loss 43.6235, Val Loss 43.6366\n",
      "Epoch 1, Batch 950, Training Loss 43.6201, Val Loss 43.6338\n",
      "Epoch 1, Batch 1000, Training Loss 43.6294, Val Loss 43.6319\n",
      "Epoch 1, Batch 1050, Training Loss 43.6237, Val Loss 43.6314\n",
      "Epoch 1, Batch 1100, Training Loss 43.6264, Val Loss 43.6275\n",
      "Epoch 1, Batch 1150, Training Loss 43.6178, Val Loss 43.6245\n",
      "Epoch 1, Batch 1200, Training Loss 43.6288, Val Loss 43.6220\n",
      "Epoch 1, Batch 1250, Training Loss 43.6285, Val Loss 43.6216\n",
      "Epoch 1, Batch 1300, Training Loss 43.6425, Val Loss 43.6254\n",
      "Epoch 1, Batch 1350, Training Loss 43.6157, Val Loss 43.6195\n",
      "Epoch 1, Batch 1400, Training Loss 43.6361, Val Loss 43.6228\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",
      "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",
      "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",
      "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",
      "Epoch 2, Batch 0, Training Loss 43.6019, Val Loss 43.6165\n",
      "Epoch 2, Batch 50, Training Loss 43.6118, Val Loss 43.6151\n",
      "Epoch 2, Batch 100, Training Loss 43.6206, Val Loss 43.6161\n",
      "Epoch 2, Batch 150, Training Loss 43.6016, Val Loss 43.6139\n",
      "Epoch 2, Batch 200, Training Loss 43.6189, Val Loss 43.6158\n",
      "Epoch 2, Batch 250, Training Loss 43.6154, Val Loss 43.6147\n",
      "Epoch 2, Batch 300, Training Loss 43.6345, Val Loss 43.6113\n",
      "Epoch 2, Batch 350, Training Loss 43.6152, Val Loss 43.6127\n",
      "Epoch 2, Batch 400, Training Loss 43.5999, Val Loss 43.6149\n",
      "Epoch 2, Batch 450, Training Loss 43.5940, Val Loss 43.6094\n",
      "Epoch 2, Batch 500, Training Loss 43.5971, Val Loss 43.6099\n",
      "Epoch 2, Batch 550, Training Loss 43.6387, Val Loss 43.6095\n",
      "Epoch 2, Batch 600, Training Loss 43.5940, Val Loss 43.6090\n",
      "Epoch 2, Batch 650, Training Loss 43.6134, Val Loss 43.6084\n",
      "Epoch 2, Batch 700, Training Loss 43.6283, Val Loss 43.6087\n",
      "Epoch 2, Batch 750, Training Loss 43.6152, Val Loss 43.6081\n",
      "Epoch 2, Batch 800, Training Loss 43.6044, Val Loss 43.6074\n",
      "Epoch 2, Batch 850, Training Loss 43.6250, Val Loss 43.6097\n",
      "Epoch 2, Batch 900, Training Loss 43.6170, Val Loss 43.6065\n",
      "Epoch 2, Batch 950, Training Loss 43.6264, Val Loss 43.6088\n",
      "Epoch 2, Batch 1000, Training Loss 43.6121, Val Loss 43.6044\n",
      "Epoch 2, Batch 1050, Training Loss 43.5995, Val Loss 43.6078\n",
      "Epoch 2, Batch 1100, Training Loss 43.6093, Val Loss 43.6053\n",
      "Epoch 2, Batch 1150, Training Loss 43.5787, Val Loss 43.6047\n",
      "Epoch 2, Batch 1200, Training Loss 43.6278, Val Loss 43.6068\n",
      "Epoch 2, Batch 1250, Training Loss 43.6025, Val Loss 43.6059\n",
      "Epoch 2, Batch 1300, Training Loss 43.5881, Val Loss 43.6090\n",
      "Epoch 2, Batch 1350, Training Loss 43.5980, Val Loss 43.6043\n",
      "Epoch 2, Batch 1400, Training Loss 43.6039, Val Loss 43.6072\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"
     ]
    }
   ],
   "source": [
    "model = model.to(device) \n",
    "num_epochs = 2\n",
    "\n",
    "train_loss_values = []\n",
    "val_loss_values = []\n",
    "batch_idx_count = []\n",
    "\n",
    "inp_val = inp_val.to(device)\n",
    "attn_val = attn_val.to(device)\n",
    "seg_val = seg_val.to(device)\n",
    "ycat_val = (torch.tensor(ycat_val)).to(device)\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for batch_idx, (inp, attn, seg, ycat) in enumerate(dataloader):\n",
    "        \n",
    "        inp = inp.to(device)\n",
    "        attn = attn.to(device)\n",
    "        seg = seg.to(device)\n",
    "        ycat = ycat.to(device)\n",
    "        \n",
    "        model.train()\n",
    "        output = model(inp, attn, seg)\n",
    "        loss = criterion(output, ycat)\n",
    "        \n",
    "        opti.zero_grad() \n",
    "        loss.backward() \n",
    "        opti.step()  \n",
    "        \n",
    "        \n",
    "        model.eval()\n",
    "        with torch.inference_mode():\n",
    "            \n",
    "            val_pred = model(inp_val, attn_val, seg_val)\n",
    "            val_loss = criterion(val_pred, ycat_val)\n",
    "            batch_idx_count.append(batch_idx)\n",
    "            train_loss_values.append(loss.detach())\n",
    "            val_loss_values.append(val_loss.detach())\n",
    "            if batch_idx % 50 ==0:\n",
    "                print(f\"Epoch {epoch+1}, Batch {batch_idx}, Training Loss {loss.item():.4f}, Val Loss {val_loss.item():.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8f956df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T20:57:50.165892Z",
     "iopub.status.busy": "2023-04-27T20:57:50.165547Z",
     "iopub.status.idle": "2023-04-27T20:57:54.542914Z",
     "shell.execute_reply": "2023-04-27T20:57:54.541883Z"
    },
    "papermill": {
     "duration": 4.393046,
     "end_time": "2023-04-27T20:57:54.545447",
     "exception": false,
     "start_time": "2023-04-27T20:57:50.152401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.eval()\n",
    "with torch.inference_mode():\n",
    "    inp_test = inp_test.to(device)\n",
    "    attn_test = attn_test.to(device)\n",
    "    seg_test = seg_test.to(device)\n",
    "    test_output = model(inp_test, attn_test, seg_test)\n",
    "\n",
    "predictions = torch.argmax(test_output, dim=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c5f7e029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T20:57:54.572601Z",
     "iopub.status.busy": "2023-04-27T20:57:54.571068Z",
     "iopub.status.idle": "2023-04-27T20:57:58.105447Z",
     "shell.execute_reply": "2023-04-27T20:57:58.104333Z"
    },
    "papermill": {
     "duration": 3.550407,
     "end_time": "2023-04-27T20:57:58.108042",
     "exception": false,
     "start_time": "2023-04-27T20:57:54.557635",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([960, 156])\n"
     ]
    }
   ],
   "source": [
    "print(predictions.shape)\n",
    "predictions = predictions.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d4516b7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T20:57:58.134829Z",
     "iopub.status.busy": "2023-04-27T20:57:58.133232Z",
     "iopub.status.idle": "2023-04-27T20:57:58.159840Z",
     "shell.execute_reply": "2023-04-27T20:57:58.158921Z"
    },
    "papermill": {
     "duration": 0.041517,
     "end_time": "2023-04-27T20:57:58.161921",
     "exception": false,
     "start_time": "2023-04-27T20:57:58.120404",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "y_test_flat = list(chain.from_iterable(y_test))\n",
    "predictions_flat = list(chain.from_iterable(predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c4e31254",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T20:57:58.187560Z",
     "iopub.status.busy": "2023-04-27T20:57:58.186610Z",
     "iopub.status.idle": "2023-04-27T20:57:58.808336Z",
     "shell.execute_reply": "2023-04-27T20:57:58.807243Z"
    },
    "papermill": {
     "duration": 0.636952,
     "end_time": "2023-04-27T20:57:58.810967",
     "exception": false,
     "start_time": "2023-04-27T20:57:58.174015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "def get_scores(predY, trueY):\n",
    "  print(\"Micro F1 score: \", f1_score(trueY, predY, average=\"micro\"))\n",
    "  print(\"Macro F1 score: \", f1_score(trueY, predY, average=\"macro\"))\n",
    "  print(\"Average F1 score: \", (f1_score(trueY, predY, average=\"micro\") + f1_score(trueY, predY, average=\"macro\")) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8e3b6a6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T20:57:58.837952Z",
     "iopub.status.busy": "2023-04-27T20:57:58.837611Z",
     "iopub.status.idle": "2023-04-27T20:57:59.317384Z",
     "shell.execute_reply": "2023-04-27T20:57:59.315941Z"
    },
    "papermill": {
     "duration": 0.495737,
     "end_time": "2023-04-27T20:57:59.319777",
     "exception": false,
     "start_time": "2023-04-27T20:57:58.824040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro F1 score:  0.9116987179487179\n",
      "Macro F1 score:  0.29023837510621286\n",
      "Average F1 score:  0.6009685465274655\n"
     ]
    }
   ],
   "source": [
    "get_scores(predictions_flat, y_test_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec3f91d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-27T20:57:59.345713Z",
     "iopub.status.busy": "2023-04-27T20:57:59.345378Z",
     "iopub.status.idle": "2023-04-27T20:57:59.690714Z",
     "shell.execute_reply": "2023-04-27T20:57:59.689643Z"
    },
    "papermill": {
     "duration": 0.360919,
     "end_time": "2023-04-27T20:57:59.693072",
     "exception": false,
     "start_time": "2023-04-27T20:57:59.332153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_idx_count = np.arange(0, len(train_loss_values))\n",
    "\n",
    "train_loss_values = torch.tensor(train_loss_values).cuda()\n",
    "train_loss_values_cpu = train_loss_values.cpu().tolist()\n",
    "\n",
    "val_loss_values = torch.tensor(val_loss_values).cuda()\n",
    "val_loss_values_cpu = val_loss_values.cpu().tolist()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(batch_idx_count, train_loss_values_cpu, label='Training Loss')\n",
    "ax.plot(batch_idx_count, val_loss_values_cpu, label='Validation Loss')\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "ax.set_xlabel('Batch Index')\n",
    "ax.set_ylabel('Loss')\n",
    "ax.set_title('Training and Validation Loss')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
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