src/bert-pretrained.ipynb

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   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "!pip install -qq transformers torch seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "827ec0c3",
   "metadata": {
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    "tags": []
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   "outputs": [],
   "source": [
    "import transformers\n",
    "from transformers import BertModel, BertTokenizer, get_linear_schedule_with_warmup\n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from collections import defaultdict\n",
    "from textwrap import wrap\n",
    "\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.optim import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ea0b0ee",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "a66aa8fc",
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "ce9a7c71",
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   "source": [
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
    "\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "\n",
    "rcParams['figure.figsize'] = 12, 8\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "np.random.seed(RANDOM_SEED)\n",
    "# torch.manual_seed(RANDOM_SEED)\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
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   "cell_type": "code",
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   },
   "outputs": [],
   "source": [
    "DATA_PATH = \"/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "df = pd.read_csv(DATA_PATH, header=None, encoding=\"ISO-8859-1\")\n",
    "df.columns = [\"target\", \"id\", \"date\", \"flag\", \"user\", \"text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "source": [
    "def to_sentiment(target):\n",
    "    # 0 - negative\n",
    "    # 4 - postive\n",
    "    # convert to\n",
    "        # 0 - negative\n",
    "        # 1 - positive \n",
    "    return int(target == 4)\n"
   ]
  },
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   "source": [
    "class_names = ['negative', 'positive']"
   ]
  },
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   "outputs": [],
   "source": [
    "PRE_TRAINED_MODEL_NAME = 'bert-base-cased'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20313496",
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   "outputs": [
    {
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   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)"
   ]
  },
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   "outputs": [],
   "source": [
    "sample_txt = 'When was I last outside? I am stuck at home for 2 weeks.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9a94b30",
   "metadata": {
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   "outputs": [],
   "source": [
    "tokens = tokenizer.tokenize(sample_txt)\n",
    "token_ids = tokenizer.convert_tokens_to_ids(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30f62c3e",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Sentence: When was I last outside? I am stuck at home for 2 weeks.\n",
      "   Tokens: ['When', 'was', 'I', 'last', 'outside', '?', 'I', 'am', 'stuck', 'at', 'home', 'for', '2', 'weeks', '.']\n",
      "Token IDs: [1332, 1108, 146, 1314, 1796, 136, 146, 1821, 5342, 1120, 1313, 1111, 123, 2277, 119]\n"
     ]
    }
   ],
   "source": [
    "print(f' Sentence: {sample_txt}')\n",
    "print(f'   Tokens: {tokens}')\n",
    "print(f'Token IDs: {token_ids}')"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "('[SEP]', 102)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.sep_token, tokenizer.sep_token_id"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "('[CLS]', 101)"
      ]
     },
     "execution_count": 17,
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   ],
   "source": [
    "tokenizer.cls_token, tokenizer.cls_token_id"
   ]
  },
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   "id": "87d77f95",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "('[PAD]', 0)"
      ]
     },
     "execution_count": 18,
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   ],
   "source": [
    "tokenizer.pad_token, tokenizer.pad_token_id"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "('[UNK]', 100)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.unk_token, tokenizer.unk_token_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84fc2cc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:04.820160Z",
     "iopub.status.busy": "2022-04-30T04:21:04.819924Z",
     "iopub.status.idle": "2022-04-30T04:21:04.832836Z",
     "shell.execute_reply": "2022-04-30T04:21:04.832080Z"
    },
    "id": "FgVi2W30oy0L",
    "outputId": "3ccdfb25-e002-4f7f-b2ce-a845d49101be",
    "papermill": {
     "duration": 0.052829,
     "end_time": "2022-04-30T04:21:04.834588",
     "exception": false,
     "start_time": "2022-04-30T04:21:04.781759",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['input_ids', 'attention_mask'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding = tokenizer.encode_plus(\n",
    "    sample_txt,\n",
    "    max_length=32,\n",
    "    add_special_tokens=True,  # Add '[CLS]' and '[SEP]'\n",
    "    return_token_type_ids=False,\n",
    "    pad_to_max_length=True,\n",
    "    return_attention_mask=True,\n",
    "    return_tensors=\"pt\",  # Return PyTorch tensors\n",
    ")\n",
    "\n",
    "encoding.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6859f979",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:04.912066Z",
     "iopub.status.busy": "2022-04-30T04:21:04.911617Z",
     "iopub.status.idle": "2022-04-30T04:21:04.929550Z",
     "shell.execute_reply": "2022-04-30T04:21:04.928758Z"
    },
    "id": "rH5tAqcwoy0N",
    "outputId": "7857564b-1ebd-48be-8593-bebcb1626f64",
    "papermill": {
     "duration": 0.058485,
     "end_time": "2022-04-30T04:21:04.931456",
     "exception": false,
     "start_time": "2022-04-30T04:21:04.872971",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([ 101, 1332, 1108,  146, 1314, 1796,  136,  146, 1821, 5342, 1120, 1313,\n",
       "        1111,  123, 2277,  119,  102,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(encoding['input_ids'][0]))\n",
    "encoding['input_ids'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a5b724d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.014958Z",
     "iopub.status.busy": "2022-04-30T04:21:05.014682Z",
     "iopub.status.idle": "2022-04-30T04:21:05.022693Z",
     "shell.execute_reply": "2022-04-30T04:21:05.021639Z"
    },
    "id": "m4CE30aFoy0O",
    "outputId": "ff41e908-5637-4da8-b8fa-5015b96bb081",
    "papermill": {
     "duration": 0.05033,
     "end_time": "2022-04-30T04:21:05.024667",
     "exception": false,
     "start_time": "2022-04-30T04:21:04.974337",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(encoding['attention_mask'][0]))\n",
    "encoding['attention_mask']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a730bb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.114686Z",
     "iopub.status.busy": "2022-04-30T04:21:05.113908Z",
     "iopub.status.idle": "2022-04-30T04:21:05.121140Z",
     "shell.execute_reply": "2022-04-30T04:21:05.120381Z"
    },
    "id": "GiIlVnojoy0P",
    "outputId": "d8ed6218-fe89-41d6-ead9-00bafebb3352",
    "papermill": {
     "duration": 0.053886,
     "end_time": "2022-04-30T04:21:05.123030",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.069144",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[CLS]',\n",
       " 'When',\n",
       " 'was',\n",
       " 'I',\n",
       " 'last',\n",
       " 'outside',\n",
       " '?',\n",
       " 'I',\n",
       " 'am',\n",
       " 'stuck',\n",
       " 'at',\n",
       " 'home',\n",
       " 'for',\n",
       " '2',\n",
       " 'weeks',\n",
       " '.',\n",
       " '[SEP]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]',\n",
       " '[PAD]']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.convert_ids_to_tokens(encoding['input_ids'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7270d134",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.208732Z",
     "iopub.status.busy": "2022-04-30T04:21:05.208004Z",
     "iopub.status.idle": "2022-04-30T04:21:05.223935Z",
     "shell.execute_reply": "2022-04-30T04:21:05.223274Z"
    },
    "id": "JiX83kRxoy0Q",
    "outputId": "d32948b4-4b0c-4637-94aa-0c0e3c866c19",
    "papermill": {
     "duration": 0.060265,
     "end_time": "2022-04-30T04:21:05.225887",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.165622",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>flag</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810369</td>\n",
       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>_TheSpecialOne_</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810672</td>\n",
       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>scotthamilton</td>\n",
       "      <td>is upset that he can't update his Facebook by ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810917</td>\n",
       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>mattycus</td>\n",
       "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811184</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>ElleCTF</td>\n",
       "      <td>my whole body feels itchy and like its on fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1467811193</td>\n",
       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Karoli</td>\n",
       "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target          id                          date      flag  \\\n",
       "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
       "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
       "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
       "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "\n",
       "              user                                               text  \n",
       "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
       "1    scotthamilton  is upset that he can't update his Facebook by ...  \n",
       "2         mattycus  @Kenichan I dived many times for the ball. Man...  \n",
       "3          ElleCTF    my whole body feels itchy and like its on fire   \n",
       "4           Karoli  @nationwideclass no, it's not behaving at all....  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9e6d7c32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.307731Z",
     "iopub.status.busy": "2022-04-30T04:21:05.307474Z",
     "iopub.status.idle": "2022-04-30T04:21:05.310882Z",
     "shell.execute_reply": "2022-04-30T04:21:05.310194Z"
    },
    "id": "W8yxq6pvoy0R",
    "papermill": {
     "duration": 0.046513,
     "end_time": "2022-04-30T04:21:05.312615",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.266102",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# token_lens = []\n",
    "\n",
    "# for i, txt in enumerate(df.text):\n",
    "#     tokens = tokenizer.encode(txt, max_length=512)\n",
    "#     token_lens.append(len(tokens))\n",
    "#     if i > 500_000:\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "718fdf29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.393506Z",
     "iopub.status.busy": "2022-04-30T04:21:05.393264Z",
     "iopub.status.idle": "2022-04-30T04:21:05.396319Z",
     "shell.execute_reply": "2022-04-30T04:21:05.395658Z"
    },
    "id": "sFG8E7jFoy0S",
    "papermill": {
     "duration": 0.045772,
     "end_time": "2022-04-30T04:21:05.398035",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.352263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sns.displot(token_lens)\n",
    "# plt.xlim([0,75])\n",
    "# plt.xlabel(\"Token count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "518508a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.479110Z",
     "iopub.status.busy": "2022-04-30T04:21:05.478887Z",
     "iopub.status.idle": "2022-04-30T04:21:05.482119Z",
     "shell.execute_reply": "2022-04-30T04:21:05.481486Z"
    },
    "id": "nmq1Gv7Poy0T",
    "papermill": {
     "duration": 0.045897,
     "end_time": "2022-04-30T04:21:05.483737",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.437840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "MAX_LEN = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c8ca5cd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.566435Z",
     "iopub.status.busy": "2022-04-30T04:21:05.565721Z",
     "iopub.status.idle": "2022-04-30T04:21:05.573416Z",
     "shell.execute_reply": "2022-04-30T04:21:05.572722Z"
    },
    "id": "TKziB24zoy0U",
    "papermill": {
     "duration": 0.05087,
     "end_time": "2022-04-30T04:21:05.575159",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.524289",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TwitterSentimentDataset(Dataset):\n",
    "    def __init__(self, df, tokenizer, max_len):\n",
    "        self.df = df\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_len = max_len\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, item):\n",
    "        text = self.df.text[item]\n",
    "        target = self.df.target[item]\n",
    "\n",
    "        encoding = self.tokenizer.encode_plus(\n",
    "            text,\n",
    "            add_special_tokens=True,\n",
    "            max_length=self.max_len,\n",
    "            # padding=\"longest\",    \n",
    "            pad_to_max_length=True,\n",
    "            return_token_type_ids=False,\n",
    "            return_attention_mask=True,\n",
    "            return_tensors=\"pt\",\n",
    "        )\n",
    "\n",
    "        return {\n",
    "            \"text\": text,\n",
    "            \"input_ids\": encoding[\"input_ids\"].flatten(),\n",
    "            \"attention_mask\": encoding[\"attention_mask\"].flatten(),\n",
    "            \"targets\": torch.tensor(target, dtype=torch.long),\n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eed21bd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.658883Z",
     "iopub.status.busy": "2022-04-30T04:21:05.658638Z",
     "iopub.status.idle": "2022-04-30T04:21:05.674004Z",
     "shell.execute_reply": "2022-04-30T04:21:05.673309Z"
    },
    "id": "qV68hQf1uMVV",
    "papermill": {
     "duration": 0.059499,
     "end_time": "2022-04-30T04:21:05.675785",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.616286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"target\"] = df[\"target\"] == 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be78599b",
   "metadata": {
    "id": "-Exijr39sn92",
    "papermill": {
     "duration": 0.04039,
     "end_time": "2022-04-30T04:21:05.757782",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.717392",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3855e92a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:05.842536Z",
     "iopub.status.busy": "2022-04-30T04:21:05.842257Z",
     "iopub.status.idle": "2022-04-30T04:21:07.691083Z",
     "shell.execute_reply": "2022-04-30T04:21:07.690332Z"
    },
    "id": "OtLT6XsZoy0W",
    "papermill": {
     "duration": 1.894569,
     "end_time": "2022-04-30T04:21:07.693237",
     "exception": false,
     "start_time": "2022-04-30T04:21:05.798668",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.3, random_state=RANDOM_SEED)\n",
    "df_train, df_val = train_test_split(df_train, test_size=0.1, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "05e366bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:07.778950Z",
     "iopub.status.busy": "2022-04-30T04:21:07.778227Z",
     "iopub.status.idle": "2022-04-30T04:21:07.783221Z",
     "shell.execute_reply": "2022-04-30T04:21:07.782504Z"
    },
    "id": "acopBVY0uwvh",
    "outputId": "39e43664-9f7a-455e-e517-513073b00cd2",
    "papermill": {
     "duration": 0.050656,
     "end_time": "2022-04-30T04:21:07.785933",
     "exception": false,
     "start_time": "2022-04-30T04:21:07.735277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1008000\n",
      "112000\n",
      "480000\n"
     ]
    }
   ],
   "source": [
    "print(len(df_train))\n",
    "print(len(df_val))\n",
    "print(len(df_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "62d3be9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:07.873391Z",
     "iopub.status.busy": "2022-04-30T04:21:07.872752Z",
     "iopub.status.idle": "2022-04-30T04:21:07.882255Z",
     "shell.execute_reply": "2022-04-30T04:21:07.881601Z"
    },
    "id": "c28UE6vsqknw",
    "papermill": {
     "duration": 0.055142,
     "end_time": "2022-04-30T04:21:07.884048",
     "exception": false,
     "start_time": "2022-04-30T04:21:07.828906",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train.reset_index(inplace=True)\n",
    "df_val.reset_index(inplace=True)\n",
    "df_test.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0ea8ba7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:07.969913Z",
     "iopub.status.busy": "2022-04-30T04:21:07.969259Z",
     "iopub.status.idle": "2022-04-30T04:21:07.973496Z",
     "shell.execute_reply": "2022-04-30T04:21:07.972765Z"
    },
    "id": "ag5_ns8Ioy0W",
    "papermill": {
     "duration": 0.048172,
     "end_time": "2022-04-30T04:21:07.975121",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_data_loader(df, tokenizer, MAX_LEN, batch_size):\n",
    "    ds = TwitterSentimentDataset(df, tokenizer, MAX_LEN)\n",
    "    return DataLoader(ds, batch_size, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a75ddfd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:08.062094Z",
     "iopub.status.busy": "2022-04-30T04:21:08.061339Z",
     "iopub.status.idle": "2022-04-30T04:21:08.066307Z",
     "shell.execute_reply": "2022-04-30T04:21:08.065563Z"
    },
    "id": "_k9T59l4oy0X",
    "papermill": {
     "duration": 0.051547,
     "end_time": "2022-04-30T04:21:08.068066",
     "exception": false,
     "start_time": "2022-04-30T04:21:08.016519",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 200\n",
    "train_data_loader = create_data_loader(df_train, tokenizer, MAX_LEN, BATCH_SIZE)\n",
    "val_data_loader = create_data_loader(df_val, tokenizer, MAX_LEN, BATCH_SIZE)\n",
    "test_data_loader = create_data_loader(df_test, tokenizer, MAX_LEN, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1f284ea2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:08.157948Z",
     "iopub.status.busy": "2022-04-30T04:21:08.157239Z",
     "iopub.status.idle": "2022-04-30T04:21:08.160799Z",
     "shell.execute_reply": "2022-04-30T04:21:08.160148Z"
    },
    "id": "S5ZSbFPCoy0Y",
    "papermill": {
     "duration": 0.050496,
     "end_time": "2022-04-30T04:21:08.162734",
     "exception": false,
     "start_time": "2022-04-30T04:21:08.112238",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# bert_model = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eec27a36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:08.250964Z",
     "iopub.status.busy": "2022-04-30T04:21:08.250726Z",
     "iopub.status.idle": "2022-04-30T04:21:08.255317Z",
     "shell.execute_reply": "2022-04-30T04:21:08.254666Z"
    },
    "id": "a-5ZevZeoy0Y",
    "papermill": {
     "duration": 0.049901,
     "end_time": "2022-04-30T04:21:08.257037",
     "exception": false,
     "start_time": "2022-04-30T04:21:08.207136",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# last_hidden_state, pooled_output = bert_model(\n",
    "#     input_ids=encoding[\"input_ids\"], attention_mask=encoding[\"attention_mask\"]\n",
    "# )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9b019467",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:08.346380Z",
     "iopub.status.busy": "2022-04-30T04:21:08.345787Z",
     "iopub.status.idle": "2022-04-30T04:21:08.352528Z",
     "shell.execute_reply": "2022-04-30T04:21:08.351788Z"
    },
    "id": "Or7Jxy7Aoy0Z",
    "papermill": {
     "duration": 0.054719,
     "end_time": "2022-04-30T04:21:08.354190",
     "exception": false,
     "start_time": "2022-04-30T04:21:08.299471",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SentimentClassifier(nn.Module):\n",
    "\n",
    "    def __init__(self, n_classes):\n",
    "        super(SentimentClassifier, self).__init__()\n",
    "        self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\n",
    "        self.drop = nn.Dropout(p=0.3)\n",
    "        self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        outs = self.bert(\n",
    "            input_ids=input_ids,\n",
    "            attention_mask=attention_mask\n",
    "        )\n",
    "        output = self.drop(outs[\"pooler_output\"])\n",
    "        return self.out(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9ca18dd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:08.448876Z",
     "iopub.status.busy": "2022-04-30T04:21:08.448015Z",
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     "shell.execute_reply": "2022-04-30T04:21:45.349095Z"
    },
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    "outputId": "60344ffe-9415-4627-9386-67b60d111569",
    "papermill": {
     "duration": 36.953831,
     "end_time": "2022-04-30T04:21:45.351985",
     "exception": false,
     "start_time": "2022-04-30T04:21:08.398154",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0da575bba7404388961e725f84fb8e46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/416M [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 BertModel: ['cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
      "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "model = SentimentClassifier(len(class_names)).to(device)\n",
    "# model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d830eb39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:45.440630Z",
     "iopub.status.busy": "2022-04-30T04:21:45.439970Z",
     "iopub.status.idle": "2022-04-30T04:21:45.446190Z",
     "shell.execute_reply": "2022-04-30T04:21:45.445548Z"
    },
    "id": "EFO8yAuzoy0a",
    "papermill": {
     "duration": 0.052933,
     "end_time": "2022-04-30T04:21:45.447806",
     "exception": false,
     "start_time": "2022-04-30T04:21:45.394873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "EPOCHS = 2\n",
    "\n",
    "optimizer = Adam(model.parameters(), lr=2e-5, weight_decay=1e-5)\n",
    "total_steps = len(train_data_loader) * EPOCHS\n",
    "\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer, num_warmup_steps=0, num_training_steps=total_steps\n",
    ")\n",
    "\n",
    "loss_fn = nn.CrossEntropyLoss().to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a4301e33",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:45.535229Z",
     "iopub.status.busy": "2022-04-30T04:21:45.534704Z",
     "iopub.status.idle": "2022-04-30T04:21:45.542180Z",
     "shell.execute_reply": "2022-04-30T04:21:45.541516Z"
    },
    "id": "AJX6xow-oy0b",
    "papermill": {
     "duration": 0.05335,
     "end_time": "2022-04-30T04:21:45.543891",
     "exception": false,
     "start_time": "2022-04-30T04:21:45.490541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_epoch(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples):\n",
    "    model = model.train()\n",
    "\n",
    "    losses = []\n",
    "    correct_predictions = 0\n",
    "\n",
    "    for i, d in enumerate(tqdm(data_loader)):\n",
    "        input_ids = d[\"input_ids\"].to(device)\n",
    "        attention_mask = d[\"attention_mask\"].to(device)\n",
    "        targets = d[\"targets\"].to(device)\n",
    "\n",
    "        outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
    "\n",
    "        _, preds = torch.max(outputs, dim=1)\n",
    "        loss = loss_fn(outputs, targets)\n",
    "\n",
    "\n",
    "        loss.backward()\n",
    "        correct_predictions += torch.sum(preds == targets)\n",
    "        losses.append(loss.item())\n",
    "        nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "    return correct_predictions.double() / n_examples, np.mean(losses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ede44d17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:45.631230Z",
     "iopub.status.busy": "2022-04-30T04:21:45.630996Z",
     "iopub.status.idle": "2022-04-30T04:21:45.637437Z",
     "shell.execute_reply": "2022-04-30T04:21:45.636767Z"
    },
    "id": "rkIUNJUFoy0c",
    "papermill": {
     "duration": 0.052214,
     "end_time": "2022-04-30T04:21:45.639067",
     "exception": false,
     "start_time": "2022-04-30T04:21:45.586853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
    "    model = model.eval()\n",
    "\n",
    "    losses = []\n",
    "    correct_predictions = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for d in tqdm(data_loader):\n",
    "            input_ids = d[\"input_ids\"].to(device)\n",
    "            attention_mask = d[\"attention_mask\"].to(device)\n",
    "            targets = d[\"targets\"].to(device)\n",
    "            outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
    "            _, preds = torch.max(outputs, dim=1)\n",
    "\n",
    "            loss = loss_fn(outputs, targets)\n",
    "\n",
    "            correct_predictions += torch.sum(preds == targets)\n",
    "            losses.append(loss.item())\n",
    "\n",
    "    return correct_predictions.double() / n_examples, np.mean(losses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ec1898e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:45.727024Z",
     "iopub.status.busy": "2022-04-30T04:21:45.726435Z",
     "iopub.status.idle": "2022-04-30T04:21:45.729961Z",
     "shell.execute_reply": "2022-04-30T04:21:45.729280Z"
    },
    "id": "e7VnydQpsVUT",
    "papermill": {
     "duration": 0.049839,
     "end_time": "2022-04-30T04:21:45.731781",
     "exception": false,
     "start_time": "2022-04-30T04:21:45.681942",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train_epoch(model, train_data_loader, loss_fn, optimizer, device, scheduler, len(df_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1e005251",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T04:21:45.819943Z",
     "iopub.status.busy": "2022-04-30T04:21:45.819693Z",
     "iopub.status.idle": "2022-04-30T07:19:58.765970Z",
     "shell.execute_reply": "2022-04-30T07:19:58.765069Z"
    },
    "id": "Shq7Gtw8oy0c",
    "outputId": "bb88332e-1638-444e-a916-cbbcb1d1cceb",
    "papermill": {
     "duration": 10692.993636,
     "end_time": "2022-04-30T07:19:58.768611",
     "exception": false,
     "start_time": "2022-04-30T04:21:45.774975",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "----------\n"
     ]
    },
    {
     "data": {
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       "model_id": "3917510e76b44f38b3b87ad5a377b95d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5040 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss 0.34432357170633854 accuracy 0.8482212301587301\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73793addcc014db1ac7d1e052cfc9cc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/560 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val   loss 0.31144987161138227 accuracy 0.8655714285714287\n",
      "\n",
      "Epoch 2/2\n",
      "----------\n"
     ]
    },
    {
     "data": {
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       "model_id": "33bfe6f614b9415eb1dbb1c2cb7ae2d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5040 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss 0.28260590803055535 accuracy 0.8801944444444444\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ecca6b4bfa5437fb98e68a9f605d38d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/560 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val   loss 0.308651945181191 accuracy 0.8688214285714286\n",
      "\n",
      "CPU times: user 2h 49min 14s, sys: 5min 48s, total: 2h 55min 3s\n",
      "Wall time: 2h 58min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "history = defaultdict(list)\n",
    "best_accuracy = 0\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "\n",
    "  print(f'Epoch {epoch + 1}/{EPOCHS}')\n",
    "  print('-' * 10)\n",
    "\n",
    "  train_acc, train_loss = train_epoch(\n",
    "    model,\n",
    "    train_data_loader,\n",
    "    loss_fn,\n",
    "    optimizer,\n",
    "    device,\n",
    "    scheduler,\n",
    "    len(df_train)\n",
    "  )\n",
    "\n",
    "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
    "\n",
    "  val_acc, val_loss = eval_model(\n",
    "    model,\n",
    "    val_data_loader,\n",
    "    loss_fn,\n",
    "    device,\n",
    "    len(df_val)\n",
    "  )\n",
    "\n",
    "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
    "  print()\n",
    "\n",
    "  history['train_acc'].append(train_acc)\n",
    "  history['train_loss'].append(train_loss)\n",
    "  history['val_acc'].append(val_acc)\n",
    "  history['val_loss'].append(val_loss)\n",
    "\n",
    "  if val_acc > best_accuracy:\n",
    "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
    "    best_accuracy = val_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0e348c5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-30T07:19:58.866041Z",
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     "iopub.status.idle": "2022-04-30T07:33:40.997598Z",
     "shell.execute_reply": "2022-04-30T07:33:40.996636Z"
    },
    "id": "TARwapYG-T5-",
    "papermill": {
     "duration": 822.183482,
     "end_time": "2022-04-30T07:33:40.999775",
     "exception": false,
     "start_time": "2022-04-30T07:19:58.816293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8dd40b2680ef4d89a658ada181c5850f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2400 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.3073 Test Accuracy: 0.8698\n"
     ]
    }
   ],
   "source": [
    "test_acc, test_loss = eval_model(model, test_data_loader, loss_fn, device, len(df_test))\n",
    "print(f\"Test loss: {test_loss:.4f} Test Accuracy: {test_acc:.4f}\")"
   ]
  }
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