diff --git "a/final BERT model.ipynb" "b/final BERT model.ipynb"
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+++ "b/final BERT model.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6f64925a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.00924,
+     "end_time": "2024-08-16T07:45:52.868042",
+     "exception": false,
+     "start_time": "2024-08-16T07:45:52.858802",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<div style='text-align: center;'>\n",
+    "    <img src='https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQzJzIHdangJTrH2mFXFgsLjuLCjpfXXwbxg&usqp=CAU' width='100'/>\n",
+    "    <h1>Sharif University of Technology</h1>\n",
+    "    <h2>Natural Language Processing</h2>\n",
+    "    <h3>Final Project</h3>\n",
+    "    <h4>Spoiler classification and summary generation</h4>\n",
+    "    <p><strong>Authors:</strong> Ali Nikkhah, Ramtin Khoshnevis, Sarina Zahedi</p>\n",
+    "    <p><strong>(Equal Contribution)</strong></p>\n",
+    "</div>\n",
+    "<hr/>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6e2de46e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:45:52.886824Z",
+     "iopub.status.busy": "2024-08-16T07:45:52.886483Z",
+     "iopub.status.idle": "2024-08-16T07:45:54.562125Z",
+     "shell.execute_reply": "2024-08-16T07:45:54.561046Z"
+    },
+    "papermill": {
+     "duration": 1.687399,
+     "end_time": "2024-08-16T07:45:54.564146",
+     "exception": false,
+     "start_time": "2024-08-16T07:45:52.876747",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from collections import Counter\n",
+    "import string\n",
+    "\n",
+    "# Download NLTK stopwords if not already downloaded\n",
+    "nltk.download('stopwords')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7353bc32",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008673,
+     "end_time": "2024-08-16T07:45:54.581724",
+     "exception": false,
+     "start_time": "2024-08-16T07:45:54.573051",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "--------------------------------------------------------------------------------------------------------------------------------------\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17777ee2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008532,
+     "end_time": "2024-08-16T07:45:54.598982",
+     "exception": false,
+     "start_time": "2024-08-16T07:45:54.590450",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### **1. Load the Dataset**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "38d202ff",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:45:54.618490Z",
+     "iopub.status.busy": "2024-08-16T07:45:54.617690Z",
+     "iopub.status.idle": "2024-08-16T07:46:16.729821Z",
+     "shell.execute_reply": "2024-08-16T07:46:16.728787Z"
+    },
+    "papermill": {
+     "duration": 22.124693,
+     "end_time": "2024-08-16T07:46:16.732387",
+     "exception": false,
+     "start_time": "2024-08-16T07:45:54.607694",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import json\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "\n",
+    "# Load the dataset\n",
+    "file_path = '/kaggle/input/imdb-spoiler-dataset/IMDB_reviews.json'\n",
+    "data = []\n",
+    "with open(file_path, 'r') as file:\n",
+    "    for line in file:\n",
+    "        data.append(json.loads(line))\n",
+    "\n",
+    "df = pd.DataFrame(data)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7af684e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009082,
+     "end_time": "2024-08-16T07:46:16.751134",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:16.742052",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### **2. Exploratory Data Analysis (EDA)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2aa679c4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:16.772714Z",
+     "iopub.status.busy": "2024-08-16T07:46:16.772150Z",
+     "iopub.status.idle": "2024-08-16T07:46:17.283393Z",
+     "shell.execute_reply": "2024-08-16T07:46:17.282410Z"
+    },
+    "papermill": {
+     "duration": 0.524028,
+     "end_time": "2024-08-16T07:46:17.285525",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:16.761497",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 573913 entries, 0 to 573912\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column          Non-Null Count   Dtype \n",
+      "---  ------          --------------   ----- \n",
+      " 0   review_date     573913 non-null  object\n",
+      " 1   movie_id        573913 non-null  object\n",
+      " 2   user_id         573913 non-null  object\n",
+      " 3   is_spoiler      573913 non-null  bool  \n",
+      " 4   review_text     573913 non-null  object\n",
+      " 5   rating          573913 non-null  object\n",
+      " 6   review_summary  573913 non-null  object\n",
+      "dtypes: bool(1), object(6)\n",
+      "memory usage: 26.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tabulate import tabulate\n",
+    "import numpy as np\n",
+    "\n",
+    "# Basic info\n",
+    "info = df.info()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "da47eb9f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:17.304674Z",
+     "iopub.status.busy": "2024-08-16T07:46:17.304341Z",
+     "iopub.status.idle": "2024-08-16T07:46:21.358313Z",
+     "shell.execute_reply": "2024-08-16T07:46:21.357521Z"
+    },
+    "papermill": {
+     "duration": 4.066309,
+     "end_time": "2024-08-16T07:46:21.360809",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:17.294500",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Describe the dataset\n",
+    "description = df.describe()\n",
+    "\n",
+    "\n",
+    "# Check for missing values\n",
+    "missing_values = df.isnull().sum()\n",
+    "\n",
+    "\n",
+    "# Distribution of spoiler vs. non-spoiler\n",
+    "spoiler_distribution = df['is_spoiler'].value_counts(normalize=True)\n",
+    "\n",
+    "# Length of reviews\n",
+    "df['review_length'] = df['review_text'].apply(len)\n",
+    "review_length_description = df['review_length'].describe()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "628c8001",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:21.380435Z",
+     "iopub.status.busy": "2024-08-16T07:46:21.380103Z",
+     "iopub.status.idle": "2024-08-16T07:46:21.388769Z",
+     "shell.execute_reply": "2024-08-16T07:46:21.387870Z"
+    },
+    "papermill": {
+     "duration": 0.021244,
+     "end_time": "2024-08-16T07:46:21.391172",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:21.369928",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Missing Values:\n",
+      "+----------------+----------------+\n",
+      "|     Column     | Missing Values |\n",
+      "+----------------+----------------+\n",
+      "|  review_date   |       0        |\n",
+      "|    movie_id    |       0        |\n",
+      "|    user_id     |       0        |\n",
+      "|   is_spoiler   |       0        |\n",
+      "|  review_text   |       0        |\n",
+      "|     rating     |       0        |\n",
+      "| review_summary |       0        |\n",
+      "+----------------+----------------+\n",
+      "\n",
+      "Spoiler vs. Non-Spoiler Distribution:\n",
+      "+------------+---------------------+\n",
+      "| Is Spoiler |     Proportion      |\n",
+      "+------------+---------------------+\n",
+      "|   False    | 0.7370263437141169  |\n",
+      "|    True    | 0.26297365628588304 |\n",
+      "+------------+---------------------+\n",
+      "\n",
+      "Review Length Description:\n",
+      "+-----------+--------------------+\n",
+      "| Statistic |       Value        |\n",
+      "+-----------+--------------------+\n",
+      "|   count   |      573913.0      |\n",
+      "|   mean    | 1460.5535246631457 |\n",
+      "|    std    | 1125.577018615146  |\n",
+      "|    min    |        18.0        |\n",
+      "|    25%    |       719.0        |\n",
+      "|    50%    |       1052.0       |\n",
+      "|    75%    |       1815.0       |\n",
+      "|    max    |      14963.0       |\n",
+      "+-----------+--------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the results\n",
+    "\n",
+    "print(\"\\nMissing Values:\")\n",
+    "print(tabulate(missing_values.items(), headers=[\"Column\", \"Missing Values\"], tablefmt=\"pretty\"))\n",
+    "\n",
+    "print(\"\\nSpoiler vs. Non-Spoiler Distribution:\")\n",
+    "print(tabulate(spoiler_distribution.items(), headers=[\"Is Spoiler\", \"Proportion\"], tablefmt=\"pretty\"))\n",
+    "\n",
+    "print(\"\\nReview Length Description:\")\n",
+    "print(tabulate(review_length_description.items(), headers=[\"Statistic\", \"Value\"], tablefmt=\"pretty\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d2373a0f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:21.411027Z",
+     "iopub.status.busy": "2024-08-16T07:46:21.410348Z",
+     "iopub.status.idle": "2024-08-16T07:46:24.167676Z",
+     "shell.execute_reply": "2024-08-16T07:46:24.166699Z"
+    },
+    "papermill": {
+     "duration": 2.769727,
+     "end_time": "2024-08-16T07:46:24.169882",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:21.400155",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Plot some samples of the dataset\n",
+    "sample_size = 2\n",
+    "\n",
+    "spoiler_samples = df[df['is_spoiler'] == True].sample(sample_size)\n",
+    "non_spoiler_samples = df[df['is_spoiler'] == False].sample(sample_size)\n",
+    "\n",
+    "plt.figure(figsize=(40, 20))\n",
+    "\n",
+    "# Spoiler samples\n",
+    "for i, review in enumerate(spoiler_samples['review_text']):\n",
+    "    plt.text(0.5, 1.0 - i*0.2, f\"Spoiler Review {i+1}: {review[:150]}...\", ha='center', va='top', wrap=True)\n",
+    "\n",
+    "# Non-Spoiler samples\n",
+    "for i, review in enumerate(non_spoiler_samples['review_text']):\n",
+    "    plt.text(0.5, 0.5 - i*0.2, f\"Non-Spoiler Review {i+1}: {review[:150]}...\", ha='center', va='top', wrap=True)\n",
+    "\n",
+    "plt.axis('off')\n",
+    "plt.title('Sample Reviews (Spoiler vs Non-Spoiler)')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e2d12852",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:24.192163Z",
+     "iopub.status.busy": "2024-08-16T07:46:24.191357Z",
+     "iopub.status.idle": "2024-08-16T07:46:27.636981Z",
+     "shell.execute_reply": "2024-08-16T07:46:27.635974Z"
+    },
+    "papermill": {
+     "duration": 3.458975,
+     "end_time": "2024-08-16T07:46:27.639034",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:24.180059",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Plot distribution of spoilers vs. non-spoilers\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(x='is_spoiler', data=df, palette='coolwarm')\n",
+    "plt.title('Distribution of Spoiler vs. Non-Spoiler Reviews')\n",
+    "plt.xlabel('Is Spoiler')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the distribution of review lengths\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df['review_length'], kde=True, bins=30, color='purple')\n",
+    "plt.title('Distribution of Review Lengths')\n",
+    "plt.xlabel('Review Length')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()\n",
+    "\n",
+    "# Correlation between review length and is_spoiler\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(x='is_spoiler', y='review_length', data=df, palette='coolwarm')\n",
+    "plt.title('Review Length vs. Spoiler')\n",
+    "plt.xlabel('Is Spoiler')\n",
+    "plt.ylabel('Review Length')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8eaead3",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011514,
+     "end_time": "2024-08-16T07:46:27.662423",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:27.650909",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### **2.B. Down sample to make two calsses uniform**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a8f85143",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:27.687575Z",
+     "iopub.status.busy": "2024-08-16T07:46:27.686947Z",
+     "iopub.status.idle": "2024-08-16T07:46:28.583084Z",
+     "shell.execute_reply": "2024-08-16T07:46:28.582026Z"
+    },
+    "papermill": {
+     "duration": 0.911341,
+     "end_time": "2024-08-16T07:46:28.585412",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:27.674071",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class_counts = df['is_spoiler'].value_counts()\n",
+    "min_class_count = class_counts.min()\n",
+    "\n",
+    "# Downsample each class to have the same number of instances as the smallest class\n",
+    "df_balanced = pd.DataFrame()  # Create an empty DataFrame to store the balanced data\n",
+    "\n",
+    "for label in class_counts.index:\n",
+    "    df_balanced = pd.concat([\n",
+    "        df_balanced,\n",
+    "        df[df['is_spoiler'] == label].sample(n=min_class_count, random_state=42)\n",
+    "    ])\n",
+    "\n",
+    "# Shuffle the DataFrame to mix the classes well\n",
+    "df_balanced = df_balanced.sample(frac=1, random_state=42).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a8e1be5b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:28.611453Z",
+     "iopub.status.busy": "2024-08-16T07:46:28.610822Z",
+     "iopub.status.idle": "2024-08-16T07:46:28.669642Z",
+     "shell.execute_reply": "2024-08-16T07:46:28.668682Z"
+    },
+    "papermill": {
+     "duration": 0.07388,
+     "end_time": "2024-08-16T07:46:28.671577",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:28.597697",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df=df_balanced"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3fcf1a76",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:28.697594Z",
+     "iopub.status.busy": "2024-08-16T07:46:28.696732Z",
+     "iopub.status.idle": "2024-08-16T07:46:30.755784Z",
+     "shell.execute_reply": "2024-08-16T07:46:30.754915Z"
+    },
+    "papermill": {
+     "duration": 2.074522,
+     "end_time": "2024-08-16T07:46:30.758245",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:28.683723",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Plot distribution of spoilers vs. non-spoilers\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(x='is_spoiler', data=df, palette='coolwarm')\n",
+    "plt.title('Distribution of Spoiler vs. Non-Spoiler Reviews')\n",
+    "plt.xlabel('Is Spoiler')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the distribution of review lengths\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df['review_length'], kde=True, bins=30, color='purple')\n",
+    "plt.title('Distribution of Review Lengths')\n",
+    "plt.xlabel('Review Length')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()\n",
+    "\n",
+    "# Correlation between review length and is_spoiler\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.boxplot(x='is_spoiler', y='review_length', data=df, palette='coolwarm')\n",
+    "plt.title('Review Length vs. Spoiler')\n",
+    "plt.xlabel('Is Spoiler')\n",
+    "plt.ylabel('Review Length')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37f4587a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.012845,
+     "end_time": "2024-08-16T07:46:30.784398",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:30.771553",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### **3. Data Preprocessing**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "681653cb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:46:30.812422Z",
+     "iopub.status.busy": "2024-08-16T07:46:30.811937Z",
+     "iopub.status.idle": "2024-08-16T07:48:00.760312Z",
+     "shell.execute_reply": "2024-08-16T07:48:00.759275Z"
+    },
+    "papermill": {
+     "duration": 89.964917,
+     "end_time": "2024-08-16T07:48:00.762642",
+     "exception": false,
+     "start_time": "2024-08-16T07:46:30.797725",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0abe9c980f1f4b19939762b120a4e9f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Cleaning Text:   0%|          | 0/301848 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import PorterStemmer\n",
+    "from tqdm.notebook import tqdm\n",
+    "# Set of English stopwords\n",
+    "stop_words = set(stopwords.words('english'))\n",
+    "\n",
+    "def clean_text(text):\n",
+    "    # Remove URLs\n",
+    "    text = re.sub(r'http\\S+', '', text)\n",
+    "    text = re.sub(r'www\\S+', '', text)\n",
+    "    \n",
+    "    # Remove emails\n",
+    "    text = re.sub(r'\\S*@\\S*\\s?', '', text)\n",
+    "    \n",
+    "    # Remove all non-word characters and digits\n",
+    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
+    "    \n",
+    "    # Normalize whitespaces\n",
+    "    text = re.sub(r'\\s+', ' ', text)\n",
+    "    \n",
+    "    # Convert text to lowercase\n",
+    "    text = text.lower()\n",
+    "    \n",
+    "    return text\n",
+    "# Set up tqdm for pandas apply\n",
+    "tqdm.pandas(desc=\"Cleaning Text\")\n",
+    "\n",
+    "# Apply the cleaning function with a progress bar\n",
+    "df['cleaned_review_text'] = df['review_text'].progress_apply(clean_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "51c4cc6c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:48:00.791648Z",
+     "iopub.status.busy": "2024-08-16T07:48:00.791293Z",
+     "iopub.status.idle": "2024-08-16T07:48:13.757187Z",
+     "shell.execute_reply": "2024-08-16T07:48:13.756196Z"
+    },
+    "papermill": {
+     "duration": 12.982821,
+     "end_time": "2024-08-16T07:48:13.759393",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:00.776572",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame saved successfully to /kaggle/working/preprocessed.json.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Specify the path where you want to save the JSON file\n",
+    "json_file_path = '/kaggle/working/preprocessed.json'  \n",
+    "\n",
+    "# Save the DataFrame to a JSON file\n",
+    "df.to_json(json_file_path, orient='records', lines=True)\n",
+    "\n",
+    "print(f\"DataFrame saved successfully to {json_file_path}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e334948c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:48:13.788310Z",
+     "iopub.status.busy": "2024-08-16T07:48:13.787993Z",
+     "iopub.status.idle": "2024-08-16T07:48:15.559539Z",
+     "shell.execute_reply": "2024-08-16T07:48:15.558562Z"
+    },
+    "papermill": {
+     "duration": 1.788951,
+     "end_time": "2024-08-16T07:48:15.562150",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:13.773199",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, Dataset\n",
+    "from transformers import BertTokenizer\n",
+    "\n",
+    "# Encode the target label\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['label'] = label_encoder.fit_transform(df['is_spoiler'])\n",
+    "train_df, temp_df = train_test_split(df, test_size=0.8, random_state=42, stratify=df['label'])\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.05, random_state=42, stratify=temp_df['label'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "48bc8443",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:48:15.591615Z",
+     "iopub.status.busy": "2024-08-16T07:48:15.590702Z",
+     "iopub.status.idle": "2024-08-16T07:48:15.600986Z",
+     "shell.execute_reply": "2024-08-16T07:48:15.600005Z"
+    },
+    "papermill": {
+     "duration": 0.026783,
+     "end_time": "2024-08-16T07:48:15.603016",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:15.576233",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class SpoilerDataset(Dataset):\n",
+    "    def __init__(self, texts, labels, tokenizer, max_length=512, stride=256):\n",
+    "        self.texts = texts\n",
+    "        self.labels = labels\n",
+    "        self.tokenizer = tokenizer\n",
+    "        self.max_length = max_length\n",
+    "        self.stride = stride\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.texts)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        text = self.texts[idx]\n",
+    "        label = self.labels[idx]\n",
+    "        inputs = self.tokenizer(\n",
+    "            text,\n",
+    "            add_special_tokens=True,\n",
+    "            return_attention_mask=True,\n",
+    "            return_tensors='pt',\n",
+    "            truncation=True,\n",
+    "            max_length=self.max_length,\n",
+    "            padding='max_length',\n",
+    "            stride=self.stride,\n",
+    "            return_overflowing_tokens=True\n",
+    "        )\n",
+    "\n",
+    "        # Here you might want to handle multiple chunks if they exist\n",
+    "        if 'overflowing_tokens' in inputs:\n",
+    "            num_chunks = len(inputs['input_ids'])\n",
+    "            chunks = [{\n",
+    "                'input_ids': inputs['input_ids'][i].flatten(),\n",
+    "                'attention_mask': inputs['attention_mask'][i].flatten(),\n",
+    "                'label': torch.tensor(label, dtype=torch.long)\n",
+    "            } for i in range(num_chunks)]\n",
+    "            return chunks\n",
+    "        else:\n",
+    "            return {\n",
+    "                'input_ids': inputs['input_ids'].flatten(),\n",
+    "                'attention_mask': inputs['attention_mask'].flatten(),\n",
+    "                'label': torch.tensor(label, dtype=torch.long)\n",
+    "            }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4b17c21f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:48:15.631657Z",
+     "iopub.status.busy": "2024-08-16T07:48:15.631325Z",
+     "iopub.status.idle": "2024-08-16T07:48:17.245490Z",
+     "shell.execute_reply": "2024-08-16T07:48:17.244611Z"
+    },
+    "papermill": {
+     "duration": 1.631282,
+     "end_time": "2024-08-16T07:48:17.247872",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:15.616590",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9399bcd4f4284470ab28ad1543ac955b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d04502224564a5880872f0937afb08b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a107680ccada42129be9e8ab2cbccc8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8f811a908544cf48db61b0c463bc48c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from transformers import BertTokenizer\n",
+    "\n",
+    "# Initialize the tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
+    "\n",
+    "# Create datasets with the resampled training data\n",
+    "train_dataset = SpoilerDataset(train_df['cleaned_review_text'].tolist(), train_df['label'].tolist(), tokenizer)\n",
+    "val_dataset = SpoilerDataset(val_df['cleaned_review_text'].tolist(), val_df['label'].tolist(), tokenizer)\n",
+    "test_dataset = SpoilerDataset(test_df['cleaned_review_text'].tolist(), test_df['label'].tolist(), tokenizer)\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
+    "val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83ac7f76",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013854,
+     "end_time": "2024-08-16T07:48:17.276630",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:17.262776",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### **4. Modeling**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f6fe0d7a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:48:17.306693Z",
+     "iopub.status.busy": "2024-08-16T07:48:17.306318Z",
+     "iopub.status.idle": "2024-08-16T07:48:21.184083Z",
+     "shell.execute_reply": "2024-08-16T07:48:21.183229Z"
+    },
+    "papermill": {
+     "duration": 3.895699,
+     "end_time": "2024-08-16T07:48:21.186495",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:17.290796",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df62b71be79f46c2a37d4137c6abb88f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import BertForSequenceClassification\n",
+    "import torch.nn as nn\n",
+    "bert_model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
+    "bert_model = bert_model.to(device)\n",
+    "\n",
+    "\n",
+    "# Define the loss function\n",
+    "loss_fn = nn.CrossEntropyLoss()\n",
+    "\n",
+    "\n",
+    "# Define optimizer (assuming the optimizer setup is missing in the provided code snippet)\n",
+    "optimizer = torch.optim.Adam(bert_model.parameters(), lr=2e-5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e2038dc",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013882,
+     "end_time": "2024-08-16T07:48:21.215112",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:21.201230",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### **5. Training and Evaluation**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5921b623",
+   "metadata": {
+    "papermill": {
+     "duration": 0.014045,
+     "end_time": "2024-08-16T07:48:21.243424",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:21.229379",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "BERT model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c0a560e0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:48:21.273714Z",
+     "iopub.status.busy": "2024-08-16T07:48:21.273177Z",
+     "iopub.status.idle": "2024-08-16T07:48:21.289702Z",
+     "shell.execute_reply": "2024-08-16T07:48:21.288322Z"
+    },
+    "papermill": {
+     "duration": 0.034949,
+     "end_time": "2024-08-16T07:48:21.292510",
+     "exception": false,
+     "start_time": "2024-08-16T07:48:21.257561",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def train(model, dataloader, optimizer, loss_fn):\n",
+    "    model.train()\n",
+    "    total_loss, total_acc = 0, 0\n",
+    "\n",
+    "    progress_bar = tqdm(dataloader, desc=\"Training\")\n",
+    "    for batch in progress_bar:\n",
+    "        # Check if the batch is a list (multiple chunks)\n",
+    "        if isinstance(batch, list):\n",
+    "            # Process each chunk individually\n",
+    "            for chunk in batch:\n",
+    "                loss, acc = process_chunk(model, chunk, optimizer, loss_fn)\n",
+    "                total_loss += loss\n",
+    "                total_acc += acc\n",
+    "        else:\n",
+    "            # Process the batch normally if it's not a list\n",
+    "            loss, acc = process_chunk(model, batch, optimizer, loss_fn)\n",
+    "            total_loss += loss\n",
+    "            total_acc += acc\n",
+    "\n",
+    "        progress_bar.set_postfix(loss=total_loss / len(dataloader), acc=total_acc / len(dataloader))\n",
+    "\n",
+    "    return total_loss / len(dataloader), total_acc / len(dataloader)\n",
+    "\n",
+    "def process_chunk(model, chunk, optimizer, loss_fn):\n",
+    "    optimizer.zero_grad()\n",
+    "    input_ids = chunk['input_ids'].to(device)\n",
+    "    attention_mask = chunk['attention_mask'].to(device)\n",
+    "    labels = chunk['label'].to(device)\n",
+    "\n",
+    "    outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
+    "    loss = outputs.loss\n",
+    "    preds = torch.argmax(outputs.logits, dim=1)\n",
+    "    acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
+    "\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    return loss.item(), acc\n",
+    "\n",
+    "def evaluate(model, dataloader):\n",
+    "    model.eval()\n",
+    "    total_acc = 0\n",
+    "    progress_bar = tqdm(dataloader, desc=\"Evaluating\")\n",
+    "\n",
+    "    for batch in progress_bar:\n",
+    "        input_ids = batch['input_ids'].to(device)\n",
+    "        attention_mask = batch['attention_mask'].to(device)\n",
+    "        labels = batch['label'].to(device)\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            outputs = model(input_ids, attention_mask=attention_mask)\n",
+    "            preds = torch.argmax(outputs.logits, dim=1)\n",
+    "            acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
+    "\n",
+    "        total_acc += acc\n",
+    "        progress_bar.set_postfix(acc=total_acc / len(dataloader))\n",
+    "\n",
+    "    return total_acc / len(dataloader)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a963e28-32d4-4f67-8f20-c6d95dea816d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_losses = []\n",
+    "train_accuracies = []\n",
+    "val_accuracies = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a33ca783",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-16T07:48:21.343098Z",
+     "iopub.status.busy": "2024-08-16T07:48:21.342735Z",
+     "iopub.status.idle": "2024-08-16T16:34:52.857638Z",
+     "shell.execute_reply": "2024-08-16T16:34:52.856423Z"
+    },
+    "papermill": {
+     "duration": 31591.533756,
+     "end_time": "2024-08-16T16:34:52.859133",
+     "exception": true,
+     "start_time": "2024-08-16T07:48:21.325377",
+     "status": "failed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7b6f8e3a1104ec98bd0aed4448e65d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: Train Loss 0.5815\n",
+      "Epoch 2/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dfc41e9c97b48a58476076c580380e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: Train Loss 0.5116\n",
+      "Epoch 3/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbec508f5ce349de938dac4880b7bb1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: Train Loss 0.3942\n",
+      "Epoch 4/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdbee59d6ce5414c904512f0da77a2e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: Train Loss 0.2470\n",
+      "Epoch 5/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "361e0d5bd4284409a6b8a6621eaa4698",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: Train Loss 0.1484\n",
+      "Epoch 6/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7317bbb3f4cf4cce8353606808311cca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: Train Loss 0.1065\n",
+      "Epoch 7/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae2ce919637f4688a4c985f4459ce180",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: Train Loss 0.0857\n",
+      "Epoch 8/8\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d01eecd090584f92989bcbcf5530a427",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training:   0%|          | 0/7547 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: Train Loss 0.0718\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from transformers import BertForSequenceClassification\n",
+    "from torch.utils.data import DataLoader  # Assuming you're using DataLoader\n",
+    "from tqdm.notebook import tqdm  # For progress bars in Jupyter Notebooks\n",
+    "import matplotlib.pyplot as plt  # For plotting graphs\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "\n",
+    "n_epochs = 8 \n",
+    "for epoch in range(n_epochs):\n",
+    "    print(f\"Epoch {epoch+1}/{n_epochs}\")\n",
+    "    \n",
+    "    train_loss, train_acc = train(bert_model, train_loader, optimizer, loss_fn)\n",
+    "    val_acc = evaluate(bert_model, val_loader)\n",
+    "    \n",
+    "    # Append metrics\n",
+    "    train_losses.append(train_loss)\n",
+    "    train_accuracies.append(train_acc)\n",
+    "    val_accuracies.append(val_acc)\n",
+    "    print(f'Epoch {epoch+1}: Train Loss {train_loss:.4f}')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d75a3fb",
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2024-08-16T07:23:43.247960Z",
+     "iopub.status.idle": "2024-08-16T07:23:43.248327Z",
+     "shell.execute_reply": "2024-08-16T07:23:43.248155Z",
+     "shell.execute_reply.started": "2024-08-16T07:23:43.248142Z"
+    },
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "torch.save(bert_model, 'bert_model_complete.pth')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0b28e8e2",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# Plotting Loss and Accuracy\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "# Plot Loss\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(range(n_epochs), train_losses, label='Train Loss', marker='o')\n",
+    "plt.title('Training Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Plot Accuracy\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(range(n_epochs), train_accuracies, label='Train Accuracy', marker='o')\n",
+    "plt.plot(range(n_epochs), val_accuracies, label='Validation Accuracy', marker='o')\n",
+    "plt.title('Accuracy')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62c6e1d1",
+   "metadata": {
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+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "source": [
+    "BERT model evaluation and illustration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c92f59b",
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2024-08-16T06:43:56.273472Z",
+     "iopub.status.idle": "2024-08-16T06:43:56.273909Z",
+     "shell.execute_reply": "2024-08-16T06:43:56.273710Z",
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+   "outputs": [],
+   "source": [
+    "# Function to get predictions and true labels\n",
+    "def get_predictions(model, dataloader):\n",
+    "    model.eval()\n",
+    "    predictions = []\n",
+    "    true_labels = []\n",
+    "    with torch.no_grad():\n",
+    "        for batch in dataloader:\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "            labels = batch['labels'].to(device)\n",
+    "            outputs = model(input_ids)\n",
+    "            preds = torch.argmax(outputs.logits, dim=1)\n",
+    "            predictions.extend(preds.tolist())\n",
+    "            true_labels.extend(labels.tolist())\n",
+    "    return predictions, true_labels\n",
+    "\n",
+    "# Assuming you have a test_loader\n",
+    "predictions, true_labels = get_predictions(bert_model, test_loader)\n",
+    "\n",
+    "# Display some sample outputs\n",
+    "for i in range(10):\n",
+    "    print(f\"Sample {i+1}: True Label: {true_labels[i]}, Prediction: {predictions[i]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "17e59bc6",
+   "metadata": {
+    "papermill": {
+     "duration": null,
+     "end_time": null,
+     "exception": null,
+     "start_time": null,
+     "status": "pending"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.82\n",
+      "Recall: 0.85\n",
+      "F1 Score: 0.81\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix\n",
+    "\n",
+    "accuracy = accuracy_score(true_labels, predictions)\n",
+    "recall = recall_score(true_labels, predictions, average='macro')\n",
+    "f1 = f1_score(true_labels, predictions, average='macro')\n",
+    "conf_matrix = confusion_matrix(true_labels, predictions)\n",
+    "\n",
+    "# Print results\n",
+    "print(f\"Accuracy: {accuracy:.2f}\")\n",
+    "print(f\"Recall: {recall:.2f}\")\n",
+    "print(f\"F1 Score: {f1:.2f}\")\n"
+   ]
+  },
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+   "id": "88c63d9e",
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+    "tags": []
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+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Create a DataFrame to display metrics\n",
+    "metrics_df = pd.DataFrame({\n",
+    "    \"Metric\": [\"Accuracy\", \"Recall\", \"F1 Score\"],\n",
+    "    \"Value\": [accuracy, recall, f1]\n",
+    "})\n",
+    "\n",
+    "print(metrics_df)\n"
+   ]
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