diff --git "a/final T5 model.ipynb" "b/final T5 model.ipynb"
new file mode 100644--- /dev/null
+++ "b/final T5 model.ipynb"
@@ -0,0 +1,971 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:17.901600Z",
+     "iopub.status.busy": "2024-08-15T20:36:17.901195Z",
+     "iopub.status.idle": "2024-08-15T20:36:17.910909Z",
+     "shell.execute_reply": "2024-08-15T20:36:17.909719Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:17.901563Z"
+    }
+   },
+   "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": 12,
+     "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",
+   "metadata": {},
+   "source": [
+    "--------------------------------------------------------------------------------------------------------------------------------------\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **1. Load the Dataset**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:18.377388Z",
+     "iopub.status.busy": "2024-08-15T20:36:18.376962Z",
+     "iopub.status.idle": "2024-08-15T20:36:28.824077Z",
+     "shell.execute_reply": "2024-08-15T20:36:28.821441Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:18.377358Z"
+    }
+   },
+   "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",
+   "metadata": {},
+   "source": [
+    "### **2. Exploratory Data Analysis (EDA)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:28.827916Z",
+     "iopub.status.busy": "2024-08-15T20:36:28.827207Z",
+     "iopub.status.idle": "2024-08-15T20:36:29.364863Z",
+     "shell.execute_reply": "2024-08-15T20:36:29.363109Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:28.827856Z"
+    }
+   },
+   "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": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:29.367078Z",
+     "iopub.status.busy": "2024-08-15T20:36:29.366653Z",
+     "iopub.status.idle": "2024-08-15T20:36:34.266048Z",
+     "shell.execute_reply": "2024-08-15T20:36:34.264813Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:29.367037Z"
+    }
+   },
+   "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": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:34.269998Z",
+     "iopub.status.busy": "2024-08-15T20:36:34.269504Z",
+     "iopub.status.idle": "2024-08-15T20:36:34.281850Z",
+     "shell.execute_reply": "2024-08-15T20:36:34.280167Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:34.269956Z"
+    }
+   },
+   "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": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:34.284083Z",
+     "iopub.status.busy": "2024-08-15T20:36:34.283649Z",
+     "iopub.status.idle": "2024-08-15T20:36:37.016511Z",
+     "shell.execute_reply": "2024-08-15T20:36:37.015129Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:34.284043Z"
+    }
+   },
+   "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": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:37.018690Z",
+     "iopub.status.busy": "2024-08-15T20:36:37.018254Z",
+     "iopub.status.idle": "2024-08-15T20:36:41.280326Z",
+     "shell.execute_reply": "2024-08-15T20:36:41.278149Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:37.018653Z"
+    }
+   },
+   "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",
+   "metadata": {},
+   "source": [
+    "### **2.B. Rule based methods, spoiler contining frequent words and phrases**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T20:36:41.282667Z",
+     "iopub.status.busy": "2024-08-15T20:36:41.282218Z",
+     "iopub.status.idle": "2024-08-15T21:03:02.309963Z",
+     "shell.execute_reply": "2024-08-15T21:03:02.308624Z",
+     "shell.execute_reply.started": "2024-08-15T20:36:41.282633Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0045c7d496964b8bb4d039fce1812329",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Cleaning Text:   0%|          | 0/573913 [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",
+    "    # Tokenize text\n",
+    "    tokens = nltk.word_tokenize(text)\n",
+    "    \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": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T21:03:02.311846Z",
+     "iopub.status.busy": "2024-08-15T21:03:02.311478Z",
+     "iopub.status.idle": "2024-08-15T21:03:30.084246Z",
+     "shell.execute_reply": "2024-08-15T21:03:30.082921Z",
+     "shell.execute_reply.started": "2024-08-15T21:03:02.311816Z"
+    }
+   },
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "### **3. Data Preprocessing**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T21:03:30.086358Z",
+     "iopub.status.busy": "2024-08-15T21:03:30.085883Z",
+     "iopub.status.idle": "2024-08-15T21:03:35.818636Z",
+     "shell.execute_reply": "2024-08-15T21:03:35.817444Z",
+     "shell.execute_reply.started": "2024-08-15T21:03:30.086319Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e94f9f334854f9aa171f335516896cf",
+       "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": "36531e2e5b62497db27de6bcdb34968c",
+       "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": "8213ba39a24b40299775c01683478bb6",
+       "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": "e3abb90457044fcfb08cde74f86a9a4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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",
+    "\n",
+    "\n",
+    "\n",
+    "# Encode the target label\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['label'] = label_encoder.fit_transform(df['is_spoiler'])\n",
+    "\n",
+    "\n",
+    "train_df, temp_df = train_test_split(df, test_size=0.2, random_state=42, stratify=df['label'])\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Re-split the validation and test sets (no need to apply SMOTE here)\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42, stratify=temp_df['label'])\n",
+    "\n",
+    "# Tokenization\n",
+    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T21:16:16.175375Z",
+     "iopub.status.busy": "2024-08-15T21:16:16.174869Z",
+     "iopub.status.idle": "2024-08-15T21:16:16.972289Z",
+     "shell.execute_reply": "2024-08-15T21:16:16.971077Z",
+     "shell.execute_reply.started": "2024-08-15T21:16:16.175305Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42, stratify=temp_df['label'])\n",
+    "\n",
+    "# Tokenization\n",
+    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T21:16:16.975612Z",
+     "iopub.status.busy": "2024-08-15T21:16:16.975045Z",
+     "iopub.status.idle": "2024-08-15T21:16:16.986935Z",
+     "shell.execute_reply": "2024-08-15T21:16:16.985447Z",
+     "shell.execute_reply.started": "2024-08-15T21:16:16.975562Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "class SpoilerDataset(Dataset):\n",
+    "    def __init__(self, texts, labels):\n",
+    "        self.texts = texts\n",
+    "        self.labels = labels\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",
+    "\n",
+    "        encoding = tokenizer.encode_plus(\n",
+    "            text,\n",
+    "            max_length=5000,\n",
+    "            add_special_tokens=True,\n",
+    "            padding='max_length',\n",
+    "            truncation=True,\n",
+    "            return_attention_mask=True,\n",
+    "            return_tensors='pt',\n",
+    "        )\n",
+    "\n",
+    "        return {\n",
+    "            'input_ids': encoding['input_ids'].flatten(),\n",
+    "            'attention_mask': encoding['attention_mask'].flatten(),\n",
+    "            'label': torch.tensor(label, dtype=torch.long)\n",
+    "        }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T21:16:16.989066Z",
+     "iopub.status.busy": "2024-08-15T21:16:16.988619Z",
+     "iopub.status.idle": "2024-08-15T21:16:17.096427Z",
+     "shell.execute_reply": "2024-08-15T21:16:17.095123Z",
+     "shell.execute_reply.started": "2024-08-15T21:16:16.988999Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Create datasets with the resampled training data\n",
+    "train_dataset = SpoilerDataset(train_df['cleaned_review_text'].tolist(), train_df['label'].tolist())\n",
+    "val_dataset = SpoilerDataset(val_df['cleaned_review_text'].tolist(), val_df['label'].tolist())\n",
+    "test_dataset = SpoilerDataset(test_df['cleaned_review_text'].tolist(), test_df['label'].tolist())\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
+    "val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **4. Modeling**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T21:16:27.227086Z",
+     "iopub.status.busy": "2024-08-15T21:16:27.226586Z",
+     "iopub.status.idle": "2024-08-15T21:16:29.599138Z",
+     "shell.execute_reply": "2024-08-15T21:16:29.597904Z",
+     "shell.execute_reply.started": "2024-08-15T21:16:27.227049Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.optim import Adam\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from tqdm.notebook import tqdm\n",
+    "from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# Load the T5 tokenizer and model\n",
+    "tokenizer = T5Tokenizer.from_pretrained('t5-base')\n",
+    "model = T5ForConditionalGeneration.from_pretrained('t5-base')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **5. Training and Evaluation**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "T5 model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-15T21:16:31.214540Z",
+     "iopub.status.busy": "2024-08-15T21:16:31.214106Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ff471c855584664be21c23906fbe770",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/573913 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Define preprocessing function for T5\n",
+    "def preprocess_function(examples):\n",
+    "    # Format the inputs and labels for text-to-text task\n",
+    "    inputs = [\"classify: \" + text for text in examples['cleaned_review_text']]\n",
+    "    targets = [str(label) for label in examples['is_spoiler']]\n",
+    "    model_inputs = tokenizer(inputs, max_length=128, truncation=True)\n",
+    "    labels = tokenizer(targets, max_length=2, truncation=True)\n",
+    "    model_inputs['labels'] = labels['input_ids']\n",
+    "    return model_inputs\n",
+    "\n",
+    "# Load and preprocess the dataset\n",
+    "dataset = load_dataset('json', data_files={'train': '/kaggle/working/preprocessed.json'})\n",
+    "tokenized_datasets = dataset.map(preprocess_function, batched=True)\n",
+    "\n",
+    "# Training arguments\n",
+    "training_args = TrainingArguments(\n",
+    "    output_dir='./results_t5',\n",
+    "    num_train_epochs=8,\n",
+    "    per_device_train_batch_size=8,\n",
+    "    per_device_eval_batch_size=8,\n",
+    "    warmup_steps=500,\n",
+    "    weight_decay=0.01,\n",
+    "    logging_dir='./logs_t5',\n",
+    ")\n",
+    "\n",
+    "# Define Trainer\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=tokenized_datasets['train'],\n",
+    "    eval_dataset=tokenized_datasets['test'],\n",
+    "    tokenizer=tokenizer,\n",
+    ")\n",
+    "\n",
+    "# Train and evaluate\n",
+    "trainer.train()\n",
+    "results_t5 = trainer.evaluate()\n",
+    "print(f\"T5 Evaluation Loss: {results_t5['eval_loss']:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "T5 model evaluation and illustration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the model and tokenizer\n",
+    "model_path = \"./saved_t5_model\"\n",
+    "trainer.save_model(model_path)\n",
+    "tokenizer.save_pretrained(model_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_t5 = trainer.evaluate()\n",
+    "print(f\"T5 Evaluation Loss: {results_t5['eval_loss']:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Function to convert logits to labels\n",
+    "def logits_to_labels(logits, tokenizer):\n",
+    "    predictions = np.argmax(logits, axis=-1)\n",
+    "    return [tokenizer.decode(pred, skip_special_tokens=True) for pred in predictions]\n",
+    "\n",
+    "# Run predictions\n",
+    "predictions = trainer.predict(tokenized_datasets['test'])\n",
+    "predicted_labels = logits_to_labels(predictions.predictions, tokenizer)\n",
+    "true_labels = [tokenizer.decode(label, skip_special_tokens=True) for label in tokenized_datasets['test']['labels']]\n",
+    "\n",
+    "# Print samples\n",
+    "for i in range(10):\n",
+    "    print(f\"Sample {i+1}: True Label: {true_labels[i]}, Predicted Label: {predicted_labels[i]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.78\n",
+      "Recall: 0.78\n",
+      "F1 Score: 0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, recall_score, f1_score, confusion_matrix\n",
+    "\n",
+    "\n",
+    "# Compute metrics\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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "datasetId": 200769,
+     "sourceId": 442620,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 30746,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}