diff --git "a/train_model.ipynb" "b/train_model.ipynb"
new file mode 100644--- /dev/null
+++ "b/train_model.ipynb"
@@ -0,0 +1,3822 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "742dd25e",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:57:34.588701Z",
+     "iopub.status.busy": "2025-04-09T14:57:34.588348Z",
+     "iopub.status.idle": "2025-04-09T14:57:36.465959Z",
+     "shell.execute_reply": "2025-04-09T14:57:36.464971Z"
+    },
+    "papermill": {
+     "duration": 1.884778,
+     "end_time": "2025-04-09T14:57:36.467568",
+     "exception": false,
+     "start_time": "2025-04-09T14:57:34.582790",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import os\n",
+    "import urllib.request\n",
+    "import sys\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6376949e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:57:36.477658Z",
+     "iopub.status.busy": "2025-04-09T14:57:36.477313Z",
+     "iopub.status.idle": "2025-04-09T14:57:44.372695Z",
+     "shell.execute_reply": "2025-04-09T14:57:44.371993Z"
+    },
+    "papermill": {
+     "duration": 7.901685,
+     "end_time": "2025-04-09T14:57:44.374160",
+     "exception": false,
+     "start_time": "2025-04-09T14:57:36.472475",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import torch.nn.functional as f\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "import torchvision\n",
+    "from torchvision.transforms import v2\n",
+    "from torchvision import models\n",
+    "from torchvision.models import resnet50, ResNet50_Weights\n",
+    "import torch.optim.lr_scheduler as lr_scheduler\n",
+    "\n",
+    "import random\n",
+    "from PIL import Image\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c2c2efd2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:57:44.384169Z",
+     "iopub.status.busy": "2025-04-09T14:57:44.383727Z",
+     "iopub.status.idle": "2025-04-09T14:57:44.850886Z",
+     "shell.execute_reply": "2025-04-09T14:57:44.849673Z"
+    },
+    "papermill": {
+     "duration": 0.474073,
+     "end_time": "2025-04-09T14:57:44.852622",
+     "exception": false,
+     "start_time": "2025-04-09T14:57:44.378549",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('/kaggle/input/fakenews-fakedit/Dataset/df_balanced_resized.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cd73260e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:57:44.862381Z",
+     "iopub.status.busy": "2025-04-09T14:57:44.862092Z",
+     "iopub.status.idle": "2025-04-09T14:57:44.892519Z",
+     "shell.execute_reply": "2025-04-09T14:57:44.891636Z"
+    },
+    "papermill": {
+     "duration": 0.036914,
+     "end_time": "2025-04-09T14:57:44.894122",
+     "exception": false,
+     "start_time": "2025-04-09T14:57:44.857208",
+     "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>author</th>\n",
+       "      <th>clean_title</th>\n",
+       "      <th>created_utc</th>\n",
+       "      <th>domain</th>\n",
+       "      <th>hasImage</th>\n",
+       "      <th>id</th>\n",
+       "      <th>image_url</th>\n",
+       "      <th>linked_submission_id</th>\n",
+       "      <th>num_comments</th>\n",
+       "      <th>score</th>\n",
+       "      <th>subreddit</th>\n",
+       "      <th>upvote_ratio</th>\n",
+       "      <th>6_way_label</th>\n",
+       "      <th>binary_label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BruxellesBlonde</td>\n",
+       "      <td>sooooo satisfying</td>\n",
+       "      <td>1.537968e+09</td>\n",
+       "      <td>i.redd.it</td>\n",
+       "      <td>True</td>\n",
+       "      <td>9j2gd1</td>\n",
+       "      <td>https://preview.redd.it/bjlftqsj3lo11.jpg?widt...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mildlyinteresting</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>korkow</td>\n",
+       "      <td>the chips fell out of this chocolate chip cookie</td>\n",
+       "      <td>1.384738e+09</td>\n",
+       "      <td>i.imgur.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1qv2m9</td>\n",
+       "      <td>https://external-preview.redd.it/ESPM-tTuK3wer...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>misleadingthumbnails</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>kyletheheroman</td>\n",
+       "      <td>has anyone seen a square cloud before</td>\n",
+       "      <td>1.551516e+09</td>\n",
+       "      <td>i.redd.it</td>\n",
+       "      <td>True</td>\n",
+       "      <td>awfsgw</td>\n",
+       "      <td>https://preview.redd.it/trqkt1rx4oj21.jpg?widt...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>mildlyinteresting</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>FledPotato</td>\n",
+       "      <td>it almost seems to smile back</td>\n",
+       "      <td>1.522871e+09</td>\n",
+       "      <td>i.redd.it</td>\n",
+       "      <td>True</td>\n",
+       "      <td>89ssvs</td>\n",
+       "      <td>https://preview.redd.it/yf1j0rvq4yp01.jpg?widt...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>212</td>\n",
+       "      <td>pareidolia</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>allhundredyears</td>\n",
+       "      <td>this tape deck has seen some shit</td>\n",
+       "      <td>1.426363e+09</td>\n",
+       "      <td>imgur.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2z1tha</td>\n",
+       "      <td>https://external-preview.redd.it/QFHuRDeu_v1cm...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>pareidolia</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>spyder_19</td>\n",
+       "      <td>saw an interesting sign at the store so i got it</td>\n",
+       "      <td>1.564682e+09</td>\n",
+       "      <td>i.redd.it</td>\n",
+       "      <td>True</td>\n",
+       "      <td>ckrv3k</td>\n",
+       "      <td>https://preview.redd.it/ketwpycjmvd31.jpg?widt...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>32</td>\n",
+       "      <td>mildlyinteresting</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Not3Shabby</td>\n",
+       "      <td>inherent paradox scrabeck</td>\n",
+       "      <td>1.489213e+09</td>\n",
+       "      <td>imgur.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5yrdcs</td>\n",
+       "      <td>https://external-preview.redd.it/g3nNRMvtW8FCT...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>fakealbumcovers</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>mikewall</td>\n",
+       "      <td>gop senate candidate kelli ward complained mcc...</td>\n",
+       "      <td>1.535296e+09</td>\n",
+       "      <td>newsweek.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>9ag51l</td>\n",
+       "      <td>https://external-preview.redd.it/3c79PddPA_og2...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>243</td>\n",
+       "      <td>nottheonion</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>worldnews_SS</td>\n",
+       "      <td>christians fleeing iraqs mosul region to provi...</td>\n",
+       "      <td>1.458731e+09</td>\n",
+       "      <td>engadget.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4bm0cs</td>\n",
+       "      <td>https://external-preview.redd.it/xNKcWQsoGpWr3...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>subredditsimulator</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>todayilearned_SS</td>\n",
+       "      <td>til pegi europes video game in only to turn to...</td>\n",
+       "      <td>1.486354e+09</td>\n",
+       "      <td>nytimes.com</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5sc05y</td>\n",
+       "      <td>https://external-preview.redd.it/PL4PyhChdGdhF...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>subredditsimulator</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             author                                        clean_title  \\\n",
+       "0   BruxellesBlonde                                  sooooo satisfying   \n",
+       "1            korkow   the chips fell out of this chocolate chip cookie   \n",
+       "2    kyletheheroman              has anyone seen a square cloud before   \n",
+       "3        FledPotato                      it almost seems to smile back   \n",
+       "4   allhundredyears                  this tape deck has seen some shit   \n",
+       "5         spyder_19   saw an interesting sign at the store so i got it   \n",
+       "6        Not3Shabby                          inherent paradox scrabeck   \n",
+       "7          mikewall  gop senate candidate kelli ward complained mcc...   \n",
+       "8      worldnews_SS  christians fleeing iraqs mosul region to provi...   \n",
+       "9  todayilearned_SS  til pegi europes video game in only to turn to...   \n",
+       "\n",
+       "    created_utc        domain  hasImage      id  \\\n",
+       "0  1.537968e+09     i.redd.it      True  9j2gd1   \n",
+       "1  1.384738e+09   i.imgur.com      True  1qv2m9   \n",
+       "2  1.551516e+09     i.redd.it      True  awfsgw   \n",
+       "3  1.522871e+09     i.redd.it      True  89ssvs   \n",
+       "4  1.426363e+09     imgur.com      True  2z1tha   \n",
+       "5  1.564682e+09     i.redd.it      True  ckrv3k   \n",
+       "6  1.489213e+09     imgur.com      True  5yrdcs   \n",
+       "7  1.535296e+09  newsweek.com      True  9ag51l   \n",
+       "8  1.458731e+09  engadget.com      True  4bm0cs   \n",
+       "9  1.486354e+09   nytimes.com      True  5sc05y   \n",
+       "\n",
+       "                                           image_url linked_submission_id  \\\n",
+       "0  https://preview.redd.it/bjlftqsj3lo11.jpg?widt...                  NaN   \n",
+       "1  https://external-preview.redd.it/ESPM-tTuK3wer...                  NaN   \n",
+       "2  https://preview.redd.it/trqkt1rx4oj21.jpg?widt...                  NaN   \n",
+       "3  https://preview.redd.it/yf1j0rvq4yp01.jpg?widt...                  NaN   \n",
+       "4  https://external-preview.redd.it/QFHuRDeu_v1cm...                  NaN   \n",
+       "5  https://preview.redd.it/ketwpycjmvd31.jpg?widt...                  NaN   \n",
+       "6  https://external-preview.redd.it/g3nNRMvtW8FCT...                  NaN   \n",
+       "7  https://external-preview.redd.it/3c79PddPA_og2...                  NaN   \n",
+       "8  https://external-preview.redd.it/xNKcWQsoGpWr3...                  NaN   \n",
+       "9  https://external-preview.redd.it/PL4PyhChdGdhF...                  NaN   \n",
+       "\n",
+       "   num_comments  score             subreddit  upvote_ratio  6_way_label  \\\n",
+       "0           3.0     15     mildlyinteresting          0.75            0   \n",
+       "1           4.0      8  misleadingthumbnails          0.59            2   \n",
+       "2           6.0      4     mildlyinteresting          0.60            0   \n",
+       "3           9.0    212            pareidolia          0.98            2   \n",
+       "4           2.0      6            pareidolia          0.88            2   \n",
+       "5           2.0     32     mildlyinteresting          0.87            0   \n",
+       "6           0.0      4       fakealbumcovers          0.83            1   \n",
+       "7          15.0    243           nottheonion          0.95            0   \n",
+       "8          20.0     10    subredditsimulator          0.92            3   \n",
+       "9          61.0      5    subredditsimulator          0.86            3   \n",
+       "\n",
+       "   binary_label  \n",
+       "0             0  \n",
+       "1             1  \n",
+       "2             0  \n",
+       "3             1  \n",
+       "4             1  \n",
+       "5             0  \n",
+       "6             1  \n",
+       "7             0  \n",
+       "8             1  \n",
+       "9             1  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "722e2299",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:57:44.905475Z",
+     "iopub.status.busy": "2025-04-09T14:57:44.905209Z",
+     "iopub.status.idle": "2025-04-09T14:57:58.592716Z",
+     "shell.execute_reply": "2025-04-09T14:57:58.591624Z"
+    },
+    "papermill": {
+     "duration": 13.695117,
+     "end_time": "2025-04-09T14:57:58.594460",
+     "exception": false,
+     "start_time": "2025-04-09T14:57:44.899343",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "\n",
+    "! pip install bert-serving-server\n",
+    "! pip install bert-serving-client\n",
+    "! pip install torch transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "09e8dffc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:57:58.605520Z",
+     "iopub.status.busy": "2025-04-09T14:57:58.605233Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.242728Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.241824Z"
+    },
+    "papermill": {
+     "duration": 17.644708,
+     "end_time": "2025-04-09T14:58:16.244174",
+     "exception": false,
+     "start_time": "2025-04-09T14:57:58.599466",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a33316103c840dcbe682c6f0aeea286",
+       "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": {
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+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da7ae94dd26a44b9851cf104543c0fd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "BertModel(\n",
+       "  (embeddings): BertEmbeddings(\n",
+       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
+       "    (position_embeddings): Embedding(512, 768)\n",
+       "    (token_type_embeddings): Embedding(2, 768)\n",
+       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "    (dropout): Dropout(p=0.1, inplace=False)\n",
+       "  )\n",
+       "  (encoder): BertEncoder(\n",
+       "    (layer): ModuleList(\n",
+       "      (0-11): 12 x BertLayer(\n",
+       "        (attention): BertAttention(\n",
+       "          (self): BertSdpaSelfAttention(\n",
+       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
+       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
+       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
+       "            (dropout): Dropout(p=0.1, inplace=False)\n",
+       "          )\n",
+       "          (output): BertSelfOutput(\n",
+       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "            (dropout): Dropout(p=0.1, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (intermediate): BertIntermediate(\n",
+       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+       "          (intermediate_act_fn): GELUActivation()\n",
+       "        )\n",
+       "        (output): BertOutput(\n",
+       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
+       "          (dropout): Dropout(p=0.1, inplace=False)\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (pooler): BertPooler(\n",
+       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+       "    (activation): Tanh()\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import BertModel, BertTokenizer\n",
+    "\n",
+    "# Load pre-trained BERT model and tokenizer\n",
+    "model_name = 'bert-base-uncased'\n",
+    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
+    "bert_model = BertModel.from_pretrained(model_name, output_hidden_states = True)\n",
+    "\n",
+    "# Put the model in evaluation mode, which turns off dropout regularization which is used in training.\n",
+    "bert_model.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "123c3656",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.256413Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.255949Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.259660Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.259038Z"
+    },
+    "papermill": {
+     "duration": 0.011103,
+     "end_time": "2025-04-09T14:58:16.261002",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.249899",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_bert_embedding(text):\n",
+    "    # Tokenize input text and get token IDs and attention mask\n",
+    "    inputs = tokenizer.encode_plus(text, add_special_tokens = True, return_tensors='pt', max_length=80, truncation=True, padding='max_length')\n",
+    "\n",
+    "    return inputs['input_ids'].squeeze(0), inputs['attention_mask'].squeeze(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f2f68f26",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.271870Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.271629Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.342935Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.342176Z"
+    },
+    "papermill": {
+     "duration": 0.078097,
+     "end_time": "2025-04-09T14:58:16.344324",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.266227",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "df_train, df_test = train_test_split(df, test_size=0.2, stratify=df[\"binary_label\"])\n",
+    "df_test, df_val = train_test_split(df_test, test_size=0.5, stratify=df_test[\"binary_label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae5603c3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.355488Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.355259Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.361355Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.360587Z"
+    },
+    "papermill": {
+     "duration": 0.01337,
+     "end_time": "2025-04-09T14:58:16.362806",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.349436",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class FakedditDataset(Dataset):\n",
+    "    def __init__(self, df, text_field=\"clean_title\", label_field=\"binary_label\", image_id=\"id\"):\n",
+    "        self.df = df.reset_index(drop=True)\n",
+    "        self.text_field = text_field\n",
+    "        self.label_field = label_field\n",
+    "        self.image_id = image_id\n",
+    "\n",
+    "        self.img_size = 256\n",
+    "        # Using the pre-calculated ImageNet mean and std values for normalization\n",
+    "        self.mean, self.std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+    "\n",
+    "        self.transform_func = v2.Compose(\n",
+    "                [   v2.Resize(256),\n",
+    "                    v2.ToImage(),\n",
+    "                    v2.ToDtype(torch.float32, scale=True),\n",
+    "                    v2.Normalize(self.mean, self.std)\n",
+    "                    ])\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        text = str(self.df.at[index, self.text_field])\n",
+    "        label = self.df.at[index, self.label_field]\n",
+    "        img_path = \"/kaggle/input/fakenews-fakedit/Dataset/images/\" + self.df.at[index, self.image_id] + \".jpg\"\n",
+    "\n",
+    "        image = Image.open(img_path)\n",
+    "        img = self.transform_func(image)\n",
+    "\n",
+    "        input_ids, attention_mask = get_bert_embedding(text)\n",
+    "\n",
+    "        return input_ids, attention_mask, label, img\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b01687f7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.373613Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.373405Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.385730Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.385182Z"
+    },
+    "papermill": {
+     "duration": 0.01933,
+     "end_time": "2025-04-09T14:58:16.387149",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.367819",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_dataset = FakedditDataset(df_train)\n",
+    "test_dataset = FakedditDataset(df_test)\n",
+    "val_dataset = FakedditDataset(df_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3ea3e972",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.398504Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.398275Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.707457Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.706508Z"
+    },
+    "papermill": {
+     "duration": 0.316775,
+     "end_time": "2025-04-09T14:58:16.709280",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.392505",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1643\n",
+      "torch.Size([32, 80]) torch.Size([32, 80]) torch.Size([32]) torch.Size([32, 3, 256, 256])\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
+    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=True)\n",
+    "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=True)\n",
+    "\n",
+    "print(len(train_loader))\n",
+    "\n",
+    "# Verifying dataset was created accurately\n",
+    "input_ids, attention_mask, label, img = next(iter(train_loader))\n",
+    "print(input_ids.shape, attention_mask.shape, label.shape, img.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2bbb61c0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.721642Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.721384Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.726400Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.725731Z"
+    },
+    "papermill": {
+     "duration": 0.012021,
+     "end_time": "2025-04-09T14:58:16.727524",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.715503",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class EarlyStopping:\n",
+    "    def __init__(self, patience=4, verbose=False, delta=0):\n",
+    "        self.patience = patience\n",
+    "        self.verbose = verbose\n",
+    "        self.counter = 0\n",
+    "        self.best_loss = None\n",
+    "        self.early_stop = False\n",
+    "        self.delta = delta\n",
+    "\n",
+    "    def __call__(self, val_loss):\n",
+    "        if self.best_loss is None:\n",
+    "            self.best_loss = val_loss\n",
+    "        elif val_loss > self.best_loss + self.delta:\n",
+    "            self.counter += 1\n",
+    "            if self.verbose:\n",
+    "                print(f\"EarlyStopping counter: {self.counter} out of {self.patience}\")\n",
+    "            if self.counter >= self.patience:\n",
+    "                self.early_stop = True\n",
+    "        else:\n",
+    "            self.best_loss = val_loss\n",
+    "            self.counter = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f2584bec",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.738341Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.738140Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.743512Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.742634Z"
+    },
+    "papermill": {
+     "duration": 0.012462,
+     "end_time": "2025-04-09T14:58:16.745020",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.732558",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 0 ... 0 1 1]\n",
+      "Unique labels: [0 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = df_train[\"binary_label\"].values  \n",
+    "\n",
+    "print(labels)\n",
+    "print(\"Unique labels:\", np.unique(labels))\n",
+    "# assert set(np.unique(labels)).issubset({0, 1}), \"🔥 Labels must be 0 or 1 for binary classification!\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "99e961ca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.757348Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.757133Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.761985Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.761219Z"
+    },
+    "papermill": {
+     "duration": 0.012382,
+     "end_time": "2025-04-09T14:58:16.763299",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.750917",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class SelfAttentionFusion(nn.Module):\n",
+    "    def __init__(self, embed_dim):\n",
+    "        super().__init__()\n",
+    "        self.attn = nn.Linear(embed_dim * 2, 2)\n",
+    "        self.softmax = nn.Softmax(dim=1)\n",
+    "\n",
+    "    def forward(self, x_text, x_img):\n",
+    "        stacked = torch.stack([x_text, x_img], dim=1)  # (B, 2, D)\n",
+    "        attn_weights = self.softmax(self.attn(torch.cat([x_text, x_img], dim=1))).unsqueeze(2)  # (B, 2, 1)\n",
+    "        fused = (attn_weights * stacked).sum(dim=1)  # (B, D)\n",
+    "        return fused"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "09b3f58a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.774550Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.774308Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.780321Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.779507Z"
+    },
+    "papermill": {
+     "duration": 0.013157,
+     "end_time": "2025-04-09T14:58:16.781710",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.768553",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class BERTResNetClassifier(nn.Module):\n",
+    "    def __init__(self, num_classes=2):\n",
+    "        super(BERTResNetClassifier, self).__init__()\n",
+    "\n",
+    "        self.num_classes = num_classes\n",
+    "\n",
+    "        # Image model\n",
+    "        self.image_model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)\n",
+    "        self.fc_image = nn.Linear(1000, 512)\n",
+    "        self.drop_img = nn.Dropout(p=0.3)\n",
+    "\n",
+    "        # Text model\n",
+    "        self.text_model = BertModel.from_pretrained(\"bert-base-uncased\")\n",
+    "        self.fc_text = nn.Linear(self.text_model.config.hidden_size, 512)\n",
+    "        self.drop_text = nn.Dropout(p=0.3)\n",
+    "\n",
+    "        # Attention-based fusion\n",
+    "        self.fusion = SelfAttentionFusion(embed_dim=512)\n",
+    "\n",
+    "        # Final classification\n",
+    "        self.fc_final = nn.Linear(512, num_classes)\n",
+    "\n",
+    "    def forward(self, image, text_input_ids, text_attention_mask):\n",
+    "        # Image path\n",
+    "        x_img = self.image_model(image)\n",
+    "        x_img = self.drop_img(x_img)\n",
+    "        x_img = self.fc_image(x_img)\n",
+    "\n",
+    "        # Text path\n",
+    "        x_text_last_hidden = self.text_model(\n",
+    "            input_ids=text_input_ids,\n",
+    "            attention_mask=text_attention_mask,\n",
+    "            return_dict=False\n",
+    "        )[0][:, 0, :]\n",
+    "        x_text = self.drop_text(x_text_last_hidden)\n",
+    "        x_text = self.fc_text(x_text)\n",
+    "\n",
+    "        # Fusion\n",
+    "        x_fused = self.fusion(x_text, x_img)\n",
+    "\n",
+    "        # Final classifier\n",
+    "        logits = self.fc_final(x_fused)\n",
+    "        return logits\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "40348647",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.792735Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.792516Z",
+     "iopub.status.idle": "2025-04-09T14:58:16.795400Z",
+     "shell.execute_reply": "2025-04-09T14:58:16.794836Z"
+    },
+    "papermill": {
+     "duration": 0.009703,
+     "end_time": "2025-04-09T14:58:16.796564",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.786861",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.utils import compute_class_weight\n",
+    "# import torch_xla.core.xla_model as xm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b8ad064a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:16.808136Z",
+     "iopub.status.busy": "2025-04-09T14:58:16.807923Z",
+     "iopub.status.idle": "2025-04-09T14:58:18.651123Z",
+     "shell.execute_reply": "2025-04-09T14:58:18.650416Z"
+    },
+    "papermill": {
+     "duration": 1.850916,
+     "end_time": "2025-04-09T14:58:18.652720",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:16.801804",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
+      "100%|██████████| 97.8M/97.8M [00:00<00:00, 172MB/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using 2 GPUs!\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(device)\n",
+    "model = BERTResNetClassifier(num_classes=2)\n",
+    "\n",
+    "if torch.cuda.device_count() > 1:\n",
+    "    print(f\"Using {torch.cuda.device_count()} GPUs!\")\n",
+    "    model = nn.DataParallel(model)\n",
+    "model = model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bafa7e70",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:18.666026Z",
+     "iopub.status.busy": "2025-04-09T14:58:18.665764Z",
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+     "start_time": "2025-04-09T14:58:18.659143",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class FocalLoss(nn.Module):\n",
+    "    def __init__(self, alpha=None, gamma=2.0, reduction='mean'):\n",
+    "        super(FocalLoss, self).__init__()\n",
+    "        self.alpha = alpha\n",
+    "        self.gamma = gamma\n",
+    "        self.reduction = reduction\n",
+    "\n",
+    "    def forward(self, inputs, targets):\n",
+    "        ce_loss = F.cross_entropy(inputs, targets, weight=self.alpha, reduction='none')\n",
+    "        pt = torch.exp(-ce_loss)  # Prob of correct class\n",
+    "        focal_loss = ((1 - pt) ** self.gamma) * ce_loss\n",
+    "\n",
+    "        if self.reduction == 'mean':\n",
+    "            return focal_loss.mean()\n",
+    "        elif self.reduction == 'sum':\n",
+    "            return focal_loss.sum()\n",
+    "        else:\n",
+    "            return focal_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9f205502",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:18.684572Z",
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+    },
+    "papermill": {
+     "duration": 0.026304,
+     "end_time": "2025-04-09T14:58:18.704195",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:18.677891",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.utils.class_weight import compute_class_weight\n",
+    "\n",
+    "class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(labels), y=labels)\n",
+    "class_weights = torch.tensor(class_weights, dtype=torch.float).to(device)\n",
+    "\n",
+    "criterion = FocalLoss(alpha=class_weights, gamma=2.0)\n",
+    "# criterion = nn.CrossEntropyLoss(weight=class_weights)\n",
+    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-5, weight_decay=1e-4)\n",
+    "optimizer = torch.optim.Adam([\n",
+    "    {'params': model.module.text_model.parameters(), 'lr': 1e-5},\n",
+    "    {'params': model.module.image_model.parameters(), 'lr': 5e-5},\n",
+    "    {'params': list(model.module.fc_image.parameters()) +\n",
+    "               list(model.module.fc_text.parameters()) +\n",
+    "               list(model.module.fusion.parameters()) +\n",
+    "               list(model.module.fc_final.parameters()), 'lr': 1e-4}\n",
+    "], weight_decay=1e-5)\n",
+    "\n",
+    "scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=0, min_lr=1e-6, verbose=True)\n",
+    "num_epochs = 20\n",
+    "\n",
+    "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs):\n",
+    "    early_stopping = EarlyStopping(patience=5, verbose=True)\n",
+    "    \n",
+    "  # Training loop\n",
+    "    for epoch in range(num_epochs):\n",
+    "        model.train()\n",
+    "        running_loss = 0.0\n",
+    "\n",
+    "        for input_ids, attention_mask, label, img in train_loader:\n",
+    "            input_ids = input_ids.to(device)\n",
+    "            attention_mask = attention_mask.to(device)\n",
+    "            label = label.to(device)\n",
+    "            img = img.to(device)\n",
+    "                \n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "          # Forward pass\n",
+    "            outputs = model(img, input_ids, attention_mask)\n",
+    "            loss = criterion(outputs, label)\n",
+    "\n",
+    "          # Backward pass and optimization\n",
+    "            loss.backward()\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            running_loss += loss.item()* img.size(0)\n",
+    "            \n",
+    "       # Validating model and ensuring loss is decreasing     \n",
+    "        model.eval()\n",
+    "        val_loss = 0.0\n",
+    "        correct_preds = 0\n",
+    "        with torch.no_grad():\n",
+    "            for input_ids, attention_mask, label, img in val_loader:\n",
+    "                input_ids = input_ids.to(device)\n",
+    "                attention_mask = attention_mask.to(device)\n",
+    "                label = label.to(device)\n",
+    "                img = img.to(device)\n",
+    "    \n",
+    "                outputs = model(img, input_ids, attention_mask)\n",
+    "                loss = criterion(outputs, label)\n",
+    "                val_loss += loss.item() * img.size(0)\n",
+    "\n",
+    "                _, preds = torch.max(outputs, 1)\n",
+    "                correct_preds += torch.sum(preds == label)\n",
+    "\n",
+    "        val_loss = val_loss / len(val_loader.dataset)\n",
+    "        accuracy = correct_preds.double() / len(val_loader.dataset)\n",
+    "        scheduler.step(val_loss)\n",
+    "        print(f'Epoch {epoch+1}/{num_epochs}, Training Loss: {running_loss/len(train_loader.dataset):.4f}, Validation Loss: {val_loss:.4f}, Accuracy: {accuracy:.4f}')\n",
+    "\n",
+    "        # Early stopping\n",
+    "        early_stopping(val_loss)\n",
+    "        if early_stopping.early_stop:\n",
+    "            print(\"Early stopping triggered. Stopping training.\")\n",
+    "            break\n",
+    "\n",
+    "    torch.save(model.state_dict(), '/kaggle/working/model2.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bec89fae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T14:58:18.716491Z",
+     "iopub.status.busy": "2025-04-09T14:58:18.716211Z",
+     "iopub.status.idle": "2025-04-09T14:58:18.722261Z",
+     "shell.execute_reply": "2025-04-09T14:58:18.721597Z"
+    },
+    "papermill": {
+     "duration": 0.013742,
+     "end_time": "2025-04-09T14:58:18.723546",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:18.709804",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import precision_score, recall_score\n",
+    "\n",
+    "def evaluate_model(model, test_loader, criterion):\n",
+    "    model.eval()\n",
+    "    val_losses = []\n",
+    "    correct_preds = 0\n",
+    "\n",
+    "    all_preds = []\n",
+    "    all_labels = []\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for input_ids, attention_mask, label, img in test_loader:\n",
+    "            input_ids = input_ids.to(device)\n",
+    "            attention_mask = attention_mask.to(device)\n",
+    "            label = label.to(device)\n",
+    "            img = img.to(device)\n",
+    "\n",
+    "            outputs = model(\n",
+    "                  image = img,\n",
+    "                  text_input_ids = input_ids,\n",
+    "                  text_attention_mask = attention_mask\n",
+    "            )\n",
+    "\n",
+    "            # Final Softmax layer returns class predictions per sample in batch\n",
+    "            # Highest probability value resembles class prediction and is assigned to preds variable\n",
+    "            _, preds = torch.max(outputs, dim=1)\n",
+    "            #print(outputs)\n",
+    "\n",
+    "            # Loss is calculated by applying Cross Entropy Loss\n",
+    "            val_loss = criterion(outputs, label)\n",
+    "\n",
+    "            # Counting correct model predictions and incrementing correct prediction count\n",
+    "            correct_preds += torch.sum(preds == label)\n",
+    "            print(preds, label)\n",
+    "\n",
+    "            # Appending current loss per batch to list of losses per epoch\n",
+    "            val_losses.append(val_loss.item())\n",
+    "            \n",
+    "            all_preds.extend(preds.cpu().numpy())\n",
+    "            all_labels.extend(label.cpu().numpy())\n",
+    "            \n",
+    "\n",
+    "    accuracy = float((correct_preds.double() / len(df_test)) * 100)\n",
+    "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
+    "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
+    "\n",
+    "    print(\"\\nAccuracy: \", accuracy)\n",
+    "    print(\"Precision: \", precision)\n",
+    "    print(\"Recall: \", recall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "73760f46",
+   "metadata": {
+    "execution": {
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+     "shell.execute_reply": "2025-04-09T17:15:30.817069Z"
+    },
+    "papermill": {
+     "duration": 8232.090989,
+     "end_time": "2025-04-09T17:15:30.820095",
+     "exception": false,
+     "start_time": "2025-04-09T14:58:18.729106",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20, Training Loss: 0.1160, Validation Loss: 0.0951, Accuracy: 0.8409\n",
+      "Epoch 2/20, Training Loss: 0.0839, Validation Loss: 0.0968, Accuracy: 0.8478\n",
+      "EarlyStopping counter: 1 out of 5\n",
+      "Epoch 3/20, Training Loss: 0.0541, Validation Loss: 0.1117, Accuracy: 0.8513\n",
+      "EarlyStopping counter: 2 out of 5\n",
+      "Epoch 4/20, Training Loss: 0.0336, Validation Loss: 0.1642, Accuracy: 0.8517\n",
+      "EarlyStopping counter: 3 out of 5\n",
+      "Epoch 5/20, Training Loss: 0.0198, Validation Loss: 0.2515, Accuracy: 0.8484\n",
+      "EarlyStopping counter: 4 out of 5\n",
+      "Epoch 6/20, Training Loss: 0.0152, Validation Loss: 0.2819, Accuracy: 0.8484\n",
+      "EarlyStopping counter: 5 out of 5\n",
+      "Early stopping triggered. Stopping training.\n",
+      "\n",
+      "\n",
+      "tensor([0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
+      "        1, 1, 0, 0, 1, 0, 1, 1], device='cuda:0') tensor([0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
+      "        1, 1, 0, 1, 0, 0, 1, 0], device='cuda:0')\n",
+      "tensor([0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
+      "        1, 0, 1, 1, 0, 1, 1, 1], device='cuda:0') tensor([0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
+      "        0, 1, 1, 1, 0, 1, 1, 1], device='cuda:0')\n",
+      "tensor([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
+      "        0, 1, 0, 0, 1, 0, 1, 1], device='cuda:0') tensor([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
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+      "tensor([0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
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+      "tensor([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1,\n",
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+      "tensor([0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
+      "        1, 1, 1, 1, 1, 1, 0, 0], device='cuda:0') tensor([0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
+      "        1, 1, 1, 1, 1, 1, 0, 0], device='cuda:0')\n",
+      "tensor([1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
+      "        1, 1, 1, 0, 1, 0, 0, 0], device='cuda:0') tensor([1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0,\n",
+      "        1, 1, 0, 0, 1, 1, 1, 0], device='cuda:0')\n",
+      "tensor([1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1,\n",
+      "        0, 1, 1, 0, 0, 0, 1, 0], device='cuda:0') tensor([1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+      "        0, 0, 0, 0, 0, 0, 1, 0], device='cuda:0')\n",
+      "tensor([1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0,\n",
+      "        0, 0, 0, 0, 0, 1, 0, 0], device='cuda:0') tensor([1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
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+      "tensor([1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
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+      "tensor([0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
+      "        0, 0, 1, 1, 0, 0, 0, 0], device='cuda:0') tensor([0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
+      "        0, 0, 1, 0, 0, 0, 0, 0], device='cuda:0')\n",
+      "tensor([1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
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+      "tensor([1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1,\n",
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+      "tensor([1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
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+      "        0, 0, 1, 1, 0, 0, 1, 1], device='cuda:0')\n",
+      "tensor([1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
+      "        0, 1, 0, 0, 0, 1, 0, 0], device='cuda:0') tensor([1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
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+      "tensor([1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
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+      "tensor([0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
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+      "        1, 1, 0, 0, 1, 0, 0, 1], device='cuda:0')\n",
+      "tensor([0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
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+      "        0, 1, 0, 1, 1, 1, 1, 1], device='cuda:0')\n",
+      "tensor([0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
+      "        1, 0, 1, 0, 1, 1, 0, 0], device='cuda:0') tensor([1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
+      "        1, 0, 1, 0, 1, 1, 0, 0], device='cuda:0')\n",
+      "tensor([0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
+      "        1, 0, 0, 0, 1, 0, 0, 1], device='cuda:0') tensor([0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n",
+      "        1, 1, 0, 0, 1, 0, 0, 1], device='cuda:0')\n",
+      "tensor([0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n",
+      "        1, 1, 0, 0, 0, 1, 1, 0], device='cuda:0') tensor([0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
+      "        0, 1, 0, 0, 0, 1, 1, 0], device='cuda:0')\n",
+      "tensor([1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
+      "        0, 0, 1, 0, 0, 0, 1, 0], device='cuda:0') tensor([0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
+      "        1, 0, 1, 0, 0, 0, 1, 0], device='cuda:0')\n",
+      "tensor([0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n",
+      "        0, 1, 1, 0, 1, 1, 1, 0], device='cuda:0') tensor([0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
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+      "tensor([0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
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+      "        0, 1, 0, 1, 1, 1, 1, 0], device='cuda:0')\n",
+      "tensor([1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1,\n",
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+      "        1, 1, 1, 0, 0, 0, 1, 0], device='cuda:0')\n",
+      "tensor([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
+      "        1, 0, 0, 0, 1, 1, 1, 1], device='cuda:0') tensor([1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1,\n",
+      "        0, 0, 0, 0, 1, 1, 1, 1], device='cuda:0')\n",
+      "tensor([1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0,\n",
+      "        0, 1, 0, 1, 1, 1, 0, 0], device='cuda:0') tensor([1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
+      "        0, 1, 0, 1, 1, 1, 1, 0], device='cuda:0')\n",
+      "tensor([1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
+      "        1, 1, 0, 0, 0, 0, 0, 0], device='cuda:0') tensor([1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
+      "        0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')\n",
+      "tensor([1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
+      "        1, 0, 0, 0, 1, 1, 0, 1], device='cuda:0') tensor([1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
+      "        0, 0, 0, 1, 1, 0, 0, 1], device='cuda:0')\n",
+      "tensor([0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
+      "        1, 0, 0, 0, 0, 1, 1, 0], device='cuda:0') tensor([0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
+      "        1, 0, 0, 0, 0, 1, 1, 0], device='cuda:0')\n",
+      "tensor([0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
+      "        1, 1, 1, 0, 1, 0, 0, 0], device='cuda:0') tensor([0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
+      "        1, 1, 1, 0, 1, 0, 0, 0], device='cuda:0')\n",
+      "tensor([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
+      "        1, 1, 0, 1, 1, 1, 0, 0], device='cuda:0') tensor([0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
+      "        0, 0, 0, 0, 1, 1, 0, 0], device='cuda:0')\n",
+      "tensor([0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
+      "        0, 1, 0, 1, 0, 0, 1, 0], device='cuda:0') tensor([1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1,\n",
+      "        0, 1, 0, 1, 0, 0, 1, 0], device='cuda:0')\n",
+      "tensor([1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
+      "        1, 0, 0, 0, 1, 1, 1, 0], device='cuda:0') tensor([1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
+      "        1, 0, 0, 0, 1, 1, 1, 0], device='cuda:0')\n",
+      "tensor([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
+      "        1, 0, 0, 1, 1, 0, 1, 0], device='cuda:0') tensor([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
+      "        1, 0, 0, 1, 1, 0, 1, 0], device='cuda:0')\n",
+      "tensor([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
+      "        0, 0, 0, 0, 0, 0, 1, 1], device='cuda:0') tensor([0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
+      "        0, 0, 1, 0, 0, 0, 1, 1], device='cuda:0')\n",
+      "tensor([1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
+      "        0, 1, 0, 1, 0, 0, 0, 0], device='cuda:0') tensor([0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
+      "        0, 1, 0, 1, 0, 1, 0, 0], device='cuda:0')\n",
+      "tensor([1, 0, 0, 1, 1, 1, 0, 0, 1], device='cuda:0') tensor([1, 0, 1, 1, 1, 0, 0, 0, 1], device='cuda:0')\n",
+      "\n",
+      "Accuracy:  85.61424874410108\n",
+      "Precision:  0.8561740091595716\n",
+      "Recall:  0.8561424874410108\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_model(model, train_loader,val_loader, criterion, optimizer, scheduler, num_epochs)\n",
+    "print(\"\\n\")\n",
+    "evaluate_model(model, test_loader, criterion)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "afc77f76",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-04-09T17:15:30.856373Z",
+     "iopub.status.busy": "2025-04-09T17:15:30.856007Z",
+     "iopub.status.idle": "2025-04-09T17:15:32.493494Z",
+     "shell.execute_reply": "2025-04-09T17:15:32.492607Z"
+    },
+    "papermill": {
+     "duration": 1.656113,
+     "end_time": "2025-04-09T17:15:32.495130",
+     "exception": false,
+     "start_time": "2025-04-09T17:15:30.839017",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "torch.save(model, \"/kaggle/working/model.pt\")\n",
+    "torch.save(model.state_dict(), \"/kaggle/working/state_dict.pth\")    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f409be3f",
+   "metadata": {
+    "papermill": {
+     "duration": 0.015863,
+     "end_time": "2025-04-09T17:15:32.527601",
+     "exception": false,
+     "start_time": "2025-04-09T17:15:32.511738",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
+   "dataSources": [
+    {
+     "datasetId": 7057067,
+     "sourceId": 11328921,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 30919,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.12"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 8283.491684,
+   "end_time": "2025-04-09T17:15:35.373455",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2025-04-09T14:57:31.881771",
+   "version": "2.6.0"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "0285041787f74b7ba095ace50a72e574": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "HTMLStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
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