diff --git "a/plagairism-fine-tuning using LLM.ipynb" "b/plagairism-fine-tuning using LLM.ipynb"
new file mode 100644--- /dev/null
+++ "b/plagairism-fine-tuning using LLM.ipynb"
@@ -0,0 +1,3389 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03ba143c",
+   "metadata": {
+    "execution": {
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+     "iopub.status.busy": "2024-11-06T15:21:58.651866Z",
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+     "start_time": "2024-11-06T15:21:58.628341",
+     "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>sentence1</th>\n",
+       "      <th>sentence2</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A person on a horse jumps over a broken down a...</td>\n",
+       "      <td>A person is at a diner, ordering an omelette.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A person on a horse jumps over a broken down a...</td>\n",
+       "      <td>A person is outdoors, on a horse.</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Children smiling and waving at camera</td>\n",
+       "      <td>There are children present</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Children smiling and waving at camera</td>\n",
+       "      <td>The kids are frowning</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A boy is jumping on skateboard in the middle o...</td>\n",
+       "      <td>The boy skates down the sidewalk.</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           sentence1  \\\n",
+       "0  A person on a horse jumps over a broken down a...   \n",
+       "1  A person on a horse jumps over a broken down a...   \n",
+       "2              Children smiling and waving at camera   \n",
+       "3              Children smiling and waving at camera   \n",
+       "4  A boy is jumping on skateboard in the middle o...   \n",
+       "\n",
+       "                                       sentence2  label  \n",
+       "0  A person is at a diner, ordering an omelette.      0  \n",
+       "1              A person is outdoors, on a horse.      1  \n",
+       "2                     There are children present      1  \n",
+       "3                          The kids are frowning      0  \n",
+       "4              The boy skates down the sidewalk.      0  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from transformers import AdamW\n",
+    "\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Load dataset\n",
+    "\n",
+    "df = pd.read_csv(\"/kaggle/input/mit-plagairism-detection-dataset/train_snli.txt\", delimiter='\\t', header=None, names=['sentence1', 'sentence2', 'label'])\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bbaf268b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:08.358426Z",
+     "iopub.status.busy": "2024-11-06T15:22:08.358091Z",
+     "iopub.status.idle": "2024-11-06T15:22:08.363719Z",
+     "shell.execute_reply": "2024-11-06T15:22:08.362934Z"
+    },
+    "papermill": {
+     "duration": 0.028454,
+     "end_time": "2024-11-06T15:22:08.365691",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:08.337237",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(367373, 3)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2a5b20d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:08.405805Z",
+     "iopub.status.busy": "2024-11-06T15:22:08.405532Z",
+     "iopub.status.idle": "2024-11-06T15:22:08.409536Z",
+     "shell.execute_reply": "2024-11-06T15:22:08.408699Z"
+    },
+    "papermill": {
+     "duration": 0.026361,
+     "end_time": "2024-11-06T15:22:08.411517",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:08.385156",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df_ = df[:1_00_000] # Using small set for initial training just to get some idea"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d0705e2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:08.451511Z",
+     "iopub.status.busy": "2024-11-06T15:22:08.451209Z",
+     "iopub.status.idle": "2024-11-06T15:22:08.481151Z",
+     "shell.execute_reply": "2024-11-06T15:22:08.480316Z"
+    },
+    "papermill": {
+     "duration": 0.052132,
+     "end_time": "2024-11-06T15:22:08.483002",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:08.430870",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sentence1    0\n",
+       "sentence2    2\n",
+       "label        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_.isna().sum() # Check for missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ebd3766c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:08.523202Z",
+     "iopub.status.busy": "2024-11-06T15:22:08.522869Z",
+     "iopub.status.idle": "2024-11-06T15:22:08.528880Z",
+     "shell.execute_reply": "2024-11-06T15:22:08.527782Z"
+    },
+    "papermill": {
+     "duration": 0.028318,
+     "end_time": "2024-11-06T15:22:08.530877",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:08.502559",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('O')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['sentence1'].dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "53cf0228",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:08.572209Z",
+     "iopub.status.busy": "2024-11-06T15:22:08.571622Z",
+     "iopub.status.idle": "2024-11-06T15:22:08.577613Z",
+     "shell.execute_reply": "2024-11-06T15:22:08.576763Z"
+    },
+    "papermill": {
+     "duration": 0.028769,
+     "end_time": "2024-11-06T15:22:08.579532",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:08.550763",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('O')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['sentence2'].dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "29726637",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:08.625015Z",
+     "iopub.status.busy": "2024-11-06T15:22:08.624082Z",
+     "iopub.status.idle": "2024-11-06T15:22:24.846453Z",
+     "shell.execute_reply": "2024-11-06T15:22:24.845686Z"
+    },
+    "papermill": {
+     "duration": 16.24535,
+     "end_time": "2024-11-06T15:22:24.848506",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:08.603156",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d324bb8922143ed9d5a0b20ed8d3ba1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/3.69k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e9a3482cfa24e7f8275f9cc6e20c336",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/801k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75db941b036b4120a925d4c0dc97563c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11bfb245682b4724afddf3f38bcc882a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/2.10M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c00e3ccfb1d04bf2a6b221aa46c445a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/831 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4d7985602524dcb9329eab6492140a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/724 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b12c12385e74041ad2aff316d4ea532",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/538M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of LlamaForSequenceClassification were not initialized from the model checkpoint at HuggingFaceTB/SmolLM-135M and are newly initialized: ['score.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": [
+    "# Load smolLM model and tokenizer\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"HuggingFaceTB/SmolLM-135M\")\n",
+    "\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\"HuggingFaceTB/SmolLM-135M\", num_labels=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b4ecb61",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:24.894013Z",
+     "iopub.status.busy": "2024-11-06T15:22:24.893135Z",
+     "iopub.status.idle": "2024-11-06T15:22:25.437798Z",
+     "shell.execute_reply": "2024-11-06T15:22:25.436468Z"
+    },
+    "papermill": {
+     "duration": 0.569784,
+     "end_time": "2024-11-06T15:22:25.439919",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:24.870135",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Padding Token: [PAD]\n",
+      "Padding Token ID: 49152\n"
+     ]
+    }
+   ],
+   "source": [
+    "class PlagiarismDataset(Dataset):\n",
+    "\n",
+    "    def __init__(self, df_, tokenizer, max_length=128):\n",
+    "\n",
+    "        self.df = df_\n",
+    "\n",
+    "        self.tokenizer = tokenizer\n",
+    "\n",
+    "        self.max_length = max_length\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    def __len__(self):\n",
+    "\n",
+    "        return len(self.df)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "\n",
+    "        row = self.df.iloc[index]\n",
+    "\n",
+    "        \n",
+    "\n",
+    "        # Ensure the sentences are strings; convert or skip if not\n",
+    "\n",
+    "        sentence1 = str(row['sentence1']) if not pd.isna(row['sentence1']) else \"\"\n",
+    "\n",
+    "        sentence2 = str(row['sentence2']) if not pd.isna(row['sentence2']) else \"\"\n",
+    "\n",
+    "        \n",
+    "\n",
+    "        inputs = self.tokenizer(\n",
+    "\n",
+    "            sentence1, sentence2,\n",
+    "\n",
+    "            add_special_tokens=True,\n",
+    "\n",
+    "            max_length=self.max_length,\n",
+    "\n",
+    "            padding='max_length',\n",
+    "\n",
+    "            truncation=True,\n",
+    "\n",
+    "            return_tensors=\"pt\"\n",
+    "\n",
+    "        )\n",
+    "\n",
+    "        \n",
+    "\n",
+    "        label = torch.tensor(row['label'], dtype=torch.long)\n",
+    "\n",
+    "        \n",
+    "\n",
+    "        return {\n",
+    "\n",
+    "            'input_ids': inputs['input_ids'].squeeze(0),\n",
+    "\n",
+    "            'attention_mask': inputs['attention_mask'].squeeze(0),\n",
+    "\n",
+    "            'label': label\n",
+    "\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def collate_fn(batch):\n",
+    "\n",
+    "    input_ids = torch.stack([item['input_ids'] for item in batch])\n",
+    "\n",
+    "    attention_masks = torch.stack([item['attention_mask'] for item in batch])\n",
+    "\n",
+    "    labels = torch.stack([item['label'] for item in batch])\n",
+    "\n",
+    "    return {\n",
+    "\n",
+    "        'input_ids': input_ids,\n",
+    "\n",
+    "        'attention_mask': attention_masks,\n",
+    "\n",
+    "        'label': labels\n",
+    "\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Add a new padding token\n",
+    "\n",
+    "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Resize the model's token embeddings to match the new tokenizer\n",
+    "\n",
+    "model.resize_token_embeddings(len(tokenizer))\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create the dataset and dataloader\n",
+    "\n",
+    "dataset = PlagiarismDataset(df, tokenizer)\n",
+    "\n",
+    "data_loader = DataLoader(dataset, batch_size=16, shuffle=True, collate_fn=collate_fn)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Check the padding token details\n",
+    "\n",
+    "print(\"Padding Token:\", tokenizer.pad_token)\n",
+    "\n",
+    "print(\"Padding Token ID:\", tokenizer.pad_token_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ff1c5e9a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:25.485057Z",
+     "iopub.status.busy": "2024-11-06T15:22:25.484389Z",
+     "iopub.status.idle": "2024-11-06T15:22:25.488574Z",
+     "shell.execute_reply": "2024-11-06T15:22:25.487793Z"
+    },
+    "papermill": {
+     "duration": 0.028512,
+     "end_time": "2024-11-06T15:22:25.490574",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:25.462062",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Set the pad token id in the model's config\n",
+    "\n",
+    "model.config.pad_token_id = tokenizer.pad_token_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "efde5d56",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:25.538383Z",
+     "iopub.status.busy": "2024-11-06T15:22:25.537648Z",
+     "iopub.status.idle": "2024-11-06T15:22:26.035308Z",
+     "shell.execute_reply": "2024-11-06T15:22:26.034537Z"
+    },
+    "papermill": {
+     "duration": 0.523203,
+     "end_time": "2024-11-06T15:22:26.037471",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:25.514268",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Training function\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model = model.to(device)\n",
+    "\n",
+    "model = torch.nn.DataParallel(model)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def train_model(model, data_loader, optimizer, loss_fn, epochs=3):\n",
+    "\n",
+    "    model.train()\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    for epoch in range(epochs):\n",
+    "\n",
+    "        total_loss = 0\n",
+    "\n",
+    "        for batch in tqdm(data_loader, desc=f\"Training Epoch {epoch+1}\"):\n",
+    "\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "\n",
+    "            attention_mask = batch['attention_mask'].to(device)\n",
+    "\n",
+    "            labels = batch['label'].to(device)\n",
+    "\n",
+    "            \n",
+    "\n",
+    "            outputs = model(input_ids, attention_mask=attention_mask)\n",
+    "\n",
+    "            loss = loss_fn(outputs.logits, labels)\n",
+    "\n",
+    "            total_loss += loss.item()\n",
+    "\n",
+    "            \n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "            loss.backward()\n",
+    "\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        \n",
+    "\n",
+    "        avg_loss = total_loss / len(data_loader)\n",
+    "\n",
+    "        print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss}\")\n",
+    "\n",
+    "        \n",
+    "\n",
+    "    print(\"Training complete!\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Evaluation function\n",
+    "\n",
+    "def evaluate_model(model, data_loader):\n",
+    "\n",
+    "    model.eval()\n",
+    "\n",
+    "    correct = 0\n",
+    "\n",
+    "    total = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "\n",
+    "        for batch in data_loader:\n",
+    "\n",
+    "            input_ids = batch['input_ids'].to(device)\n",
+    "\n",
+    "            attention_mask = batch['attention_mask'].to(device)\n",
+    "\n",
+    "            labels = batch['label'].to(device)\n",
+    "\n",
+    "            \n",
+    "\n",
+    "            outputs = model(input_ids, attention_mask=attention_mask)\n",
+    "\n",
+    "            predictions = torch.argmax(outputs.logits, dim=1)\n",
+    "\n",
+    "            correct += (predictions == labels).sum().item()\n",
+    "\n",
+    "            total += labels.size(0)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    accuracy = correct / total\n",
+    "\n",
+    "    print(f\"Accuracy: {accuracy:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2428dc76",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:26.081218Z",
+     "iopub.status.busy": "2024-11-06T15:22:26.080919Z",
+     "iopub.status.idle": "2024-11-06T15:22:26.803331Z",
+     "shell.execute_reply": "2024-11-06T15:22:26.802560Z"
+    },
+    "papermill": {
+     "duration": 0.746637,
+     "end_time": "2024-11-06T15:22:26.805509",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:26.058872",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cuda\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set up training\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "model.to(device)\n",
+    "\n",
+    "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
+    "\n",
+    "loss_fn = CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a727a46b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T15:22:26.849899Z",
+     "iopub.status.busy": "2024-11-06T15:22:26.849448Z",
+     "iopub.status.idle": "2024-11-06T22:39:51.491368Z",
+     "shell.execute_reply": "2024-11-06T22:39:51.490286Z"
+    },
+    "papermill": {
+     "duration": 26244.666261,
+     "end_time": "2024-11-06T22:39:51.493528",
+     "exception": false,
+     "start_time": "2024-11-06T15:22:26.827267",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch 1:   0%|          | 0/22961 [00:00<?, ?it/s]/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/parallel_apply.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with torch.cuda.device(device), torch.cuda.stream(stream), autocast(enabled=autocast_enabled):\n",
+      "Training Epoch 1:   0%|          | 1/22961 [00:20<128:03:57, 20.08s/it]/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/parallel_apply.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
+      "  with torch.cuda.device(device), torch.cuda.stream(stream), autocast(enabled=autocast_enabled):\n",
+      "Training Epoch 1: 100%|██████████| 22961/22961 [2:25:56<00:00,  2.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/3, Loss: 0.1226412019093822\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch 2: 100%|██████████| 22961/22961 [2:25:56<00:00,  2.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/3, Loss: 0.05606692839428341\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch 3: 100%|██████████| 22961/22961 [2:25:31<00:00,  2.63it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/3, Loss: 0.02446798090389194\n",
+      "Training complete!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "\n",
+    "train_model(model, data_loader, optimizer, loss_fn, epochs=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2d703969",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T22:40:03.076003Z",
+     "iopub.status.busy": "2024-11-06T22:40:03.074709Z",
+     "iopub.status.idle": "2024-11-06T23:35:12.450461Z",
+     "shell.execute_reply": "2024-11-06T23:35:12.449557Z"
+    },
+    "papermill": {
+     "duration": 3320.760935,
+     "end_time": "2024-11-06T23:35:18.145340",
+     "exception": false,
+     "start_time": "2024-11-06T22:39:57.384405",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9966\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the model\n",
+    "\n",
+    "evaluate_model(model, data_loader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b34c1e1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-06T23:35:29.336673Z",
+     "iopub.status.busy": "2024-11-06T23:35:29.335795Z",
+     "iopub.status.idle": "2024-11-06T23:35:31.299195Z",
+     "shell.execute_reply": "2024-11-06T23:35:31.298188Z"
+    },
+    "papermill": {
+     "duration": 7.649007,
+     "end_time": "2024-11-06T23:35:31.301195",
+     "exception": false,
+     "start_time": "2024-11-06T23:35:23.652188",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model and tokenizer saved to /kaggle/working/\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_directory = \"/kaggle/working/\"\n",
+    "\n",
+    "# Save the model and tokenizer to the directory\n",
+    "model_to_save = model.module if hasattr(model, \"module\") else model\n",
+    "\n",
+    "# Save the fine-tuned model\n",
+    "model_to_save.save_pretrained(save_directory)\n",
+    "\n",
+    "# Save the tokenizer\n",
+    "tokenizer.save_pretrained(save_directory)\n",
+    "\n",
+    "print(f\"Model and tokenizer saved to {save_directory}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
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