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     "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",
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       "\n",
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       "        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",
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      "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()"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "(367373, 3)"
      ]
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     "execution_count": 2,
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   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2a5b20d",
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   "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",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentence1    0\n",
       "sentence2    2\n",
       "label        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_.isna().sum() # Check for missing values"
   ]
  },
  {
   "cell_type": "code",
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   "id": "ebd3766c",
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    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   "source": [
    "df['sentence1'].dtype"
   ]
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   "id": "53cf0228",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['sentence2'].dtype"
   ]
  },
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   "cell_type": "code",
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   "id": "29726637",
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       "tokenizer_config.json:   0%|          | 0.00/3.69k [00:00<?, ?B/s]"
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      "text/plain": [
       "vocab.json:   0%|          | 0.00/801k [00:00<?, ?B/s]"
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       "merges.txt:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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       "tokenizer.json:   0%|          | 0.00/2.10M [00:00<?, ?B/s]"
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       "special_tokens_map.json:   0%|          | 0.00/831 [00:00<?, ?B/s]"
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       "config.json:   0%|          | 0.00/724 [00:00<?, ?B/s]"
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     "data": {
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       "model_id": "7b12c12385e74041ad2aff316d4ea532",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/538M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "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",
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   "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,
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   "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",
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   "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}\")"
   ]
  },
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     "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()"
   ]
  },
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   "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)"
   ]
  },
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     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "\n",
    "evaluate_model(model, data_loader)"
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  },
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     "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}\")"
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