EvalBot(Fine-Tuning).ipynb

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   "source": [
    "## Importing libraries\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch.utils.data import DataLoader,TensorDataset\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "from transformers import BertTokenizer,BertForSequenceClassification,AdamW\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
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   "source": [
    "## Loading the training data\n",
    "data=pd.read_csv('/kaggle/input/evalbot/training.csv')"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "(99887, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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    }
   ],
   "source": [
    "## Dimensions of data\n",
    "data.shape"
   ]
  },
  {
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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",
       "        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>similarity</th>\n",
       "      <th>sentence1</th>\n",
       "      <th>sentence2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>person horse jumps broken airplane</td>\n",
       "      <td>person training horse competition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>person horse jumps broken airplane</td>\n",
       "      <td>person diner ordering omelette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>person horse jumps broken airplane</td>\n",
       "      <td>person outdoors horse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>children smiling waving camera</td>\n",
       "      <td>smiling parents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>children smiling waving camera</td>\n",
       "      <td>children present</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   similarity                           sentence1  \\\n",
       "0           1  person horse jumps broken airplane   \n",
       "1           0  person horse jumps broken airplane   \n",
       "2           2  person horse jumps broken airplane   \n",
       "3           1      children smiling waving camera   \n",
       "4           2      children smiling waving camera   \n",
       "\n",
       "                           sentence2  \n",
       "0  person training horse competition  \n",
       "1     person diner ordering omelette  \n",
       "2              person outdoors horse  \n",
       "3                    smiling parents  \n",
       "4                   children present  "
      ]
     },
     "execution_count": 16,
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    }
   ],
   "source": [
    "## First 5 rows of data\n",
    "data.head()"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99887 entries, 0 to 99886\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   similarity  99887 non-null  int64 \n",
      " 1   sentence1   99887 non-null  object\n",
      " 2   sentence2   99884 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "## Basic information of data\n",
    "data.info()"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "## Drop null values\n",
    "data=data.dropna()"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "similarity    0\n",
       "sentence1     0\n",
       "sentence2     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check for null values\n",
    "data.isnull().sum()"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "## Forming pairs of sentences\n",
    "training_data=[(row['sentence1'],row['sentence2']) for index,row in data.iterrows()]"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "99884"
      ]
     },
     "execution_count": 21,
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   ],
   "source": [
    "## Length of training pairs\n",
    "len(training_data)"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "('person horse jumps broken airplane', 'person training horse competition')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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    }
   ],
   "source": [
    "training_data[0]"
   ]
  },
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     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "## Load BERT Tokenizer and Model\n",
    "model_name='bert-base-uncased'\n",
    "tokenizer=BertTokenizer.from_pretrained(model_name)\n",
    "model=BertForSequenceClassification.from_pretrained(model_name,num_labels=3)"
   ]
  },
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   "source": [
    "## Tokenization\n",
    "def tokenization(sent1,sent2):\n",
    "  encoded=tokenizer.encode_plus(\n",
    "      sent1,sent2,\n",
    "      add_special_tokens=True,\n",
    "      padding=True,\n",
    "      truncation=True,\n",
    "      return_tensors='pt'\n",
    "  )\n",
    "  input_ids=encoded['input_ids']\n",
    "  attention_masks=encoded['attention_mask']\n",
    "  return input_ids,attention_masks\n",
    "\n",
    "input_ids=[]\n",
    "attention_masks=[]\n",
    "for sent1,sent2 in training_data:\n",
    "  ids,masks=tokenization(sent1,sent2)\n",
    "  input_ids.append(ids[0])\n",
    "  attention_masks.append(masks[0])\n",
    "\n",
    "input_ids=pad_sequence(input_ids,batch_first=True)\n",
    "attention_masks=pad_sequence(attention_masks,batch_first=True)\n",
    "similarity_tensor=torch.tensor(data['similarity'].values)\n",
    "dataset=TensorDataset(input_ids,attention_masks,similarity_tensor)\n",
    "dataloader=DataLoader(dataset,batch_size=32)"
   ]
  },
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     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): 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",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)"
   ]
  },
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   "source": [
    "## Initialize hyper-parameters\n",
    "optimizer1=torch.optim.AdamW(model.parameters(),lr=1e-5)\n",
    "optimizer2=torch.optim.AdamW(model.parameters(),lr=2e-5)\n",
    "optimizer3=torch.optim.AdamW(model.parameters(),lr=5e-5)\n",
    "epochs=10"
   ]
  },
  {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch :  1 ----> Loss :  0.6337909205153842\n",
      "Epoch :  2 ----> Loss :  0.4555837016368928\n",
      "Epoch :  3 ----> Loss :  0.3649554422565525\n",
      "Epoch :  4 ----> Loss :  0.2931244809641088\n",
      "Epoch :  5 ----> Loss :  0.2363479535860856\n",
      "Epoch :  6 ----> Loss :  0.1943885262740548\n",
      "Epoch :  7 ----> Loss :  0.16114755553264387\n",
      "Epoch :  8 ----> Loss :  0.13830374421379998\n",
      "Epoch :  9 ----> Loss :  0.11543103792238682\n",
      "Epoch :  10 ----> Loss :  0.10421650026746519\n"
     ]
    }
   ],
   "source": [
    "## Fine-tuning of model with optimizer 1\n",
    "for epoch in range(epochs):\n",
    "  model.train()\n",
    "  total_loss=0\n",
    "  for batch in dataloader:\n",
    "    input_ids,attention_masks,labels=batch\n",
    "    input_ids,attention_masks,labels=input_ids.to(device),attention_masks.to(device),labels.to(device)\n",
    "    outputs=model(input_ids,attention_mask=attention_masks,labels=labels)\n",
    "    loss=outputs.loss\n",
    "    total_loss+=loss.item()\n",
    "    optimizer1.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer1.step()\n",
    "  loss=total_loss/len(dataloader)\n",
    "  print('Epoch : ',epoch+1,'----> Loss : ',loss)"
   ]
  },
  {
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    "execution": {
     "iopub.execute_input": "2024-10-29T14:12:05.361261Z",
     "iopub.status.busy": "2024-10-29T14:12:05.360845Z",
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    "outputId": "60d27d2e-6874-448f-d597-03d0fa5a5986",
    "trusted": true
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   "outputs": [],
   "source": [
    "## Save fine-tuned model\n",
    "model.save_pretrained('/kaggle/working/fine-tuned-bert1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "text": [
      "Epoch :  1 ----> Loss :  0.5814655830535959\n",
      "Epoch :  2 ----> Loss :  0.4049869488559611\n",
      "Epoch :  3 ----> Loss :  0.2976359235913917\n",
      "Epoch :  4 ----> Loss :  0.2200380824515232\n",
      "Epoch :  5 ----> Loss :  0.17517459309170075\n",
      "Epoch :  6 ----> Loss :  0.14198081307682942\n",
      "Epoch :  7 ----> Loss :  0.11953141586154026\n",
      "Epoch :  8 ----> Loss :  0.09930353646065496\n",
      "Epoch :  9 ----> Loss :  0.08608571486547589\n",
      "Epoch :  10 ----> Loss :  0.07473612968541574\n"
     ]
    }
   ],
   "source": [
    "## Fine-tuning of model with optimizer 2\n",
    "for epoch in range(epochs):\n",
    "  model.train()\n",
    "  total_loss=0\n",
    "  for batch in dataloader:\n",
    "    input_ids,attention_masks,labels=batch\n",
    "    input_ids,attention_masks,labels=input_ids.to(device),attention_masks.to(device),labels.to(device)\n",
    "    outputs=model(input_ids,attention_mask=attention_masks,labels=labels)\n",
    "    loss=outputs.loss\n",
    "    total_loss+=loss.item()\n",
    "    optimizer2.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer2.step()\n",
    "  loss=total_loss/len(dataloader)\n",
    "  print('Epoch : ',epoch+1,'----> Loss : ',loss)"
   ]
  },
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   "execution_count": null,
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    "execution": {
     "iopub.execute_input": "2024-10-30T06:26:23.001813Z",
     "iopub.status.busy": "2024-10-30T06:26:23.001001Z",
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    },
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   "outputs": [],
   "source": [
    "## Save fine-tuned model\n",
    "model.save_pretrained('/kaggle/working/fine-tuned-bert2')"
   ]
  },
  {
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   "outputs": [
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     "text": [
      "Epoch :  1 ----> Loss :  0.5851726678464368\n",
      "Epoch :  2 ----> Loss :  0.40096190740986176\n",
      "Epoch :  3 ----> Loss :  0.2900092475400244\n",
      "Epoch :  4 ----> Loss :  0.22516892129354882\n",
      "Epoch :  5 ----> Loss :  0.18167134091791426\n",
      "Epoch :  6 ----> Loss :  0.15366139025400552\n",
      "Epoch :  7 ----> Loss :  0.13023175942710935\n",
      "Epoch :  8 ----> Loss :  0.11334886096340642\n",
      "Epoch :  9 ----> Loss :  0.10210506791833007\n",
      "Epoch :  10 ----> Loss :  0.08756085024920783\n"
     ]
    }
   ],
   "source": [
    "## Fine-tuning of model with optimizer 3\n",
    "for epoch in range(epochs):\n",
    "  model.train()\n",
    "  total_loss=0\n",
    "  for batch in dataloader:\n",
    "    input_ids,attention_masks,labels=batch\n",
    "    input_ids,attention_masks,labels=input_ids.to(device),attention_masks.to(device),labels.to(device)\n",
    "    outputs=model(input_ids,attention_mask=attention_masks,labels=labels)\n",
    "    loss=outputs.loss\n",
    "    total_loss+=loss.item()\n",
    "    optimizer3.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer3.step()\n",
    "  loss=total_loss/len(dataloader)\n",
    "  print('Epoch : ',epoch+1,'----> Loss : ',loss)"
   ]
  },
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    "execution": {
     "iopub.execute_input": "2024-11-02T09:40:41.112002Z",
     "iopub.status.busy": "2024-11-02T09:40:41.111617Z",
     "iopub.status.idle": "2024-11-02T09:40:42.347650Z",
     "shell.execute_reply": "2024-11-02T09:40:42.346729Z",
     "shell.execute_reply.started": "2024-11-02T09:40:41.111966Z"
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   },
   "outputs": [],
   "source": [
    "## Save fine-tuned model\n",
    "model.save_pretrained('/kaggle/working/fine-tuned-bert3')"
   ]
  }
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