BERT-IMDb.ipynb

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   "source": [
    "### Training Process"
   ]
  },
  {
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   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import re"
   ]
  },
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   "source": [
    "def seq_processing(s):\n",
    "    s = s.lower()\n",
    "    s = re.sub(r'(@.?)[\\s]', ' ', s)\n",
    "    s = re.sub(r'\\s+', ' ', s)\n",
    "    s = re.sub(r'&amp', '&', s)\n",
    "    return s"
   ]
  },
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       "Downloading builder script:   0%|          | 0.00/1.79k [00:00<?, ?B/s]"
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     "text": [
      "Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.02 MiB, post-processed: Unknown size, total: 207.25 MiB) to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1...\n"
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      "text/plain": [
       "Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"
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     "text": [
      "Dataset imdb downloaded and prepared to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1. Subsequent calls will reuse this data.\n"
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     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Epoch 1/3: 100%|██████████| 1563/1563 [25:23<00:00,  1.03it/s]\n"
     ]
    },
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     "name": "stdout",
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     "text": [
      "Training Loss: 0.2597\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2/3: 100%|██████████| 1563/1563 [25:21<00:00,  1.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Loss: 0.1568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3/3: 100%|██████████| 1563/1563 [25:19<00:00,  1.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Loss: 0.0966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from transformers import BertTokenizer, BertModel\n",
    "from sklearn.metrics import classification_report, accuracy_score, f1_score\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "from tqdm import tqdm\n",
    "from datasets import load_dataset\n",
    "\n",
    "# Define constants\n",
    "MAX_SEQ_LEN = 512\n",
    "BATCH_SIZE = 16\n",
    "NUM_CLASSES = 1\n",
    "LEARNING_RATE = 1e-5\n",
    "NUM_EPOCHS = 3\n",
    "\n",
    "# Check if GPU is available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "# Load the IMDb dataset\n",
    "dataset = load_dataset('imdb')\n",
    "\n",
    "# Split the dataset into train and test sets\n",
    "train_dataset, test_dataset = dataset['train'], dataset['test']\n",
    "train_texts, train_labels = train_dataset['text'], train_dataset['label']\n",
    "test_texts, test_labels = test_dataset['text'], test_dataset['label']\n",
    "\n",
    "# Create the tokenizer\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "\n",
    "# Define the IMDbDataset class\n",
    "class IMDbDataset(Dataset):\n",
    "    def __init__(self, texts, labels, tokenizer):\n",
    "        self.texts = texts\n",
    "        self.labels = labels\n",
    "        self.tokenizer = tokenizer\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        text = self.texts[idx]\n",
    "        label = self.labels[idx]\n",
    "        encoding = self.tokenizer.encode_plus(\n",
    "            text,\n",
    "            max_length=MAX_SEQ_LEN,\n",
    "            padding='max_length',\n",
    "            truncation=True,\n",
    "            return_tensors='pt'\n",
    "        )\n",
    "        input_ids = encoding['input_ids'].squeeze()\n",
    "        attention_mask = encoding['attention_mask'].squeeze()\n",
    "#         label = torch.tensor(label)\n",
    "        return input_ids, attention_mask, label\n",
    "\n",
    "# Create the dataset objects\n",
    "train_dataset = IMDbDataset(train_texts, train_labels, tokenizer)\n",
    "test_dataset = IMDbDataset(test_texts, test_labels, tokenizer)\n",
    "\n",
    "# Create the data loaders\n",
    "def collate_fn(batch):\n",
    "    input_ids_list, attention_mask_list, labels = zip(*batch)\n",
    "    input_ids_padded = pad_sequence(input_ids_list, batch_first=True)\n",
    "    attention_mask_padded = pad_sequence(attention_mask_list, batch_first=True)\n",
    "    labels_tensor = torch.tensor(labels).to(device)\n",
    "    return input_ids_padded, attention_mask_padded, labels_tensor\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n",
    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn)\n",
    "\n",
    "# Define the IMDbClassifier model\n",
    "class IMDbClassifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(IMDbClassifier, self).__init__()\n",
    "        self.bert = BertModel.from_pretrained('bert-base-uncased')\n",
    "        self.dropout = nn.Dropout(0.1)\n",
    "        self.bn = nn.BatchNorm1d(self.bert.config.hidden_size)\n",
    "        self.fc = nn.Linear(self.bert.config.hidden_size, NUM_CLASSES)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.criterion = nn.BCELoss()\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=False)\n",
    "        output = self.dropout(pooled_output['pooler_output'])\n",
    "        output = self.bn(output)\n",
    "        output = self.fc(output)\n",
    "        output = self.sigmoid(output)\n",
    "        return output\n",
    "\n",
    "# Create the model\n",
    "model = IMDbClassifier().to(device)\n",
    "\n",
    "# Define the optimizer\n",
    "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
    "\n",
    "# Training loop\n",
    "model.train()\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    running_loss = 0.0\n",
    "    for input_ids, attention_mask, labels in tqdm(train_loader, desc=f'Epoch {epoch + 1}/{NUM_EPOCHS}'):\n",
    "        input_ids = input_ids.to(device)\n",
    "        attention_mask = attention_mask.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(input_ids, attention_mask)\n",
    "        loss = model.criterion(outputs.squeeze(), labels.float())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "    epoch_loss = running_loss / len(train_loader)\n",
    "    print(f'Training Loss: {epoch_loss:.4f}')"
   ]
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    {
     "name": "stderr",
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     "text": [
      "Evaluating: 100%|██████████| 1563/1563 [10:53<00:00,  2.39it/s]"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.94      0.94     12500\n",
      "         1.0       0.94      0.94      0.94     12500\n",
      "\n",
      "    accuracy                           0.94     25000\n",
      "   macro avg       0.94      0.94      0.94     25000\n",
      "weighted avg       0.94      0.94      0.94     25000\n",
      "\n",
      "Test Accuracy: 0.9399\n",
      "Test F1 Score: 0.9398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation loop\n",
    "model.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "real, pred = torch.Tensor([]), torch.Tensor([])\n",
    "\n",
    "with torch.no_grad():\n",
    "    for input_ids, attention_mask, labels in tqdm(\n",
    "        test_loader, desc=\"Evaluating\"\n",
    "    ):\n",
    "        input_ids = input_ids.to(device)\n",
    "        attention_mask = attention_mask.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(input_ids, attention_mask)\n",
    "        predicted = torch.round(outputs).squeeze()\n",
    "\n",
    "        real = torch.cat([real, labels.detach().cpu()], dim = 0)\n",
    "        pred = torch.cat([pred, predicted.detach().cpu()], dim = 0)\n",
    "\n",
    "accuracy = accuracy_score(real, pred)\n",
    "f1 = f1_score(real, pred)\n",
    "\n",
    "print(classification_report(real, pred))\n",
    "print(f\"Test Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Test F1 Score: {f1:.4f}\")"
   ]
  },
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   "source": [
    "torch.save(model.state_dict(), 'bert-weight.pt')"
   ]
  },
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   "source": [
    "### Evaluating Process"
   ]
  },
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted sentence: this movie sucks.\n",
      "Output: Negative\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from transformers import BertTokenizer, BertModel\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "from tqdm import tqdm\n",
    "from datasets import load_dataset\n",
    "import re\n",
    "\n",
    "# Define constants\n",
    "MAX_SEQ_LEN = 512\n",
    "BATCH_SIZE = 16\n",
    "NUM_CLASSES = 1\n",
    "LEARNING_RATE = 1e-5\n",
    "NUM_EPOCHS = 5\n",
    "\n",
    "# Check if GPU is available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "# Create the tokenizer\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "\n",
    "def seq_processing(s):\n",
    "    s = s.lower()\n",
    "    s = re.sub(r'(@.?)[\\s]', ' ', s)\n",
    "    s = re.sub(r'\\s+', ' ', s)\n",
    "    s = re.sub(r'&amp', '&', s)\n",
    "    return s\n",
    "\n",
    "# Define the IMDbClassifier model\n",
    "class IMDbClassifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(IMDbClassifier, self).__init__()\n",
    "        self.bert = BertModel.from_pretrained('bert-base-uncased')\n",
    "        self.dropout = nn.Dropout(0.1)\n",
    "        self.bn = nn.BatchNorm1d(self.bert.config.hidden_size)\n",
    "        self.fc = nn.Linear(self.bert.config.hidden_size, NUM_CLASSES)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.criterion = nn.BCELoss()\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        pooled_output = self.bert(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=False)\n",
    "        output = self.dropout(pooled_output['pooler_output'])\n",
    "        output = self.bn(output)\n",
    "        output = self.fc(output)\n",
    "        output = self.sigmoid(output)\n",
    "        return output\n",
    "\n",
    "#########    \n",
    "\n",
    "sentence = seq_processing('This movie sucks.')\n",
    "encoding = tokenizer.encode_plus(\n",
    "    sentence,\n",
    "    max_length=MAX_SEQ_LEN,\n",
    "    padding='max_length',\n",
    "    truncation=True,\n",
    "    return_tensors='pt'\n",
    ")\n",
    "input_ids = encoding['input_ids']\n",
    "attention_mask = encoding['attention_mask']\n",
    "\n",
    "class_names = ['Negative', 'Positive']\n",
    "\n",
    "######### \n",
    "\n",
    "# Create the model\n",
    "model = IMDbClassifier().to(device)\n",
    "model.load_state_dict(torch.load('bert-weight.pt'))\n",
    "\n",
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    input_ids = input_ids.to(device)\n",
    "    attention_mask = attention_mask.to(device)\n",
    "    output = model(input_ids, attention_mask).squeeze()\n",
    "    print(f'The predicted sentence: {sentence}')\n",
    "    print(f'Output: {class_names[round(float(output))]}')"
   ]
  }
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