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pip install datasets","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Quora Insincere Questions Classification","metadata":{"id":"SmueePyrUnhz"}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.optim import Adam\nfrom tqdm import tqdm","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Load the data","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv('../input/quora-insincere-questions-classification/train.csv')\ntest_df = pd.read_csv('../input/quora-insincere-questions-classification/test.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Split the training data into train and validation sets","metadata":{}},{"cell_type":"code","source":"train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=42)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Load pre-trained embeddings","metadata":{}},{"cell_type":"code","source":"embeddings_index = {}\nwith open('../input/quora-insincere-questions-classification/embeddings/glove.840B.300d/glove.840B.300d.txt', encoding='utf-8') as f:\n    for line in f:\n        values = line.split()\n        word = values[0]\n        coefs = np.asarray(values[1:], dtype='float32')\n        embeddings_index[word] = coefs","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Prepare tokenizer","metadata":{}},{"cell_type":"code","source":"def tokenize(text):\n    return text.lower().split()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create vocabulary","metadata":{}},{"cell_type":"code","source":"vocab = set()\ntrain_df['question_text'].apply(lambda x: vocab.update(tokenize(x)))\nval_df['question_text'].apply(lambda x: vocab.update(tokenize(x)))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create word to index mapping","metadata":{}},{"cell_type":"code","source":"word_to_index = {word: i+1 for i, word in enumerate(vocab)}\nindex_to_word = {i+1: word for i, word in enumerate(vocab)}","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create embedding matrix","metadata":{}},{"cell_type":"code","source":"embedding_matrix = np.zeros((len(word_to_index) + 1, 300))\nfor word, i in word_to_index.items():\n    embedding_vector = embeddings_index.get(word)\n    if embedding_vector is not None:\n        embedding_matrix[i] = embedding_vector","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Tokenize and pad sequences","metadata":{}},{"cell_type":"code","source":"max_length = 100\n\ndef text_to_sequence(text, max_length):\n    sequence = [word_to_index.get(word, 0) for word in tokenize(text)[:max_length]]\n    return sequence + [0] * (max_length - len(sequence))\n\nX_train = np.array([text_to_sequence(text, max_length) for text in train_df['question_text']])\ny_train = train_df['target'].values\nX_val = np.array([text_to_sequence(text, max_length) for text in val_df['question_text']])\ny_val = val_df['target'].values\nX_test = np.array([text_to_sequence(text, max_length) for text in test_df['question_text']])","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Define model","metadata":{}},{"cell_type":"code","source":"class QuoraModel(nn.Module):\n    def __init__(self, vocab_size, embedding_dim, hidden_dim):\n        super(QuoraModel, self).__init__()\n        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)\n        self.fc = nn.Linear(hidden_dim, 1)\n        \n    def forward(self, x):\n        x = self.embedding(x)\n        _, (hidden, _) = self.lstm(x)\n        return self.fc(hidden.squeeze(0))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Initialize model","metadata":{}},{"cell_type":"code","source":"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel = QuoraModel(len(word_to_index) + 1, 300, 128).to(device)\nmodel.embedding.weight.data.copy_(torch.from_numpy(embedding_matrix))\nmodel.embedding.weight.requires_grad = False","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Prepare data loaders","metadata":{}},{"cell_type":"code","source":"train_dataset = TensorDataset(torch.tensor(X_train, dtype=torch.long), torch.tensor(y_train, dtype=torch.float))\nval_dataset = TensorDataset(torch.tensor(X_val, dtype=torch.long), torch.tensor(y_val, dtype=torch.float))\ntrain_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\nval_loader = DataLoader(val_dataset, batch_size=64)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Training loop","metadata":{}},{"cell_type":"code","source":"criterion = nn.BCEWithLogitsLoss()\noptimizer = Adam(model.parameters())\nnum_epochs = 3\n\nfor epoch in range(num_epochs):\n    model.train()\n    for batch in tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\"):\n        inputs, targets = [b.to(device) for b in batch]\n        optimizer.zero_grad()\n        outputs = model(inputs).squeeze()\n        loss = criterion(outputs, targets)\n        loss.backward()\n        optimizer.step()\n    \n    model.eval()\n    val_loss = 0\n    correct = 0\n    with torch.no_grad():\n        for inputs, targets in val_loader:\n            inputs, targets = inputs.to(device), targets.to(device)\n            outputs = model(inputs).squeeze()\n            val_loss += criterion(outputs, targets).item()\n            correct += ((outputs > 0) == targets).sum().item()\n    \n    val_loss /= len(val_loader)\n    accuracy = correct / len(val_dataset)\n    print(f\"Validation Loss: {val_loss:.4f}, Accuracy: {accuracy:.4f}\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Make predictions on the test set","metadata":{}},{"cell_type":"code","source":"model.eval()\ntest_dataset = TensorDataset(torch.tensor(X_test, dtype=torch.long))\ntest_loader = DataLoader(test_dataset, batch_size=64)\n\npredictions = []\nwith torch.no_grad():\n    for inputs in tqdm(test_loader, desc=\"Predicting\"):\n        inputs = inputs[0].to(device)\n        outputs = model(inputs).squeeze()\n        predictions.extend((outputs > 0).cpu().numpy())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create submission file","metadata":{}},{"cell_type":"code","source":"submission_df = pd.DataFrame({\n    'qid': test_df['qid'],\n    'prediction': predictions\n})\nsubmission_df.to_csv('submission.csv', index=False)\nprint(\"Submission file created: submission.csv\")","metadata":{},"execution_count":null,"outputs":[]}]}