Sentiment Analysis of Movie Reviews.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Du_NLzSY3P4X"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import nltk\n",
        "import torch\n",
        "import numpy as np\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import BertTokenizer, BertForSequenceClassification, AdamW\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, LSTM, Dense\n",
        "from keras.preprocessing.sequence import pad_sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "av1XWUPRBrSm",
        "outputId": "fb3c2748-b725-437c-86d8-ad24b70505a0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Download NLTK resources\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ry0JrcvR_NEl"
      },
      "outputs": [],
      "source": [
        "# Load the data\n",
        "train_df = pd.read_csv(\"Train.csv\")\n",
        "valid_df = pd.read_csv(\"Valid.csv\")\n",
        "test_df = pd.read_csv(\"Test.csv\")\n",
        "\n",
        "# Define a function for text preprocessing\n",
        "def preprocess_text(text):\n",
        "    # Tokenization\n",
        "    tokens = nltk.word_tokenize(text)\n",
        "\n",
        "    # Lowercasing\n",
        "    tokens = [token.lower() for token in tokens]\n",
        "\n",
        "    # Removing punctuation\n",
        "    tokens = [re.sub(r'[^\\w\\s]', '', token) for token in tokens]\n",
        "\n",
        "    # Removing stopwords\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    tokens = [token for token in tokens if token not in stop_words]\n",
        "\n",
        "    # Lemmatization\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
        "\n",
        "    # Join tokens back into text\n",
        "    preprocessed_text = ' '.join(tokens)\n",
        "\n",
        "    return preprocessed_text\n",
        "\n",
        "# Apply preprocessing to the text data\n",
        "train_df['preprocessed_text'] = train_df['text'].apply(preprocess_text)\n",
        "valid_df['preprocessed_text'] = valid_df['text'].apply(preprocess_text)\n",
        "test_df['preprocessed_text'] = test_df['text'].apply(preprocess_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "IXWMNf0-U1fJ"
      },
      "outputs": [],
      "source": [
        "# Vectorize the text data\n",
        "vectorizer = TfidfVectorizer(max_features=10000)\n",
        "X_train_vec = vectorizer.fit_transform(train_df['preprocessed_text'])\n",
        "X_valid_vec = vectorizer.transform(valid_df['preprocessed_text'])\n",
        "X_test_vec = vectorizer.transform(test_df['preprocessed_text'])\n",
        "y_train = train_df['label']\n",
        "y_valid = valid_df['label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8GpYIc-o9UH",
        "outputId": "b3d9c1ff-80c3-483c-f6d0-14d43794d929"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<40000x1000 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 2217505 stored elements in Compressed Sparse Row format>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_vec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "03MfmDiZpdlx",
        "outputId": "f8c27030-319f-4ea6-e809-d7d14d9696c2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate class distribution\n",
        "class_distribution = y_train.value_counts()\n",
        "\n",
        "# Plot pie chart\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(class_distribution, labels=class_distribution.index, autopct='%1.1f%%', startangle=140)\n",
        "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "plt.title('Class Distribution')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-W0SXrYBD9_",
        "outputId": "f4bb9b34-aadd-4bea-ed84-33d48f67c992"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 40000 entries, 0 to 39999\n",
            "Data columns (total 3 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   text               40000 non-null  object\n",
            " 1   label              40000 non-null  int64 \n",
            " 2   preprocessed_text  40000 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 937.6+ KB\n"
          ]
        }
      ],
      "source": [
        "train_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZgyu3ulYPLv",
        "outputId": "28a91a74-baa7-4aaf-afaf-9372856e6196"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic Regression Accuracy: 0.8882\n",
            "Confusion Matrix:\n",
            "[[2164  322]\n",
            " [ 237 2277]]\n",
            "Precision: 0.8761061946902655\n",
            "Recall: 0.905727923627685\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
        "\n",
        "# Train and evaluate Logistic Regression model\n",
        "logreg_model = LogisticRegression()\n",
        "logreg_model.fit(X_train_vec, y_train)\n",
        "logreg_preds_valid = logreg_model.predict(X_valid_vec)\n",
        "logreg_accuracy = accuracy_score(y_valid, logreg_preds_valid)\n",
        "print(\"Logistic Regression Accuracy:\", logreg_accuracy)\n",
        "\n",
        "# Compute confusion matrix\n",
        "conf_matrix = confusion_matrix(y_valid, logreg_preds_valid)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Compute precision\n",
        "precision = precision_score(y_valid, logreg_preds_valid)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Compute recall\n",
        "recall = recall_score(y_valid, logreg_preds_valid)\n",
        "print(\"Recall:\", recall)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "ZDMYOK80-Qle",
        "outputId": "4b51fde8-c9a1-43c1-8c7b-8447167dc7c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJ7UWWHtoK5k",
        "outputId": "4062d163-581e-4917-eeb3-3b1714fcef05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Naive Bayes Accuracy: 0.8572\n",
            "Confusion Matrix:\n",
            "[[2129  357]\n",
            " [ 357 2157]]\n",
            "Precision: 0.8579952267303103\n",
            "Recall: 0.8579952267303103\n"
          ]
        }
      ],
      "source": [
        "# Train and evaluate Naive Bayes model\n",
        "nb_model = MultinomialNB()\n",
        "nb_model.fit(X_train_vec, y_train)\n",
        "nb_preds_valid = nb_model.predict(X_valid_vec)\n",
        "nb_accuracy = accuracy_score(y_valid, nb_preds_valid)\n",
        "print(\"Naive Bayes Accuracy:\", nb_accuracy)\n",
        "\n",
        "# Compute confusion matrix\n",
        "conf_matrix = confusion_matrix(y_valid, nb_preds_valid)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Compute precision\n",
        "precision = precision_score(y_valid, nb_preds_valid)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Compute recall\n",
        "recall = recall_score(y_valid, nb_preds_valid)\n",
        "print(\"Recall:\", recall)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tan_hYAJ_m9k",
        "outputId": "26ecf8ad-a1ed-448b-bced-1543de5cf4ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SVM Accuracy: 0.8936\n",
            "Confusion Matrix:\n",
            "[[2177  309]\n",
            " [ 223 2291]]\n",
            "Precision: 0.8811538461538462\n",
            "Recall: 0.911296738265712\n"
          ]
        }
      ],
      "source": [
        "# Train and evaluate Support Vector Machine (SVM) model\n",
        "svm_model = SVC()\n",
        "svm_model.fit(X_train_vec, y_train)\n",
        "svm_preds_valid = svm_model.predict(X_valid_vec)\n",
        "svm_accuracy = accuracy_score(y_valid, svm_preds_valid)\n",
        "print(\"SVM Accuracy:\", svm_accuracy)\n",
        "\n",
        "# Compute confusion matrix\n",
        "conf_matrix = confusion_matrix(y_valid, svm_preds_valid)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Compute precision\n",
        "precision = precision_score(y_valid, svm_preds_valid)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Compute recall\n",
        "recall = recall_score(y_valid, svm_preds_valid)\n",
        "print(\"Recall:\", recall)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZLn6O3d_loG",
        "outputId": "6ec11207-8e03-48cc-d7d0-7c00c0304af4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest Accuracy: 0.8506\n",
            "Confusion Matrix:\n",
            "[[2111  375]\n",
            " [ 372 2142]]\n",
            "Precision: 0.8510131108462455\n",
            "Recall: 0.8520286396181385\n"
          ]
        }
      ],
      "source": [
        "# Train and evaluate Random Forest model\n",
        "rf_model = RandomForestClassifier()\n",
        "rf_model.fit(X_train_vec, y_train)\n",
        "rf_preds_valid = rf_model.predict(X_valid_vec)\n",
        "rf_accuracy = accuracy_score(y_valid, rf_preds_valid)\n",
        "print(\"Random Forest Accuracy:\", rf_accuracy)\n",
        "\n",
        "\n",
        "# Compute confusion matrix\n",
        "conf_matrix = confusion_matrix(y_valid, rf_preds_valid)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Compute precision\n",
        "precision = precision_score(y_valid, rf_preds_valid)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Compute recall\n",
        "recall = recall_score(y_valid, rf_preds_valid)\n",
        "print(\"Recall:\", recall)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "q9OY_Rh2oWQT"
      },
      "outputs": [],
      "source": [
        "# Train and evaluate BERT model\n",
        "class CustomDatasetTrain(Dataset):\n",
        "    def __init__(self, texts, labels, tokenizer, max_len):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_len = max_len\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",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            return_token_type_ids=False,\n",
        "            padding='max_length',\n",
        "            truncation=True,\n",
        "            return_attention_mask=True,\n",
        "            return_tensors='pt'\n",
        "        )\n",
        "        return {\n",
        "            'text': text,\n",
        "            'input_ids': encoding['input_ids'].flatten(),\n",
        "            'attention_mask': encoding['attention_mask'].flatten(),\n",
        "            'labels': torch.tensor(label, dtype=torch.long)\n",
        "        }\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
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        "outputId": "b62d825d-3ab8-4c64-d559-f0c6a88b6b2f"
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d1d4449c7d314b8fa7dbef4df7a320c4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a68762239a6948dab1c12e597dd0dc7e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6223fe4b1404b04941f454f3550aa2e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86abe5e3867e47ec9c49f8b277b49391",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "77ead761920e4f2da8056c779ef47ed7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "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",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:521: 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": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "max_len = 256\n",
        "batch_size = 32\n",
        "\n",
        "train_dataset = CustomDatasetTrain(train_df['preprocessed_text'].values, train_df['label'].values, tokenizer, max_len)\n",
        "valid_dataset = CustomDatasetTrain(valid_df['preprocessed_text'].values, valid_df['label'].values, tokenizer, max_len)\n",
        "test_dataset = CustomDatasetTrain(test_df['preprocessed_text'].values, test_df['label'].values, tokenizer, max_len)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
        "model.to(device)\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdyVZMTeoXFN",
        "outputId": "b30e0ba0-3805-480d-e6a6-3141ba753475"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Epoch 1/3, Train Loss: 0.2935, Train Accuracy: 0.8730\n",
            "Validation Accuracy: 0.9102\n",
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            "Epoch 2/3, Train Loss: 0.1678, Train Accuracy: 0.9368\n",
            "Validation Accuracy: 0.9174\n",
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            "Epoch 3/3, Train Loss: 0.0906, Train Accuracy: 0.9688\n",
            "Validation Accuracy: 0.9092\n"
          ]
        }
      ],
      "source": [
        "def train_epoch(model, data_loader, optimizer, device):\n",
        "    i=0\n",
        "    model.train()\n",
        "    losses = []\n",
        "    correct_predictions = 0\n",
        "    print(len(data_loader))\n",
        "    for batch in data_loader:\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        labels = batch['labels'].to(device)\n",
        "\n",
        "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs.loss\n",
        "        logits = outputs.logits\n",
        "\n",
        "        _, preds = torch.max(logits, dim=1)\n",
        "        correct_predictions += torch.sum(preds == labels)\n",
        "\n",
        "        losses.append(loss.item())\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        print(f\"....... {i}\")\n",
        "        i = i+1\n",
        "\n",
        "    return correct_predictions.double() / len(data_loader.dataset), np.mean(losses)\n",
        "\n",
        "def eval_model(model, data_loader, device):\n",
        "    model.eval()\n",
        "    losses = []\n",
        "    correct_predictions = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in data_loader:\n",
        "            input_ids = batch['input_ids'].to(device)\n",
        "            attention_mask = batch['attention_mask'].to(device)\n",
        "            labels = batch['labels'].to(device)\n",
        "\n",
        "            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "            loss = outputs.loss\n",
        "            logits = outputs.logits\n",
        "\n",
        "            _, preds = torch.max(logits, dim=1)\n",
        "            correct_predictions += torch.sum(preds == labels)\n",
        "\n",
        "            losses.append(loss.item())\n",
        "\n",
        "    return correct_predictions.double() / len(data_loader.dataset), np.mean(losses)\n",
        "\n",
        "num_epochs = 3\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    train_acc, train_loss = train_epoch(model, train_loader, optimizer, device)\n",
        "    print(f'Epoch {epoch + 1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_acc:.4f}')\n",
        "    valid_acc, _ = eval_model(model, valid_loader, device)\n",
        "    print(f'Validation Accuracy: {valid_acc:.4f}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJdRzyH3AbVf",
        "outputId": "a7a2f5b8-59a6-466d-ddfe-be7d29b2228b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            "[[2218  268]\n",
            " [ 186 2328]]\n",
            "Precision: 0.8967642526964561\n",
            "Recall: 0.9260143198090692\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
        "\n",
        "# Define a function to get predictions for the validation set\n",
        "def get_predictions(model, data_loader, device):\n",
        "    model.eval()\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in data_loader:\n",
        "            input_ids = batch['input_ids'].to(device)\n",
        "            attention_mask = batch['attention_mask'].to(device)\n",
        "            labels = batch['labels'].to(device)\n",
        "\n",
        "            outputs = model(input_ids, attention_mask=attention_mask)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            _, preds = torch.max(logits, dim=1)\n",
        "            all_preds.extend(preds.cpu().tolist())\n",
        "            all_labels.extend(labels.cpu().tolist())\n",
        "\n",
        "    return all_preds, all_labels\n",
        "\n",
        "# Get predictions for the validation set\n",
        "val_preds, val_labels = get_predictions(model, valid_loader, device)\n",
        "\n",
        "# Compute confusion matrix\n",
        "conf_matrix = confusion_matrix(val_labels, val_preds)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Compute precision\n",
        "precision = precision_score(val_labels, val_preds)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Compute recall\n",
        "recall = recall_score(val_labels, val_preds)\n",
        "print(\"Recall:\", recall)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "FAua3vNaoiK9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import BertTokenizer\n",
        "import torch\n",
        "\n",
        "# Define your model class here if not already defined\n",
        "\n",
        "# Define a custom dataset class to load the test data\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, dataframe, tokenizer, max_length):\n",
        "        self.data = dataframe\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        text = self.data.iloc[index]['text']\n",
        "\n",
        "        inputs = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            None,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_length,\n",
        "            padding='max_length',\n",
        "            truncation=True,\n",
        "            return_token_type_ids=True\n",
        "        )\n",
        "\n",
        "        input_ids = inputs['input_ids']\n",
        "        attention_mask = inputs['attention_mask']\n",
        "\n",
        "        return {\n",
        "            'input_ids': torch.tensor(input_ids, dtype=torch.long),\n",
        "            'attention_mask': torch.tensor(attention_mask, dtype=torch.long)\n",
        "        }\n",
        "\n",
        "# Load the test data from the CSV file\n",
        "test_data = pd.read_csv('Test.csv')\n",
        "\n",
        "# Define the parameters\n",
        "max_length = 256  # Adjust this according to your model's maximum input length\n",
        "batch_size = 32  # Adjust batch size based on your system's memory\n",
        "\n",
        "# Load the BERT tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Create a DataLoader for the test data\n",
        "test_dataset = CustomDataset(test_data, tokenizer, max_length)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# Assuming your model is already defined and loaded, proceed with making predictions\n",
        "test_preds = []\n",
        "with torch.no_grad():\n",
        "    for batch in test_loader:\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "        logits = outputs.logits\n",
        "        _, preds = torch.max(logits, dim=1)\n",
        "\n",
        "        test_preds.extend(preds.cpu().tolist())\n",
        "\n",
        "# Add the predicted labels to the test DataFrame\n",
        "test_data['label'] = test_preds\n",
        "\n",
        "# Save the results to a submission file\n",
        "test_data[['id', 'label']].to_csv('submission.csv', index=False)"
      ]
    }
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