Multi_class_setence_classifcation_BERT.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vxsFWNDc1MWK"
      },
      "outputs": [],
      "source": [
        "\n",
        "file_id = '1tTCOug6ktdMLOtKFtvRoCIs-U6ywXBaj'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from google.colab import drive\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "2cKz1Rem2379"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "download = drive.CreateFile({'id': file_id})\n",
        "# Download the file to a local disc\n",
        "download.GetContentFile('data.csv')"
      ],
      "metadata": {
        "id": "eXlbvqvQ2qbL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "xWlbaAmx3Ct2"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the CSV file without headers\n",
        "df = pd.read_csv('data.csv', header=None)\n",
        "\n",
        "# Assign headers 'class' and 'sentence'\n",
        "df.columns = ['class', 'sentence']\n",
        "\n",
        "# Now you can use the modified DataFrame 'df' for further processing\n"
      ],
      "metadata": {
        "id": "e7ApSVkv27O4"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers torch\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GiKjuPkw3F03",
        "outputId": "c2e97568-72e1-43c9-d2b5-e2ff01312fd6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.30.1-py3-none-any.whl (7.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m52.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m29.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m110.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m86.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import BertTokenizer, BertForSequenceClassification, AdamW\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, f1_score"
      ],
      "metadata": {
        "id": "SCz1jiPz4Nxk"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the BERT model and tokenizer\n",
        "model_name = 'bert-base-uncased'\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = BertForSequenceClassification.from_pretrained(model_name, num_labels=df['class'].nunique())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256,
          "referenced_widgets": [
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        "outputId": "573943ca-4aca-468d-d8d1-1af52df964c0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "bc27fa09d9704a32869f65e0366297db"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_full = df.copy()"
      ],
      "metadata": {
        "id": "K1Wx9yEG5L3m"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df= df_full.sample(10000)\n",
        "df['class'].nunique()\n",
        "label_encoder = LabelEncoder()\n",
        "df['label'] = label_encoder.fit_transform(df['class'])"
      ],
      "metadata": {
        "id": "N83CtKGb5rxM"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df, val_df = train_test_split(df, test_size=0.3, random_state=42)\n"
      ],
      "metadata": {
        "id": "kPvOORP19pdx"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 128\n",
        "train_encodings = tokenizer(list(train_df['sentence']), truncation=True, padding=True, max_length=max_length)"
      ],
      "metadata": {
        "id": "qImdYZEV9rks"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_encodings = tokenizer(list(val_df['sentence']), truncation=True, padding=True, max_length=max_length)\n"
      ],
      "metadata": {
        "id": "0Qkmata89yAA"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the tokenized data to PyTorch tensors\n",
        "train_dataset = TensorDataset(torch.tensor(train_encodings['input_ids']),\n",
        "                              torch.tensor(train_encodings['attention_mask']),\n",
        "                              torch.tensor(train_df['label'].values))"
      ],
      "metadata": {
        "id": "VLH6oRiV927G"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_dataset = TensorDataset(torch.tensor(val_encodings['input_ids']),\n",
        "                            torch.tensor(val_encodings['attention_mask']),\n",
        "                            torch.tensor(val_df['label'].values))"
      ],
      "metadata": {
        "id": "ue61F1Vk_FzS"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create data loaders for training and validation\n",
        "batch_size = 16\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=batch_size)\n"
      ],
      "metadata": {
        "id": "KPocWe1h_JXD"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the device for training\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Set the optimizer and learning rate\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "# Training loop\n",
        "epochs = 3\n",
        "model.to(device)\n",
        "model.train()\n",
        "for epoch in range(epochs):\n",
        "    total_loss = 0\n",
        "    total_correct = 0\n",
        "    total_samples = 0\n",
        "    all_predicted_labels = []\n",
        "    all_true_labels = []\n",
        "\n",
        "    for batch in train_dataloader:\n",
        "        inputs = {'input_ids': batch[0].to(device),\n",
        "                  'attention_mask': batch[1].to(device),\n",
        "                  'labels': batch[2].to(device)}\n",
        "        outputs = model(**inputs)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        predicted_labels = torch.argmax(outputs.logits, dim=1)\n",
        "        true_labels = inputs['labels']\n",
        "        total_loss += loss.item()\n",
        "        total_correct += (predicted_labels == true_labels).sum().item()\n",
        "        total_samples += len(true_labels)\n",
        "        all_predicted_labels.extend(predicted_labels.tolist())\n",
        "        all_true_labels.extend(true_labels.tolist())\n",
        "\n",
        "    # Calculate accuracy and F1 score\n",
        "    epoch_loss = total_loss / len(train_dataloader)\n",
        "    epoch_accuracy = total_correct / total_samples\n",
        "    epoch_f1_score = f1_score(all_true_labels, all_predicted_labels, average='weighted')\n",
        "\n",
        "    print(f\"Epoch {epoch+1}/{epochs} - Loss: {epoch_loss:.4f} - Accuracy: {epoch_accuracy:.4f} - F1 Score: {epoch_f1_score:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9O332D4-4gKJ",
        "outputId": "d1bbff36-fd1a-4add-e759-0591526b596d"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3 - Loss: 0.0687 - Accuracy: 0.9833 - F1 Score: 0.9833\n",
            "Epoch 2/3 - Loss: 0.0455 - Accuracy: 0.9896 - F1 Score: 0.9896\n",
            "Epoch 3/3 - Loss: 0.0363 - Accuracy: 0.9907 - F1 Score: 0.9907\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Evaluation\n",
        "model.eval()\n",
        "all_predicted_labels = []\n",
        "all_true_labels = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch in val_dataloader:\n",
        "        inputs = {'input_ids': batch[0].to(device),\n",
        "                  'attention_mask': batch[1].to(device),\n",
        "                  'labels': batch[2].to(device)}\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "        predicted_labels = torch.argmax(logits, dim=1)\n",
        "        all_predicted_labels.extend(predicted_labels.cpu().numpy())\n",
        "        all_true_labels.extend(inputs['labels'].cpu().numpy())\n",
        "\n",
        "# Calculate accuracy and F1 score\n",
        "val_accuracy = accuracy_score(all_true_labels, all_predicted_labels)\n",
        "val_f1_score = f1_score(all_true_labels, all_predicted_labels, average='weighted')\n",
        "\n",
        "print(f\"Validation Accuracy: {val_accuracy:.4f}\")\n",
        "print(f\"Validation F1 Score: {val_f1_score:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4S8C21Qb_U4h",
        "outputId": "babd7818-fb40-44de-a3e9-3e47edb47871"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 0.9563\n",
            "Validation F1 Score: 0.9563\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQElLpiWCGow",
        "outputId": "c5b3a13a-8d5c-42ac-c7b0-ac4dc96348f8"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19464                     Books\n",
              "25891                     Books\n",
              "48700               Electronics\n",
              "17877                 Household\n",
              "35536    Clothing & Accessories\n",
              "                  ...          \n",
              "16476                 Household\n",
              "3791                  Household\n",
              "42652               Electronics\n",
              "18423                 Household\n",
              "8456                  Household\n",
              "Name: class, Length: 10000, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    }
  ]
}