Domain_Adaptation_with_Task_Specific_Fine_Tuning.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStardust/blob/main/Domain_Adaptation_with_Task_Specific_Fine_Tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "pip install transformers torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertForSequenceClassification, BertTokenizer, AdamW\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "# Load BERT model and tokenizer\n",
        "model_name = \"bert-base-uncased\"\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = BertForSequenceClassification.from_pretrained(model_name)\n",
        "\n",
        "# Example medical dataset (replace with your actual dataset)\n",
        "texts = [\"Patient has a headache.\", \"CT scan shows no abnormalities.\"]\n",
        "labels = [1, 0]  # Example labels\n",
        "\n",
        "# Tokenize texts\n",
        "inputs = tokenizer(texts, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "# Create DataLoader\n",
        "dataset = TensorDataset(inputs['input_ids'], inputs['attention_mask'], labels)\n",
        "data_loader = DataLoader(dataset, batch_size=2, shuffle=True)\n",
        "\n",
        "# Set up optimizer\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "# Fine-tuning loop\n",
        "model.train()\n",
        "for epoch in range(3):  # Number of epochs\n",
        "    for batch in data_loader:\n",
        "        input_ids, attention_mask, labels = batch\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        print(f\"Epoch {epoch}, Loss: {loss.item()}\")\n",
        "\n",
        "print(\"Fine-tuning completed.\")"
      ],
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      "execution_count": null,
      "outputs": []
    }
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