notebooks/03_Working_with_HuggingFaces_Transformers_Library.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Working with HuggingFace's Transformers Library\n",
        "\n",
        "In this tutorial, we'll explore the Transformers library by HuggingFace, a powerful tool for working with state-of-the-art NLP models like BERT, GPT-2, and more.\n",
        "\n",
        "## Prerequisites:\n",
        "Basic understanding of PyTorch.\n",
        "Familiarity with deep learning concepts.\n",
        "\n",
        "## Setting up the Environment\n",
        "First, let's set up our Colab environment:"
      ],
      "metadata": {
        "id": "qgCk0792h9G5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q torch torchvision transformers"
      ],
      "metadata": {
        "id": "ulfVRKbDiJfJ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Loading Pre-trained Models\n",
        "HuggingFace's Transformers library provides a plethora of pre-trained models. Let's start by loading the BERT model."
      ],
      "metadata": {
        "id": "EKjqOs_liO-0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertModel\n",
        "\n",
        "# Load pre-trained BERT tokenizer and model\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertModel.from_pretrained('bert-base-uncased')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        "outputId": "21ca6a74-4b97-445f-be09-f6e8a56b05ba"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Tokenization\n",
        "Before feeding text to BERT, we need to tokenize it. The tokenizer will convert our text into tokens that correspond to BERT's vocabulary."
      ],
      "metadata": {
        "id": "r26qz94UiXG-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Hello, HuggingFace!\"\n",
        "encoded_input = tokenizer(text, return_tensors='pt')\n",
        "print(encoded_input)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gsDrG_5iTtE",
        "outputId": "81b6549c-796f-43c8-b4a3-c4ed2d2de22f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101,  7592,  1010, 17662, 12172,   999,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Model Inference\n",
        "Now, let's use the BERT model to get embeddings for our text."
      ],
      "metadata": {
        "id": "njOfxEj2ifZ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "with torch.no_grad():\n",
        "    output = model(**encoded_input)\n",
        "\n",
        "# Extract the sequence output (representations for each token in the input)\n",
        "sequence_output = output.last_hidden_state\n",
        "print(sequence_output)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZTD67vqicPF",
        "outputId": "dd9de4d5-f9f7-4bc9-85dc-f3569f3fe353"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[[-0.2291,  0.0135, -0.1188,  ..., -0.2613, -0.0115,  0.6596],\n",
            "         [-0.0107,  0.0749,  0.6104,  ..., -0.1752,  0.3824, -0.0338],\n",
            "         [-0.7685,  0.8493,  0.3917,  ..., -1.2588, -0.3544,  0.0577],\n",
            "         ...,\n",
            "         [ 0.2830, -0.0994,  1.1351,  ..., -0.4314,  0.6670, -0.0023],\n",
            "         [-0.5194, -0.1928, -0.3060,  ...,  0.6108, -0.3512,  0.1036],\n",
            "         [ 0.7359,  0.3968, -0.0619,  ...,  0.4613, -0.4899, -0.3350]]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Using Transformer Models for Tasks\n",
        "HuggingFace provides interfaces for specific tasks like sequence classification, token classification, etc. Let's use BERT for sequence classification."
      ],
      "metadata": {
        "id": "Wiq2XyOgisxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertForSequenceClassification\n",
        "\n",
        "# Load the BERT model for sequence classification\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Example input text\n",
        "texts = [\"Hello, HuggingFace!\", \"Transformers are awesome!\"]\n",
        "\n",
        "# Tokenize and get predictions\n",
        "encoded_inputs = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')\n",
        "outputs = model(**encoded_inputs)\n",
        "\n",
        "# Get the logits from the model's output\n",
        "logits = outputs.logits\n",
        "print(logits)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcBhYEdKijjr",
        "outputId": "610e014b-270f-4e8c-d0df-a5f86f1eb805"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.5261, 0.4207],\n",
            "        [0.4768, 0.3721]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Fine-tuning on Custom Data\n",
        "For this example, let's assume we have a binary classification task. We'll create dummy data and fine-tune BERT on it."
      ],
      "metadata": {
        "id": "Te_HqP0AiyST"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dummy data\n",
        "texts = [\"I love Transformers.\", \"I don't like this library.\"]\n",
        "labels = torch.tensor([1, 0])  # 1: Positive, 0: Negative\n",
        "\n",
        "# Tokenize the data\n",
        "encoded_inputs = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')\n",
        "encoded_inputs[\"labels\"] = labels\n",
        "\n",
        "# Fine-tuning\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)\n",
        "model.train()\n",
        "\n",
        "for epoch in range(3):\n",
        "    optimizer.zero_grad()\n",
        "    outputs = model(**encoded_inputs)\n",
        "    loss = outputs.loss\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    print(f\"Epoch {epoch + 1}, Loss: {loss.item()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yclKXOIiwLV",
        "outputId": "f6297ade-6f8a-4967-e285-748b0151f361"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Loss: 0.6741079092025757\n",
            "Epoch 2, Loss: 0.6127657294273376\n",
            "Epoch 3, Loss: 0.6706300973892212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Saving & Loading Fine-tuned Models\n",
        "After fine-tuning, you might want to save your model for later use."
      ],
      "metadata": {
        "id": "ADid9pKni4St"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save model\n",
        "model.save_pretrained(\"./my_bert_model\")\n",
        "\n",
        "# Load model\n",
        "loaded_model = BertForSequenceClassification.from_pretrained(\"./my_bert_model\")\n"
      ],
      "metadata": {
        "id": "EoqCvDwXi1Mm"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bNOCAKtei7FU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}