notebooks/09_Question_Answering_Systems_with_Transformers.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Question Answering Systems with Transformers using Python, PyTorch, and Huggingface\n",
        "\n",
        "Question Answering (QA) systems are designed to answer questions posed in natural language. With the advent of transformer models, building powerful QA systems has become more accessible.\n",
        "\n",
        "## 1. Introduction to Question Answering Systems\n",
        "\n",
        "QA systems have been a significant area of research in NLP. The goal is to provide concise, relevant answers to user queries. Transformers, with their self-attention mechanism, have shown state-of-the-art performance in this domain.\n",
        "\n",
        "## 2. Setting up the Environment\n",
        "\n",
        "We'll be using Google Colab for this tutorial, which offers free GPU resources.\n"
      ],
      "metadata": {
        "id": "YahT8Lple3RW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install necessary packages\n",
        "!pip install transformers\n",
        "!pip install torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvuUiciMeLOH",
        "outputId": "32660a7b-103f-4b12-aa65-d2780a11e127"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.33.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.17.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\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: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement 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 torch) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.27.4.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After installation, let's verify the versions:\n"
      ],
      "metadata": {
        "id": "g-oFR6v2fArx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "import torch\n",
        "\n",
        "print(\"Transformers version:\", transformers.__version__)\n",
        "print(\"PyTorch version:\", torch.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcsOYHc9e-2v",
        "outputId": "46e54391-3f69-4b63-931c-c35ee9aa1e98"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformers version: 4.33.2\n",
            "PyTorch version: 2.0.1+cu118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Loading Pre-trained Models\n",
        "\n",
        "For QA tasks, the `BertForQuestionAnswering` model is commonly used.\n"
      ],
      "metadata": {
        "id": "kwd72dlIfEkE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertForQuestionAnswering\n",
        "from transformers import pipeline\n",
        "\n",
        "# Load pre-trained model and tokenizer\n",
        "model = BertForQuestionAnswering.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "\n",
        "# QA pipeline\n",
        "qa_pipeline = pipeline(\"question-answering\", model=model, tokenizer=tokenizer)"
      ],
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        "outputId": "e4089ebb-6858-4f4b-b3ef-3de117df8bf1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/443 [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 model.safetensors:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-large-uncased-whole-word-masking-finetuned-squad were not used when initializing BertForQuestionAnswering: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
            "- This IS expected if you are initializing BertForQuestionAnswering 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 BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Using the QA System\n",
        "\n",
        "With the model loaded, we can now pose questions based on a provided context.\n"
      ],
      "metadata": {
        "id": "v5tWFWR3fLj7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "context = \"Transformers are a type of deep learning model that have gained immense popularity in recent years due to their effectiveness in various NLP tasks.\"\n",
        "question = \"What have transformers gained in recent years?\"\n",
        "\n",
        "answer = qa_pipeline({'context': context, 'question': question})\n",
        "print(answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vF-LJAhffJlG",
        "outputId": "7631c450-7598-438d-8cbd-3cb752f79d8f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'score': 0.8668745160102844, 'start': 64, 'end': 82, 'answer': 'immense popularity'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Understanding the Output\n",
        "\n",
        "The output will be a dictionary containing:\n",
        "\n",
        "- `score`: Confidence score of the answer.\n",
        "- `start`: Start position of the answer in the context.\n",
        "- `end`: End position of the answer in the context.\n",
        "- `answer`: The extracted answer from the context.\n",
        "\n",
        "## 6. Conclusion\n",
        "\n",
        "Question Answering systems have a wide range of applications, from chatbots to search engines. Transformers have revolutionized the performance and capabilities of these systems. This tutorial offers a glimpse into building a basic QA system. For advanced features and optimizations, the official documentation and related research papers are invaluable resources.\n",
        "\n",
        "## 7. Reproducibility\n",
        "\n",
        "Ensuring reproducibility involves:\n",
        "\n",
        "1. Using the same package versions.\n",
        "2. Setting random seeds for all libraries.\n",
        "3. Using consistent model weights and architectures."
      ],
      "metadata": {
        "id": "17w0ofe7fQHE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This tutorial serves as an introduction to QA systems using transformers. Dive deeper by exploring the official documentation, community discussions, and academic research on the topic.\n"
      ],
      "metadata": {
        "id": "oJaiAhwvfUZh"
      }
    }
  ]
}