question_answering_subjqa.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "Y23hYA_ZNycI",
        "outputId": "a4079373-f141-4e87-b1d8-4c2953237fd6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'notebooks'...\n",
            "remote: Enumerating objects: 526, done.\u001b[K\n",
            "remote: Counting objects: 100% (173/173), done.\u001b[K\n",
            "remote: Compressing objects: 100% (47/47), done.\u001b[K\n",
            "remote: Total 526 (delta 143), reused 135 (delta 126), pack-reused 353\u001b[K\n",
            "Receiving objects: 100% (526/526), 28.62 MiB | 10.91 MiB/s, done.\n",
            "Resolving deltas: 100% (250/250), done.\n",
            "/content/notebooks/notebooks\n"
          ]
        }
      ],
      "source": [
        "# Uncomment and run this cell if you're on Colab or Kaggle\n",
        "!git clone https://github.com/nlp-with-transformers/notebooks.git\n",
        "%cd notebooks\n",
        "from install import *\n",
        "install_requirements(is_chapter7=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch\n",
        "!pip install transformers\n",
        "!pip install matplotlib\n",
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzScnhWaOaM1",
        "outputId": "e9e4a69e-385f-42de-9b1d-c6e0cb12ba90"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "NxXOSAxiNycK",
        "outputId": "31e7ec1a-84de-45ff-9bc1-509336ae531f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using transformers v4.36.2\n",
            "Using datasets v2.16.1\n"
          ]
        }
      ],
      "source": [
        "#hide\n",
        "from utils import *\n",
        "setup_chapter()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "bIwhx33UNycL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c1e9d61-dae5-41a8-99ef-a6784b218a31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: TOKENIZERS_PARALLELISM=false\n"
          ]
        }
      ],
      "source": [
        "#hide\n",
        "%env TOKENIZERS_PARALLELISM=false"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "uymuQhJGNycL"
      },
      "outputs": [],
      "source": [
        "#hide\n",
        "# Suppress Haystack logging\n",
        "import logging\n",
        "for module in [\"farm.utils\", \"farm.infer\", \"haystack.reader.farm.FARMReader\",\n",
        "              \"farm.modeling.prediction_head\", \"elasticsearch\", \"haystack.eval\",\n",
        "               \"haystack.document_store.base\", \"haystack.retriever.base\",\n",
        "              \"farm.data_handler.dataset\"]:\n",
        "    module_logger = logging.getLogger(module)\n",
        "    module_logger.setLevel(logging.ERROR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AuYMcdYrNycM"
      },
      "source": [
        "# Question Answering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SMNoGm5NycM"
      },
      "source": [
        "<img alt=\"Marie Curie\" width=\"500\" caption=\"A Google search query and corresponding answer snippet\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_marie-curie.png?raw=1\" id=\"marie-curie\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gO7y8pOGNycN"
      },
      "source": [
        "## Building a Review-Based QA System"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM-2ZUNrNycN"
      },
      "source": [
        "### The Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3lIRb14NycO"
      },
      "source": [
        "<img alt=\"Phone with Query\" width=\"400\" caption=\"A question about a product and the corresponding review (the answer span is underlined)\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_phone.png?raw=1\" id=\"phone\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "bFP_9IxbNycO",
        "outputId": "777ed63b-3b73-4afe-fc2b-de9e78bafd5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['books', 'electronics', 'grocery', 'movies', 'restaurants', 'tripadvisor']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "from datasets import get_dataset_config_names\n",
        "\n",
        "domains = get_dataset_config_names(\"subjqa\")\n",
        "domains"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "d--7ESmPNycP"
      },
      "outputs": [],
      "source": [
        "#hide_output\n",
        "from datasets import load_dataset\n",
        "\n",
        "subjqa = load_dataset(\"subjqa\", name=\"electronics\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "6CPjTs5cNycP",
        "outputId": "f1d6c3db-c469-4af7-a782-2d0c22066f36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'text': ['Bass is weak as expected', 'Bass is weak as expected, even with EQ\n",
            "adjusted up'], 'answer_start': [1302, 1302], 'answer_subj_level': [1, 1],\n",
            "'ans_subj_score': [0.5083333253860474, 0.5083333253860474], 'is_ans_subjective':\n",
            "[True, True]}\n"
          ]
        }
      ],
      "source": [
        "print(subjqa[\"train\"][\"answers\"][1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "nNNXFPGNNycP",
        "outputId": "1efb4fc3-1f68-4372-ccff-9fa532ea84f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of questions in train: 1295\n",
            "Number of questions in test: 358\n",
            "Number of questions in validation: 255\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "dfs = {split: dset.to_pandas() for split, dset in subjqa.flatten().items()}\n",
        "\n",
        "for split, df in dfs.items():\n",
        "    print(f\"Number of questions in {split}: {df['id'].nunique()}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "1Mu3qgmMNycP",
        "outputId": "8d786291-06c8-4974-dc71-91b0381b0814",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           title                        question                answers.text  \\\n",
              "791   B005DKZTMG  Does the keyboard lightweight?  [this keyboard is compact]   \n",
              "1159  B00AAIPT76             How is the battery?                          []   \n",
              "\n",
              "     answers.answer_start                                            context  \n",
              "791                 [215]  I really like this keyboard.  I give it 4 star...  \n",
              "1159                   []  I bought this after the first spare gopro batt...  "
            ],
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              "      <th>791</th>\n",
              "      <td>B005DKZTMG</td>\n",
              "      <td>Does the keyboard lightweight?</td>\n",
              "      <td>[this keyboard is compact]</td>\n",
              "      <td>[215]</td>\n",
              "      <td>I really like this keyboard.  I give it 4 star...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1159</th>\n",
              "      <td>B00AAIPT76</td>\n",
              "      <td>How is the battery?</td>\n",
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              "      <td>[]</td>\n",
              "      <td>I bought this after the first spare gopro batt...</td>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6a74bf6d-4867-456d-a289-4dba353ae3c7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-6a74bf6d-4867-456d-a289-4dba353ae3c7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_543dcebb-7146-4229-be57-25747d402314\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sample_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sample_df');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#hide_output\n",
        "qa_cols = [\"title\", \"question\", \"answers.text\",\n",
        "           \"answers.answer_start\", \"context\"]\n",
        "sample_df = dfs[\"train\"][qa_cols].sample(2, random_state=7)\n",
        "sample_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "_cdWfE88NycQ",
        "outputId": "f80788bb-9a5a-418c-a102-c1bca3a945aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'this keyboard is compact'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "start_idx = sample_df[\"answers.answer_start\"].iloc[0][0]\n",
        "end_idx = start_idx + len(sample_df[\"answers.text\"].iloc[0][0])\n",
        "sample_df[\"context\"].iloc[0][start_idx:end_idx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "oiw5qJIHNycQ",
        "outputId": "1f61b130-b504-4775-8292-618c0ba4bd58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "counts = {}\n",
        "question_types = [\"What\", \"How\", \"Is\", \"Does\", \"Do\", \"Was\", \"Where\", \"Why\"]\n",
        "\n",
        "for q in question_types:\n",
        "    counts[q] = dfs[\"train\"][\"question\"].str.startswith(q).value_counts()[True]\n",
        "\n",
        "pd.Series(counts).sort_values().plot.barh()\n",
        "plt.title(\"Frequency of Question Types\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "abGydpoGNycQ",
        "outputId": "4eb3f206-7301-43cc-c866-58449e9fb417",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "How is the camera?\n",
            "How do you like the control?\n",
            "How fast is the charger?\n",
            "What is direction?\n",
            "What is the quality of the construction of the bag?\n",
            "What is your impression of the product?\n",
            "Is this how zoom works?\n",
            "Is sound clear?\n",
            "Is it a wireless keyboard?\n"
          ]
        }
      ],
      "source": [
        "for question_type in [\"How\", \"What\", \"Is\"]:\n",
        "    for question in (\n",
        "        dfs[\"train\"][dfs[\"train\"].question.str.startswith(question_type)]\n",
        "        .sample(n=3, random_state=42)['question']):\n",
        "        print(question)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KaVDFZ6XNycQ"
      },
      "source": [
        "### Sidebar: The Stanford Question Answering Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gh1ZtgfTNycQ"
      },
      "source": [
        "<img alt=\"SQuAD SotA\" width=\"600\" caption=\"Progress on the SQuAD 2.0 benchmark (image from Papers with Code)\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_squad-sota.png?raw=1\" id=\"squad-sota\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWuocOSDNycQ"
      },
      "source": [
        "### End sidebar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "im8z5ozkNycQ"
      },
      "source": [
        "### Extracting Answers from Text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtX8x8KkNycQ"
      },
      "source": [
        "#### Span classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ug1FukfnNycQ"
      },
      "source": [
        "<img alt=\"QA Head\" caption=\"The span classification head for QA tasks\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_qa-head.png?raw=1\" id=\"qa-head\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNaPsTAlNycQ"
      },
      "source": [
        "<img alt=\"SQuAD models\" width=\"600\" caption=\"A selection of extractive QA models on the Hugging Face Hub\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_squad-models.png?raw=1\" id=\"squad-models\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbssrQKXNycQ"
      },
      "source": [
        "#### Tokenizing text for QA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "LvwdDZpANycQ"
      },
      "outputs": [],
      "source": [
        "#hide_output\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_ckpt = \"deepset/minilm-uncased-squad2\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "vXoAAXkWNycR"
      },
      "outputs": [],
      "source": [
        "question = \"How much music can this hold?\"\n",
        "context = \"\"\"An MP3 is about 1 MB/minute, so about 6000 hours depending on \\\n",
        "file size.\"\"\"\n",
        "inputs = tokenizer(question, context, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "9f7XYTLQNycR",
        "outputId": "1334bced-b028-4577-f071-121e320ad742",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 0     1     2     3     4     5     6     7    8     9   ...  \\\n",
              "input_ids       101  2129  2172  2189  2064  2023  2907  1029  102  2019  ...   \n",
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              "\n",
              "                  18    19     20    21    22    23    24    25    26   27  \n",
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              "attention_mask     1     1      1     1     1     1     1     1     1    1  \n",
              "\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-44b2ed86-7590-48bb-a0c3-720b26835ee6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-44b2ed86-7590-48bb-a0c3-720b26835ee6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "  }\n",
              "\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9eac53d0-001c-4adb-844b-c630a0eb9e4f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8028ff9c-2491-4054-89e6-8b46667929cd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('input_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8028ff9c-2491-4054-89e6-8b46667929cd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('input_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#hide_input\n",
        "input_df = pd.DataFrame.from_dict(tokenizer(question, context), orient=\"index\")\n",
        "input_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "NJdXCfWVNycR",
        "outputId": "ba36d2d8-c9ee-41cf-e776-48ccd505255e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] how much music can this hold? [SEP] an mp3 is about 1 mb / minute, so\n",
            "about 6000 hours depending on file size. [SEP]\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer.decode(inputs[\"input_ids\"][0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "An7qllLYNycR",
        "outputId": "9d6eb14d-eb26-4bd9-ea6f-d08df8a10e22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "QuestionAnsweringModelOutput(loss=None, start_logits=tensor([[-0.9862, -4.7750,\n",
            "-5.4025, -5.2378, -5.2863, -5.5117, -4.9819, -6.1880,\n",
            "         -0.9862,  0.2596, -0.2144, -1.7136,  3.7806,  4.8561, -1.0546, -3.9097,\n",
            "         -1.7374, -4.5944, -1.4278,  3.9949,  5.0391, -0.2018, -3.0193, -4.8549,\n",
            "         -2.3107, -3.5110, -3.5713, -0.9862]]), end_logits=tensor([[-0.9623,\n",
            "-5.4733, -5.0326, -5.1639, -5.4278, -5.5151, -5.1749, -4.6233,\n",
            "         -0.9623, -3.7855, -0.8715, -3.7745, -3.0161, -1.1780,  0.1758, -2.7365,\n",
            "          4.8934,  0.3046, -3.1761, -3.2762,  0.8937,  5.6606, -0.3623, -4.9554,\n",
            "         -3.2531, -0.0914,  1.6211, -0.9623]]), hidden_states=None,\n",
            "attentions=None)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForQuestionAnswering\n",
        "\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(model_ckpt)\n",
        "\n",
        "with torch.no_grad():\n",
        "    outputs = model(**inputs)\n",
        "print(outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "VlJYKE79NycR"
      },
      "outputs": [],
      "source": [
        "start_logits = outputs.start_logits\n",
        "end_logits = outputs.end_logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "BKZ__4AiNycR",
        "outputId": "d1d68c3a-8d53-4e3f-e63c-1880092f066f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input IDs shape: torch.Size([1, 28])\n",
            "Start logits shape: torch.Size([1, 28])\n",
            "End logits shape: torch.Size([1, 28])\n"
          ]
        }
      ],
      "source": [
        "print(f\"Input IDs shape: {inputs.input_ids.size()}\")\n",
        "print(f\"Start logits shape: {start_logits.size()}\")\n",
        "print(f\"End logits shape: {end_logits.size()}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "w7XGqIN0NycR",
        "outputId": "f19491d6-5510-40fe-e887-a008c597bf34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#hide_input\n",
        "#id qa-scores\n",
        "#caption Predicted logits for the start and end tokens; the token with the highest score is colored in orange\n",
        "\n",
        "# The idea for this visualisation comes from https://mccormickml.com/2020/03/10/question-answering-with-a-fine-tuned-BERT\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "s_scores = start_logits.detach().numpy().flatten()\n",
        "e_scores = end_logits.detach().numpy().flatten()\n",
        "tokens = tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"][0])\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
        "colors = [\"C0\" if s != np.max(s_scores) else \"C1\" for s in s_scores]\n",
        "ax1.bar(x=tokens, height=s_scores, color=colors)\n",
        "ax1.set_ylabel(\"Start Scores\")\n",
        "colors = [\"C0\" if s != np.max(e_scores) else \"C1\" for s in e_scores]\n",
        "ax2.bar(x=tokens, height=e_scores, color=colors)\n",
        "ax2.set_ylabel(\"End Scores\")\n",
        "plt.xticks(rotation=\"vertical\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "HQZ0K4wrNycR",
        "outputId": "862f2854-6d15-49c5-a636-ab3b047d7fcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: How much music can this hold?\n",
            "Answer: 6000 hours\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "start_idx = torch.argmax(start_logits)\n",
        "end_idx = torch.argmax(end_logits) + 1\n",
        "answer_span = inputs[\"input_ids\"][0][start_idx:end_idx]\n",
        "answer = tokenizer.decode(answer_span)\n",
        "print(f\"Question: {question}\")\n",
        "print(f\"Answer: {answer}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "JO1WLD0GNycR",
        "outputId": "f2a66626-c8c6-4e11-c6a9-39fbf4b967a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/question_answering.py:326: UserWarning: topk parameter is deprecated, use top_k instead\n",
            "  warnings.warn(\"topk parameter is deprecated, use top_k instead\", UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.2651609182357788, 'start': 38, 'end': 48, 'answer': '6000 hours'},\n",
              " {'score': 0.22082965075969696,\n",
              "  'start': 16,\n",
              "  'end': 48,\n",
              "  'answer': '1 MB/minute, so about 6000 hours'},\n",
              " {'score': 0.10253587365150452,\n",
              "  'start': 16,\n",
              "  'end': 27,\n",
              "  'answer': '1 MB/minute'}]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"question-answering\", model=model, tokenizer=tokenizer)\n",
        "pipe(question=question, context=context, topk=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "GpDbnsQ1NycR",
        "outputId": "9f57e7e3-f529-4bb3-9511-72de037c316c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.9068412780761719, 'start': 0, 'end': 0, 'answer': ''}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "pipe(question=\"Why is there no data?\", context=context,\n",
        "     handle_impossible_answer=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lj9_32H1NycS"
      },
      "source": [
        "#### Dealing with long passages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "i40GiRp8NycS",
        "outputId": "9c7b54ea-740a-441a-fd2f-067a6924faef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#hide_input\n",
        "#id subjqa-dist\n",
        "#caption Distribution of tokens for each question-context pair in the SubjQA training set\n",
        "def compute_input_length(row):\n",
        "    inputs = tokenizer(row[\"question\"], row[\"context\"])\n",
        "    return len(inputs[\"input_ids\"])\n",
        "\n",
        "dfs[\"train\"][\"n_tokens\"] = dfs[\"train\"].apply(compute_input_length, axis=1)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "dfs[\"train\"][\"n_tokens\"].hist(bins=100, grid=False, ec=\"C0\", ax=ax)\n",
        "plt.xlabel(\"Number of tokens in question-context pair\")\n",
        "ax.axvline(x=512, ymin=0, ymax=1, linestyle=\"--\", color=\"C1\",\n",
        "           label=\"Maximum sequence length\")\n",
        "plt.legend()\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXC3XPtGNycS"
      },
      "source": [
        "<img alt=\"Sliding window\" caption=\"How the sliding window creates multiple question-context pairs for long documents—the first bar corresponds to the question, while the second bar is the context captured in each window\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_sliding-window.png?raw=1\" id=\"sliding-window\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "1d7vIymnNycW"
      },
      "outputs": [],
      "source": [
        "example = dfs[\"train\"].iloc[0][[\"question\", \"context\"]]\n",
        "tokenized_example = tokenizer(example[\"question\"], example[\"context\"],\n",
        "                              return_overflowing_tokens=True, max_length=100,\n",
        "                              stride=25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "LKTh1jgnNycW",
        "outputId": "c481a0b2-ac50-4e9e-caee-72045f7fc54a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window #0 has 100 tokens\n",
            "Window #1 has 88 tokens\n"
          ]
        }
      ],
      "source": [
        "for idx, window in enumerate(tokenized_example[\"input_ids\"]):\n",
        "    print(f\"Window #{idx} has {len(window)} tokens\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "N--NpEAsNycW",
        "outputId": "c70d9f93-f2cc-4c43-d9c6-388f9bb427f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] how is the bass? [SEP] i have had koss headphones in the past, pro 4aa and\n",
            "qz - 99. the koss portapro is portable and has great bass response. the work\n",
            "great with my android phone and can be \" rolled up \" to be carried in my\n",
            "motorcycle jacket or computer bag without getting crunched. they are very light\n",
            "and do not feel heavy or bear down on your ears even after listening to music\n",
            "with them on all day. the sound is [SEP]\n",
            "\n",
            "[CLS] how is the bass? [SEP] and do not feel heavy or bear down on your ears\n",
            "even after listening to music with them on all day. the sound is night and day\n",
            "better than any ear - bud could be and are almost as good as the pro 4aa. they\n",
            "are \" open air \" headphones so you cannot match the bass to the sealed types,\n",
            "but it comes close. for $ 32, you cannot go wrong. [SEP]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for window in tokenized_example[\"input_ids\"]:\n",
        "    print(f\"{tokenizer.decode(window)} \\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MW2JiyVNycX"
      },
      "source": [
        "### Using Haystack to Build a QA Pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jw22kWeRNycX"
      },
      "source": [
        "<img alt=\"QA Architecture\" caption=\"The retriever-reader architecture for modern QA systems\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_retriever-reader.png?raw=1\" id=\"retriever-reader\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fh_GL4vlNycX"
      },
      "source": [
        "#### Initializing a document store"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "LQ9rioOANycX"
      },
      "outputs": [],
      "source": [
        "url = \"\"\"https://artifacts.elastic.co/downloads/elasticsearch/\\\n",
        "elasticsearch-7.9.2-linux-x86_64.tar.gz\"\"\"\n",
        "!wget -nc -q {url}\n",
        "!tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "r0hVxkfDNycX"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from subprocess import Popen, PIPE, STDOUT\n",
        "\n",
        "# Run Elasticsearch as a background process\n",
        "!chown -R daemon:daemon elasticsearch-7.9.2\n",
        "es_server = Popen(args=['elasticsearch-7.9.2/bin/elasticsearch'],\n",
        "                  stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1))\n",
        "# Wait until Elasticsearch has started\n",
        "!sleep 30"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -X GET \"localhost:9200/?pretty\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BWgsFqcCTT-t",
        "outputId": "c2f71594-abad-417c-ae14-dd704d092b32"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"name\" : \"2266a1885d70\",\n",
            "  \"cluster_name\" : \"elasticsearch\",\n",
            "  \"cluster_uuid\" : \"k9Y9sHqPREe9UIor9h3cGQ\",\n",
            "  \"version\" : {\n",
            "    \"number\" : \"7.9.2\",\n",
            "    \"build_flavor\" : \"default\",\n",
            "    \"build_type\" : \"tar\",\n",
            "    \"build_hash\" : \"d34da0ea4a966c4e49417f2da2f244e3e97b4e6e\",\n",
            "    \"build_date\" : \"2020-09-23T00:45:33.626720Z\",\n",
            "    \"build_snapshot\" : false,\n",
            "    \"lucene_version\" : \"8.6.2\",\n",
            "    \"minimum_wire_compatibility_version\" : \"6.8.0\",\n",
            "    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\n",
            "  },\n",
            "  \"tagline\" : \"You Know, for Search\"\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "RxTb_Nw_NycX"
      },
      "outputs": [],
      "source": [
        "# #hide\n",
        "# # Alternative if Docker is installed\n",
        "# from haystack.utils import launch_es\n",
        "\n",
        "# launch_es()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "92iskm_7NycX",
        "outputId": "910c29bf-6946-43aa-de09-9c3b72f7dd1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"name\" : \"96938eee37cd\",\n",
            "  \"cluster_name\" : \"docker-cluster\",\n",
            "  \"cluster_uuid\" : \"ABGDdvbbRWmMb9Umz79HbA\",\n",
            "  \"version\" : {\n",
            "    \"number\" : \"7.9.2\",\n",
            "    \"build_flavor\" : \"default\",\n",
            "    \"build_type\" : \"docker\",\n",
            "    \"build_hash\" : \"d34da0ea4a966c4e49417f2da2f244e3e97b4e6e\",\n",
            "    \"build_date\" : \"2020-09-23T00:45:33.626720Z\",\n",
            "    \"build_snapshot\" : false,\n",
            "    \"lucene_version\" : \"8.6.2\",\n",
            "    \"minimum_wire_compatibility_version\" : \"6.8.0\",\n",
            "    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\n",
            "  },\n",
            "  \"tagline\" : \"You Know, for Search\"\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "!curl -X GET \"localhost:9200/?pretty\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Instantiate a Document Store**"
      ],
      "metadata": {
        "id": "jSawoBuPTdwl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install farm-haystack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZ9evfB1TsZ1",
        "outputId": "bccfb378-ed4b-49d6-8818-b580d8c4ef8c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: farm-haystack in /usr/local/lib/python3.10/dist-packages (1.24.0)\n",
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            "Requirement already satisfied: events in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (0.5)\n",
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            "Requirement already satisfied: lazy-imports==0.3.1 in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (0.3.1)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (10.1.0)\n",
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            "Requirement already satisfied: platformdirs in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (4.1.0)\n",
            "Requirement already satisfied: posthog in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (3.3.3)\n",
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            "Requirement already satisfied: quantulum3 in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (0.9.0)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (2.31.0)\n",
            "Requirement already satisfied: requests-cache<1.0.0 in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (0.9.8)\n",
            "Requirement already satisfied: scikit-learn>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (1.4.0)\n",
            "Requirement already satisfied: sseclient-py in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (1.8.0)\n",
            "Requirement already satisfied: tenacity in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (8.2.3)\n",
            "Requirement already satisfied: tiktoken>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (0.5.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (4.66.1)\n",
            "Requirement already satisfied: transformers==4.36.2 in /usr/local/lib/python3.10/dist-packages (from farm-haystack) (4.36.2)\n",
            "Requirement already satisfied: pyyaml<7.0,>=6.0 in /usr/local/lib/python3.10/dist-packages (from prompthub-py==4.0.0->farm-haystack) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack) (23.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack) (0.15.1)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack) (0.4.1)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2->farm-haystack) (4.9.0)\n",
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            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->farm-haystack) (0.32.1)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->farm-haystack) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->farm-haystack) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack) (1.16.0)\n",
            "Requirement already satisfied: monotonic>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack) (1.6)\n",
            "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack) (2.2.1)\n",
            "Requirement already satisfied: inflect in /usr/local/lib/python3.10/dist-packages (from quantulum3->farm-haystack) (7.0.0)\n",
            "Requirement already satisfied: num2words in /usr/local/lib/python3.10/dist-packages (from quantulum3->farm-haystack) (0.5.13)\n",
            "Requirement already satisfied: exceptiongroup>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from cattrs>=22.2->requests-cache<1.0.0->farm-haystack) (1.2.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.2->farm-haystack) (2023.6.0)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.10/dist-packages (from num2words->quantulum3->farm-haystack) (0.6.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install farm-haystack[elasticsearch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S43CN5DSYSen",
        "outputId": "aa1f4ee6-c197-4ba1-bd73-cf25c04c76ad"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: farm-haystack[elasticsearch] in /usr/local/lib/python3.10/dist-packages (1.24.0)\n",
            "Requirement already satisfied: boilerpy3 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (1.0.7)\n",
            "Requirement already satisfied: events in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (0.5)\n",
            "Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (0.26.0)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (4.19.2)\n",
            "Requirement already satisfied: lazy-imports==0.3.1 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (0.3.1)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (10.1.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (3.2.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (1.5.3)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (9.0.0)\n",
            "Requirement already satisfied: platformdirs in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (4.1.0)\n",
            "Requirement already satisfied: posthog in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (3.3.3)\n",
            "Requirement already satisfied: prompthub-py==4.0.0 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (4.0.0)\n",
            "Requirement already satisfied: pydantic<2 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (1.10.14)\n",
            "Requirement already satisfied: quantulum3 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (0.9.0)\n",
            "Requirement already satisfied: rank-bm25 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (0.2.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (2.31.0)\n",
            "Requirement already satisfied: requests-cache<1.0.0 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (0.9.8)\n",
            "Requirement already satisfied: scikit-learn>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (1.4.0)\n",
            "Requirement already satisfied: sseclient-py in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (1.8.0)\n",
            "Requirement already satisfied: tenacity in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (8.2.3)\n",
            "Requirement already satisfied: tiktoken>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (0.5.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (4.66.1)\n",
            "Requirement already satisfied: transformers==4.36.2 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (4.36.2)\n",
            "Requirement already satisfied: pyyaml<7.0,>=6.0 in /usr/local/lib/python3.10/dist-packages (from prompthub-py==4.0.0->farm-haystack[elasticsearch]) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack[elasticsearch]) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack[elasticsearch]) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack[elasticsearch]) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack[elasticsearch]) (23.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack[elasticsearch]) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack[elasticsearch]) (0.15.1)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.2->farm-haystack[elasticsearch]) (0.4.1)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2->farm-haystack[elasticsearch]) (4.9.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->farm-haystack[elasticsearch]) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->farm-haystack[elasticsearch]) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->farm-haystack[elasticsearch]) (1.26.18)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->farm-haystack[elasticsearch]) (2023.11.17)\n",
            "Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.10/dist-packages (from requests-cache<1.0.0->farm-haystack[elasticsearch]) (1.4.4)\n",
            "Requirement already satisfied: attrs>=21.2 in /usr/local/lib/python3.10/dist-packages (from requests-cache<1.0.0->farm-haystack[elasticsearch]) (23.2.0)\n",
            "Requirement already satisfied: cattrs>=22.2 in /usr/local/lib/python3.10/dist-packages (from requests-cache<1.0.0->farm-haystack[elasticsearch]) (23.2.3)\n",
            "Requirement already satisfied: url-normalize>=1.4 in /usr/local/lib/python3.10/dist-packages (from requests-cache<1.0.0->farm-haystack[elasticsearch]) (1.4.3)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.3.0->farm-haystack[elasticsearch]) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.3.0->farm-haystack[elasticsearch]) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.3.0->farm-haystack[elasticsearch]) (3.2.0)\n",
            "Requirement already satisfied: elastic-transport<8 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (7.16.0)\n",
            "Requirement already satisfied: elasticsearch<8,>=7.17 in /usr/local/lib/python3.10/dist-packages (from farm-haystack[elasticsearch]) (7.17.9)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx->farm-haystack[elasticsearch]) (3.7.1)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->farm-haystack[elasticsearch]) (1.0.2)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->farm-haystack[elasticsearch]) (1.3.0)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx->farm-haystack[elasticsearch]) (0.14.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema->farm-haystack[elasticsearch]) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema->farm-haystack[elasticsearch]) (0.32.1)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema->farm-haystack[elasticsearch]) (0.17.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->farm-haystack[elasticsearch]) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->farm-haystack[elasticsearch]) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack[elasticsearch]) (1.16.0)\n",
            "Requirement already satisfied: monotonic>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack[elasticsearch]) (1.6)\n",
            "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from posthog->farm-haystack[elasticsearch]) (2.2.1)\n",
            "Requirement already satisfied: inflect in /usr/local/lib/python3.10/dist-packages (from quantulum3->farm-haystack[elasticsearch]) (7.0.0)\n",
            "Requirement already satisfied: num2words in /usr/local/lib/python3.10/dist-packages (from quantulum3->farm-haystack[elasticsearch]) (0.5.13)\n",
            "Requirement already satisfied: exceptiongroup>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from cattrs>=22.2->requests-cache<1.0.0->farm-haystack[elasticsearch]) (1.2.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.2->farm-haystack[elasticsearch]) (2023.6.0)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.10/dist-packages (from num2words->quantulum3->farm-haystack[elasticsearch]) (0.6.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "RDfLmjfFNycX"
      },
      "outputs": [],
      "source": [
        "# from haystack.document_store.elasticsearch import ElasticsearchDocumentStore\n",
        "from haystack.document_stores import ElasticsearchDocumentStore\n",
        "\n",
        "# Return the document embedding for later use with dense retriever\n",
        "document_store = ElasticsearchDocumentStore(return_embedding=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "cCe6X59nNycX"
      },
      "outputs": [],
      "source": [
        "#hide\n",
        "# It's a good idea to flush Elasticsearch with each notebook restart\n",
        "if len(document_store.get_all_documents()) or len(document_store.get_all_labels()) > 0:\n",
        "    document_store.delete_documents(\"document\")\n",
        "    document_store.delete_documents(\"label\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://stackoverflow.com/questions/69909352/typeerror-init-missing-1-required-positional-argument-content\n",
        "the document.store.write_documents(data_json) that throws the exception. As the format for the argument has been changed from { 'text': str, 'meta': obj} to {'content': str, 'meta': obj}."
      ],
      "metadata": {
        "id": "V9QEY1GTdpQb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "1jcN9vo9NycX",
        "outputId": "4eaa05a6-b182-48d1-d64a-2421e5a10dc7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 1615 documents\n"
          ]
        }
      ],
      "source": [
        "for split, df in dfs.items():\n",
        "    # Exclude duplicate reviews\n",
        "    docs = [{\"content\": row[\"context\"],\n",
        "             \"meta\":{\"item_id\": row[\"title\"], \"question_id\": row[\"id\"],\n",
        "                     \"split\": split}}\n",
        "        for _,row in df.drop_duplicates(subset=\"context\").iterrows()]\n",
        "    document_store.write_documents(docs, index=\"document\")\n",
        "\n",
        "print(f\"Loaded {document_store.get_document_count()} documents\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kd5Eb9JNycX"
      },
      "source": [
        "#### Initializing a retriever"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "BM25 is an improved version of the classic Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and represents the question and context as sparse vectors that can be searched efficiently on Elasticsearch. The BM25 score measures how much matched text is about a search query and improves on TF-IDF by saturating TF values quickly and normalizing the document length so that short documents are favored over long ones."
      ],
      "metadata": {
        "id": "gwXt7tyrkIbz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install farm-haystack[colab,ocr,preprocessing,file-conversion,pdf]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJp5k-Vxlo8k",
        "outputId": "c6f655ad-feaa-4562-fd61-9851ab85f858"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import haystack; dir(haystack)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHnKwzBvoHpo",
        "outputId": "0f2c0a57-6062-4519-cb7e-bc9681924447"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Answer',\n",
              " 'BaseComponent',\n",
              " 'Document',\n",
              " 'EvaluationResult',\n",
              " 'Label',\n",
              " 'MultiLabel',\n",
              " 'Pipeline',\n",
              " 'Span',\n",
              " 'TableCell',\n",
              " '__annotations__',\n",
              " '__builtins__',\n",
              " '__cached__',\n",
              " '__doc__',\n",
              " '__file__',\n",
              " '__loader__',\n",
              " '__name__',\n",
              " '__package__',\n",
              " '__path__',\n",
              " '__spec__',\n",
              " '__version__',\n",
              " 'document_stores',\n",
              " 'environment',\n",
              " 'errors',\n",
              " 'hash128',\n",
              " 'haystack',\n",
              " 'lazy_imports',\n",
              " 'metadata',\n",
              " 'mmh3',\n",
              " 'modeling',\n",
              " 'nodes',\n",
              " 'pipelines',\n",
              " 'schema',\n",
              " 'set_pytorch_secure_model_loading',\n",
              " 'silenceable_tqdm',\n",
              " 'telemetry',\n",
              " 'utils']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://docs.haystack.deepset.ai/docs/migration\n",
        "\n",
        "---\n",
        "BM25=Best Match 25\n",
        "\n"
      ],
      "metadata": {
        "id": "tHqJI2NysJWF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "fOprt7A_NycY"
      },
      "outputs": [],
      "source": [
        "# from haystack.retriever.sparse import ElasticsearchRetriever\n",
        "from haystack.nodes import BM25Retriever\n",
        "\n",
        "es_retriever = BM25Retriever(document_store=document_store)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "gcat_TbXNycY"
      },
      "outputs": [],
      "source": [
        "item_id = \"B0074BW614\"\n",
        "query = \"Is it good for reading?\"\n",
        "retrieved_docs = es_retriever.retrieve(\n",
        "    query=query, top_k=3, filters={\"item_id\":[item_id], \"split\":[\"train\"]})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "Bgsuw1EsNycY",
        "outputId": "978b764b-8c4c-4f27-c115-e78e46d8dc65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<Document: id=252e83e25d52df7311d597dc89eef9f6, content='This is a gift to\n",
            "myself.  I have been a kindle user for 4 years and this is my third one.  I\n",
            "never ...'>\n"
          ]
        }
      ],
      "source": [
        "print(retrieved_docs[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piXKbfWpNycY"
      },
      "source": [
        "#### Initializing a reader"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://docs.haystack.deepset.ai/docs/migration"
      ],
      "metadata": {
        "id": "MvKrTZfHjC1k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install farm-haystack[inference]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_oMAEmnjRQd",
        "outputId": "102233e7-6afe-49c9-9cdb-fa20f12c6790"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: farm-haystack[inference] in /usr/local/lib/python3.10/dist-packages (1.24.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "ZC8srf6oNycY"
      },
      "outputs": [],
      "source": [
        "#hide_output\n",
        "# from haystack.reader.farm import FARMReader\n",
        "from haystack.nodes import FARMReader\n",
        "\n",
        "model_ckpt = \"deepset/minilm-uncased-squad2\"\n",
        "max_seq_length, doc_stride = 384, 128\n",
        "reader = FARMReader(model_name_or_path=model_ckpt, progress_bar=False,\n",
        "                    max_seq_len=max_seq_length, doc_stride=doc_stride,\n",
        "                    return_no_answer=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "8CTyJrkcNycY",
        "outputId": "6b304e4c-bcb8-4418-8931-a805fa53a4a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'query': 'How much music can this hold?', 'no_ans_gap': 12.648088693618774,\n",
            "'answers': [<Answer {'answer': '6000 hours', 'type': 'extractive', 'score':\n",
            "0.5293056964874268, 'context': 'An MP3 is about 1 MB/minute, so about 6000 hours\n",
            "depending on file size.', 'offsets_in_document': [{'start': 38, 'end': 48}],\n",
            "'offsets_in_context': [{'start': 38, 'end': 48}], 'document_ids':\n",
            "['e344757014e804eff50faa3ecf1c9c75'], 'meta': {}}>]}\n"
          ]
        }
      ],
      "source": [
        "print(reader.predict_on_texts(question=question, texts=[context], top_k=1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrxT-8dBNycY"
      },
      "source": [
        "#### Putting it all together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "wkSQE6D5NycY"
      },
      "outputs": [],
      "source": [
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "\n",
        "pipe = ExtractiveQAPipeline(reader, es_retriever)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "d4WtTzbRNycY",
        "outputId": "5f05ea85-795c-48f9-b468-b192e2a4d2d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: Is it good for reading?\n",
            "\n",
            "'Query: Is it good for reading?'\n",
            "'Answers:'\n",
            "[   <Answer {'answer': 'it is great for reading books when no light is available', 'type': 'extractive', 'score': 0.5443066358566284, 'context': 'ecoming addicted to hers! Our son LOVES it and it is great for reading books when no light is availa...', 'offsets_in_document': [{'start': 216, 'end': 272}], 'offsets_in_context': [{'start': 47, 'end': 103}], 'document_ids': ['41f4a1710c6f3be65c7814326b662ab6'], 'meta': {'item_id': 'B0074BW614', 'question_id': '998d564607f10bf6dbbd20b33b8fbbf1', 'split': 'train'}}>,\n",
            "    <Answer {'answer': 'I mainly use it for book reading', 'type': 'extractive', 'score': 0.4695245623588562, 'context': ' is my third one.  I never thought I would want a fire for I mainly use it for book reading.  I deci...', 'offsets_in_document': [{'start': 132, 'end': 164}], 'offsets_in_context': [{'start': 59, 'end': 91}], 'document_ids': ['252e83e25d52df7311d597dc89eef9f6'], 'meta': {'item_id': 'B0074BW614', 'question_id': '868e311275e26dbafe5af70774a300f3', 'split': 'train'}}>,\n",
            "    <Answer {'answer': '', 'type': 'extractive', 'score': 0.45532437576070817, 'context': None, 'offsets_in_document': [{'start': 0, 'end': 0}], 'offsets_in_context': [{'start': 0, 'end': 0}], 'document_ids': None, 'meta': {}}>]\n",
            "Answer 1: <Answer: answer='it is great for reading books when no light is\n",
            "available', score=0.5443066358566284, context='ecoming addicted to hers! Our son\n",
            "LOVES it and it ...'>\n",
            "Answer 1: it is great for reading books when no light is available\n",
            "Review snippet: ...ecoming addicted to hers! Our son LOVES it and it is great\n",
            "for reading books when no light is availa......\n",
            "\n",
            "\n",
            "\n",
            "Answer 2: <Answer: answer='I mainly use it for book reading',\n",
            "score=0.4695245623588562, context=' is my third one.  I never thought I would\n",
            "want a ...'>\n",
            "Answer 2: I mainly use it for book reading\n",
            "Review snippet: ... is my third one.  I never thought I would want a fire for I\n",
            "mainly use it for book reading.  I deci......\n",
            "\n",
            "\n",
            "\n",
            "Answer 3: <Answer: answer='', score=0.45532437576070817, context=None>\n",
            "Answer 3:\n",
            "Review snippet: ...None...\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "n_answers = 3\n",
        "# preds = pipe.run(query=query, top_k_retriever=3, top_k_reader=n_answers,\n",
        "#                  filters={\"item_id\": [item_id], \"split\":[\"train\"]})\n",
        "preds = pipe.run(query=query,\n",
        "                 params={\"Retriever\": {\"top_k\": 3}, \"Reader\": {\"top_k\": 3},\n",
        "                         \"filters\": {\"item_id\": [item_id], \"split\":[\"train\"]}})#,)\n",
        "                        #  filters={\"item_id\": [item_id], \"split\":[\"train\"]})\n",
        "\n",
        "print(f\"Question: {preds['query']} \\n\")\n",
        "# print(preds)\n",
        "from haystack.utils import print_answers\n",
        "print_answers(preds, details=\"all\", max_text_len=100)\n",
        "ans = [x.to_dict() for x in preds[\"answers\"]]\n",
        "\n",
        "for idx in range(n_answers):\n",
        "    print(f\"Answer {idx+1}: {preds['answers'][idx]}\")\n",
        "    print(f\"Answer {idx+1}: {ans[idx]['answer']}\")\n",
        "    print(f\"Review snippet: ...{ans[idx]['context']}...\")\n",
        "    print(\"\\n\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QECxHKnxNycY"
      },
      "source": [
        "## Improving Our QA Pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABBz4MzuNycZ"
      },
      "source": [
        "### Evaluating the Retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "5AZqgE9GNycZ"
      },
      "outputs": [],
      "source": [
        "# from haystack.pipelines import Pipeline\n",
        "# from haystack.eval import EvalDocuments\n",
        "\n",
        "# class EvalRetrieverPipeline:\n",
        "#     def __init__(self, retriever):\n",
        "#         self.retriever = retriever\n",
        "#         self.eval_retriever = EvalDocuments()\n",
        "#         pipe = Pipeline()\n",
        "#         pipe.add_node(component=self.retriever, name=\"ESRetriever\",\n",
        "#                       inputs=[\"Query\"])\n",
        "#         pipe.add_node(component=self.eval_retriever, name=\"EvalRetriever\",\n",
        "#                       inputs=[\"ESRetriever\"])\n",
        "#         self.pipeline = pipe\n",
        "\n",
        "\n",
        "# pipe = EvalRetrieverPipeline(es_retriever)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`EvalDocuments` and `EvalAnswer` nodes are deprecated since December 2021 and we recommend to use Pipeline.eval instead since then.\n",
        "https://github.com/deepset-ai/haystack/issues/4192"
      ],
      "metadata": {
        "id": "EtJ_SmXvlXDv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.pipelines import DocumentSearchPipeline\n",
        "\n",
        "pipe = DocumentSearchPipeline(retriever=es_retriever)"
      ],
      "metadata": {
        "id": "feu2pTWg4hIz"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack import Label, Answer, Document\n",
        "\n",
        "labels = []\n",
        "for i, row in dfs[\"test\"].iterrows():\n",
        "    # Metadata used for filtering in the Retriever\n",
        "    meta = {\"item_id\": row[\"title\"], \"question_id\": row[\"id\"]}\n",
        "    # Populate labels for questions with answers\n",
        "    if len(row[\"answers.text\"]):\n",
        "        for answer in row[\"answers.text\"]:\n",
        "            label = Label(\n",
        "                query=row[\"question\"], answer=Answer(answer=answer), origin=\"gold-label\", document=Document(content=row[\"context\"], id=row[\"review_id\"]),\n",
        "                meta=meta, is_correct_answer=True, is_correct_document=True,\n",
        "                no_answer=False, filters={\"item_id\": [meta[\"item_id\"]], \"split\":[\"test\"]})\n",
        "    # Populate labels for questions without answers\n",
        "    else:\n",
        "        label = Label(\n",
        "            query=row[\"question\"], answer=Answer(answer=\"\"), origin=\"gold-label\", document=Document(content=row[\"context\"], id=row[\"review_id\"]),\n",
        "            meta=meta, is_correct_answer=True, is_correct_document=True,\n",
        "            no_answer=True, filters={\"item_id\": [row[\"title\"]], \"split\":[\"test\"]})\n",
        "    labels.append(label)"
      ],
      "metadata": {
        "id": "qkX8fZht4g77"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(labels[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvU2rvszt_K0",
        "outputId": "74e03c00-35dd-4b0d-ed8d-184578574703"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<Label: {'id': '1d792375-3bf9-4437-93fe-df0797ea3619', 'query': 'What is the\n",
            "tonal balance of these headphones?', 'document': {'id':\n",
            "'f011c75fb8d26066217e64b3ff470d84', 'content': \"I have been a headphone fanatic\n",
            "for thirty years and have owned and used a variety of headphones over those\n",
            "years, to include Stax SR-5, Sennheiser HD-424 and HD-580.  The Sony MDRV6\n",
            "excells as the best value of any headphone that I've ever owned.  They are\n",
            "especially good at producing natural-sounding deep bass, and the overall\n",
            "octave-to-octave balance is excellent.  The sound quality is all in all\n",
            "comparable to other headphones that cost considerably more.The MDRV6 is\n",
            "especially well-suited for travel due to the collapsible design, and for noisy\n",
            "environments or for quiet environments such as a library where the sound emitted\n",
            "by open-back headphones would distract others.The MDRV6 is not quite as\n",
            "comfortable as some other headphones, but the comfort can be improved enormously\n",
            "by replacing the pads with the velour pads from BeyerDynamic.  The pads that\n",
            "come on the MDRV6 have a non-breathable cover, and significant additional\n",
            "discomfort is caused by the way that the thin foam cover is glued to the pad\n",
            "around the inner circumference of the pad, which prevents the top and back of\n",
            "your ear from slipping into the space between the pad and the face of the\n",
            "headphone.  This forces the pad to rest on the back of the ear, which compresses\n",
            "the ear and eventually becomes uncomfortable.  I read on a web forum where\n",
            "several people had replaced the pads (which eventually come apart) with a velour\n",
            "pad made by BeyerDynamic, and after I did this replacement on my one pair that\n",
            "had a damaged pad, the increase in comfort was so great that I immediately did\n",
            "the other pair.  The thin cover can be glued down to the headphone face if\n",
            "needed to hold it in place, which then allows the top and rear of your ear to\n",
            "naturally  slide under the inner circumference of the pad, which is considerably\n",
            "more comfortable.  I can recommend this trivial modification without reservation\n",
            "to anyone who owns these headphones.  Even if you don't replace the pads with\n",
            "the BeyerDynamic velour pads, I suggest removing the pads anyway, then carefully\n",
            "separating the thin cover from the pad, and glueing the cover directly to the\n",
            "headphone surface.\", 'content_type': 'text', 'meta': {}, 'id_hash_keys':\n",
            "['content'], 'score': None, 'embedding': None}, 'is_correct_answer': True,\n",
            "'is_correct_document': True, 'origin': 'gold-label', 'answer': {'answer': 'I\n",
            "have been a headphone fanatic for thirty years', 'type': 'extractive', 'score':\n",
            "None, 'context': None, 'offsets_in_document': None, 'offsets_in_context': None,\n",
            "'document_ids': None, 'meta': {}}, 'pipeline_id': None, 'created_at':\n",
            "'2024-01-29 02:02:27', 'updated_at': None, 'meta': {'item_id': 'B00001WRSJ',\n",
            "'question_id': 'd0781d13200014aa25860e44da9d5ea7'}, 'filters': {'item_id':\n",
            "['B00001WRSJ'], 'split': ['test']}}>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "dPBouNAvNycZ",
        "outputId": "d518c242-687f-436e-af45-faeb0d74dcb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 358 question-answer pairs\n"
          ]
        }
      ],
      "source": [
        "document_store.write_labels(labels, index=\"label\")\n",
        "print(f\"\"\"Loaded {document_store.get_label_count(index=\"label\")} \\\n",
        "question-answer pairs\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "t3UyYnIhNycZ",
        "outputId": "be5d867c-da2d-4285-b4b4-521133441e90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "330\n"
          ]
        }
      ],
      "source": [
        "labels_agg = document_store.get_all_labels_aggregated(\n",
        "    index=\"label\",\n",
        "    open_domain=True,\n",
        "    aggregate_by_meta=[\"item_id\"]\n",
        ")\n",
        "print(len(labels_agg))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "9x6nM11vNycZ",
        "outputId": "994c6631-7e49-4898-8997-a68ff870ee8b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<MultiLabel: {'labels': [{'id': '95f441de-f7f7-46cc-9c83-863eaf337712', 'query':\n",
            "'How does the fan work?', 'document': {'id': 'a5db4d52bb4d0fcadff2b8a6f4e54d75',\n",
            "'content': 'the usb on the back caused my mac to restart when i would plug\n",
            "something into about 50% of the time.  very annoying but the fan is really\n",
            "really good!', 'content_type': 'text', 'meta': {}, 'id_hash_keys': ['content'],\n",
            "'score': None, 'embedding': None}, 'is_correct_answer': True,\n",
            "'is_correct_document': True, 'origin': 'gold-label', 'answer': {'answer': 'the\n",
            "fan is really really good', 'type': 'extractive', 'score': None, 'context':\n",
            "None, 'offsets_in_document': None, 'offsets_in_context': None, 'document_ids':\n",
            "None, 'meta': {}}, 'pipeline_id': None, 'created_at': '2024-01-29 02:02:27',\n",
            "'updated_at': '2024-01-29 02:02:27', 'meta': {'item_id': 'B002MU1ZRS',\n",
            "'question_id': '5a9b7616541f700f103d21f8ad41bc4b'}, 'filters': {'item_id':\n",
            "['B002MU1ZRS'], 'split': ['test']}}, {'id':\n",
            "'0976c8f8-8c6f-48bd-ad18-76b31bda758b', 'query': 'How does the fan work?',\n",
            "'document': {'id': '5c70ce373bb8b93ca23bf3d696dbb8eb', 'content': 'In most\n",
            "demanding games, my GPU will consistently soar to 90-93 degrees Celsius. I\n",
            "didn\\'t like seeing temps in the 90s so I figured I would pick up this Cooler\n",
            "Master for a shade over $20. Is it worth it? I think it is.My Sager NP8130 is\n",
            "15.6\" and this cooler fits it just about perfectly - I have maybe 1\" of extra\n",
            "room. My laptop\\'s rubber stops on the bottom keep it anchored pretty well, and\n",
            "the fan itself isn\\'t super loud. There is an adjustable dial to change fan\n",
            "speed, but when I\\'m gaming, I generally just have it on max, and when I\\'m not\n",
            "gaming, I turn the cooling pad off altogether.The one thing I would make sure\n",
            "you do is use the little legs on the bottom of the cooling pad to prop it up an\n",
            "extra half inch or so. It makes a world of difference in how much more air can\n",
            "flow through under the pad and up to your laptop. Also, beware of using this on\n",
            "top of a table cloth - it seems to impede air flow as well. With the legs\n",
            "propping up the cooling pad, my GPU temps only hit about 84-86 degrees, but\n",
            "without it propped up, my GPU is still 90-91 degrees.Overall, it\\'s a small ~$20\n",
            "investment to potentially increase the life of my laptop as a whole.',\n",
            "'content_type': 'text', 'meta': {}, 'id_hash_keys': ['content'], 'score': None,\n",
            "'embedding': None}, 'is_correct_answer': True, 'is_correct_document': True,\n",
            "'origin': 'gold-label', 'answer': {'answer': \"the fan itself isn't super loud.\n",
            "There is an adjustable dial to change fan speed\", 'type': 'extractive', 'score':\n",
            "None, 'context': None, 'offsets_in_document': None, 'offsets_in_context': None,\n",
            "'document_ids': None, 'meta': {}}, 'pipeline_id': None, 'created_at':\n",
            "'2024-01-29 02:02:27', 'updated_at': '2024-01-29 02:02:27', 'meta': {'item_id':\n",
            "'B002MU1ZRS', 'question_id': 'f20dae56410f31632d6a9f8f8284657a'}, 'filters':\n",
            "{'item_id': ['B002MU1ZRS'], 'split': ['test']}}], 'query': 'How does the fan\n",
            "work?', 'filters': {'item_id': ['B002MU1ZRS'], 'split': ['test']}, 'id':\n",
            "'1643333b1f69c6de39072fb1e8b6d06f', 'no_answer': False, 'answers': ['the fan is\n",
            "really really good', \"the fan itself isn't super loud. There is an adjustable\n",
            "dial to change fan speed\"], 'offsets_in_documents': [], 'offsets_in_contexts':\n",
            "[], 'document_ids': ['a5db4d52bb4d0fcadff2b8a6f4e54d75',\n",
            "'5c70ce373bb8b93ca23bf3d696dbb8eb'], 'contexts': ['the usb on the back caused my\n",
            "mac to restart when i would plug something into about 50% of the time.  very\n",
            "annoying but the fan is really really good!', 'In most demanding games, my GPU\n",
            "will consistently soar to 90-93 degrees Celsius. I didn\\'t like seeing temps in\n",
            "the 90s so I figured I would pick up this Cooler Master for a shade over $20. Is\n",
            "it worth it? I think it is.My Sager NP8130 is 15.6\" and this cooler fits it just\n",
            "about perfectly - I have maybe 1\" of extra room. My laptop\\'s rubber stops on\n",
            "the bottom keep it anchored pretty well, and the fan itself isn\\'t super loud.\n",
            "There is an adjustable dial to change fan speed, but when I\\'m gaming, I\n",
            "generally just have it on max, and when I\\'m not gaming, I turn the cooling pad\n",
            "off altogether.The one thing I would make sure you do is use the little legs on\n",
            "the bottom of the cooling pad to prop it up an extra half inch or so. It makes a\n",
            "world of difference in how much more air can flow through under the pad and up\n",
            "to your laptop. Also, beware of using this on top of a table cloth - it seems to\n",
            "impede air flow as well. With the legs propping up the cooling pad, my GPU temps\n",
            "only hit about 84-86 degrees, but without it propped up, my GPU is still 90-91\n",
            "degrees.Overall, it\\'s a small ~$20 investment to potentially increase the life\n",
            "of my laptop as a whole.']}>\n"
          ]
        }
      ],
      "source": [
        "print(labels_agg[109])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install farm-haystack[metrics]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGj-2f6g7Kom",
        "outputId": "e2ed9720-d735-4661-e70a-eef3a7d08169"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: farm-haystack[metrics] in /usr/local/lib/python3.10/dist-packages (1.24.0)\n",
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            "Requirement already satisfied: num2words in /usr/local/lib/python3.10/dist-packages (from quantulum3->farm-haystack[metrics]) (0.5.13)\n",
            "Requirement already satisfied: Mako in /usr/local/lib/python3.10/dist-packages (from alembic!=1.10.0,<2->mlflow->farm-haystack[metrics]) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from cattrs>=22.2->requests-cache<1.0.0->farm-haystack[metrics]) (1.2.0)\n",
            "Requirement already satisfied: pyjwt>=1.7.0 in /usr/lib/python3/dist-packages (from databricks-cli<1,>=0.8.7->mlflow->farm-haystack[metrics]) (2.3.0)\n",
            "Requirement already satisfied: oauthlib>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from databricks-cli<1,>=0.8.7->mlflow->farm-haystack[metrics]) (3.2.2)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from databricks-cli<1,>=0.8.7->mlflow->farm-haystack[metrics]) (0.9.0)\n",
            "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from Flask<4->mlflow->farm-haystack[metrics]) (3.0.1)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from Flask<4->mlflow->farm-haystack[metrics]) (2.1.2)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from gitpython<4,>=2.1.0->mlflow->farm-haystack[metrics]) (4.0.11)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.2->farm-haystack[metrics]) (2023.6.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata!=4.7.0,<8,>=3.7.0->mlflow->farm-haystack[metrics]) (3.17.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2<4,>=2.11->mlflow->farm-haystack[metrics]) (2.1.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4->mlflow->farm-haystack[metrics]) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4->mlflow->farm-haystack[metrics]) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4->mlflow->farm-haystack[metrics]) (4.47.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4->mlflow->farm-haystack[metrics]) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib<4->mlflow->farm-haystack[metrics]) (3.1.1)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy<3,>=1.4.0->mlflow->farm-haystack[metrics]) (3.0.3)\n",
            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.10/dist-packages (from num2words->quantulum3->farm-haystack[metrics]) (0.6.2)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->gitpython<4,>=2.1.0->mlflow->farm-haystack[metrics]) (5.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rapidfuzz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SYTMooZ7hx8",
        "outputId": "a83e00a1-8c0c-4513-ebba-44ff62eaae0a"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rapidfuzz in /usr/local/lib/python3.10/dist-packages (2.7.0)\n",
            "Requirement already satisfied: jarowinkler<2.0.0,>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from rapidfuzz) (1.2.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "phpI2RwcNycZ"
      },
      "outputs": [],
      "source": [
        "eval_result = pipe.eval(\n",
        "    labels=labels_agg,\n",
        "    params={\"Retriever\": {\"top_k\": 3}},\n",
        ")\n",
        "metrics = eval_result.calculate_metrics()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Recall@3: {metrics['Retriever']['recall_single_hit']:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYyfm6137F9r",
        "outputId": "a5c0b912-cfc5-445b-f6e9-5ef116b93ded"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall@3: 0.95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "-rCONdolNycZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51e1875b-6d2a-42ea-dd50-e93a5f6210b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/haystack/schema.py:1464: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  documents[\"matched_label_idxs\"] = documents.apply(find_matched_label_idxs, axis=1)\n",
            "/usr/local/lib/python3.10/dist-packages/haystack/schema.py:1464: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  documents[\"matched_label_idxs\"] = documents.apply(find_matched_label_idxs, axis=1)\n",
            "/usr/local/lib/python3.10/dist-packages/haystack/schema.py:1464: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  documents[\"matched_label_idxs\"] = documents.apply(find_matched_label_idxs, axis=1)\n"
          ]
        }
      ],
      "source": [
        "#hide_output\n",
        "def evaluate_retriever(retriever, topk_values = [1,3,5,10,20]):\n",
        "    topk_results = {}\n",
        "\n",
        "    # Calculate the maximum top_k\n",
        "    max_top_k = max(topk_values)\n",
        "\n",
        "    # Create a pipeline\n",
        "    p = DocumentSearchPipeline(retriever=retriever)\n",
        "\n",
        "    # Run inference at most top_k by traversing question-answer pairs in the test set\n",
        "    eval_result = p.eval(\n",
        "        labels=labels_agg,\n",
        "        params={\"Retriever\": {\"top_k\": max_top_k}},\n",
        "    )\n",
        "    # Calculate recall for each top_k value\n",
        "    for topk in topk_values:\n",
        "        metrics = eval_result.calculate_metrics(simulated_top_k_retriever=topk)\n",
        "        topk_results[topk] = {\"recall\": metrics[\"Retriever\"][\"recall_single_hit\"]}\n",
        "\n",
        "    return pd.DataFrame.from_dict(topk_results, orient=\"index\")\n",
        "\n",
        "es_topk_df = evaluate_retriever(es_retriever)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "L9ijc8epNycZ",
        "outputId": "c4debec5-f866-47d7-8988-804d86d6de53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def plot_retriever_eval(dfs, retriever_names):\n",
        "    fig, ax = plt.subplots()\n",
        "    for df, retriever_name in zip(dfs, retriever_names):\n",
        "        df.plot(y=\"recall\", ax=ax, label=retriever_name)\n",
        "    plt.xticks(df.index)\n",
        "    plt.ylabel(\"Top-k Recall\")\n",
        "    plt.xlabel(\"k\")\n",
        "    plt.show()\n",
        "\n",
        "plot_retriever_eval([es_topk_df], [\"BM25\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_30v6HFYNycZ"
      },
      "source": [
        "#### Dense Passage Retrieval"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zW4zaIo3Nyca"
      },
      "source": [
        "<img alt=\"DPR Architecture\" caption=\"DPR's bi-encoder architecture for computing the relevance of a document and query\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_dpr.png?raw=1\" id=\"dpr\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "4HHWpMNaNyca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393,
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            "b2dac9c5fac444dca5450c62bae03447",
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            "96ab78ec622f436fb14274f8ce01ae8c",
            "88624eb330ad4a86b3d483673928334b",
            "2dca7bd889bc45708925d240d069a9b0",
            "afca6fb7030641f6b797eeed73611d65",
            "008ef50c05764d75af8a43335b51b7c9",
            "faa86266eef14b35b327a80e1a3272ab",
            "d1e77a4162974d4e87484072b8c67f83",
            "a37d282dc90f40fea956c82949bf6a9e"
          ]
        },
        "outputId": "1e4a3fa6-c0e3-403f-df55-b1bc0c5a349f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "25f10e110417419e81087b097a3b08d3"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f324e427626e4bdea9d78b0547cf3d17"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "87dbc239dc6f4af28185c21e94a398b0"
            }
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a1ede5d930c4190a59f1b36054709f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b7cbb16ae224186b8094c3cc24cfe15"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "  return self.fget.__get__(instance, owner)()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c677ee12e133480c9471b79ed826bcc3"
            }
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          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "17fcd39cfbe34d6a8c5dc0417f5ec909"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0659cc95fea14b3aa15dcc40b8aab5b6"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0780b7b2789147fb92a8febf0f7abd40"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34291ab2591648fd9d43f86993bf033e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#hide_output\n",
        "# from haystack.retriever.dense import DensePassageRetriever\n",
        "from haystack.nodes import DensePassageRetriever\n",
        "\n",
        "dpr_retriever = DensePassageRetriever(document_store=document_store,\n",
        "    query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
        "    passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
        "    embed_title=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "-KMLunT0Nyca",
        "outputId": "0df7da73-e55c-4689-eff9-7ebc9d21dfb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "Updating embeddings: 10000 Docs [00:41, 240.04 Docs/s]\n"
          ]
        }
      ],
      "source": [
        "#hide_output\n",
        "document_store.update_embeddings(retriever=dpr_retriever)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "TxE4batmNyca",
        "outputId": "63dfc6cc-4356-4460-dc80-b703ad3a50e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/haystack/schema.py:1464: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  documents[\"matched_label_idxs\"] = documents.apply(find_matched_label_idxs, axis=1)\n",
            "/usr/local/lib/python3.10/dist-packages/haystack/schema.py:1464: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  documents[\"matched_label_idxs\"] = documents.apply(find_matched_label_idxs, axis=1)\n",
            "/usr/local/lib/python3.10/dist-packages/haystack/schema.py:1464: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  documents[\"matched_label_idxs\"] = documents.apply(find_matched_label_idxs, axis=1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "dpr_topk_df = evaluate_retriever(dpr_retriever)\n",
        "plot_retriever_eval([es_topk_df, dpr_topk_df], [\"BM25\", \"DPR\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concluded DPR is better than BM25"
      ],
      "metadata": {
        "id": "EjWmLnS9AsyG"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dePrS-0WNyca"
      },
      "source": [
        "### Evaluating the Reader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "GwsxuRXTNyca",
        "outputId": "f6a8309d-72bf-4d8b-8321-5c3a2591cc6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EM: 0\n",
            "F1: 0.8\n"
          ]
        }
      ],
      "source": [
        "# from farm.evaluation.squad_evaluation import compute_f1, compute_exact\n",
        "from haystack.modeling.evaluation.squad import compute_f1, compute_exact\n",
        "\n",
        "pred = \"about 6000 hours\"\n",
        "label = \"6000 hours\"\n",
        "print(f\"EM: {compute_exact(label, pred)}\")\n",
        "print(f\"F1: {compute_f1(label, pred)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "9RaqEeDyNyca",
        "outputId": "c835361c-6993-4de8-a0dc-bdef660d5ff6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EM: 0\n",
            "F1: 0.4\n"
          ]
        }
      ],
      "source": [
        "pred = \"about 6000 dollars\"\n",
        "print(f\"EM: {compute_exact(label, pred)}\")\n",
        "print(f\"F1: {compute_f1(label, pred)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "KUvGQ3blNyca"
      },
      "outputs": [],
      "source": [
        "#hide_output\n",
        "from haystack.pipelines import Pipeline\n",
        "def evaluate_reader(reader):\n",
        "    score_keys = ['exact_match', 'f1']\n",
        "    p = Pipeline()\n",
        "    p.add_node(component=reader, name=\"Reader\", inputs=[\"Query\"])\n",
        "\n",
        "    eval_result = p.eval(\n",
        "        labels=labels_agg,\n",
        "        documents= [[label.document for label in multilabel.labels] for multilabel in labels_agg],\n",
        "        params={},\n",
        "    )\n",
        "    metrics = eval_result.calculate_metrics(simulated_top_k_reader=1)\n",
        "\n",
        "    return {k:v for k,v in metrics[\"Reader\"].items() if k in score_keys}\n",
        "\n",
        "reader_eval = {}\n",
        "reader_eval[\"Fine-tune on SQuAD\"] = evaluate_reader(reader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "ngGsgQfONyca",
        "outputId": "6b091597-7744-4666-c6ee-49b190ba6e70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def plot_reader_eval(reader_eval):\n",
        "    fig, ax = plt.subplots()\n",
        "    df = pd.DataFrame.from_dict(reader_eval)\n",
        "    df.plot(kind=\"bar\", ylabel=\"Score\", rot=0, ax=ax)\n",
        "    ax.set_xticklabels([\"EM\", \"F1\"])\n",
        "    plt.legend(loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "plot_reader_eval(reader_eval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofTEWxV6Nyca"
      },
      "source": [
        "### Domain Adaptation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrXumormNyca"
      },
      "source": [
        "<img alt=\"SQuAD Schema\" caption=\"Visualization of the SQuAD JSON format\" src=\"https://github.com/nlp-with-transformers/notebooks/blob/main/images/chapter07_squad-schema.png?raw=1\" id=\"squad-schema\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "AYEsvztMNyca"
      },
      "outputs": [],
      "source": [
        "def create_paragraphs(df):\n",
        "    paragraphs = []\n",
        "    id2context = dict(zip(df[\"review_id\"], df[\"context\"]))\n",
        "    for review_id, review in id2context.items():\n",
        "        qas = []\n",
        "        # Filter for all question-answer pairs about a specific context\n",
        "        review_df = df.query(f\"review_id == '{review_id}'\")\n",
        "        id2question = dict(zip(review_df[\"id\"], review_df[\"question\"]))\n",
        "        # Build up the qas array\n",
        "        for qid, question in id2question.items():\n",
        "            # Filter for a single question ID\n",
        "            question_df = df.query(f\"id == '{qid}'\").to_dict(orient=\"list\")\n",
        "            ans_start_idxs = question_df[\"answers.answer_start\"][0].tolist()\n",
        "            ans_text = question_df[\"answers.text\"][0].tolist()\n",
        "            # Fill answerable questions\n",
        "            if len(ans_start_idxs):\n",
        "                answers = [\n",
        "                    {\"text\": text, \"answer_start\": answer_start}\n",
        "                    for text, answer_start in zip(ans_text, ans_start_idxs)]\n",
        "                is_impossible = False\n",
        "            else:\n",
        "                answers = []\n",
        "                is_impossible = True\n",
        "            # Add question-answer pairs to qas\n",
        "            qas.append({\"question\": question, \"id\": qid,\n",
        "                        \"is_impossible\": is_impossible, \"answers\": answers})\n",
        "        # Add context and question-answer pairs to paragraphs\n",
        "        paragraphs.append({\"qas\": qas, \"context\": review})\n",
        "    return paragraphs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "1E0itowSNycb",
        "outputId": "b47067b1-a35b-4bfe-ea39-4810601e532a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'qas': [{'question': 'How is the bass?',\n",
              "    'id': '2543d296da9766d8d17d040ecc781699',\n",
              "    'is_impossible': True,\n",
              "    'answers': []}],\n",
              "  'context': 'I have had Koss headphones in the past, Pro 4AA and QZ-99.  The Koss Portapro is portable AND has great bass response.  The work great with my Android phone and can be \"rolled up\" to be carried in my motorcycle jacket or computer bag without getting crunched.  They are very light and do not feel heavy or bear down on your ears even after listening to music with them on all day.  The sound is night and day better than any ear-bud could be and are almost as good as the Pro 4AA.  They are \"open air\" headphones so you cannot match the bass to the sealed types, but it comes close. For $32, you cannot go wrong.'},\n",
              " {'qas': [{'question': 'Is this music song have a goo bass?',\n",
              "    'id': 'd476830bf9282e2b9033e2bb44bbb995',\n",
              "    'is_impossible': False,\n",
              "    'answers': [{'text': 'Bass is weak as expected', 'answer_start': 1302},\n",
              "     {'text': 'Bass is weak as expected, even with EQ adjusted up',\n",
              "      'answer_start': 1302}]}],\n",
              "  'context': 'To anyone who hasn\\'t tried all the various types of headphones, it is important to remember exactly what these are: cheap portable on-ear headphones. They give a totally different sound then in-ears or closed design phones, but for what they are I would say they\\'re good. I currently own six pairs of phones, from stock apple earbuds to Sennheiser HD 518s. Gave my Portapros a run on both my computer\\'s sound card and mp3 player, using 256 kbps mp3s or better. The clarity is good and they\\'re very lightweight. The folding design is simple but effective. The look is certainly retro and unique, although I didn\\'t find it as comfortable as many have claimed. Earpads are *very* thin and made my ears sore after 30 minutes of listening, although this can be remedied to a point by adjusting the \"comfort zone\" feature (tightening the temple pads while loosening the ear pads). The cord seems to be an average thickness, but I wouldn\\'t get too rough with these. The steel headband adjusts smoothly and easily, just watch out that the slider doesn\\'t catch your hair. Despite the sore ears, the phones are very lightweight overall.Back to the sound: as you would expect, it\\'s good for a portable phone, but hardly earth shattering. At flat EQ the clarity is good, although the highs can sometimes be harsh. Bass is weak as expected, even with EQ adjusted up. To be fair, a portable on-ear would have a tough time comparing to the bass of an in-ear with a good seal or a pair with larger drivers. No sound isolation offered if you\\'re into that sort of thing. Cool 80s phones, though I\\'ve certainly owned better portable on-ears (Sony makes excellent phones in this category). Soundstage is very narrow and lacks body. A good value if you can get them for under thirty, otherwise I\\'d rather invest in a nicer pair of phones. If we\\'re talking about value, they\\'re a good buy compared to new stock apple buds. If you\\'re trying to compare the sound quality of this product to serious headphones, there\\'s really no comparison at all.Update: After 100 hours of burn-in time the sound has not been affected in any appreciable way. Highs are still harsh, and bass is still underwhelming. I sometimes use these as a convenience but they have been largely replaced in my collection.'},\n",
              " {'qas': [{'question': 'How is the bass?',\n",
              "    'id': '455575557886d6dfeea5aa19577e5de4',\n",
              "    'is_impossible': False,\n",
              "    'answers': [{'text': 'The only fault in the sound is the bass',\n",
              "      'answer_start': 650}]}],\n",
              "  'context': \"I have had many sub-$100 headphones from $5 Panasonic to $100 Sony, with Sennheiser HD 433, 202, PX100 II (I really wanted to like these PX100-II, they were so very well designed), and even a Grado SR60 for awhile.  And what it basically comes down to is value.  I have never heard sound as good as these headphones in the $35 range, easily the best under $75.  I can't believe they're over 25 years old.It's hard to describe how much detail these headphones bring out without making it too harsh or dull.  I listen to every type of music from classical to hip hop to electronic to country, and these headphones are suitable for all types of music.  The only fault in the sound is the bass.  It's just a *slight* bit boomy, but you get to like it after a while to be honest.The design is from the 80s as you all have probably figured out.  It could use a update but it seems like Koss has tried to perfect this formula and failed in the past.  I don't really care about the looks or the way it folds up or the fact that my hair gets caught up in it (I have very short hair, even for a male).But despite it's design flaws, it's the most comfortable headphones I have ever worn, and the best part is that it's also the best sounding pair of headphones I have ever heard under $75.If you can get over the design flaws or if sound is the most important feature of headphones for you, there is nothing even close to this at this price range.This one is an absolute GEM.  I loved these so much I ordered two of the 25th Anniversary ones for a bit more.Update: I read some reviews about the PX100-II being much improved and better sounding than the PortaPro.  Since the PX100-II is relatively new, I thought I'd give it another listen.  This time I noticed something different.  The sound is warm, mellow, and neutral, but it loses a lot of detail at the expense of these attributes.  I still prefer higher-detail Portapro, but some may prefer the more mellow sound of the PX100-II.Oh by the way the Portapro comes in the straight plug now, not the angled plug anymore.  It's supposed to be for better compatibility with the iPods and iPhones out there.\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "#hide_output\n",
        "product = dfs[\"train\"].query(\"title == 'B00001P4ZH'\")\n",
        "create_paragraphs(product)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4sbftEUNycb"
      },
      "source": [
        "```python\n",
        "[{'qas': [{'question': 'How is the bass?',\n",
        "    'id': '2543d296da9766d8d17d040ecc781699',\n",
        "    'is_impossible': True,\n",
        "    'answers': []}],\n",
        "  'context': 'I have had Koss headphones ...',\n",
        "    'id': 'd476830bf9282e2b9033e2bb44bbb995',\n",
        "    'is_impossible': False,\n",
        "    'answers': [{'text': 'Bass is weak as expected', 'answer_start': 1302},\n",
        "     {'text': 'Bass is weak as expected, even with EQ adjusted up',\n",
        "      'answer_start': 1302}]}],\n",
        "  'context': 'To anyone who hasn\\'t tried all ...'},\n",
        " {'qas': [{'question': 'How is the bass?',\n",
        "    'id': '455575557886d6dfeea5aa19577e5de4',\n",
        "    'is_impossible': False,\n",
        "    'answers': [{'text': 'The only fault in the sound is the bass',\n",
        "      'answer_start': 650}]}],\n",
        "  'context': \"I have had many sub-$100 headphones ...\"}]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "n1nk58fiNycb"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "def convert_to_squad(dfs):\n",
        "    for split, df in dfs.items():\n",
        "        subjqa_data = {}\n",
        "        # Create `paragraphs` for each product ID\n",
        "        groups = (df.groupby(\"title\").apply(create_paragraphs)\n",
        "            .to_frame(name=\"paragraphs\").reset_index())\n",
        "        subjqa_data[\"data\"] = groups.to_dict(orient=\"records\")\n",
        "        # Save the result to disk\n",
        "        with open(f\"electronics-{split}.json\", \"w+\", encoding=\"utf-8\") as f:\n",
        "            json.dump(subjqa_data, f)\n",
        "\n",
        "convert_to_squad(dfs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "s-rIQg46Nycb",
        "outputId": "b405b426-dbbc-4a92-a8fd-78f1dba9f17c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rPreprocessing dataset:   0%|          | 0/3 [00:00<?, ? Dicts/s]WARNING:haystack.modeling.data_handler.processor:Answer 'These earbuds are nothing special. Their best attribute is that they are cheap. The sound from them is nothing special. In comparison to my standard white IPod buds these seem to emphasize the higher frequencies. There seems to be nothing happening in the bass range with these. I don't think they are efficient enough to have much bass at all. To be fair, my IPod buds overly emphasize bass so they are not that accurate either. I also compared these to a pricy Sennheiser set that I've owned for a couple of years and let's just say there is a reason why the Sennheisers cost more.Surely these Panasonic buds are very nice to use if you think they might get damaged somehow in use as it will not break your heart to have to replace them. These might be especially useful if you only listen to talk radio or have high frequency hearing loss. If you really like to listen to the most of your music and you must wear earbuds then these are not for you.I also have to note that these are packaged in a difficult to open blister pack that required several passes with utility shears to break into. I hate to see all the plastic that has to be discarded just to get this product out of the package.Cosmetically, they are OK. They don't hurt my ears and are comfortable to me. They come with a couple of other size ear pieces if you are hard to fit. The wires take a set when folded so they are not very well-behaved and don't coil nicely and lie flat when you put them away. These will be a little bird's nest of tangled wires when you set them aside. ANSWERNOTFOUND' not contained in context.\n",
            "Example will not be converted for training/evaluation.\n",
            "Preprocessing dataset:  33%|███▎      | 1/3 [00:00<00:01,  1.36 Dicts/s]WARNING:haystack.modeling.data_handler.processor:Answer using start/end indices is '  Operation of the menus and contro' while gold label text is 'Operation of the menus and controls'.\n",
            "Example will not be converted for training/evaluation.\n",
            "WARNING:haystack.modeling.data_handler.processor:Answer using start/end indices is '  This camera performs like the pros.  Fast accurate and easy to operat' while gold label text is 'This camera performs like the pros.  Fast accurate and easy to operated'.\n",
            "Example will not be converted for training/evaluation.\n",
            "Preprocessing dataset:  67%|██████▋   | 2/3 [00:01<00:00,  1.25 Dicts/s]WARNING:haystack.modeling.data_handler.processor:Answer 'enjoy my music.  When I used my old one ear headset for music, the connection was not that good even if I put the phone in my pants pocket.  I used it on iPhone 4S and iPad 3 for music, phone calls, and audible app.  All Bluetooth controls work fine. ANSWERNOTFOUND' not contained in context.\n",
            "Example will not be converted for training/evaluation.\n",
            "WARNING:haystack.modeling.data_handler.processor:Answer 'is good.  First unit defective.  Directions are weak and limited, Net support just plain bad. Roku insists on registration, [I think SEVEN times for us] before asking dumb questions and forcing us to run from computer to TV and back.  This is a Roku 3, and apparently it's too new for them to handle. ANSWERNOTFOUND' not contained in context.\n",
            "Example will not be converted for training/evaluation.\n",
            "Preprocessing dataset: 100%|██████████| 3/3 [00:02<00:00,  1.34 Dicts/s]\n",
            "ERROR:haystack.modeling.data_handler.processor:Unable to convert 5 samples to features. Their ids are : 471-0-0, 83-0-0, 143-0-0, 75-0-0, 60-0-0\n",
            "Preprocessing dataset: 100%|██████████| 1/1 [00:00<00:00,  1.36 Dicts/s]\n",
            "ERROR:haystack.modeling.data_handler.processor:Unable to convert 5 samples to features. Their ids are : 83-0-0, 471-0-0, 60-0-0, 143-0-0, 75-0-0\n"
          ]
        }
      ],
      "source": [
        "#hide_output\n",
        "train_filename = \"electronics-train.json\"\n",
        "dev_filename = \"electronics-validation.json\"\n",
        "# GPU utilization till: 6.4GBs\n",
        "reader.train(data_dir=\".\", use_gpu=True, n_epochs=1, batch_size=16,\n",
        "             train_filename=train_filename, dev_filename=dev_filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "nFYWunCuNycc",
        "outputId": "eab6e38e-d546-449e-d2a8-871ebcabe7d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "reader_eval[\"Fine-tune on SQuAD + SubjQA\"] = evaluate_reader(reader)\n",
        "plot_reader_eval(reader_eval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "joDpSGzbNycc",
        "outputId": "277f424e-1386-4e27-9bf3-30ecf28956ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "f1102e0ba2bd416182e567179dffbcb5",
            "0e412d8e1d39455990fab62e7438a3b1",
            "00da876f3cd2470ab634e9da72a21865",
            "cba3ca3652f946bbbc3e6fae61264a46",
            "9f65becc6e39442c92fc5fd84a517b43",
            "a3097e1451344a828ed869cb93bda40f",
            "de9d2a36988d4226a53603778e221f21",
            "fc772bfe48c146238be4b9cc531e0044",
            "7332646012174798939904cbeac13aca",
            "ea73c05adfa64fa4a6d775f5f8081cb2",
            "ab1e22a404d54560a45b6d24a95bfa1a",
            "95fc2ecb9f914f6ea1dc1378da508b79",
            "96376b1177a84c6ba9d45534ec835852",
            "f3e7ba641b904b55954af996e31d5515",
            "86ed062e94a9437a90184baea0f7e24d",
            "0d3ff9d516054b38989cd79f381a71b0",
            "bb145b47c2794f64838d77511dd23079",
            "9c803e7590084bf4be913f20463e777f",
            "c075646eb54048ed9783fec755d14b33",
            "403addf3fa2b488fac4a3ef496a7a3c0",
            "c62d180aab6c4ddc8f9c1a67fe8bac6b",
            "96222b9ecf58491abb067acd7c15dbc9",
            "940aa3f9ea6b4c899d631139c9c1b2be",
            "a9d69e6a2bc3440f9dd62c4b12fd9abd",
            "be6d11f01cd04ac9b2e2b7558749cb89",
            "5ae6486230bb4a3bb3400303addcd76c",
            "f2c7c1ea9c994417ac703a376c2076f1",
            "f4a1e3f56a004879b0bd9027f4a106f3",
            "0b6af91358104420a36311ee26a12e9a",
            "bf866bdb6906457a966bb8413fa41e17",
            "92a34430466e4b3b8203e9f251362b0b",
            "4fc3c1eb781f41409185a585f7eb6717",
            "8160531f258f4539963c65fc2d1e5cb4",
            "681e28ad97824c4ca181ab56b5e9dff6",
            "4fef6cb9f6c944f686baed08e3904a7c",
            "58fb29a0b44a415d8ac8781d99f433c7",
            "af96cebc21374ecbb99d258d52b5e676",
            "7dc0cd186e924401b63dcbf7b8a9d251",
            "b5c7490a5536441c8c4a7510d0ef0bc9",
            "5f0d7477017f407ab63b5eebcd658b21",
            "b5758b774c09430a932241c1ba72bfba",
            "a48cd8feceb9431ab93aad173f58045c",
            "fa2cc06f1bc04d6489baf470a491f776",
            "6232f69564474d1f9cf692aa7eeee76c",
            "45cbfefe081e472aa02b5e4fa4ea9afa",
            "07663d4a10194cf3b3d7b1c5c7045204",
            "45c0b51fade0447091809b6126180c9a",
            "7246821553fd40b4a67327ee4a3d2afa",
            "cec1e17e05f64045b2bfe1a41975e242",
            "07748b38663444a5b75ca0595dd5f062",
            "e17fa07814ea47c8bbf1543af24bc9ac",
            "feac74a58fc7477390dca888599c34f1",
            "a19097ed2cd340129d03844b79f8dd19",
            "7b0012e494d3494bb6dcdce163f88cf9",
            "aba1a04ce2844590bf7e467f4c7f863a"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/385 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1102e0ba2bd416182e567179dffbcb5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/133M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95fc2ecb9f914f6ea1dc1378da508b79"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "940aa3f9ea6b4c899d631139c9c1b2be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "681e28ad97824c4ca181ab56b5e9dff6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45cbfefe081e472aa02b5e4fa4ea9afa"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#hide_output\n",
        "minilm_ckpt = \"microsoft/MiniLM-L12-H384-uncased\"\n",
        "minilm_reader = FARMReader(model_name_or_path=minilm_ckpt, progress_bar=False,\n",
        "                           max_seq_len=max_seq_length, doc_stride=doc_stride,\n",
        "                           return_no_answer=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "jf78yinJNycc",
        "outputId": "a5ea9a48-fbcc-4783-da66-9eeacd624cd4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rPreprocessing dataset:   0%|          | 0/3 [00:00<?, ? Dicts/s]WARNING:haystack.modeling.data_handler.processor:Answer 'These earbuds are nothing special. Their best attribute is that they are cheap. The sound from them is nothing special. In comparison to my standard white IPod buds these seem to emphasize the higher frequencies. There seems to be nothing happening in the bass range with these. I don't think they are efficient enough to have much bass at all. To be fair, my IPod buds overly emphasize bass so they are not that accurate either. I also compared these to a pricy Sennheiser set that I've owned for a couple of years and let's just say there is a reason why the Sennheisers cost more.Surely these Panasonic buds are very nice to use if you think they might get damaged somehow in use as it will not break your heart to have to replace them. These might be especially useful if you only listen to talk radio or have high frequency hearing loss. If you really like to listen to the most of your music and you must wear earbuds then these are not for you.I also have to note that these are packaged in a difficult to open blister pack that required several passes with utility shears to break into. I hate to see all the plastic that has to be discarded just to get this product out of the package.Cosmetically, they are OK. They don't hurt my ears and are comfortable to me. They come with a couple of other size ear pieces if you are hard to fit. The wires take a set when folded so they are not very well-behaved and don't coil nicely and lie flat when you put them away. These will be a little bird's nest of tangled wires when you set them aside. ANSWERNOTFOUND' not contained in context.\n",
            "Example will not be converted for training/evaluation.\n",
            "Preprocessing dataset:  33%|███▎      | 1/3 [00:01<00:02,  1.24s/ Dicts]WARNING:haystack.modeling.data_handler.processor:Answer using start/end indices is '  Operation of the menus and contro' while gold label text is 'Operation of the menus and controls'.\n",
            "Example will not be converted for training/evaluation.\n",
            "WARNING:haystack.modeling.data_handler.processor:Answer using start/end indices is '  This camera performs like the pros.  Fast accurate and easy to operat' while gold label text is 'This camera performs like the pros.  Fast accurate and easy to operated'.\n",
            "Example will not be converted for training/evaluation.\n",
            "Preprocessing dataset:  67%|██████▋   | 2/3 [00:02<00:01,  1.16s/ Dicts]WARNING:haystack.modeling.data_handler.processor:Answer 'enjoy my music.  When I used my old one ear headset for music, the connection was not that good even if I put the phone in my pants pocket.  I used it on iPhone 4S and iPad 3 for music, phone calls, and audible app.  All Bluetooth controls work fine. ANSWERNOTFOUND' not contained in context.\n",
            "Example will not be converted for training/evaluation.\n",
            "WARNING:haystack.modeling.data_handler.processor:Answer 'is good.  First unit defective.  Directions are weak and limited, Net support just plain bad. Roku insists on registration, [I think SEVEN times for us] before asking dumb questions and forcing us to run from computer to TV and back.  This is a Roku 3, and apparently it's too new for them to handle. ANSWERNOTFOUND' not contained in context.\n",
            "Example will not be converted for training/evaluation.\n",
            "Preprocessing dataset: 100%|██████████| 3/3 [00:02<00:00,  1.05 Dicts/s]\n",
            "ERROR:haystack.modeling.data_handler.processor:Unable to convert 5 samples to features. Their ids are : 471-0-0, 83-0-0, 143-0-0, 75-0-0, 60-0-0\n",
            "Preprocessing dataset: 100%|██████████| 1/1 [00:00<00:00,  2.48 Dicts/s]\n",
            "ERROR:haystack.modeling.data_handler.processor:Unable to convert 5 samples to features. Their ids are : 83-0-0, 471-0-0, 60-0-0, 143-0-0, 75-0-0\n"
          ]
        }
      ],
      "source": [
        "#hide_output\n",
        "minilm_reader.train(data_dir=\".\", use_gpu=True, n_epochs=1, batch_size=16,\n",
        "             train_filename=train_filename, dev_filename=dev_filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "rH5d0jslNycc",
        "outputId": "7b900fad-e7d5-4b06-b894-6c5892953b0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "reader_eval[\"Fine-tune on SubjQA\"] = evaluate_reader(minilm_reader)\n",
        "plot_reader_eval(reader_eval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXIhIEApNycc"
      },
      "source": [
        "### Evaluating the Whole QA Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "4MJlUiCGNycc"
      },
      "outputs": [],
      "source": [
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "pipe = ExtractiveQAPipeline(retriever=es_retriever, reader=reader)\n",
        "\n",
        "# Evaluate\n",
        "eval_result = pipe.eval(\n",
        "    labels=labels_agg,\n",
        "    params={},\n",
        ")\n",
        "metrics = eval_result.calculate_metrics(simulated_top_k_reader=1)\n",
        "# Extract Reader metrics\n",
        "reader_eval[\"QA Pipeline (top-1)\"] = {\n",
        "    k:v for k,v in metrics[\"Reader\"].items()\n",
        "    if k in [\"exact_match\", \"f1\"]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "xzEaf0tgNyce",
        "outputId": "8ddc7e1b-1297-4846-86d0-76d5327ab566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          },
          "metadata": {}
        }
      ],
      "source": [
        "#hide_input\n",
        "#id reader-vs-pipeline\n",
        "#caption Comparison of EM and _F_~1~ scores for the reader against the whole QA pipeline\n",
        "plot_reader_eval({\"Reader\": reader_eval[\"Fine-tune on SQuAD + SubjQA\"],\n",
        "                  \"QA pipeline (top-1)\": reader_eval[\"QA Pipeline (top-1)\"]})"
      ]
    },
    {
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      "metadata": {
        "id": "i-LtaSnNNycf"
      },
      "source": [
        "END"
      ]
    }
  ],
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