wikiQA_fine-turned_converted_query.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aggregate-Intellect/xir/blob/main/wikiQA_fine-turned_converted_query.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install beir\n",
        "!pip install tensorflow-text\n",
        "!pip install farm-haystack\n",
        "!pip install --upgrade pip\n",
        "# !pip install git+https://github.com/deepset-ai/haystack.git"
      ],
      "metadata": {
        "id": "yYOtcGRQIr6Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import List\n",
        "import requests\n",
        "import pandas as pd\n",
        "from haystack import Document\n",
        "from haystack.document_stores import FAISSDocumentStore\n",
        "from haystack.nodes import RAGenerator, EmbeddingRetriever\n",
        "from haystack.utils import fetch_archive_from_http, print_answers, print_documents\n",
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "from haystack.pipelines import DocumentSearchPipeline"
      ],
      "metadata": {
        "id": "hDIFRLN6AiYg"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAa7RWt2NgBd",
        "outputId": "3b6ae19c-39c8-4749-9f63-2c5b517c5abe"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load wikiQA dataset"
      ],
      "metadata": {
        "id": "A9nE-peqKFT2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "wikiqa_data = load_dataset(\n",
        "    'wiki_qa',\n",
        "    split='train',\n",
        ")\n",
        "next(iter(wikiqa_data))"
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
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              "Downloading metadata:   0%|          | 0.00/830 [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING - datasets.builder -  Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset wiki_qa/default (download: 6.77 MiB, generated: 6.10 MiB, post-processed: Unknown size, total: 12.87 MiB) to /root/.cache/huggingface/datasets/wiki_qa/default/0.1.0/d2d236b5cbdc6fbdab45d168b4d678a002e06ddea3525733a24558150585951c...\n"
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        {
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            "text/plain": [
              "Downloading data:   0%|          | 0.00/7.09M [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/6165 [00:00<?, ? examples/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/2733 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/20360 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset wiki_qa downloaded and prepared to /root/.cache/huggingface/datasets/wiki_qa/default/0.1.0/d2d236b5cbdc6fbdab45d168b4d678a002e06ddea3525733a24558150585951c. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'answer': 'A partly submerged glacier cave on Perito Moreno Glacier .',\n",
              " 'document_title': 'Glacier cave',\n",
              " 'label': 0,\n",
              " 'question': 'how are glacier caves formed?',\n",
              " 'question_id': 'Q1'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "\n",
        "pd_wikiqa_data = pd.DataFrame(wikiqa_data)\n",
        "pd_wikiqa_data"
      ],
      "metadata": {
        "colab": {
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              "      question_id                             question  \\\n",
              "0              Q1        how are glacier caves formed?   \n",
              "1              Q1        how are glacier caves formed?   \n",
              "2              Q1        how are glacier caves formed?   \n",
              "3              Q1        how are glacier caves formed?   \n",
              "4              Q1        how are glacier caves formed?   \n",
              "...           ...                                  ...   \n",
              "20355       Q3043        what is section eight housing   \n",
              "20356       Q3043        what is section eight housing   \n",
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              "20358       Q3046     what is us dollar worth based on   \n",
              "20359       Q3046     what is us dollar worth based on   \n",
              "\n",
              "                            document_title  \\\n",
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              "4                             Glacier cave   \n",
              "...                                    ...   \n",
              "20355                  Section 8 (housing)   \n",
              "20356                  Section 8 (housing)   \n",
              "20357        Category:Types of restaurants   \n",
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              "20359  History of the United States dollar   \n",
              "\n",
              "                                                                                answer  \\\n",
              "0                           A partly submerged glacier cave on Perito Moreno Glacier .   \n",
              "1                                            The ice facade is approximately 60 m high   \n",
              "2                                            Ice formations in the Titlis glacier cave   \n",
              "3                        A glacier cave is a cave formed within the ice of a glacier .   \n",
              "4      Glacier caves are often called ice caves , but this term is properly used to...   \n",
              "...                                                                                ...   \n",
              "20355  A tenant who leaves a subsidized project will lose access to the project-bas...   \n",
              "20356  The United States Department of Housing and Urban Development and United Sta...   \n",
              "20357     Restaurants categorized by type and information about these different types.   \n",
              "20358                                      U.S. Federal Reserve notes in the mid-1990s   \n",
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              "      <td>Glacier cave</td>\n",
              "      <td>A partly submerged glacier cave on Perito Moreno Glacier .</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Q1</td>\n",
              "      <td>how are glacier caves formed?</td>\n",
              "      <td>Glacier cave</td>\n",
              "      <td>The ice facade is approximately 60 m high</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Q1</td>\n",
              "      <td>how are glacier caves formed?</td>\n",
              "      <td>Glacier cave</td>\n",
              "      <td>Ice formations in the Titlis glacier cave</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Q1</td>\n",
              "      <td>how are glacier caves formed?</td>\n",
              "      <td>Glacier cave</td>\n",
              "      <td>A glacier cave is a cave formed within the ice of a glacier .</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Q1</td>\n",
              "      <td>how are glacier caves formed?</td>\n",
              "      <td>Glacier cave</td>\n",
              "      <td>Glacier caves are often called ice caves , but this term is properly used to...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20355</th>\n",
              "      <td>Q3043</td>\n",
              "      <td>what is section eight housing</td>\n",
              "      <td>Section 8 (housing)</td>\n",
              "      <td>A tenant who leaves a subsidized project will lose access to the project-bas...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20356</th>\n",
              "      <td>Q3043</td>\n",
              "      <td>what is section eight housing</td>\n",
              "      <td>Section 8 (housing)</td>\n",
              "      <td>The United States Department of Housing and Urban Development and United Sta...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20357</th>\n",
              "      <td>Q3044</td>\n",
              "      <td>what is the main type of restaurant</td>\n",
              "      <td>Category:Types of restaurants</td>\n",
              "      <td>Restaurants categorized by type and information about these different types.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20358</th>\n",
              "      <td>Q3046</td>\n",
              "      <td>what is us dollar worth based on</td>\n",
              "      <td>History of the United States dollar</td>\n",
              "      <td>U.S. Federal Reserve notes in the mid-1990s</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20359</th>\n",
              "      <td>Q3046</td>\n",
              "      <td>what is us dollar worth based on</td>\n",
              "      <td>History of the United States dollar</td>\n",
              "      <td>The history of the United States dollar covers more than 200 years.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20360 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ba1ec08-9495-4227-a537-74ee70800cbc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "      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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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2ba1ec08-9495-4227-a537-74ee70800cbc');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing and document store"
      ],
      "metadata": {
        "id": "P9Z2fchHKZ2M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd_data = pd.DataFrame(wikiqa_data)\n",
        "\n",
        "# make sure all sentences ending with .\n",
        "# if last character is not . ? !, add . at the end\n",
        "for index in range(0,pd_data.shape[0]):\n",
        "  last_char = pd_data['answer'][index][-1]\n",
        "  if not (last_char =='.' or last_char =='!' or last_char =='?'):\n",
        "    pd_data['answer'][index] = pd_data['answer'][index] + '.'\n",
        "\n",
        "pd_data.to_csv(\"wikiQA_dataset.csv\", index=False)\n",
        "\n",
        "processed_wikiqa = pd.DataFrame(columns = [\"question_id\", \"question\", \"document_title\", \"document_content\", \"answer\"])\n",
        "processed_all_wikiqa = pd.DataFrame(columns = [\"question_id\", \"document_title\", \"document_content\"])\n",
        "\n",
        "for question in pd_data['question_id'].unique().tolist():\n",
        "    pd_question = pd_data.loc[pd_data['question_id'] == question]   \n",
        "    content = ' '.join(pd_question[\"answer\"].tolist())\n",
        "    pd_answer = pd_question[pd_question['label'] == 1]\n",
        "    processed_all_wikiqa = processed_all_wikiqa.append({\"question_id\": question,\n",
        "                                                        \"document_title\": pd_question[\"document_title\"].iloc[0],\n",
        "                                                        \"document_content\" : content}, ignore_index=True)\n",
        "    if not pd_answer.empty:\n",
        "        answer = ' '.join(pd_answer['answer'].tolist())\n",
        "        new_row = {\"question_id\": pd_question[\"question_id\"].iloc[0], \n",
        "                  \"question\" : pd_question[\"question\"].iloc[0],\n",
        "                  \"document_title\" : pd_question[\"document_title\"].iloc[0],\n",
        "                  \"document_content\" : content,\n",
        "                  \"answer\": answer}\n",
        "        processed_wikiqa = processed_wikiqa.append(new_row, ignore_index=True)\n",
        "processed_wikiqa.to_csv(\"processed_wikiqa.csv\", index=False)\n",
        "\n",
        "docs = []\n",
        "for index, row in processed_all_wikiqa.iterrows():\n",
        "    # create haystack document object with text content and doc metadata\n",
        "    doc = Document(\n",
        "        content = row['document_content'],\n",
        "        meta = {\n",
        "            \"document_title\": row['document_title']\n",
        "        }\n",
        "    )\n",
        "    docs.append(doc)\n",
        "\n",
        "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\", return_embedding=True)\n",
        "\n",
        "# Delete existing documents in documents store\n",
        "document_store.delete_documents()\n",
        "\n",
        "document_store.write_documents(docs)\n",
        "document_store.get_document_count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
          "referenced_widgets": [
            "2b268d0068684d66b61fb9197780e932",
            "c7f1eb4d45e142e5aa297eb189096fa2",
            "78e5f68983234ad2a77c34e9f1aedeb0",
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            "ebcd7cf7831c48449d100c8ef758bd8b",
            "331a449cff0142409a7bbbffe980160d"
          ]
        },
        "id": "nsGZGT6-AdBl",
        "outputId": "93c7229d-8146-4b95-dcfb-6eb85d3eb5ec"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  \n",
            "INFO - haystack.telemetry -  Haystack sends anonymous usage data to understand the actual usage and steer dev efforts towards features that are most meaningful to users. You can opt-out at anytime by calling disable_telemetry() or by manually setting the environment variable HAYSTACK_TELEMETRY_ENABLED as described for different operating systems on the documentation page. More information at https://haystack.deepset.ai/guides/telemetry\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing Documents:   0%|          | 0/2118 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b268d0068684d66b61fb9197780e932"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1994"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine turning on retriver model"
      ],
      "metadata": {
        "id": "BTDkWong1jaU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = []\n",
        "for question_id in pd_wikiqa_data['question_id'].unique().tolist():\n",
        "    pd_question = pd_wikiqa_data.loc[pd_wikiqa_data['question_id'] == question_id]   \n",
        "    pd_neg = pd_question[pd_question['label'] == 0]\n",
        "    neg_content = ' '.join(pd_neg[\"answer\"].tolist())\n",
        "    pd_answer = pd_question[pd_question['label'] == 1.0]\n",
        "    if not pd_answer.empty:\n",
        "        answer = ' '.join(pd_answer['answer'].tolist())\n",
        "        new_row = {\"question\" : pd_question[\"question\"].iloc[0],\n",
        "                  \"neg_doc\": neg_content,\n",
        "                  \"pos_doc\": answer,\n",
        "                  \"score\": 1.0}\n",
        "        data.append(new_row)"
      ],
      "metadata": {
        "id": "aJ7v7MBnDtMl"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fine_turned_retriever = EmbeddingRetriever(\n",
        "            document_store=document_store,\n",
        "            embedding_model=\"flax-sentence-embeddings/all_datasets_v3_mpnet-base\",\n",
        "            model_format=\"sentence_transformers\"\n",
        ")\n",
        "fine_turned_retriever.train(training_data = data, learning_rate = 2e-5, n_epochs = 1, num_warmup_steps = None, batch_size = 1)"
      ],
      "metadata": {
        "colab": {
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            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
            "INFO - haystack.nodes.retriever.dense -  Init retriever using embeddings of model flax-sentence-embeddings/all_datasets_v3_mpnet-base\n"
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          "text": [
            "WARNING - haystack.nodes.retriever._embedding_encoder -  You are using a Sentence Transformer with the dot_product function. We recommend using cosine instead. This can be set when initializing the DocumentStore\n",
            "INFO - haystack.nodes.retriever._embedding_encoder -  GPL training/adapting SentenceTransformer(\n",
            "  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel \n",
            "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
            "  (2): Normalize()\n",
            ") with 873 examples\n"
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        },
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              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Union\n",
        "fine_turned_retriever.save(\"/content/my retriever\") "
      ],
      "metadata": {
        "id": "jdaUGaNLsjTE"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Validation"
      ],
      "metadata": {
        "id": "P9K4AkCB9Jaj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load test dataset"
      ],
      "metadata": {
        "id": "4Og2FrxC1urU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vali_wikiqa_data = load_dataset(\n",
        "    'wiki_qa',\n",
        "    split='test',\n",
        ")\n",
        "next(iter(vali_wikiqa_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GMBqStmx7-L",
        "outputId": "76df96a9-9f2d-4b6e-8b7f-3c46c0e5edfd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING - datasets.builder -  Using custom data configuration default\n",
            "WARNING - datasets.builder -  Reusing dataset wiki_qa (/root/.cache/huggingface/datasets/wiki_qa/default/0.1.0/d2d236b5cbdc6fbdab45d168b4d678a002e06ddea3525733a24558150585951c)\n"
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        {
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          "data": {
            "text/plain": [
              "{'answer': 'African immigration to the United States refers to immigrants to the United States who are or were nationals of Africa .',\n",
              " 'document_title': 'African immigration to the United States',\n",
              " 'label': 0,\n",
              " 'question': 'HOW AFRICAN AMERICANS WERE IMMIGRATED TO THE US',\n",
              " 'question_id': 'Q0'}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd_test_data = pd.DataFrame(vali_wikiqa_data)\n",
        "pd_test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
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        "outputId": "27f0599f-ece6-4a17-8c6b-a65cf04c27fb"
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      "execution_count": 12,
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              "      <td>what is an open mare?</td>\n",
              "      <td>Mare</td>\n",
              "      <td>An adult male horse is called a stallion , or, if castrated , a gelding .</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6164</th>\n",
              "      <td>Q3045</td>\n",
              "      <td>what is an open mare?</td>\n",
              "      <td>Mare</td>\n",
              "      <td>Occasionally the term \"horse\" is used in a restrictive sense to designate on...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6165 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb579fe4-0385-44e6-b7dc-71053b0a9a67')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bb579fe4-0385-44e6-b7dc-71053b0a9a67');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd_test_data = pd.DataFrame(vali_wikiqa_data)\n",
        "\n",
        "# make sure all sentences ending with .\n",
        "# if last character is not . ? !, add . at the end\n",
        "for index in range(0,pd_test_data.shape[0]):\n",
        "  last_char = pd_test_data['answer'][index][-1]\n",
        "  if not (last_char =='.' or last_char =='!' or last_char =='?'):\n",
        "    pd_test_data['answer'][index] = pd_test_data['answer'][index] + '.'\n",
        "\n",
        "pd_test_data.to_csv(\"wikiQA_dataset.csv\", index=False)\n",
        "\n",
        "processed_wikiqa = pd.DataFrame(columns = [\"question_id\", \"question\", \"document_title\", \"document_content\", \"answer\"])\n",
        "processed_all_wikiqa = pd.DataFrame(columns = [\"question_id\", \"document_title\", \"document_content\"])\n",
        "\n",
        "for question_id in pd_test_data['question_id'].unique().tolist():\n",
        "    # print(question_id)\n",
        "    pd_question = pd_test_data.loc[pd_test_data['question_id'] == question_id]   \n",
        "    # print(pd_question)\n",
        "    content = ' '.join(pd_question[\"answer\"].tolist())\n",
        "    pd_answer = pd_question[pd_question['label'] == 1]\n",
        "    # print(pd_question[\"document_title\"].iloc[0])\n",
        "    processed_all_wikiqa = processed_all_wikiqa.append({\"question_id\": question_id,\n",
        "                                                        \"document_title\": pd_question[\"document_title\"].iloc[0],\n",
        "                                                        \"document_content\" : content}, ignore_index=True)\n",
        "    if not pd_answer.empty:\n",
        "        answer = ' '.join(pd_answer['answer'].tolist())\n",
        "        new_row = {\"question_id\": pd_question[\"question_id\"].iloc[0], \n",
        "                  \"question\" : pd_question[\"question\"].iloc[0],\n",
        "                  \"document_title\" : pd_question[\"document_title\"].iloc[0],\n",
        "                  \"document_content\" : content,\n",
        "                  \"answer\": answer}\n",
        "        processed_wikiqa = processed_wikiqa.append(new_row, ignore_index=True)\n",
        "\n",
        "processed_wikiqa"
      ],
      "metadata": {
        "id": "7NG95tF39MMh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "outputId": "fea35372-5e37-4062-dfca-098d3f450d36"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    question_id                                         question  \\\n",
              "0            Q0  HOW AFRICAN AMERICANS WERE IMMIGRATED TO THE US   \n",
              "1            Q4                           how a water pump works   \n",
              "2           Q20        how old was sue lyon when she made lolita   \n",
              "3           Q33                       how are antibodies used in   \n",
              "4           Q59                   HOW MUCH IS CENTAVOS IN MEXICO   \n",
              "..          ...                                              ...   \n",
              "238       Q2990           what is the main component of vaccines   \n",
              "239       Q2994                        what is preciosa crystal?   \n",
              "240       Q3004                who are all of the jonas brothers   \n",
              "241       Q3008                   who is mary matalin married to   \n",
              "242       Q3012                        where is the brisket from   \n",
              "\n",
              "                               document_title  \\\n",
              "0    African immigration to the United States   \n",
              "1                                        Pump   \n",
              "2                          Lolita (1962 film)   \n",
              "3                                    antibody   \n",
              "4                                Mexican peso   \n",
              "..                                        ...   \n",
              "238                                   vaccine   \n",
              "239                    Preciosa (corporation)   \n",
              "240                            Jonas Brothers   \n",
              "241                              Mary Matalin   \n",
              "242                                   Brisket   \n",
              "\n",
              "                                                                    document_content  \\\n",
              "0    African immigration to the United States refers to immigrants to the United ...   \n",
              "1    A small, electrically powered pump. A large, electrically driven pump (elect...   \n",
              "2    Lolita is a 1962 comedy-drama film by Stanley Kubrick based on the classic n...   \n",
              "3    Each antibody binds to a specific antigen ; an interaction similar to a lock...   \n",
              "4    The peso ( sign : $; code : MXN) is the currency of Mexico . Modern peso and...   \n",
              "..                                                                               ...   \n",
              "238  Jonas Salk in 1955 holds two bottles of a culture used to grow polio vaccine...   \n",
              "239  Preciosa is the luxury brand name for the range of precision-cut lead crysta...   \n",
              "240  The Jonas Brothers are an American pop rock band. Formed in 2005, they have ...   \n",
              "241  This is about the political professional. For the actress, see Marlee Matlin...   \n",
              "242  American cuts of beef including the brisket. Brisket is a cut of meat from t...   \n",
              "\n",
              "                                                                              answer  \n",
              "0    As such, African immigrants are to be distinguished from African American pe...  \n",
              "1    Pumps operate by some mechanism (typically reciprocating or rotary ), and co...  \n",
              "2     The actress who played Lolita, Sue Lyon , was fourteen at the time of filming.  \n",
              "3    An antibody (Ab), also known as an immunoglobulin (Ig), is a large Y-shaped ...  \n",
              "4                    The peso is subdivided into 100 centavos, represented by \" ¢ \".  \n",
              "..                                                                               ...  \n",
              "238  The agent stimulates the body's immune system to recognize the agent as fore...  \n",
              "239  Preciosa is the luxury brand name for the range of precision-cut lead crysta...  \n",
              "240  Formed in 2005, they have gained popularity from the Disney Channel children...  \n",
              "241               She is married to Democratic political consultant James Carville .  \n",
              "242        Brisket is a cut of meat from the breast or lower chest of beef or veal .  \n",
              "\n",
              "[243 rows x 5 columns]"
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              "      <td>African immigration to the United States</td>\n",
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              "      <td>how a water pump works</td>\n",
              "      <td>Pump</td>\n",
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              "      <td>how old was sue lyon when she made lolita</td>\n",
              "      <td>Lolita (1962 film)</td>\n",
              "      <td>Lolita is a 1962 comedy-drama film by Stanley Kubrick based on the classic n...</td>\n",
              "      <td>The actress who played Lolita, Sue Lyon , was fourteen at the time of filming.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Q33</td>\n",
              "      <td>how are antibodies used in</td>\n",
              "      <td>antibody</td>\n",
              "      <td>Each antibody binds to a specific antigen ; an interaction similar to a lock...</td>\n",
              "      <td>An antibody (Ab), also known as an immunoglobulin (Ig), is a large Y-shaped ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Q59</td>\n",
              "      <td>HOW MUCH IS CENTAVOS IN MEXICO</td>\n",
              "      <td>Mexican peso</td>\n",
              "      <td>The peso ( sign : $; code : MXN) is the currency of Mexico . Modern peso and...</td>\n",
              "      <td>The peso is subdivided into 100 centavos, represented by \" ¢ \".</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>238</th>\n",
              "      <td>Q2990</td>\n",
              "      <td>what is the main component of vaccines</td>\n",
              "      <td>vaccine</td>\n",
              "      <td>Jonas Salk in 1955 holds two bottles of a culture used to grow polio vaccine...</td>\n",
              "      <td>The agent stimulates the body's immune system to recognize the agent as fore...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>Q2994</td>\n",
              "      <td>what is preciosa crystal?</td>\n",
              "      <td>Preciosa (corporation)</td>\n",
              "      <td>Preciosa is the luxury brand name for the range of precision-cut lead crysta...</td>\n",
              "      <td>Preciosa is the luxury brand name for the range of precision-cut lead crysta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>Q3004</td>\n",
              "      <td>who are all of the jonas brothers</td>\n",
              "      <td>Jonas Brothers</td>\n",
              "      <td>The Jonas Brothers are an American pop rock band. Formed in 2005, they have ...</td>\n",
              "      <td>Formed in 2005, they have gained popularity from the Disney Channel children...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>Q3008</td>\n",
              "      <td>who is mary matalin married to</td>\n",
              "      <td>Mary Matalin</td>\n",
              "      <td>This is about the political professional. For the actress, see Marlee Matlin...</td>\n",
              "      <td>She is married to Democratic political consultant James Carville .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>Q3012</td>\n",
              "      <td>where is the brisket from</td>\n",
              "      <td>Brisket</td>\n",
              "      <td>American cuts of beef including the brisket. Brisket is a cut of meat from t...</td>\n",
              "      <td>Brisket is a cut of meat from the breast or lower chest of beef or veal .</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>243 rows × 5 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-652f102f-d9a3-4f95-b331-b829dbaa48db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-652f102f-d9a3-4f95-b331-b829dbaa48db');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs = []\n",
        "for index, row in processed_all_wikiqa.iterrows():\n",
        "    # create haystack document object with text content and doc metadata\n",
        "    doc = Document(\n",
        "        content = row['document_content'],\n",
        "        meta = {\n",
        "            \"document_title\": row['document_title']\n",
        "        }\n",
        "    )\n",
        "    docs.append(doc)\n",
        "\n",
        "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\", return_embedding=True)\n",
        "\n",
        "# Delete existing documents in documents store\n",
        "document_store.delete_documents()\n",
        "\n",
        "document_store.write_documents(docs)\n",
        "document_store.get_document_count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
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        "   model_format=\"sentence_transformers\",\n",
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        ")\n",
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          "text": [
            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
            "INFO - haystack.nodes.retriever.dense -  Init retriever using embeddings of model flax-sentence-embeddings/all_datasets_v3_mpnet-base\n",
            "WARNING - haystack.nodes.retriever._embedding_encoder -  You are using a Sentence Transformer with the dot_product function. We recommend using cosine instead. This can be set when initializing the DocumentStore\n",
            "INFO - haystack.document_stores.faiss -  Updating embeddings for 619 docs...\n"
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              "    question_id                                         question  \\\n",
              "0            Q0  HOW AFRICAN AMERICANS WERE IMMIGRATED TO THE US   \n",
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              "..          ...                                              ...   \n",
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              "                               document_title  \\\n",
              "0    African immigration to the United States   \n",
              "1                                        Pump   \n",
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              "4                                Mexican peso   \n",
              "..                                        ...   \n",
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              "239                    Preciosa (corporation)   \n",
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              "242                                   Brisket   \n",
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              "                                                                    document_content  \\\n",
              "0    African immigration to the United States refers to immigrants to the United ...   \n",
              "1    A small, electrically powered pump. A large, electrically driven pump (elect...   \n",
              "2    Lolita is a 1962 comedy-drama film by Stanley Kubrick based on the classic n...   \n",
              "3    Each antibody binds to a specific antigen ; an interaction similar to a lock...   \n",
              "4    The peso ( sign : $; code : MXN) is the currency of Mexico . Modern peso and...   \n",
              "..                                                                               ...   \n",
              "238  Jonas Salk in 1955 holds two bottles of a culture used to grow polio vaccine...   \n",
              "239  Preciosa is the luxury brand name for the range of precision-cut lead crysta...   \n",
              "240  The Jonas Brothers are an American pop rock band. Formed in 2005, they have ...   \n",
              "241  This is about the political professional. For the actress, see Marlee Matlin...   \n",
              "242  American cuts of beef including the brisket. Brisket is a cut of meat from t...   \n",
              "\n",
              "                                                                              answer  \n",
              "0    As such, African immigrants are to be distinguished from African American pe...  \n",
              "1    Pumps operate by some mechanism (typically reciprocating or rotary ), and co...  \n",
              "2     The actress who played Lolita, Sue Lyon , was fourteen at the time of filming.  \n",
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              "4                    The peso is subdivided into 100 centavos, represented by \" ¢ \".  \n",
              "..                                                                               ...  \n",
              "238  The agent stimulates the body's immune system to recognize the agent as fore...  \n",
              "239  Preciosa is the luxury brand name for the range of precision-cut lead crysta...  \n",
              "240  Formed in 2005, they have gained popularity from the Disney Channel children...  \n",
              "241               She is married to Democratic political consultant James Carville .  \n",
              "242        Brisket is a cut of meat from the breast or lower chest of beef or veal .  \n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Accuracy of fine-turned model and converted query"
      ],
      "metadata": {
        "id": "5gX-Aeei1826"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "converted_test_data = pd.read_csv(\"converted_test_data.csv\")"
      ],
      "metadata": {
        "id": "5e8XjSzxKMIq"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "converted_test_data"
      ],
      "metadata": {
        "id": "-Edo6DGqpNJ7",
        "outputId": "d126386f-0c2f-4b17-d61b-368a241d1c6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0 question_id                                         question  \\\n",
              "0             0          Q0  HOW AFRICAN AMERICANS WERE IMMIGRATED TO THE US   \n",
              "1             1          Q4                           how a water pump works   \n",
              "2             2         Q20        how old was sue lyon when she made lolita   \n",
              "3             3         Q33                       how are antibodies used in   \n",
              "4             4         Q59                   HOW MUCH IS CENTAVOS IN MEXICO   \n",
              "..          ...         ...                                              ...   \n",
              "238         238       Q2990           what is the main component of vaccines   \n",
              "239         239       Q2994                        what is preciosa crystal?   \n",
              "240         240       Q3004                who are all of the jonas brothers   \n",
              "241         241       Q3008                   who is mary matalin married to   \n",
              "242         242       Q3012                        where is the brisket from   \n",
              "\n",
              "                           converted_query  \\\n",
              "0    African American immigrated to the US   \n",
              "1                         water pump works   \n",
              "2      Sue Lyon's age when she made lolita   \n",
              "3                       antibodies used in   \n",
              "4               centavos's price in mexico   \n",
              "..                                     ...   \n",
              "238                                    NaN   \n",
              "239                                    NaN   \n",
              "240                                    NaN   \n",
              "241                                    NaN   \n",
              "242                                    NaN   \n",
              "\n",
              "                               document_title  \\\n",
              "0    African immigration to the United States   \n",
              "1                                        Pump   \n",
              "2                          Lolita (1962 film)   \n",
              "3                                    antibody   \n",
              "4                                Mexican peso   \n",
              "..                                        ...   \n",
              "238                                   vaccine   \n",
              "239                    Preciosa (corporation)   \n",
              "240                            Jonas Brothers   \n",
              "241                              Mary Matalin   \n",
              "242                                   Brisket   \n",
              "\n",
              "                                                                    document_content  \\\n",
              "0    African immigration to the United States refers to immigrants to the United ...   \n",
              "1    A small, electrically powered pump. A large, electrically driven pump (elect...   \n",
              "2    Lolita is a 1962 comedy-drama film by Stanley Kubrick based on the classic n...   \n",
              "3    Each antibody binds to a specific antigen ; an interaction similar to a lock...   \n",
              "4    The peso ( sign : $; code : MXN) is the currency of Mexico . Modern peso and...   \n",
              "..                                                                               ...   \n",
              "238  Jonas Salk in 1955 holds two bottles of a culture used to grow polio vaccine...   \n",
              "239  Preciosa is the luxury brand name for the range of precision-cut lead crysta...   \n",
              "240  The Jonas Brothers are an American pop rock band. Formed in 2005, they have ...   \n",
              "241  This is about the political professional. For the actress, see Marlee Matlin...   \n",
              "242  American cuts of beef including the brisket. Brisket is a cut of meat from t...   \n",
              "\n",
              "                                                                              answer  \n",
              "0    As such, African immigrants are to be distinguished from African American pe...  \n",
              "1    Pumps operate by some mechanism (typically reciprocating or rotary ), and co...  \n",
              "2     The actress who played Lolita, Sue Lyon , was fourteen at the time of filming.  \n",
              "3    An antibody (Ab), also known as an immunoglobulin (Ig), is a large Y-shaped ...  \n",
              "4                    The peso is subdivided into 100 centavos, represented by \" ¢ \".  \n",
              "..                                                                               ...  \n",
              "238  The agent stimulates the body's immune system to recognize the agent as fore...  \n",
              "239  Preciosa is the luxury brand name for the range of precision-cut lead crysta...  \n",
              "240  Formed in 2005, they have gained popularity from the Disney Channel children...  \n",
              "241               She is married to Democratic political consultant James Carville .  \n",
              "242        Brisket is a cut of meat from the breast or lower chest of beef or veal .  \n",
              "\n",
              "[243 rows x 7 columns]"
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              "      <td>Q0</td>\n",
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              "      <td>African American immigrated to the US</td>\n",
              "      <td>African immigration to the United States</td>\n",
              "      <td>African immigration to the United States refers to immigrants to the United ...</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Q4</td>\n",
              "      <td>how a water pump works</td>\n",
              "      <td>water pump works</td>\n",
              "      <td>Pump</td>\n",
              "      <td>A small, electrically powered pump. A large, electrically driven pump (elect...</td>\n",
              "      <td>Pumps operate by some mechanism (typically reciprocating or rotary ), and co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Q20</td>\n",
              "      <td>how old was sue lyon when she made lolita</td>\n",
              "      <td>Sue Lyon's age when she made lolita</td>\n",
              "      <td>Lolita (1962 film)</td>\n",
              "      <td>Lolita is a 1962 comedy-drama film by Stanley Kubrick based on the classic n...</td>\n",
              "      <td>The actress who played Lolita, Sue Lyon , was fourteen at the time of filming.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Q33</td>\n",
              "      <td>how are antibodies used in</td>\n",
              "      <td>antibodies used in</td>\n",
              "      <td>antibody</td>\n",
              "      <td>Each antibody binds to a specific antigen ; an interaction similar to a lock...</td>\n",
              "      <td>An antibody (Ab), also known as an immunoglobulin (Ig), is a large Y-shaped ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Q59</td>\n",
              "      <td>HOW MUCH IS CENTAVOS IN MEXICO</td>\n",
              "      <td>centavos's price in mexico</td>\n",
              "      <td>Mexican peso</td>\n",
              "      <td>The peso ( sign : $; code : MXN) is the currency of Mexico . Modern peso and...</td>\n",
              "      <td>The peso is subdivided into 100 centavos, represented by \" ¢ \".</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>238</td>\n",
              "      <td>Q2990</td>\n",
              "      <td>what is the main component of vaccines</td>\n",
              "      <td>NaN</td>\n",
              "      <td>vaccine</td>\n",
              "      <td>Jonas Salk in 1955 holds two bottles of a culture used to grow polio vaccine...</td>\n",
              "      <td>The agent stimulates the body's immune system to recognize the agent as fore...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>239</td>\n",
              "      <td>Q2994</td>\n",
              "      <td>what is preciosa crystal?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Preciosa (corporation)</td>\n",
              "      <td>Preciosa is the luxury brand name for the range of precision-cut lead crysta...</td>\n",
              "      <td>Preciosa is the luxury brand name for the range of precision-cut lead crysta...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>240</td>\n",
              "      <td>Q3004</td>\n",
              "      <td>who are all of the jonas brothers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jonas Brothers</td>\n",
              "      <td>The Jonas Brothers are an American pop rock band. Formed in 2005, they have ...</td>\n",
              "      <td>Formed in 2005, they have gained popularity from the Disney Channel children...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>241</td>\n",
              "      <td>Q3008</td>\n",
              "      <td>who is mary matalin married to</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mary Matalin</td>\n",
              "      <td>This is about the political professional. For the actress, see Marlee Matlin...</td>\n",
              "      <td>She is married to Democratic political consultant James Carville .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>242</td>\n",
              "      <td>Q3012</td>\n",
              "      <td>where is the brisket from</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brisket</td>\n",
              "      <td>American cuts of beef including the brisket. Brisket is a cut of meat from t...</td>\n",
              "      <td>Brisket is a cut of meat from the breast or lower chest of beef or veal .</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>243 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-918dc181-e4da-4e59-bd80-9dc07da29fd0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-918dc181-e4da-4e59-bd80-9dc07da29fd0 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-918dc181-e4da-4e59-bd80-9dc07da29fd0');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test the first 100 data"
      ],
      "metadata": {
        "id": "r4nw3BVKRV-x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.document_stores import InMemoryDocumentStore\n",
        "\n",
        "number_correctly_retrieved_passage = 0\n",
        "number_correctly_retrieved_sentence = 0\n",
        "number_documents = 244\n",
        "\n",
        "\n",
        "for index, row in converted_test_data.iterrows():\n",
        "    if index == 101:\n",
        "        break\n",
        "    query = row['converted_query']\n",
        "    result = IR_pipeline.run(\n",
        "             query=query, \n",
        "             params={\"Retriever\": {\"top_k\": 1}}\n",
        "             )\n",
        "    retrieved_document = result[\"documents\"][0].content\n",
        "    target_document = converted_test_data['document_content'][index]\n",
        "    \n",
        "    if retrieved_document == target_document:\n",
        "        number_correctly_retrieved_passage += 1\n",
        "        \n",
        "        sentence_document_store = InMemoryDocumentStore()\n",
        "        sentence_document_store.delete_documents()\n",
        "        docs = []\n",
        "        sentence_list = nltk.tokenize.sent_tokenize(retrieved_document)\n",
        "        for sentence in sentence_list:\n",
        "            doc = Document(content=sentence,\n",
        "                           meta={\"article_title\": converted_test_data['document_title']})\n",
        "            docs.append(doc)\n",
        "            sentence_document_store.write_documents(docs)\n",
        "        \n",
        "        sentence_retriever = EmbeddingRetriever(\n",
        "            document_store=sentence_document_store,\n",
        "            embedding_model=\"/content/my retriever\",\n",
        "            model_format=\"sentence_transformers\",\n",
        "            scale_score = False\n",
        "        )\n",
        "\n",
        "        sentence_document_store.update_embeddings(sentence_retriever)\n",
        "\n",
        "        sentence_pipeline = DocumentSearchPipeline(retriever = sentence_retriever)\n",
        "        \n",
        "        sentence_result = sentence_pipeline.run(query=query, \n",
        "                                                params={\"Retriever\": {\"top_k\": 1}})\n",
        "        print(\"index: \", index)\n",
        "        print(number_correctly_retrieved_passage)\n",
        "        if sentence_result['documents'] != []:\n",
        "            retrieved_sentence = sentence_result[\"documents\"][0].content\n",
        "        target_sentence = converted_test_data['answer'][index]\n",
        "        if str(retrieved_sentence) in \" \".join(str(target_sentence).split()):\n",
        "            number_correctly_retrieved_sentence += 1\n",
        "            print(\"Found\")\n",
        "            print(number_correctly_retrieved_sentence)\n",
        "        else:\n",
        "            print(\"Not Found\")\n",
        "            print(number_correctly_retrieved_sentence)"
      ],
      "metadata": {
        "id": "IEnYLDIgAkKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(number_correctly_retrieved_passage)\n",
        "print(number_correctly_retrieved_sentence)\n"
      ],
      "metadata": {
        "id": "-pVPfjGyB3DY",
        "outputId": "dc4b248b-27a0-4078-a0fe-5df961d32f33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100\n",
            "75\n"
          ]
        }
      ]
    }
  ]
}