Bert_Answer_and_Question.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/adarsharegmi/Bert-Pre-trained-model/blob/main/Bert_Answer_and_Question.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4c1-jFlEdc-"
      },
      "source": [
        "# Hugging Face and Transformers\n",
        "\n",
        "> Hugging face is the open source NLP.\n",
        "\n",
        "> Transformers is their NLP library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_IOMPNoYFHTH"
      },
      "source": [
        "# CoQA conversational Question answering dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hutw3ZC0FOal"
      },
      "source": [
        "# BERT \n",
        "\n",
        "  Bidirectional Encoder Representations from Transformers.It is one of the most popular and widely used NLP models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4u5PdXxCFGCX",
        "outputId": "87b4ba7a-60bc-4332-ce60-ce970299d3d9"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.10.3-py3-none-any.whl (2.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.8 MB 5.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 57.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 73.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.1)\n",
            "Collecting huggingface-hub>=0.0.12\n",
            "  Downloading huggingface_hub-0.0.17-py3-none-any.whl (52 kB)\n",
            "\u001b[K     |████████████████████████████████| 52 kB 1.4 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 36.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.0.12->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Installing collected packages: tokenizers, sacremoses, pyyaml, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.0.17 pyyaml-5.4.1 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.10.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyvANeAnFj9e"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from transformers import BertForQuestionAnswering\n",
        "from transformers import BertTokenizer"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "YtGSaaX8Fwvs",
        "outputId": "69234ac6-c22b-4f4c-fd85-474ef12602e2"
      },
      "source": [
        "coqa = pd.read_json('http://downloads.cs.stanford.edu/nlp/data/coqa/coqa-train-v1.0.json')\n",
        "coqa.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>version</th>\n",
              "      <th>data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>{'source': 'wikipedia', 'id': '3zotghdk5ibi9ce...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>{'source': 'cnn', 'id': '3wj1oxy92agboo5nlq4r7...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>{'source': 'gutenberg', 'id': '3bdcf01ogxu7zdn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>{'source': 'cnn', 'id': '3ewijtffvo7wwchw6rtya...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>{'source': 'gutenberg', 'id': '3urfvvm165iantk...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   version                                               data\n",
              "0        1  {'source': 'wikipedia', 'id': '3zotghdk5ibi9ce...\n",
              "1        1  {'source': 'cnn', 'id': '3wj1oxy92agboo5nlq4r7...\n",
              "2        1  {'source': 'gutenberg', 'id': '3bdcf01ogxu7zdn...\n",
              "3        1  {'source': 'cnn', 'id': '3ewijtffvo7wwchw6rtya...\n",
              "4        1  {'source': 'gutenberg', 'id': '3urfvvm165iantk..."
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K84wacZ8F2p-"
      },
      "source": [
        "# DATA CLEANING\n",
        "del coqa[\"version\"]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "RX9S4RncF-VC",
        "outputId": "9a03b087-9cf9-4801-96c8-42de0d137cd0"
      },
      "source": [
        "# required cols\n",
        "cols = [\"text\", \"question\",\"answer\"]\n",
        "\n",
        "comp_list = []\n",
        "\n",
        "for index, row in coqa.iterrows():\n",
        "  for i in range(len(row[\"data\"][\"questions\"])):\n",
        "    temp_list = []\n",
        "    temp_list.append(row[\"data\"][\"story\"])\n",
        "    temp_list.append(row[\"data\"][\"questions\"][i][\"input_text\"])\n",
        "    temp_list.append(row[\"data\"][\"answers\"][i][\"input_text\"])\n",
        "    comp_list.append(temp_list)\n",
        "\n",
        "new_df = pd.DataFrame(comp_list, columns=cols)\n",
        "new_df"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>When was the Vat formally opened?</td>\n",
              "      <td>It was formally established in 1475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>what is the library for?</td>\n",
              "      <td>research</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>for what subjects?</td>\n",
              "      <td>history, and law</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>and?</td>\n",
              "      <td>philosophy, science and theology</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>what was started in 2014?</td>\n",
              "      <td>a  project</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108642</th>\n",
              "      <td>(CNN) -- Cristiano Ronaldo provided the perfec...</td>\n",
              "      <td>Who was a sub?</td>\n",
              "      <td>Xabi Alonso</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108643</th>\n",
              "      <td>(CNN) -- Cristiano Ronaldo provided the perfec...</td>\n",
              "      <td>Was it his first game this year?</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108644</th>\n",
              "      <td>(CNN) -- Cristiano Ronaldo provided the perfec...</td>\n",
              "      <td>What position did the team reach?</td>\n",
              "      <td>third</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108645</th>\n",
              "      <td>(CNN) -- Cristiano Ronaldo provided the perfec...</td>\n",
              "      <td>Who was ahead of them?</td>\n",
              "      <td>Barca.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108646</th>\n",
              "      <td>(CNN) -- Cristiano Ronaldo provided the perfec...</td>\n",
              "      <td>By how much?</td>\n",
              "      <td>six points</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>108647 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                     text  ...                               answer\n",
              "0       The Vatican Apostolic Library (), more commonl...  ...  It was formally established in 1475\n",
              "1       The Vatican Apostolic Library (), more commonl...  ...                             research\n",
              "2       The Vatican Apostolic Library (), more commonl...  ...                     history, and law\n",
              "3       The Vatican Apostolic Library (), more commonl...  ...     philosophy, science and theology\n",
              "4       The Vatican Apostolic Library (), more commonl...  ...                           a  project\n",
              "...                                                   ...  ...                                  ...\n",
              "108642  (CNN) -- Cristiano Ronaldo provided the perfec...  ...                          Xabi Alonso\n",
              "108643  (CNN) -- Cristiano Ronaldo provided the perfec...  ...                                  Yes\n",
              "108644  (CNN) -- Cristiano Ronaldo provided the perfec...  ...                                third\n",
              "108645  (CNN) -- Cristiano Ronaldo provided the perfec...  ...                               Barca.\n",
              "108646  (CNN) -- Cristiano Ronaldo provided the perfec...  ...                           six points\n",
              "\n",
              "[108647 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FNYbQyXG2Lm"
      },
      "source": [
        "new_df.to_csv(\"CoQA_data.csv\", index=False)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "gVN4SlDVHUaf",
        "outputId": "de736b8e-e72e-400d-a892-727a69910bff"
      },
      "source": [
        "data = pd.read_csv(\"CoQA_data.csv\")\n",
        "data.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>When was the Vat formally opened?</td>\n",
              "      <td>It was formally established in 1475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>what is the library for?</td>\n",
              "      <td>research</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>for what subjects?</td>\n",
              "      <td>history, and law</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>and?</td>\n",
              "      <td>philosophy, science and theology</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The Vatican Apostolic Library (), more commonl...</td>\n",
              "      <td>what was started in 2014?</td>\n",
              "      <td>a  project</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  ...                               answer\n",
              "0  The Vatican Apostolic Library (), more commonl...  ...  It was formally established in 1475\n",
              "1  The Vatican Apostolic Library (), more commonl...  ...                             research\n",
              "2  The Vatican Apostolic Library (), more commonl...  ...                     history, and law\n",
              "3  The Vatican Apostolic Library (), more commonl...  ...     philosophy, science and theology\n",
              "4  The Vatican Apostolic Library (), more commonl...  ...                           a  project\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHBrqWtHHXeL",
        "outputId": "99e25754-9b81-475b-eaf8-9899c7efb6e8"
      },
      "source": [
        "print(\"Number of question and answers: \", len(data))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of question and answers:  108647\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "FGlMlcTaHZ7m",
        "outputId": "529fd57b-3340-4d49-f00f-c96c0f630249"
      },
      "source": [
        "# BUILDING CHATBOT\n",
        "model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "232c8216ff0c4270b736832492b1d56c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/443 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5378fe29214744708c54f15d8e83a81c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2
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            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2
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            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "149b45cc63c94e2191edd2dbf198bb10",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdR2x8DgHgo5"
      },
      "source": [
        "random_num = np.random.randint(0,len(data))\n",
        "\n",
        "question = data[\"question\"][random_num]\n",
        "text = data[\"text\"][random_num]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5CkEzRFHoaZ",
        "outputId": "0a42398c-25b1-43e2-f168-ed2f1acfeb29"
      },
      "source": [
        "\n",
        "print(question, \"\\n\", text)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Did he do a good job? \n",
            " Many people like animals and raise one or more as pets--dogs, cat or some kinds of birds. I love dogs, too. My aunt gave me a dog on my birthday. We call it Wangwang. It is a little black dog. He is friendly and helpful. Most of the time he likes to run and play with me. Sometimes he follows my father around in the fields. One day, my father took off his grey coat and put it on the ground under a big tree. Wangwang stood watching him. My father said, \"Watch over my coat, Wangwant.\" Wangwant sat down beside the coat. My father went on working. After he finished his work, he forgot all about his coat and went home. Late in the evening I didn't see my dog. I looked everywhere for him calling, \"Wangwang, Wangwang!\" But Wangwang didn't come back. Soon my father wanted something that was in his coat pocket. Then he remembered what he had done. He went back to the big tree. What do you think he saw? Wangwang was sitting on the coat so that nobody could take it away.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioUyqn3SHrRr",
        "outputId": "77dc46df-65e5-4f3f-bdb9-08ed3ae55dc9"
      },
      "source": [
        "input_ids = tokenizer.encode(question, text)\n",
        "print(\"The input has a total of {} tokens.\".format(len(input_ids)))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The input has a total of 249 tokens.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9bvPKSUeHs7M",
        "outputId": "e3c09785-a467-4c71-dbfe-298a40d1693a"
      },
      "source": [
        "tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
        "\n",
        "for token, id in zip(tokens, input_ids):\n",
        "    print('{:8}{:8,}'.format(token,id))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS]        101\n",
            "did        2,106\n",
            "he         2,002\n",
            "do         2,079\n",
            "a          1,037\n",
            "good       2,204\n",
            "job        3,105\n",
            "?          1,029\n",
            "[SEP]        102\n",
            "many       2,116\n",
            "people     2,111\n",
            "like       2,066\n",
            "animals    4,176\n",
            "and        1,998\n",
            "raise      5,333\n",
            "one        2,028\n",
            "or         2,030\n",
            "more       2,062\n",
            "as         2,004\n",
            "pets      18,551\n",
            "-          1,011\n",
            "-          1,011\n",
            "dogs       6,077\n",
            ",          1,010\n",
            "cat        4,937\n",
            "or         2,030\n",
            "some       2,070\n",
            "kinds      7,957\n",
            "of         1,997\n",
            "birds      5,055\n",
            ".          1,012\n",
            "i          1,045\n",
            "love       2,293\n",
            "dogs       6,077\n",
            ",          1,010\n",
            "too        2,205\n",
            ".          1,012\n",
            "my         2,026\n",
            "aunt       5,916\n",
            "gave       2,435\n",
            "me         2,033\n",
            "a          1,037\n",
            "dog        3,899\n",
            "on         2,006\n",
            "my         2,026\n",
            "birthday   5,798\n",
            ".          1,012\n",
            "we         2,057\n",
            "call       2,655\n",
            "it         2,009\n",
            "wang       7,418\n",
            "##wang    16,600\n",
            ".          1,012\n",
            "it         2,009\n",
            "is         2,003\n",
            "a          1,037\n",
            "little     2,210\n",
            "black      2,304\n",
            "dog        3,899\n",
            ".          1,012\n",
            "he         2,002\n",
            "is         2,003\n",
            "friendly   5,379\n",
            "and        1,998\n",
            "helpful   14,044\n",
            ".          1,012\n",
            "most       2,087\n",
            "of         1,997\n",
            "the        1,996\n",
            "time       2,051\n",
            "he         2,002\n",
            "likes      7,777\n",
            "to         2,000\n",
            "run        2,448\n",
            "and        1,998\n",
            "play       2,377\n",
            "with       2,007\n",
            "me         2,033\n",
            ".          1,012\n",
            "sometimes   2,823\n",
            "he         2,002\n",
            "follows    4,076\n",
            "my         2,026\n",
            "father     2,269\n",
            "around     2,105\n",
            "in         1,999\n",
            "the        1,996\n",
            "fields     4,249\n",
            ".          1,012\n",
            "one        2,028\n",
            "day        2,154\n",
            ",          1,010\n",
            "my         2,026\n",
            "father     2,269\n",
            "took       2,165\n",
            "off        2,125\n",
            "his        2,010\n",
            "grey       4,462\n",
            "coat       5,435\n",
            "and        1,998\n",
            "put        2,404\n",
            "it         2,009\n",
            "on         2,006\n",
            "the        1,996\n",
            "ground     2,598\n",
            "under      2,104\n",
            "a          1,037\n",
            "big        2,502\n",
            "tree       3,392\n",
            ".          1,012\n",
            "wang       7,418\n",
            "##wang    16,600\n",
            "stood      2,768\n",
            "watching   3,666\n",
            "him        2,032\n",
            ".          1,012\n",
            "my         2,026\n",
            "father     2,269\n",
            "said       2,056\n",
            ",          1,010\n",
            "\"          1,000\n",
            "watch      3,422\n",
            "over       2,058\n",
            "my         2,026\n",
            "coat       5,435\n",
            ",          1,010\n",
            "wang       7,418\n",
            "##wan      7,447\n",
            "##t        2,102\n",
            ".          1,012\n",
            "\"          1,000\n",
            "wang       7,418\n",
            "##wan      7,447\n",
            "##t        2,102\n",
            "sat        2,938\n",
            "down       2,091\n",
            "beside     3,875\n",
            "the        1,996\n",
            "coat       5,435\n",
            ".          1,012\n",
            "my         2,026\n",
            "father     2,269\n",
            "went       2,253\n",
            "on         2,006\n",
            "working    2,551\n",
            ".          1,012\n",
            "after      2,044\n",
            "he         2,002\n",
            "finished   2,736\n",
            "his        2,010\n",
            "work       2,147\n",
            ",          1,010\n",
            "he         2,002\n",
            "forgot     9,471\n",
            "all        2,035\n",
            "about      2,055\n",
            "his        2,010\n",
            "coat       5,435\n",
            "and        1,998\n",
            "went       2,253\n",
            "home       2,188\n",
            ".          1,012\n",
            "late       2,397\n",
            "in         1,999\n",
            "the        1,996\n",
            "evening    3,944\n",
            "i          1,045\n",
            "didn       2,134\n",
            "'          1,005\n",
            "t          1,056\n",
            "see        2,156\n",
            "my         2,026\n",
            "dog        3,899\n",
            ".          1,012\n",
            "i          1,045\n",
            "looked     2,246\n",
            "everywhere   7,249\n",
            "for        2,005\n",
            "him        2,032\n",
            "calling    4,214\n",
            ",          1,010\n",
            "\"          1,000\n",
            "wang       7,418\n",
            "##wang    16,600\n",
            ",          1,010\n",
            "wang       7,418\n",
            "##wang    16,600\n",
            "!            999\n",
            "\"          1,000\n",
            "but        2,021\n",
            "wang       7,418\n",
            "##wang    16,600\n",
            "didn       2,134\n",
            "'          1,005\n",
            "t          1,056\n",
            "come       2,272\n",
            "back       2,067\n",
            ".          1,012\n",
            "soon       2,574\n",
            "my         2,026\n",
            "father     2,269\n",
            "wanted     2,359\n",
            "something   2,242\n",
            "that       2,008\n",
            "was        2,001\n",
            "in         1,999\n",
            "his        2,010\n",
            "coat       5,435\n",
            "pocket     4,979\n",
            ".          1,012\n",
            "then       2,059\n",
            "he         2,002\n",
            "remembered   4,622\n",
            "what       2,054\n",
            "he         2,002\n",
            "had        2,018\n",
            "done       2,589\n",
            ".          1,012\n",
            "he         2,002\n",
            "went       2,253\n",
            "back       2,067\n",
            "to         2,000\n",
            "the        1,996\n",
            "big        2,502\n",
            "tree       3,392\n",
            ".          1,012\n",
            "what       2,054\n",
            "do         2,079\n",
            "you        2,017\n",
            "think      2,228\n",
            "he         2,002\n",
            "saw        2,387\n",
            "?          1,029\n",
            "wang       7,418\n",
            "##wang    16,600\n",
            "was        2,001\n",
            "sitting    3,564\n",
            "on         2,006\n",
            "the        1,996\n",
            "coat       5,435\n",
            "so         2,061\n",
            "that       2,008\n",
            "nobody     6,343\n",
            "could      2,071\n",
            "take       2,202\n",
            "it         2,009\n",
            "away       2,185\n",
            ".          1,012\n",
            "[SEP]        102\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGOQ34lFHvWB",
        "outputId": "ebec1622-d028-4e35-f9d3-c952dbabfe44"
      },
      "source": [
        "#first occurence of [SEP] token\n",
        "sep_idx = input_ids.index(tokenizer.sep_token_id)\n",
        "print(sep_idx)\n",
        "\n",
        "#number of tokens in segment A - question\n",
        "num_seg_a = sep_idx+1\n",
        "print(num_seg_a)\n",
        "\n",
        "#number of tokens in segment B - text\n",
        "num_seg_b = len(input_ids) - num_seg_a\n",
        "print(num_seg_b)\n",
        "\n",
        "segment_ids = [0]*num_seg_a + [1]*num_seg_b\n",
        "print(segment_ids)\n",
        "\n",
        "assert len(segment_ids) == len(input_ids)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8\n",
            "9\n",
            "240\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPnkIF0yHzpO"
      },
      "source": [
        "#token input_ids to represent the input\n",
        "#token segment_ids to differentiate our segments - text and question \n",
        "output = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([segment_ids]))\n",
        "#print(output.start_logits, output.end_logits)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mX-lOel3H1zs"
      },
      "source": [
        "\n",
        "#tokens with highest start and end scores\n",
        "answer_start = torch.argmax(output.start_logits)\n",
        "answer_end = torch.argmax(output.end_logits)\n",
        "#print(answer_start, answer_end)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vJCX9NnH4M3",
        "outputId": "adcd5fa8-18b3-441e-9af8-3610e056269d"
      },
      "source": [
        "if answer_end >= answer_start:\n",
        "    answer = \" \".join(tokens[answer_start:answer_end+1])\n",
        "else:\n",
        "    print(\"I am unable to find the answer to this question. Can you please ask another question?\")\n",
        "    \n",
        "print(\"Text:\\n{}\".format(text.capitalize()))\n",
        "print(\"\\nQuestion:\\n{}\".format(question.capitalize()))\n",
        "print(\"\\nAnswer:\\n{}.\".format(answer.capitalize()))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Text:\n",
            "Many people like animals and raise one or more as pets--dogs, cat or some kinds of birds. i love dogs, too. my aunt gave me a dog on my birthday. we call it wangwang. it is a little black dog. he is friendly and helpful. most of the time he likes to run and play with me. sometimes he follows my father around in the fields. one day, my father took off his grey coat and put it on the ground under a big tree. wangwang stood watching him. my father said, \"watch over my coat, wangwant.\" wangwant sat down beside the coat. my father went on working. after he finished his work, he forgot all about his coat and went home. late in the evening i didn't see my dog. i looked everywhere for him calling, \"wangwang, wangwang!\" but wangwang didn't come back. soon my father wanted something that was in his coat pocket. then he remembered what he had done. he went back to the big tree. what do you think he saw? wangwang was sitting on the coat so that nobody could take it away.\n",
            "\n",
            "Question:\n",
            "Did he do a good job?\n",
            "\n",
            "Answer:\n",
            "He forgot all about his coat and went home.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBH59Nw8H54H"
      },
      "source": [
        "start_scores = output.start_logits.detach().numpy().flatten()\n",
        "end_scores = output.end_logits.detach().numpy().flatten()\n",
        "\n",
        "token_labels = []\n",
        "for i, token in enumerate(tokens):\n",
        "    token_labels.append(\"{}-{}\".format(token,i))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVka8eMaH8A-",
        "outputId": "ffa34ecb-eceb-411d-a42c-e4ccc11e73cc"
      },
      "source": [
        "print(len(token_labels))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "249\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "eMiu2cIvH-Av",
        "outputId": "5349fb9c-b162-4aa0-8cc4-60dfc81f4686"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#first 100 tokens\n",
        "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
        "ax = sns.barplot(x=token_labels[:80], y=start_scores[:80], ci=None)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "ax.grid(True)\n",
        "plt.title(\"Start word scores\")\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "gD4lv8GQH_om",
        "outputId": "83fc21d0-aa8e-4d7b-83ac-5467f60e7b0f"
      },
      "source": [
        "\n",
        "#last 100 tokens\n",
        "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
        "ax = sns.barplot(x=token_labels[-80:], y=start_scores[-80:], ci=None)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "ax.grid(True)\n",
        "plt.title(\"Start word scores\")\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "cvQgE2gKIKN0",
        "outputId": "1942d9f3-961a-4dd4-fa94-9eb3990e6aa6"
      },
      "source": [
        "#first 100 tokens\n",
        "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
        "ax = sns.barplot(x=token_labels[:80], y=end_scores[:80], ci=None)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n",
        "ax.grid(True)\n",
        "plt.title(\"End word scores\")\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzUkiR9LIMBO"
      },
      "source": [
        "# code to join broken words\n",
        "\n",
        "answer = tokens[answer_start]\n",
        "\n",
        "for i in range(answer_start+1, answer_end+1):\n",
        "    if tokens[i][0:2] == \"##\":\n",
        "        answer += tokens[i][2:]\n",
        "    else:\n",
        "        answer += \" \" + tokens[i]"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-MIR0JTISlf"
      },
      "source": [
        "# FUNCTION\n",
        "\n",
        "def question_answer(question, text):\n",
        "    \n",
        "    #tokenize question and text in ids as a pair\n",
        "    input_ids = tokenizer.encode(question, text)\n",
        "    \n",
        "    #string version of tokenized ids\n",
        "    tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
        "    \n",
        "    #segment IDs\n",
        "    #first occurence of [SEP] token\n",
        "    sep_idx = input_ids.index(tokenizer.sep_token_id)\n",
        "\n",
        "    #number of tokens in segment A - question\n",
        "    num_seg_a = sep_idx+1\n",
        "\n",
        "    #number of tokens in segment B - text\n",
        "    num_seg_b = len(input_ids) - num_seg_a\n",
        "    \n",
        "    #list of 0s and 1s\n",
        "    segment_ids = [0]*num_seg_a + [1]*num_seg_b\n",
        "    \n",
        "    assert len(segment_ids) == len(input_ids)\n",
        "    \n",
        "    #model output using input_ids and segment_ids\n",
        "    output = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([segment_ids]))\n",
        "    \n",
        "    #reconstructing the answer\n",
        "    answer_start = torch.argmax(output.start_logits)\n",
        "    answer_end = torch.argmax(output.end_logits)\n",
        "\n",
        "    if answer_end >= answer_start:\n",
        "        answer = tokens[answer_start]\n",
        "        for i in range(answer_start+1, answer_end+1):\n",
        "            if tokens[i][0:2] == \"##\":\n",
        "                answer += tokens[i][2:]\n",
        "            else:\n",
        "                answer += \" \" + tokens[i]\n",
        "                \n",
        "    if answer.startswith(\"[CLS]\"):\n",
        "        answer = \"Unable to find the answer to your question.\"\n",
        "    \n",
        "#     print(\"Text:\\n{}\".format(text.capitalize()))\n",
        "#     print(\"\\nQuestion:\\n{}\".format(question.capitalize()))\n",
        "    print(\"\\nAnswer:\\n{}\".format(answer.capitalize()))"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1cAsgk9IWyf",
        "outputId": "94def462-02f2-447c-b4db-a0e3c2cf1476"
      },
      "source": [
        "text = \"\"\"New York (CNN) -- More than 80 Michael Jackson collectibles -- including the late pop star's famous rhinestone-studded glove from a 1983 performance -- were auctioned off Saturday, reaping a total $2 million. Profits from the auction at the Hard Rock Cafe in New York's Times Square crushed pre-sale expectations of only $120,000 in sales. The highly prized memorabilia, which included items spanning the many stages of Jackson's career, came from more than 30 fans, associates and family members, who contacted Julien's Auctions to sell their gifts and mementos of the singer. Jackson's flashy glove was the big-ticket item of the night, fetching $420,000 from a buyer in Hong Kong, China. Jackson wore the glove at a 1983 performance during \\\"Motown 25,\\\" an NBC special where he debuted his revolutionary moonwalk. Fellow Motown star Walter \\\"Clyde\\\" Orange of the Commodores, who also performed in the special 26 years ago, said he asked for Jackson's autograph at the time, but Jackson gave him the glove instead. \"The legacy that [Jackson] left behind is bigger than life for me,\\\" Orange said. \\\"I hope that through that glove people can see what he was trying to say in his music and what he said in his music.\\\" Orange said he plans to give a portion of the proceeds to charity. Hoffman Ma, who bought the glove on behalf of Ponte 16 Resort in Macau, paid a 25 percent buyer's premium, which was tacked onto all final sales over $50,000. Winners of items less than $50,000 paid a 20 percent premium.\"\"\"\n",
        "question = \"Where was the Auction held?\"\n",
        "\n",
        "question_answer(question, text)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Answer:\n",
            "Hard rock cafe in new york ' s times square\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJAkhbCiIZVu",
        "outputId": "08e69c56-4951-402b-e37f-c772f5d4a7b6"
      },
      "source": [
        "print(\"Original answer:\\n\", data.loc[data[\"question\"] == question][\"answer\"].values[0])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original answer:\n",
            " Hard Rock Cafe\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qi6IeBRyIwAr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}