team3-chatbot-qa.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cqwAETvxd3L"
      },
      "source": [
        "# Advanced Generative Q&A Chatbot Using distilBERT model\n",
        "## AAI-520 Team 3 Final Project\n",
        "\n",
        "Team 3 Members:  Tyler Foreman, Ahmed Ahmed, Tursun Alkam\n",
        "\n",
        "Date:  October 23, 2023\n",
        "\n",
        "GitHub Repository: https://github.com/t4ai/aai520-team3-final\n",
        "\n",
        "References used: https://huggingface.co/learn/nlp-course/chapter0/1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y7_4jfHy15Vi"
      },
      "outputs": [],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install evaluate\n",
        "!pip install nvidia-smi\n",
        "!pip install --upgrade nvidia-pyindex"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "WQGAegI2xd3T"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "import random\n",
        "import statistics\n",
        "import spacy\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "import os\n",
        "import json\n",
        "\n",
        "from transformers import BertModel, BertTokenizerFast, AdamW\n",
        "import tensorflow as tf\n",
        "\n",
        "import keras\n",
        "from keras.layers import Input, Lambda, Dense\n",
        "from keras.models import Model\n",
        "import keras.backend as K"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRVwYCnBxd3b"
      },
      "source": [
        "#### Install notes on MacOS\n",
        "\n",
        "1. upgrade pip: pip install --upgrade pip\n",
        "2. intall rust compiler: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh\n",
        "3. source \"$HOME/.cargo/env\"\n",
        "4. pip install transformers\n",
        "5. pip install tensorflow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfEa70fuxd3c"
      },
      "source": [
        "## Load Dataset and Process into Format for BERT Tokenizer\n",
        "\n",
        " - For Dev and Training:\n",
        " - Load data and process into contexts, questions and answers { text, start, end}\n",
        " --  For each question/answer: add context entry to contexts[], add question to questions[] and answer to answers[]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaH_OrxP1q7R",
        "outputId": "27046a22-302a-4d99-ffaa-4decb8749431"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Oct 12 22:43:54 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   62C    P8    10W /  70W |      3MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# Run if in Colab and check GPU\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zi8FVyasxl3n",
        "outputId": "f7207158-cb29-4121-c54c-62ed6c48c2f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Run if in Colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "RNzLOCAzxd3d"
      },
      "outputs": [],
      "source": [
        "# helper function to load dataset from JSON file\n",
        "def load_squad_data_file(file_path):\n",
        "    #open file and parse JSON\n",
        "    f = open(file_path, 'rb')\n",
        "    squad_data = json.load(f)\n",
        "\n",
        "    # setup output lists that will be populated\n",
        "    contexts = []\n",
        "    questions = []\n",
        "    answers = []\n",
        "    idmap = []\n",
        "\n",
        "    # traverse JSON object tree and populate object lists\n",
        "    # since there are many answers to questions and contexts, create entries for each answer\n",
        "    for json_groups in squad_data['data']:\n",
        "        for paragraph in json_groups['paragraphs']:\n",
        "            curr_context = paragraph['context']\n",
        "            for qa in paragraph['qas']:\n",
        "                curr_question = qa['question']\n",
        "                curr_id = qa['id']\n",
        "                for answer in qa['answers']:\n",
        "                    # add entries for each answer to all lists\n",
        "                    contexts.append(curr_context.lower())\n",
        "                    questions.append(curr_question.lower())\n",
        "                    answers.append(answer)\n",
        "                    idmap.append(curr_id)\n",
        "\n",
        "\n",
        "    return contexts, questions, answers, idmap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfZiDf74DZEF"
      },
      "source": [
        "### Load the Data from the JSON File\n",
        "\n",
        "Use the appropriate block below - first one is setup to load from Google Colab mounted drive, second block loads from local filesystem (if running this notbook locally)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "w3qQNMfExwp2"
      },
      "outputs": [],
      "source": [
        "# load the datasets from JSON - in Colab\n",
        "contexts_train, questions_train, answers_train, id_map_train = load_squad_data_file('/content/drive/MyDrive/USD/datasets/train-v1.1.json')\n",
        "contexts_test, questions_test, answers_test, id_map_test = load_squad_data_file('/content/drive/MyDrive/USD/datasets/dev-v1.1.json')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "znAlCjCXxd3e"
      },
      "outputs": [],
      "source": [
        "# load the datasets from JSON - Local\n",
        "contexts_train, questions_train, answers_train, id_map_train = load_squad_data_file('./dataset/train-v1.1.json')\n",
        "contexts_test, questions_test, answers_test, id_map_test = load_squad_data_file('./dataset/dev-v1.1.json')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mX7wZ8iXxd3e",
        "outputId": "ffc4be09-50c1-4af6-da50-a4f61d655e51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Train Samples]\n",
            "architecturally, the school has a catholic character. atop the main building's gold dome is a golden statue of the virgin mary. immediately in front of the main building and facing it, is a copper statue of christ with arms upraised with the legend \"venite ad me omnes\". next to the main building is the basilica of the sacred heart. immediately behind the basilica is the grotto, a marian place of prayer and reflection. it is a replica of the grotto at lourdes, france where the virgin mary reputedly appeared to saint bernadette soubirous in 1858. at the end of the main drive (and in a direct line that connects through 3 statues and the gold dome), is a simple, modern stone statue of mary.\n",
            "['to whom did the virgin mary allegedly appear in 1858 in lourdes france?', 'what is in front of the notre dame main building?', 'the basilica of the sacred heart at notre dame is beside to which structure?', 'what is the grotto at notre dame?', 'what sits on top of the main building at notre dame?']\n",
            "[{'answer_start': 515, 'text': 'Saint Bernadette Soubirous'}, {'answer_start': 188, 'text': 'a copper statue of Christ'}, {'answer_start': 279, 'text': 'the Main Building'}, {'answer_start': 381, 'text': 'a Marian place of prayer and reflection'}, {'answer_start': 92, 'text': 'a golden statue of the Virgin Mary'}]\n",
            "['5733be284776f41900661182', '5733be284776f4190066117f', '5733be284776f41900661180', '5733be284776f41900661181', '5733be284776f4190066117e']\n",
            "\n",
            "[Test Samples]\n",
            "super bowl 50 was an american football game to determine the champion of the national football league (nfl) for the 2015 season. the american football conference (afc) champion denver broncos defeated the national football conference (nfc) champion carolina panthers 24–10 to earn their third super bowl title. the game was played on february 7, 2016, at levi's stadium in the san francisco bay area at santa clara, california. as this was the 50th super bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each super bowl game with roman numerals (under which the game would have been known as \"super bowl l\"), so that the logo could prominently feature the arabic numerals 50.\n",
            "['which nfl team represented the afc at super bowl 50?', 'which nfl team represented the afc at super bowl 50?', 'which nfl team represented the afc at super bowl 50?', 'which nfl team represented the nfc at super bowl 50?', 'which nfl team represented the nfc at super bowl 50?']\n",
            "[{'answer_start': 177, 'text': 'Denver Broncos'}, {'answer_start': 177, 'text': 'Denver Broncos'}, {'answer_start': 177, 'text': 'Denver Broncos'}, {'answer_start': 249, 'text': 'Carolina Panthers'}, {'answer_start': 249, 'text': 'Carolina Panthers'}]\n",
            "['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ec', '56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ed']\n"
          ]
        }
      ],
      "source": [
        "# inspect some samples to make sure everything was loaded correctly\n",
        "print(\"[Train Samples]\")\n",
        "print(contexts_train[0])\n",
        "print(questions_train[:5])\n",
        "print(answers_train[:5])\n",
        "print(id_map_train[:5])\n",
        "\n",
        "\n",
        "print(\"\\n[Test Samples]\")\n",
        "print(contexts_test[0])\n",
        "print(questions_test[:5])\n",
        "print(answers_test[:5])\n",
        "print(id_map_test[:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahUGc364xd3h"
      },
      "source": [
        "## Exploratory Data Analysis\n",
        "-  Derive some descriptive statistics from processed datasets\n",
        "-  Conduct analysis on context lengths, question lenghths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25C8P14Zxd3h",
        "outputId": "da1f67bd-0bc6-4d6d-e385-7eb576f38874"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Training] # contexts: 87599\n",
            "[Training] # questions: 87599\n",
            "[Training] # answers: 87599\n",
            "\n",
            "[Test] # contexts: 34726\n",
            "[Test] # questions: 34726\n",
            "[Test] # answers: 34726\n"
          ]
        }
      ],
      "source": [
        "# get basic counts of Q&A pairs to confirm consistency\n",
        "print(\"[Training] # contexts: {}\".format(len(contexts_train)))\n",
        "print(\"[Training] # questions: {}\".format(len(questions_train)))\n",
        "print(\"[Training] # answers: {}\".format(len(answers_train)))\n",
        "\n",
        "print(\"\\n[Test] # contexts: {}\".format(len(contexts_test)))\n",
        "print(\"[Test] # questions: {}\".format(len(questions_test)))\n",
        "print(\"[Test] # answers: {}\".format(len(answers_test)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4l7ByVUyrCx",
        "outputId": "2b4ced83-7a83-483a-e9b9-198f7eda0a93"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to\n",
            "[nltk_data]     /Users/tylerforeman/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WnhdDNtqxd3i"
      },
      "outputs": [],
      "source": [
        "# check text lengths\n",
        "context_train_tok_dist = []\n",
        "for doc in contexts_train:\n",
        "    context_train_tok_dist.append(len(word_tokenize(doc)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c5cz7L0_xd3p"
      },
      "outputs": [],
      "source": [
        "# check question lengths\n",
        "question_train_tok_dist = []\n",
        "for doc in questions_train:\n",
        "    question_train_tok_dist.append(len(word_tokenize(doc)))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "KxYbcE11xd3q",
        "outputId": "cccb50b7-bbe1-4210-ca71-6a7787e75765"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot histogram to visually analyze distribution\n",
        "sns.histplot(data=context_train_tok_dist).set(title='Distribution of word/token counts of context text [train]')\n",
        "plt.xlabel( \"Token/Word Count\" , size = 12 )\n",
        "plt.ylabel( \"Context count\" , size = 12 )\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsUKLLmIxd3q",
        "outputId": "b16fea1e-de04-4cd9-f46e-d73d1186361e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[Training] Mean context token length: 138\n",
            "[Training] Min content token length: 22\n",
            "[Training] Max content token length: 766\n"
          ]
        }
      ],
      "source": [
        "# get descriptive statistics of context text token lengths\n",
        "print(\"[Training] Mean context token length: {}\".format(round(np.mean(context_train_tok_dist))))\n",
        "print(\"[Training] Min content token length: {}\".format(min(context_train_tok_dist)))\n",
        "print(\"[Training] Max content token length: {}\".format(max(context_train_tok_dist)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "URcKUH25xd3s",
        "outputId": "9f9bd251-9b61-43d4-dfd5-922d106fa627"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot histogram to visually analyze distribution\n",
        "sns.histplot(data=question_train_tok_dist).set(title='Distribution of word/token counts of question text [train]')\n",
        "plt.xlabel( \"Token/Word Count\", size = 12 )\n",
        "plt.ylabel( \"Question count\", size = 12 )\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Acbd1q0bxd3s",
        "outputId": "d1a21f58-fedb-4629-cfd4-d0af2070ff83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[Training] Mean question token length: 11\n",
            "[Training] Min question token length: 1\n",
            "[Training] Max question token length: 60\n"
          ]
        }
      ],
      "source": [
        "# get descriptive statistics of question text token lengths\n",
        "print(\"[Training] Mean question token length: {}\".format(round(np.mean(question_train_tok_dist))))\n",
        "print(\"[Training] Min question token length: {}\".format(min(question_train_tok_dist)))\n",
        "print(\"[Training] Max question token length: {}\".format(max(question_train_tok_dist)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tg-3AxJixd3s"
      },
      "source": [
        "## Feature Extraction\n",
        "-  Tokenize the context and question text using HF tokenizer\n",
        "-  Use pre-trained BERT model from HF \"distilbert-base-cased\" (https://huggingface.co/distilbertbert-base-cased).  This model is suitable for Q&A tasks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "0e010e2ce83349bf922f6cd009dbd5f2",
            "56b4d120c231409b83fc736ba98300bc",
            "90ac7c18c96a4eb5bd0f5d291c20e039",
            "89c3996292f2461f94665ecfeed9de1a",
            "4790f1af82e940dab751183aee19ccfc",
            "1b4ddd5e3dee4b26883551e2210b5685",
            "c3430072a15848bd97265b66f4e2bb67",
            "814a223d9f1d436aaa6d0e832abe101c",
            "2238c03daa7e4cd281715e06037406c0",
            "058010f7519645aeab1d3dd445833d97",
            "c413f6fc42d440008fa94c5983f843be",
            "b5b740d4d4814020a7a04f0cc66dfa31",
            "5e27f9829041482e9843d9a8930b9832",
            "82908f0258c84891a5e3f07ba83a9984",
            "bdfad691b3d9498facb3fcd3990a7854",
            "36da1aa622054b1aae72e64091190b3b",
            "5584477da795408887f8e5e332a6b583",
            "0344b364c956438a84047ab1d2df2303",
            "8628a900cf2748c8a752680cc0049f0b",
            "011d123e697f403d8eab96b17b653116",
            "ae7ce5394c0849f3b537c23a312e98cc",
            "214a1f8825a54187bc1da7cd8994ff54",
            "8790ca558c9244e6aa960d5fe94a6e51",
            "e39865300747421489bd7d41df1e32f2",
            "4004b45b408544ec9afb5bc2aaee987d",
            "d79547a08d4c476f8f9294eab1aa18cd",
            "4b3f0ac9930b45ce8d60c66bb466b7ae",
            "9dffb84130fd4af187b9d37a4931c8a7",
            "df86d154c3a14b909e95c6b901fbb05c",
            "84a1b11b8e19478d8167ae6d101a14cb",
            "4c154386f62e49b1948e6b7f9f4d9487",
            "aa682a0d7b7b40a99119033379de7d31",
            "c8f3bd6cfd6d4ee69aca74a3ab54f3e6",
            "03221d103fc04d809add1964e4115230",
            "05fb64ba3c2f497db581fd52f6e44fd9",
            "0f64d3e1f020422b8ed3652553c002de",
            "bcbe6add6b5e48d89f0d5d9aab29b1a4",
            "8e5ddf16b7a1437081529a6eaa05efd6",
            "0bc4e71f90d14539a20030813181b12c",
            "30ed9122f0314669b28077a267ff4212",
            "b64ac65e981741748385eb65603db6fd",
            "74c8deb785ab4a85850bd04887ae3e29",
            "e95ac5db115d4c4a88f8abfb73f8b015",
            "5c4b8ce794f543d4bdd200282e14c578"
          ]
        },
        "id": "RERrY6UExd3t",
        "outputId": "5c914978-da74-4f44-b506-4f5f6e90cf39"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e010e2ce83349bf922f6cd009dbd5f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/465 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5b740d4d4814020a7a04f0cc66dfa31"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8790ca558c9244e6aa960d5fe94a6e51"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03221d103fc04d809add1964e4115230"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Initiatlize tokenizer from HF transformer lib\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"distilbert-base-cased\"\n",
        "tokenizer1 = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fh_h25S4xd3t",
        "outputId": "2f083bf9-f3e7-4ab1-9f33-a8bc9968c20c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('which', (0, 5)),\n",
              " ('organization', (6, 18)),\n",
              " ('declared', (19, 27)),\n",
              " ('the', (28, 31)),\n",
              " ('first', (32, 37)),\n",
              " ('year', (38, 42)),\n",
              " ('of', (43, 45)),\n",
              " ('studies', (46, 53)),\n",
              " ('program', (54, 61)),\n",
              " ('at', (62, 64)),\n",
              " ('notre', (65, 70)),\n",
              " ('dame', (71, 75)),\n",
              " ('\"', (76, 77)),\n",
              " ('outstanding', (77, 88)),\n",
              " ('?', (88, 89)),\n",
              " ('\"', (89, 90))]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# test pre-tokenizer on sample\n",
        "tokenizer1.backend_tokenizer.pre_tokenizer.pre_tokenize_str(questions_train[23])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "KNz0EQg4xd3u",
        "outputId": "5a0f97bf-353f-4581-f85e-293f305f6e00"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] which organization declared the first year of studies program at notre dame \" outstanding? \" [SEP] all of notre dame\\'s undergraduate students are a part of one of the five undergraduate colleges at the school or are in the first year of studies program. the first year of studies program was established in 1962 to guide incoming freshmen in their first year at the school before they have declared a major. each student is given an academic advisor from the program who helps them to choose classes that give them exposure to any major in which they are interested. the program also includes a learning resource center which provides time management, collaborative learning, and subject tutoring. this program has been recognized previously, by u. s. news & world report, as outstanding. [SEP]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# test HF tokenizer on a sample from train dataset\n",
        "inputs = tokenizer1(questions_train[23], contexts_train[23])\n",
        "tokenizer1.decode(inputs[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfvXOferxd3v"
      },
      "source": [
        "### Need to handle some pre-processing along with tokenization:\n",
        "- Our max input token length for the model pipeline is 384 tokens - so we need to truncate samples that are longer than this.  \n",
        "    - Only truncate the context - not the question (\"only_second)\n",
        "    - Truncated samples will create multiple new samples, each with different but overlapping section of the context (but same question)\n",
        "    - Stride will provide overlap/moving window between the context text in the multiple samples (stride defines # of overlap tokens)\n",
        "    \n",
        "- Our label for the model (and ultimately what the model will predict) will be the start_pos and end_pos within in the context where the answer is found\n",
        "    - Our dataset provides start_pos plus the text, making it possible to derive end_pos\n",
        "    - However, we need to map these from overflow token positions\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "1iRFjVpBxd3w"
      },
      "outputs": [],
      "source": [
        "# initialize new tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "HhRup6Ckxd3y"
      },
      "outputs": [],
      "source": [
        "# helper functions for feature extraction and tokenization\n",
        "max_tok_len = 384\n",
        "stride_len = 128\n",
        "\n",
        "# tokenize inputs.  since we may have overflow, we need to post-process to create our label set with start/end tokens\n",
        "def train_tokenize_process(questions, contexts, answers):\n",
        "\n",
        "    # process all training contexts + questions through tokenizer\n",
        "    inputs_train = tokenizer(\n",
        "        questions,\n",
        "        contexts,\n",
        "        max_length=max_tok_len,\n",
        "        truncation=\"only_second\",\n",
        "        stride=stride_len,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    # get offset mapping and overflow mappings\n",
        "    offset_mapping = inputs_train.pop(\"offset_mapping\")\n",
        "    sample_map = inputs_train.pop(\"overflow_to_sample_mapping\")\n",
        "\n",
        "    # build the corresponding label sets for our overflow\n",
        "    start_positions = []\n",
        "    end_positions = []\n",
        "\n",
        "    for i, offset in enumerate(offset_mapping):\n",
        "\n",
        "        # setup sample idx, answer and seq ids\n",
        "        qa_sample_idx = sample_map[i]\n",
        "        answer = answers[qa_sample_idx]\n",
        "        answer_start_idx = answer['answer_start']\n",
        "        answer_end_idx = answer_start_idx + len(answer['text'])\n",
        "        seq_ids = inputs_train.sequence_ids(i)\n",
        "\n",
        "        # need to traverse to the context so we can get start and end pos\n",
        "        context_start, context_end = traverse_sequence_ids(seq_ids)\n",
        "\n",
        "        # context_start gives us the start token of the context, and context_end gives us the end token idx of the context\n",
        "        # use this to index into offsets to get the start char and end char\n",
        "        if offset[context_start][0] > answer_start_idx or offset[context_end][1] < answer_end_idx:\n",
        "            start_positions.append(0)\n",
        "            end_positions.append(0)\n",
        "        else:\n",
        "            # Otherwise it's the start and end token positions\n",
        "            idx = context_start\n",
        "            while idx <= context_end and offset[idx][0] <= answer_start_idx:\n",
        "                idx += 1\n",
        "            start_positions.append(idx - 1)\n",
        "\n",
        "            idx = context_end\n",
        "            while idx >= context_start and offset[idx][1] >= answer_end_idx:\n",
        "                idx -= 1\n",
        "            end_positions.append(idx + 1)\n",
        "\n",
        "    inputs_train[\"start_positions\"] = start_positions\n",
        "    inputs_train[\"end_positions\"] = end_positions\n",
        "\n",
        "    return inputs_train\n",
        "\n",
        "\n",
        "# tokenize inputs - don't need to worry about mapping labels but we need to map the source ID so we can correlate in metrics calcs\n",
        "def test_tokenize_process(questions, contexts, id_map):\n",
        "\n",
        "    # process all training contexts + questions through tokenizer\n",
        "    inputs_test = tokenizer(\n",
        "        questions,\n",
        "        contexts,\n",
        "        max_length=max_tok_len,\n",
        "        truncation=\"only_second\",\n",
        "        stride=stride_len,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    example_ids = []\n",
        "    sample_map = inputs_test.pop(\"overflow_to_sample_mapping\")\n",
        "\n",
        "    # use id_map to correlate samples to original ID.  This will be used later to calculate metrics.\n",
        "    for i in range(len(inputs_test[\"input_ids\"])):\n",
        "        sample_idx = sample_map[i]\n",
        "        example_ids.append(id_map[sample_idx])\n",
        "\n",
        "        sequence_ids = inputs_test.sequence_ids(i)\n",
        "        offset = inputs_test[\"offset_mapping\"][i]\n",
        "        inputs_test[\"offset_mapping\"][i] = [\n",
        "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
        "        ]\n",
        "\n",
        "    inputs_test[\"example_id\"] = example_ids\n",
        "\n",
        "    return inputs_test\n",
        "\n",
        "\n",
        "# helper function to extract context start and end from sequence ids\n",
        "# the sequence IDs encode the tokens so we know where question is and where context is (and delimiter).  ie: None, 0, 0, 0, None, 1, 1, 1, 1 = <delimiter><question tokens (0)><delimiter><context tokens=1>\n",
        "def traverse_sequence_ids(sequence_ids):\n",
        "    pos = 0\n",
        "\n",
        "    # first, traverse until we hit the first \"1\", the start of the context\n",
        "    while(sequence_ids[pos] != 1):\n",
        "        pos += 1\n",
        "    start_pos = pos\n",
        "\n",
        "    # next, traverse until we hit a non \"1\", the end of the context\n",
        "    while(sequence_ids[pos] == 1):\n",
        "        pos += 1\n",
        "    end_pos = pos - 1\n",
        "\n",
        "    return start_pos, end_pos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WjfvBi3dxd3y"
      },
      "source": [
        "#### Run some tests on a few samples to make sure mappings are working as expected in truncation case"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-uqCVgLxd3z",
        "outputId": "6963b2df-c4a9-4191-e400-825354d6a1b0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "447\n"
          ]
        }
      ],
      "source": [
        "# test - find some samples that need truncation\n",
        "max(context_train_tok_dist)\n",
        "i = 0\n",
        "for x in context_train_tok_dist:\n",
        "    if(x > 384):\n",
        "        print(i)\n",
        "        if (i > 10):\n",
        "          break\n",
        "    i = i + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "3barpqrHxd30",
        "outputId": "3ff61178-09e7-411e-96f3-2cf20924e2e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[238, 24, 0, 94, 0, 253, 0, 0, 165]\n",
            "[240, 26, 0, 99, 0, 253, 0, 0, 168]\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[CLS] beyonce got married in 2008 to whom? [SEP] \", and singles \" diva \", \" ego \", \" broken - hearted girl \" and \" video phone \". the music video for \" single ladies \" has been parodied and imitated around the world, spawning the \" first major dance craze \" of the internet age according to the toronto star. the video has won several awards, including best video at the 2009 mtv europe music awards, the 2009 scottish mobo awards, and the 2009 bet awards. at the 2009 mtv video music awards, the video was nominated for nine awards, ultimately winning three including video of the year. its failure to win the best female video category, which went to american country pop singer taylor swift\\'s \" you belong with me \", led to kanye west interrupting the ceremony and beyoncé improvising a re - presentation of swift\\'s award during her own acceptance speech. in march 2009, beyoncé embarked on the i am... world tour, her second headlining worldwide concert tour, consisting of 108 shows, grossing $ 119. 5 million. [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# test and inspect truncation is working\n",
        "st = 446\n",
        "in_train_exp = train_tokenize_process(questions_train[st:st+5], contexts_train[st:st+5], answers_train[st:st+5])\n",
        "\n",
        "print(in_train_exp[\"start_positions\"])\n",
        "print(in_train_exp[\"end_positions\"])\n",
        "\n",
        "tokenizer.decode(in_train_exp[\"input_ids\"][2])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWCSyo19xd30"
      },
      "source": [
        "### Run training data and test data through preprocessor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "AYoY-TE0xd30"
      },
      "outputs": [],
      "source": [
        "# run the full dataset through the tokenizer\n",
        "train_input = train_tokenize_process(questions_train, contexts_train, answers_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKFI4Eylxd30",
        "outputId": "6765fe46-bed3-4cb3-d178-3a12b2e0f8a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 225, 0, 35, 211, 261, 204, 125, 137, 207, 259, 263, 224, 198, 36, 204, 214, 200, 256, 80]\n",
            "[0, 229, 0, 36, 214, 265, 205, 126, 139, 210, 263, 267, 224, 201, 37, 207, 215, 203, 260, 80]\n"
          ]
        }
      ],
      "source": [
        "# take a peak at some samples\n",
        "rn = random.randint(1, 10000)\n",
        "print(train_input[\"start_positions\"][rn:rn+20])\n",
        "print(train_input[\"end_positions\"][rn:rn+20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g0YlUYamxd30"
      },
      "outputs": [],
      "source": [
        "# run test data through pre-processing\n",
        "test_input = test_tokenize_process(questions_test, contexts_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "OFFEqPAeDZEO"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "iCnnPfFvxd31"
      },
      "outputs": [],
      "source": [
        "# load our datasets into HuggingFace Datasets\n",
        "train_dataset = Dataset.from_dict(train_input)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HFhNjYOxd31"
      },
      "source": [
        "# Fine-Tune Pre-trained distilBERT Model\n",
        "\n",
        "- Start with simple distilBERT model: \"distilbert-base-cased\" (https://huggingface.co/distilbert-base-cased)\n",
        "- Note: need to pip install ipywidgets to support notebook_login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "97hJQmnlxd32"
      },
      "outputs": [],
      "source": [
        "from transformers import TFAutoModelForQuestionAnswering\n",
        "from huggingface_hub import notebook_login\n",
        "from transformers import DefaultDataCollator\n",
        "from transformers import create_optimizer\n",
        "from transformers.keras_callbacks import PushToHubCallback"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "e8656f1943a1432baf32bd950fe7353d",
            "1ec55ebbdc524eb88ff601f7024b1ac9",
            "64b0d9c06f34496ba8c547379d1b8776",
            "7299219c101245158f67b1046f15f305",
            "7ba7862909334a6a9490147992790599",
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            "3f6fd345402e42c59635c2f32c4a7878"
          ]
        },
        "id": "o4IIGiUqxd34",
        "outputId": "7cb10430-138b-4a99-e862-4d69c462a778"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8656f1943a1432baf32bd950fe7353d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# login to HF Hub\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5tXOzj-xd35",
        "outputId": "ce8d3fde-ca56-4ef9-811e-2447cf6d5dc1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForQuestionAnswering: ['vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_transform.weight']\n",
            "- This IS expected if you are initializing TFDistilBertForQuestionAnswering from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForQuestionAnswering from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForQuestionAnswering were not initialized from the PyTorch model and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# get the pre-trained model\n",
        "model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "VC3XOFn7xd36"
      },
      "outputs": [],
      "source": [
        "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "2tQfEIQmxd38"
      },
      "outputs": [],
      "source": [
        "# setup train and test datasets for TensorFlow\n",
        "tf_train_dataset = model.prepare_tf_dataset(\n",
        "    train_dataset,\n",
        "    collate_fn=data_collator,\n",
        "    shuffle=True,\n",
        "    batch_size=16,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ov1poVx5xd38"
      },
      "outputs": [],
      "source": [
        "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
        "num_train_epochs = 3\n",
        "num_train_steps = len(tf_train_dataset) * num_train_epochs\n",
        "optimizer, schedule = create_optimizer(\n",
        "    init_lr=2e-5,\n",
        "    num_warmup_steps=0,\n",
        "    num_train_steps=num_train_steps,\n",
        "    weight_decay_rate=0.01,\n",
        ")\n",
        "model.compile(optimizer=optimizer)\n",
        "\n",
        "# Train in mixed-precision float16\n",
        "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
          "referenced_widgets": [
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          ]
        },
        "id": "9sT2T-k6xd3-",
        "outputId": "0781cc79-0785-42df-a8a4-3cc162745444"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/t4ai/distilbert-finetuned-t3-qa into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/t4ai/distilbert-finetuned-t3-qa into local empty directory.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Download file tf_model.h5:   0%|          | 7.38k/249M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aaed64c2ca8847c78b49ceb504a661a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Clean file tf_model.h5:   0%|          | 1.00k/249M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b46e1f352894e91966f0a91ffe0911c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "5585/5585 [==============================] - 1766s 314ms/step - loss: 1.5389\n",
            "Epoch 2/3\n",
            "5585/5585 [==============================] - 1753s 314ms/step - loss: 0.9645\n",
            "Epoch 3/3\n",
            "5585/5585 [==============================] - 1750s 313ms/step - loss: 0.7523\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7893b003cca0>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "callback = PushToHubCallback(output_dir=\"distilbert-finetuned-t3-qa\", tokenizer=tokenizer)\n",
        "\n",
        "# train the model\n",
        "model.fit(tf_train_dataset, callbacks=[callback], epochs=num_train_epochs, verbose=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmw7hMCnxd4I"
      },
      "source": [
        "# Evaluate Model Performance on Test Data\n",
        "\n",
        "- Evaluation routine and metrics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203,
          "referenced_widgets": [
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        },
        "id": "frOkzlOhDZEY",
        "outputId": "966615a0-47b2-470f-a152-21a0ac9f205f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:472: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/554 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7f4aa0722864d93a6fa09e98b390321"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/261M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a444af918dd4778b9a2192c47a15632"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at t4ai/distilbert-finetuned-t3-qa were not used when initializing TFDistilBertForQuestionAnswering: ['dropout_39']\n",
            "- This IS expected if you are initializing TFDistilBertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForQuestionAnswering were not initialized from the model checkpoint at t4ai/distilbert-finetuned-t3-qa and are newly initialized: ['dropout_59']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# get the fine tuned model\n",
        "model = TFAutoModelForQuestionAnswering.from_pretrained(\"t4ai/distilbert-finetuned-t3-qa\", use_auth_token=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "QsJUYmB1DZEZ"
      },
      "outputs": [],
      "source": [
        "from tqdm.auto import tqdm\n",
        "import collections\n",
        "import evaluate\n",
        "n_best = 20\n",
        "max_answer_length = 50\n",
        "\n",
        "metric = evaluate.load(\"squad\")\n",
        "\n",
        "def compute_metrics(start_logits, end_logits, features, ref_answers, contexts, id_map):\n",
        "\n",
        "    example_to_features = collections.defaultdict(list)\n",
        "    for idx, feature in enumerate(features):\n",
        "        example_to_features[feature['example_id']].append(idx)\n",
        "\n",
        "    predicted_answers = []\n",
        "    for idx, context in enumerate(contexts):\n",
        "        example_id = id_map[idx]\n",
        "        answers = []\n",
        "\n",
        "        # Loop through all features associated with that example\n",
        "        for feature_index in example_to_features[example_id]:\n",
        "            start_logit = start_logits[feature_index]\n",
        "            end_logit = end_logits[feature_index]\n",
        "            offsets = features[feature_index][\"offset_mapping\"]\n",
        "\n",
        "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
        "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
        "            for start_index in start_indexes:\n",
        "                for end_index in end_indexes:\n",
        "                    # Skip answers that are not fully in the context\n",
        "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
        "                        continue\n",
        "                    # Skip answers with a length that is either < 0 or > max_answer_length\n",
        "                    if (\n",
        "                        end_index < start_index\n",
        "                        or end_index - start_index + 1 > max_answer_length\n",
        "                    ):\n",
        "                        continue\n",
        "\n",
        "                    answer = {\n",
        "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
        "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
        "                    }\n",
        "                    answers.append(answer)\n",
        "\n",
        "        # Select the answer with the best score\n",
        "        if len(answers) > 0:\n",
        "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
        "            predicted_answers.append(\n",
        "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
        "            )\n",
        "        else:\n",
        "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
        "\n",
        "    # post process answers for metric format\n",
        "    formatted_answers = []\n",
        "    for idx, ans in enumerate(ref_answers):\n",
        "        formatted_answers.append({'answers': {'answer_start': [ans['answer_start']], 'text': [str.lower(ans['text'])]}, 'id': id_map[idx]})\n",
        "\n",
        "    return metric.compute(predictions=predicted_answers, references=formatted_answers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLc2DQGlDZEZ"
      },
      "source": [
        "### Test first with a small validation dataset\n",
        "\n",
        "- Create a small validation set extracted from our test set.  This will be extracted permanantly and not included in test set to avoid data leakage\n",
        "- Validation set is only 20 samples - mainly will be used to test data preparation, predictions and metrics calculations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "omuVT88FDZEZ"
      },
      "outputs": [],
      "source": [
        "# create dataset from test tokens\n",
        "val_questions = questions_test[0:20]\n",
        "val_answers = answers_test[0:20]\n",
        "val_contexts = contexts_test[0:20]\n",
        "val_ids = id_map_test[0:20]\n",
        "val_inputs = test_tokenize_process(val_questions, val_contexts, val_ids)\n",
        "val_dataset = Dataset.from_dict(val_inputs)\n",
        "tf_val_dataset = model.prepare_tf_dataset(\n",
        "    val_dataset,\n",
        "    collate_fn=data_collator,\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BQfEBJGsDZEZ",
        "outputId": "61ed5177-9941-4629-9e24-e468d528f3fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 10s 2s/step\n"
          ]
        }
      ],
      "source": [
        "# run test dataset through model\n",
        "val_predictions = model.predict(tf_val_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cgZLiUyFDZEZ"
      },
      "outputs": [],
      "source": [
        "# extract the logits from the model predictions\n",
        "start_logits = val_predictions.start_logits\n",
        "end_logits = val_predictions.end_logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PLjCJgyZDZEa",
        "outputId": "d215ef4a-4d9a-436c-eaa4-00ddfd3402ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'exact_match': 55.0, 'f1': 62.68131868131869}"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#theoretical, predicted =\n",
        "compute_metrics(start_logits, end_logits, val_dataset, val_answers, val_contexts, val_ids)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2QvOJ1k-DZEa"
      },
      "source": [
        "### Run with test dataset\n",
        "\n",
        "- Performance evaluation metrics used are: Exact Match and F1 Score\n",
        "- Full test dataset does not include the samples used in validation dataset\n",
        "- Full test dataset will give us the full performance metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "XH1WB3VCDZEa"
      },
      "outputs": [],
      "source": [
        "# create dataset from test tokens - leave out data used in val to avoid data leakage\n",
        "questions_test_sub = questions_test[20:]\n",
        "answers_test_sub = answers_test[20:]\n",
        "contexts_test_sub = contexts_test[20:]\n",
        "test_ids = id_map_test[20:]\n",
        "test_inputs = test_tokenize_process(questions_test_sub, contexts_test_sub, test_ids)\n",
        "test_dataset = Dataset.from_dict(test_inputs)\n",
        "tf_test_dataset = model.prepare_tf_dataset(\n",
        "    test_dataset,\n",
        "    collate_fn=data_collator,\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGsPmNkGDZEa",
        "outputId": "755cd6d3-3daa-4899-ab13-ab2e118ac560"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2237/2237 [==============================] - 3509s 2s/step\n"
          ]
        }
      ],
      "source": [
        "# run test dataset through model\n",
        "test_predictions = model.predict(tf_test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "OyEwzj4fDZEa"
      },
      "outputs": [],
      "source": [
        "# extract the logits from the model predictions\n",
        "start_logits_test = test_predictions.start_logits\n",
        "end_logits_test = test_predictions.end_logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFO740dZDZEb",
        "outputId": "3fcf82a3-e825-4ea4-d017-c5f8068a5dc4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'exact_match': 61.50521523655852, 'f1': 76.27598367426225}"
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          },
          "metadata": {},
          "execution_count": 53
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      ],
      "source": [
        "# calculate the performance metrics\n",
        "compute_metrics(start_logits_test, end_logits_test, test_dataset, answers_test_sub, contexts_test_sub, test_ids)"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "# Create Model Pipeline and Run Some Samples\n",
        "\n"
      ]
    },
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      "metadata": {
        "id": "F9Vyfc44DZEb"
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          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at t4ai/distilbert-finetuned-t3-qa were not used when initializing TFDistilBertForQuestionAnswering: ['dropout_39']\n",
            "- This IS expected if you are initializing TFDistilBertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForQuestionAnswering were not initialized from the model checkpoint at t4ai/distilbert-finetuned-t3-qa and are newly initialized: ['dropout_79']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
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            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.23k [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
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          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/669k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/82.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
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          "output_type": "stream",
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Setup chatbot pipeline\n",
        "chatbot_qa = pipeline(\"question-answering\", model=\"t4ai/distilbert-finetuned-t3-qa\", use_auth_token=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
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          "output_type": "execute_result",
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            "text/plain": [
              "{'score': 0.1673583984375, 'start': 5, 'end': 16, 'answer': 'pound-force'}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "context = \"\"\"\n",
        "The pound-force has a metric counterpart, less commonly used than the newton: the kilogram-force (kgf) (sometimes kilopond), is the force exerted by standard gravity on one kilogram of mass. The kilogram-force leads to an alternate, but rarely used unit of mass: the metric slug (sometimes mug or hyl) is that mass that accelerates at 1 m\\u00b7s\\u22122 when subjected to a force of 1 kgf. The kilogram-force is not a part of the modern SI system, and is generally deprecated; however it still sees use for some purposes as expressing aircraft weight, jet thrust, bicycle spoke tension, torque wrench settings and engine output torque. Other arcane units of force include the sth\\u00e8ne, which is equivalent to 1000 N, and the kip, which is equivalent to 1000 lbf.\n",
        "\"\"\"\n",
        "question = \"What is the metric term less used than the Newton?\"\n",
        "chatbot_qa(question=question, context=context)"
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    },
    {
      "cell_type": "code",
      "execution_count": 56,
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              "{'score': 0.67138671875, 'start': 115, 'end': 123, 'answer': 'kilopond'}"
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      "source": [
        "question = \"What is the kilogram-force sometimes reffered to as?\"\n",
        "chatbot_qa(question=question, context=context)"
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