Himani/Milestone3/bert Phrase.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ho88Li_Rndor"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers==4.27.4\n",
        "!pip install wandb\n",
        "!pip install datasets\n",
        "!pip install bert_score\n",
        "!pip install evaluate\n",
        "!pip install accelerate\n",
        "!pip install gradio --upgrade\n",
        "!pip install --upgrade torch\n",
        "!pip install sentencepiece"
      ],
      "metadata": {
        "id": "3Cai5iutRCI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa605305-2cd9-416b-84bf-be455d2ca897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio)\n",
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            "Collecting starlette<0.27.0,>=0.26.1 (from fastapi->gradio)\n",
            "  Downloading starlette-0.26.1-py3-none-any.whl (66 kB)\n",
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            "  Downloading httpcore-0.17.0-py3-none-any.whl (70 kB)\n",
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            "Collecting uc-micro-py (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio)\n",
            "  Downloading uc_micro_py-1.0.2-py3-none-any.whl (6.2 kB)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->gradio) (1.16.0)\n",
            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4694 sha256=82c08d7a29bb5d6d290731f0bd4ad8519beefbd2338a8c2a00020b42817176dc\n",
            "  Stored in directory: /root/.cache/pip/wheels/0c/c2/0e/3b9c6845c6a4e35beb90910cc70d9ac9ab5d47402bd62af0df\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, linkify-it-py, httpcore, httpx, fastapi, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.1.0 fastapi-0.95.1 ffmpy-0.3.0 gradio-3.30.0 gradio-client-0.2.4 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 linkify-it-py-2.0.2 mdit-py-plugins-0.3.3 orjson-3.8.12 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.0+cu118)\n",
            "Collecting torch\n",
            "  Downloading torch-2.0.1-cp310-cp310-manylinux1_x86_64.whl (619.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m619.9/619.9 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Collecting nvidia-cuda-nvrtc-cu11==11.7.99 (from torch)\n",
            "  Downloading nvidia_cuda_nvrtc_cu11-11.7.99-2-py3-none-manylinux1_x86_64.whl (21.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.0/21.0 MB\u001b[0m \u001b[31m76.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cuda-runtime-cu11==11.7.99 (from torch)\n",
            "  Downloading nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl (849 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m849.3/849.3 kB\u001b[0m \u001b[31m65.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cuda-cupti-cu11==11.7.101 (from torch)\n",
            "  Downloading nvidia_cuda_cupti_cu11-11.7.101-py3-none-manylinux1_x86_64.whl (11.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.8/11.8 MB\u001b[0m \u001b[31m92.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cudnn-cu11==8.5.0.96 (from torch)\n",
            "  Downloading nvidia_cudnn_cu11-8.5.0.96-2-py3-none-manylinux1_x86_64.whl (557.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m557.1/557.1 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cublas-cu11==11.10.3.66 (from torch)\n",
            "  Downloading nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl (317.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.1/317.1 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cufft-cu11==10.9.0.58 (from torch)\n",
            "  Downloading nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.4/168.4 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-curand-cu11==10.2.10.91 (from torch)\n",
            "  Downloading nvidia_curand_cu11-10.2.10.91-py3-none-manylinux1_x86_64.whl (54.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.6/54.6 MB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cusolver-cu11==11.4.0.1 (from torch)\n",
            "  Downloading nvidia_cusolver_cu11-11.4.0.1-2-py3-none-manylinux1_x86_64.whl (102.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.6/102.6 MB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cusparse-cu11==11.7.4.91 (from torch)\n",
            "  Downloading nvidia_cusparse_cu11-11.7.4.91-py3-none-manylinux1_x86_64.whl (173.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.2/173.2 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nccl-cu11==2.14.3 (from torch)\n",
            "  Downloading nvidia_nccl_cu11-2.14.3-py3-none-manylinux1_x86_64.whl (177.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.1/177.1 MB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nvtx-cu11==11.7.91 (from torch)\n",
            "  Downloading nvidia_nvtx_cu11-11.7.91-py3-none-manylinux1_x86_64.whl (98 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.6/98.6 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch) (67.7.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch) (0.40.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, nvidia-cusolver-cu11, nvidia-cudnn-cu11, torch\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.0+cu118\n",
            "    Uninstalling torch-2.0.0+cu118:\n",
            "      Successfully uninstalled torch-2.0.0+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.0.1+cu118 requires torch==2.0.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchdata 0.6.0 requires torch==2.0.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchtext 0.15.1 requires torch==2.0.0, but you have torch 2.0.1 which is incompatible.\n",
            "torchvision 0.15.1+cu118 requires torch==2.0.0, but you have torch 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 torch-2.0.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C1RmIef-3xjc"
      },
      "outputs": [],
      "source": [
        "#text preprocessing\n",
        "def parse_spoiler_for_correct_spoiler_positions(df):\n",
        "    spoiler = []        \n",
        "    for sp in df['spoilerPositions']:\n",
        "        if len(sp) <=1:\n",
        "          df['spoilerPositions'] = [[[4, 37],[4, 222]]]  # this data is manually checked and assigned because it was throwing error\n",
        "        else:\n",
        "          start,end = sp[0],sp[1]\n",
        "          spoiler.append(df['targetParagraphs'][start[0]][start[1]:end[1]])\n",
        "        \n",
        "    return spoiler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#text preprocessing\n",
        "def find_token_positions(x):    \n",
        "    token_position = []\n",
        "    for position in x['spoilerPositions']:\n",
        "        if len(position) <2:\n",
        "          print(\"format of data is not right for uuid\", x['uuid'])\n",
        "        else:\n",
        "          index = 0\n",
        "          start,end = position\n",
        "          \n",
        "          for idx,element in enumerate([x['targetTitle']] + x['targetParagraphs']):\n",
        "              if idx==start[0]+1:\n",
        "                  # because character is starting at given index +1\n",
        "                  end_index = index + end[1]\n",
        "                  start_index = index+start[1]\n",
        "                  token_position.append([start_index,end_index])\n",
        "                  break\n",
        "              if idx==0:\n",
        "                  index+=len(element)+3\n",
        "              else:\n",
        "                  index+=len(element)+1\n",
        "        \n",
        "    return token_position"
      ],
      "metadata": {
        "id": "cqov0H9OsoJt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#formatting dataframe columns accroding to requirements\n",
        "def formatted_df(df):\n",
        "  \n",
        "  df['spoilerParsed'] = df.apply(parse_spoiler_for_correct_spoiler_positions,1)\n",
        "  df['combinedParagraphs'] = df['targetParagraphs'].apply(lambda x:\" \".join(x),1)\n",
        "  df.combinedParagraphs = df.targetTitle + \" - \" + df.combinedParagraphs\n",
        "  df['tokenPosition'] = df.apply(find_token_positions,1) \n",
        "  df['tags'] = df.tags.apply(lambda x:x[0],1)\n",
        "  df['postText'] = df.postText.apply(lambda x:x[0],1)\n",
        "  df['label'] = df['tags'].map({\"phrase\":0,\"passage\":1,\"multi\":2})\n",
        "  return df\n",
        "    "
      ],
      "metadata": {
        "id": "pdHyLLV2scyf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = './train.jsonl'\n",
        "df = pd.read_json(data_path, lines=True)\n",
        "df_valid = pd.read_json(\"./validation.jsonl\", lines=True)\n",
        "formatted_df(df)\n",
        "formatted_df(df_valid)"
      ],
      "metadata": {
        "id": "0fdEQoZBsgc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6798deb7-d4ab-4c60-c94a-220311bf631a"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <td>[Christopher Suprun]</td>\n",
              "      <td>Texas GOP elector announces he won't vote for ...</td>\n",
              "      <td>[[246, 264]]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>797</th>\n",
              "      <td>1fdf71e8-ec14-4c3b-a7c5-ca678c6f8ccb</td>\n",
              "      <td>847331053991813120</td>\n",
              "      <td>This beauty queen cured her acne with one diet...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>[Her inspirational journey is encouraging othe...</td>\n",
              "      <td>UK beauty queen cured her severe acne with one...</td>\n",
              "      <td>A beauty pageant contestant has taken to Insta...</td>\n",
              "      <td>acne, Skincare, beauty, Beauty pageant, Dermat...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>{'source': 'anonymized', 'humanSpoiler': None,...</td>\n",
              "      <td>[Rachel Crawley, High fat vegan plant based di...</td>\n",
              "      <td>[[[2, 144], [2, 158]], [[6, 56], [6, 124]]]</td>\n",
              "      <td>multi</td>\n",
              "      <td>[Rachel Crawley, High fat vegan plant based di...</td>\n",
              "      <td>UK beauty queen cured her severe acne with one...</td>\n",
              "      <td>[[390, 404], [1044, 1112]]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>798</th>\n",
              "      <td>17f6b540-cf8d-4ddf-8321-1c9ce2315d71</td>\n",
              "      <td>788056531304583168</td>\n",
              "      <td>WikiLeaks' Julian Assange Reported Dead</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>[On 16 October 2016, WikiLeaks posted a series...</td>\n",
              "      <td>WikiLeaks’ Julian Assange Isn’t Dead, Just Off...</td>\n",
              "      <td>A series of mysterious tweets from WikiLeaks l...</td>\n",
              "      <td>dead man's switch, julian assange, wikileaks</td>\n",
              "      <td>[http://static.snopes.com/app/themes/snopes-th...</td>\n",
              "      <td>http://trib.al/leR8lNw</td>\n",
              "      <td>{'source': 'anonymized', 'humanSpoiler': 'It w...</td>\n",
              "      <td>[Julian Assange’s internet link has been inten...</td>\n",
              "      <td>[[[11, 0], [11, 78]]]</td>\n",
              "      <td>passage</td>\n",
              "      <td>[Julian Assange’s internet link has been inten...</td>\n",
              "      <td>WikiLeaks’ Julian Assange Isn’t Dead, Just Off...</td>\n",
              "      <td>[[3420, 3498]]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>799</th>\n",
              "      <td>89dcad77-d8ad-4705-8676-717b26fda2ad</td>\n",
              "      <td>388308677494444032</td>\n",
              "      <td>Original \"Law &amp;amp; Order: SVU\" cast member le...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>[Richard Belzer is leaving \"Law &amp; Order: SVU.\"...</td>\n",
              "      <td>Richard Belzer Leaving 'Law &amp; Order: SVU': Mun...</td>\n",
              "      <td>Richard Belzer is leaving \"Law &amp; Order: SVU.\" ...</td>\n",
              "      <td>TV Canada,richard belzer leaving law and order...</td>\n",
              "      <td>[http://s.m.huffpost.com/assets/Logo_Huffingto...</td>\n",
              "      <td>http://huff.to/19m7Itl</td>\n",
              "      <td>{'source': 'anonymized', 'humanSpoiler': 'Rich...</td>\n",
              "      <td>[Richard Belzer]</td>\n",
              "      <td>[[[0, 0], [0, 14]]]</td>\n",
              "      <td>phrase</td>\n",
              "      <td>[Richard Belzer]</td>\n",
              "      <td>Richard Belzer Leaving 'Law &amp; Order: SVU': Mun...</td>\n",
              "      <td>[[63, 77]]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>800 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54dc2d99-3d10-4195-ae79-944097097ea8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-54dc2d99-3d10-4195-ae79-944097097ea8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-54dc2d99-3d10-4195-ae79-944097097ea8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C-mvsSzF3xjl",
        "colab": {
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          "height": 145,
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        },
        "outputId": "04c4d4f0-bbcf-4604-957a-9de698dfd933"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "83699d63bcc24f46bc4f7c908f8d1536"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/477 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "918c9323d9124af68e1174c02f44e6f7"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6851b9dfb7644ba3a281cca4852ab8bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "8137e482ad114aa4995a89622eb1ad0f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "#model_checkpoint = \"csarron/bert-base-uncased-squad-v1\"\n",
        "#model_checkpoint =  \"MaRiOrOsSi/t5-base-finetuned-question-answering\"\n",
        "model_name = \"csarron/bert-base-uncased-squad-v1\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lAqM4z-B3xjm",
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        },
        "outputId": "a910dcfe-3d9d-41e7-a26a-87cfbf9fd979"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.94k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/1.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa1b12cf89cd4ca2922330fe3b70e09a"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/3.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Downloading builder script:   0%|          | 0.00/4.53k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/3.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93026c53f4cb4099b4238bb84819b1a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.81k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5699c103ee8f4ba39bd407c873a19d9f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "import evaluate\n",
        "bleu = evaluate.load(\"bleu\",n=4)\n",
        "squad_metric = evaluate.load(\"squad\")\n",
        "meteor = evaluate.load(\"meteor\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o-4I60_33xjn"
      },
      "outputs": [],
      "source": [
        "def generate_new_qa_form_df(df):\n",
        "    df_new = df[['uuid','targetTitle','postText',\"combinedParagraphs\",\"tokenPosition\",\"spoiler\"]]\n",
        "    df_new[\"answers\"] = df_new.apply(lambda x: {'text':x['spoiler'], \"answer_start\":[x['tokenPosition'][0][0]]},1)\n",
        "    df_new = df_new.drop(columns=[\"tokenPosition\",\"spoiler\"])\n",
        "    df_new.columns = [\"id\",\"title\",\"question\",\"context\",\"answers\"]\n",
        "    \n",
        "    return df_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PDJ2D9413xjn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35343547-802e-47f4-cf32-6781b5b27344"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1367, 335)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "spoiler_type = \"phrase\"\n",
        "train_df = df[df.tags==spoiler_type]\n",
        "val_df = df_valid[df_valid.tags==spoiler_type]\n",
        "\n",
        "len(train_df),len(val_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LJdx5uDy3xjp"
      },
      "outputs": [],
      "source": [
        "max_length = 512\n",
        "stride = 128\n",
        "\n",
        "#huggingface documentation\n",
        "def preprocess_training_examples(examples):\n",
        "    questions = [q.strip() for q in examples[\"question\"]]\n",
        "    inputs = tokenizer(\n",
        "        questions,\n",
        "        examples[\"context\"],\n",
        "        max_length=max_length,\n",
        "        truncation=\"only_second\",\n",
        "        stride=stride,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
        "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
        "    answers = examples[\"answers\"]\n",
        "    start_positions = []\n",
        "    end_positions = []\n",
        "\n",
        "    for i, offset in enumerate(offset_mapping):\n",
        "        sample_idx = sample_map[i]\n",
        "        answer = answers[sample_idx]\n",
        "        start_char = answer[\"answer_start\"][0]\n",
        "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
        "        sequence_ids = inputs.sequence_ids(i)\n",
        "\n",
        "        # Find the start and end of the context\n",
        "        idx = 0\n",
        "        while sequence_ids[idx] != 1:\n",
        "            idx += 1\n",
        "        context_start = idx\n",
        "        while sequence_ids[idx] == 1:\n",
        "            idx += 1\n",
        "        context_end = idx - 1\n",
        "\n",
        "        # If the answer is not fully inside the context, label is (0, 0)\n",
        "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\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] <= start_char:\n",
        "                idx += 1\n",
        "            start_positions.append(idx - 1)\n",
        "\n",
        "            idx = context_end\n",
        "            while idx >= context_start and offset[idx][1] >= end_char:\n",
        "                idx -= 1\n",
        "            end_positions.append(idx + 1)\n",
        "\n",
        "    inputs[\"start_positions\"] = start_positions\n",
        "    inputs[\"end_positions\"] = end_positions\n",
        "    return inputs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head(2)"
      ],
      "metadata": {
        "id": "rdUeGbKfwISl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "b0160e3e-3498-4a1c-c74a-7cdd7582cc2d"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                   uuid              postId  \\\n",
              "1  b1a1f63d-8853-4a11-89e8-6b2952a393ec  411701128456593408   \n",
              "2  008b7b19-0445-4e16-8f9e-075b73f80ca4  380537005123190784   \n",
              "\n",
              "                                            postText postPlatform  \\\n",
              "1     NASA sets date for full recovery of ozone hole      Twitter   \n",
              "2  This is what makes employees happy -- and it's...      Twitter   \n",
              "\n",
              "                                    targetParagraphs  \\\n",
              "1  [2070 is shaping up to be a great year for Mot...   \n",
              "2  [Despite common belief, money isn't the key to...   \n",
              "\n",
              "                                         targetTitle  \\\n",
              "1  Hole In Ozone Layer Expected To Make Full Reco...   \n",
              "2  Intellectual Stimulation Trumps Money For Empl...   \n",
              "\n",
              "                                   targetDescription  \\\n",
              "1  2070 is shaping up to be a great year for Moth...   \n",
              "2  By: Chad Brooks \\r\\nPublished: 09/18/2013 06:4...   \n",
              "\n",
              "                                      targetKeywords  \\\n",
              "1  ozone layer,ozone hole determined by weather,M...   \n",
              "2  employee happiness money,employee happiness in...   \n",
              "\n",
              "                                         targetMedia               targetUrl  \\\n",
              "1  [http://s.m.huffpost.com/assets/Logo_Huffingto...  http://huff.to/1cH672Z   \n",
              "2  [http://i.huffpost.com/gen/1359674/images/o-HA...  http://huff.to/1epfeaw   \n",
              "\n",
              "                                          provenance  \\\n",
              "1  {'source': 'anonymized', 'humanSpoiler': '2070...   \n",
              "2  {'source': 'anonymized', 'humanSpoiler': 'Inte...   \n",
              "\n",
              "                      spoiler        spoilerPositions    tags  \\\n",
              "1                      [2070]      [[[0, 0], [0, 4]]]  phrase   \n",
              "2  [intellectual stimulation]  [[[1, 186], [1, 210]]]  phrase   \n",
              "\n",
              "                spoilerParsed  \\\n",
              "1                      [2070]   \n",
              "2  [intellectual stimulation]   \n",
              "\n",
              "                                  combinedParagraphs tokenPosition  label  \n",
              "1  Hole In Ozone Layer Expected To Make Full Reco...    [[67, 71]]      0  \n",
              "2  Intellectual Stimulation Trumps Money For Empl...  [[349, 373]]      0  "
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              "      <td>phrase</td>\n",
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              "      <td>http://huff.to/1epfeaw</td>\n",
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              "      <td>phrase</td>\n",
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              "          const element = document.querySelector('#df-f43f9d51-f2f1-4a05-80f8-c9ec93fe9d2f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YOBd67vj3xjq"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "train_df = generate_new_qa_form_df(train_df)\n",
        "val_df = generate_new_qa_form_df(val_df)\n",
        "\n",
        "train_data = Dataset.from_pandas(train_df.reset_index(drop=True), split=\"train\")\n",
        "val_data = Dataset.from_pandas(val_df.reset_index(drop=True), split=\"test\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vQY2K6nV5Bh0",
        "colab": {
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          "height": 367
        },
        "outputId": "73abc0ff-f446-4d22-8de6-a4c122b602fa"
      },
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              "2  Intellectual Stimulation Trumps Money For Empl...   \n",
              "\n",
              "                                            question  \\\n",
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              "2  This is what makes employees happy -- and it's...   \n",
              "\n",
              "                                             context  \\\n",
              "1  Hole In Ozone Layer Expected To Make Full Reco...   \n",
              "2  Intellectual Stimulation Trumps Money For Empl...   \n",
              "\n",
              "                                             answers  \n",
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              "2  {'text': ['intellectual stimulation'], 'answer...  "
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ceaedb7-a276-4cff-b2e8-23b5098d74a6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ceaedb7-a276-4cff-b2e8-23b5098d74a6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "train_df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_validation_examples(examples):\n",
        "    questions = [q.strip() for q in examples[\"question\"]]\n",
        "    inputs = tokenizer(\n",
        "        questions,\n",
        "        examples[\"context\"],\n",
        "        max_length=max_length,\n",
        "        truncation=\"only_second\",\n",
        "        stride=stride,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
        "    example_ids = []\n",
        "\n",
        "    for i in range(len(inputs[\"input_ids\"])):\n",
        "        sample_idx = sample_map[i]\n",
        "        example_ids.append(examples[\"id\"][sample_idx])\n",
        "\n",
        "        sequence_ids = inputs.sequence_ids(i)\n",
        "        offset = inputs[\"offset_mapping\"][i]\n",
        "        inputs[\"offset_mapping\"][i] = [\n",
        "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
        "        ]\n",
        "\n",
        "    inputs[\"example_id\"] = example_ids\n",
        "    return inputs"
      ],
      "metadata": {
        "id": "sls5fC4wTT5p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NCZccq7P5EQ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa567396-935d-4090-e724-3a5e2716940e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'question', 'context', 'answers'],\n",
              "    num_rows: 1367\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNBEKg9D3xjr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
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          ]
        },
        "outputId": "7289ab20-83b2-48a8-9749-ba4c52d3569a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1367 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8ea0de04273431d9e181cc17b2f4d6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/335 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ad80a11359e4fb2902229f4e890563f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_dataset = train_data.map(\n",
        "    preprocess_training_examples,\n",
        "    batched=True,\n",
        "    remove_columns=train_data.column_names,\n",
        ")\n",
        "validation_dataset = val_data.map(\n",
        "    preprocess_validation_examples,\n",
        "    batched=True,\n",
        "    remove_columns=val_data.column_names,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-sKA7x0v3xjt"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "from collections import defaultdict\n",
        "from tqdm.auto import tqdm\n",
        "def compute_metrics(start_logits, end_logits, features, examples, predictOnly=False):\n",
        "\n",
        "    example_to_features = defaultdict(list)\n",
        "    for idx, feature in enumerate(features):\n",
        "        example_to_features[feature[\"example_id\"]].append(idx)\n",
        "\n",
        "    predicted_spoilers = []\n",
        "    for example in tqdm(examples):\n",
        "        example_id = example[\"id\"]\n",
        "        context = example[\"context\"]\n",
        "        spoilers = []\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",
        "                    spoilers.append(answer)\n",
        "\n",
        "        # Select the answer with the best score\n",
        "        if len(spoilers) > 0:\n",
        "            best_answer = max(spoilers, key=lambda x: x[\"logit_score\"])\n",
        "            predicted_spoilers.append(\n",
        "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
        "            )\n",
        "        else:\n",
        "            predicted_spoilers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
        "            \n",
        "    predicted_texts = [i['prediction_text'] for i in predicted_spoilers]\n",
        "    \n",
        "    if predictOnly:\n",
        "        return predicted_texts\n",
        "    \n",
        "    actual_spoilers_squad = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
        "    actual_spoilers = [i['answers']['text'][0] for i in actual_spoilers_squad]    \n",
        "    \n",
        "    \n",
        "    bleu_eval = bleu.compute(predictions=predicted_texts, references=actual_spoilers)\n",
        "    meteor_eval = meteor.compute(predictions=predicted_texts,references = actual_spoilers)\n",
        "    squad_metrics_eval = squad_metric.compute(predictions=predicted_spoilers, references=actual_spoilers_squad)\n",
        "   \n",
        "    return [squad_metrics_eval,meteor_eval,bleu_eval],actual_spoilers,predicted_texts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaA_DjnN3xju",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d839bfdb5aa34234b6ae54559ee18323",
            "2fe266b7495c41cfb7be741a5c14ebd3",
            "4fb6777cd09b480285846126738736ca",
            "24f87e4f6a244680937a78b8d3583900",
            "49738235cdc34161a92100ea5c16c2f3",
            "af30d8e3da2243b1b761558f0e2a9c00",
            "056cf37b80f544be9b37ddd3105e7c60",
            "3b893c4ac28948a789ce299276f69bcb",
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            "3d757faf73214450882e4716f64067fe",
            "33a3c30b60e945f68e7a1716a4933b68"
          ]
        },
        "outputId": "4901fcb2-6fac-4442-df1a-5a04210482fc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d839bfdb5aa34234b6ae54559ee18323"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW, RobertaTokenizer, AutoModelForQuestionAnswering,T5Tokenizer, T5ForConditionalGeneration\n",
        "\n",
        "\n",
        "model_name = \"csarron/bert-base-uncased-squad-v1\"\n",
        "\n",
        "#Phrase Generation\n",
        "config = dict(\n",
        "max_length = 512,\n",
        "stride = 128,\n",
        "n_best = 25,\n",
        "max_answer_length = 30,\n",
        "batch_size = 8,\n",
        "epochs = 10,\n",
        "learning_rate = 1e-6,\n",
        "model_name = model_name,\n",
        "spoiler_type = \"phrase\"\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "max_length = config[\"max_length\"]\n",
        "stride = config[\"stride\"]\n",
        "n_best = config[\"n_best\"]\n",
        "max_answer_length = config[\"max_answer_length\"]\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(config[\"model_name\"])\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(config[\"model_name\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_liPZ60X3xjv"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **BERT FINE TUNING**"
      ],
      "metadata": {
        "id": "TVkAgAhJacoh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PoGvgKMm3xjy"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "args = TrainingArguments(\n",
        "    #\"deepset/roberta-large-squad2\",\n",
        "    #'deepset/roberta-base-squad2',\n",
        "    #'deepset/minilm-uncased-squad2',\n",
        "    model_name,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    logging_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    num_train_epochs=10,\n",
        "    weight_decay=0.01,\n",
        "    \n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=validation_dataset,\n",
        "    tokenizer=tokenizer,\n",
        ")\n"
      ],
      "metadata": {
        "id": "nL-6pTHBQvhl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768,
          "referenced_widgets": [
            "b5e70b1deec84c40a50970fb1823f393",
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        },
        "id": "XVpIl-pvQ6YZ",
        "outputId": "c8c1c03b-43f7-4d4e-baff-68ea1e77691d"
      },
      "execution_count": 24,
      "outputs": [
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          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
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              "      [ 847/2990 11:37 < 29:28, 1.21 it/s, Epoch 2.83/10]\n",
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              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.426000</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.095700</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.062200</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.020900</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.022600</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.013100</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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