Himani/Milestone3/roberta.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "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": "85bcdb7b-09dd-40bd-c2cf-7e7c83d50f2e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\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=045b45d75c18a189c18f1efb5ae2ec68b019edbcfe6a9cb1a8c8f4ab899832a9\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[31m2.7 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[31m47.0 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[31m38.8 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[31m47.7 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[31m3.1 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[31m2.9 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[31m7.8 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[31m11.5 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[31m9.0 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[31m2.3 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[31m7.1 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[31m10.8 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[31m21.5 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": 3,
      "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": 4,
      "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": 5,
      "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": "86446e52-2218-47c7-aa13-12d93c62d9c3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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-503b258a-45a9-4f30-9ea9-2f9e4d3bb77c')\"\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-503b258a-45a9-4f30-9ea9-2f9e4d3bb77c 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-503b258a-45a9-4f30-9ea9-2f9e4d3bb77c');\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": 8,
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        "outputId": "6f295b40-098e-4196-c904-edbf306ab58a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/79.0 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d786d8209cf14ba89978ab2c82b1278d"
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          "metadata": {}
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        {
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          "data": {
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              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3e6212e118b4b8aae334d85e904292e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/772 [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "model_name = 'deepset/roberta-base-squad2'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
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        "colab": {
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        "outputId": "a3ef94be-765e-4f74-a796-dbffac760871"
      },
      "outputs": [
        {
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          "data": {
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        {
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          "data": {
            "text/plain": [
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        {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "35aef703297d4b36bb23c51e8b580204"
            }
          },
          "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": 10,
      "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": 11,
      "metadata": {
        "id": "PDJ2D9413xjn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3b4abbc-fe53-412a-d944-a1757c484cf3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1367, 335)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "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": 12,
      "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": "19fadc7c-d577-45fd-ecac-96b197e26209"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "  <thead>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b1a1f63d-8853-4a11-89e8-6b2952a393ec</td>\n",
              "      <td>411701128456593408</td>\n",
              "      <td>NASA sets date for full recovery of ozone hole</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>[2070 is shaping up to be a great year for Mot...</td>\n",
              "      <td>Hole In Ozone Layer Expected To Make Full Reco...</td>\n",
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              "      <td>ozone layer,ozone hole determined by weather,M...</td>\n",
              "      <td>[http://s.m.huffpost.com/assets/Logo_Huffingto...</td>\n",
              "      <td>http://huff.to/1cH672Z</td>\n",
              "      <td>{'source': 'anonymized', 'humanSpoiler': '2070...</td>\n",
              "      <td>[2070]</td>\n",
              "      <td>[[[0, 0], [0, 4]]]</td>\n",
              "      <td>phrase</td>\n",
              "      <td>[2070]</td>\n",
              "      <td>Hole In Ozone Layer Expected To Make Full Reco...</td>\n",
              "      <td>[[67, 71]]</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>008b7b19-0445-4e16-8f9e-075b73f80ca4</td>\n",
              "      <td>380537005123190784</td>\n",
              "      <td>This is what makes employees happy -- and it's...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>[Despite common belief, money isn't the key to...</td>\n",
              "      <td>Intellectual Stimulation Trumps Money For Empl...</td>\n",
              "      <td>By: Chad Brooks \\r\\nPublished: 09/18/2013 06:4...</td>\n",
              "      <td>employee happiness money,employee happiness in...</td>\n",
              "      <td>[http://i.huffpost.com/gen/1359674/images/o-HA...</td>\n",
              "      <td>http://huff.to/1epfeaw</td>\n",
              "      <td>{'source': 'anonymized', 'humanSpoiler': 'Inte...</td>\n",
              "      <td>[intellectual stimulation]</td>\n",
              "      <td>[[[1, 186], [1, 210]]]</td>\n",
              "      <td>phrase</td>\n",
              "      <td>[intellectual stimulation]</td>\n",
              "      <td>Intellectual Stimulation Trumps Money For Empl...</td>\n",
              "      <td>[[349, 373]]</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-312f38af-4d20-450d-a8d6-367822e4cb07 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-312f38af-4d20-450d-a8d6-367822e4cb07');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "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": 15,
      "metadata": {
        "id": "vQY2K6nV5Bh0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "1fe6b5c2-b7d5-4d43-de82-fe5e7afa0951"
      },
      "outputs": [
        {
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          "data": {
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              "                                     id  \\\n",
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              "\n",
              "                                               title  \\\n",
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              "2  Intellectual Stimulation Trumps Money For Empl...   \n",
              "\n",
              "                                            question  \\\n",
              "1     NASA sets date for full recovery of ozone hole   \n",
              "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",
              "1           {'text': ['2070'], 'answer_start': [67]}  \n",
              "2  {'text': ['intellectual stimulation'], 'answer...  "
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              "    }\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-f9659f67-31d0-4974-944c-452111550375 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-f9659f67-31d0-4974-944c-452111550375');\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": 15
        }
      ],
      "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "NCZccq7P5EQ9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72ee7a99-d86b-40b4-dac7-37d0d8f56c67"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'question', 'context', 'answers'],\n",
              "    num_rows: 1367\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "GNBEKg9D3xjr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "b1757732f0d1488ca18a204df217142d",
            "1b449768e9f54ebdbc4ce0a4debf6a6b",
            "60acc108eb464d329093abd6da77acc3",
            "6cb56a85526f421982cf2549b94c480d",
            "39372a756d28422e96aaebf967ad0531",
            "62d658aa21b44d08bb41b6fb202afc92",
            "7feed4a7c1eb4953a68a8715ce9eda60",
            "f5c9c73df3bf471c980f6466a5347e83",
            "3af8225727aa439a89baa545d7ba7c01",
            "da11e89d692a48b8a04ca957cf5732f5",
            "d34c7c73d15843ddb791892eee95fefb",
            "944cb93713d14e01b8ded669fc7a1a01",
            "7bf8e6a63b93476886acabe6bc4eff49",
            "9155f2a3bb78485ba4e651052f718aa7",
            "86f970a5e2b64f42b75c51c22b20fb56",
            "4d5b5636aca643f9b0663604729d2509",
            "34f8f7a99b934f56ad20cb9d5c7568be",
            "d7e9e22fbf6f436fb9f888fb9b308831",
            "69c387ab80bb41fe88dd2c20ac13f083",
            "460e99ced3364508b227e378aa75683d",
            "e1184921c9704f1d834bbcce42634401",
            "f0835cc5e6454dd59a2791b7621adce8"
          ]
        },
        "outputId": "9a6f71e8-97a5-4c3a-8750-4b87b80606ba"
      },
      "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": "b1757732f0d1488ca18a204df217142d"
            }
          },
          "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": "944cb93713d14e01b8ded669fc7a1a01"
            }
          },
          "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": 19,
      "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": 20,
      "metadata": {
        "id": "PaA_DjnN3xju",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "202ed2ab864c4ae08d4bd2389b5632cf",
            "21776a9c5320449c987d0905c387e963",
            "92d9212454b3464594f20f80ed659a1d",
            "1d333502a9ee4b9b969190acd0f0dd76",
            "9e5db8d1480e4b7b8c84a14c87a4de2a",
            "b47595d635e541b7a727f2b8f1e6103e",
            "f8223d1e12274ea5af9ae195ddfaca77",
            "0c8a85e7361a443e87e071765716c700",
            "56b7c102f2764f0d8a64c0a8322a0a1e",
            "9bf4b5cc4a6f4566bef9b1aa5ee3beb6",
            "b5adb53ebbfa44a692b03d0326c7e7aa"
          ]
        },
        "outputId": "d227d783-cd1e-4523-a386-2bd7838e2497"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/496M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "202ed2ab864c4ae08d4bd2389b5632cf"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW, RobertaTokenizer, AutoModelForQuestionAnswering,T5Tokenizer, T5ForConditionalGeneration\n",
        "\n",
        "\n",
        "model_name = 'deepset/roberta-base-squad2'\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": "markdown",
      "source": [
        "# **ROBERTA FINE TUNING**"
      ],
      "metadata": {
        "id": "TVkAgAhJacoh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "PoGvgKMm3xjy"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "args = TrainingArguments(\n",
        "   \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": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "XVpIl-pvQ6YZ",
        "outputId": "75f6e4d0-1aa7-4899-ee49-bd41fb9e2d15"
      },
      "execution_count": 23,
      "outputs": [
        {
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              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
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              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
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              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
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              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
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        {
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          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.15.2"
            ]
          },
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              "<IPython.core.display.HTML object>"
            ],
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              "Run data is saved locally in <code>/content/wandb/run-20230515_002454-odsq980z</code>"
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        },
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/rog1/huggingface/runs/odsq980z' target=\"_blank\">azure-yogurt-19</a></strong> to <a href='https://wandb.ai/rog1/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/rog1/huggingface' target=\"_blank\">https://wandb.ai/rog1/huggingface</a>"
            ]
          },
          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View run at <a href='https://wandb.ai/rog1/huggingface/runs/odsq980z' target=\"_blank\">https://wandb.ai/rog1/huggingface/runs/odsq980z</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a RobertaTokenizerFast 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2970' max='2970' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2970/2970 46:00, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.446200</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.733600</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.357600</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.202200</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.122000</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.071200</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.049200</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.028300</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.017600</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>No log</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
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