indic_supervised_text_classification.ipynb

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    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aswinpradeep/indic-supervised-text-classification/blob/main/indic_supervised_text_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Indic supervised text classification**\n",
        "\n",
        "Notebook contains several experiments to perform domain classification for text in Indian languages.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "kzqcL9qE4ytG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install necessary libraries"
      ],
      "metadata": {
        "id": "zALxTHAwRqCW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install indic-nlp-library\n",
        "!pip install transformers\n",
        "!pip install sentencepiece"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7l4hrNIRRwjV",
        "outputId": "5962bd1d-b169-4caf-c835-59d3a4879209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting indic-nlp-library\n",
            "  Downloading indic_nlp_library-0.81-py3-none-any.whl (40 kB)\n",
            "\u001b[?25l\r\u001b[K     |████████▏                       | 10 kB 31.2 MB/s eta 0:00:01\r\u001b[K     |████████████████▍               | 20 kB 8.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 30 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 40 kB 2.4 MB/s \n",
            "\u001b[?25hCollecting sphinx-rtd-theme\n",
            "  Downloading sphinx_rtd_theme-1.0.0-py2.py3-none-any.whl (2.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.8 MB 8.6 MB/s \n",
            "\u001b[?25hCollecting sphinx-argparse\n",
            "  Downloading sphinx_argparse-0.3.1-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from indic-nlp-library) (1.19.5)\n",
            "Collecting morfessor\n",
            "  Downloading Morfessor-2.0.6-py3-none-any.whl (35 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from indic-nlp-library) (1.1.5)\n",
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            "Requirement already satisfied: requests>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.23.0)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (21.3)\n",
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            "Requirement already satisfied: docutils<0.18,>=0.11 in /usr/local/lib/python3.7/dist-packages (from sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (0.17.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.3->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.0.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (2021.10.8)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (3.0.6)\n",
            "Requirement already satisfied: sphinxcontrib-serializinghtml in /usr/local/lib/python3.7/dist-packages (from sphinxcontrib-websupport->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.1.5)\n",
            "Installing collected packages: sphinx-rtd-theme, sphinx-argparse, morfessor, indic-nlp-library\n",
            "Successfully installed indic-nlp-library-0.81 morfessor-2.0.6 sphinx-argparse-0.3.1 sphinx-rtd-theme-1.0.0\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 5.3 MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 34.6 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 446 kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 46.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 47.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.2.1 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.10.3 transformers-4.15.0\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 5.4 MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Download & Create dataset"
      ],
      "metadata": {
        "id": "z-6nlKZX_2O4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Download and extract files\n",
        "\n",
        "!wget https://anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com/drivespark-en-hi-20210303.zip\n",
        "!unzip drivespark-en-hi-20210303.zip\n",
        "\n",
        "\n",
        "!wget https://anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com/mk_20210320_en_hi.zip\n",
        "!unzip mk_20210320_en_hi.zip\n",
        "\n",
        "!wget https://anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com/ie_business_20210320_en_hi.zip\n",
        "!unzip ie_business_20210320_en_hi.zip\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4WVfiww4yRg",
        "outputId": "28bf5f74-4c5d-4f09-9612-9ff18a23b1c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-10 10:44:53--  https://anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com/drivespark-en-hi-20210303.zip\n",
            "Resolving anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com (anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com)... 52.218.232.185\n",
            "Connecting to anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com (anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com)|52.218.232.185|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3099461 (3.0M) [application/zip]\n",
            "Saving to: ‘drivespark-en-hi-20210303.zip’\n",
            "\n",
            "drivespark-en-hi-20 100%[===================>]   2.96M  5.83MB/s    in 0.5s    \n",
            "\n",
            "2022-01-10 10:44:54 (5.83 MB/s) - ‘drivespark-en-hi-20210303.zip’ saved [3099461/3099461]\n",
            "\n",
            "Archive:  drivespark-en-hi-20210303.zip\n",
            "   creating: en-hi/\n",
            "  inflating: en-hi/ds_train.hi       \n",
            "  inflating: en-hi/ds_train.en       \n",
            "  inflating: __MACOSX/en-hi/._ds_train.en  \n",
            "--2022-01-10 10:44:54--  https://anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com/mk_20210320_en_hi.zip\n",
            "Resolving anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com (anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com)... 52.218.232.185\n",
            "Connecting to anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com (anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com)|52.218.232.185|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1231605 (1.2M) [application/zip]\n",
            "Saving to: ‘mk_20210320_en_hi.zip’\n",
            "\n",
            "mk_20210320_en_hi.z 100%[===================>]   1.17M  3.07MB/s    in 0.4s    \n",
            "\n",
            "2022-01-10 10:44:55 (3.07 MB/s) - ‘mk_20210320_en_hi.zip’ saved [1231605/1231605]\n",
            "\n",
            "Archive:  mk_20210320_en_hi.zip\n",
            "  inflating: en-hi/mk_train.en       \n",
            "  inflating: en-hi/mk_train.hi       \n",
            "--2022-01-10 10:44:55--  https://anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com/ie_business_20210320_en_hi.zip\n",
            "Resolving anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com (anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com)... 52.218.232.185\n",
            "Connecting to anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com (anuvaad-parallel-corpus.s3-us-west-2.amazonaws.com)|52.218.232.185|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1867544 (1.8M) [application/zip]\n",
            "Saving to: ‘ie_business_20210320_en_hi.zip’\n",
            "\n",
            "ie_business_2021032 100%[===================>]   1.78M  4.76MB/s    in 0.4s    \n",
            "\n",
            "2022-01-10 10:44:55 (4.76 MB/s) - ‘ie_business_20210320_en_hi.zip’ saved [1867544/1867544]\n",
            "\n",
            "Archive:  ie_business_20210320_en_hi.zip\n",
            "  inflating: en-hi/ie_business_train.hi  \n",
            "  inflating: en-hi/ie_business_train.en  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Hardcoded list of and dataset size\n",
        "\n",
        "DOMAIN0 = \"AUTOMOTIVE\"\n",
        "D0_FILE = \"/content/en-hi/ds_train.hi\"\n",
        "\n",
        "DOMAIN1 = \"SPORTS\"\n",
        "D1_FILE = \"/content/en-hi/mk_train.hi\"\n",
        "\n",
        "DOMAIN2 = \"FINANCE\"\n",
        "D2_FILE = \"/content/en-hi/ie_business_train.hi\"\n",
        "\n",
        "DOMAINS = [DOMAIN0, DOMAIN1, DOMAIN2]\n",
        "PER_DOMAIN_DATA_LEN = 500\n",
        "DATASET_LEN = 500"
      ],
      "metadata": {
        "id": "uGYEXO9F4r_a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Duhp8azX4hSM",
        "outputId": "b4f8f6ae-6c06-48e0-ef56-de9b86d87419"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>वर्तमान में मनेसर संयंत्र में कुल 1,100 कर्मचा...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>20 फरवरी को बैंगलुरू में आईपीएल खिलाड़ियों की ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>बजाज ऑटो ने दोपहिया बाजार में किया धमाल, बिक्र...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>सिडनी, 26 नवंबरः एशियाई फुटबाल परिसंघ (एएफसी) ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>376</th>\n",
              "      <td>पिछले साल के अगस्त महीने के मुकाबले बिक्री में...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>481</th>\n",
              "      <td>पूर्व विश्व चैम्पियन बुलगारिया के वेसलिन टोपाल...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>ऑडी ने पेश की नयी लगजरी कार ए8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fe3c32c-1ac4-4d3f-b350-076651ef9489')\"\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-4fe3c32c-1ac4-4d3f-b350-076651ef9489 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-4fe3c32c-1ac4-4d3f-b350-076651ef9489');\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",
              "  "
            ],
            "text/plain": [
              "                                                  text  domain\n",
              "384  टीम इंडिया के लिए वर्ष 2015 मिला जुला वर्ष रहा...       1\n",
              "154  टीम इंडिया इंग्लैंड के खिलाफ वनडे और टी-20 सीर...       1\n",
              "466                     टॉस जीतकर भारत ने शुरु की पारी       1\n",
              "254  वर्तमान में मनेसर संयंत्र में कुल 1,100 कर्मचा...       0\n",
              "244  20 फरवरी को बैंगलुरू में आईपीएल खिलाड़ियों की ...       1\n",
              "..                                                 ...     ...\n",
              "182  बजाज ऑटो ने दोपहिया बाजार में किया धमाल, बिक्र...       0\n",
              "467  सिडनी, 26 नवंबरः एशियाई फुटबाल परिसंघ (एएफसी) ...       1\n",
              "376  पिछले साल के अगस्त महीने के मुकाबले बिक्री में...       2\n",
              "481  पूर्व विश्व चैम्पियन बुलगारिया के वेसलिन टोपाल...       1\n",
              "177                     ऑडी ने पेश की नयी लगजरी कार ए8       0\n",
              "\n",
              "[500 rows x 2 columns]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd \n",
        "df0 =  pd.read_csv(D0_FILE, sep=\"\\t\", header=None).head(PER_DOMAIN_DATA_LEN)\n",
        "df0.columns = [\"text\"]\n",
        "df0[\"domain\"] = 0\n",
        "\n",
        "df1 =  pd.read_csv(D1_FILE, sep=\"\\t\", header=None).head(PER_DOMAIN_DATA_LEN)\n",
        "df1.columns = [\"text\"]\n",
        "df1[\"domain\"] = 1\n",
        "\n",
        "df2 =  pd.read_csv(D2_FILE, sep=\"\\t\", header=None).head(PER_DOMAIN_DATA_LEN)\n",
        "df2.columns = [\"text\"]\n",
        "df2[\"domain\"] = 2\n",
        "\n",
        "df = pd.concat([df0, df1, df2], axis=0)\n",
        "df = df.sample(frac = 1)\n",
        "df = df.head(DATASET_LEN)\n",
        "#df['domain'] = df['domain'].map({'financial':0, 'automotive':1 , 'sports':2})\n",
        "display(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Basic data cleanup"
      ],
      "metadata": {
        "id": "uZrG0hcI_qkU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "import re\n",
        "\n",
        "\n",
        "def clean_data(text):\n",
        "    #fixes html tags displayed in encoded format\n",
        "    text = BeautifulSoup(text, features = \"lxml\").string\n",
        "\n",
        "    #remove html markup\n",
        "    text = re.sub(\"(<.*?>)\", \"\", text)\n",
        "\n",
        "    #remove urls\n",
        "    text = re.sub(r'http\\S+', '', text)\n",
        "\n",
        "    return text\n",
        "\n",
        "df['text'] = df['text'].apply(clean_data)\n",
        "df = df.dropna()\n",
        "display(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4efY4dlk9vT-",
        "outputId": "fb813dd4-9818-4221-c28b-9489cabc575c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3114c9f9-a357-4d71-8dca-69db8dd1fef5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>domain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>384</th>\n",
              "      <td>टीम इंडिया के लिए वर्ष 2015 मिला जुला वर्ष रहा...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>टीम इंडिया इंग्लैंड के खिलाफ वनडे और टी-20 सीर...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466</th>\n",
              "      <td>टॉस जीतकर भारत ने शुरु की पारी</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>वर्तमान में मनेसर संयंत्र में कुल 1,100 कर्मचा...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>20 फरवरी को बैंगलुरू में आईपीएल खिलाड़ियों की ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>बजाज ऑटो ने दोपहिया बाजार में किया धमाल, बिक्र...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>सिडनी, 26 नवंबरः एशियाई फुटबाल परिसंघ (एएफसी) ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>376</th>\n",
              "      <td>पिछले साल के अगस्त महीने के मुकाबले बिक्री में...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>481</th>\n",
              "      <td>पूर्व विश्व चैम्पियन बुलगारिया के वेसलिन टोपाल...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>ऑडी ने पेश की नयी लगजरी कार ए8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3114c9f9-a357-4d71-8dca-69db8dd1fef5')\"\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-3114c9f9-a357-4d71-8dca-69db8dd1fef5 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-3114c9f9-a357-4d71-8dca-69db8dd1fef5');\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",
              "  "
            ],
            "text/plain": [
              "                                                  text  domain\n",
              "384  टीम इंडिया के लिए वर्ष 2015 मिला जुला वर्ष रहा...       1\n",
              "154  टीम इंडिया इंग्लैंड के खिलाफ वनडे और टी-20 सीर...       1\n",
              "466                     टॉस जीतकर भारत ने शुरु की पारी       1\n",
              "254  वर्तमान में मनेसर संयंत्र में कुल 1,100 कर्मचा...       0\n",
              "244  20 फरवरी को बैंगलुरू में आईपीएल खिलाड़ियों की ...       1\n",
              "..                                                 ...     ...\n",
              "182  बजाज ऑटो ने दोपहिया बाजार में किया धमाल, बिक्र...       0\n",
              "467  सिडनी, 26 नवंबरः एशियाई फुटबाल परिसंघ (एएफसी) ...       1\n",
              "376  पिछले साल के अगस्त महीने के मुकाबले बिक्री में...       2\n",
              "481  पूर्व विश्व चैम्पियन बुलगारिया के वेसलिन टोपाल...       1\n",
              "177                     ऑडी ने पेश की नयी लगजरी कार ए8       0\n",
              "\n",
              "[500 rows x 2 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#split into train and test dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "df = df.head(DATASET_LEN)\n",
        "train_df, test_df = train_test_split(df, test_size=0.2)\n",
        "train_df\n",
        "\n",
        "train_df.to_csv(\"train.csv\")\n",
        "test_df.to_csv(\"test.csv\")"
      ],
      "metadata": {
        "id": "y2JahAknCI2q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import pandas as pd\n",
        "# train_df = pd.read_csv(\"/content/train.csv\")\n",
        "# test_df = pd.read_csv(\"/content/test.csv\")"
      ],
      "metadata": {
        "id": "IIUHMNy_YJqi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = list(train_df[\"text\"])\n",
        "X_test = list(test_df[\"text\"])\n",
        "y_train = list(train_df[\"domain\"])\n",
        "y_test = list(test_df[\"domain\"])"
      ],
      "metadata": {
        "id": "jiG65tq3hpec"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Statistics"
      ],
      "metadata": {
        "id": "u7xoVX-_B5gT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.value_counts(train_df['domain']).plot.bar()\n",
        "print(train_df.value_counts(train_df['domain']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "BVtS9bZSAnBO",
        "outputId": "a5169d85-e95e-4ec9-84e8-4f297289a816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "domain\n",
            "1    143\n",
            "0    137\n",
            "2    120\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.value_counts(test_df['domain']).plot.bar()\n",
        "print(test_df.value_counts(test_df['domain']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "InNbfgt5iqod",
        "outputId": "f454660c-0d92-454f-ef2c-da1e1ea0cdc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "domain\n",
            "1    44\n",
            "0    29\n",
            "2    27\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conventional approaches "
      ],
      "metadata": {
        "id": "Q0NX1gZHMsER"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNN"
      ],
      "metadata": {
        "id": "sLzC8ophQovd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer , CountVectorizer\n",
        "from indicnlp.tokenize import indic_tokenize  \n",
        "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import pickle"
      ],
      "metadata": {
        "id": "o40xDEuhIvNy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# hindi_stop_words = [\"अंदर\",\"अत\",\"अदि\",\"अप\",\"अपना\",\"अपनि\",\"अपनी\",\"अपने\",\"अभि\",\"अभी\",\"आदि\",\"आप\",\"इंहिं\",\"इंहें\",\"इंहों\",\"इतयादि\",\"इत्यादि\",\"इन\",\"इनका\",\"इन्हीं\",\"इन्हें\",\"इन्हों\",\"इस\",\"इसका\",\"इसकि\",\"इसकी\",\"इसके\",\"इसमें\",\"इसि\",\"इसी\",\"इसे\",\"उंहिं\",\"उंहें\",\"उंहों\",\"उन\",\"उनका\",\"उनकि\",\"उनकी\",\"उनके\",\"उनको\",\"उन्हीं\",\"उन्हें\",\"उन्हों\",\"उस\",\"उसके\",\"उसि\",\"उसी\",\"उसे\",\"एक\",\"एवं\",\"एस\",\"एसे\",\"ऐसे\",\"ओर\",\"और\",\"कइ\",\"कई\",\"कर\",\"करता\",\"करते\",\"करना\",\"करने\",\"करें\",\"कहते\",\"कहा\",\"का\",\"काफि\",\"काफ़ी\",\"कि\",\"किंहें\",\"किंहों\",\"कितना\",\"किन्हें\",\"किन्हों\",\"किया\",\"किर\",\"किस\",\"किसि\",\"किसी\",\"किसे\",\"की\",\"कुछ\",\"कुल\",\"के\",\"को\",\"कोइ\",\"कोई\",\"कोन\",\"कोनसा\",\"कौन\",\"कौनसा\",\"गया\",\"घर\",\"जब\",\"जहाँ\",\"जहां\",\"जा\",\"जिंहें\",\"जिंहों\",\"जितना\",\"जिधर\",\"जिन\",\"जिन्हें\",\"जिन्हों\",\"जिस\",\"जिसे\",\"जीधर\",\"जेसा\",\"जेसे\",\"जैसा\",\"जैसे\",\"जो\",\"तक\",\"तब\",\"तरह\",\"तिंहें\",\"तिंहों\",\"तिन\",\"तिन्हें\",\"तिन्हों\",\"तिस\",\"तिसे\",\"तो\",\"था\",\"थि\",\"थी\",\"थे\",\"दबारा\",\"दवारा\",\"दिया\",\"दुसरा\",\"दुसरे\",\"दूसरे\",\"दो\",\"द्वारा\",\"न\",\"नहिं\",\"नहीं\",\"ना\",\"निचे\",\"निहायत\",\"नीचे\",\"ने\",\"पर\",\"पहले\",\"पुरा\",\"पूरा\",\"पे\",\"फिर\",\"बनि\",\"बनी\",\"बहि\",\"बही\",\"बहुत\",\"बाद\",\"बाला\",\"बिलकुल\",\"भि\",\"भितर\",\"भी\",\"भीतर\",\"मगर\",\"मानो\",\"मे\",\"में\",\"यदि\",\"यह\",\"यहाँ\",\"यहां\",\"यहि\",\"यही\",\"या\",\"यिह\",\"ये\",\"रखें\",\"रवासा\",\"रहा\",\"रहे\",\"ऱ्वासा\",\"लिए\",\"लिये\",\"लेकिन\",\"व\",\"वगेरह\",\"वरग\",\"वर्ग\",\"वह\",\"वहाँ\",\"वहां\",\"वहिं\",\"वहीं\",\"वाले\",\"वुह\",\"वे\",\"वग़ैरह\",\"संग\",\"सकता\",\"सकते\",\"सबसे\",\"सभि\",\"सभी\",\"साथ\",\"साबुत\",\"साभ\",\"सारा\",\"से\",\"सो\",\"हि\",\"ही\",\"हुअ\",\"हुआ\",\"हुइ\",\"हुई\",\"हुए\",\"हे\",\"हें\",\"है\",\"हैं\",\"हो\",\"होता\",\"होति\",\"होती\",\"होते\",\"होना\",\"होने\"]\n",
        "# vectorizer = CountVectorizer()\n",
        "vectorizer = TfidfVectorizer(tokenizer = lambda x: indic_tokenize.trivial_tokenize(x))\n",
        "\n",
        "X_train_vec = vectorizer.fit_transform(X_train)\n",
        "X_test_vec = vectorizer.transform(X_test)\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=2)\n",
        "knn.fit(X_train_vec, y_train)\n",
        "\n",
        "knn_score = knn.score(X_test_vec, y_test)\n",
        "\n",
        "print(\"Results for KNN Classifier with Tfidf vectorizer\")\n",
        "print(knn_score)\n",
        "\n",
        "y_pred_knn = knn.predict(X_test_vec)\n",
        "#Confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_knn)\n",
        "cmd = ConfusionMatrixDisplay(cm, display_labels=DOMAINS)\n",
        "cmd.plot()\n",
        "\n",
        "\n",
        "knnPickle = open('knnpickle_file', 'wb') \n",
        "\n",
        "# source, destination \n",
        "pickle.dump(knn, knnPickle)   \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "Em5s3nEYluu7",
        "outputId": "88416288-62b9-48a1-bdcd-2c0b60c03ce7"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results for KNN Classifier with Tfidf vectorizer\n",
            "0.8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neural Network / BERT approaches\n"
      ],
      "metadata": {
        "id": "A0NG73r3Phph"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
        "import torch\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from transformers import BertTokenizer, BertForSequenceClassification,AutoTokenizer,AutoModelForSequenceClassification\n",
        "from transformers import EarlyStoppingCallback"
      ],
      "metadata": {
        "id": "iPesWn09uMRg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "  # Create torch dataset\n",
        "  class Dataset(torch.utils.data.Dataset):\n",
        "      def __init__(self, encodings, labels=None):\n",
        "          self.encodings = encodings\n",
        "          self.labels = labels\n",
        "\n",
        "      def __getitem__(self, idx):\n",
        "          item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "          if self.labels:\n",
        "              item[\"labels\"] = torch.tensor(self.labels[idx])\n",
        "          return item\n",
        "\n",
        "      def __len__(self):\n",
        "          return len(self.encodings[\"input_ids\"])\n",
        "\n"
      ],
      "metadata": {
        "id": "TkU_zc8MuKtR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " def compute_metrics(p):\n",
        "      pred, labels = p\n",
        "      pred = np.argmax(pred, axis=1)\n",
        "\n",
        "      accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
        "      recall = recall_score(y_true=labels, y_pred=pred, average='micro')\n",
        "      precision = precision_score(y_true=labels, y_pred=pred, average='micro')\n",
        "      f1 = f1_score(y_true=labels, y_pred=pred, average='micro')\n",
        "      return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}\n"
      ],
      "metadata": {
        "id": "7bwYdf346Nwg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"ai4bharat/indic-bert\"\n",
        "indicbert_tokenizer = AutoTokenizer.from_pretrained(model_name, keep_accents=True)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)\n",
        " \n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "X_train_tokenized = indicbert_tokenizer(X_train, padding=True, truncation=True, max_length=512)\n",
        "X_val_tokenized = indicbert_tokenizer(X_test, padding=True, truncation=True, max_length=512)\n",
        "\n",
        "train_dataset = Dataset(X_train_tokenized, y_train)\n",
        "val_dataset = Dataset(X_val_tokenized, y_test)\n",
        "\n",
        "\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"output/indic-bert\",\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=50,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    num_train_epochs=3,\n",
        "    seed=0,\n",
        "    load_best_model_at_end=True,\n",
        "    logging_dir='logs',           \n",
        "    logging_steps=50,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        ")\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n",
        "    \n",
        ")\n",
        "\n",
        "trainer.train()\n",
        "indicbert_model_path = \"output/indic-bert\"\n",
        "model.save_pretrained(indicbert_model_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772,
          "referenced_widgets": [
            "a9c0e49dc68a465682994a5a96efb117",
            "78f5533f5d2c485885a4732844d40432",
            "904352483acc4e8381f5c682571fc3ec",
            "53043a14fef84eb181c6c037831de05d",
            "f3065c8b1e894a518aa89905b4632fe5",
            "192560152334446fb10564caccda0091",
            "24e6d1b47cfa499fb5d0ed04aa3baea1",
            "3835724a38b34fc6a95894895a10774d",
            "e72a05c15fef4bd59280c5a9144a424d",
            "e3c2b9c94df44c5dbde7665a9e50b0b9",
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          "data": {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at ai4bharat/indic-bert were not used when initializing AlbertForSequenceClassification: ['predictions.bias', 'sop_classifier.classifier.weight', 'predictions.decoder.weight', 'predictions.decoder.bias', 'predictions.dense.bias', 'predictions.LayerNorm.bias', 'sop_classifier.classifier.bias', 'predictions.LayerNorm.weight', 'predictions.dense.weight']\n",
            "- This IS expected if you are initializing AlbertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing AlbertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at ai4bharat/indic-bert and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "***** Running training *****\n",
            "  Num examples = 400\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 150\n"
          ]
        },
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              "      \n",
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              "      [150/150 00:37, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>50</td>\n",
              "      <td>1.065700</td>\n",
              "      <td>0.921311</td>\n",
              "      <td>0.710000</td>\n",
              "      <td>0.710000</td>\n",
              "      <td>0.710000</td>\n",
              "      <td>0.710000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.637100</td>\n",
              "      <td>0.459224</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.371900</td>\n",
              "      <td>0.382246</td>\n",
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              "      <td>0.910000</td>\n",
              "      <td>0.910000</td>\n",
              "      <td>0.910000</td>\n",
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        {
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          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Configuration saved in output/indic-bert/config.json\n",
            "Model weights saved in output/indic-bert/pytorch_model.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_tokenized = indicbert_tokenizer(X_test, padding=True, truncation=True, max_length=512)\n",
        "\n",
        "# Create torch dataset\n",
        "test_dataset = Dataset(X_test_tokenized)\n",
        "\n",
        "# Load trained model\n",
        "\n",
        "# model = BertForSequenceClassification.from_pretrained(model_path, num_labels=3)\n",
        "indicbert_out_model = AutoModelForSequenceClassification.from_pretrained(indicbert_model_path, num_labels=3)\n",
        "indicbert_out_model.to(device)\n",
        "\n",
        "\n",
        "# Define test trainer\n",
        "test_trainer = Trainer(indicbert_out_model)\n",
        "\n",
        "# Make prediction\n",
        "raw_pred, _, _ = test_trainer.predict(test_dataset)\n",
        "\n",
        "# Preprocess raw predictions\n",
        "y_pred = np.argmax(raw_pred, axis=1)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RcmTKvQ1AYnc",
        "outputId": "fac6ae4f-0c75-44a9-c2ee-0be7c902b161"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file output/indic-bert/config.json\n",
            "Model config AlbertConfig {\n",
            "  \"_name_or_path\": \"output/indic-bert\",\n",
            "  \"architectures\": [\n",
            "    \"AlbertForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0,\n",
            "  \"bos_token_id\": 2,\n",
            "  \"classifier_dropout_prob\": 0.1,\n",
            "  \"down_scale_factor\": 1,\n",
            "  \"embedding_size\": 128,\n",
            "  \"eos_token_id\": 3,\n",
            "  \"gap_size\": 0,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"inner_group_num\": 1,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"albert\",\n",
            "  \"net_structure_type\": 0,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_groups\": 1,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_memory_blocks\": 0,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"vocab_size\": 200000\n",
            "}\n",
            "\n",
            "loading weights file output/indic-bert/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing AlbertForSequenceClassification.\n",
            "\n",
            "All the weights of AlbertForSequenceClassification were initialized from the model checkpoint at output/indic-bert.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use AlbertForSequenceClassification for predictions without further training.\n",
            "No `TrainingArguments` passed, using `output_dir=tmp_trainer`.\n",
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "***** Running Prediction *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "      \n",
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              "      [13/13 00:00]\n",
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    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cmd = ConfusionMatrixDisplay(cm, display_labels=DOMAINS)\n",
        "cmd.plot()\n",
        "accuracy_score(y_true=y_test, y_pred=y_pred)"
      ],
      "metadata": {
        "id": "NPDnFF7ZBRMm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "265c95df-6a4f-4300-e11b-803e79d7f4fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91"
            ]
          },
          "metadata": {},
          "execution_count": 18
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"google/muril-large-cased\"\n",
        "muril_tokenizer = AutoTokenizer.from_pretrained(model_name, keep_accents=True)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)\n",
        " \n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "X_train_tokenized = muril_tokenizer(X_train, padding=True, truncation=True, max_length=512)\n",
        "X_val_tokenized = muril_tokenizer(X_test, padding=True, truncation=True, max_length=512)\n",
        "\n",
        "train_dataset = Dataset(X_train_tokenized, y_train)\n",
        "val_dataset = Dataset(X_val_tokenized, y_test)\n",
        "\n",
        "\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"output/muril\",\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=50,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    num_train_epochs=3,\n",
        "    seed=0,\n",
        "    load_best_model_at_end=True,\n",
        "    logging_dir='logs',           \n",
        "    logging_steps=50,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        ")\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n",
        "    \n",
        ")\n",
        "\n",
        "trainer.train()\n",
        "muril_model_path = \"output/muril-large-cased\"\n",
        "model.save_pretrained(muril_model_path)\n"
      ],
      "metadata": {
        "id": "Y3eROlrGBi-6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "loading configuration file https://huggingface.co/google/muril-large-cased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/fbda239b28cad45a6080fac5120af123da0ab1e614b767bc9d46a114ce6153a6.ef3bfb0d9b54a567d874d535fea34f5386e733032065b5d841165d64dcbf0b72\n",
            "Model config BertConfig {\n",
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            "  ],\n",
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            "\n",
            "loading configuration file https://huggingface.co/google/muril-large-cased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/fbda239b28cad45a6080fac5120af123da0ab1e614b767bc9d46a114ce6153a6.ef3bfb0d9b54a567d874d535fea34f5386e733032065b5d841165d64dcbf0b72\n",
            "Model config BertConfig {\n",
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            "\n",
            "loading configuration file https://huggingface.co/google/muril-large-cased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/fbda239b28cad45a6080fac5120af123da0ab1e614b767bc9d46a114ce6153a6.ef3bfb0d9b54a567d874d535fea34f5386e733032065b5d841165d64dcbf0b72\n",
            "Model config BertConfig {\n",
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          "text": [
            "storing https://huggingface.co/google/muril-large-cased/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/dd0d122a98e678842ba177db8e6af4b241eabb3873d4abb1fb076cb95b844ad4.3c28b8ebf6e4133342d2f89ab4ed734abab1a3d61b1236e3c784682333cb4c7a\n",
            "creating metadata file for /root/.cache/huggingface/transformers/dd0d122a98e678842ba177db8e6af4b241eabb3873d4abb1fb076cb95b844ad4.3c28b8ebf6e4133342d2f89ab4ed734abab1a3d61b1236e3c784682333cb4c7a\n",
            "loading weights file https://huggingface.co/google/muril-large-cased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/dd0d122a98e678842ba177db8e6af4b241eabb3873d4abb1fb076cb95b844ad4.3c28b8ebf6e4133342d2f89ab4ed734abab1a3d61b1236e3c784682333cb4c7a\n",
            "Some weights of the model checkpoint at google/muril-large-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at google/muril-large-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "***** Running training *****\n",
            "  Num examples = 400\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 150\n"
          ]
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              "      [150/150 02:44, Epoch 3/3]\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
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              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
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          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Configuration saved in output/muril-large-cased/config.json\n",
            "Model weights saved in output/muril-large-cased/pytorch_model.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_tokenized = muril_tokenizer(X_test, padding=True, truncation=True, max_length=512)\n",
        "\n",
        "# Create torch dataset\n",
        "test_dataset = Dataset(X_test_tokenized)\n",
        "\n",
        "# Load trained model\n",
        "\n",
        "# model = BertForSequenceClassification.from_pretrained(model_path, num_labels=3)\n",
        "muril_out_model = AutoModelForSequenceClassification.from_pretrained(muril_model_path, num_labels=3)\n",
        "\n",
        "\n",
        "# Define test trainer\n",
        "test_trainer = Trainer(muril_out_model)\n",
        "\n",
        "# Make prediction\n",
        "raw_pred, _, _ = test_trainer.predict(test_dataset)\n",
        "\n",
        "# Preprocess raw predictions\n",
        "y_pred = np.argmax(raw_pred, axis=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "bkSlwj3KIGHB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        },
        "outputId": "6f7b600b-195d-4bdc-b2d8-7105dea70881"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file output/muril-large-cased/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"output/muril-large-cased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForSequenceClassification\"\n",
            "  ],\n",
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            "  \"classifier_dropout\": null,\n",
            "  \"embedding_size\": 1024,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 197285\n",
            "}\n",
            "\n",
            "loading weights file output/muril-large-cased/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing BertForSequenceClassification.\n",
            "\n",
            "All the weights of BertForSequenceClassification were initialized from the model checkpoint at output/muril-large-cased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.\n",
            "No `TrainingArguments` passed, using `output_dir=tmp_trainer`.\n",
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "***** Running Prediction *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='13' max='13' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [13/13 00:03]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cmd = ConfusionMatrixDisplay(cm, display_labels=DOMAINS)\n",
        "cmd.plot()\n",
        "accuracy_score(y_true=y_test, y_pred=y_pred)"
      ],
      "metadata": {
        "id": "vTNpV2mTIIda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "fc3b474d-4a7e-4612-e0d6-06dafa20c784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.96"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "qp7txX9lNV6w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_sentence = \"सचिन ने पिछले क्रिकेट मैच में कुल 20 रन बनाए और 2 विकेट लिए\"\n",
        "\n",
        "encoding = muril_tokenizer([input_sentence], return_tensors='pt').to(device)\n",
        "predictions = muril_out_model(**encoding).logits.argmax(-1)\n",
        "\n",
        "print(\" Muril prediction : \", DOMAINS[predictions.item()])\n",
        "\n",
        "encoding = indicbert_tokenizer([input_sentence], return_tensors='pt').to(device)\n",
        "predictions = indicbert_out_model(**encoding).logits.argmax(-1)\n",
        "\n",
        "print(\" Indic bert prediction : \", DOMAINS[predictions.item()])\n",
        "\n",
        "\n",
        "knn_model = pickle.load(open('knnpickle_file', 'rb'))\n",
        "\n",
        "vct = vectorizer.transform([input_sentence])\n",
        "predictions = knn_model.predict(vct)\n",
        "\n",
        "print(\" KNN prediction : \", DOMAINS[predictions[0]])\n"
      ],
      "metadata": {
        "id": "9conQW6JF97w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37519a1b-430b-4ad1-c544-dc016c3c9081"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Muril prediction :  SPORTS\n",
            " Indic bert prediction :  SPORTS\n",
            " KNN prediction :  SPORTS\n"
          ]
        }
      ]
    }
  ]
}