FineTuned_AI4Bharat(EN_MR).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anujsahani01/English-Marathi-Translation/blob/main/FineTuned_AI4Bharat(EN_MR).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IG59w-15z5eQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "984927db-21aa-484d-de8f-e4c2d89c3aa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Found existing installation: transformers 4.30.2\n",
            "Uninstalling transformers-4.30.2:\n",
            "  Would remove:\n",
            "    /usr/local/bin/transformers-cli\n",
            "    /usr/local/lib/python3.10/dist-packages/transformers-4.30.2.dist-info/*\n",
            "    /usr/local/lib/python3.10/dist-packages/transformers/*\n",
            "Proceed (Y/n)? Y\n",
            "  Successfully uninstalled transformers-4.30.2\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n",
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            "Installing collected packages: transformers\n",
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          ]
        }
      ],
      "source": [
        "!pip install kaggle\n",
        "!pip install datasets\n",
        "!pip install --upgrade accelerate\n",
        "!pip install wandb\n",
        "!pip uninstall transformers\n",
        "!pip install --no-cache-dir transformers sentencepiece\n",
        "!pip install evaluate\n",
        "!pip install rouge_score\n",
        "! pip install sacremoses\n",
        "! pip install sacrebleu"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URjw3tgNGltN",
        "outputId": "cb41143b-9a3f-4571-df9b-f4e776817318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aVwdqQVohzLd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f66925ca-dd1f-4f72-d2c0-81cdf917a3c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ],
      "source": [
        "!mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uMo7PK_Rhzge"
      },
      "outputs": [],
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lELagXTNh0wS"
      },
      "outputs": [],
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sHbjU3I3h3S4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2edc538b-66ab-4181-d7a6-2abba3ca265f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "samanantar.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d mathurinache/samanantar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jz17FTgkh43C"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "\n",
        "with zipfile.ZipFile('/content/samanantar.zip') as zf:\n",
        "    zf.extract('final_data/en-mr/train.mr', 'data')\n",
        "\n",
        "with zipfile.ZipFile('/content/samanantar.zip') as zf:\n",
        "    zf.extract('final_data/en-mr/train.en', 'data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZRwFpmPjh8eJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KpP34tTAh8yz"
      },
      "outputs": [],
      "source": [
        "english_text = ''\n",
        "marathi_text = ''\n",
        "\n",
        "with open('/content/data/final_data/en-mr/train.en' , 'r', encoding = 'utf-8') as f:\n",
        "  english_text = f.read()\n",
        "\n",
        "with open('/content/data/final_data/en-mr/train.mr' , 'r', encoding = 'utf-8') as f:\n",
        "  marathi_text = f.read()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oMJNg-0lh-kx"
      },
      "outputs": [],
      "source": [
        "english = []\n",
        "marathi = []\n",
        "english = english_text.split('\\n')\n",
        "marathi = marathi_text.split('\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-8U_8Ky8iAR8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb78e48d-02d3-43d4-ec19-12c866220029"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-e9738c3977d1>:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
            "  df1.column = ['inputs', 'targets']\n"
          ]
        }
      ],
      "source": [
        "df1 = pd.DataFrame()\n",
        "df1.column = ['inputs', 'targets']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c8YTkISPiBrB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "658dc003-0dd6-42ed-88f6-b60d47ae3d7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3322057 3322057\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(len(english), len(marathi))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EwsdfvqbiDIp"
      },
      "outputs": [],
      "source": [
        "df1['inputs'] = english\n",
        "df1['targets'] = marathi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nCGh6RcbiEVv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df2 = pd.read_csv('/content/data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.read_csv('/content/data1.csv')\n",
        "df4 = pd.read_csv('/content/data2.csv')\n",
        "# df5 = pd.read_csv('/content/data3.csv')"
      ],
      "metadata": {
        "id": "-GsXvG-hLw2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J_TZ3Oa7iRW8"
      },
      "outputs": [],
      "source": [
        "frames = [df1, df2, df3, df4]\n",
        "df_data = pd.concat(frames)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hNXDp4XqigUc"
      },
      "outputs": [],
      "source": [
        "df_data.columns = ['english', 'marathi']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uG6hZd2riSDN"
      },
      "outputs": [],
      "source": [
        "df_data.dropna(inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "rwUqRrxHiSF_",
        "outputId": "7d2e7147-7da1-4c22-bfd6-be8405bf01f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             english  \\\n",
              "0         Next few months are really crucial for us.   \n",
              "1  Sharad Pawar, Supriya Sule, Ajit Pawar, Praful...   \n",
              "2  \"Kanwal Singh Chauhan, the president of Sonepa...   \n",
              "3               His demise is a huge loss for India.   \n",
              "4                        Photographer and raconteur.   \n",
              "5  He has won several competitions at the state a...   \n",
              "6                       This is a worrying increase.   \n",
              "7                   Most of the victims were Dalits.   \n",
              "8                     This has shocked the Congress.   \n",
              "9                             This was not the idea.   \n",
              "\n",
              "                                             marathi  \n",
              "0  पुढील तीन महिने आमच्यासाठी खूप महत्त्वपूर्ण आहेत.  \n",
              "1  या बैठकील पक्षाध्यक्ष शरद पवार, केंद्रीय मंत्र...  \n",
              "2  वृत्तसंस्थेने दिलेल्या माहितीनुसार, विरोध करण्...  \n",
              "3  त्यांच्या मृत्यूमुळे भारताचे खूप मोठे नुकसान झ...  \n",
              "4                     छायाचित्रकार आणि छायाचित्रकार.  \n",
              "5  राज्य व राष्ट्रीय पातळीवरील अनेक मल्ल त्यांनी ...  \n",
              "6                           ही वाढ चिंताजनक तर आहेच.  \n",
              "7  त्यातील बहुतांशी घटनांत पीडित व्यक्ती या दलित ...  \n",
              "8               त्यामुळे काँग्रेसला मोठा धक्का बसला.  \n",
              "9                             हा कल्पनाविलास नव्हता.  "
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>Sharad Pawar, Supriya Sule, Ajit Pawar, Praful...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>\"Kanwal Singh Chauhan, the president of Sonepa...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>His demise is a huge loss for India.</td>\n",
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              "      <th>4</th>\n",
              "      <td>Photographer and raconteur.</td>\n",
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              "    </tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>This is a worrying increase.</td>\n",
              "      <td>ही वाढ चिंताजनक तर आहेच.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Most of the victims were Dalits.</td>\n",
              "      <td>त्यातील बहुतांशी घटनांत पीडित व्यक्ती या दलित ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>This has shocked the Congress.</td>\n",
              "      <td>त्यामुळे काँग्रेसला मोठा धक्का बसला.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>This was not the idea.</td>\n",
              "      <td>हा कल्पनाविलास नव्हता.</td>\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "df_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JfL2ZdeiSII",
        "outputId": "14598ca7-a663-4a99-e6df-51854670dbd7"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3400084"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "len(df_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hKLZrdI6iSO7"
      },
      "outputs": [],
      "source": [
        "l = len(df_data)\n",
        "split = int(0.75*l)\n",
        "df_train = df_data[:split]\n",
        "# shuffle the DataFrame rows\n",
        "df_train = df_train.sample(frac = 1)\n",
        "df_train.reset_index(inplace = True)\n",
        "df_train.drop('index', axis = 1, inplace = True)\n",
        "\n",
        "df_test = df_data[split:]\n",
        "df_test = df_test.sample(frac = 1)\n",
        "df_test.reset_index(inplace = True)\n",
        "df_test.drop('index', axis = 1, inplace = True);"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head(10)"
      ],
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          "height": 363
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        "id": "FDkKN1I5IMlR",
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "                                             english  \\\n",
              "0  Director Karan Johar and producer Boney Kapoor...   \n",
              "1  After hearing the arguments of both sides, the...   \n",
              "2           The traffic was restored by the evening.   \n",
              "3                                  Material benefits   \n",
              "4     Police have registered a case on the accident.   \n",
              "5  This was informed by Finance Minister Nirmala ...   \n",
              "6  Additional responsibilities in job too might b...   \n",
              "7   The Congress won only one seat and the NCP four.   \n",
              "8                       The media cannot be muzzled.   \n",
              "9  Forest officials and police personnel reached ...   \n",
              "\n",
              "                                             marathi  \n",
              "0  निर्माता आणि दिग्दर्शक करण जोहर यानेही बाबा बन...  \n",
              "1  दोन्ही पक्षांची बाजू ऐकल्यानंतर सर्वोच्च न्याय...  \n",
              "2              रात्री ही वाहतूक पूर्ववत करण्यात आली.  \n",
              "3                                      साहित्य फायदे  \n",
              "4       अपघाताची कवठेमहांकाळ पोलिसांत नोंद झाली आहे.  \n",
              "5  अर्थमंत्री निर्मला सितारमण यांनी संसदेतील भाषण...  \n",
              "6            नोकरीमध्ये अतिरिक्त जबाबदारी मिळू शकते.  \n",
              "7  राष्ट्रवादीने चार जागा जिंकल्या तर काँग्रेसला ...  \n",
              "8                      माध्यमांचे काही होऊ शकत नाही.  \n",
              "9  वनविभागाचे कर्मचारी आणि पालिका अधिकारी घटनास्थ...  "
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              "    }\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>english</th>\n",
              "      <th>marathi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Director Karan Johar and producer Boney Kapoor...</td>\n",
              "      <td>निर्माता आणि दिग्दर्शक करण जोहर यानेही बाबा बन...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>After hearing the arguments of both sides, the...</td>\n",
              "      <td>दोन्ही पक्षांची बाजू ऐकल्यानंतर सर्वोच्च न्याय...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The traffic was restored by the evening.</td>\n",
              "      <td>रात्री ही वाहतूक पूर्ववत करण्यात आली.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Material benefits</td>\n",
              "      <td>साहित्य फायदे</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Police have registered a case on the accident.</td>\n",
              "      <td>अपघाताची कवठेमहांकाळ पोलिसांत नोंद झाली आहे.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>This was informed by Finance Minister Nirmala ...</td>\n",
              "      <td>अर्थमंत्री निर्मला सितारमण यांनी संसदेतील भाषण...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Additional responsibilities in job too might b...</td>\n",
              "      <td>नोकरीमध्ये अतिरिक्त जबाबदारी मिळू शकते.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>The Congress won only one seat and the NCP four.</td>\n",
              "      <td>राष्ट्रवादीने चार जागा जिंकल्या तर काँग्रेसला ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The media cannot be muzzled.</td>\n",
              "      <td>माध्यमांचे काही होऊ शकत नाही.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Forest officials and police personnel reached ...</td>\n",
              "      <td>वनविभागाचे कर्मचारी आणि पालिका अधिकारी घटनास्थ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28a14b0e-1c59-4b42-bf1b-f7abdc4f40a0')\"\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-28a14b0e-1c59-4b42-bf1b-f7abdc4f40a0 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-28a14b0e-1c59-4b42-bf1b-f7abdc4f40a0');\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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnnzRqkKiSQ3",
        "outputId": "450239ba-dba6-4348-d1ff-e6c822b3655e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2550063 850021\n"
          ]
        }
      ],
      "source": [
        "print(len(df_train), len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['length'] = df_train['english'].apply(\n",
        "    lambda row: min(len(row.split(\" \")), len(row)) if isinstance(row, str) else None\n",
        ")"
      ],
      "metadata": {
        "id": "I22SI0uZHj9t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train['length'].min())\n",
        "print(df_train['length'].max())\n",
        "print(df_train['length'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6knfMkAHj1D",
        "outputId": "1bc3c589-6f5b-47ee-9681-f81e300dbfce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "10263\n",
            "10.831284560420665\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.drop(['length'], axis = 1, inplace = True);"
      ],
      "metadata": {
        "id": "_to_ukpsHjst"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train.loc[(df_train['english'].str.len() <= 80) & (df_train['english'].str.len() >= 40)]"
      ],
      "metadata": {
        "id": "5rlMdAhPHjkS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = df_test.loc[(df_test['english'].str.len() <= 80) & (df_test['english'].str.len() >= 40)]"
      ],
      "metadata": {
        "id": "aYIZ6F1xHjb-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_train), len(df_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3kaImVIHsK7",
        "outputId": "ac325094-c378-4b9b-9ca3-5d45c3f665c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "882869 281254\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.reset_index(inplace = True)\n",
        "df_test.reset_index(inplace = True)"
      ],
      "metadata": {
        "id": "xD7Aqrf7Hx0-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.drop(['index'], axis = 1, inplace = True)\n",
        "df_test.drop(['index'], axis = 1, inplace= True)"
      ],
      "metadata": {
        "id": "almLcMvoHxNh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.to_csv('train.csv', encoding = 'utf-8', index= False)"
      ],
      "metadata": {
        "id": "_LINiYWtN3Ag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import DatasetDict, Dataset\n",
        "\n",
        "dataset = DatasetDict({\n",
        "    \"train\" : Dataset.from_pandas(df_train[:int(len(df_train)/2)]),\n",
        "    \"test\" : Dataset.from_pandas(df_test[:int(len(df_test)/2)])\n",
        "})"
      ],
      "metadata": {
        "id": "420TaoAUH4Xl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pa43-hiJH4K_",
        "outputId": "02c96953-85ff-444c-cf93-5b305db78f51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['english', 'marathi'],\n",
              "        num_rows: 441434\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['english', 'marathi'],\n",
              "        num_rows: 140627\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QBRBqb-qmLe8"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5FlQVZGamVQB"
      },
      "outputs": [],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5zj7U72RlgUx"
      },
      "outputs": [],
      "source": [
        "# from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer\n",
        "from transformers import FSMTForConditionalGeneration, FSMTTokenizer, AutoTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRb6Yq49mZ0u"
      },
      "outputs": [],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('ai4bharat/indic-bert', keep_accents=True, eos_token = '[CLS]', model_max_length = 512)\n",
        "tokenizer.bos_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = tokenizer('She is not at home. She is in the garden.In the garden she plays with her friends.', text_target = 'ती घरी नाही. ती बागेत आहे.बागेत ती तिच्या मैत्रिणींसोबत खेळते.',  max_length= 50, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "out1 = tokenizer(dataset['train']['english'][0], text_target =  dataset['train']['marathi'][0],  max_length= 20, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')"
      ],
      "metadata": {
        "id": "ip2dOm1eIaDl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEIBksNksFUW",
        "outputId": "3b8ff704-3cd0-42bc-875e-f550a3578e97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[    2,  4548,     8, 82828, 55011, 10322,    24, 20650, 59575,   335,\n",
              "         15988, 75454,   297,   271,  3368,     9,     3,     0,     0,     0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]), 'length': tensor([20]), 'labels': tensor([[     2,  13547,     86,  26882,  16896,    219,   7189,     75,  54838,\n",
              "           4798,   1311,   5891, 158254, 121957,  13028,   3608,   4134,    343,\n",
              "              9,      3]])}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(out['input_ids'])\n",
        "print(out['attention_mask'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iy2klikkOkyo",
        "outputId": "be5f4813-f8e1-4e96-b00b-20e7d88abdb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[    2,   839,    54,   179,   117,  2279,     9,   839,    54,    26,\n",
            "            11, 69284,     9,  1197,    11, 69284,   839, 21581,    90,   481,\n",
            "         10574,     9,     3,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0]])\n",
            "tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwUTBkRm1e9Y",
        "outputId": "d9ef75de-0d7c-4c85-cd05-71ec99407d38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS] ती घरी नाही. ती बागेत आहे.बागेत ती तिच्या मैत्रिणींसोबत खेळते.[SEP]<pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>']\n"
          ]
        }
      ],
      "source": [
        "decoded_preds = tokenizer.batch_decode(out['labels'])\n",
        "print(decoded_preds)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaDHDtnqMTNS",
        "outputId": "63ec8af0-e028-46ec-f831-5f95f7798bda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AlbertTokenizerFast(name_or_path='ai4bharat/indic-bert', vocab_size=200000, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '[CLS]', 'eos_token': '[CLS]', 'unk_token': '<unk>', 'sep_token': '[SEP]', 'pad_token': '<pad>', 'cls_token': '[CLS]', 'mask_token': AddedToken(\"[MASK]\", rstrip=False, lstrip=True, single_word=False, normalized=False)}, clean_up_tokenization_spaces=True)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "5Ygq4e_BTrNm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 70\n",
        "context_length = 70\n",
        "\n",
        "def tokenize(data):\n",
        "  data = tokenizer(data['english'], text_target =  data['marathi'],  max_length= max_length, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "  inp_ids = []\n",
        "  if len(data['input_ids']) != 0:\n",
        "    for length, ids in zip(data[\"length\"], data[\"input_ids\"]):\n",
        "      inp_ids.append(ids)\n",
        "    label = []\n",
        "    for length, lb in zip(data[\"length\"], data[\"labels\"]):\n",
        "      label.append(lb)\n",
        "  return {\"input_ids\": inp_ids,\n",
        "          'labels' : label}"
      ],
      "metadata": {
        "id": "AyyZLPNgIPd9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "8c348266bcfe4f01b095f3daff116912",
            "556e03a5f11943ec9ff1fa364d8ed022",
            "c95fa91d28a348f7b8ee2f3cf35b25e7",
            "245bcddac90844ab99d2072221608610",
            "173bcae539ab4ce0aada3b1dcd52a4a2",
            "b246a46f7def4577b2ab7f07bb62672a",
            "7d7a1889da064d579e7e4fb45483cff6",
            "4795d183142b4f618f05db88a56d34fa",
            "619019a688f241fc8dcfe3bc57ddc452",
            "7d74973ad2754c6c95a17a86e11ccaa3",
            "17e166f26eac41f39459873852520a1b",
            "7ba0890caea04c508913161362c2c240",
            "a29fad5f28394682a7355adbc601f6b6",
            "96831d86a83348fd81fb11e5b7818c85",
            "c76aa79b7c4d4e1486396c354fcb1bf9",
            "cc71defde64d4bfb93fc14c3e8ad9044",
            "de90c63f7b5e471791ba63382fab5ad0",
            "1320d54d988540eda0476bda06a8ef84",
            "5b8df631f43447bb9ae4d6767185e3d5",
            "da714cbdb01f41f89a106ab27a7f73cd",
            "a4c369df46694c778d00d116994a9813",
            "68116bc21c844953acaa38f7d3e085e1"
          ]
        },
        "id": "0wmpuArWnIb6",
        "outputId": "d29b5b2f-b0f0-47fd-c8f4-c8226b14fb83"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/441434 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c348266bcfe4f01b095f3daff116912"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/140627 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ba0890caea04c508913161362c2c240"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_dataset = dataset.map(\n",
        "    tokenize,\n",
        "    batched=True,\n",
        "    remove_columns = dataset['train'].column_names,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQLXrP97nY_i",
        "outputId": "64ef7324-f13f-43ee-d35e-0d6c79a10982"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'labels'],\n",
              "        num_rows: 441434\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'labels'],\n",
              "        num_rows: 140627\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "tokenized_dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inp = tokenized_dataset['train']['input_ids']"
      ],
      "metadata": {
        "id": "Q_yNprNbPwm7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr = []\n",
        "df_final = pd.DataFrame()\n",
        "df_final['tokenized_eng'] = inp"
      ],
      "metadata": {
        "id": "7FALpoDVJPQO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_final['length'] = df_final['tokenized_eng'].apply(\n",
        "    lambda row: len(row)\n",
        ")"
      ],
      "metadata": {
        "id": "F91cOgwSQ7Dg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_final['length'].min())\n",
        "print(df_final['length'].max())\n",
        "print(df_final['length'].min())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WxwUTeJJSZv",
        "outputId": "ed4a4812-7ddd-4f34-bff2-8bca711443ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "70\n",
            "70\n",
            "70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out = tokenized_dataset['train'][0]"
      ],
      "metadata": {
        "id": "IbQbqVq3JVxf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(out['labels'])\n",
        "print(out['input_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M6Sk0HrRJVmV",
        "outputId": "f7402039-2070-4ad6-d78a-1c076f83c6bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2, 13547, 86, 26882, 16896, 219, 7189, 75, 54838, 4798, 1311, 5891, 158254, 121957, 13028, 3608, 4134, 343, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[2, 4548, 8, 82828, 55011, 10322, 24, 20650, 59575, 335, 15988, 75454, 297, 271, 3368, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hUkl84elm4m5"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModel"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -U neptune transformers"
      ],
      "metadata": {
        "id": "It8gSAjfX-rU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2b0ced1-90d6-4566-aff5-16bcfb360fcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: neptune in /usr/local/lib/python3.10/dist-packages (1.3.2)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.30.2)\n",
            "Requirement already satisfied: GitPython>=2.0.8 in /usr/local/lib/python3.10/dist-packages (from neptune) (3.1.31)\n",
            "Requirement already satisfied: Pillow>=1.1.6 in /usr/local/lib/python3.10/dist-packages (from neptune) (8.4.0)\n",
            "Requirement already satisfied: PyJWT in /usr/local/lib/python3.10/dist-packages (from neptune) (2.7.0)\n",
            "Requirement already satisfied: backoff in /usr/local/lib/python3.10/dist-packages (from neptune) (2.2.1)\n",
            "Requirement already satisfied: boto3>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.27.1)\n",
            "Requirement already satisfied: bravado<12.0.0,>=11.0.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (11.0.3)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (8.1.3)\n",
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            "Requirement already satisfied: oauthlib>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (3.2.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from neptune) (23.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from neptune) (1.5.3)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from neptune) (5.9.5)\n",
            "Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (2.27.1)\n",
            "Requirement already satisfied: requests-oauthlib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.3.1)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.16.0)\n",
            "Requirement already satisfied: swagger-spec-validator>=2.7.4 in /usr/local/lib/python3.10/dist-packages (from neptune) (3.0.3)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.26.16)\n",
            "Requirement already satisfied: websocket-client!=1.0.0,>=0.35.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.16.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: botocore<1.31.0,>=1.30.1 in /usr/local/lib/python3.10/dist-packages (from boto3>=1.16.0->neptune) (1.30.1)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from boto3>=1.16.0->neptune) (1.0.1)\n",
            "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from boto3>=1.16.0->neptune) (0.6.1)\n",
            "Requirement already satisfied: bravado-core>=5.16.1 in /usr/local/lib/python3.10/dist-packages (from bravado<12.0.0,>=11.0.0->neptune) (5.17.1)\n",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.10/dist-packages (from bravado<12.0.0,>=11.0.0->neptune) (1.0.5)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from bravado<12.0.0,>=11.0.0->neptune) (2.8.2)\n",
            "Requirement already satisfied: simplejson in /usr/local/lib/python3.10/dist-packages (from bravado<12.0.0,>=11.0.0->neptune) (3.19.1)\n",
            "Requirement already satisfied: monotonic in /usr/local/lib/python3.10/dist-packages (from bravado<12.0.0,>=11.0.0->neptune) (1.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from bravado<12.0.0,>=11.0.0->neptune) (4.6.3)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from GitPython>=2.0.8->neptune) (4.0.10)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->neptune) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->neptune) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->neptune) (3.4)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from swagger-spec-validator>=2.7.4->neptune) (4.3.3)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->neptune) (2022.7.1)\n",
            "Requirement already satisfied: jsonref in /usr/local/lib/python3.10/dist-packages (from bravado-core>=5.16.1->bravado<12.0.0,>=11.0.0->neptune) (1.1.0)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.8->neptune) (5.0.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (23.1.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (0.19.3)\n",
            "Requirement already satisfied: fqdn in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (1.5.1)\n",
            "Requirement already satisfied: isoduration in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (20.11.0)\n",
            "Requirement already satisfied: jsonpointer>1.13 in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (2.4)\n",
            "Requirement already satisfied: rfc3339-validator in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (0.1.4)\n",
            "Requirement already satisfied: rfc3987 in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (1.3.8)\n",
            "Requirement already satisfied: uri-template in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (1.3.0)\n",
            "Requirement already satisfied: webcolors>=1.11 in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (1.13)\n",
            "Requirement already satisfied: arrow>=0.15.0 in /usr/local/lib/python3.10/dist-packages (from isoduration->jsonschema->swagger-spec-validator>=2.7.4->neptune) (1.2.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from getpass import getpass\n",
        "from transformers.integrations import NeptuneCallback\n",
        "import neptune\n",
        "token=my_api_token = getpass(\"Enter your Neptune API token:\")"
      ],
      "metadata": {
        "id": "oJOZRExhX9WD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17af4fed-79de-4eac-bbe4-aebe060831f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your Neptune API token:··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_project = \"anujsahani01/ITStudio\"\n",
        "run = neptune.init_run(\n",
        "    project=\"anujsahani01/ITStudio\",\n",
        "    api_token = 'eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiJkMzNmZWY5My0yOGY1LTRiZDItODA1MS05ZTM3MTc0MTI2NTYifQ==',\n",
        "    source_files= [\"/content/drive/MyDrive/AI4Bharat_optimized(FINALL).ipynb\"]\n",
        ")"
      ],
      "metadata": {
        "id": "t1j7EVqrX5NG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5f40fe6-d78f-4aef-d0cc-dc381c3546b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-57-d9acc3b93ba8>:2: NeptuneWarning: To avoid unintended consumption of logging hours during interactive sessions, the following monitoring options are disabled unless set to 'True' when initializing the run: 'capture_stdout', 'capture_stderr', and 'capture_hardware_metrics'.\n",
            "  run = neptune.init_run(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://app.neptune.ai/anujsahani01/ITStudio/e/MODEL-57\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run[\"train_dataset\"].track_files(\"./train.csv\")"
      ],
      "metadata": {
        "id": "6GJt7e2LOJW5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ve_Qb3027xEG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be1e4ce2-8335-4964-ed38-01e22268ca90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using a model of type albert to instantiate a model of type mbart. This is not supported for all configurations of models and can yield errors.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoConfig, AlbertConfig, MBartConfig\n",
        "model_checkpoint  = 'ai4bharat/indic-bert'\n",
        "config = MBartConfig.from_pretrained(model_checkpoint, vocab_size=len(tokenizer), use_auth_token=True)\n",
        "config.vocab_size = tokenizer.vocab_size"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.models.auto.modeling_auto import AutoModelForCausalLM, AutoModel, AutoModelForSeq2SeqLM\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, config = config).to('cuda')\n",
        "\n",
        "model.resize_token_embeddings(len(tokenizer))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QTTvmBHy-h0",
        "outputId": "34734be0-1135-477f-a8e6-e4fbfab47295"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at ai4bharat/indic-bert were not used when initializing MBartForConditionalGeneration: ['albert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.weight', 'predictions.decoder.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.weight', 'albert.embeddings.word_embeddings.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias', 'albert.pooler.bias', 'albert.embeddings.LayerNorm.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.weight', 'sop_classifier.classifier.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.weight', 'predictions.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.bias', 'albert.encoder.embedding_hidden_mapping_in.bias', 'predictions.LayerNorm.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.bias', 'predictions.LayerNorm.weight', 'predictions.dense.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.bias', 'sop_classifier.classifier.bias', 'albert.embeddings.token_type_embeddings.weight', 'albert.encoder.embedding_hidden_mapping_in.weight', 'albert.embeddings.position_embeddings.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.weight', 'predictions.dense.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.bias', 'albert.pooler.weight', 'albert.embeddings.LayerNorm.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight', 'predictions.decoder.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.bias']\n",
            "- This IS expected if you are initializing MBartForConditionalGeneration 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 MBartForConditionalGeneration 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 MBartForConditionalGeneration were not initialized from the model checkpoint at ai4bharat/indic-bert and are newly initialized: ['decoder.layers.11.final_layer_norm.weight', 'decoder.layers.11.encoder_attn.v_proj.weight', 'decoder.layers.3.encoder_attn.out_proj.bias', 'decoder.layers.10.self_attn.q_proj.weight', 'decoder.layers.10.encoder_attn.v_proj.bias', 'decoder.layers.8.self_attn.k_proj.weight', 'encoder.layers.6.self_attn.k_proj.bias', 'decoder.layers.7.encoder_attn.q_proj.weight', 'decoder.layers.10.fc1.bias', 'decoder.layers.4.final_layer_norm.bias', 'decoder.layers.7.self_attn.v_proj.bias', 'decoder.layers.2.fc1.weight', 'decoder.layers.4.encoder_attn.q_proj.bias', 'decoder.layers.5.encoder_attn_layer_norm.weight', 'decoder.layers.9.self_attn.v_proj.weight', 'decoder.layers.10.encoder_attn.out_proj.bias', 'decoder.layers.4.final_layer_norm.weight', 'decoder.layers.7.fc2.weight', 'decoder.layers.1.encoder_attn.v_proj.bias', 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'decoder.layers.10.encoder_attn.q_proj.bias', 'decoder.layers.4.encoder_attn.out_proj.weight', 'decoder.layers.11.self_attn.q_proj.bias', 'decoder.layers.5.encoder_attn.v_proj.weight', 'decoder.layers.5.encoder_attn.q_proj.bias', 'encoder.layers.8.self_attn.k_proj.weight', 'decoder.layers.10.self_attn.out_proj.bias', 'encoder.layers.10.self_attn.k_proj.bias', 'decoder.layers.0.encoder_attn.out_proj.weight', 'decoder.layers.7.encoder_attn.v_proj.weight', 'encoder.layers.11.self_attn.v_proj.bias', 'decoder.layers.6.encoder_attn_layer_norm.bias', 'encoder.layers.2.self_attn.k_proj.bias', 'decoder.layers.7.self_attn_layer_norm.bias', 'encoder.layers.10.fc2.bias', 'encoder.layers.2.self_attn.k_proj.weight', 'encoder.layers.11.final_layer_norm.weight', 'decoder.layers.0.self_attn.k_proj.bias', 'encoder.layers.7.fc1.weight', 'encoder.layer_norm.weight', 'encoder.layers.2.fc2.bias', 'encoder.layers.3.self_attn.out_proj.bias', 'encoder.layers.10.self_attn.out_proj.bias', 'encoder.layers.2.fc2.weight', 'decoder.layers.8.fc1.weight', 'encoder.layers.1.self_attn_layer_norm.bias', 'encoder.layers.8.self_attn.k_proj.bias', 'decoder.layers.1.encoder_attn.q_proj.weight', 'decoder.layers.2.self_attn_layer_norm.bias', 'encoder.layers.8.self_attn.v_proj.weight', 'decoder.layers.9.encoder_attn.k_proj.weight', 'encoder.layernorm_embedding.weight', 'encoder.layers.5.self_attn.k_proj.weight', 'decoder.layers.1.self_attn.out_proj.weight', 'decoder.layers.2.final_layer_norm.weight', 'decoder.layers.1.encoder_attn.k_proj.weight', 'encoder.layers.9.self_attn.out_proj.weight', 'decoder.layers.0.self_attn.k_proj.weight', 'decoder.layers.8.self_attn.out_proj.bias', 'decoder.layers.9.self_attn.k_proj.bias', 'encoder.layers.10.self_attn.k_proj.weight', 'decoder.layers.2.encoder_attn.v_proj.bias', 'decoder.layers.6.final_layer_norm.bias', 'decoder.layers.3.encoder_attn.out_proj.weight', 'encoder.layers.5.self_attn_layer_norm.weight', 'decoder.layers.0.encoder_attn.v_proj.bias', 'encoder.layers.1.self_attn.k_proj.weight', 'encoder.layers.7.self_attn.q_proj.bias', 'decoder.layers.2.encoder_attn_layer_norm.bias', 'decoder.layers.8.self_attn.v_proj.weight', 'decoder.layers.1.encoder_attn.v_proj.weight', 'encoder.layers.10.final_layer_norm.weight', 'decoder.layers.11.fc1.bias', 'decoder.layernorm_embedding.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embedding(200000, 768, padding_idx=1)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaZGfrVATkvZ",
        "outputId": "060b2528-d376-4562-af79-7c1b4b59f2df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MBartConfig {\n",
              "  \"_name_or_path\": \"ai4bharat/indic-bert\",\n",
              "  \"activation_dropout\": 0.0,\n",
              "  \"activation_function\": \"gelu\",\n",
              "  \"attention_dropout\": 0.0,\n",
              "  \"attention_probs_dropout_prob\": 0,\n",
              "  \"bos_token_id\": 0,\n",
              "  \"classifier_dropout\": 0.0,\n",
              "  \"d_model\": 768,\n",
              "  \"decoder_attention_heads\": 16,\n",
              "  \"decoder_ffn_dim\": 4096,\n",
              "  \"decoder_layerdrop\": 0.0,\n",
              "  \"decoder_layers\": 12,\n",
              "  \"down_scale_factor\": 1,\n",
              "  \"dropout\": 0.1,\n",
              "  \"embedding_size\": 128,\n",
              "  \"encoder_attention_heads\": 12,\n",
              "  \"encoder_ffn_dim\": 4096,\n",
              "  \"encoder_layerdrop\": 0.0,\n",
              "  \"encoder_layers\": 12,\n",
              "  \"eos_token_id\": 2,\n",
              "  \"forced_eos_token_id\": 2,\n",
              "  \"gap_size\": 0,\n",
              "  \"hidden_act\": \"gelu\",\n",
              "  \"hidden_dropout_prob\": 0,\n",
              "  \"init_std\": 0.02,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"inner_group_num\": 1,\n",
              "  \"intermediate_size\": 3072,\n",
              "  \"is_encoder_decoder\": true,\n",
              "  \"max_position_embeddings\": 512,\n",
              "  \"model_type\": \"mbart\",\n",
              "  \"net_structure_type\": 0,\n",
              "  \"num_hidden_groups\": 1,\n",
              "  \"num_hidden_layers\": 12,\n",
              "  \"num_memory_blocks\": 0,\n",
              "  \"pad_token_id\": 1,\n",
              "  \"scale_embedding\": false,\n",
              "  \"transformers_version\": \"4.30.2\",\n",
              "  \"type_vocab_size\": 2,\n",
              "  \"use_cache\": true,\n",
              "  \"vocab_size\": 200000\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tt7I8dFtrItB",
        "outputId": "d1a6c074-f310-417f-9048-ea14976d9296"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MBartForConditionalGeneration(\n",
            "  (model): MBartModel(\n",
            "    (shared): Embedding(200000, 768, padding_idx=1)\n",
            "    (encoder): MBartEncoder(\n",
            "      (embed_tokens): Embedding(200000, 768, padding_idx=1)\n",
            "      (embed_positions): MBartLearnedPositionalEmbedding(514, 768)\n",
            "      (layers): ModuleList(\n",
            "        (0-11): 12 x MBartEncoderLayer(\n",
            "          (self_attn): MBartAttention(\n",
            "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          )\n",
            "          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          (activation_fn): GELUActivation()\n",
            "          (fc1): Linear(in_features=768, out_features=4096, bias=True)\n",
            "          (fc2): Linear(in_features=4096, out_features=768, bias=True)\n",
            "          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (layernorm_embedding): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "      (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "    (decoder): MBartDecoder(\n",
            "      (embed_tokens): Embedding(200000, 768, padding_idx=1)\n",
            "      (embed_positions): MBartLearnedPositionalEmbedding(514, 768)\n",
            "      (layers): ModuleList(\n",
            "        (0-11): 12 x MBartDecoderLayer(\n",
            "          (self_attn): MBartAttention(\n",
            "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          )\n",
            "          (activation_fn): GELUActivation()\n",
            "          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          (encoder_attn): MBartAttention(\n",
            "            (k_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (v_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (q_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (out_proj): Linear(in_features=768, out_features=768, bias=True)\n",
            "          )\n",
            "          (encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          (fc1): Linear(in_features=768, out_features=4096, bias=True)\n",
            "          (fc2): Linear(in_features=4096, out_features=768, bias=True)\n",
            "          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (layernorm_embedding): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "      (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "  )\n",
            "  (lm_head): Linear(in_features=768, out_features=200000, bias=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pJkic0cQrM4J"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XjTvQD4vv9AT"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "metric = evaluate.load(\"sacrebleu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yyeS6u7CwLda"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "  preds, labels = eval_preds\n",
        "  # In case the model returns more than the prediction logits\n",
        "  if isinstance(preds, tuple):\n",
        "      preds = preds[0]\n",
        "\n",
        "  decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "\n",
        "  # Replace -100s in the labels as we can't decode them\n",
        "  labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "  decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "  # Some simple post-processing\n",
        "  decoded_preds = [pred.strip() for pred in decoded_preds]\n",
        "  decoded_labels = [[label.strip()] for label in decoded_labels]\n",
        "\n",
        "  result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "  return {\"bleu\": result[\"score\"]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_u5-WC66xOFc"
      },
      "outputs": [],
      "source": [
        "params = {'lr':0.0005, 'max_steps': 8000, 'warmup_steps': 50, 'weight_decay': 0.01, 'per_device_train_batch_size': 12, 'per_device_eval_batch_size': 12, 'evaluation_strategy' :\"no\", 'save_total_limit' :1, 'num_train_epochs': 1, 'predict_with_generate' : True, 'fp16' : False, 'push_to_hub' : True, 'remove_unused_columns' :False, 'dropout': 0.5}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9JNS_ufnxIZD"
      },
      "outputs": [],
      "source": [
        "from transformers import  Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "args =  Seq2SeqTrainingArguments(\n",
        "    f\"./finetuned_AI4Bharat_en_mr\",\n",
        "    evaluation_strategy= params['evaluation_strategy'],\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate= params['lr'] ,\n",
        "    per_device_train_batch_size=params['per_device_train_batch_size'],\n",
        "    per_device_eval_batch_size= params['per_device_eval_batch_size'],\n",
        "    warmup_steps = params['warmup_steps'],\n",
        "    weight_decay= params['weight_decay'],\n",
        "    save_total_limit= params['save_total_limit'],\n",
        "    num_train_epochs= params['num_train_epochs'],\n",
        "    predict_with_generate= params['predict_with_generate'],\n",
        "    fp16= params['fp16'],\n",
        "    push_to_hub= params['push_to_hubacc'],\n",
        "    max_steps = params['max_steps'],\n",
        "    remove_unused_columns= params['remove_unused_columns'],\n",
        "    report_to=\"neptune\",\n",
        "    # predict_with_generate = True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ipc8ZK9txae2",
        "outputId": "309cd3cd-5f01-498f-95ee-90df348a15d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/./finetuned_AI4Bharat_en_mr is already a clone of https://huggingface.co/anujsahani01/finetuned_AI4Bharat_en_mr. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/./finetuned_AI4Bharat_en_mr is already a clone of https://huggingface.co/anujsahani01/finetuned_AI4Bharat_en_mr. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        }
      ],
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model.to('cuda'),\n",
        "    args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PW-B7wXzRFj4"
      },
      "outputs": [],
      "source": [
        "for cb in trainer.callback_handler.callbacks:\n",
        "  if isinstance(cb, NeptuneCallback):\n",
        "    trainer.callback_handler.remove_callback(cb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wX5tyoKoyCG4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "outputId": "a86d3120-0ef4-437e-90b3-4f5907155c92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a AlbertTokenizerFast 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='8000' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [8000/8000 1:47:19, Epoch 0/1]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.507700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.297200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.240900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>1.203000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>1.168900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.133100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>1.107700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>1.084100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>1.071700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>1.049000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>1.018100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>1.013300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.998100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.989900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.980900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.977900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n",
            "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=8000, training_loss=1.115094051361084, metrics={'train_runtime': 6440.7353, 'train_samples_per_second': 14.905, 'train_steps_per_second': 1.242, 'total_flos': 9525810954240000.0, 'train_loss': 1.115094051361084, 'epoch': 0.22})"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = trainer.state.log_history"
      ],
      "metadata": {
        "id": "ONb6ZMHLIgdG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for key, value in params.items():\n",
        "  run[key] = value"
      ],
      "metadata": {
        "id": "vaifoa-XIoc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = []\n",
        "train_loss = []\n",
        "steps =  []\n",
        "epoch = []"
      ],
      "metadata": {
        "id": "DJuqUtdkIuAd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(metrics)-1):\n",
        "  train_loss.append(metrics[i]['loss'])\n",
        "  steps.append(metrics[i]['step'])\n",
        "  lr.append(metrics[i]['learning_rate'])\n",
        "  epoch.append(metrics[i]['epoch'])"
      ],
      "metadata": {
        "id": "fiOY7nODIt5r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(train_loss)):\n",
        "  run['train/loss'].append(train_loss[i])"
      ],
      "metadata": {
        "id": "a2Y58xYJItSs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(epoch)):\n",
        "  run['epoch'].append(epoch[i])"
      ],
      "metadata": {
        "id": "vNfYgnZHItLq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(lr)):\n",
        "  run[\"learning_rate\"].append(lr[i])"
      ],
      "metadata": {
        "id": "3skHhMeAI2GI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(steps)):\n",
        "  run[\"steps\"].append(steps[i])"
      ],
      "metadata": {
        "id": "ZnFLmVX1I6QS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8iPbH6ZOyO4g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360,
          "referenced_widgets": [
            "d967c3a060194584bd80a114066e77fb",
            "b0d6cf3f36664671b7298ba7ed4fddc6",
            "8783f3eb543c4f5193fff8815ecfa30e",
            "ad7d7f336bd64a319a0e51e26a16854e",
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            "563a5ba69f284c9f97d390427f40ca77",
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            "8ba14248e5d74ce49a19033e621bd249",
            "c127d28702f4477b90be385584905b53",
            "a724144615734ca4bfa424d620e1c061"
          ]
        },
        "outputId": "fe261879-74bf-4697-bd8d-31825f370a23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/1.46G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d967c3a060194584bd80a114066e77fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_en_mr\n",
            "   8d6deef..954a26c  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_en_mr\n",
            "   8d6deef..954a26c  main -> main\n",
            "\n",
            "To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_en_mr\n",
            "   954a26c..a8420e2  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_en_mr\n",
            "   954a26c..a8420e2  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/anujsahani01/finetuned_AI4Bharat_en_mr/commit/954a26c161ddcaf9a0b4bbc93b0280bcc1436bf0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!  pip install sacremoses"
      ],
      "metadata": {
        "id": "dt9KoTlLI9s7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e10ca5b-d4a7-4179-a725-bf188b63b4c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.10/dist-packages (0.0.53)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from sacremoses) (2022.10.31)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from sacremoses) (8.1.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.2.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sacremoses) (4.65.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, TranslationPipeline, Text2TextGenerationPipeline, pipeline\n",
        "\n",
        "model_ckpt = \"anujsahani01/finetuned_AI4Bharat_en_mr\"\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"anujsahani01/finetuned_AI4Bharat_en_mr\")\n",
        "pipe = pipeline('text2text-generation', \"anujsahani01/finetuned_AI4Bharat_en_mr\")"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.19k [00:00<?, ?B/s]"
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        {
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          "data": {
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              "Downloading pytorch_model.bin:   0%|          | 0.00/1.56G [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/519 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/5.65M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "Downloading tokenizer.json:   0%|          | 0.00/15.3M [00:00<?, ?B/s]"
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        {
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              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/286 [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "src_text = 'he is playing outside'\n",
        "pred = pipe(src_text, num_return_sequences=1)\n",
        "pred"
      ],
      "metadata": {
        "id": "EooGqmw-2PIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00c57744-bd82-4569-d952-b0313f859131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': ''}]"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run.stop()"
      ],
      "metadata": {
        "id": "wQjIyyiyJDRY",
        "colab": {
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        },
        "outputId": "03ea346f-f92c-4685-ca00-d99bac951908"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shutting down background jobs, please wait a moment...\n",
            "Done!\n",
            "All 0 operations synced, thanks for waiting!\n",
            "Explore the metadata in the Neptune app:\n",
            "https://app.neptune.ai/anujsahani01/ITStudio/e/MODEL-57/metadata\n"
          ]
        }
      ]
    },
    {
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      "execution_count": null,
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