FineTuned_AI4Bharat(MR_EN).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/anujsahani01/English-Marathi-Translation/blob/main/FineTuned_AI4Bharat(MR_EN).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",
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        "outputId": "f463f54d-13c2-4f8a-cdab-ea21735ecb64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, transformers\n",
            "Successfully installed safetensors-0.3.1 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.30.2\n",
            "Collecting evaluate\n",
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            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.0 responses-0.18.0\n",
            "Collecting rouge_score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.4.0)\n",
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            "Building wheels for collected packages: rouge_score\n",
            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24934 sha256=a24aed2ed5ffe756a58606100ca810c0efc636e9ede88d7f39df79991aaafcaa\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge_score\n",
            "Installing collected packages: rouge_score\n",
            "Successfully installed rouge_score-0.1.2\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
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            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895241 sha256=81cdabdd3d39c54b73bfaa08cdd30e5ca1dd070a898713e3dca2f0f6268df351\n",
            "  Stored in directory: /root/.cache/pip/wheels/00/24/97/a2ea5324f36bc626e1ea0267f33db6aa80d157ee977e9e42fb\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses\n",
            "Successfully installed sacremoses-0.0.53\n",
            "Collecting sacrebleu\n",
            "  Downloading sacrebleu-2.3.1-py3-none-any.whl (118 kB)\n",
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            "\u001b[?25hCollecting portalocker (from sacrebleu)\n",
            "  Downloading portalocker-2.7.0-py2.py3-none-any.whl (15 kB)\n",
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            "Collecting colorama (from sacrebleu)\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
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            "Installing collected packages: portalocker, colorama, sacrebleu\n",
            "Successfully installed colorama-0.4.6 portalocker-2.7.0 sacrebleu-2.3.1\n"
          ]
        }
      ],
      "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": "0f4d4635-8a1d-484e-8b93-0db278ae0a19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aVwdqQVohzLd"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uMo7PK_Rhzge",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9ab9372-79e2-40b1-f768-575644368895"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n"
          ]
        }
      ],
      "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": "715d5d58-a3f5-454e-acf0-0a8eb3cfed66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading samanantar.zip to /content\n",
            "100% 3.44G/3.44G [00:36<00:00, 60.8MB/s]\n",
            "100% 3.44G/3.44G [00:36<00:00, 102MB/s] \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": "73e2ab4c-5ac9-46ad-b31e-5e607d51540e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-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": "7c478578-7b5b-43f5-f99b-dc8c2ae243b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3322057 3322057\n"
          ]
        }
      ],
      "source": [
        "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": "34c40435-7813-4bf7-b083-236ae3979570"
      },
      "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.  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8db63e54-ff47-4f9e-9076-419475a45ba5\">\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>english</th>\n",
              "      <th>marathi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Next few months are really crucial for us.</td>\n",
              "      <td>पुढील तीन महिने आमच्यासाठी खूप महत्त्वपूर्ण आहेत.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sharad Pawar, Supriya Sule, Ajit Pawar, Praful...</td>\n",
              "      <td>या बैठकील पक्षाध्यक्ष शरद पवार, केंद्रीय मंत्र...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>\"Kanwal Singh Chauhan, the president of Sonepa...</td>\n",
              "      <td>वृत्तसंस्थेने दिलेल्या माहितीनुसार, विरोध करण्...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>His demise is a huge loss for India.</td>\n",
              "      <td>त्यांच्या मृत्यूमुळे भारताचे खूप मोठे नुकसान झ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Photographer and raconteur.</td>\n",
              "      <td>छायाचित्रकार आणि छायाचित्रकार.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>He has won several competitions at the state a...</td>\n",
              "      <td>राज्य व राष्ट्रीय पातळीवरील अनेक मल्ल त्यांनी ...</td>\n",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8db63e54-ff47-4f9e-9076-419475a45ba5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
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              "      \n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8db63e54-ff47-4f9e-9076-419475a45ba5 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-8db63e54-ff47-4f9e-9076-419475a45ba5');\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": 21
        }
      ],
      "source": [
        "df_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JfL2ZdeiSII",
        "outputId": "f692fd19-5ab3-4413-8409-9fe29b1619fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3400084"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "FDkKN1I5IMlR",
        "outputId": "69a0dea5-2a51-495e-a756-11c47e7c3efe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             english  \\\n",
              "0  He later met BJP president Amit Shah at the BJ...   \n",
              "1                             Rahul Gandhi resigned.   \n",
              "2                                         Call them.   \n",
              "3  Police have filed a case under the Information...   \n",
              "4      How will obedience in this matter benefit me?   \n",
              "5                  Will the Shiv Sena and BJP split?   \n",
              "6    This exercise strengthens the pectoral muscles.   \n",
              "7                                     Nothing helps.   \n",
              "8  In harmony with that, in our time the ingather...   \n",
              "9                         1 pinch of cardamom powder   \n",
              "\n",
              "                                             marathi  \n",
              "0  भाजपाध्यक्ष अमित शहांच्या बरोबर त्यांनी भाजपा ...  \n",
              "1     यामध्ये राहुल गांधींनी राजीनामा देऊ केला होता.  \n",
              "2                                  हाकला रे त्यांना.  \n",
              "3  याप्रकरणी पवई पोलीसांनी भादविसह माहिती-तंत्रज्...  \n",
              "4                            ते खरोखरच सुखी आहेत का?  \n",
              "5             शिवसेना आणि भाजपचा वेगळा जाहीरनामा का?  \n",
              "6              या आसनामुळे पाठीचे स्नायू बळकट होतात.  \n",
              "7                                    काही सूचत नाही.  \n",
              "8  ( उत्पत्ति १०: १ - २९) त्याच धर्तीवर, आपल्या क...  \n",
              "9                                 १ वाटी मोहरीची डाळ  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43c4aa40-5ae3-4a91-886c-51ed071b9269\">\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>english</th>\n",
              "      <th>marathi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>He later met BJP president Amit Shah at the BJ...</td>\n",
              "      <td>भाजपाध्यक्ष अमित शहांच्या बरोबर त्यांनी भाजपा ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rahul Gandhi resigned.</td>\n",
              "      <td>यामध्ये राहुल गांधींनी राजीनामा देऊ केला होता.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Call them.</td>\n",
              "      <td>हाकला रे त्यांना.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Police have filed a case under the Information...</td>\n",
              "      <td>याप्रकरणी पवई पोलीसांनी भादविसह माहिती-तंत्रज्...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>How will obedience in this matter benefit me?</td>\n",
              "      <td>ते खरोखरच सुखी आहेत का?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Will the Shiv Sena and BJP split?</td>\n",
              "      <td>शिवसेना आणि भाजपचा वेगळा जाहीरनामा का?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>This exercise strengthens the pectoral muscles.</td>\n",
              "      <td>या आसनामुळे पाठीचे स्नायू बळकट होतात.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nothing helps.</td>\n",
              "      <td>काही सूचत नाही.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>In harmony with that, in our time the ingather...</td>\n",
              "      <td>( उत्पत्ति १०: १ - २९) त्याच धर्तीवर, आपल्या क...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1 pinch of cardamom powder</td>\n",
              "      <td>१ वाटी मोहरीची डाळ</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43c4aa40-5ae3-4a91-886c-51ed071b9269')\"\n",
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              "\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-43c4aa40-5ae3-4a91-886c-51ed071b9269 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-43c4aa40-5ae3-4a91-886c-51ed071b9269');\n",
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              "            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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnnzRqkKiSQ3",
        "outputId": "4cface4d-54d6-4a0b-f037-19bf7188278a"
      },
      "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": "d297fc8e-2be0-411f-e339-282c32296249"
      },
      "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": "784f6b70-6b80-4137-b0e8-4b31dc152656"
      },
      "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": "2814bd98-1823-4378-b175-7bef94de5cb2"
      },
      "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": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QBRBqb-qmLe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "8ddd6a26d325462fa84a9e32c2b29560",
            "6bb68fdf17e845d9832d65068bc418a6",
            "c4ea100d0fb448148375abc39e6c9408",
            "d6e38024d46b4f859f15482cdcf8ee45",
            "31a24310023f491ba222d913e82563b7",
            "f00cf497a8df474c91dcd332b8530583",
            "2c4c81273fa543b6b0b108a8cb7cf4f9",
            "929291ed00664bfc9f7aabf51e1820f4",
            "e7f7f54ace3247ee8e5b0152d24104f6",
            "86b2d0cfcb8146f081b46064ffa89d21",
            "18879b919d7e4539a4898a60bb29ed65",
            "11281fd78bbc4a81a74e3c6c36c7ab36",
            "189e5c231a4b43d589ad2687331e834a",
            "8acbf4fe05044699a25306d476780980",
            "7d72aaeba3134c0fa0f1e8e9042aec01",
            "3c2a9f7ef76d4cddaac40f559de4ad50",
            "265bf773327445ff87c05b43933fa179",
            "8038e90b398349c7a9d9b4b322a3f91a",
            "b4cf5cf38b98452295dbe4e8e523c075",
            "8055a4ca6c1d4ad19a97c0e13760bfea",
            "073b470594e34502a72b154ba7ef4bfd",
            "1cff855d0c504ad1afe0023ca1a7437d",
            "aa31b8f1cc0e4043b4bb1340ccd6fb25",
            "1811a8ebfdb942129027df02723ab4d7",
            "97bba507581c4e91a090e7f128227270",
            "78826c253dda4182ab4c4f4bf5cd831f",
            "10ae6ee6eb8e443680d2504afac7aeca",
            "8accd5e5199747a4a89f9651a8c176ac",
            "fec21dabdec54eda94a4a08dfbc30382",
            "8f2082a88a134c8f8c65c51530b380cf",
            "f2e51cb606ac4096bd8d55783ceaa504",
            "f6340275045b4221ace15ea78e3651c6"
          ]
        },
        "outputId": "4a6310df-0c50-4726-9f92-6fab489ca5b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ddd6a26d325462fa84a9e32c2b29560"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5FlQVZGamVQB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e59b996-7ae7-4224-986c-4a205e946646"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "e001309f341949669cc7b2214a07c35e",
            "ce3a4d43c8b9422699173da74d0b0023",
            "efa4a8aa902c459d913962de52cf4bde",
            "7d7dead426d54f02bbbb70bee866a7a0",
            "b13adefb8824402e8c5466481a90b0f3",
            "79a5db0bac144e7c9de66b04e65fa09b",
            "fab84d1d29c94933b2dcbb6470330f8e",
            "ac3341d5444e4475a4db44182d720622",
            "bcdd46878bd44d8b92d5502f033b38d6",
            "c36c0d81186145c6976d1caf5176c0ac",
            "53e21c56bc084ffa8566eb8b0be17bfa",
            "ba3adcc8a3e645a9887392ea9fa228ef",
            "01c698edfebc43d982de4475b1bf3f95",
            "9a9a460701c74f2dae0571d44018aaea",
            "c3852caef23f4a47a9b8ef5126e8f16d",
            "268ac3e83735406aba16cbd0e9c81718",
            "791dbdcb78874a20bad4740009103f32",
            "2f79354393fd4f928b6b8d77d42004cc",
            "406375534c9749d48a65d6729444851e",
            "2470488de606415bb316c46ed639c3c6",
            "842694b4bf994a519e15f54238911202",
            "93d3d719df384a8c92f9da682ccbe06b"
          ]
        },
        "outputId": "33522fd4-8ea6-4b47-d76c-74952b6339d5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/507 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e001309f341949669cc7b2214a07c35e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/5.65M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba3adcc8a3e645a9887392ea9fa228ef"
            }
          },
          "metadata": {}
        }
      ],
      "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('ती घरी नाही. ती बागेत आहे.बागेत ती तिच्या मैत्रिणींसोबत खेळते.', text_target = 'She is not at home. She is in the garden.In the garden she plays with her friends.',  max_length= 50, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "out1 = tokenizer(dataset['train']['marathi'][0], text_target =  dataset['train']['english'][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": "baa29489-680d-4564-ee08-a9c27565bcff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[     2,    798, 128140,   3881,  37041,   1322,  25538,   1037,    798,\n",
              "         168127,  43687,  30316,      9,      3,      0,      0,      0,      0,\n",
              "              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, 0, 0, 0, 0, 0, 0]]), 'length': tensor([20]), 'labels': tensor([[    2,   193,  4066,  6976,  4841, 52490,  3013,    33, 35162,     8,\n",
              "         59837,   117,    11,  4841, 52490, 28637,    26,    11,  4392,     3]])}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(out['input_ids'])\n",
        "print(out['attention_mask'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iy2klikkOkyo",
        "outputId": "5395c456-b1b6-49dd-c38f-7dbd0aa0fab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[     2,   1409,   7685,    228,      9,   1409, 116544,    229,     47,\n",
            "              9,  20079,   9865,   1409,   5625,  79946,  95876,   4868,    691,\n",
            "              9,      3,      0,      0,      0,      0,      0,      0,      0,\n",
            "              0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
            "              0,      0,      0,      0,      0,      0,      0,      0,      0,\n",
            "              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, 0, 0, 0, 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": "56b82fa2-6ce0-4b96-b26c-6dee07b6008a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS] she is not at home. she is in the garden.in the garden she plays with her friends.[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>']\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": "3764d585-6a80-4573-b507-704ce82ebb58"
      },
      "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": 45
        }
      ]
    },
    {
      "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['marathi'], text_target =  data['english'],  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": [
            "d5d5f4f501f54912b1760891aa72bd16",
            "580e864fadc04c4797d1fc228043af52",
            "80c52276294348a2b41afd2f86a42ed6",
            "0a29a4ea45d74b888925d8bce6853e27",
            "0feb248827f0464491318544e1c6f218",
            "da7244e230ed43ad9981cfaa578b0b20",
            "2a61db03d2c64ea39436119a5a91ecac",
            "46a69b1a2f494be281b9b8adcd0932d0",
            "026fb937fae84d2ead6ca251711e141a",
            "b0d3da4ec94445e68dff8916249966df",
            "3193be41261446a4b7b77452212abc81",
            "097c446c7d4e424c90431d26e2780926",
            "76f67852f437412cb7ce95b542edfa4a",
            "e1997907c9bd404e8d507fdaa42ef7de",
            "e00ee9b127534b7e95eb286ae5c9bc7e",
            "fb07178fb61146ca956a169830ecd8f7",
            "75ad5a653ded4f6b9b8d980e5ec57fa7",
            "3e43b913ca9f40e8a3bd541dcf5d06a9",
            "40a4210387b74c32898f5dfc88dfc98c",
            "eba016d684bf4e8d8b8576062ff139e9",
            "15e9494c0922441e9fcb4094a750f1b9",
            "0cdb7ac456514af6b583ba22407ec5a0"
          ]
        },
        "id": "0wmpuArWnIb6",
        "outputId": "8e883147-3682-4b6e-f944-bb40d7967950"
      },
      "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": "d5d5f4f501f54912b1760891aa72bd16"
            }
          },
          "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": "097c446c7d4e424c90431d26e2780926"
            }
          },
          "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": "d13e5591-2b70-4ec5-86b5-a1ab31afcd08"
      },
      "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": 49
        }
      ],
      "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": "3d458a27-e05d-4c9a-ede6-aa8da0f611c3"
      },
      "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": "80591787-8f6b-4efb-d036-ce0c20b2ffe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2, 193, 4066, 6976, 4841, 52490, 3013, 33, 35162, 8, 59837, 117, 11, 4841, 52490, 28637, 26, 11, 4392, 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]\n",
            "[2, 798, 128140, 3881, 37041, 1322, 25538, 1037, 798, 168127, 43687, 30316, 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, 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": "d9da0220-f9e5-43c1-c56e-1186f175060b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting neptune\n",
            "  Downloading neptune-1.3.2-py3-none-any.whl (455 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/455.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m \u001b[32m317.4/455.3 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m455.3/455.3 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.30.2)\n",
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            "  Downloading backoff-2.2.1-py3-none-any.whl (15 kB)\n",
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            "\u001b[?25hCollecting bravado<12.0.0,>=11.0.0 (from neptune)\n",
            "  Downloading bravado-11.0.3-py2.py3-none-any.whl (38 kB)\n",
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            "Collecting swagger-spec-validator>=2.7.4 (from neptune)\n",
            "  Downloading swagger_spec_validator-3.0.3-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.26.16)\n",
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            "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",
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            "Collecting botocore<1.31.0,>=1.30.0 (from boto3>=1.16.0->neptune)\n",
            "  Downloading botocore-1.30.0-py3-none-any.whl (11.0 MB)\n",
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            "\u001b[?25hCollecting jmespath<2.0.0,>=0.7.1 (from boto3>=1.16.0->neptune)\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Collecting s3transfer<0.7.0,>=0.6.0 (from boto3>=1.16.0->neptune)\n",
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            "\u001b[?25hCollecting bravado-core>=5.16.1 (from bravado<12.0.0,>=11.0.0->neptune)\n",
            "  Downloading bravado_core-5.17.1-py2.py3-none-any.whl (67 kB)\n",
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            "\u001b[?25hRequirement 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",
            "Collecting simplejson (from bravado<12.0.0,>=11.0.0->neptune)\n",
            "  Downloading simplejson-3.19.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (137 kB)\n",
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            "\u001b[?25hCollecting monotonic (from bravado<12.0.0,>=11.0.0->neptune)\n",
            "  Downloading monotonic-1.6-py2.py3-none-any.whl (8.2 kB)\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",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->neptune) (2022.7.1)\n",
            "Collecting jsonref (from bravado-core>=5.16.1->bravado<12.0.0,>=11.0.0->neptune)\n",
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            "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",
            "Collecting fqdn (from jsonschema->swagger-spec-validator>=2.7.4->neptune)\n",
            "  Downloading fqdn-1.5.1-py3-none-any.whl (9.1 kB)\n",
            "Collecting isoduration (from jsonschema->swagger-spec-validator>=2.7.4->neptune)\n",
            "  Downloading isoduration-20.11.0-py3-none-any.whl (11 kB)\n",
            "Collecting jsonpointer>1.13 (from jsonschema->swagger-spec-validator>=2.7.4->neptune)\n",
            "  Downloading jsonpointer-2.4-py2.py3-none-any.whl (7.8 kB)\n",
            "Collecting rfc3339-validator (from jsonschema->swagger-spec-validator>=2.7.4->neptune)\n",
            "  Downloading rfc3339_validator-0.1.4-py2.py3-none-any.whl (3.5 kB)\n",
            "Collecting rfc3987 (from jsonschema->swagger-spec-validator>=2.7.4->neptune)\n",
            "  Downloading rfc3987-1.3.8-py2.py3-none-any.whl (13 kB)\n",
            "Collecting uri-template (from jsonschema->swagger-spec-validator>=2.7.4->neptune)\n",
            "  Downloading uri_template-1.3.0-py3-none-any.whl (11 kB)\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",
            "Collecting arrow>=0.15.0 (from isoduration->jsonschema->swagger-spec-validator>=2.7.4->neptune)\n",
            "  Downloading arrow-1.2.3-py3-none-any.whl (66 kB)\n",
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            "\u001b[?25hInstalling collected packages: rfc3987, monotonic, uri-template, simplejson, rfc3339-validator, PyJWT, jsonref, jsonpointer, jmespath, fqdn, backoff, swagger-spec-validator, botocore, arrow, s3transfer, isoduration, boto3, bravado-core, bravado, neptune\n",
            "Successfully installed PyJWT-2.7.0 arrow-1.2.3 backoff-2.2.1 boto3-1.27.0 botocore-1.30.0 bravado-11.0.3 bravado-core-5.17.1 fqdn-1.5.1 isoduration-20.11.0 jmespath-1.0.1 jsonpointer-2.4 jsonref-1.1.0 monotonic-1.6 neptune-1.3.2 rfc3339-validator-0.1.4 rfc3987-1.3.8 s3transfer-0.6.1 simplejson-3.19.1 swagger-spec-validator-3.0.3 uri-template-1.3.0\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": "65042ffe-cee8-48f2-8001-1c6660b4806c"
      },
      "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": "759d4106-f02f-47a2-f0ab-8bdc3e5a7c80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-59-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-51\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": "37ed8a4f-121a-403d-df13-aeeaad11f158"
      },
      "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/",
          "height": 179,
          "referenced_widgets": [
            "a926d229f3c1469a9d9eb92b10ccca83",
            "962436d8f1f545d8916bd5ad2655825c",
            "819b1bbd4f604ea0bfd838da25d35497",
            "467c726ee2c64e5a9e8df5a8fc13e1c5",
            "557b07a0319044c9b426c1b57e4fb045",
            "1628fe6d31c24a2eafd58f5f40a988c8",
            "42d4faf05c864dcd9c7ad8b1b1ef363e",
            "65f3b183efd04fe5af010ac41f4a6be7",
            "d77b85d50f744f86be36ac7f20a5fd0e",
            "1332ae12a69a476ba20a92155a1a5270",
            "6d0f5ab1d8a24a47b7d951f95432621b"
          ]
        },
        "id": "3QTTvmBHy-h0",
        "outputId": "6dbe1d24-92ac-4e7e-9197-c7c3085863b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/135M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
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          "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.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.weight', 'predictions.bias', 'predictions.LayerNorm.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.weight', 'albert.pooler.bias', 'albert.embeddings.LayerNorm.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.bias', 'albert.encoder.embedding_hidden_mapping_in.bias', 'albert.embeddings.position_embeddings.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.weight', 'predictions.LayerNorm.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.bias', 'albert.embeddings.token_type_embeddings.weight', 'predictions.decoder.weight', 'predictions.dense.weight', 'albert.embeddings.LayerNorm.bias', 'albert.pooler.weight', 'albert.embeddings.word_embeddings.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.bias', 'sop_classifier.classifier.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.weight', 'albert.encoder.embedding_hidden_mapping_in.weight', 'predictions.decoder.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.weight', 'predictions.dense.bias', 'sop_classifier.classifier.weight']\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.8.encoder_attn.out_proj.weight', 'decoder.layers.8.fc1.bias', 'encoder.layers.0.self_attn.v_proj.bias', 'decoder.layers.2.fc1.bias', 'encoder.layers.5.self_attn.q_proj.weight', 'decoder.layers.4.self_attn.q_proj.bias', 'decoder.layers.2.encoder_attn.q_proj.bias', 'decoder.layers.9.fc2.bias', 'decoder.layers.10.self_attn.q_proj.bias', 'encoder.layers.4.self_attn.k_proj.weight', 'encoder.layers.10.self_attn.out_proj.weight', 'encoder.layer_norm.weight', 'encoder.layers.2.self_attn.k_proj.bias', 'decoder.layers.6.fc1.bias', 'encoder.layers.3.self_attn.q_proj.bias', 'decoder.layers.3.self_attn.q_proj.bias', 'decoder.layers.7.final_layer_norm.weight', 'decoder.layers.8.encoder_attn_layer_norm.bias', 'decoder.layers.0.fc2.weight', 'decoder.layers.1.encoder_attn.k_proj.bias', 'decoder.layers.1.fc1.bias', 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'encoder.layers.5.self_attn.out_proj.weight', 'decoder.layers.8.self_attn.v_proj.weight', 'decoder.layers.4.encoder_attn.out_proj.bias', 'encoder.layers.1.self_attn.q_proj.weight', 'encoder.layers.3.self_attn.v_proj.bias', 'decoder.layers.11.encoder_attn_layer_norm.bias', 'decoder.layers.9.fc2.weight', 'encoder.layers.2.fc2.bias', 'encoder.layers.11.fc2.bias', 'encoder.layers.1.fc1.weight', 'encoder.layers.11.self_attn.q_proj.weight', 'encoder.layers.9.fc2.bias', 'decoder.layers.10.encoder_attn.v_proj.bias', 'decoder.layers.5.encoder_attn.k_proj.bias', 'encoder.layers.1.self_attn.out_proj.weight', 'encoder.layers.1.final_layer_norm.bias', 'decoder.layers.8.fc1.weight', 'decoder.layers.3.fc2.bias', 'decoder.layers.7.self_attn.v_proj.bias', 'encoder.layers.10.self_attn.q_proj.bias', 'encoder.layers.2.fc1.weight', 'encoder.layers.0.self_attn_layer_norm.bias', 'encoder.layers.4.self_attn.out_proj.bias', 'decoder.layers.2.fc2.weight', 'decoder.layers.11.fc2.weight', 'encoder.layers.0.fc2.bias', 'decoder.layer_norm.weight', 'decoder.layers.3.encoder_attn.k_proj.bias', 'encoder.layers.7.self_attn_layer_norm.bias', 'encoder.layers.11.fc1.weight', 'decoder.layers.5.self_attn_layer_norm.bias', 'decoder.layers.11.self_attn.k_proj.weight', 'encoder.layers.8.self_attn.v_proj.weight', 'encoder.layers.9.fc1.bias', 'decoder.layers.10.self_attn_layer_norm.bias', 'encoder.layers.10.final_layer_norm.weight', 'encoder.layers.2.self_attn_layer_norm.weight', 'decoder.layers.1.self_attn.k_proj.weight', 'encoder.layers.8.self_attn.out_proj.weight', 'encoder.layers.1.self_attn.out_proj.bias', 'decoder.layers.0.self_attn.k_proj.weight', 'encoder.layers.2.self_attn.q_proj.bias', 'encoder.layers.3.self_attn.out_proj.weight', 'decoder.layers.5.encoder_attn.out_proj.weight', 'decoder.layers.9.self_attn.out_proj.bias', 'encoder.layers.7.fc1.weight', 'decoder.layers.11.self_attn.q_proj.bias', 'decoder.layers.10.encoder_attn_layer_norm.bias', 'decoder.layers.3.self_attn.out_proj.bias', 'decoder.layers.4.fc2.bias', 'decoder.layers.2.encoder_attn.k_proj.bias', 'decoder.layers.10.encoder_attn.q_proj.weight', 'encoder.layers.5.self_attn.q_proj.bias', 'decoder.layers.1.fc1.weight', 'encoder.layers.8.fc1.weight', 'encoder.layers.11.fc2.weight', 'decoder.layers.8.self_attn.q_proj.bias', 'decoder.layers.0.self_attn_layer_norm.weight', 'decoder.layers.2.fc2.bias', 'decoder.layers.0.self_attn.q_proj.bias', 'decoder.layers.7.encoder_attn.q_proj.weight', 'decoder.layers.11.encoder_attn.q_proj.weight', 'decoder.layers.2.encoder_attn.out_proj.bias', 'decoder.layers.7.self_attn.v_proj.weight', 'decoder.layers.7.encoder_attn.out_proj.bias', 'decoder.layers.1.encoder_attn.q_proj.bias', 'decoder.layers.0.final_layer_norm.weight', 'decoder.layers.7.fc1.bias', 'decoder.layers.3.encoder_attn.v_proj.weight', 'decoder.layers.6.final_layer_norm.bias', 'encoder.layers.5.fc2.weight', 'decoder.layers.6.self_attn.k_proj.weight', 'decoder.layers.5.self_attn.q_proj.weight', 'decoder.layers.4.final_layer_norm.weight', 'decoder.layers.6.fc2.weight', 'encoder.layers.8.self_attn.v_proj.bias', 'decoder.layers.4.encoder_attn.v_proj.bias', 'encoder.layers.0.fc1.bias', 'decoder.layers.3.encoder_attn.out_proj.weight', 'decoder.layers.7.encoder_attn.k_proj.weight', 'decoder.layers.5.final_layer_norm.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": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaZGfrVATkvZ",
        "outputId": "4c655309-4278-49a6-91fa-9ac247154e86"
      },
      "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": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tt7I8dFtrItB",
        "outputId": "dd8987ef-f4bb-4fc2-e679-d171591d839c"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7e459f911fed4a8f8f12758c6665f28e",
            "fadba6afbfc94f19b0b118f0d6f4c7db",
            "d1c10424b6ca4812b7cc250b3448f675",
            "dcc7077d9e4e4cd28c95e5c7b2141ea0",
            "b3470fcf5721495481a142ef4e1eccf6",
            "674998cdc15049caa4c52f89d3865b62",
            "356e99c610b0491c9954ddf42d9370a8",
            "cdb00aadc1654733ba290a12544e5bfa",
            "f672b4db96644e35be2950e9103c25d1",
            "05475ff406b5467787d6122ce4acb1f9",
            "0cb8c2b1012a49c4aed2509c7f5d3aa2"
          ]
        },
        "outputId": "7d56a708-fbb9-41dc-ce6d-f308aaf742d0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e459f911fed4a8f8f12758c6665f28e"
            }
          },
          "metadata": {}
        }
      ],
      "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':0.001, 'max_steps': 6000, 'warmup_steps': 50, 'weight_decay': 0.0001, 'per_device_train_batch_size': 32 ,'per_device_eval_batch_size': 32, '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}\n",
        "# params = {'lr':0.0001, 'max_steps': 10000, 'warmup_steps': 50, 'weight_decay': 0.01, 'per_device_train_batch_size': 16 ,'per_device_eval_batch_size': 16, '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}\n",
        "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_mr_en\",\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_hub'],\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": "d8bef180-099c-4cee-b234-599816dd71d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/anujsahani01/finetuned_AI4Bharat_mr_en into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/anujsahani01/finetuned_AI4Bharat_mr_en into local empty directory.\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": "9de3b8b0-42b5-45a0-8738-c4c6662e5644"
      },
      "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 1:55:05, 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>1.317900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.075300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.007300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.960600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.934700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.909600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.899400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.873600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.857500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.850900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.829300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.824000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.801200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.797000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.791900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.777500</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=0.9067293319702149, metrics={'train_runtime': 6906.8759, 'train_samples_per_second': 13.899, 'train_steps_per_second': 1.158, 'total_flos': 9525810954240000.0, 'train_loss': 0.9067293319702149, 'epoch': 0.22})"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "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": [
            "2769c457897d427e966ca6db234eab90",
            "a8af172cda0c424590437956a9474e6c",
            "f3894792e98c43b19790ccfb1452bb6f",
            "25c64f3dd5034b7584bf82d43882ae3d",
            "41a081ddcb3942ebb4628ef6d425b9de",
            "53fd74e7f6704e26adc32a169d9b2c56",
            "870127dc311843d798c421b05cb99ba0",
            "6886c233016d479f8cf40293dbe825e8",
            "6348ae24ebad483291899164a06d3747",
            "0e5c8ddfce3c4a6a936151b71619fd62",
            "ec11b9a860964f0ca4fadb4f67c442f0"
          ]
        },
        "outputId": "1a014131-26a5-415c-ddaf-2c810490a62d"
      },
      "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": "2769c457897d427e966ca6db234eab90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_mr_en\n",
            "   fdbe850..bc3e49b  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_mr_en\n",
            "   fdbe850..bc3e49b  main -> main\n",
            "\n",
            "To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_mr_en\n",
            "   bc3e49b..cf4c9ce  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_AI4Bharat_mr_en\n",
            "   bc3e49b..cf4c9ce  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/anujsahani01/finetuned_AI4Bharat_mr_en/commit/bc3e49be28d108bafc9b4d0646c4b91b6b8b822b'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!  pip install sacremoses"
      ],
      "metadata": {
        "id": "dt9KoTlLI9s7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ada5a18f-0205-455f-d1bc-9bab2d6f468b"
      },
      "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_mr_en\"\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"anujsahani01/finetuned_AI4Bharat_mr_en\")\n",
        "pipe = pipeline('translation_mr_to_en', \"anujsahani01/finetuned_AI4Bharat_mr_en\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
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            "29a5bfb3f701485695e649c794ca9d28"
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        },
        "id": "FItYNjK1LEnO",
        "outputId": "80fa8a11-d49d-4899-b497-2f091505cadd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.19k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4abca0a65bb746028df29b6f0d174d24"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.56G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cd717bd56af4e2d97fafd0b5f86e993"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b44fdc6bf2de45dc82ea98bc83307dde"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/519 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c6dd7c5e080498f83d8c24fec042bbb"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/5.65M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "321c1aeac27c467c9f83ef3bfa195e51"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.json:   0%|          | 0.00/15.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a758f3ec3c5e4d568ab42f41b01521a2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/286 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "6d0fa4f705174375ac62ff57ae80310c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "src_text = 'माझ्या बहिणीला काही काम नाही.'\n",
        "pred = pipe(src_text, num_return_sequences=1)\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DP3pxngWLlUg",
        "outputId": "1dd51a1c-d50e-42ee-d2e8-003d84db18ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': ''}]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "run.stop()"
      ],
      "metadata": {
        "id": "wQjIyyiyJDRY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b2ed690-a6c1-4cfc-84c1-c125bf3f8f1c"
      },
      "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-51/metadata\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LJXMYS6C3KL-"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
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            "_model_name": "VBoxModel",
            "_view_count": null,
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            "_view_name": "VBoxView",
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              "IPY_MODEL_1811a8ebfdb942129027df02723ab4d7"
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