FineTuned_Mbart(EN_MR).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anujsahani01/English-Marathi-Translation/blob/main/FineTuned_Mbart(EN_MR).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IG59w-15z5eQ",
        "outputId": "56d96226-3550-4040-9e67-4aad752d84fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.1)\n",
            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (9.0.0)\n",
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          ]
        }
      ],
      "source": [
        "!pip install kaggle\n",
        "!pip install datasets\n",
        "!pip install --upgrade accelerate\n",
        "!pip install wandb\n",
        "!pip uninstall transformers\n",
        "!pip install --no-cache-dir transformers sentencepiece\n",
        "!pip install evaluate\n",
        "!pip install rouge_score\n",
        "!pip install sacremoses\n",
        "!pip install sacrebleu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "invav3dLL6z8"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aVwdqQVohzLd"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uMo7PK_Rhzge"
      },
      "outputs": [],
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lELagXTNh0wS"
      },
      "outputs": [],
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sHbjU3I3h3S4"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "source": [
        "df1 = pd.DataFrame()\n",
        "df1.column = ['inputs', 'targets']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c8YTkISPiBrB"
      },
      "outputs": [],
      "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": "WTMApbKkBVdM"
      },
      "outputs": [],
      "source": [
        "df1.head(10)"
      ]
    },
    {
      "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",
      "execution_count": null,
      "metadata": {
        "id": "9Kaqqmd6MMCd"
      },
      "outputs": [],
      "source": [
        "df3 = pd.read_csv('/content/data1.csv')\n",
        "df4 = pd.read_csv('/content/data2.csv')\n",
        "df5 = pd.read_csv('/content/data3.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J_TZ3Oa7iRW8"
      },
      "outputs": [],
      "source": [
        "frames = [df1, df2, df3, df4, df5]\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": "0eff8659-8dad-4320-bb94-c857cc515e86"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "      <div>\n",
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              "      <th></th>\n",
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              "      <th>marathi</th>\n",
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              "  <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",
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            "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.  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JfL2ZdeiSII",
        "outputId": "62246b17-fdd1-4f68-9e7f-a77ea175b10b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3517283"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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",
      "execution_count": null,
      "metadata": {
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        },
        "id": "judfh_gxDqsh",
        "outputId": "8e1b2544-ca91-4c38-ebc9-b45c2bfd5cb8"
      },
      "outputs": [
        {
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              "    }\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>The Union Minister for Petroleum &amp; Natural Gas...</td>\n",
              "      <td>यावेळी कौशल्य विकास आणि उद्योजकता मंत्री धमेंद...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The letter conveying that decision also reveal...</td>\n",
              "      <td>नियमन मंडळाकडून मिळालेल्या या नवीन सल्ल्याचं म...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MJ Akbar defamation case: Journalist Priya Ram...</td>\n",
              "      <td>नवी दिल्ली – पत्रकार प्रिया रमानी यांना दिल्ली...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>And Moses and Aaron went into the tabernacle o...</td>\n",
              "      <td>2मोशे व अहरोन दर्शन मंडपात गेले आणि बाहेर आल्य...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>On the other hand, in the grand alliance, RJD ...</td>\n",
              "      <td>महायुतीत आरजेडी १४४, कॉंग्रेस ७० आणि डावे पक्ष...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Change Currently Registered Email Address...</td>\n",
              "      <td>सध्या नोंदणीकृत ईमेल पत्ता बदला...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Suddenly, I lost my flashy car, my money, my e...</td>\n",
              "      <td>खऱ्‍याचं खोटं आणि खोट्याचं खरं करून तो मला सो...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>India has already committed $500 million to th...</td>\n",
              "      <td>या मोहिमेसाठी भारताला 500 कोटी इतका खर्च झाला ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>We are in an independent India.</td>\n",
              "      <td>आपण स्वतंत्र भारतात राहतो.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>There were a number of events prior to and dur...</td>\n",
              "      <td>हे कळल्यावरही आमच्यापैकी काहींनी अर्ज भरला.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4aa60612-84bd-4513-aa14-8ce5551c94ce')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4aa60612-84bd-4513-aa14-8ce5551c94ce 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-4aa60612-84bd-4513-aa14-8ce5551c94ce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                             english  \\\n",
              "0  The Union Minister for Petroleum & Natural Gas...   \n",
              "1  The letter conveying that decision also reveal...   \n",
              "2  MJ Akbar defamation case: Journalist Priya Ram...   \n",
              "3  And Moses and Aaron went into the tabernacle o...   \n",
              "4  On the other hand, in the grand alliance, RJD ...   \n",
              "5       Change Currently Registered Email Address...   \n",
              "6  Suddenly, I lost my flashy car, my money, my e...   \n",
              "7  India has already committed $500 million to th...   \n",
              "8                    We are in an independent India.   \n",
              "9  There were a number of events prior to and dur...   \n",
              "\n",
              "                                             marathi  \n",
              "0  यावेळी कौशल्य विकास आणि उद्योजकता मंत्री धमेंद...  \n",
              "1  नियमन मंडळाकडून मिळालेल्या या नवीन सल्ल्याचं म...  \n",
              "2  नवी दिल्ली – पत्रकार प्रिया रमानी यांना दिल्ली...  \n",
              "3  2मोशे व अहरोन दर्शन मंडपात गेले आणि बाहेर आल्य...  \n",
              "4  महायुतीत आरजेडी १४४, कॉंग्रेस ७० आणि डावे पक्ष...  \n",
              "5                 सध्या नोंदणीकृत ईमेल पत्ता बदला...  \n",
              "6  खऱ्‍याचं खोटं आणि खोट्याचं खरं करून तो मला सो...  \n",
              "7  या मोहिमेसाठी भारताला 500 कोटी इतका खर्च झाला ...  \n",
              "8                         आपण स्वतंत्र भारतात राहतो.  \n",
              "9        हे कळल्यावरही आमच्यापैकी काहींनी अर्ज भरला.  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnnzRqkKiSQ3",
        "outputId": "3610e34e-dc0d-4e91-d417-75e6c0210177"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2637962 879321\n"
          ]
        }
      ],
      "source": [
        "print(len(df_train), len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4PHf8_54VqGL"
      },
      "outputs": [],
      "source": [
        "df_train['length'] = df_train['english'].apply(\n",
        "    lambda row: min(len(row.split(\" \")), len(row)) if isinstance(row, str) else None\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGYcfS_pWvye",
        "outputId": "c8cc9c95-6cdf-4139-8edc-e2b40c41d53d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n",
            "10263\n",
            "10.831806902449694\n"
          ]
        }
      ],
      "source": [
        "print(df_train['length'].min())\n",
        "print(df_train['length'].max())\n",
        "print(df_train['length'].mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rHYWrmx2cRuj"
      },
      "outputs": [],
      "source": [
        "df_train.drop(['length'], axis = 1, inplace = True);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hSTTlUXda3VN"
      },
      "outputs": [],
      "source": [
        "df_train = df_train.loc[df_train['english'].str.len() >= 40]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NwBEDM8-en_E"
      },
      "outputs": [],
      "source": [
        "df_test = df_test.loc[df_test['english'].str.len() >= 40]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6v3LFcq4iSTD",
        "outputId": "7d542d51-69e8-4334-f7bb-204a14455872"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1556706 555556\n"
          ]
        }
      ],
      "source": [
        "print(len(df_train), len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Sggy6SZfE8O"
      },
      "outputs": [],
      "source": [
        "df_train.reset_index(inplace = True)\n",
        "df_test.reset_index(inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I485GT0iMq8K"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df_train.to_csv('train.csv', encoding = 'utf-8', index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7pbcE7ISgI18"
      },
      "outputs": [],
      "source": [
        "df_train.drop(['index'], axis = 1, inplace = True)\n",
        "df_test.drop(['index'], axis = 1, inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zkJT3RVNiSXR"
      },
      "outputs": [],
      "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",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xm1dSnOliSbb",
        "outputId": "a04efbdf-34ea-4139-fc6e-9aa81e7e2a02"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['english', 'marathi'],\n",
              "        num_rows: 778353\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['english', 'marathi'],\n",
              "        num_rows: 277778\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTuAlnW3KDdy",
        "outputId": "47d18a54-a265-4053-9ecb-973bb2a1a27c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Further action will be taken according to this.\n",
            "त्यानुसार या प्रकरणात पुढील कारवाई करण्यात येईल.\n"
          ]
        }
      ],
      "source": [
        "print(dataset['train']['english'][128453])\n",
        "print(dataset['train']['marathi'][128453])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "b720318b84384c669b38effce4490e4f",
            "735cbadc1b2b4bcf80f15b6c8296b57a",
            "0c89fad470de4f51a922205159c317f7",
            "63d731f8c54b48bab86b830e40f75d6a",
            "7946d93d267243c48251efaac0cb2099",
            "835ee13aa48b4000a9c2743547ffad69",
            "0e2984b1cd35486ea08d211ec2e534f4",
            "536f0ba6315d46cca5adb01ff9e22359",
            "868f2ef4585748fa8f497720efe19284",
            "47df0f0a672445c4b38694ad775e6734",
            "5f49855b2a864795a4827c8c72a50b90",
            "0fa0488dd16a439dbe9a31d4cd0472d7",
            "84827a83f5b8490face9f4a5c64a0f70",
            "f06ddadca0c94264a6ac454a96ec2aaa",
            "a4810180f26648298e2ab68342f81a4c",
            "627b6dd6a1944ab8922ba95765551596",
            "3858517b785a45409ed05e886b2447e5",
            "867165c5160f42ceae391fd03f4a582e",
            "e75fb20880ed4886866299b34c0f37ed",
            "2e525db4e338407495d04d5b92071758",
            "30f880df1cff44b49ce600a580e011ef",
            "47def64eeec04197b31de1c192ff8db4",
            "ccae5fc09eb348a7a2b140ffd3e2e33d",
            "017dc625ab8145678fb493bad35bfa25",
            "a071e3b368c04fcb8cfbc05232de0dd3",
            "f940221eacab42c488d92f58a0cda1a8",
            "2ff7d3dde8d34c76ae11c06274a7bae9",
            "9f6cb063a71e4410b0afbc609ab9d34c",
            "db805204e07f475a97352ffd00e06911",
            "00a8b27fc73545ffac85dcb8be4622ed",
            "c386a6dd138647f4b1294627274cc217",
            "0c05aa6c3e6b4c3cb1ad3ee1be0a9a23"
          ]
        },
        "id": "QBRBqb-qmLe8",
        "outputId": "f69a0bc5-81c5-4300-de0f-66bbbbb2288a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b720318b84384c669b38effce4490e4f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FlQVZGamVQB",
        "outputId": "c27c5183-c61d-4224-c11f-2603a8569fd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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: "
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jMBplEvW-spc"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "00641bc17a1a4628903e7af672d9c404",
            "2f0f5cbf24a649c79b0d8506c1c0de3c",
            "571cc9d0ef5240ed910202c76d624178",
            "a42bb92bc623435fb0b20d788ef8b408",
            "eb5056e5ee28402796d7e95394c57c16",
            "6ed5eac5d3b44051a42f773152aaf251",
            "7536de9afd21419197d52ef1076f8dd0",
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            "c28333f1c8f445dcafe9cab2d0624dd8"
          ]
        },
        "id": "vRb6Yq49mZ0u",
        "outputId": "6fc3c6dd-a19c-4b56-c652-7fc9910d67d4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "00641bc17a1a4628903e7af672d9c404",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/531 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "70a1d5e62b5b45868b790274d9a1f0b9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "df3e455bf5f143378927503883e55b74",
              "version_major": 2,
              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9f5e52f1540742afaa499d4feadf17d2",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/649 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('facebook/mbart-large-50', src_lang=\"en_XX\", tgt_lang=\"mr_IN\", model_max_length = 512)\n",
        "tokenizer.bos_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDkpwR82w6Uy",
        "outputId": "4beb7ff0-5972-4705-ae15-e322e062d055"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MBart50TokenizerFast(name_or_path='facebook/mbart-large-50', vocab_size=250054, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '</s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'sep_token': '</s>', 'pad_token': '<pad>', 'cls_token': '<s>', 'mask_token': '<mask>', 'additional_special_tokens': ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']}, clean_up_tokenization_spaces=True)"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7n3FdUjmw4tp",
        "outputId": "891aae05-be07-440f-fe09-c080abd511e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "We have added 1 tokens\n"
          ]
        }
      ],
      "source": [
        "special_tokens_dict = {\"eos_token\": \"<CLS>\"}\n",
        "\n",
        "num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n",
        "print(\"We have added\", num_added_toks, \"tokens\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ip2dOm1eIaDl"
      },
      "outputs": [],
      "source": [
        "out = tokenizer('She is not at home. She is in the garden.In the garden she plays with her friends.', text_target = 'ती घरी नाही. ती बागेत आहे.बागेत ती तिच्या मैत्रिणींसोबत खेळते.',  max_length= 50, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "out1 = tokenizer(dataset['train']['english'][0], text_target =  dataset['train']['marathi'][0],  max_length= 20, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iy2klikkOkyo",
        "outputId": "1cb39470-fa1b-4352-8fcd-52ff9fa9db32"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(out['input_ids'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwUTBkRm1e9Y",
        "outputId": "9939aded-9d24-4c4a-b064-562df33fa03b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['en_XX She is not at home. She is in the garden.In the garden she plays with her friends.<CLS><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['input_ids'])\n",
        "print(decoded_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Me9PrTaBwDtd",
        "outputId": "9a9424ff-552e-4722-9c1c-4f39f84247f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['mr_IN ती घरी नाही. ती बागेत आहे.बागेत ती तिच्या मैत्रिणींसोबत खेळते.<CLS><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>']"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.batch_decode(out['labels'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Ygq4e_BTrNm"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SbbEqFtinIqH"
      },
      "outputs": [],
      "source": [
        "max_length = 70\n",
        "context_length = 70\n",
        "\n",
        "def tokenize(data):\n",
        "  data = tokenizer(data['english'], text_target =  data['marathi'],  max_length= max_length, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "  inp_ids = []\n",
        "  if len(data['input_ids']) != 0:\n",
        "    for length, ids in zip(data[\"length\"], data[\"input_ids\"]):\n",
        "      inp_ids.append(ids)\n",
        "    label = []\n",
        "    for length, lb in zip(data[\"length\"], data[\"labels\"]):\n",
        "      label.append(lb)\n",
        "\n",
        "\n",
        "  return {\"input_ids\": inp_ids,\n",
        "          'labels' : label}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "05bc8ccea7ba45089922aa89ab8e1297",
            "0ab6d9886949476dbe230209c96b814b",
            "febca28a81a04934bba4e565076fae54",
            "42fdfd2c534b472e8c6b8d66d78418df",
            "c296d7489bf5474cafb9dc3b9113ffdc",
            "e6285786fb204be8b51b1721830b5842",
            "c8766f65bf7a45b29a7ea6466ad96617",
            "2ed6aa269b704263b27927a1c9c7bcef",
            "e51da388c6c249469ad2d3fd552165d5",
            "2105dd9c219241f6a93a8f59a7ba188a",
            "2419da60cb8941c5919902d9379312ea",
            "2ef7e94591394f35b8ba9963933c9882",
            "a57e961974ca4bbb923ef479776d7ce8",
            "e5eddf2f1aed4ec5bf20ebcf57bcfb01",
            "8339355f585848be8592671417799235",
            "317e5aeaff094b3193129d76364274f5",
            "a977a6d02d734ff3a4c5848b1af7aa3c",
            "1cf5b76a317b4f82bfc0f7f5af66b55d",
            "e6e72081388448f1a7286a2d9790825f",
            "871a470bc5b14202814acaddabcf6cfe",
            "ae07c24d24b944acbfb8be46a59f7d96",
            "e42550b77c4848878fea3ab290d74e57"
          ]
        },
        "id": "0wmpuArWnIb6",
        "outputId": "92b9c50d-8af6-4b55-e6fd-634ef5735b4b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05bc8ccea7ba45089922aa89ab8e1297",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/441434 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ef7e94591394f35b8ba9963933c9882",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/140627 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": "ba43ecc2-6608-44db-fa96-bca0419d1907"
      },
      "outputs": [
        {
          "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",
              "})"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mF6WQL64mV9Q"
      },
      "outputs": [],
      "source": [
        "inp = tokenized_dataset['train']['input_ids']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z6p39mNkZmG2"
      },
      "outputs": [],
      "source": [
        "arr = []\n",
        "df_final = pd.DataFrame()\n",
        "df_final['tokenized_eng'] = inp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kIsErTkxZyLQ"
      },
      "outputs": [],
      "source": [
        "df_final['length'] = df_final['tokenized_eng'].apply(\n",
        "    lambda row: len(row)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfIHkA8cly58",
        "outputId": "fb49e43b-c5d7-48e2-fcde-1167de88fa8e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "70\n",
            "70\n",
            "70\n"
          ]
        }
      ],
      "source": [
        "print(df_final['length'].min())\n",
        "print(df_final['length'].max())\n",
        "print(df_final['length'].min())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q_yNprNbPwm7"
      },
      "outputs": [],
      "source": [
        "out = tokenized_dataset['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F91cOgwSQ7Dg",
        "outputId": "22b3a65b-86a3-44d6-ead5-7160388b6703"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[250038, 172323, 79060, 70240, 48491, 53405, 6576, 2535, 105899, 31211, 44981, 641, 217713, 56063, 3360, 15, 9337, 14032, 6377, 16, 9933, 3396, 41870, 3044, 22176, 22582, 169812, 1356, 10195, 118138, 5673, 9188, 535, 8358, 5, 250054, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[250004, 602, 21356, 509, 40, 27686, 11737, 47, 34202, 111, 663, 605, 3010, 26995, 7712, 99, 70, 405, 26973, 299, 5, 250054, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ],
      "source": [
        "print(out['labels'])\n",
        "print(out['input_ids'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7F9hSdiYNL0V",
        "outputId": "cd8c1579-8f70-48c0-a004-1ecfde1af96f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting neptune\n",
            "  Downloading neptune-1.3.1-py3-none-any.whl (450 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m450.4/450.4 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25hCollecting monotonic (from bravado<12.0.0,>=11.0.0->neptune)\n",
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            "Collecting docopt (from pipreqs>=0.4->neptune)\n",
            "  Downloading docopt-0.6.2.tar.gz (25 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hBuilding wheels for collected packages: docopt\n",
            "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13707 sha256=3d62715495dd0244924d851e88f47cb7d47dcb8d933f6b79b7e00ba9f8ce1831\n",
            "  Stored in directory: /root/.cache/pip/wheels/fc/ab/d4/5da2067ac95b36618c629a5f93f809425700506f72c9732fac\n",
            "Successfully built docopt\n",
            "Installing collected packages: rfc3987, monotonic, docopt, uri-template, simplejson, rfc3339-validator, PyJWT, jsonref, jsonpointer, jmespath, fqdn, yarg, swagger-spec-validator, botocore, arrow, s3transfer, pipreqs, isoduration, boto3, bravado-core, bravado, neptune\n",
            "Successfully installed PyJWT-2.7.0 arrow-1.2.3 boto3-1.26.165 botocore-1.29.165 bravado-11.0.3 bravado-core-5.17.1 docopt-0.6.2 fqdn-1.5.1 isoduration-20.11.0 jmespath-1.0.1 jsonpointer-2.4 jsonref-1.1.0 monotonic-1.6 neptune-1.3.1 pipreqs-0.4.13 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 yarg-0.1.9\n"
          ]
        }
      ],
      "source": [
        "! pip install -U neptune transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UiOSOMq6NLrc",
        "outputId": "c6fde6bc-4a0b-4543-82c1-50635d326bef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your Neptune API token:··········\n"
          ]
        }
      ],
      "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:\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6Z4CKXDNLcY",
        "outputId": "b66e83de-02b6-47cd-f9cc-f5ba726365fd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-59-b6d931fdc011>: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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "https://app.neptune.ai/anujsahani01/ITStudio/e/MODEL-45\n"
          ]
        }
      ],
      "source": [
        "my_project = \"anujsahani01/ITStudio\"\n",
        "run = neptune.init_run(\n",
        "    project=\"anujsahani01/ITStudio\",\n",
        "    api_token = 'eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiJkMzNmZWY5My0yOGY1LTRiZDItODA1MS05ZTM3MTc0MTI2NTYifQ==',\n",
        "    source_files= [\"/content/drive/MyDrive/Mbart(Finetuning).ipynb\"]\n",
        ")\n",
        "\n",
        "\n",
        "neptune_callback = NeptuneCallback(run=run)\n",
        "# run[\"train_dataset\"].track_files(\"./train.csv\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hUkl84elm4m5"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "3898051552224d6886f772ab3e5e9f5e",
            "06729d0ba8094d3fa8338ba5e0e9426b",
            "e37c52ec75e44ef4bb666037524db30c",
            "894f9680f30043869c694378f07077eb",
            "9a29e41b93ab41a38f7c66a963e0a2fb",
            "f8c67a11d8004391bcf3aa1d831f002c",
            "1380cf2e275c4240b572dec547d8ba8e",
            "7d4fd2376bf642839f5a33053faad47f",
            "f2e0b5458e0b42feb393caf978403b14",
            "494c06bfb1de4232b49cd4ab52c55949",
            "0dfc4fffb3e647e7a5879748ff3345b1",
            "78e5ea992831438da4878096a3afee56",
            "0f1dff22c0ee40148ef2e63b9df2c911",
            "ab6dfc27081543439f62e4bbb96719d6",
            "853480710c4c44888ffe209c9bed3313",
            "7c15ba5e23004cb08aa6d48068b2408c",
            "de878b49d080455d83c4324948ddb53f",
            "86421e031357475096dc8a7cec981e11",
            "fc5ea364961549ff995aa538f3e12373",
            "b58f2e37edb24c2280d47ec82393f4ff",
            "7930ee2711e94a71a88ab42776eff74c",
            "b54e3f12723c49209b430216cd310b50"
          ]
        },
        "id": "CKE3SkqH42na",
        "outputId": "66716e41-6ad4-4cbb-e313-56e13b9b3587"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3898051552224d6886f772ab3e5e9f5e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.44G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "78e5ea992831438da4878096a3afee56",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/261 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Embedding(250055, 1024)"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoConfig\n",
        "model_checkpoint  = 'facebook/mbart-large-50'\n",
        "config = AutoConfig.from_pretrained(model_checkpoint, vocab_size=len(tokenizer), use_auth_token=True,)\n",
        "                                    # encoder_layerdrop = 0.2,\n",
        "                                    # decoder_layerdrop = 0.2)\n",
        "                                    # attention_dropout = 0.4,\n",
        "                                    # dropout = 0.4,\n",
        "                                    # activation_dropout = 0.4)\n",
        "config.vocab_size = tokenizer.vocab_size\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, config = config).to('cuda')\n",
        "model.resize_token_embeddings(len(tokenizer))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tt7I8dFtrItB",
        "outputId": "1a505b2c-2b07-44f4-d5d7-6465a418c32b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MBartForConditionalGeneration(\n",
            "  (model): MBartModel(\n",
            "    (shared): Embedding(250055, 1024)\n",
            "    (encoder): MBartEncoder(\n",
            "      (embed_tokens): Embedding(250055, 1024)\n",
            "      (embed_positions): MBartLearnedPositionalEmbedding(1026, 1024)\n",
            "      (layers): ModuleList(\n",
            "        (0-11): 12 x MBartEncoderLayer(\n",
            "          (self_attn): MBartAttention(\n",
            "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "          )\n",
            "          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "          (activation_fn): GELUActivation()\n",
            "          (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
            "          (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
            "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "    (decoder): MBartDecoder(\n",
            "      (embed_tokens): Embedding(250055, 1024)\n",
            "      (embed_positions): MBartLearnedPositionalEmbedding(1026, 1024)\n",
            "      (layers): ModuleList(\n",
            "        (0-11): 12 x MBartDecoderLayer(\n",
            "          (self_attn): MBartAttention(\n",
            "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "          )\n",
            "          (activation_fn): GELUActivation()\n",
            "          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "          (encoder_attn): MBartAttention(\n",
            "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "          )\n",
            "          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "          (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
            "          (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
            "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "      (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "    )\n",
            "  )\n",
            "  (lm_head): Linear(in_features=1024, out_features=250055, 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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3JqJsFIr8xt",
        "outputId": "bb1dfdaa-6a2c-4f30-82bf-c0932824a3c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: evaluate in /usr/local/lib/python3.10/dist-packages (0.4.0)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.1)\n",
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          ]
        }
      ],
      "source": [
        "! pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2157c4a196454d80818d87a07241b383",
            "d567c605ce154246944c24710a905a19",
            "0fd1a1d2a0424f0388aecd4277c8b55a",
            "1e25ec0805084037a7751d2d01db6ba0",
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            "bddd6468d531437f9f3c73f6e6f402f1",
            "c5e85b188a4646eeafd45e264aede8c2",
            "6837fee92461415cbb756d8d45115e0f",
            "ab7d433145694d6a860924e1a5abd794"
          ]
        },
        "id": "XjTvQD4vv9AT",
        "outputId": "f36ec9b6-8472-40e7-97b6-01e05d1d7195"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2157c4a196454d80818d87a07241b383",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import evaluate\n",
        "metric = evaluate.load(\"sacrebleu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yyeS6u7CwLda"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "  preds, labels = eval_preds\n",
        "  # In case the model returns more than the prediction logits\n",
        "  if isinstance(preds, tuple):\n",
        "      preds = preds[0]\n",
        "\n",
        "  decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "\n",
        "  # Replace -100s in the labels as we can't decode them\n",
        "  labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "  decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "  # Some simple post-processing\n",
        "  decoded_preds = [pred.strip() for pred in decoded_preds]\n",
        "  decoded_labels = [[label.strip()] for label in decoded_labels]\n",
        "\n",
        "  result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "  return {\"bleu\": result[\"score\"]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_u5-WC66xOFc"
      },
      "outputs": [],
      "source": [
        "params = {'lr':0.0005, 'max_steps': 10000, '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}\n",
        "# params = {'lr':0.001, '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_Mbart\",\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": {
        "id": "Nr1lDNXdO7xv"
      },
      "outputs": [],
      "source": [
        "neptune_callback = NeptuneCallback(run=run)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692,
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        "id": "Ipc8ZK9txae2",
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/anujsahani01/finetuned_Mbart into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/anujsahani01/finetuned_Mbart into local empty directory.\n"
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        {
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            ]
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        },
        {
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              "model_id": "f393d3ce70c24209a48c3cfaea35dfb3",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "132ba362e75e411ea6d1579540d9915d",
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          },
          "metadata": {},
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      ],
      "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,)\n",
        "    # callbacks=[neptune_callback])"
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wX5tyoKoyCG4",
        "outputId": "6829fb66-c699-4d70-f5c5-073df990f012"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "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 MBart50TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
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              "      \n",
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              "      [ 9326/10000 3:09:13 < 13:40, 0.82 it/s, Epoch 0.25/1]\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",
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              "      <td>8500</td>\n",
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              "      [10000/10000 3:24:00, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>500</td>\n",
              "      <td>2.036700</td>\n",
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              "    <tr>\n",
              "      <td>6000</td>\n",
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              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.915200</td>\n",
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              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.899500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.879600</td>\n",
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              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.865600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.857700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.836500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.829800</td>\n",
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              "    <tr>\n",
              "      <td>10000</td>\n",
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              "</table><p>"
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10000, training_loss=1.0457263916015624, metrics={'train_runtime': 12242.398, 'train_samples_per_second': 9.802, 'train_steps_per_second': 0.817, 'total_flos': 1.77772363776e+16, 'train_loss': 1.0457263916015624, 'epoch': 0.27})"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0NsUtP75PZ5n"
      },
      "outputs": [],
      "source": [
        "metrics = trainer.state.log_history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TX7G957dPZfy"
      },
      "outputs": [],
      "source": [
        "for key, value in params.items():\n",
        "  run[key] = value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ql48PohYPZCa"
      },
      "outputs": [],
      "source": [
        "lr = []\n",
        "train_loss = []\n",
        "steps =  []\n",
        "epoch = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kvnIY7p9PY7x"
      },
      "outputs": [],
      "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'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yJnfdG5fPY0q"
      },
      "outputs": [],
      "source": [
        "for i in range(len(train_loss)):\n",
        "  run['train/loss'].append(train_loss[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BknXAO70PiR8"
      },
      "outputs": [],
      "source": [
        "for i in range(len(epoch)):\n",
        "  run['epoch'].append(epoch[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ht6jWusePjAk"
      },
      "outputs": [],
      "source": [
        "for i in range(len(lr)):\n",
        "  run[\"learning_rate\"].append(lr[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "psNXrIoOPjEQ"
      },
      "outputs": [],
      "source": [
        "for i in range(len(steps)):\n",
        "  run[\"steps\"].append(steps[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "8iPbH6ZOyO4g",
        "outputId": "bd560a17-a813-4cc6-aedf-6fef402b5b40"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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",
            "To https://huggingface.co/anujsahani01/finetuned_Mbart\n",
            "   291badc..0a2515d  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_Mbart\n",
            "   291badc..0a2515d  main -> main\n",
            "\n",
            "To https://huggingface.co/anujsahani01/finetuned_Mbart\n",
            "   0a2515d..6fe108a  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_Mbart\n",
            "   0a2515d..6fe108a  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/anujsahani01/finetuned_Mbart/commit/0a2515d2d07fcc4ff1951f14d05f2135982d4a2e'"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcs8tam2PqR5",
        "outputId": "1def826a-ebcc-444f-f2df-748885a02d20"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "! pip install sacremoses"
      ]
    },
    {
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        },
        "id": "sdiX8klwPv9A",
        "outputId": "9ccbd3e1-75d2-4d31-f15b-db268399e3de"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "221f86f6746b49cfb47efa251ddf64b8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.41k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32958e5b9b5f4020ab2be99ebea2a62c",
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              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/2.44G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "007ff075566c4effa29416cd32e7fae6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/256 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "eb04bedc32f3454eab2d330e4b136ea2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4155320bc6044206a3e54191110c68d6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "b3a4d1ebcfa843379b9b518321f9b6c7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b9a71591db445d78ab9cb9227fc4038",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3ed843b2e6e84819bd038a0d20eab7e1",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, TranslationPipeline, Text2TextGenerationPipeline, pipeline\n",
        "\n",
        "model_ckpt = \"anujsahani01/finetuned_Mbart\"\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"anujsahani01/finetuned_Mbart\")\n",
        "pipe = pipeline('translation_en_to_mr', \"anujsahani01/finetuned_Mbart\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1bS9IK1PvZh",
        "outputId": "b0ae4cba-c274-4fa9-aec6-cccc60880570"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'translation_text': 'mr'},\n",
              " {'translation_text': 'mr .'},\n",
              " {'translation_text': 'mr, पाट्या प्रमाणात बंद करण्यात आला आहे.'}]"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "src_text = 'he is playing outside'\n",
        "pred = pipe(src_text, num_return_sequences=3)\n",
        "pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r9dnHBlZO9GY"
      },
      "outputs": [],
      "source": [
        "run[\"train_dataset\"].track_files(\"./train.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VltBsxONyxS4"
      },
      "outputs": [],
      "source": [
        "from evaluate import TranslationEvaluator\n",
        "metric = TranslationEvaluator(task = 'translation', default_metric_name = \"bleu\")\n",
        "evaluation_results = metric.compute(model_or_pipeline = model, tokenizer = tokenizer,  metric = 'bleu', data = dataset['test'][:100] , device = 0, split = 'test' ,input_column = 'english', label_column = 'marathi')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vyqoeYDlyxkg"
      },
      "outputs": [],
      "source": [
        "run[\"bleu\"].append(evaluation_results[\"bleu\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4YJCy_2Q16r",
        "outputId": "9b86c33a-ea6b-483e-a4de-d3152de1a2af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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-45/metadata\n"
          ]
        }
      ],
      "source": [
        "run.stop()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dqFeNnFIPFfl"
      },
      "outputs": [],
      "source": []
    }
  ],
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    "accelerator": "GPU",
    "colab": {
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      "machine_shape": "hm",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
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