FineTuned_Helsinki_NLP(EN_MR).ipynb

{
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/anujsahani01/English-Marathi-Translation/blob/main/FineTuned_Helsinki_NLP(EN_MR).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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        "outputId": "d6eb9df2-c3a6-46c2-9b33-5cb18980186c"
      },
      "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",
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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",
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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=dc49d4b69f51f3d66af1fefe3da8ad370fbdd67c260c7a38ad0bd208283cfd13\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 neptune-prophet\n",
            "  Downloading neptune_prophet-1.0.0-py3-none-any.whl (12 kB)\n",
            "Collecting matplotlib<=3.4.3 (from neptune-prophet)\n",
            "  Downloading matplotlib-3.4.3.tar.gz (37.9 MB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: matplotlib\n",
            "  Building wheel for matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for matplotlib: filename=matplotlib-3.4.3-cp310-cp310-linux_x86_64.whl size=10523217 sha256=420d1b3c900ee233686e8e17e1e7e9d1f13f6bd7787f0499d4bf9e166a424696\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/af/e4/d399b616d3e7ae88374c2ebab2d5d3ecf776a3590d4f5f768f\n",
            "Successfully built matplotlib\n",
            "Installing collected packages: matplotlib, neptune-prophet\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.7.1\n",
            "    Uninstalling matplotlib-3.7.1:\n",
            "      Successfully uninstalled matplotlib-3.7.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "mizani 0.8.1 requires matplotlib>=3.5.0, but you have matplotlib 3.4.3 which is incompatible.\n",
            "plotnine 0.10.1 requires matplotlib>=3.5.0, but you have matplotlib 3.4.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed matplotlib-3.4.3 neptune-prophet-1.0.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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 neptune-prophet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DwSjzCCNfA-",
        "outputId": "c12b4ea6-807d-41a4-829b-8cf75d63229f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eQpaC6w8QxsH"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilzM2dTCQ6uH",
        "outputId": "d02d8e33-f340-4450-ba9e-48fb5174e7f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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": "eDsfIBdeQ8ii"
      },
      "outputs": [],
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XuUr8beRAj0",
        "outputId": "715aa5b4-2bcf-48a9-eb9a-659c627e5916"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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% 3.43G/3.44G [00:35<00:00, 90.8MB/s]\n",
            "100% 3.44G/3.44G [00:35<00:00, 104MB/s] \n"
          ]
        }
      ],
      "source": [
        "! kaggle datasets download -d mathurinache/samanantar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j_G_EbeZRB8b"
      },
      "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": "HontZ2KLRErQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YUl1bIceRG7f"
      },
      "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": "ENWfrrn4RItB"
      },
      "outputs": [],
      "source": [
        "english = []\n",
        "marathi = []\n",
        "english = english_text.split('\\n')\n",
        "marathi = marathi_text.split('\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bovnx4DyRKdC",
        "outputId": "ffc39811-286a-46a5-d6e9-808a66c5d714"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-11-e9738c3977d1>:2: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
            "  df1.column = ['inputs', 'targets']\n"
          ]
        }
      ],
      "source": [
        "df1 = pd.DataFrame()\n",
        "df1.column = ['inputs', 'targets']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LM01SWOIRNuG",
        "outputId": "3ceefc0f-2489-4eec-c98e-8aa9beacd2a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3322057 3322057\n"
          ]
        }
      ],
      "source": [
        "print(len(english), len(marathi))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zNsIyULWRPDt"
      },
      "outputs": [],
      "source": [
        "df1['inputs'] = english\n",
        "df1['targets'] = marathi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "35kcJp3FRQbS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df2 = pd.read_csv('/content/data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2id9Gyqtkqcf"
      },
      "outputs": [],
      "source": [
        "df3 = pd.read_csv('/content/data1.csv')\n",
        "df4 = pd.read_csv('/content/data2.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xcDzuVXMRX-S"
      },
      "outputs": [],
      "source": [
        "frames = [df1, df2, df3, df4]\n",
        "df_data = pd.concat(frames)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "12BefZStRZYm"
      },
      "outputs": [],
      "source": [
        "df_data.columns = ['english', 'marathi']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3sGJZ4OURazu"
      },
      "outputs": [],
      "source": [
        "df_data.dropna(inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "SQgUNb-nRiNG",
        "outputId": "f6731f04-a0e9-4cbe-d533-ff7dff5ca454"
      },
      "outputs": [
        {
          "data": {
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              "<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",
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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",
              "  "
            ],
            "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": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FSAwYgjRkCR",
        "outputId": "30953884-9dd0-4fc5-9339-04afa6d76bfa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3400084"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZl1Rk-uS8Mt"
      },
      "outputs": [],
      "source": [
        "df_data.to_csv('final_data.csv', encoding = 'utf-8', index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l5YHRoyBRnRS"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k9Zib-SkRop9",
        "outputId": "7a10bff8-6ed3-415e-f227-47ec3c57ac9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2550063 850021\n"
          ]
        }
      ],
      "source": [
        "print(len(df_train), len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "axcpiAGtR0b9",
        "outputId": "ed8008a0-caf7-4628-80c1-e6ef7eba9247"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>english</th>\n",
              "      <th>marathi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>You'll forever stay in our thoughts.</td>\n",
              "      <td>तुझा आठव हा आमच्या मनात सदैव राहू दे.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>We married.</td>\n",
              "      <td>आम्ही लग्न करत आहोत.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>So, it is important to normalize for the size ...</td>\n",
              "      <td>त्यामुळे इलेक्ट्रोडच्या आकारासाठी सामान्य करणे...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The movie also stars Aamir Khan, Katrina Kaif ...</td>\n",
              "      <td>या चित्रपटात त्यांच्यासोबत आमिर खान, कॅटरिना क...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Later the police resorted to firing at the mob.</td>\n",
              "      <td>त्यानंतर पोलिसांना जमावाला पांगविण्यासाठी गोळी...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>India-Australia</td>\n",
              "      <td>हिंदुस्थान – ऑस्ट्रेलिया</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>The Indian government again demanded action fr...</td>\n",
              "      <td>यासंदर्भात भारत सरकारने पाकिस्तानला कडक कारवाई...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>India needs a better opposition: Abhijit Banerjee</td>\n",
              "      <td>भारताला चांगल्या विरोधी पक्षाची गरज - अभिजित ब...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Prime Minister will also unveil plaque to inau...</td>\n",
              "      <td>दमण आणि दीव तसेच दादरा आणि नगर हवेली इथे विविध...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>I have been saying.</td>\n",
              "      <td>ऊनही मी म्हणत होते.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "                                             english  \\\n",
              "0               You'll forever stay in our thoughts.   \n",
              "1                                        We married.   \n",
              "2  So, it is important to normalize for the size ...   \n",
              "3  The movie also stars Aamir Khan, Katrina Kaif ...   \n",
              "4    Later the police resorted to firing at the mob.   \n",
              "5                                    India-Australia   \n",
              "6  The Indian government again demanded action fr...   \n",
              "7  India needs a better opposition: Abhijit Banerjee   \n",
              "8  Prime Minister will also unveil plaque to inau...   \n",
              "9                                I have been saying.   \n",
              "\n",
              "                                             marathi  \n",
              "0              तुझा आठव हा आमच्या मनात सदैव राहू दे.  \n",
              "1                               आम्ही लग्न करत आहोत.  \n",
              "2  त्यामुळे इलेक्ट्रोडच्या आकारासाठी सामान्य करणे...  \n",
              "3  या चित्रपटात त्यांच्यासोबत आमिर खान, कॅटरिना क...  \n",
              "4  त्यानंतर पोलिसांना जमावाला पांगविण्यासाठी गोळी...  \n",
              "5                           हिंदुस्थान – ऑस्ट्रेलिया  \n",
              "6  यासंदर्भात भारत सरकारने पाकिस्तानला कडक कारवाई...  \n",
              "7  भारताला चांगल्या विरोधी पक्षाची गरज - अभिजित ब...  \n",
              "8  दमण आणि दीव तसेच दादरा आणि नगर हवेली इथे विविध...  \n",
              "9                                ऊनही मी म्हणत होते.  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WXz83VVgFIec"
      },
      "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": "FpCYvNgdzX8x"
      },
      "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": "YrlqFvcgzX0R",
        "outputId": "86748d1d-9596-4526-cdce-6ee9513affb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n",
            "10263\n",
            "10.831284560420665\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": "NaSC1hAdzXsd"
      },
      "outputs": [],
      "source": [
        "df_train.drop(['length'], axis = 1, inplace = True);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w6MDvcDizXk7"
      },
      "outputs": [],
      "source": [
        "df_train = df_train.loc[(df_train['english'].str.len() <= 80) & (df_train['english'].str.len() >= 40)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NolzoSBczXSb"
      },
      "outputs": [],
      "source": [
        "df_test = df_test.loc[(df_test['english'].str.len() <= 80) & (df_test['english'].str.len() >= 40)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7X2kAUKTzXKn",
        "outputId": "648f9b1f-33fb-435e-9447-16c838f358b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "882869 281254\n"
          ]
        }
      ],
      "source": [
        "print(len(df_train), len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Me--3NPDzgoq"
      },
      "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": "eFT0sKGFR4Lw",
        "outputId": "4abc6a11-0ebe-4bff-afd6-a81cf78e2ede"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['english', 'marathi', '__index_level_0__'],\n",
              "        num_rows: 441434\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['english', 'marathi', '__index_level_0__'],\n",
              "        num_rows: 140627\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "478a2cf780d24a2f8080f3c2f9528a93",
            "fafd7ce7d9cd4a3cb2cb255b90db6bb9",
            "f1027c8f97994333947af0331f039127",
            "76300d5f08d64f60b96cded2f10a4fc3",
            "dc573ca041664593b3adc610135e4675",
            "bc730a3f99654dbb9c413ce2435a7991",
            "5ffdb132f0174f3ca04eee5883a449ad",
            "b3fd1032535042eea54621b65da7ce17",
            "d32cc6bdfd5c416ea94791b45f68c124",
            "848bcb8d5f9f4c3eb7ab15f20ba0c48f",
            "d79bcbb097fe42009688f19fbbb1f696",
            "2a587cdd28d04634b6553cfe9832b54f",
            "fc17246965c84b4c933fe86be0722587",
            "baaa967aa761443499a0f50d04d15c02",
            "a4d827eb0e8848aaa7c01e7de6d54261",
            "73fc56fae3c94105b8dd2b654eeb3f20",
            "6959ca3afced47828cbdc472de6fc60b",
            "b7e2d2a79aaf4551bb004e2c099c19cb",
            "40358d12ff4a49eb97fa11f2e4e115b5",
            "4f82df6003b34a00a5620243984242ca",
            "034d7f84bbe24fa7ad9bf1e4597ac469",
            "efb6633997ad46f7bcaf792e4b426fab",
            "7a3a59873166415485c2c77c92abacd6",
            "f62704001c5a4924aec872be2b73f9c1",
            "261c8be5bbcd466f82a78ae864c9da7d",
            "54f7f0fbe0754958909e5b180d6f3394",
            "00d67e1e385f4db6a121b47ffa259403",
            "23b412f539c8422fbaae2d6ff7b2a64f",
            "18171e7a919745469e5cf48b2347c540",
            "0c063a004b5c4290849c47e5b62f1b08",
            "b322739a518f47a7842029a2f7ce127a",
            "5ca17881996f4607acf6154008d5fdcf"
          ]
        },
        "id": "O9UKMDw5R5zC",
        "outputId": "86d02f41-d81e-414a-8a4c-9a4337b4830e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "478a2cf780d24a2f8080f3c2f9528a93",
              "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": "mbmwobjCSLWW",
        "outputId": "ec1131ac-5c11-4a67-d779-5bcc6432ec4d"
      },
      "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: \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": "1Dphq_tPSSuy"
      },
      "outputs": [],
      "source": [
        "from transformers import FSMTForConditionalGeneration, FSMTTokenizer, AutoTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213,
          "referenced_widgets": [
            "bd46758d7cb44fb997b110dbb4a2b99f",
            "159286f2e23b4495bdedfcd0c485af32",
            "b03555b6e69c48b48d543f29a291376a",
            "fd2909ba38194e8bb98a88662b483830",
            "e81d48c70459423589507ac8bbf4e9bf",
            "2d74ec58b0b049e1ad1cab6a7d5f20cb",
            "c02934c82c454da8bfa1cfd54615c47e",
            "cab3c732ca7c4cc89ed74db5a6ad88b3",
            "ce2f4251cf024767bc86a4d283d18f1d",
            "3210bf85eaaf4fea9b2bac12d076e993",
            "b2398c55e73d4fe184b02575e8da4073",
            "776cd4cf389e4fd9925afe1a29c0d6bc",
            "9f8a022dd99d4c25bcbc55fc41ba2fcc",
            "06787709741848bfa9a6bb6788fe9c81",
            "8a45046f98bd457f807964680102dfc5",
            "348739a6a81b4d4883378815015e46da",
            "edb32dc20c044295b6afd13660e62b95",
            "b2888d683ae44057ac2fe9240a452e09",
            "dddd8ad3ec3f482cb0b07c22f590723a",
            "705316134ac749a98a3527e7a09e879d",
            "b0e77ea4e01846f389869c899c2330ff",
            "8397cd2dea124ca49191423bdfb22b9e",
            "7eacc01a87cd45c4a067d3412d9e1aa4",
            "06ed893e67d4480390e4a5e4f1c1d3b3",
            "32a52c5990e8418d96be486ff8c552bc",
            "730737ebbf3e4c629b36280c1206f19a",
            "b7977ead0759428f8bfdca1c1885af4a",
            "8e4695df20f14c3991156d841591a6c7",
            "2ab2cb946d074ca48bfe80c7134c1785",
            "1428cdcf070d4b6b8e22448a4fc17bdf",
            "c1ba711e5ecb438f961476159063049a",
            "b98e3bf9fb7548d9be3fee8b2e150764",
            "02721f3034074e2aadd35a154915507f",
            "26fd16bdc13540a0a9a1c25be769d5ae",
            "89c2a6ef18fa4cb683b2869d8f81e0ee",
            "4cfe1f6f299f4586b9d1496e8a421596",
            "ed2cd1e67afa4578aa59f9875205075b",
            "d1c354050659463e8942240e533fd7e0",
            "169ace578bdc43ab8c2236bff7deb9b8",
            "e4b0d3a140304c26b6dce1155a5ad66a",
            "a51800e3a8634b84a6f538c5a47deefc",
            "c96e268b14d6478bbe5931b01ad00138",
            "fa8fd9b07fa14bdead67ba0c86c79130",
            "cc8782082d3e4f169a8254f5567c55a9",
            "8408553623c045a1ab65c47d7c78146f",
            "2c84fa6138cf439d9b7b4b3e59530dd8",
            "7e5cd494c4bd4c1aa3291ccc9771c063",
            "ad586326a7454fb0aad0d3141ad31bfa",
            "5f6d0b26d4fd44a89bbdafb50025ddaa",
            "d4945986a9dd45c5b438b518e1e18279",
            "7625fbcf36424c7e8efbc5e9ab3f467d",
            "558ad708d81b4b328dfb8ca55455412d",
            "1f9ca250c11f48b5b08d5691d45534a0",
            "bdace48c9f1b4bd899deae1945f67e9e",
            "87bae29ef424437cabdb3009ce3e8b65"
          ]
        },
        "id": "YxUflz2sSX4m",
        "outputId": "bf88e19d-cd79-4f03-9b7f-31e65e62783c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd46758d7cb44fb997b110dbb4a2b99f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "776cd4cf389e4fd9925afe1a29c0d6bc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7eacc01a87cd45c4a067d3412d9e1aa4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/source.spm:   0%|          | 0.00/813k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26fd16bdc13540a0a9a1c25be769d5ae",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/target.spm:   0%|          | 0.00/1.17M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8408553623c045a1ab65c47d7c78146f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/2.31M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-mr\", return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgWPPr124Aht",
        "outputId": "1cbc4fe5-6b96-453d-f48c-a48ebada99dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MarianTokenizer(name_or_path='Helsinki-NLP/opus-mt-en-mr', vocab_size=61674, model_max_length=512, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}, clean_up_tokenization_spaces=True)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKL4-v5P4AZx",
        "outputId": "bedfa806-11da-4a85-ac95-e8cc44001e17"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  380,     3,    39,    17,   591,     7,  4655, 10246,    20,     4,\n",
              "          5290,     6,     4, 59912,  4752,     2,     0, 61673, 61673, 61673]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]), 'length': tensor([20]), 'labels': tensor([[  370,  2066,   482, 16298, 17794,  1377,    62,  3348,   840,  3719,\n",
              "           418, 11302,    16,     2,     0, 61673, 61673, 61673, 61673, 61673]])}"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "out = 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')\n",
        "out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "977Wpjgu4AO9",
        "outputId": "a6cbc973-f546-45f9-8686-1ad4937876f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['So, it is▁important to▁normalize for the▁size of the▁electrode.</s> <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": "9nXEXisC4RBQ",
        "outputId": "c1b324a5-1a38-4539-8ff6-046345cab12b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['त्यामुळे इलेक्ट्रोडच्या आकारासाठी सामान्य करणे आवश्यक आहे.</s> <pad> <pad> <pad> <pad> <pad>']"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.batch_decode(out['labels'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aJLWqSm6z9f",
        "outputId": "5d091c9c-4cda-4305-85eb-ffcacc5d267e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  380,     3,    39,    17,   591,     7,  4655, 10246,    20,     4,\n",
              "          5290,     6,     4, 59912,  4752,     2,     0, 61673, 61673, 61673]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]), 'length': tensor([20])}"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.src_lang = \"en\"\n",
        "eng = tokenizer(dataset['train']['english'][0], max_length=20, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "eng"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lIXs9qB7Hku",
        "outputId": "1e6c54f4-8986-414b-e8ea-fc94d75396f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['So, it is▁important to▁normalize for the▁size of the▁electrode.</s> <pad> <pad> <pad>']\n"
          ]
        }
      ],
      "source": [
        "decoded_preds = tokenizer.batch_decode(eng['input_ids'])\n",
        "print(decoded_preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LmTU72n_Q_jq"
      },
      "outputs": [],
      "source": [
        "# bos_id = tokenizer._convert_token_to_id_with_added_voc(\"<s>\")\n",
        "# eos_id = tokenizer._convert_token_to_id_with_added_voc(\"</s>\")\n",
        "# pad_id = tokenizer._convert_token_to_id_with_added_voc(\"<pad>\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XmGRc2VW_AfL"
      },
      "outputs": [],
      "source": [
        "max_length = 100\n",
        "context_length = 100\n",
        "\n",
        "def tokenize(data):\n",
        "  data = tokenizer(data['english'], text_target =  data['marathi'],  max_length= max_length, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "  inp_ids = []\n",
        "  if len(data['input_ids']) != 0:\n",
        "    for length, ids in zip(data[\"length\"], data[\"input_ids\"]):\n",
        "      inp_ids.append(ids)\n",
        "    label = []\n",
        "    for length, lb in zip(data[\"length\"], data[\"labels\"]):\n",
        "      label.append(lb)\n",
        "  return {\"input_ids\": inp_ids,\n",
        "          'labels' : label}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jHQNQNBASfC7"
      },
      "outputs": [],
      "source": [
        "# max_length = 63\n",
        "# context_length = 63\n",
        "\n",
        "# def tokenize(data):\n",
        "#   tokenizer.src_lang = \"en\"\n",
        "#   # inp = tokenizer(\"I am a boy </s> <2en>\", add_special_tokens=False, return_tensors=\"pt\", padding=True).input_ids\n",
        "#   eng = tokenizer(data['english'], max_length=max_length, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "#   eng_batch = []\n",
        "#   for length, ids in zip(eng[\"length\"], eng[\"input_ids\"]):\n",
        "#     eng_batch.append(ids)\n",
        "#   tokenizer.src_lang = \"mr\"\n",
        "#   mth = tokenizer(data['marathi'], max_length=max_length, padding = 'max_length', truncation=True, return_length = True, return_tensors = 'pt')\n",
        "#   mth_batch = []\n",
        "#   for length, target in zip(mth[\"length\"], mth[\"input_ids\"]):\n",
        "#     mth_batch.append(target)\n",
        "#   return {\n",
        "#       'input_ids' : eng_batch,\n",
        "#       'labels' : mth_batch\n",
        "#   }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "821ab95e76e64b16a8167286a935c495",
            "c08a3fcc36684498a5a8dd494d81107d",
            "1bfd852cba174f94869529f3e083806a",
            "eb2b241382aa4c5a9beaeae8c337080a",
            "d1c73250f0f545b1bc7b2f8ba5195424",
            "5ec038a5fc9b4f2b9618db85495ce74a",
            "2f8500931eda424abda0db08a221f639",
            "68d0775f5953495e9c61345d64c1be40",
            "a27b59d79a894f1c841aff124894caeb",
            "89d16a4c8ed04f22baf1afc1c1dcb2d6",
            "2d279911a1c34c4db9d04d03bc2c1a95",
            "d332ffbca27e463db9f7c3e5e385ea39",
            "9ea53a8e6fb14ea5ad998d2754c55575",
            "9bffe762bacd4db0bb85a982542035f6",
            "d835b184abea4804b738f4b0e5bc84cf",
            "1408ca189ff6444a9681abdb3e8d813b",
            "71e0b711e0d64adcabe064f9f0aacf2d",
            "d29377ddf4b54f678162706657527353",
            "3d734aeef59a473b9b4eecad1ca3847b",
            "cb35eee577994344b473e36caebf19d6",
            "eb91160932cb4152865d3aa1dc650e48",
            "86fc79d40618445a8a6de08f3869cb2b"
          ]
        },
        "id": "Hp7s_qPPSjsh",
        "outputId": "6f60236c-6721-4b19-945f-d1d21048e681"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "821ab95e76e64b16a8167286a935c495",
              "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": "d332ffbca27e463db9f7c3e5e385ea39",
              "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": "E9UfWvQrS9Z2",
        "outputId": "04421a63-85b4-4774-e447-a7b26844989e"
      },
      "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": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZr8J9RQO0yv",
        "outputId": "704ef677-3803-472c-d722-0f8e975e496a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
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            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.30.2)\n",
            "Requirement already satisfied: GitPython>=2.0.8 in /usr/local/lib/python3.10/dist-packages (from neptune) (3.1.31)\n",
            "Requirement already satisfied: Pillow>=1.1.6 in /usr/local/lib/python3.10/dist-packages (from neptune) (8.4.0)\n",
            "Collecting PyJWT (from neptune)\n",
            "  Downloading PyJWT-2.7.0-py3-none-any.whl (22 kB)\n",
            "Collecting backoff (from neptune)\n",
            "  Downloading backoff-2.2.1-py3-none-any.whl (15 kB)\n",
            "Collecting boto3>=1.16.0 (from neptune)\n",
            "  Downloading boto3-1.27.1-py3-none-any.whl (135 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.9/135.9 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\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",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (8.1.3)\n",
            "Requirement already satisfied: future>=0.17.1 in /usr/local/lib/python3.10/dist-packages (from neptune) (0.18.3)\n",
            "Requirement already satisfied: oauthlib>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (3.2.2)\n",
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            "Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (2.27.1)\n",
            "Requirement already satisfied: requests-oauthlib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.3.1)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.16.0)\n",
            "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",
            "Requirement already satisfied: websocket-client!=1.0.0,>=0.35.0 in /usr/local/lib/python3.10/dist-packages (from neptune) (1.6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Collecting botocore<1.31.0,>=1.30.1 (from boto3>=1.16.0->neptune)\n",
            "  Downloading botocore-1.30.1-py3-none-any.whl (11.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.0/11.0 MB\u001b[0m \u001b[31m86.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\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",
            "  Downloading s3transfer-0.6.1-py3-none-any.whl (79 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\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",
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            "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: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->neptune) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20.0->neptune) (2.0.12)\n",
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            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.10/dist-packages (from swagger-spec-validator>=2.7.4->neptune) (4.3.3)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->neptune) (2022.7.1)\n",
            "Collecting jsonref (from bravado-core>=5.16.1->bravado<12.0.0,>=11.0.0->neptune)\n",
            "  Downloading jsonref-1.1.0-py3-none-any.whl (9.4 kB)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.8->neptune) (5.0.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (23.1.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema->swagger-spec-validator>=2.7.4->neptune) (0.19.3)\n",
            "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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\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 boto3-1.27.1 botocore-1.30.1 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"
          ]
        }
      ],
      "source": [
        "! pip install -U neptune transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pF9vsLIAOzp5",
        "outputId": "ba8cd637-aedb-42f8-e51b-1530237a0d3a"
      },
      "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": "6fHGcw1oOvVP",
        "outputId": "62af7e18-0e1f-4068-c261-786c6dde449c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "https://app.neptune.ai/anujsahani01/ITStudio/e/MODEL-60\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/Helsinki_NLP_optimied(FINALL).ipynb\"]\n",
        ")\n",
        "\n",
        "# run[\"train_dataset\"].track_files(\"./train.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u4GYCyidS94S"
      },
      "outputs": [],
      "source": [
        "# params = {'lr':0.01, 'max_steps': 10000, 'warmup_steps': 50, 'weight_decay': 0.001, 'per_device_train_batch_size': 96, 'per_device_eval_batch_size': 96, 'evaluation_strategy' :\"no\",  'num_train_epochs': 2, 'predict_with_generate' : True, 'fp16' : False, 'push_to_hub' : True, 'remove_unused_columns' :False, 'dropout': 0.5}\n",
        "params = {'lr':0.0005, 'max_steps': 10000, 'warmup_steps': 50, 'weight_decay': 0.01, 'per_device_train_batch_size': 64, 'per_device_eval_batch_size': 64, '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": "mrqQeShjTCWm"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoConfig\n",
        "model_checkpoint  = \"Helsinki-NLP/opus-mt-en-mr\"\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, ignore_mismatched_sizes=True).to('cuda')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1r-s75V3TIHy",
        "outputId": "912a44cd-3bd4-4be3-d1c3-47b3083fdaa4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MarianMTModel(\n",
            "  (model): MarianModel(\n",
            "    (shared): Embedding(61674, 512, padding_idx=61673)\n",
            "    (encoder): MarianEncoder(\n",
            "      (embed_tokens): Embedding(61674, 512, padding_idx=61673)\n",
            "      (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
            "      (layers): ModuleList(\n",
            "        (0-5): 6 x MarianEncoderLayer(\n",
            "          (self_attn): MarianAttention(\n",
            "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "          )\n",
            "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
            "          (activation_fn): SiLUActivation()\n",
            "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
            "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
            "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (decoder): MarianDecoder(\n",
            "      (embed_tokens): Embedding(61674, 512, padding_idx=61673)\n",
            "      (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
            "      (layers): ModuleList(\n",
            "        (0-5): 6 x MarianDecoderLayer(\n",
            "          (self_attn): MarianAttention(\n",
            "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "          )\n",
            "          (activation_fn): SiLUActivation()\n",
            "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
            "          (encoder_attn): MarianAttention(\n",
            "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
            "          )\n",
            "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
            "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
            "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
            "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (lm_head): Linear(in_features=512, out_features=61674, bias=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ond_E89jTKpN"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xq86iWCZTWeU",
        "outputId": "3bb99181-2e07-41a6-dd67-a0031b5dcd13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting sacrebleu\n",
            "  Using cached sacrebleu-2.3.1-py3-none-any.whl (118 kB)\n",
            "Requirement already satisfied: portalocker in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (2.7.0)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (2022.10.31)\n",
            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (0.8.10)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (1.22.4)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (0.4.6)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (4.9.2)\n",
            "Installing collected packages: sacrebleu\n",
            "Successfully installed sacrebleu-2.3.1\n"
          ]
        }
      ],
      "source": [
        "! pip install sacrebleu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mi09qgthTORv"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "metric = evaluate.load(\"sacrebleu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S15oMG-HTQ9w"
      },
      "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": "YxJRNq4BNLzw"
      },
      "outputs": [],
      "source": [
        "device = 'cuda'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8XzUCEtwT5_2"
      },
      "outputs": [],
      "source": [
        "from transformers import  Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "args =  Seq2SeqTrainingArguments(\n",
        "    f\"./finetuned_Helsinki-NLP-en-mr\",\n",
        "    evaluation_strategy= params['evaluation_strategy'],\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate= params['lr'] ,\n",
        "    per_device_train_batch_size=params['per_device_train_batch_size'],\n",
        "    per_device_eval_batch_size= params['per_device_eval_batch_size'],\n",
        "    warmup_steps = params['warmup_steps'],\n",
        "    weight_decay= params['weight_decay'],\n",
        "    save_total_limit= params['save_total_limit'],\n",
        "    num_train_epochs= params['num_train_epochs'],\n",
        "    predict_with_generate= params['predict_with_generate'],\n",
        "    fp16= params['fp16'],\n",
        "    push_to_hub= params['push_to_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": "CpK4vbSWUgV4",
        "outputId": "614baacf-37fa-47e5-b26c-f336baa09405"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/anujsahani01/finetuned_Helsinki-NLP-en-mr into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/anujsahani01/finetuned_Helsinki-NLP-en-mr 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": "lHWErN1WUm85"
      },
      "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": 832
        },
        "id": "QjvY39NkUsEc",
        "outputId": "13b7b914-87f7-410d-e43b-3806cdc75cb2"
      },
      "outputs": [
        {
          "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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10000' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10000/10000 2:33:58, Epoch 1/2]\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>1.225700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.860200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.828200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.768800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.748700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.748800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.708800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.637600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.608400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.606700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.582300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.571600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.557400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.537600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.530800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.524800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.523300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.518300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.517400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n",
            "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10000, training_loss=0.6677646087646485, metrics={'train_runtime': 9240.4668, 'train_samples_per_second': 69.261, 'train_steps_per_second': 1.082, 'total_flos': 1.69481761062912e+16, 'train_loss': 0.6677646087646485, 'epoch': 1.45})"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zRtuGBThLQFJ"
      },
      "outputs": [],
      "source": [
        "metrics = trainer.state.log_history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G4RcKURUGEGq"
      },
      "outputs": [],
      "source": [
        "for key, value in params.items():\n",
        "  run[key] = value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WO-nraLEOVZ2"
      },
      "outputs": [],
      "source": [
        "lr = []\n",
        "train_loss = []\n",
        "steps =  []\n",
        "epoch = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dxMXckksNwd_"
      },
      "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": "dMI56Mk4FVR8"
      },
      "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": "nUCc21ndGKxT"
      },
      "outputs": [],
      "source": [
        "for i in range(len(epoch)):\n",
        "  run['epoch'].append(epoch[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hmLFdjIiGwFK"
      },
      "outputs": [],
      "source": [
        "for i in range(len(lr)):\n",
        "  run[\"learning_rate\"].append(lr[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vqo9dErIO6Gg"
      },
      "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": 255
        },
        "id": "mtVzYGrOiJgl",
        "outputId": "6ea16cdf-5a31-4e1c-f4d5-920d44d5c03e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/anujsahani01/finetuned_Helsinki-NLP-en-mr\n",
            "   b967adc..f0facad  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_Helsinki-NLP-en-mr\n",
            "   b967adc..f0facad  main -> main\n",
            "\n",
            "To https://huggingface.co/anujsahani01/finetuned_Helsinki-NLP-en-mr\n",
            "   f0facad..82ad163  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/anujsahani01/finetuned_Helsinki-NLP-en-mr\n",
            "   f0facad..82ad163  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/anujsahani01/finetuned_Helsinki-NLP-en-mr/commit/f0facad2dcceb318234bd6c52b94f74290203227'"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "le6bo-DnLxRZ",
        "outputId": "d6a0b238-69ad-4bbe-db0e-2595321e6d96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/880.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\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",
            "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=db2bb845d541703ce816c713216c403a4b484f03fc2e33bc27cbc74ca36349cd\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"
          ]
        }
      ],
      "source": [
        "!  pip install sacremoses"
      ]
    },
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        "id": "UhQm4_oGiCa9",
        "outputId": "71ab897a-da43-4c25-c322-3728587dd947"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e84c48859e04efd8dda8ac9d92631af",
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            "text/plain": [
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            ]
          },
          "metadata": {},
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        {
          "data": {
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              "model_id": "298e7ea0e2524453bde768eeffedd295",
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            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/303M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            ]
          },
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        },
        {
          "data": {
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            },
            "text/plain": [
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            ]
          },
          "metadata": {},
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        },
        {
          "data": {
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            "text/plain": [
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        {
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              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "ab765dda1e6a442c9ccb595025465948",
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            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/2.43M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "331ddec2fce241859da08a75f11903e2",
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, TranslationPipeline, Text2TextGenerationPipeline, pipeline\n",
        "\n",
        "model_ckpt = \"anujsahani01/finetuned_Helsinki-NLP-en-mr\"\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"anujsahani01/finetuned_Helsinki-NLP-en-mr\")\n",
        "pipe = pipeline('translation_en_to_mr', \"anujsahani01/finetuned_Helsinki-NLP-en-mr\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pDe5nOjj8qLN",
        "outputId": "58492b5f-ef37-4ea2-a634-d51e3471d1ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[{'translation_text': 'ऋषी कपूरच्या सिम्बा या चित्रपटातून बॉलिवूडमध्ये पदार्पण करत आहे.'},\n",
              "  {'translation_text': 'ऋषी कपूरच्या सिम्बा या चित्रपटातून बॉलिवूडमध्ये पदार्पण करणार आहे.'},\n",
              "  {'translation_text': 'ऋषी कपूरच्या सिम्बा या चित्रपटातून बॉलिवूडमध्ये पदार्पण करणार आहेत.'}]]"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "src_text = ['my name is anuj sahani']\n",
        "pred = pipe(src_text, num_return_sequences=3)\n",
        "pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o7ILTRbuZbWX"
      },
      "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": "1eHIjfKzZ2Id"
      },
      "outputs": [],
      "source": [
        "run[\"bleu\"].append(evaluation_results[\"bleu\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaRI_UH-ABaH",
        "outputId": "aeae350e-d324-4e73-afa1-a89fef533b52"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Unassigned field at \"source_code//content/drive/MyDrive/Helsinki-NLP(Finetuning).ipynb\">"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "run['source_code'] [\"/content/drive/MyDrive/Helsinki-NLP(Finetuning).ipynb\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tF8ICsBX0FT",
        "outputId": "6834857a-56e0-4ef1-eb47-baaaaf85123c"
      },
      "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-60/metadata\n"
          ]
        }
      ],
      "source": [
        "run.stop()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EN-o4u9TYKQ6"
      },
      "outputs": [],
      "source": []
    }
  ],
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