Fine_Tune_Neural_Machine_Translation.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction\n",
        "It is often very difficult and expensive to train a full-blown Machine Translation model from scratch on a large parallel corpora. Hence, here we will try to fine-tune pretrained model(`IndicBART`) with our training data. We will be using the libraries provided by Hugging Face to do so."
      ],
      "metadata": {
        "id": "fH6DEuhnHtn4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mounting Google Drive"
      ],
      "metadata": {
        "id": "5WEPgANaJxKa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Y1fLvEdJzMq",
        "outputId": "ef33a6a4-657a-4a4a-80f6-0c6559a55dea"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd \"/content/drive/MyDrive/IASNLP\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z94wggUjLxSD",
        "outputId": "e2a89831-1e04-4e73-ec46-de573727a549"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/IASNLP\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Imporrting Necessary Packages"
      ],
      "metadata": {
        "id": "6mqn_5paJGkP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[sentencepiece]\n",
        "!pip install datasets\n",
        "!pip install sacrebleu\n",
        "!pip install sentencepiece\n",
        "!pip install indic-nlp-library"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_9QTYVOI8Ew",
        "outputId": "63dd5865-dc51-43f2-b939-3583da617acc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
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            "      Successfully uninstalled PyYAML-3.13\n",
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            "\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",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.25.11)\n",
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            "Requirement already satisfied: sphinxcontrib-serializinghtml in /usr/local/lib/python3.7/dist-packages (from sphinxcontrib-websupport->sphinx>=1.2.0->sphinx-argparse->indic-nlp-library) (1.1.5)\n",
            "Installing collected packages: sphinx-rtd-theme, sphinx-argparse, morfessor, indic-nlp-library\n",
            "Successfully installed indic-nlp-library-0.81 morfessor-2.0.6 sphinx-argparse-0.3.1 sphinx-rtd-theme-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from indicnlp.transliterate.unicode_transliterate import UnicodeIndicTransliterator\n",
        "import transformers\n",
        "import sentencepiece\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "from datasets import load_dataset, load_metric"
      ],
      "metadata": {
        "id": "feLiWGj7JN0S"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "Hh4R8XKkIpH5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have saved the train data and test data previously. We will straight away load it."
      ],
      "metadata": {
        "id": "_O1DL5khJfcP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185,
          "referenced_widgets": [
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        },
        "id": "VuUsTiZVHjIY",
        "outputId": "6e9405d1-211a-4622-bec7-802f6f6ac861"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-b1b37da2b3a2df75\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-b1b37da2b3a2df75/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "169414c1cce9443990eb2b88ffa35367"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f7a443ec4194b578187712a9b8b31fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8a1e09c561f44899c39c1e9e58b13fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8287727c678246da929ae1fd67f92d85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-b1b37da2b3a2df75/0.0.0/51cce309a08df9c4d82ffd9363bbe090bf173197fc01a71b034e8594995a1a58. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "675d7f73f84c4cc187e80a566c9ba1f3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "data = load_dataset('csv', data_files={'train': ['train_data.csv'], 'test': ['train_dev.csv']})"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below, we can see the distribution of the data."
      ],
      "metadata": {
        "id": "uRWHBZYcdyfI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.remove_columns('Unnamed: 0')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QEFCcpc_LtoD",
        "outputId": "f2513adf-0a9d-4856-9e38-70e17d9838fe"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['src', 'tgt'],\n",
              "        num_rows: 111020\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['src', 'tgt'],\n",
              "        num_rows: 4626\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Train Data"
      ],
      "metadata": {
        "id": "JXUVQWaHlBfu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(data['train'][:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "a3f6YI9rfgCk",
        "outputId": "dd4a8e90-f0cd-4321-9b7f-90ab25b2b6df"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                                                 src  \\\n",
              "0                     But the shoot was a tough one.   \n",
              "1                         Road construction started.   \n",
              "2                            Why did he pay so much?   \n",
              "3      \"AT ITS worst, this has been Satan's century.   \n",
              "4                            That's our only demand.   \n",
              "5                     He leads his life by teaching.   \n",
              "6                                    Im not leaving.   \n",
              "7  Under the instructions of the caliph Uthman ib...   \n",
              "8  The only way of weaning him off the ventilator...   \n",
              "9                    Gaibandha death toll rises to 5   \n",
              "\n",
              "                                                 tgt  \n",
              "0                      তবে শ্যুটটা খুব মুশকিলের ছিল।  \n",
              "1                         রাস্তা নির্মাণ শুরু হয়েছে।  \n",
              "2                            কেন তিনি এত টাকা দিতেন?  \n",
              "3  \"এই শতাব্দীর প্রচণ্ড ভয়াবহতা এটাকে শয়তানের এক ...  \n",
              "4                         সেটাই আমাদের একমাত্র দাবি।  \n",
              "5                  সে শিক্ষকতা করে জীবন পরিচালনা করে  \n",
              "6                                আমি এলাকা ছাড়ব না।  \n",
              "7  খলিফা উসমান ইবনে আফফানের নির্দেশে মুয়াবিয়া এ...  \n",
              "8  তাকে বাঁচানোর একমাত্র উপায় বায়ুরন্ধ্র বন্ধ করে...  \n",
              "9      গাইবান্ধায় নিহতের সংখ্যা বেড়ে ৭, গৌরনদীতে ৩  "
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              "      <td>But the shoot was a tough one.</td>\n",
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              "      <td>Road construction started.</td>\n",
              "      <td>রাস্তা নির্মাণ শুরু হয়েছে।</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Why did he pay so much?</td>\n",
              "      <td>কেন তিনি এত টাকা দিতেন?</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>\"AT ITS worst, this has been Satan's century.</td>\n",
              "      <td>\"এই শতাব্দীর প্রচণ্ড ভয়াবহতা এটাকে শয়তানের এক ...</td>\n",
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              "      <th>4</th>\n",
              "      <td>That's our only demand.</td>\n",
              "      <td>সেটাই আমাদের একমাত্র দাবি।</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>He leads his life by teaching.</td>\n",
              "      <td>সে শিক্ষকতা করে জীবন পরিচালনা করে</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Im not leaving.</td>\n",
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              "    <tr>\n",
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              "      <td>Under the instructions of the caliph Uthman ib...</td>\n",
              "      <td>খলিফা উসমান ইবনে আফফানের নির্দেশে মুয়াবিয়া এ...</td>\n",
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              "      <td>তাকে বাঁচানোর একমাত্র উপায় বায়ুরন্ধ্র বন্ধ করে...</td>\n",
              "    </tr>\n",
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              "          document.querySelector('#df-da80d573-6462-4791-b3da-81ccf3a43ed7 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-da80d573-6462-4791-b3da-81ccf3a43ed7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Train-Dev Data"
      ],
      "metadata": {
        "id": "8lIV0E7vlE_1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(data['test'][:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0641Cf0LlH91",
        "outputId": "a956cb78-b2f7-4fb2-aa06-49d9f79b661f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                 src  \\\n",
              "0  We beg our Protestant and Jewish friends to pu...   \n",
              "1  Sa'd advised Muhammad: \"Don't be hard on him. ...   \n",
              "2                       Photo by 'Save Gaza Project'   \n",
              "3  So, therefore, we need to test batteries under...   \n",
              "4    This party is also contesting in the elections.   \n",
              "5                        Roads and houses collapsed.   \n",
              "6  When a piece of paper is rolled up, Hitotsuyam...   \n",
              "7  The founder of the modern Catholic movement Op...   \n",
              "8  Research has shown that exercise also helps in...   \n",
              "9                            It will be so much fun.   \n",
              "\n",
              "                                                 tgt  \n",
              "0  কোন কোন ক্ষেত্রে কর্তৃপক্ষ এবং ধর্মীয় নেতারা ...  \n",
              "1  সা'দ মুহাম্মাদকে বলেন: \"তার প্রতি কঠোর হবেন না...  \n",
              "2                        ছবি \"সেভ গাজা প্রজেক্টের\"।'  \n",
              "3  অতএব, আমআদের কিছুটা মান অবস্থাগুলির অধীনে ব্যা...  \n",
              "4     নির্বাচনে এই দলের মধ্যেই প্রতিদ্বন্দ্বিতা হবে।  \n",
              "5                      তলিয়ে গেছে ঘরবাড়ি, রাস্তাঘাট।  \n",
              "6  হিতোসুয়েমা কাগজ দিয়ে ম্যাশে কৌশল অবলম্বন করে...  \n",
              "7  আধুনিক ক্যাথলিক সংঘের প্রতিষ্ঠাতা ওপাস ডেই, হো...  \n",
              "8  এছাড়া গবেষণায় দেখা গেছে, শরীরচর্চা উদ্বেগ ও মা...  \n",
              "9                                  অনেক মজা হবে তখন।  "
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              "      <td>We beg our Protestant and Jewish friends to pu...</td>\n",
              "      <td>কোন কোন ক্ষেত্রে কর্তৃপক্ষ এবং ধর্মীয় নেতারা ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sa'd advised Muhammad: \"Don't be hard on him. ...</td>\n",
              "      <td>সা'দ মুহাম্মাদকে বলেন: \"তার প্রতি কঠোর হবেন না...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Photo by 'Save Gaza Project'</td>\n",
              "      <td>ছবি \"সেভ গাজা প্রজেক্টের\"।'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>So, therefore, we need to test batteries under...</td>\n",
              "      <td>অতএব, আমআদের কিছুটা মান অবস্থাগুলির অধীনে ব্যা...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This party is also contesting in the elections.</td>\n",
              "      <td>নির্বাচনে এই দলের মধ্যেই প্রতিদ্বন্দ্বিতা হবে।</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Roads and houses collapsed.</td>\n",
              "      <td>তলিয়ে গেছে ঘরবাড়ি, রাস্তাঘাট।</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>When a piece of paper is rolled up, Hitotsuyam...</td>\n",
              "      <td>হিতোসুয়েমা কাগজ দিয়ে ম্যাশে কৌশল অবলম্বন করে...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>The founder of the modern Catholic movement Op...</td>\n",
              "      <td>আধুনিক ক্যাথলিক সংঘের প্রতিষ্ঠাতা ওপাস ডেই, হো...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Research has shown that exercise also helps in...</td>\n",
              "      <td>এছাড়া গবেষণায় দেখা গেছে, শরীরচর্চা উদ্বেগ ও মা...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>It will be so much fun.</td>\n",
              "      <td>অনেক মজা হবে তখন।</td>\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "ig7LusYYeFyG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will start by preprocessing data. For that we have to get the model configurations of the pretrained model which we are going to fine-tune."
      ],
      "metadata": {
        "id": "nJuw63xRePXL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"ai4bharat/IndicBART\""
      ],
      "metadata": {
        "id": "WE-IE_QMMb4X"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's set the metric for evaluation we will be using."
      ],
      "metadata": {
        "id": "XEgL_6VdfKso"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metric = load_metric(\"sacrebleu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.85k [00:00<?, ?B/s]"
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    {
      "cell_type": "markdown",
      "source": [
        "## Tokenization & Normalization"
      ],
      "metadata": {
        "id": "dzqYGjpLlu4O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use the tokenizer that is consistent with the vocabulary and method which is used in `IndicBART`"
      ],
      "metadata": {
        "id": "mTjrKGU6mEDC"
      }
    },
    {
      "cell_type": "code",
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        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, do_lower_case=False, use_fast=False, keep_accents=True)"
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "b22906fdb7504465b5b2765c00444ac4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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>\")\n",
        "en_id = tokenizer._convert_token_to_id_with_added_voc(\"<2en>\")\n",
        "bn_id = tokenizer._convert_token_to_id_with_added_voc(\"<2bn>\")"
      ],
      "metadata": {
        "id": "NR2g0qmPQ5ex"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transliteration"
      ],
      "metadata": {
        "id": "4USjsATh4oxl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One key thing to keep in mind is `IndicBART` is pretrained on 11 different Indian Languages, where all other languages except Hindi and Marathi are transliterated to Devnagri Script. Hence, to use it for Bengali we had to transliterate the Bengali text to Devnagri script as show below. Later we would also need to convert Devnagri to Bengali."
      ],
      "metadata": {
        "id": "AaaRzy_gzD7-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ben_dev = UnicodeIndicTransliterator()"
      ],
      "metadata": {
        "id": "PpA61VkAqzjH"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "beng_sent = \"আমি তোমাকে ভালোবাসি।\"\n",
        "print(\"Bengali: \", beng_sent)\n",
        "print(\"Hindi: \", ben_dev.transliterate(beng_sent, \"bn\", \"hi\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUpr1Stlvt4r",
        "outputId": "af3d9aad-19dd-43bb-c0db-7539fbd1cf5e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bengali:  আমি তোমাকে ভালোবাসি।\n",
            "Hindi:  आमि तोमाके भालोबासि।\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below, we apply the above two steps to `data`."
      ],
      "metadata": {
        "id": "_EiLvAQ84srK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prefix = \"\"\n",
        "max_input_length = 128\n",
        "max_target_length = 128\n",
        "def preprocess_function(examples):\n",
        "    input_conv = [sent + \" </s> <2en>\" for sent in examples['src']]\n",
        "    model_inputs = tokenizer(input_conv, max_length=max_input_length, truncation=True)\n",
        "    output_conv = [\"<2hi> \" + ben_dev.transliterate(sent, \"bn\", \"hi\") + \" </s>\" for sent in examples['tgt']]\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(output_conv, max_length=max_target_length, truncation=True)\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ],
      "metadata": {
        "id": "p6zgGVe9zBZO"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_data = data.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "2b52d24f5cff4394adf8f5e4f18e6ed9",
            "0b1fe63b91734acea06a29b679368922",
            "bffbc8efc7564a3fb4d16a564af83ed4",
            "05ea877fe0224302b367559c024a4baf",
            "b761aea2e2144776b54672d69461abfb",
            "d315848f274f44df8c5c5ae1673ae66a",
            "ecd8380f4f214fe082ee1fd02a24f5e6",
            "17899c61563a474d823516b09ef238db",
            "28a44918ad194aa68dbaba894e1ea0eb",
            "9ddd32c8b2ab4baaa4444f3ba51a6103",
            "3a788b3ae5b944868b4b2d8d8a242f12",
            "c7dc44866fd343c592b0f3e9e9e056d6",
            "1866824d7fc449bdba09d3e759c96a3a",
            "65a3969722c34a10b3e42e40a83133a8",
            "87ddafcf3480442cb0f484d5a537a492",
            "c1b26ec6088d4e56a4d6cf6c3f66ae53",
            "57e1ab31c761439d9ae58c191f1967e9",
            "89876de95fa54c5e94ce0abaedb62837",
            "3ce7d45c33e64364978ef350fadfe8f0",
            "b0d7278953f54940aeb5ad493a150ec3",
            "b306b0bd7f8b491b88555468dbeb9e56",
            "428f9dacfe1148acb782185493124639"
          ]
        },
        "id": "E1r4DbhI4Ght",
        "outputId": "85c11d8a-8eeb-4ce0-e054-a221be75bf6c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Parameter 'function'=<function preprocess_function at 0x7faa6166f950> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/112 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b52d24f5cff4394adf8f5e4f18e6ed9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7dc44866fd343c592b0f3e9e9e056d6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the form of the tokenized(ready-to-feed) data."
      ],
      "metadata": {
        "id": "JaoQZoaiYDKE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(tokenized_data['train'][:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "wLrIKpA-V7ER",
        "outputId": "ecdee2cb-005a-4fd5-f40c-022e6090708a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 src  \\\n",
              "0                     But the shoot was a tough one.   \n",
              "1                         Road construction started.   \n",
              "2                            Why did he pay so much?   \n",
              "3      \"AT ITS worst, this has been Satan's century.   \n",
              "4                            That's our only demand.   \n",
              "5                     He leads his life by teaching.   \n",
              "6                                    Im not leaving.   \n",
              "7  Under the instructions of the caliph Uthman ib...   \n",
              "8  The only way of weaning him off the ventilator...   \n",
              "9                    Gaibandha death toll rises to 5   \n",
              "\n",
              "                                                 tgt  \\\n",
              "0                      তবে শ্যুটটা খুব মুশকিলের ছিল।   \n",
              "1                         রাস্তা নির্মাণ শুরু হয়েছে।   \n",
              "2                            কেন তিনি এত টাকা দিতেন?   \n",
              "3  \"এই শতাব্দীর প্রচণ্ড ভয়াবহতা এটাকে শয়তানের এক ...   \n",
              "4                         সেটাই আমাদের একমাত্র দাবি।   \n",
              "5                  সে শিক্ষকতা করে জীবন পরিচালনা করে   \n",
              "6                                আমি এলাকা ছাড়ব না।   \n",
              "7  খলিফা উসমান ইবনে আফফানের নির্দেশে মুয়াবিয়া এ...   \n",
              "8  তাকে বাঁচানোর একমাত্র উপায় বায়ুরন্ধ্র বন্ধ করে...   \n",
              "9      গাইবান্ধায় নিহতের সংখ্যা বেড়ে ৭, গৌরনদীতে ৩   \n",
              "\n",
              "                                           input_ids  \\\n",
              "0  [2, 2485, 22, 32110, 241, 80, 26560, 1052, 6, ...   \n",
              "1        [2, 17312, 15948, 7047, 6, 64001, 64004, 3]   \n",
              "2  [2, 28913, 4357, 450, 10006, 1771, 4618, 108, ...   \n",
              "3  [2, 131, 13817, 466, 23869, 37426, 7, 631, 292...   \n",
              "4  [2, 9951, 142, 36, 2145, 1916, 10122, 6, 64001...   \n",
              "5  [2, 1265, 45960, 496, 4140, 271, 45841, 6, 640...   \n",
              "6         [2, 40131, 457, 32045, 6, 64001, 64004, 3]   \n",
              "7  [2, 23570, 22, 55958, 51, 22, 4927, 10785, 104...   \n",
              "8  [2, 202, 1916, 4052, 51, 1075, 1397, 193, 2173...   \n",
              "9  [2, 8548, 924, 33242, 5092, 9542, 41785, 14833...   \n",
              "\n",
              "                                      token_type_ids  \\\n",
              "0               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "1                           [0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "2                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "4                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "5                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "6                           [0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "7  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "8  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "9         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "\n",
              "                                      attention_mask  \\\n",
              "0               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "1                           [1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "2                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "3  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "4                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "5                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "6                           [1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "7  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "8  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "9         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "\n",
              "                                              labels  \n",
              "0  [2, 64006, 1140, 410, 25252, 252, 1974, 26838,...  \n",
              "1      [2, 64006, 2743, 637, 1119, 443, 8, 64001, 3]  \n",
              "2  [2, 64006, 4138, 336, 4789, 1380, 2206, 40, 10...  \n",
              "3  [2, 64006, 131, 7763, 50185, 25078, 14062, 86,...  \n",
              "4   [2, 64006, 31100, 1373, 5867, 1434, 8, 64001, 3]  \n",
              "5  [2, 64006, 42, 825, 86, 155, 2297, 11547, 155,...  \n",
              "6   [2, 64006, 528, 9173, 9561, 85, 97, 8, 64001, 3]  \n",
              "7  [2, 64006, 606, 29947, 10, 288, 682, 40832, 53...  \n",
              "8  [2, 64006, 2111, 28317, 9630, 5867, 6092, 1237...  \n",
              "9  [2, 64006, 54111, 220, 36126, 1142, 11111, 124...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9743eedb-535e-45df-b734-da6ed9e3098d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>src</th>\n",
              "      <th>tgt</th>\n",
              "      <th>input_ids</th>\n",
              "      <th>token_type_ids</th>\n",
              "      <th>attention_mask</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>But the shoot was a tough one.</td>\n",
              "      <td>তবে শ্যুটটা খুব মুশকিলের ছিল।</td>\n",
              "      <td>[2, 2485, 22, 32110, 241, 80, 26560, 1052, 6, ...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 1140, 410, 25252, 252, 1974, 26838,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Road construction started.</td>\n",
              "      <td>রাস্তা নির্মাণ শুরু হয়েছে।</td>\n",
              "      <td>[2, 17312, 15948, 7047, 6, 64001, 64004, 3]</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 2743, 637, 1119, 443, 8, 64001, 3]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Why did he pay so much?</td>\n",
              "      <td>কেন তিনি এত টাকা দিতেন?</td>\n",
              "      <td>[2, 28913, 4357, 450, 10006, 1771, 4618, 108, ...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 4138, 336, 4789, 1380, 2206, 40, 10...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>\"AT ITS worst, this has been Satan's century.</td>\n",
              "      <td>\"এই শতাব্দীর প্রচণ্ড ভয়াবহতা এটাকে শয়তানের এক ...</td>\n",
              "      <td>[2, 131, 13817, 466, 23869, 37426, 7, 631, 292...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 131, 7763, 50185, 25078, 14062, 86,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>That's our only demand.</td>\n",
              "      <td>সেটাই আমাদের একমাত্র দাবি।</td>\n",
              "      <td>[2, 9951, 142, 36, 2145, 1916, 10122, 6, 64001...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 31100, 1373, 5867, 1434, 8, 64001, 3]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>He leads his life by teaching.</td>\n",
              "      <td>সে শিক্ষকতা করে জীবন পরিচালনা করে</td>\n",
              "      <td>[2, 1265, 45960, 496, 4140, 271, 45841, 6, 640...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 42, 825, 86, 155, 2297, 11547, 155,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Im not leaving.</td>\n",
              "      <td>আমি এলাকা ছাড়ব না।</td>\n",
              "      <td>[2, 40131, 457, 32045, 6, 64001, 64004, 3]</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 528, 9173, 9561, 85, 97, 8, 64001, 3]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Under the instructions of the caliph Uthman ib...</td>\n",
              "      <td>খলিফা উসমান ইবনে আফফানের নির্দেশে মুয়াবিয়া এ...</td>\n",
              "      <td>[2, 23570, 22, 55958, 51, 22, 4927, 10785, 104...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 606, 29947, 10, 288, 682, 40832, 53...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The only way of weaning him off the ventilator...</td>\n",
              "      <td>তাকে বাঁচানোর একমাত্র উপায় বায়ুরন্ধ্র বন্ধ করে...</td>\n",
              "      <td>[2, 202, 1916, 4052, 51, 1075, 1397, 193, 2173...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 2111, 28317, 9630, 5867, 6092, 1237...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Gaibandha death toll rises to 5</td>\n",
              "      <td>গাইবান্ধায় নিহতের সংখ্যা বেড়ে ৭, গৌরনদীতে ৩</td>\n",
              "      <td>[2, 8548, 924, 33242, 5092, 9542, 41785, 14833...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 54111, 220, 36126, 1142, 11111, 124...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9743eedb-535e-45df-b734-da6ed9e3098d');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(tokenized_data['test'][:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "i9JVOqQKabEn",
        "outputId": "59cd25f8-a642-4b71-fa37-673e5e408c26"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 src  \\\n",
              "0  We beg our Protestant and Jewish friends to pu...   \n",
              "1  Sa'd advised Muhammad: \"Don't be hard on him. ...   \n",
              "2                       Photo by 'Save Gaza Project'   \n",
              "3  So, therefore, we need to test batteries under...   \n",
              "4    This party is also contesting in the elections.   \n",
              "5                        Roads and houses collapsed.   \n",
              "6  When a piece of paper is rolled up, Hitotsuyam...   \n",
              "7  The founder of the modern Catholic movement Op...   \n",
              "8  Research has shown that exercise also helps in...   \n",
              "9                            It will be so much fun.   \n",
              "\n",
              "                                                 tgt  \\\n",
              "0  কোন কোন ক্ষেত্রে কর্তৃপক্ষ এবং ধর্মীয় নেতারা ...   \n",
              "1  সা'দ মুহাম্মাদকে বলেন: \"তার প্রতি কঠোর হবেন না...   \n",
              "2                        ছবি \"সেভ গাজা প্রজেক্টের\"।'   \n",
              "3  অতএব, আমআদের কিছুটা মান অবস্থাগুলির অধীনে ব্যা...   \n",
              "4     নির্বাচনে এই দলের মধ্যেই প্রতিদ্বন্দ্বিতা হবে।   \n",
              "5                      তলিয়ে গেছে ঘরবাড়ি, রাস্তাঘাট।   \n",
              "6  হিতোসুয়েমা কাগজ দিয়ে ম্যাশে কৌশল অবলম্বন করে...   \n",
              "7  আধুনিক ক্যাথলিক সংঘের প্রতিষ্ঠাতা ওপাস ডেই, হো...   \n",
              "8  এছাড়া গবেষণায় দেখা গেছে, শরীরচর্চা উদ্বেগ ও মা...   \n",
              "9                                  অনেক মজা হবে তখন।   \n",
              "\n",
              "                                           input_ids  \\\n",
              "0  [2, 2855, 281, 1498, 2145, 7373, 35777, 4187, ...   \n",
              "1  [2, 5336, 142, 343, 49343, 44765, 53, 131, 200...   \n",
              "2  [2, 24649, 271, 82, 1326, 19315, 8548, 10552, ...   \n",
              "3  [2, 3867, 7, 35894, 7, 1075, 4212, 57, 13496, ...   \n",
              "4  [2, 2520, 2397, 158, 657, 53429, 67, 22, 6881,...   \n",
              "5  [2, 17312, 36, 62, 24573, 41590, 343, 6, 64001...   \n",
              "6  [2, 8181, 80, 31612, 51, 24670, 158, 25156, 19...   \n",
              "7  [2, 202, 35407, 51, 22, 24081, 1149, 12888, 39...   \n",
              "8  [2, 23075, 292, 29396, 181, 21838, 657, 37287,...   \n",
              "9  [2, 1280, 424, 281, 1771, 4618, 28639, 6, 6400...   \n",
              "\n",
              "                                      token_type_ids  \\\n",
              "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "2            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "4            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "5                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "6  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "7  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "8  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "9                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
              "\n",
              "                                      attention_mask  \\\n",
              "0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "2            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "3  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "4            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "5                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "6  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "7  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "8  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "9                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "\n",
              "                                              labels  \n",
              "0  [2, 64006, 1503, 1503, 4321, 8275, 210, 13928,...  \n",
              "1  [2, 64006, 750, 142, 64, 3547, 10, 8429, 64, 1...  \n",
              "2  [2, 64006, 1902, 131, 525, 523, 17188, 10, 456...  \n",
              "3  [2, 64006, 45953, 7, 651, 344, 444, 14372, 802...  \n",
              "4  [2, 64006, 7125, 114, 2849, 12807, 28198, 10, ...  \n",
              "5  [2, 64006, 1630, 1965, 2249, 401, 10784, 7, 51...  \n",
              "6  [2, 64006, 501, 200, 862, 407, 209, 6235, 514,...  \n",
              "7  [2, 64006, 1652, 823, 521, 4971, 869, 58, 2712...  \n",
              "8  [2, 64006, 10019, 44502, 576, 2249, 7, 1218, 4...  \n",
              "9      [2, 64006, 300, 7861, 481, 2442, 8, 64001, 3]  "
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              "      <th></th>\n",
              "      <th>src</th>\n",
              "      <th>tgt</th>\n",
              "      <th>input_ids</th>\n",
              "      <th>token_type_ids</th>\n",
              "      <th>attention_mask</th>\n",
              "      <th>labels</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>We beg our Protestant and Jewish friends to pu...</td>\n",
              "      <td>কোন কোন ক্ষেত্রে কর্তৃপক্ষ এবং ধর্মীয় নেতারা ...</td>\n",
              "      <td>[2, 2855, 281, 1498, 2145, 7373, 35777, 4187, ...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 1503, 1503, 4321, 8275, 210, 13928,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sa'd advised Muhammad: \"Don't be hard on him. ...</td>\n",
              "      <td>সা'দ মুহাম্মাদকে বলেন: \"তার প্রতি কঠোর হবেন না...</td>\n",
              "      <td>[2, 5336, 142, 343, 49343, 44765, 53, 131, 200...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 750, 142, 64, 3547, 10, 8429, 64, 1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Photo by 'Save Gaza Project'</td>\n",
              "      <td>ছবি \"সেভ গাজা প্রজেক্টের\"।'</td>\n",
              "      <td>[2, 24649, 271, 82, 1326, 19315, 8548, 10552, ...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 1902, 131, 525, 523, 17188, 10, 456...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>So, therefore, we need to test batteries under...</td>\n",
              "      <td>অতএব, আমআদের কিছুটা মান অবস্থাগুলির অধীনে ব্যা...</td>\n",
              "      <td>[2, 3867, 7, 35894, 7, 1075, 4212, 57, 13496, ...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 45953, 7, 651, 344, 444, 14372, 802...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This party is also contesting in the elections.</td>\n",
              "      <td>নির্বাচনে এই দলের মধ্যেই প্রতিদ্বন্দ্বিতা হবে।</td>\n",
              "      <td>[2, 2520, 2397, 158, 657, 53429, 67, 22, 6881,...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 7125, 114, 2849, 12807, 28198, 10, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Roads and houses collapsed.</td>\n",
              "      <td>তলিয়ে গেছে ঘরবাড়ি, রাস্তাঘাট।</td>\n",
              "      <td>[2, 17312, 36, 62, 24573, 41590, 343, 6, 64001...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 1630, 1965, 2249, 401, 10784, 7, 51...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>When a piece of paper is rolled up, Hitotsuyam...</td>\n",
              "      <td>হিতোসুয়েমা কাগজ দিয়ে ম্যাশে কৌশল অবলম্বন করে...</td>\n",
              "      <td>[2, 8181, 80, 31612, 51, 24670, 158, 25156, 19...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 501, 200, 862, 407, 209, 6235, 514,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>The founder of the modern Catholic movement Op...</td>\n",
              "      <td>আধুনিক ক্যাথলিক সংঘের প্রতিষ্ঠাতা ওপাস ডেই, হো...</td>\n",
              "      <td>[2, 202, 35407, 51, 22, 24081, 1149, 12888, 39...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 1652, 823, 521, 4971, 869, 58, 2712...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Research has shown that exercise also helps in...</td>\n",
              "      <td>এছাড়া গবেষণায় দেখা গেছে, শরীরচর্চা উদ্বেগ ও মা...</td>\n",
              "      <td>[2, 23075, 292, 29396, 181, 21838, 657, 37287,...</td>\n",
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              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[2, 64006, 10019, 44502, 576, 2249, 7, 1218, 4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>It will be so much fun.</td>\n",
              "      <td>অনেক মজা হবে তখন।</td>\n",
              "      <td>[2, 1280, 424, 281, 1771, 4618, 28639, 6, 6400...</td>\n",
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              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[2, 64006, 300, 7861, 481, 2442, 8, 64001, 3]</td>\n",
              "    </tr>\n",
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              "</table>\n",
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              "    }\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-9c2b242d-58fe-4407-8103-f03ad7aa6ccb 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-9c2b242d-58fe-4407-8103-f03ad7aa6ccb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Decoded English input_ids: \", tokenizer.decode(tokenized_data['train']['input_ids'][0], skip_special_tokens=True, clean_up_tokenization_spaces=False))\n",
        "print(\"Decoded Devnagri labels: \", tokenizer.decode(tokenized_data['train']['labels'][0], skip_special_tokens=True, clean_up_tokenization_spaces=False))\n",
        "print(\"Transliterated Bengali labels: \", ben_dev.transliterate(tokenizer.decode(tokenized_data['train']['labels'][0], skip_special_tokens=True, clean_up_tokenization_spaces=False), \"hi\", \"bn\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMpth6ETXVGt",
        "outputId": "47dbdd33-daa2-4a42-fa8d-00ea3bb3ec0a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decoded English input_ids:  But the shoot was a tough one.\n",
            "Decoded Devnagri labels:  तबे श्युटटा खुब मुशकिलेर छिल।\n",
            "Transliterated Bengali labels:  তবে শ্যুটটা খুব মুশকিলের ছিল।\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# metric.compute(predictions=[tokenizer(\"<2hi>तोमाके भालोबासि। </s>\")], references=[tokenizer(\"<2hi> आमि तोमाके भालोबासि। </s>\")])"
      ],
      "metadata": {
        "id": "yInZm1LncX7p"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Fine-Tuning"
      ],
      "metadata": {
        "id": "9pg1qNhr5K8X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before going ahead to fine-tune model, we start by loading the pre-trained model."
      ],
      "metadata": {
        "id": "ktmqrnRW5PB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "269b03430a514f1c913e0194ed45c1e2",
            "c9a2127b77a441348d94303c9086e592",
            "c29defc7275a4bfa8c8bbd3f4fa391f8",
            "6d16a291d80a46db953fc94f1a220640",
            "32cdea696e404bcbbe520d8dbfd9c98f",
            "16c9179ea34647c39e9ac0797e4e6a33",
            "5aeaa6f649e048e284357335fdf7d8c4",
            "4c7181bfcb7c499f87b713bd98eacda7",
            "e71827f739944ef382f65655935cd10d",
            "53cfdae6f11b41e786746183d54dfed4",
            "59bf1aa3f6da4937b1346f6c8daf341c"
          ]
        },
        "id": "274lO_9j4rnG",
        "outputId": "88ff3bf7-8913-4106-ce7c-8334bcd942d8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/931M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "269b03430a514f1c913e0194ed45c1e2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Collator takes care of the padding."
      ],
      "metadata": {
        "id": "EM343c5cjJV4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ],
      "metadata": {
        "id": "5kaNb-9pjGGl"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below are the hyperparameters using which we would fine tune `IndicBART` for NMT downstream task."
      ],
      "metadata": {
        "id": "-AOQGsqdexs_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "args = Seq2SeqTrainingArguments(\n",
        "    f\"{model_name}-finetuned-en-to-bn\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=16,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=10,\n",
        "    num_train_epochs=10,\n",
        "    predict_with_generate=True)"
      ],
      "metadata": {
        "id": "RwQeA82K5a85"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [[label.strip()] for label in labels]\n",
        "    return preds, labels\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "    result = {\"bleu\": result[\"score\"]}\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    result = {k: round(v, 4) for k, v in result.items()}\n",
        "    return result"
      ],
      "metadata": {
        "id": "DAhbTqxn7pqN"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_data[\"train\"],\n",
        "    eval_dataset=tokenized_data[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics)"
      ],
      "metadata": {
        "id": "oglviu1CkgeE"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "5w1zskzIkv0V",
        "outputId": "021d0031-b18d-4b18-faff-3fed593d2818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `MBartForConditionalGeneration.forward` and have been ignored: src, token_type_ids, tgt. If src, token_type_ids, tgt are not expected by `MBartForConditionalGeneration.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: 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",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 111020\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 34700\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1341' max='34700' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 1341/34700 23:41 < 9:50:14, 0.94 it/s, Epoch 0.39/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to IndicBART-finetuned-en-to-bn/checkpoint-500\n",
            "Configuration saved in IndicBART-finetuned-en-to-bn/checkpoint-500/config.json\n",
            "Model weights saved in IndicBART-finetuned-en-to-bn/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in IndicBART-finetuned-en-to-bn/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in IndicBART-finetuned-en-to-bn/checkpoint-500/special_tokens_map.json\n",
            "added tokens file saved in IndicBART-finetuned-en-to-bn/checkpoint-500/added_tokens.json\n",
            "Saving model checkpoint to IndicBART-finetuned-en-to-bn/checkpoint-1000\n",
            "Configuration saved in IndicBART-finetuned-en-to-bn/checkpoint-1000/config.json\n",
            "Model weights saved in IndicBART-finetuned-en-to-bn/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in IndicBART-finetuned-en-to-bn/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in IndicBART-finetuned-en-to-bn/checkpoint-1000/special_tokens_map.json\n",
            "added tokens file saved in IndicBART-finetuned-en-to-bn/checkpoint-1000/added_tokens.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7Ib-UpFtn-td"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}