En_Hi_language_translation.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#Checking if GPU is running or not\n",
        "\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KALYvZycPe8N",
        "outputId": "8779b13c-154d-4b50-c78d-83606313e124"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Dec  3 17:42:04 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   43C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets transformers[sentencepiece] sacrebleu -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3XQ_jhwe0m5",
        "outputId": "613723d9-a33a-4b28-c63d-b15b94e4acc3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.4/106.4 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m52.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "fr_hhoAFefza"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "import transformers\n",
        "import tensorflow as tf\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n",
        "from transformers import AdamWeightDecay\n",
        "from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-dbDT2O9fJEp"
      },
      "outputs": [],
      "source": [
        "model_checkpoint = \"Helsinki-NLP/opus-mt-en-hi\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Q8AHfSWWMLm"
      },
      "source": [
        "## Helsinki-NLP/opus-mt-en-hi model\n",
        "\n",
        "source: https://huggingface.co/Helsinki-NLP/opus-mt-en-hi\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8H3frhJQ_Lym"
      },
      "source": [
        "# The Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFmXYJQHBetF"
      },
      "source": [
        "Source: https://huggingface.co/datasets/cfilt/iitb-english-hindi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476,
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        },
        "id": "daj9JBV6jDGK",
        "outputId": "09eea34a-211c-45e6-9930-34782b6463b4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/3.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1e036b106b146e2911133a29797a7e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/repocard.py:105: UserWarning: Repo card metadata block was not found. Setting CardData to empty.\n",
            "  warnings.warn(\"Repo card metadata block was not found. Setting CardData to empty.\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/953 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e0f4efe9c88485a87629da2a77fd84f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b093e08729104554bce4373e3c32ed34"
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3301b2c2e7614c22804802fa1a956a12"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4af8b429d7f741969cb507fa9589ca78"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f34881a30a3048feab7d71442f94c18f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bdc1eadbd8b45c39835c9f6d6489b19"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1659083 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec0935b8d92d42cd88a48da22b07b783"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/520 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86456baedcab4fb8b79bcd68eece143d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/2507 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3fcb92e73716429fafe4ab8aaac4dad4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "raw_datasets = load_dataset(\"cfilt/iitb-english-hindi\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHdy5o8zsGhm",
        "outputId": "a96a2607-f855-4c57-d718-83cf20e979ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['translation'],\n",
              "        num_rows: 1659083\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['translation'],\n",
              "        num_rows: 520\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['translation'],\n",
              "        num_rows: 2507\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfaNkUHNqU11",
        "outputId": "9ab84ebf-7197-4f78-d4fe-7a3cf7e6be63"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'translation': {'en': 'Accerciser Accessibility Explorer',\n",
              "  'hi': 'एक्सेर्साइसर पहुंचनीयता अन्वेषक'}}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "raw_datasets['train'][1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4x6TtxnEEDuw"
      },
      "source": [
        "#Preprocessing the data"
      ]
    },
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        },
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        "outputId": "f22e3869-4e65-4901-8de1-192aaa469fbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/44.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "target.spm:   0%|          | 0.00/1.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/2.10M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b0f9f5e1dbd473a8da66c4720f4f0cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:197: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYCILyEK0HXG",
        "outputId": "1394c39a-4bfc-402f-8c5d-0ea3dd990f27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [12110, 2, 90, 23, 19, 8800, 61, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "tokenizer(\"Hello, this is a sentence!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDgcI2Nz0SZN",
        "outputId": "f802d690-9acc-43d5-875c-910f986b17e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[12110, 2, 90, 23, 19, 8800, 61, 0], [239, 23, 414, 8800, 3, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "tokenizer([\"Hello, this is a sentence!\", \"This is another sentence.\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nh0epnye1JJk",
        "outputId": "8c15b4fd-e7ef-4f5f-fbbc-6374170f1d55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [[26618, 16155, 346, 33383, 0]], 'attention_mask': [[1, 1, 1, 1, 1]]}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3856: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "with tokenizer.as_target_tokenizer():\n",
        "    print(tokenizer([\"एक्सेर्साइसर पहुंचनीयता अन्वेषक\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "XHaFoHKG3snJ"
      },
      "outputs": [],
      "source": [
        "max_input_length = 128\n",
        "max_target_length = 128\n",
        "\n",
        "source_lang = \"en\"\n",
        "target_lang = \"hi\"\n",
        "\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [ex[source_lang] for ex in examples[\"translation\"]]\n",
        "    targets = [ex[target_lang] for ex in examples[\"translation\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
        "\n",
        "    # Setup the tokenizer for targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DX9vN5Yo4kuG",
        "outputId": "beae5b79-0882-4bcd-b9bc-420b0fd9b0fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[3872, 85, 2501, 132, 15441, 36398, 0], [32643, 28541, 36253, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1]], 'labels': [[63, 2025, 18, 16155, 346, 20311, 24, 2279, 679, 0], [26618, 16155, 346, 33383, 0]]}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "preprocess_function(raw_datasets[\"train\"][:2])"
      ]
    },
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      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        "outputId": "a2664e45-021d-4ebb-92f1-8ffcfe85c139"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1659083 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "852a21f7133743d3911ad0b49d14e104"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/520 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2507 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
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          "height": 170,
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        "id": "LEbE41CbU1bL",
        "outputId": "aaddb9b6-27cd-4b9b-cb6a-7ffc505c940a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tf_model.h5:   0%|          | 0.00/306M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19c75a74fba84b0c8893bbc059338850"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFMarianMTModel.\n",
            "\n",
            "All the layers of TFMarianMTModel were initialized from the model checkpoint at Helsinki-NLP/opus-mt-en-hi.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFMarianMTModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7366be29081f474f85d3f3efd7cbfc85"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "GOJU15XzX5iy"
      },
      "outputs": [],
      "source": [
        "batch_size = 16\n",
        "learning_rate = 2e-5\n",
        "weight_decay = 0.01\n",
        "num_train_epochs = 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "NCoU8kNtX6Tk"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "4IbSTkjuYZ8Z"
      },
      "outputs": [],
      "source": [
        "generation_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\", pad_to_multiple_of=128)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "AgvwEEvDZP7F"
      },
      "outputs": [],
      "source": [
        "train_dataset = model.prepare_tf_dataset(\n",
        "    tokenized_datasets[\"test\"],\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "E_1LNLwvZQvD"
      },
      "outputs": [],
      "source": [
        "validation_dataset = model.prepare_tf_dataset(\n",
        "    tokenized_datasets[\"validation\"],\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False,\n",
        "    collate_fn=data_collator,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "GYF9ApeHZTR5"
      },
      "outputs": [],
      "source": [
        "generation_dataset = model.prepare_tf_dataset(\n",
        "    tokenized_datasets[\"validation\"],\n",
        "    batch_size=8,\n",
        "    shuffle=False,\n",
        "    collate_fn=generation_data_collator,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Wx6p1NQ_ZVXb"
      },
      "outputs": [],
      "source": [
        "optimizer = AdamWeightDecay(learning_rate=learning_rate, weight_decay_rate=weight_decay)\n",
        "model.compile(optimizer=optimizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-G5WXVp0Z15g",
        "outputId": "3cfeafac-4772-4014-94c5-d90dbf6e27b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "156/156 [==============================] - 84s 356ms/step - loss: 3.7569 - val_loss: 3.9570\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a2dba1a6110>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "model.fit(train_dataset, validation_data=validation_dataset, epochs=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "4EWkhFnyZ3Z8"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"tf_model/\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcGNz4yrMY4F"
      },
      "source": [
        "# Model Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTkqZ2fuLbd1",
        "outputId": "45c20aa5-3b5f-48c6-8c28-74783b711171"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:197: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
            "All model checkpoint layers were used when initializing TFMarianMTModel.\n",
            "\n",
            "All the layers of TFMarianMTModel were initialized from the model checkpoint at tf_model/.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFMarianMTModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "model = TFAutoModelForSeq2SeqLM.from_pretrained(\"tf_model/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4-NztUuM6pk",
        "outputId": "c2e81ef7-c4ad-4a2b-9a30-2c9bd97d5715"
      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[61949   500   179  4598 14918   130     5     2   500   118  2136  4186\n",
            "   4850   179     5  4404     3  5674     3   906     3    12     3  2518\n",
            "      3     5    40     0 61949]], shape=(1, 29), dtype=int32)\n"
          ]
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      "source": [
        "input_text  = \"My name Bappy. My youtube channel name is DSwithBappy\"\n",
        "\n",
        "tokenized = tokenizer([input_text], return_tensors='np')\n",
        "out = model.generate(**tokenized, max_length=128)\n",
        "print(out)"
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          "text": [
            "मेरा नाम बाप्पी है, मेरा आपटबे चैनल नाम है डी.एस.के. से.सी. है।\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3856: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
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        "with tokenizer.as_target_tokenizer():\n",
        "    print(tokenizer.decode(out[0], skip_special_tokens=True))"
      ]
    },
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      "source": [],
      "metadata": {
        "id": "_WjVcYschmbn"
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