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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Model: https://huggingface.co/Helsinki-NLP/opus-mt-en-hi\n",
        "\n",
        "### Data: https://huggingface.co/datasets/cfilt/iitb-english-hindi"
      ],
      "metadata": {
        "id": "-lJN3mUnJRPv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "nQu320h7_iKn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "253e38f3-23e0-48b0-d535-de0a297f6f11"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Sep 18 11:12:27 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\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   46C    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": "goiXOT5AIAJN",
        "outputId": "ecb02998-9c08-4595-a748-fcb22f3f8fe3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.8/51.8 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.3/474.3 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.0/104.0 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m20.1 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[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade accelerate\n",
        "!pip uninstall -y transformers accelerate\n",
        "!pip install transformers accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBUf_3mNIaVc",
        "outputId": "1299b326-ba5d-457c-b533-6cf6bd1e296d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.34.2)\n",
            "Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.2)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.4.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.24.7)\n",
            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (3.16.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (2024.6.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (4.66.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.8)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2024.8.30)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Found existing installation: transformers 4.44.2\n",
            "Uninstalling transformers-4.44.2:\n",
            "  Successfully uninstalled transformers-4.44.2\n",
            "Found existing installation: accelerate 0.34.2\n",
            "Uninstalling accelerate-0.34.2:\n",
            "  Successfully uninstalled accelerate-0.34.2\n",
            "Collecting transformers\n",
            "  Using cached transformers-4.44.2-py3-none-any.whl.metadata (43 kB)\n",
            "Collecting accelerate\n",
            "  Using cached accelerate-0.34.2-py3-none-any.whl.metadata (19 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.4.0+cu121)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.8)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Using cached transformers-4.44.2-py3-none-any.whl (9.5 MB)\n",
            "Using cached accelerate-0.34.2-py3-none-any.whl (324 kB)\n",
            "Installing collected packages: accelerate, transformers\n",
            "Successfully installed accelerate-0.34.2 transformers-4.44.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "pRyFlwDMIaTN"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"Helsinki-NLP/opus-mt-en-hi\""
      ],
      "metadata": {
        "id": "1k9ftQ_LIaQc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets = load_dataset(\"cfilt/iitb-english-hindi\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
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        "id": "L2M4mkExJNBd",
        "outputId": "3d52ce89-202f-4501-ea82-ea4f8f8e20fc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/3.14k [00:00<?, ?B/s]"
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          "metadata": {}
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        {
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            "text/plain": [
              "dataset_infos.json:   0%|          | 0.00/953 [00:00<?, ?B/s]"
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/190M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "validation-00000-of-00001.parquet:   0%|          | 0.00/85.7k [00:00<?, ?B/s]"
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test-00000-of-00001.parquet:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3714296f1cb34948851ff9a5d56f9df6"
            }
          },
          "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": "3cee605394e3490abd341930fc430c27"
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          },
          "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": "901057bd4a4048c78b8dcaa0ebaddbe5"
            }
          },
          "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": "a264f67267f6469a9e1f39e9bb685e77"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBXpbdnJJjd3",
        "outputId": "2f272f80-1c2e-4760-9081-5a8052a5c331"
      },
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3DPVS1qJoVc",
        "outputId": "e21a039b-41ef-418e-bedb-a7ef131c7c2a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'translation': {'en': 'Accerciser Accessibility Explorer',\n",
              "  'hi': 'एक्सेर्साइसर पहुंचनीयता अन्वेषक'}}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266,
          "referenced_widgets": [
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        "id": "lpGdmZIIKAzO",
        "outputId": "7367ce06-776c-4962-81ce-c274a8f299b4"
      },
      "execution_count": 9,
      "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": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "target.spm:   0%|          | 0.00/1.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "514b50222b8e4418b48530b7eb5fec8e"
            }
          },
          "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": "244eac2210ab4d529d95ec18d256a2d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(\"Bappy, this is a sentence!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3L0QXStKSQN",
        "outputId": "e65dd56a-bfff-4fca-eaa4-8f69d14e49af"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [1102, 9331, 667, 2, 90, 23, 19, 8800, 61, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer([\"Hello, this is a sentence!\", \"This is another sentence.\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbtMfA_WKSOE",
        "outputId": "3fd4f1d4-bce4-450b-f4f4-5c287af8ae55"
      },
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# \"Hello\": 12110\n",
        "# \"this\": 90\n",
        "# \"Bappy\""
      ],
      "metadata": {
        "id": "QsRiVlK4Klhs"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##b ##a ##p ##p ##y"
      ],
      "metadata": {
        "id": "5gWqKVcXK8ns"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 'b' :2\n",
        "# 'a':4\n",
        "# 'p':9\n",
        "# 'y':10"
      ],
      "metadata": {
        "id": "T6JVgY61LZv2"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 249910"
      ],
      "metadata": {
        "id": "KjgqWXIFLgmA"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with tokenizer.as_target_tokenizer():\n",
        "    print(tokenizer([\"एक्सेर्साइसर पहुंचनीयता अन्वेषक\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHbb0NehL3H9",
        "outputId": "b5c465bd-db12-4e1b-c51e-380874357879"
      },
      "execution_count": 16,
      "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:4126: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "chWKclhqMH5X"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "2169638f7dbf4bd3bb3e3d96588324dd",
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            "feafd6c3e8b64c2bbb06d93a466ad3ef",
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            "247ff874b594498da4fa3979423ef142",
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            "81ab21f1cc01464a98c8721386ad852b",
            "927769a02aca4a3bbf29e82342f627bf",
            "1d079f46ddc041b9a60ead6b5153d350",
            "56b3fa886e554a38bea5932788c52cc1",
            "436c8ecff970495f9c04b71401265399",
            "9697f93829e340c0b3db6748c3779405",
            "e1dd130e334d4e8eb9721e57a48ed48f"
          ]
        },
        "id": "Et6QYmznMtVd",
        "outputId": "1aafb4be-87be-4fc6-b8dc-0fb3e744f597"
      },
      "execution_count": 18,
      "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,
              "version_minor": 0,
              "model_id": "2169638f7dbf4bd3bb3e3d96588324dd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/520 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4be3eb3963774cb4aa7412f2e0bc1b21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2507 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de51e6a5e0d34d6185bac07a178b23b8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150,
          "referenced_widgets": [
            "87d800a5952a454c916fe97bcde5da40",
            "5a0091b7b83a4073bfc7c278bb970b68",
            "623e5232d15749f7ad54f2c44bf03139",
            "0f1aa15ac61a414ab52e16eab3352831",
            "d9aa2a0dce93413f92a7957ff3c66dfc",
            "d4c5978a80544879a102c7cc43247bfd",
            "6f58fbb996274372876e57242d233a4c",
            "29ff52b3ba3744158f52967075d0a02f",
            "a1c86b5da8bb4b3d986cbe84e3e90b31",
            "0ae00bda13614594b0c2715341fbd158",
            "8e105754843f415cb4d7c84982fa80de",
            "0bf9905110df4f7e8f9a14e8f092f4bc",
            "e33f692040384ab9a15a1d50679c6817",
            "32d28d187ee74a10a3b26f9ec5d516dc",
            "8a23dbfcb6ba4cecba808ac584db24f3",
            "8d77ca57adac4b7dbbd98af9c7929ba6",
            "145c6c2cc6da43eda7faab4934f153f5",
            "86290352fb6f42cbaa27a948a0dcf72e",
            "63151a2c251e4456bb372d18d1ebe226",
            "438d25f5b7ae4603832f962408f19377",
            "954532c3e0f046eabe38c4f55edc8d59",
            "ee54282626c049fb8c74125556f08f9f"
          ]
        },
        "id": "oC6BJ5BmMx60",
        "outputId": "b1a8c7ba-2cb3-4246-f5ad-506ea5e60000"
      },
      "execution_count": 21,
      "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": "87d800a5952a454c916fe97bcde5da40"
            }
          },
          "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": "0bf9905110df4f7e8f9a14e8f092f4bc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 16\n",
        "learning_rate = 2e-5\n",
        "weight_decay = 0.01\n",
        "num_train_epochs = 1"
      ],
      "metadata": {
        "id": "fHzNF_EKOi0l"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "027Cq5TwOiyV"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\", pad_to_multiple_of=128)"
      ],
      "metadata": {
        "id": "1TUvW-P3OwBc"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "id": "4-tVfVIv6fMm",
        "outputId": "a895ee04-9f19-41f7-d8e1-e92467127ef4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 42,
      "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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'].shape"
      ],
      "metadata": {
        "id": "ge2Zld0d6pVs",
        "outputId": "88f14e94-7dd8-4392-9f53-0b4509da5868",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1659083, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['validation'].shape"
      ],
      "metadata": {
        "id": "dcd53_2r7EQ7",
        "outputId": "6e4a3fbd-80b1-4158-c93d-0a86c3e9b383",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(520, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenized_datasets['train'][:200]"
      ],
      "metadata": {
        "id": "PEStJjqk7Wn_"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# raw_datasets['train'][:20000]"
      ],
      "metadata": {
        "id": "vBvKhxpP6tHm"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = model.prepare_tf_dataset(\n",
        "    tokenized_datasets[\"train\"].select(range(10000)),\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator,\n",
        ")\n"
      ],
      "metadata": {
        "id": "-O1h6HxrPPf_"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        ")"
      ],
      "metadata": {
        "id": "gUBoM2h4PPd6"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_dataset = model.prepare_tf_dataset(\n",
        "    tokenized_datasets[\"test\"],\n",
        "    batch_size=8,\n",
        "    shuffle=False,\n",
        "    collate_fn=generation_data_collator,\n",
        ")"
      ],
      "metadata": {
        "id": "zH_JP9UMPmkt"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamWeightDecay(learning_rate=learning_rate, weight_decay_rate=weight_decay)\n",
        "model.compile(optimizer=optimizer)"
      ],
      "metadata": {
        "id": "Tf7jIwzQPpU8"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset, validation_data=validation_dataset, epochs=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqB-17hNP02d",
        "outputId": "eb5fdbda-668c-4258-aea2-9a3f9bf1d669"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "625/625 [==============================] - 162s 216ms/step - loss: 0.9453 - val_loss: 4.0425\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf_keras.src.callbacks.History at 0x77ff9ab36770>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interencing"
      ],
      "metadata": {
        "id": "xQcLT05ZRCFD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"tf_model/\")"
      ],
      "metadata": {
        "id": "anlKOmJ9P44d",
        "outputId": "6bad1e95-94c5-4f08-8aa3-f585b12991b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 512, 'num_beams': 4, 'bad_words_ids': [[61949]], 'forced_eos_token_id': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "model = TFAutoModelForSeq2SeqLM.from_pretrained(\"tf_model/\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v95RKWX1P41h",
        "outputId": "6a582b07-424c-4c64-a232-3c18f673f3fd"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_text  = \"I am learning Coding. How are you\"\n",
        "\n",
        "tokenized = tokenizer([input_text], return_tensors='np')\n",
        "out = model.generate(**tokenized, max_length=128)\n",
        "print(out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XkXXgu5KP4zk",
        "outputId": "ec538cc7-5ab6-4c9b-863b-d17adcf88503"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[61949   104    18 26904   187  2407   153   254     3   118   280    28\n",
            "     22     0 61949 61949]], shape=(1, 16), dtype=int32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with tokenizer.as_target_tokenizer():\n",
        "    print(tokenizer.decode(out[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fE8PfauHP4w7",
        "outputId": "091d297f-f19b-41d1-ec97-9da716e307ed"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "मैं कोडिंग करना सीख रहा हूँ. आप कैसे हैं?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1zGENKwmSBbn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}