Translation_English_2_French.ipynb

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  "cells": [
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBaOV-WeQJ-8",
        "outputId": "128567c7-9808-4d96-ac9c-5515fd053f07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m110.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m113.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "#install transformers library\n",
        "!pip install transformers -U -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install sentencepiece library\n",
        "!pip install sentencepiece"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ni2zZnLYQwEx",
        "outputId": "b5134ef1-1bf0-4d9f-fee3-b87998fcf9ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import MBartForConditionalGeneration, MBart50TokenizerFast"
      ],
      "metadata": {
        "id": "B-alK7z0QPYf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# download and save model\n",
        "model = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50-one-to-many-mmt\")"
      ],
      "metadata": {
        "colab": {
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        {
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            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.44G [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/261 [00:00<?, ?B/s]"
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      "cell_type": "code",
      "source": [
        "tokenizer = MBart50TokenizerFast.from_pretrained(\"facebook/mbart-large-50-one-to-many-mmt\", src_lang=\"en_XX\")"
      ],
      "metadata": {
        "colab": {
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/528 [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
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        {
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          "data": {
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              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/717 [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "input_text = [\"He has done a good job in this match\", \"However his team is struggling at the moment\"]\n",
        "\n",
        "model_inputs = tokenizer(input_text, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "\n",
        "# translate from English to Kiswahili\n",
        "generated_tokens = model.generate(\n",
        "    **model_inputs,\n",
        "    forced_bos_token_id=tokenizer.lang_code_to_id[\"fr_XX\"]\n",
        ")\n",
        "\n",
        "translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "oYc6AXHcQZSd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "translation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2nPXuo_dSCjp",
        "outputId": "fd8d7339-370f-4bb1-8cdc-fc4c0c2e9c85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Il a fait un bon travail dans ce match',\n",
              " 'Cependant, son équipe se heurte actuellement à des difficultés.']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ]
}