C3TR_Adapter_batch_translation_sample.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/webbigdata-jp/python_sample/blob/main/C3TR_Adapter_batch_translation_sample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tCLTsfT_qgA"
      },
      "source": [
        "# [C3TR-Adapter](https://huggingface.co/webbigdata/C3TR-Adapter) batch translaion sample.\n",
        "\n",
        "アップロードされたファイルを英語から日本語、または日本語から英語に一括翻訳し、ファイルとして出力します。  \n",
        "Translate uploaded file from English to Japanese or from Japanese to English in bulk and output it as file.  \n",
        "\n",
        "上段メニューの「ランタイム」→「すべてのセルを実行」で実行してください  \n",
        "Please execute it by clicking \"Runtime\" -> \"Execute All Cells\" in the upper menu  \n",
        "  \n",
        "\n",
        "以下は既知の問題です。  \n",
        "Below are the known issues  \n",
        "\n",
        "- 長い文章を入力するとエラーになります。\n",
        "- 意味のない文章や日本語でない文章を入力すると、出力がおかしくなることがあります。\n",
        "\n",
        "- If you give a long sentence, an error will occur.(This is a limitation of free Colab)\n",
        "- If you provide meaningless sentences or sentences that are not Japanese, the output may become strange."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (1)Install required libraries"
      ],
      "metadata": {
        "id": "zedYDn634dMn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ygJ-cNOc_qTi",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "%%shell\n",
        "#@title Install Required Libraries\n",
        "pip install peft==0.9.0 bitsandbytes==0.42.0"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (2)Setting Up"
      ],
      "metadata": {
        "id": "pXAuW7B54q1o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Upload Text File(.txt only)\n",
        "import os\n",
        "from google.colab import files\n",
        "import shutil\n",
        "\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "id": "7crRQAAE5CGP",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Translation Setting\n",
        "Translation_direction = 'Japanese to English' #@param [\"Japanese to English\", \"English to Japanese\"]"
      ],
      "metadata": {
        "id": "0yUosS3ZK9Pt",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "#@title Download Model (may take a few minutes)\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from peft import PeftModel\n",
        "\n",
        "model_id = \"unsloth/gemma-7b-bnb-4bit\"\n",
        "peft_model_id = \"webbigdata/C3TR-Adapter\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\")\n",
        "model = PeftModel.from_pretrained(model = model, model_id = peft_model_id)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "import re\n",
        "\n",
        "\n",
        "def contains_japanese(text):\n",
        "    # 日本語の文字範囲を確認するための正規表現パターン\n",
        "    # 平仮名: 3040-309F, 片仮名: 30A0-30FF, 漢字: 4E00-9FAF (旧字体、新字体)\n",
        "    pattern = re.compile('[\\u3040-\\u309F\\u30A0-\\u30FF\\u4E00-\\u9FAF]')\n",
        "    return re.search(pattern, text) is not None\n",
        "\n",
        "def trans(prompt, model, tokenizer, Translation_direction):\n",
        "    input_ids = tokenizer(prompt, return_tensors=\"pt\",\n",
        "        padding=True, max_length=1600, truncation=True).input_ids.cuda()\n",
        "\n",
        "    # Translation\n",
        "    generated_ids = model.generate(input_ids=input_ids,\n",
        "        max_new_tokens=800,\n",
        "        use_cache=True,\n",
        "        prompt_lookup_num_tokens=10\n",
        "        )\n",
        "    full_outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)\n",
        "    return full_outputs[0].split(\"### Answer:\\n\")[-1].strip()\n"
      ],
      "metadata": {
        "id": "aypy-hpcKSaG",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## (3)Do translation"
      ],
      "metadata": {
        "id": "2A6hrL9rptoi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UewwoJ2FAOP4",
        "cellView": "form",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "#@title Translate line by line and write to file\n",
        "import chardet  # Required for character encoding detection\n",
        "\n",
        "for filename in uploaded.keys():\n",
        "    translated_sentences = []\n",
        "\n",
        "    with open(filename, \"rb\") as file:  # Read file in binary mode\n",
        "        binary_content = file.read()\n",
        "        detected_encoding = chardet.detect(binary_content)[\"encoding\"] or \"sjis\"\n",
        "        content = binary_content.decode(detected_encoding).encode(\"utf-8\").decode(\"utf-8\")  # Convert to utf-8\n",
        "\n",
        "        if Translation_direction == 'Japanese to English':\n",
        "            if contains_japanese(content):\n",
        "              sentences = [s for s in content.split('。') if s]\n",
        "              sentences = [item for sublist in [s.split('\\n') for s in sentences] for item in sublist]\n",
        "            else:\n",
        "              print(content)\n",
        "              sentences = [content]\n",
        "        else:\n",
        "            sentences = [s for s in content.split('.') if s]\n",
        "            sentences = [item for sublist in [s.split('\\n') for s in sentences] for item in sublist]\n",
        "\n",
        "    for sentence in sentences:\n",
        "        sentence = sentence.strip()\n",
        "        if len(sentence) > 0:\n",
        "            if Translation_direction == 'Japanese to English':\n",
        "                if contains_japanese(content):\n",
        "                    ja_prompt = f\"### Instructions:\\nTranslate Japanese to English.\\n### Input:\\n{sentence}\\n### Answer:\\n\"\n",
        "                    translated_sentences.append(trans(ja_prompt, model, tokenizer, Translation_direction))\n",
        "                else:\n",
        "                    translated_sentences.append(sentence)\n",
        "            else:\n",
        "                en_prompt = f\"### Instructions:\\nTranslate English to Japanese.\\n### Input:\\n{sentence}\\n### Answer:\\n\"\n",
        "                translated_sentences.append(trans(en_prompt, model, tokenizer, Translation_direction))\n",
        "        else:\n",
        "          translated_sentences.append(\"\")\n",
        "\n",
        "    output_filename = filename.replace('.txt', '_Ja_to_En.txt') if Translation_direction == 'Japanese to English' else filename.replace('.txt', '_En_to_Ja.txt')\n",
        "    with open(output_filename, 'w', encoding='utf-8') as f:\n",
        "        f.write('\\n'.join(translated_sentences))\n",
        "\n",
        "    print(f\"Translation compleated. please download files.: {output_filename}\")\n",
        "    files.download(output_filename)\n"
      ]
    }
  ],
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