rinna_japanese_gpt2_demo.ipynb

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        "id": "q_SozNmyQBR_"
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      "source": [
        "GitHub \n",
        "https://github.com/rinnakk/japanese-pretrained-models   \n",
        "https://huggingface.co/rinna/japanese-gpt-1b  \n",
        "\n",
        "<a href=\"https://colab.research.google.com/github/kaz12tech/ai_demos/blob/master/rinna_japanese_gpt2_demo.ipynb\" target=\"_blank\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXY8afVuL6qd"
      },
      "source": [
        "# ランタイムの設定\n",
        "「ランタイム」→「ランタイムのタイプを変更」→「ハードウェアアクセラレータ」をGPUに変更"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hT3XhzHBL6qf"
      },
      "source": [
        "# 実行方法\n",
        "「ランタイム」→「すべてのセルを実行」を選択"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HT-h6gYBPwdt"
      },
      "source": [
        "# ライブラリインストール"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOom59zQKnwk",
        "outputId": "f75e0988-3cd7-4c34-ef3f-2450d2c25c7f"
      },
      "outputs": [],
      "source": [
        "# Huggingface Transformers\n",
        "!pip install transformers==4.16.0\n",
        "# Sentencepiece\n",
        "!pip install sentencepiece==0.1.96"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukd64Su5L6ql"
      },
      "source": [
        "# トークナイザーとモデルのロード"
      ]
    },
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      "source": [
        "import torch\n",
        "from transformers import T5Tokenizer, AutoModelForCausalLM\n",
        "import re\n",
        "\n",
        "# トークナイザーとモデルのロード\n",
        "tokenizer = T5Tokenizer.from_pretrained(\"rinna/japanese-gpt-1b\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"rinna/japanese-gpt-1b\")\n",
        "\n",
        "# GPU使用\n",
        "if torch.cuda.is_available():\n",
        "    model = model.to(\"cuda\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmYqH08aL6qn"
      },
      "source": [
        "# 自問自答を定義"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GU9-yNgaTswd"
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        "#@markdown ## 独り言回数\n",
        "round_trip = 20 #@param {type:\"slider\", min:0, max:100, step:1}\n",
        "\n",
        "#@markdown ## 独り言のトピック\n",
        "topic = \"\\u4EBA\\u9593\\u3068\\u4EBA\\u5DE5\\u77E5\\u80FD\\u306F\\u3069\\u3046\\u306A\\u3063\\u3066\\u3044\\u304F\\u306E\\u3060\\u308D\\u3046\\u304B\\u3002\" #@param {type:\"string\"}\n",
        "text = \"りんなちゃんの独り言。りんなちゃん:「おはよう私」りんなちゃん:「\" + topic + \"」りんなちゃん:「\"\n",
        "\n",
        "#@markdown ### parameter変更(Option)\n",
        "#@markdown 次のトークン確率をモジュール化するために使用される値\n",
        "temperature = 1 #@param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
        "#@markdown 繰り返しペナルティのパラメータ。1.0はペナルティなし\n",
        "repetition_penalty = 1 #@param {type:\"slider\", min:0.0, max:1.0, step:0.1}\n",
        "#@markdown 長さに対する指数関数的なペナルティ。1.0はペナルティなし\n",
        "length_penalty = 1.0 #@param {type:\"slider\", min:0.0, max:1.0, step:0.1}"
      ]
    },
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      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "Nfw739YpU4G0",
        "outputId": "ea8e7cbb-69f2-42d2-f371-83c050f666e3"
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      "outputs": [],
      "source": [
        "print(\"りんなちゃんの独り言。topic:\", topic)\n",
        "\n",
        "pos = 3 # 括弧の取得位置\n",
        "for round_num in range(round_trip):\n",
        "  token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors=\"pt\")\n",
        "  max_length = 100\n",
        "  if max_length < len(text):\n",
        "    max_length = len(text) + 30\n",
        "\n",
        "  # りんなちゃんのテキスト生成\n",
        "  with torch.no_grad():\n",
        "    output_ids = model.generate(\n",
        "        token_ids.to(model.device),\n",
        "        max_length=max_length,\n",
        "        min_length=50,\n",
        "        do_sample=True,\n",
        "        top_k=500,\n",
        "        top_p=0.95,\n",
        "        pad_token_id=tokenizer.pad_token_id,\n",
        "        bos_token_id=tokenizer.bos_token_id,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "        bad_word_ids=[[tokenizer.unk_token_id]],\n",
        "        temperature = temperature,\n",
        "        repetition_penalty = repetition_penalty,\n",
        "        length_penalty = length_penalty\n",
        "        )\n",
        "  output = tokenizer.decode(output_ids.tolist()[0])\n",
        "  # 半角を全角に正規化\n",
        "  output = output.translate(str.maketrans({chr(0xFF01 + i): chr(0x21 + i) for i in range(94)}))\n",
        "\n",
        "  # りんなちゃんの先頭の独り言のみ取得\n",
        "  prefix = \"りんなちゃん:「\"\n",
        "  suffix = \"」\"\n",
        "  pre = output.split(prefix)\n",
        "  post = pre[pos].split(suffix)\n",
        "\n",
        "  # 」で閉じずにりんなちゃんが次の独り言を続けた場合に対処\n",
        "  if \"りんなちゃん:\" in post[0]:\n",
        "    post[0] = post[0].split(\"りんなちゃん:\")[0]\n",
        "  # 」で閉じずに終了した場合\n",
        "  if \"</s>\" in post[0]:\n",
        "    post[0] = post[0].replace(\"</s>\", \"\")\n",
        "\n",
        "  print(\"独り言\", str(round_num+1), \":\", post[0])\n",
        "  \n",
        "  # textに付加\n",
        "  text += post[0] + \"りんなちゃん:「\"\n",
        "\n",
        "  # 次回取得位置更新\n",
        "  pos += 1\n"
      ]
    }
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