chapter05/5-4-sts-analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5bf742b1",
      "metadata": {
        "id": "5bf742b1"
      },
      "source": [
        "# 第5章 大規模言語モデルのファインチューニング"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.4 自然言語推論・意味的類似度計算・多肢選択式質問応答モデルの実装"
      ],
      "metadata": {
        "id": "LpSKoKlJUyFF"
      },
      "id": "LpSKoKlJUyFF"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.4.2 意味的類似度計算"
      ],
      "metadata": {
        "id": "g8knrgrFUy-G"
      },
      "id": "g8knrgrFUy-G"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7a26676",
      "metadata": {
        "id": "b7a26676",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "status": "ok",
          "timestamp": 1689256465486,
          "user_tz": -540,
          "elapsed": 76837,
          "user": {
            "displayName": "Ryokan Ri",
            "userId": "09390037946487494940"
          }
        },
        "outputId": "636c75ca-25bd-4e99-b147-0bc9008ed2b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "    Uninstalling wasabi-1.1.2:\n",
            "      Successfully uninstalled wasabi-1.1.2\n",
            "Successfully installed accelerate-0.20.3 datasets-2.13.1 dill-0.3.6 fugashi-1.2.1 huggingface-hub-0.16.4 ipadic-1.0.0 japanize-matplotlib-1.1.3 multiprocess-0.70.14 plac-1.3.5 rhoknp-1.3.0 safetensors-0.3.1 sudachidict-core-20230110 sudachipy-0.6.7 tokenizers-0.13.3 transformers-4.30.2 unidic-1.1.0 unidic-lite-1.0.8 wasabi-0.10.1 xxhash-3.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets transformers[ja,torch] matplotlib japanize-matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ef914bc8",
      "metadata": {
        "id": "ef914bc8"
      },
      "source": [
        "#### モデルの予測結果の取得"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "517166a4",
      "metadata": {
        "id": "517166a4",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1689256501531,
          "user_tz": -540,
          "elapsed": 36054,
          "user": {
            "displayName": "Ryokan Ri",
            "userId": "09390037946487494940"
          }
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213,
          "referenced_widgets": [
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          ]
        },
        "outputId": "5f30d7bc-f82e-41c5-c9c9-033ecdf8fb45"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/796 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "709935e3e6d5481c98039041f93374ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/445M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82914cd621db4f7096caf1edbe4c8cf1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/606 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a02787ff538c40218a7f5e69a71f41cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/231k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "366aaa9bf99441b0b8e4446592c37782"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1bca25c8f880424cbe92c907c8a4fcfb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "model_name = \"llm-book/bert-base-japanese-v3-jsts\"\n",
        "text_sim_pipeline = pipeline(\n",
        "    model=\"llm-book/bert-base-japanese-v3-jsts\",\n",
        "    function_to_apply=\"none\",  # 出力に適用する関数の指定\n",
        "    device=\"cuda:0\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0dd6808",
      "metadata": {
        "id": "a0dd6808",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297,
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        },
        "executionInfo": {
          "status": "ok",
          "timestamp": 1689256505586,
          "user_tz": -540,
          "elapsed": 4062,
          "user": {
            "displayName": "Ryokan Ri",
            "userId": "09390037946487494940"
          }
        },
        "outputId": "7a18ef45-6896-4e48-e8b2-c75c8dafd373"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/13.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4dd1b77619124c16aa10ed5926b1c7d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.90k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c377cc329284e808940f404930f578b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/9.00k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fddea124078745d6ba6181eba26f9537"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset jglue/JSTS to /root/.cache/huggingface/datasets/llm-book___jglue/JSTS/1.1.0/afed02e914319785e72f3ea981b4bd3e00089f2361b1137820c183c6b8173edd...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5900f870eb843fd80ac6ad958494c24"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/653k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92ba4bf0e35b413bacc4bdcbb2098b64"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/84.2k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbcfa2fdcbf14aa98a7876c196479761"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd5de0d2d5a4456782b57c4d2955a6a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc3b132070bf42aba4b9c523f5f06dd7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18c7689bc8da4b87a0de4d1c4c9a037a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset jglue downloaded and prepared to /root/.cache/huggingface/datasets/llm-book___jglue/JSTS/1.1.0/afed02e914319785e72f3ea981b4bd3e00089f2361b1137820c183c6b8173edd. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "valid_dataset = load_dataset(\n",
        "    \"llm-book/JGLUE\", name=\"JSTS\", split=\"validation\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "81421c7d",
      "metadata": {
        "id": "81421c7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "status": "ok",
          "timestamp": 1689256548647,
          "user_tz": -540,
          "elapsed": 43067,
          "user": {
            "displayName": "Ryokan Ri",
            "userId": "09390037946487494940"
          }
        },
        "outputId": "73d7c166-941d-4d42-e98f-2ab97faf4a8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "10it [00:00, 16.44it/s]/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1081: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "1457it [00:43, 33.62it/s]\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "# ラベル名の情報を取得するためのClassLabelインスタンス\n",
        "class_label = valid_dataset.features[\"label\"]\n",
        "\n",
        "results: list[dict[str, float | str]] = []\n",
        "for i, example in tqdm(enumerate(valid_dataset)):\n",
        "    # モデルの予測結果を取得\n",
        "    model_prediction = text_sim_pipeline({\"text\": example[\"sentence1\"], \"text_pair\": example[\"sentence2\"]})\n",
        "\n",
        "    # resultsに分析に必要な情報を格納\n",
        "    results.append(\n",
        "        {\n",
        "            \"example_id\": i,\n",
        "            \"pred_score\": model_prediction[\"score\"],\n",
        "            \"true_score\": example[\"label\"],\n",
        "        }\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c176fe9",
      "metadata": {
        "id": "0c176fe9"
      },
      "source": [
        "#### 全体的な傾向の分析"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import japanize_matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams[\"font.size\"] = 16  # 文字サイズを設定\n",
        "\n",
        "plt.scatter(\n",
        "    [i[\"true_score\"] for i in results],\n",
        "    [i[\"pred_score\"] for i in results],\n",
        "    alpha=0.5\n",
        ")\n",
        "plt.xlabel(\"正解スコア\")\n",
        "plt.ylabel(\"予測スコア\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "Yjy3tKxAYmae",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1689256549525,
          "user_tz": -540,
          "elapsed": 887,
          "user": {
            "displayName": "Ryokan Ri",
            "userId": "09390037946487494940"
          }
        },
        "outputId": "3f88b3e6-fe88-4ff5-d766-db1544774f0d"
      },
      "id": "Yjy3tKxAYmae",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fec1cfaf",
      "metadata": {
        "id": "fec1cfaf"
      },
      "source": [
        "#### エラー分析"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cb5fdfb4",
      "metadata": {
        "id": "cb5fdfb4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "status": "ok",
          "timestamp": 1689256583107,
          "user_tz": -540,
          "elapsed": 447,
          "user": {
            "displayName": "Ryokan Ri",
            "userId": "09390037946487494940"
          }
        },
        "outputId": "c40d622f-30c5-4a87-a60e-154c323f1467",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "テキスト１：建物の中で一頭の牛が人に引かれて立っています。\n",
            "テキスト２：会場内で男性がホルスタインを連れています\n",
            "予測スコア：1.2813432216644287\n",
            "正解スコア：3.799999952316284\n",
            "----------------\n",
            "テキスト１：ジャンボジェット機が建物の上を低空飛行しています。\n",
            "テキスト２：ジャンボジェット機が建物の上を高空飛行しています。\n",
            "予測スコア：4.614662170410156\n",
            "正解スコア：2.4000000953674316\n",
            "----------------\n",
            "テキスト１：ゲレンデのこぶでスキージャンプしています。\n",
            "テキスト２：雪上でモーグルを楽しむ水色のウェアを着た女性。\n",
            "予測スコア：1.2380545139312744\n",
            "正解スコア：3.200000047683716\n",
            "----------------\n",
            "テキスト１：猫がぬいぐるみを抱いて寝ています。\n",
            "テキスト２：ぬいぐるみの横で黒猫が寝ています。\n",
            "予測スコア：4.13502311706543\n",
            "正解スコア：2.200000047683716\n",
            "----------------\n",
            "テキスト１：真っ白なトイレと洗面台が備え付けられたバスルームの写真です。\n",
            "テキスト２：全体を白に基調にしたユニットバスの写真です。\n",
            "予測スコア：1.8714594841003418\n",
            "正解スコア：3.799999952316284\n",
            "----------------\n"
          ]
        }
      ],
      "source": [
        "# モデルの予測誤差が高い順にソート\n",
        "sorted_results = sorted(\n",
        "    results, key=lambda x: -abs(x[\"true_score\"] - x[\"pred_score\"])\n",
        ")\n",
        "# 高い確率で予測しながら誤った事例の上位を表示\n",
        "for top_result in sorted_results[:5]:\n",
        "    sentence1 = valid_dataset[top_result[\"example_id\"]][\"sentence1\"]\n",
        "    sentence2 = valid_dataset[top_result[\"example_id\"]][\"sentence2\"]\n",
        "\n",
        "    print(f\"テキスト１：{sentence1}\")\n",
        "    print(f\"テキスト２：{sentence2}\")\n",
        "\n",
        "    print(f\"予測スコア：{top_result['pred_score']}\")\n",
        "    print(f\"正解スコア：{top_result['true_score']}\")\n",
        "    # print(f\"予測確率: {top_result['pred_prob']:.4f}\")\n",
        "    print(\"----------------\")"
      ]
    }
  ],
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          "file_id": "1mYgMJGOVLx1k-d4war1qQgXLdP8UmLqd",
          "timestamp": 1689236837588
        },
        {
          "file_id": "1rMnb-uRtCTVBZ6Ik7BXsYV6j7dlu6ENG",
          "timestamp": 1689235728824
        }
      ],
      "gpuType": "T4"
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
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      "display_name": "Python 3"
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