chapter05/5-3-sentiment-analysis-analysis-wrime.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": [
        "**注意**\n",
        "2023/7/28現在、MARC-jaのデータセットの配布元のリンクが切れており、書籍上の5.2、5.3、5.5.4節に掲載されているコードにおいて、データセット読み込みの箇所でエラーが出る状態です。こちらのノートブックは、MARC-jaと同様の感情分析のデータセットであるWRIMEを用いて書籍と同様のコードを実行するために用意されています。"
      ],
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.3 感情分析モデルのエラー分析"
      ],
      "metadata": {
        "id": "MWL8eXCGUXpK"
      },
      "id": "MWL8eXCGUXpK"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7a26676",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7a26676",
        "outputId": "0658e05d-4f6e-4882-f143-69d9ccb26292"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: ipadic, sudachidict-core, unidic, unidic-lite\n",
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            "Successfully built ipadic sudachidict-core unidic unidic-lite\n",
            "Installing collected packages: wasabi, unidic-lite, tokenizers, sudachipy, safetensors, plac, ipadic, xxhash, sudachidict-core, rhoknp, fugashi, dill, unidic, multiprocess, huggingface-hub, transformers, datasets, accelerate\n",
            "  Attempting uninstall: wasabi\n",
            "    Found existing installation: wasabi 1.1.2\n",
            "    Uninstalling wasabi-1.1.2:\n",
            "      Successfully uninstalled wasabi-1.1.2\n",
            "Successfully installed accelerate-0.21.0 datasets-2.14.1 dill-0.3.7 fugashi-1.2.1 huggingface-hub-0.16.4 ipadic-1.0.0 multiprocess-0.70.15 plac-1.3.5 rhoknp-1.3.0 safetensors-0.3.1 sudachidict-core-20230110 sudachipy-0.6.7 tokenizers-0.13.3 transformers-4.31.0 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 scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ef914bc8",
      "metadata": {
        "id": "ef914bc8"
      },
      "source": [
        "### 5.3.1 モデルの予測結果の取得"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "517166a4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213,
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        "id": "517166a4",
        "outputId": "224329b0-e60b-49a8-f55f-a1fcb2675110"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/826 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/445M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/529 [00:00<?, ?B/s]"
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        {
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          "data": {
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              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/231k [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "version_major": 2,
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              "model_id": "592b435d52b14ff5a4d98822a769d5da"
            }
          },
          "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-wrime-sentiment\"\n",
        "sentiment_pipeline = pipeline(model=model_name, device=\"cuda:0\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0dd6808",
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        },
        "id": "a0dd6808",
        "outputId": "a136851c-dfaf-423b-db4f-bddf85d45110"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.33k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cec03431258d4de8b3eef72030776590"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.28k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7daefeaf4ff548148fb8ca5accf0ee2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.30M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96a3f2f22e8f408d9fedaa26487610e3"
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          },
          "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": "fb3eaf0254594ba99e92227e4bb4a07c"
            }
          },
          "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": "a34ebfa46ca94f7d9a9e111ccb938eb4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d79d2bc92d4c47c0875d8a26d471f1a3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "valid_dataset = load_dataset(\"llm-book/wrime-sentiment\", split=\"validation\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "81421c7d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81421c7d",
        "outputId": "ad6a5333-c1e0-4cd6-b8ac-da7579059495"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "10it [00:03,  4.06it/s]/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1083: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  warnings.warn(\n",
            "1608it [00:22, 72.95it/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 = sentiment_pipeline(example[\"sentence\"])[0]\n",
        "    # 正解のラベルIDをラベル名に変換\n",
        "    true_label = class_label.int2str(example[\"label\"])\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_prob\": model_prediction[\"score\"],\n",
        "            \"pred_label\": model_prediction[\"label\"],\n",
        "            \"true_label\": true_label,\n",
        "        }\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c176fe9",
      "metadata": {
        "id": "0c176fe9"
      },
      "source": [
        "### 5.3.2 全体的な傾向の分析"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a710acb",
      "metadata": {
        "id": "2a710acb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "76753fe2-7a1a-4685-9b12-2712d9bd0e75"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7b974c165f00>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "\n",
        "plt.rcParams[\"font.size\"] = 18  # 文字サイズを大きくする\n",
        "\n",
        "# 混同行列の作成\n",
        "confusion_matrix = confusion_matrix(\n",
        "    y_true=[result[\"true_label\"] for result in results],\n",
        "    y_pred=[result[\"pred_label\"] for result in results],\n",
        "    labels=class_label.names,\n",
        ")\n",
        "# 混同行列を画像として表示\n",
        "ConfusionMatrixDisplay(\n",
        "    confusion_matrix, display_labels=class_label.names\n",
        ").plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fec1cfaf",
      "metadata": {
        "id": "fec1cfaf"
      },
      "source": [
        "### 5.3.3 モデルのショートカットに注意"
      ]
    },
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      "cell_type": "code",
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      "id": "cb5fdfb4",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb4f84e8-abff-4d42-e70b-ecadc7e68f71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "レビュー文：何のこれしき会社員だった頃の辛さに比べれば。まあ種類が違うけど。\n",
            "\n",
            "\n",
            "予測：negative\n",
            "正解：positive\n",
            "予測確率: 0.9993\n",
            "----------------\n",
            "レビュー文：今年のプリキュアの決め台詞が、\n",
            " \n",
            "「お大事に」\n",
            "\n",
            "今年にマッチングし過ぎてて、プリキュアスタッフの先見の明がすご過ぎるし、何が言いたいかと言うと変身する棒を買わされました。\n",
            "予測：positive\n",
            "正解：negative\n",
            "予測確率: 0.9993\n",
            "----------------\n",
            "レビュー文：ツイッター早朝部、いくわよ！！ あと諸事情により数人ブロックさせて頂きました…申し訳ない……\n",
            "予測：negative\n",
            "正解：positive\n",
            "予測確率: 0.9992\n",
            "----------------\n",
            "レビュー文：酒はぬるめの 燗がいい 肴は炙った イカでいい←もっといいもの食えよ\n",
            "予測：positive\n",
            "正解：negative\n",
            "予測確率: 0.9992\n",
            "----------------\n",
            "レビュー文：できれば今すぐお線香あげにいきたい\n",
            "予測：positive\n",
            "正解：negative\n",
            "予測確率: 0.9992\n",
            "----------------\n"
          ]
        }
      ],
      "source": [
        "# 予測が誤った事例を収集\n",
        "failed_results = [\n",
        "    res for res in results if res[\"pred_label\"] != res[\"true_label\"]\n",
        "]\n",
        "# モデルの予測確率が高い順にソート\n",
        "sorted_failed_results = sorted(\n",
        "    failed_results, key=lambda x: -x[\"pred_prob\"]\n",
        ")\n",
        "# 高い確率で予測しながら誤った事例の上位2件を表示\n",
        "for top_result in sorted_failed_results[:5]:\n",
        "    review_text = valid_dataset[top_result[\"example_id\"]][\"sentence\"]\n",
        "    print(f\"レビュー文：{review_text}\")\n",
        "    print(f\"予測：{top_result['pred_label']}\")\n",
        "    print(f\"正解：{top_result['true_label']}\")\n",
        "    print(f\"予測確率: {top_result['pred_prob']:.4f}\")\n",
        "    print(\"----------------\")"
      ]
    }
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