{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "694ywzJro_xb", "outputId": "23db38ab-a6da-4348-d18e-f97cd104efb4" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fri May 2 12:44:12 AM UTC 2025\n", "Python 3.11.12\n" ] } ], "source": [ "!date\n", "!python --version" ] }, { "cell_type": "markdown", "source": [ "更新履歴\n", "- 2025年5月1日\n", " - 解説追加。\n", " - TrainingArguments\n", " - evaluation_strategyをeval_strategyに変更。\n", " - `report_to=\"none\"`を追加。（デフォルトだとwandbを使うため、APIキー入力待ちになってしまう）" ], "metadata": { "id": "ZPtWgVBKpzTh" } }, { "cell_type": "markdown", "source": [ "# AutoModelForSequenceClassificationを用いたファインチューニング例\n", "このノートブックでは[Japanese Realistic Textual Entailment Corpus](https://github.com/megagonlabs/jrte-corpus)のpn.tsvをデータセットとし、BERT（\"tohoku-nlp/bert-base-japanese-v3\"）を用いてファインチューニングする例を示している。\n", "\n", "pn.tsvは実際のレビュー文に対して5名の被験者が極性ラベルを付与したデータセットである。[上記サイトのデータ説明](https://github.com/megagonlabs/jrte-corpus?tab=readme-ov-file#datapntsv)にあるとおり、ID, Label, Text, Judges, Usage の5列のデータがサンプル毎に用意されている。\n", "- ID: ユニークなID\n", "- Label: 1 (Positive), 0 (Neutral), -1 (Negative)\n", "- Text: レビュー文そのもの\n", "- Judges: JSONフォーマットによる極性判定結果。例: `{\"0\": 1, \"1\": 4}`\n", "- Usage: train, deve, testの種別。\n", "\n", "全体の流れは以下の通り。\n", "\n", "- 環境構築: fugashi, accelerateをインストール\n", "- モジュール読み込み\n", "- データ前処理: LLMにおける分類タスクでは教師ラベルを「0から始まる整数」として割り振る必要があるため、ラベルを設定し直した。\n", "- モデルの用意: tokenizerの動作確認を含む。\n", "- LLM用にデータを整形: tokenizerの出力と教師ラベルを合わせてDataset型に変換。\n", "- 学習\n", "- テストデータに対する詳細結果\n", "- 学習データに対する詳細結果" ], "metadata": { "id": "YQry16tI1iXY" } }, { "cell_type": "code", "source": [ "!curl -O https://raw.githubusercontent.com/megagonlabs/jrte-corpus/refs/heads/master/data/pn.tsv\n", "!head pn.tsv" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uwHzUemBm8H5", "outputId": "912c903a-41c8-4257-aa7c-a815e575d085" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 473k 100 473k 0 0 1275k 0 --:--:-- --:--:-- --:--:-- 1275k\n", "pn17q00001\t0\t出張でお世話になりました。\t{\"0\": 3}\ttest\n", "pn17q00002\t0\t朝食は普通でした。\t{\"0\": 3}\ttest\n", "pn17q00003\t1\tまた是非行きたいです。\t{\"1\": 3}\ttest\n", "pn17q00004\t1\tまた利用したいと思えるホテルでした。\t{\"1\": 3}\ttest\n", "pn17q00005\t1\t駅から近くて便利でした。\t{\"0\": 1, \"1\": 2}\ttest\n", "pn17q00006\t1\tまた来年も利用したいと思います。\t{\"0\": 1, \"1\": 2}\ttest\n", "pn17q00007\t0\t新婚旅行で利用しました。\t{\"0\": 3}\ttest\n", "pn17q00008\t1\tまた利用いたします。\t{\"0\": 1, \"1\": 2}\ttest\n", "pn17q00009\t1\tコストパフォーマンス最高です。\t{\"1\": 3}\ttest\n", "pn17q00010\t0\tお心遣いありがとうございました。\t{\"0\": 2, \"1\": 1}\ttest\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 注意\n", "### GPUを指定する\n", "LLMを用いた学習を行っている都合上、デフォルト（CPU）実行すると極めて時間がかかる。おそらく数時間要するだろう。自身でも動作確認したい人は、GPUを指定して実行することをお勧めする。\n", "\n", "GPUを指定するには以下の手順を取る。\n", "- 「ランタイム」から「ランタイムのタイプの変更」を選ぶ。\n", "- 「ハードウェア アクセラレータ」からGPUを選ぶ。\n", " - T4 GPU を選ぶと良い。この中では低スペックだが十分早い。T4 GPUなら、10エポックの学習が約14分で終了する。\n", "\n", "### リソース使用制限\n", "Google Colabは無料で利用できるが、利用度合いに応じてリソースが制限されることがある。特にGPUは使えなくなることが多いため、不必要に何度も実行することは避けよう。\n", "\n", "詳細: [最適な Colab のプランを選択する](https://colab.research.google.com/signup/pricing?hl=ja)\n", "\n", "### 必要に応じてモデルや結果をファイル保存する\n", "このノートブックではノートブック内に出力しつつ、学習したモデルはセッション内に保存しているだけで終えている。このため後日「学習結果を利用したい」場合には改めて学習し直す必要がある。それが面倒に思う人はファイル保存するようにしよう。\n", "\n", "なお、正確には「ファイル」ではなく「複数のファイルを含むフォルダ」として保存されている。このフォルダ単位でダウンロードしたり、アップロードしてモデルを復元する必要があることに注意しよう。\n", "\n", "またモデルはとてもファイルサイズが大きく、実際にダウンロード＆アップロードするにはとても時間がかかる。そのためGoogleドライブにアクセス許可した上で自身のドライブ内に保存する方が楽だ。\n", "\n", "```Python\n", "# ノートブックからドライブへのアクセスを許可する\n", "from google.colab import drive\n", "drive.mount('/content/drive')\n", "```" ], "metadata": { "id": "ddjbPBwC63Fi" } }, { "cell_type": "markdown", "source": [ "## 環境構築" ], "metadata": { "id": "gmO5jzDI1zl0" } }, { "cell_type": "code", "source": [ "!pip install fugashi[unidic-lite]\n", "!pip install accelerate -U" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WXcMonRRpPW2", "outputId": "7f6e4508-d5af-46b7-a05a-5241e0ace47e" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting fugashi[unidic-lite]\n", " Downloading fugashi-1.4.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.9 kB)\n", "Collecting unidic-lite (from fugashi[unidic-lite])\n", " Downloading unidic-lite-1.0.8.tar.gz (47.4 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.4/47.4 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "Downloading 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"\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n", " Attempting uninstall: nvidia-nvjitlink-cu12\n", " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", " Attempting uninstall: nvidia-curand-cu12\n", " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", " Attempting uninstall: nvidia-cufft-cu12\n", " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", " Attempting uninstall: nvidia-cuda-runtime-cu12\n", " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-cupti-cu12\n", " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", " Attempting uninstall: nvidia-cublas-cu12\n", " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", " Attempting uninstall: nvidia-cusparse-cu12\n", " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", " Attempting uninstall: nvidia-cudnn-cu12\n", " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", " Attempting uninstall: nvidia-cusolver-cu12\n", " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n" ] } ] }, { "cell_type": "markdown", "source": [ "## モジュール読み込み、データ前処理\n", "元データでは極性が -1, 0, 1 で付与されている。しかし教師ラベルとしては0から始まる整数で設定する必要がある。そのためここでは read_csv() で読み込んだ後、map関数により -1を0に、0を1に、1を2に置換している。" ], "metadata": { "id": "L-AQS9sy12nO" } }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "jOIVctCTo_xf", "outputId": "4fae80c8-0efe-4ea0-dca6-f5072ca811db" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " id sentiment text judges-json usage\n", "0 pn17q00001 0 出張でお世話になりました。 {\"0\": 3} test\n", "1 pn17q00002 0 朝食は普通でした。 {\"0\": 3} test\n", "2 pn17q00003 1 また是非行きたいです。 {\"1\": 3} test\n", "3 pn17q00004 1 また利用したいと思えるホテルでした。 {\"1\": 3} test\n", "4 pn17q00005 1 駅から近くて便利でした。 {\"0\": 1, \"1\": 2} test" ], "text/html": [ "\n", "
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idsentimenttextjudges-jsonusage
0pn17q000010出張でお世話になりました。{\"0\": 3}test
1pn17q000020朝食は普通でした。{\"0\": 3}test
2pn17q000031また是非行きたいです。{\"1\": 3}test
3pn17q000041また利用したいと思えるホテルでした。{\"1\": 3}test
4pn17q000051駅から近くて便利でした。{\"0\": 1, \"1\": 2}test
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 5553,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5553,\n \"samples\": [\n \"pn17q01971\",\n \"pn17q03940\",\n \"pn17q02861\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": -1,\n \"max\": 1,\n \"num_unique_values\": 3,\n \"samples\": [\n 0,\n 1,\n -1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5553,\n \"samples\": [\n \"\\u30d0\\u30a4\\u30ad\\u30f3\\u30b0\\u306e\\u671d\\u98df\\u3082\\u7f8e\\u5473\\u3057\\u304b\\u3063\\u305f\\u3067\\u3059\\u3002\",\n \"\\u30d5\\u30ed\\u30f3\\u30c8\\u5bfe\\u5fdc\\u3082\\u4e01\\u5be7\\u3067\\u3059\\u3002\",\n \"\\u671d\\u98df\\u306f\\u7f8e\\u5473\\u304b\\u3063\\u305f\\u3002\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"judges-json\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"{\\\"-1\\\": 1, \\\"1\\\": 2}\",\n \"{\\\"1\\\": 3}\",\n \"{\\\"-1\\\": 3}\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"usage\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"test\",\n \"train\",\n \"dev\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 4 } ], "source": [ "import torch\n", "import pandas as pd\n", "from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n", "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n", "import numpy as np\n", "\n", "# データの読み込み\n", "filename = \"pn.tsv\"\n", "columns = [\"id\", \"sentiment\", \"text\", \"judges-json\", \"usage\"]\n", "df = pd.read_csv(filename, sep=\"\\t\", names=columns)\n", "df.head()" ] }, { "cell_type": "code", "source": [ "# ラベルの付け替え\n", "# AutoModelForSequenceClassificationではラベルは「0から始まる整数」である必要がある。\n", "# このため -1, 0, 1 => 0, 1, 2（ネガティブ0、ノーマル1、ポジティブ2）に付け替える。\n", "df['sentiment'] = df['sentiment'].map({-1: 0, 0: 1, 1: 2})\n", "df.head()" ], "metadata": { "id": "pkm-AzaZjTRk", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "2e58f8ed-936f-4bd1-e910-9c68ce4fbbaa" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " id sentiment text judges-json usage\n", "0 pn17q00001 1 出張でお世話になりました。 {\"0\": 3} test\n", "1 pn17q00002 1 朝食は普通でした。 {\"0\": 3} test\n", "2 pn17q00003 2 また是非行きたいです。 {\"1\": 3} test\n", "3 pn17q00004 2 また利用したいと思えるホテルでした。 {\"1\": 3} test\n", "4 pn17q00005 2 駅から近くて便利でした。 {\"0\": 1, \"1\": 2} test" ], "text/html": [ "\n", "
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idsentimenttextjudges-jsonusage
0pn17q000011出張でお世話になりました。{\"0\": 3}test
1pn17q000021朝食は普通でした。{\"0\": 3}test
2pn17q000032また是非行きたいです。{\"1\": 3}test
3pn17q000042また利用したいと思えるホテルでした。{\"1\": 3}test
4pn17q000052駅から近くて便利でした。{\"0\": 1, \"1\": 2}test
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 5553,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5553,\n \"samples\": [\n \"pn17q01971\",\n \"pn17q03940\",\n \"pn17q02861\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 2,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n 2,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5553,\n \"samples\": [\n \"\\u30d0\\u30a4\\u30ad\\u30f3\\u30b0\\u306e\\u671d\\u98df\\u3082\\u7f8e\\u5473\\u3057\\u304b\\u3063\\u305f\\u3067\\u3059\\u3002\",\n \"\\u30d5\\u30ed\\u30f3\\u30c8\\u5bfe\\u5fdc\\u3082\\u4e01\\u5be7\\u3067\\u3059\\u3002\",\n \"\\u671d\\u98df\\u306f\\u7f8e\\u5473\\u304b\\u3063\\u305f\\u3002\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"judges-json\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"{\\\"-1\\\": 1, \\\"1\\\": 2}\",\n \"{\\\"1\\\": 3}\",\n \"{\\\"-1\\\": 3}\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"usage\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"test\",\n \"train\",\n \"dev\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "# データセットの分割\n", "train_texts = df[df[\"usage\"] == \"train\"][\"text\"].tolist()\n", "train_labels = df[df[\"usage\"] == \"train\"][\"sentiment\"].tolist()\n", "dev_texts = df[df[\"usage\"] == \"dev\"][\"text\"].tolist()\n", "dev_labels = df[df[\"usage\"] == \"dev\"][\"sentiment\"].tolist()\n", "test_texts = df[df[\"usage\"] == \"test\"][\"text\"].tolist()\n", "test_labels = df[df[\"usage\"] == \"test\"][\"sentiment\"].tolist()\n", "\n", "# 動作確認\n", "print(f\"{train_texts[0]=}\")\n", "print(f\"{train_labels[0]=}\")\n", "print(df['sentiment'].value_counts())" ], "metadata": { "id": "H2oGt5Xthwsy", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d6da3d4d-b4c9-44e8-dbbc-4f7509aef52e" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "train_texts[0]='(笑)'\n", "train_labels[0]=1\n", "sentiment\n", "2 3406\n", "1 1329\n", "0 818\n", "Name: count, dtype: int64\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "ETXKmVVYo_xg" }, "source": [ "## モデルの用意\n", "- ここでは \"tohoku-nlp/bert-base-japanese-v3\" を利用。\n", "- 長文だが、最初の出力「`Some weights of BertForSequenceClassification were not initialized from the model checkpoint at tohoku-nlp/bert-base-japanese-v3 and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.`」は、このままでは利用できない（ので学習が必要だろう）ということを指摘している。この理由は、AutoModelForSequenceClassificationが「LLMの最後尾に新たな線形層（[torch.nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html)）を追加しているためだ。この線形層はクラス数と同数のユニットを持つように設定されており（引数num_labelsで設定）、各ユニットに対するスコアを求めるようにモデルを拡張している。\n", "- このようなにモデルを拡張しているということは、拡張した部分（Linear層のパラメータ）については重みがまっさらな状態である。このままでは当然推定できない（でたらめになる）ため、「You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.」と注意を促している。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 384, "referenced_widgets": [ "e1e113c9b2034983a48c322b9c77fd83", "72f0f6f3095345b5998e699d5d6c7cb6", "325f14486f4142da983aa43855195e40", "613e973f5cf54f21884da91545d2b0e2", "72a7bc647e1f46efa345547143d49a46", "e5b548bc39e141fb8b23388142032b3f", "0349448fe7eb4ff595ecf438d7c79574", "6dbb7b01e3af46c09d3e39385aee4fa0", "20f695e8bb7f49568881f214a1833f96", "f7ab322a9018409bac6c0716ca82c39f", "65b40695869c472bbe05a97534f77e96", "6accad342c3f477cb9113c70452b0738", "4be71da5a98c4b58a798180629ed853d", "1c8069f42fe24988930203d7765e25dd", "d0b22e266aa74c6b896eaac59bdf10d2", "c31e201f9b3a4bcf8c61e74f477076d7", "d256ad649a2c4fc98535ed481531a739", "28a0f9c0e7804686870308200ee4caa1", "ceb7fccbd47643f886b08f2d47ee1eea", "46947fe9e2a1463daf82ba854b255a8b", "9a0b334b526f47e7ab9f840e13a1806e", "2df113687bf148ec8796f2f1f41b9196", "04ef6e02b51247b08e651397fcee0d42", "2e8d4f2f77fa4a3b9fb2d0eb1ac5cc74", "66b6fd8426e6422d8feb0cb22294896d", "c47a8602ac144b40bb07211f167bddf4", "fa799fafd1f447589ed648eb32771baa", "61fa4dc07cc742c9995c5ffbcc95225e", "32a12c03610c4d718a00472249d24a15", "f9f3e94cb6af4b14b062f32e257022dc", "5a43ce8d466047609e97f112ba63d85e", "74c94a9dc8194c8c902d989ed9269230", "3bdd49a6dec04b5aa484ba8b8c583ebd", "3e70c1d2d83c46fb845e8159a70950d0", "5d894253ea5b435389ab72f749b82cfb", "0489dc7134ea4a52a382eb1bd0ead726", "5338b621a4304a0d8fd32c9b8a6c8fad", "bdf3717814b24b59abf1d7f1ad3ac115", "bb67b2b1e3b44bb8a1b2c9315735aee9", "0962a495fdf94d83a1f3f0567caf8052", "6f0c662fa67e49529e4c0754bcc9648e", "1dce1e6afd394576b07d2fae5187cb17", "25a1abc6014044059177088de6d71d51", "155cdc297d984f0483928f9dafc5e3be", "0b52104a96164b68b605914303968a4b", "1e03065f13c0421eac2b67a2636a735c", "4f4305820d304fd59f9ad41c6708f2d3", "dd9a9a5b7366478fb05e0887498e0c8c", "8135925fe02446139bcd40f0c835b67d", "8c8b3f690f934266b1f37debc4a43e63", "3a41f0df8a124c67920e78971c21c93e", "a395af14f2214165a69ac588cce57049", "6cbe1ec6340c4c82b33eaaf919c8ae5b", "50779aa05b71429c9b0ea78c784fcfe4", "b52ca6f490554505b5190a0a42fa083a" ] }, "id": "uzl27_c_o_xh", "outputId": "cd90e7cd-0a67-4b3e-c0cc-675141951e0b" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/251 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [2430/2430 13:12, Epoch 10/10]\n", "
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EpochTraining LossValidation LossAccuracyF1PrecisionRecall
10.4043000.4083810.8471220.7920120.8216420.776139
20.2904000.3700820.8597120.8159320.8206560.811538
30.1962000.4321910.8776980.8386910.8452390.837207
40.1512000.4789740.8848920.8440250.8461120.845028
50.0793000.5551780.8714030.8312010.8329010.829776
60.0527000.7227240.8633090.8244310.8238940.826944
70.0013000.7583870.8750000.8312570.8352240.829333
80.0230000.8352250.8758990.8283960.8386850.821027
90.0003000.8641220.8758990.8318510.8374950.826545
100.0006000.8662640.8758990.8312690.8380460.824955

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Test Accuracy: 0.8553345388788427\n", "Test F1 Score: 0.8221032582050153\n", "Test Precision: 0.8452179386146287\n", "Test Recall: 0.8034550381672684\n" ] } ], "source": [ "# トレーニングの設定\n", "training_args = TrainingArguments(\n", " output_dir=output_dir,\n", " num_train_epochs=num_train_epochs,\n", " per_device_train_batch_size=batch_size,\n", " per_device_eval_batch_size=batch_size,\n", " warmup_steps=500,\n", " weight_decay=0.01,\n", " logging_dir=logging_dir,\n", " logging_steps=10,\n", " eval_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " report_to=\"none\" # wandbを使わない\n", ")\n", "\n", "# 精度の計算\n", "def compute_metrics(p):\n", " pred, labels = p\n", " pred = np.argmax(pred, axis=1)\n", " precision, recall, f1, _ = precision_recall_fscore_support(labels, pred, average='macro')\n", " acc = accuracy_score(labels, pred)\n", " return {\n", " 'accuracy': acc,\n", " 'f1': f1,\n", " 'precision': precision,\n", " 'recall': recall\n", " }\n", "\n", "# トレーナーの設定\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=dev_dataset,\n", " compute_metrics=compute_metrics\n", ")\n", "\n", "# モデルのトレーニング\n", "trainer.train()\n", "\n", "# モデルの保存\n", "trainer.save_model(\"./sentiment_model\")\n", "\n", "# テストセットでの評価\n", "results = trainer.evaluate(eval_dataset=test_dataset)\n", "\n", "# 結果の表示\n", "print(f\"Test Accuracy: {results['eval_accuracy']}\")\n", "print(f\"Test F1 Score: {results['eval_f1']}\")\n", "print(f\"Test Precision: {results['eval_precision']}\")\n", "print(f\"Test Recall: {results['eval_recall']}\")" ] }, { "cell_type": "markdown", "source": [ "## テストデータに対する詳細結果" ], "metadata": { "id": "rvOyFqp_5fhs" } }, { "cell_type": "code", "source": [ "# テストデータに対する詳細結果\n", "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n", "#import numpy as np\n", "\n", "predictions, labels, _ = trainer.predict(test_dataset)\n", "predicted_labels = np.argmax(predictions, axis=1)\n", "labels = ['negative', 'normal', 'positive']\n", "\n", "# 分類レポートの表示\n", "print(classification_report(test_labels, predicted_labels, target_names=labels))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 187 }, "id": "UGxJIbUKy1QY", "outputId": "f0f1eacd-ac69-4986-a856-f5cafd105afe" }, "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " negative 0.91 0.79 0.85 78\n", " normal 0.74 0.67 0.70 139\n", " positive 0.89 0.95 0.92 336\n", "\n", " accuracy 0.86 553\n", " macro avg 0.85 0.80 0.82 553\n", "weighted avg 0.85 0.86 0.85 553\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "# 混同行列の表示\n", "conf_matrix = confusion_matrix(test_labels, predicted_labels)\n", "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=labels)\n", "disp.plot(cmap='Blues')\n", "disp.ax_.set_title('Confusion Matrix')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 489 }, "id": "kDwMrlHszDFO", "outputId": "70e4dbd4-5cdb-4724-8234-3ca60b638d85" }, "execution_count": 12, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Confusion Matrix')" ] }, "metadata": {}, "execution_count": 12 }, { "output_type": "display_data", "data": { "text/plain": [ "
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hvL295W0aN24Me3t7REVFAQCioqLQrFkzeeICAD4+PsjKypJXbyqLyQsREZEGs7Ozg4mJiXwJDg6usO3169dhZGQEqVSKyZMnY/fu3XBzc0Nqaip0dXVhamqq0N7KygqpqakAgNTUVIXEpXR/6T5lsI5JREQkRlqSZ4sqxwNITk5W6DaSSivuinJxcUFMTAwyMzOxc+dO+Pn54eTJk68ewyti8kJERCRGVTRVunT2UGXo6urCyckJANC6dWtcunQJq1evxvDhw1FQUICMjAyF6suDBw9gbW0NALC2tsbFixcVzlc6G6m0TWWx24iIiIheSUlJCfLz89G6dWvUqlULERER8n1xcXFISkqCu7s7AMDd3R3Xr19HWlqavE14eDhkMhnc3NyUui4rL0RERGJUw0/YnTNnDnr37g17e3s8efIE27ZtQ2RkJI4cOQITExOMHz8eM2fOhLm5OWQyGaZOnQp3d3d06NABANCzZ0+4ublh9OjRWLp0KVJTU/H555/D39//hV1V5WHyQkREJEY1/ITdtLQ0jBkzBikpKTAxMUHz5s1x5MgR9OjRAwCwcuVKaGlpYciQIcjPz4ePjw/WrVsnP15bWxv79+/HlClT4O7uDkNDQ/j5+WHhwoXKh87nvLw++JwXzcLnvGgWPudFM9Toc14850Oio/fK5xGK8pB/ckG1xlpd+N1EREQkRnwxIxEREYmKBr+YkckLERGRGGlw5UW8aRcRERFpJFZeiIiIxIjdRkRERCQq7DYiIiIiEgdWXoiIiERJxW4jEdcvmLwQERGJEbuNiIiIiMSBlRciIiIxkkhUnG0k3soLkxciIiIx0uCp0uKNnIiIiDQSKy9ERERipMEDdpm8EBERiZEGdxsxeSEiIhIjDa68iDftIiIiIo3EygsREZEYsduIiIiIRIXdRkRERETiwMoLERGRCEkkEkg0tPLC5IWIiEiENDl5YbcRERERiQorL0RERGIk+f9FleNFiskLERGRCLHbiIiIiEgkWHkhIiISIU2uvDB5ISIiEiEmL1RGUFAQ9uzZg5iYGHWH8sZIS8/Et6GHce5KHPLzC1HPpjbmThsKV+d6KCoqRsj/juJcdBz+Tn0EI0M9tG3hBP8xvWBRW6bu0ElJ52MSsH7bcVyPS8aD9Cxs/OI99OrSXL7/wyVb8cuhSwrHeLVrjK0rJtd0qFTFzl6Jx9otx3D1zySk/pOF/309EX29Wqg7rDcSkxcNJ5FIsHv3bgwcOFC+LTAwEFOnTlVfUG+YrOynmDQ7BK2aNcSq+eNgJjNEUso/MDbSBwDk5RciLuE+3hveDc6ONsjKfoqVP+xD4JLNCFsRoOboSVm5T/Ph5mSLEX3bY8JnP5bbpmv7xljx6Sj5um4t/jh6E+Q+zUfTRnXx7tvuGP3x9+oOh95Q/GlRASMjIxgZGak7jDfGll0nYVnHFPOmD5Vvs7U2l//ZyFAPaxeNVzgm8P23Me6jdUh9mAFrC9OaCpWqQDd3N3Rzd3thG11dHViyqvbG6dGxCXp0bKLuMDSDBk+VVutsIy8vL0ybNg0ff/wxzM3NYW1tjaCgIPn+jIwMTJgwARYWFpDJZOjWrRuuXr2qcI7FixfD0tISxsbGmDBhAj755BO0bNlSvv/SpUvo0aMH6tSpAxMTE3h6euLKlSvy/Y6OjgCAQYMGQSKRyNeDgoLk5zl69Cj09PSQkZGhcO3p06ejW7du8vUzZ86gc+fO0NfXh52dHaZNm4acnByVP6c3wamLsXB1qos5X25Fr9GLMXr6Guw5cvGFx2Tn5EMikcDIUK+GoqSaFPV7PJr3+xydRy7BJ8t+xqNMfq8QKaO020iVRazUPlU6LCwMhoaGuHDhApYuXYqFCxciPDwcAPDOO+8gLS0Nhw4dQnR0NFq1aoXu3bvj0aNHAICtW7diyZIl+OqrrxAdHQ17e3usX79e4fxPnjyBn58fzpw5g/Pnz8PZ2Rl9+vTBkydPADxLbgBg06ZNSElJka//V/fu3WFqaopdu3bJtxUXF2PHjh3w9fUFACQkJKBXr14YMmQIrl27hh07duDMmTMICGCXBwDcT32EXw9dgJ1tHawOeg+De3fAiu/34UBEdLnt8wsK8U3YIfTs0hxGBkxe3jRd27ti9efvYsfqD/DZlP44H5OA0YEbUFxcou7QiEgE1N5t1Lx5c8yfPx8A4OzsjG+++QYRERHQ19fHxYsXkZaWBqlUCgBYtmwZ9uzZg507d2LSpElYu3Ytxo8fj3HjxgEA5s2bh6NHjyI7O1t+/v9WRgDgu+++g6mpKU6ePIl+/frBwsICAGBqagpra+tyY9TW1saIESOwbds2jB//rGsjIiICGRkZGDJkCAAgODgYvr6++PDDD+X3smbNGnh6emL9+vXQ0yv7Czg/Px/5+fny9aysLKU/P7EoEQS4OtXFB2N8AAAuDW1xOykVvx6+gL7dWyu0LSoqxmdLtwMC8PGUgWqIlqrbAO9W8j+7NrSFa0NbeAxfjHO/x6Nzm0ZqjIxIPCQSqDhgt+piqWlqr7w0b95cYd3GxgZpaWm4evUqsrOzUbt2bfn4EyMjIyQmJiIhIQEAEBcXh3bt2ikc//z6gwcPMHHiRDg7O8PExAQymQzZ2dlISkpSKk5fX19ERkbi/v37AJ5Vffr27QtTU1MAwNWrVxEaGqoQq4+PD0pKSpCYmFjuOYODg2FiYiJf7OzslIpJTOqYGaO+naXCNsd6lnjwMFNhW1FRMT5dug0paY+xduF7rLpoCIe6dWBuaog79x6qOxQi0ZBAxW4jEWcvaq+81KpVS2FdIpGgpKQE2dnZsLGxQWRkZJljShOGyvDz80N6ejpWr14NBwcHSKVSuLu7o6CgQKk427Zti4YNG+Knn37ClClTsHv3boSGhsr3Z2dn4/3338e0adPKHGtvb1/uOefMmYOZM2fK17Oyst7YBKa5qwPu/v2Pwrak+//A2tJUvl6auCTfT8e6JRNgIjOs4ShJXe6nZeBxZi6s6pioOxQiEgG1Jy8VadWqFVJTU6GjoyMfRPs8FxcXXLp0CWPGjJFve37MytmzZ7Fu3Tr06dMHAJCcnIx//lH8JVqrVi0UFxe/NCZfX19s3boV9erVg5aWFvr27asQ782bN+Hk5FTZW4RUKpV3ib3pRg7oiAkfhyD05xPo3qkZbt66hz1HLmKO/yAAzxKXT77cirjb97F8rh9KSgSkP342LklmpI9anEYrKjm5+Uj8+98qSlLKI/xx6x7MjA1hKjPAik2H0cezBSxrG+PO3+lYsu43ONatA892jdUYNVWF7Nx8JCb/+7W/ez8d1+PuwdTEAHb/mWFIquNzXl5D3t7ecHd3x8CBA7F06VI0atQI9+/fx4EDBzBo0CC0adMGU6dOxcSJE9GmTRt4eHhgx44duHbtGho0aCA/j7OzM7Zs2YI2bdogKysLs2bNgr6+vsK1HB0dERERgY4dO0IqlcLMzKzcmHx9fREUFIQlS5Zg6NChConH7Nmz0aFDBwQEBGDChAkwNDTEzZs3ER4ejm+++aZ6PiQRcXO2w9JP38W6zUewccdx2FqZYcaEfujl9RYAIC09C6cvxgIARk9fo3DsuiUT0bpZgzLnpNfX1T+T8M60b+XrC9buAQC807stggPfQWzCffxy6BKysp/Cqo4Mnm0bY9bEPpDqvrY/kqiSYmLvov/kf7+HP1v5KwBgZN/2WBc0Wl1hvZk0eKr0a/uTQiKR4ODBg/jss88wbtw4PHz4ENbW1ujSpQusrKwAPEsmbt++jcDAQOTl5WHYsGEYO3YsLl78dwruxo0bMWnSJLRq1Qp2dnb44osvEBgYqHCt5cuXY+bMmfj+++9Rt25d3Llzp9yYnJyc0K5dO1y8eBGrVq1S2Ne8eXOcPHkSn332GTp37gxBENCwYUMMHz68Sj8XMevU1hWd2rqWu8/WygwXfguu4Yiouni0csbfZ1ZVuH/biik1FwzVqE6tG+HxJf6DjaqXRBAEQd1BVKUePXrA2toaW7ZsUXcoSsvKyoKJiQnO3rgHI2M+vOtNV89c/+WN6I1hIH1t/61IVSgrKwtWtU2QmZkJmax6fo6X/q4wG7kRWroGr3yekoJcPN4+vlpjrS6i/m7Kzc1FSEgIfHx8oK2tje3bt+PYsWPy58QQERG9qVQd8yLmh9SJOnkp7VpasmQJ8vLy4OLigl27dsHb21vdoREREVUrJi8ipa+vj2PHjqk7DCIiIqpBok5eiIiINBZnGxEREZGYaHK3kdpfD0BERESkDFZeiIiIRIiVFyIiIhIVlV7K+AqJT3BwMNq2bQtjY2NYWlpi4MCBiIuLU2jj5eVV5hqTJ09WaJOUlIS+ffvCwMAAlpaWmDVrFoqKipSKhZUXIiIieqmTJ0/C398fbdu2RVFRET799FP07NkTN2/ehKHhvy/SnThxIhYuXChfNzD490F6xcXF6Nu3L6ytrXHu3DmkpKRgzJgxqFWrFr744otKx8LkhYiISIRqutvo8OHDCuuhoaGwtLREdHQ0unTpIt9uYGAAa2vrcs9x9OhR3Lx5E8eOHYOVlRVatmyJRYsWYfbs2QgKCoKurm6lYmG3ERERkRhJqmDBs9cN/HfJz8+v1OUzMzMBAObmim8L37p1K+rUqYOmTZtizpw5yM3Nle+LiopCs2bN5O8oBAAfHx9kZWXhxo0blb51Vl6IiIg0mJ2dncL6/PnzERQU9MJjSkpK8OGHH6Jjx45o2rSpfPuoUaPg4OAAW1tbXLt2DbNnz0ZcXBx+/fXZ28VTU1MVEhcA8vXU1NRKx8zkhYiISISqqtsoOTlZ4cWMUqn0pcf6+/vjjz/+wJkzZxS2T5o0Sf7nZs2awcbGBt27d0dCQgIaNmz4yrE+j91GREREIlRVs41kMpnC8rLkJSAgAPv378eJEydQr169F7Zt3749ACA+Ph4AYG1tjQcPHii0KV2vaJxMeZi8EBERiVBNT5UWBAEBAQHYvXs3jh8/jvr167/0mJiYGACAjY0NAMDd3R3Xr19HWlqavE14eDhkMhnc3NwqHQu7jYiIiOil/P39sW3bNuzduxfGxsbyMSomJibQ19dHQkICtm3bhj59+qB27dq4du0aZsyYgS5duqB58+YAgJ49e8LNzQ2jR4/G0qVLkZqais8//xz+/v6V6q4qxcoLERGRGFXRbKPKWr9+PTIzM+Hl5QUbGxv5smPHDgCArq4ujh07hp49e6Jx48b46KOPMGTIEOzbt09+Dm1tbezfvx/a2tpwd3fHu+++izFjxig8F6YyWHkhIiISoZp+zosgCC/cb2dnh5MnT770PA4ODjh48KBS134eKy9EREQkKqy8EBERiZAmv5iRyQsREZEISaBi8qLsoJfXCLuNiIiISFRYeSEiIhIhdhsRERGRuLzCdOcyx4sUu42IiIhIVFh5ISIiEiF2GxEREZGoMHkhIiIiUZFIni2qHC9WHPNCREREosLKCxERkQg9q7yo0m1UhcHUMCYvREREYqRitxGnShMRERHVEFZeiIiIRIizjYiIiEhUONuIiIiISCRYeSEiIhIhLS0JtLRevXwiqHCsujF5ISIiEiF2GxERERGJBCsvREREIsTZRkRERCQqmtxtxOSFiIhIhDS58sIxL0RERCQqrLwQERGJkCZXXpi8EBERiZAmj3lhtxERERGJCisvREREIiSBit1GEG/phckLERGRCLHbiIiIiEgkWHkhIiISIc42IiIiIlFhtxERERGRSLDyQkREJELsNiIiIiJR0eRuIyYvREREIqTJlReOeSEiIiJRYeXlNeRoYQiZzFDdYVA1O5eQru4QqAY5WRipOwSqAU+ePK25i6nYbSTiB+wyeSEiIhIjdhsRERERiQQrL0RERCLE2UZEREQkKuw2IiIiIhIJVl6IiIhEiN1GREREJCrsNiIiIiISCVZeiIiIRIiVFyIiIhKV0jEvqizKCA4ORtu2bWFsbAxLS0sMHDgQcXFxCm3y8vLg7++P2rVrw8jICEOGDMGDBw8U2iQlJaFv374wMDCApaUlZs2ahaKiIqViYfJCREQkQqWVF1UWZZw8eRL+/v44f/48wsPDUVhYiJ49eyInJ0feZsaMGdi3bx9++eUXnDx5Evfv38fgwYPl+4uLi9G3b18UFBTg3LlzCAsLQ2hoKObNm6dULOw2IiIiopc6fPiwwnpoaCgsLS0RHR2NLl26IDMzExs3bsS2bdvQrVs3AMCmTZvg6uqK8+fPo0OHDjh69Chu3ryJY8eOwcrKCi1btsSiRYswe/ZsBAUFQVdXt1KxsPJCREQkQlXVbZSVlaWw5OfnV+r6mZmZAABzc3MAQHR0NAoLC+Ht7S1v07hxY9jb2yMqKgoAEBUVhWbNmsHKykrexsfHB1lZWbhx40al753JCxERkQhVVbeRnZ0dTExM5EtwcPBLr11SUoIPP/wQHTt2RNOmTQEAqamp0NXVhampqUJbKysrpKamytv8N3Ep3V+6r7LYbURERKTBkpOTIZPJ5OtSqfSlx/j7++OPP/7AmTNnqjO0CjF5ISIiEiEJVHzC7v//XyaTKSQvLxMQEID9+/fj1KlTqFevnny7tbU1CgoKkJGRoVB9efDgAaytreVtLl68qHC+0tlIpW0qg91GREREIqQlkai8KEMQBAQEBGD37t04fvw46tevr7C/devWqFWrFiIiIuTb4uLikJSUBHd3dwCAu7s7rl+/jrS0NHmb8PBwyGQyuLm5VToWVl6IiIjopfz9/bFt2zbs3bsXxsbG8jEqJiYm0NfXh4mJCcaPH4+ZM2fC3NwcMpkMU6dOhbu7Ozp06AAA6NmzJ9zc3DB69GgsXboUqamp+Pzzz+Hv71+p7qpSTF6IiIhEqKZfzLh+/XoAgJeXl8L2TZs2YezYsQCAlStXQktLC0OGDEF+fj58fHywbt06eVttbW3s378fU6ZMgbu7OwwNDeHn54eFCxcqFQuTFyIiIhGq6dcDCILw0jZ6enr49ttv8e2331bYxsHBAQcPHlTq2s9j8kJERCRCWpJniyrHixUH7BIREZGosPJCREQkRhIV3wwt4soLkxciIiIRqukBu68TdhsRERGRqLDyQkREJEKS//9PlePFiskLERGRCHG2EREREZFIsPJCREQkQjX9kLrXSaWSl99++63SJ3z77bdfORgiIiKqHE2ebVSp5GXgwIGVOplEIkFxcbEq8RARERG9UKWSl5KSkuqOg4iIiJSgJZFAS4XyiSrHqptKY17y8vKgp6dXVbEQERFRJWlyt5HSs42Ki4uxaNEi1K1bF0ZGRrh9+zYAYO7cudi4cWOVB0hERERllQ7YVWURK6WTlyVLliA0NBRLly6Frq6ufHvTpk3xww8/VGlwRERERM9TOnnZvHkzvvvuO/j6+kJbW1u+vUWLFvjzzz+rNDgiIiIqX2m3kSqLWCk95uXvv/+Gk5NTme0lJSUoLCyskqCIiIjoxTR5wK7SlRc3NzecPn26zPadO3firbfeqpKgiIiIiCqidOVl3rx58PPzw99//42SkhL8+uuviIuLw+bNm7F///7qiJGIiIieI/n/RZXjxUrpysuAAQOwb98+HDt2DIaGhpg3bx5iY2Oxb98+9OjRozpiJCIioudo8myjV3rOS+fOnREeHl7VsRARERG91Cs/pO7y5cuIjY0F8GwcTOvWrassKCIiInoxLcmzRZXjxUrp5OXevXsYOXIkzp49C1NTUwBARkYGPDw88NNPP6FevXpVHSMRERE9R5PfKq30mJcJEyagsLAQsbGxePToER49eoTY2FiUlJRgwoQJ1REjERERkZzSlZeTJ0/i3LlzcHFxkW9zcXHB2rVr0blz5yoNjoiIiCom4uKJSpROXuzs7Mp9GF1xcTFsbW2rJCgiIiJ6MXYbKeHrr7/G1KlTcfnyZfm2y5cvY/r06Vi2bFmVBkdERETlKx2wq8oiVpWqvJiZmSlkaDk5OWjfvj10dJ4dXlRUBB0dHbz33nsYOHBgtQRKREREBFQyeVm1alU1h0FERETK0ORuo0olL35+ftUdBxERESlBk18P8MoPqQOAvLw8FBQUKGyTyWQqBURERET0IkonLzk5OZg9ezZ+/vlnpKenl9lfXFxcJYERERFRxbQkEmip0PWjyrHqpvRso48//hjHjx/H+vXrIZVK8cMPP2DBggWwtbXF5s2bqyNGIiIieo5EovoiVkpXXvbt24fNmzfDy8sL48aNQ+fOneHk5AQHBwds3boVvr6+1REnEREREYBXqLw8evQIDRo0APBsfMujR48AAJ06dcKpU6eqNjoiIiIqV+lsI1UWsVK68tKgQQMkJibC3t4ejRs3xs8//4x27dph37598hc1ElVGSloGFn77GyKibuJpfiHq16uDNZ/7oqWrvbpDIxXlPs3Htp0ncP7Sn8jMykF9R2tMHN0Lzg3rAgC274rE6ag/8M+jLOhoa6NhfRu8O6wbXJz4Ylcx+/6n41j14yG8O6gT5kwZAADILyjE0g37cCjyKgoKi9CxTSPMnToYdcyM1Ryt+Kna9SPi3EX5ysu4ceNw9epVAMAnn3yCb7/9Fnp6epgxYwZmzZpV5QGKmaOjI5+RU4GMrFz0nbQKOjra+GnlFJzZ/ikWTBsIE2N9dYdGVeCb7/ch5vptzJgyCGu+nIK3mjXEvOAtSH+UBQCwta6NSWP7YM2XU/Dl/HGwtDBF0Jf/Q2ZWjpojp1d1PS4Zvxw4j0YNbBS2fxXyGyLPx2LF56MRtmwKHqZnYfqCMDVFSW8KpSsvM2bMkP/Z29sbf/75J6Kjo+Hk5ITmzZtXaXD05lqz5RhsrUyxdu6/Y6QcbGurMSKqKvkFhYi6dBOfzRyBJq4OAICRQ7xw6cpfOHTsMt4d1g2eHZspHDPe1wfHIn/HnaQHaNG0gTrCJhXkPM3H7C+3YcGModiwLUK+/UnOU+w6fAlLPxmFDm85AQAWfzQc/Sd8jauxd9Hi//9+0KvhbCMVODg4YPDgwaJMXJ5/Rg3VnCOnr6Olqz3e+/RHuPb+FF3HfIUte86pOyyqAsXFJSgpEVCrluK/jXR1dRD7V1KZ9oVFxThyIhqGBlLUd7CuqTCpCi1euxtd2rnCvVUjhe03/vobRUXFcG/lLN/WwN4SNpamiLl5t6bDfONwttFLrFmzptInnDZt2isH8zJeXl5o3rw59PT08MMPP0BXVxeTJ09GUFAQACApKQlTp05FREQEtLS00KtXL6xduxZWVlYAgKCgIOzZswcBAQFYsmQJ7t69i5KSEkgkEoSEhGDfvn04fvw4HBwc8OOPP8LCwgITJkzApUuX0KJFC2zZsgUNGzYEACQkJGDmzJk4f/48cnJy4OrqiuDgYHh7e1fb/b9J7t5PR+ivZzB5ZFd86NcDMbFJ+HTlLtSqpY0RfdurOzxSgYG+FC7O9fDznlOoV9cCpiaGOH3uD8Tdugdra3N5u0tX/sKyb3Yiv6AQZqbGWPDJaMiMDdQYOb2KgydiEBv/N3Z8U/Zn/z+Pn6BWLW3IjBS7g2ubGeOfx09qKsQ3Fl8P8BIrV66s1MkkEkm1Ji8AEBYWhpkzZ+LChQuIiorC2LFj0bFjR3Tv3h0DBgyAkZERTp48iaKiIvj7+2P48OGIjIyUHx8fH49du3bh119/hba2tnz7okWLsGLFCqxYsQKzZ8/GqFGj0KBBA8yZMwf29vZ47733EBAQgEOHDgEAsrOz0adPHyxZsgRSqRSbN29G//79ERcXB3v7yg04zc/PR35+vnw9Kyuraj4kESgpEdDS1Q6fT+kPAGjuYofYhBSE7T7L5OUNMGPKIKz97je8F7ACWloSNHS0QWePpkhITJG3aebmiFVfTEbWk1wcPRGNpWt34usFE2BqYqjGyEkZKWkZ+HL9Xnz/5URIdWupOxzSIJVKXhITE6s7jkpr3rw55s+fDwBwdnbGN998g4iIZ32s169fR2JiIuzs7AAAmzdvRpMmTXDp0iW0bdsWwLOuos2bN8PCwkLhvOPGjcOwYcMAALNnz4a7uzvmzp0LHx8fAMD06dMxbtw4efsWLVqgRYsW8vVFixZh9+7d+O233xAQEFCpewkODsaCBQte5WMQPas6MjRyVOwiaORohf2RV9UUEVUlGytzfDF3LPLyCpD7NB/mZsZYumYnrCzN5G309HRhY20OG2tzuDjXw+SZa3Es8gqGDuisxshJGTdv3UN6Rjbe+WC1fFtxSQkuX0/E9r3n8F3wBBQWFiMr+6lC9SX98RPONqoCWlBt7IfK40bUSKV3G6nD82NrbGxskJaWhtjYWNjZ2ckTFwBwc3ODqakpYmNj5cmLg4NDmcTl+fOWdjM1a9ZMYVteXh6ysrIgk8mQnZ2NoKAgHDhwACkpKSgqKsLTp0+RlFS2T78ic+bMwcyZM+XrWVlZCvG/ydo1b4D4pDSFbQnJD2FnbVbBESRGenq60NPTRXbOU8Rcj4ffyB4VthUEAYVFfL2ImHR4ywl7NnyksO2z5TvQwM4S44d1hbWlCXR0tHH+91vo2fnZz9jE5DSkpGWgpRsH66qK3UYiUquWYmlSIpGgpKSk0scbGpZfkv7veUu/oOVtK71WYGAgwsPDsWzZMjg5OUFfXx9Dhw5VahCwVCqFVCqtdPs3yeQRXugzcSVWhh7FgO5v4febd7Flzzks/2S4ukOjKnDlWjwgAHVtaiPlwSOEbgtHXZs66N6lJfLyCvDL3tNo18oFZqZGyMrOxcHwS0h/nIWO7d3UHTopwdBAD871FSuoBnq6MJEZyLcP6dUWSzfsg4mxAYwM9PDFuj1o6ebAmUakEtElLxVxdXVFcnIykpOT5dWLmzdvIiMjA25uVf8D8ezZsxg7diwGDRoE4NkYmDt37lT5dd5Ub7k5IOyrCVi8fh+W/3gY9ja1sfjDwRjaq626Q6MqkJubjy07IvDPoywYG+nDva0r3h3WDTo62igpEXDv/j84fvoqsp7kwthIH84N6iJ47jjY17NUd+hUxWZPfhsSiQQfLtqMwoIidGzjgs+nDlJ3WG8EiQTQ0tCH1L0xyYu3tzeaNWsGX19frFq1CkVFRfjggw/g6emJNm3aVPn1nJ2d8euvv6J///6QSCSYO3euUhUgAnp2aoqenZqqOwyqBp06NEGnDk3K3aerq4M5M1hhe1OFLpuisC7VrYW5Uwdj7tTBaorozaWlYvKiyrHqJubxOgokEgn27t0LMzMzdOnSBd7e3mjQoAF27NhRLddbsWIFzMzM4OHhgf79+8PHxwetWrWqlmsRERHRvySCIAjKHnT69Gls2LABCQkJ2LlzJ+rWrYstW7agfv366NSpU3XEqRGysrJgYmKCv9MeQyaTqTscqmbnEtLVHQLVICcLI3WHQDXgyZMstGxojczMzGr7OV76u8L/p8uQGrz636v83Gx8O6JNpWM9deoUvv76a0RHRyMlJQW7d+/GwIED5fvHjh2LsDDFVz/4+Pjg8OHD8vVHjx5h6tSp2LdvH7S0tDBkyBCsXr0aRkbK3YfSlZddu3bBx8cH+vr6+P333+XPKcnMzMQXX3yh7OmIiIjoFZR2G6myKCMnJwctWrTAt99+W2GbXr16ISUlRb5s375dYb+vry9u3LiB8PBw7N+/H6dOncKkSZOUvnelx7wsXrwYISEhGDNmDH766Sf59o4dO2Lx4sVKB0BERESvv969e6N3794vbCOVSmFtXf5rPmJjY3H48GFcunRJPhZ17dq16NOnD5YtWwZbW9tKx6J05SUuLg5dunQps93ExAQZGRnKno6IiIheQVW92ygrK0th+e+T35UVGRkJS0tLuLi4YMqUKUhP/7d7PCoqCqampgqTaLy9vaGlpYULFy4odR2lkxdra2vEx8eX2X7mzBk0aMC3wRIREdWE0rdKq7IAgJ2dHUxMTORLcHDwK8XTq1cvbN68GREREfjqq69w8uRJ9O7dG8XFzx4+mZqaCktLxcch6OjowNzcHKmpqUpdS+luo4kTJ2L69On48ccfIZFIcP/+fURFRSEwMBBz585V9nRERET0Cqrq9QDJyckKA3Zf9eGpI0aMkP+5WbNmaN68ORo2bIjIyEh0795dhUjLUjp5+eSTT1BSUoLu3bsjNzcXXbp0gVQqRWBgIKZOnVqlwREREVH1kslk1TIzqkGDBqhTpw7i4+PRvXt3WFtbIy1N8bUwRUVFePToUYXjZCqidPIikUjw2WefYdasWYiPj0d2djbc3NyUnuZEREREr+6/41Ze9fjqdO/ePaSnp8PGxgYA4O7ujoyMDERHR6N169YAgOPHj6OkpATt27dX6tyv/IRdXV3dannsPhEREb2cFv4dt/KqxysjOztbYcxrYmIiYmJiYG5uDnNzcyxYsABDhgyBtbU1EhIS8PHHH8PJyQk+Pj4Anr3Gp1evXpg4cSJCQkJQWFiIgIAAjBgxQqmZRsArJC9du3Z94Zsojx8/ruwpiYiI6DV3+fJldO3aVb4+c+ZMAICfnx/Wr1+Pa9euISwsDBkZGbC1tUXPnj2xaNEihTE0W7duRUBAALp37y5/SN2aNWuUjkXp5KVly5YK64WFhYiJicEff/wBPz8/pQMgIiIi5dV0t5GXlxde9FD+I0eOvPQc5ubm2LZtm3IXLofSycvKlSvL3R4UFITs7GyVAyIiIqKX44sZq8C7776LH3/8sapOR0RERFSuVx6w+7yoqCjo6elV1emIiIjoBSQSqDRgt7pnG1UnpZOXwYMHK6wLgoCUlBRcvnyZD6kjIiKqIa/7VOnqpHTyYmJiorCupaUFFxcXLFy4ED179qyywIiIiIjKo1TyUlxcjHHjxqFZs2YwMzOrrpiIiIjoJThgt5K0tbXRs2dPvj2aiIhIzSRV8J9YKT3bqGnTprh9+3Z1xEJERESVVFp5UWURK6WTl8WLFyMwMBD79+9HSkoKsrKyFBYiIiKi6lTpMS8LFy7ERx99hD59+gAA3n77bYXXBAiCAIlEguLi4qqPkoiIiBRo8piXSicvCxYswOTJk3HixInqjIeIiIgqQSKRvPBdg5U5XqwqnbyUvs/A09Oz2oIhIiIiehmlpkqLOUsjIiJ6k7DbqJIaNWr00gTm0aNHKgVEREREL8cn7FbSggULyjxhl4iIiKgmKZW8jBgxApaWltUVCxEREVWSlkSi0osZVTlW3SqdvHC8CxER0etDk8e8VPohdaWzjYiIiIjUqdKVl5KSkuqMg4iIiJSh4oBdEb/aSLkxL0RERPR60IIEWipkIKocq25MXoiIiERIk6dKK/1iRiIiIiJ1YuWFiIhIhDR5thGTFyIiIhHS5Oe8sNuIiIiIRIWVFyIiIhHS5AG7TF6IiIhESAsqdhuJeKo0u42IiIhIVFh5ISIiEiF2GxEREZGoaEG17hMxd72IOXYiIiLSQKy8EBERiZBEIoFEhb4fVY5VNyYvREREIiSBai+GFm/qwuSFiIhIlPiEXSIiIiKRYOWFiIhIpMRbO1ENkxciIiIR0uTnvLDbiIiIiESFlRciIiIR4lRpIiIiEhU+YZeIiIhIJFh5ISIiEiF2GxEREZGoaPITdtltRERERKLCystrSEdbCzrazCvfdK3tzdQdAtWgep0/VHcIVAOE4oIauxa7jYiIiEhUNHm2EZMXIiIiEdLkyouYEy8iIiKqIadOnUL//v1ha2sLiUSCPXv2KOwXBAHz5s2DjY0N9PX14e3tjVu3bim0efToEXx9fSGTyWBqaorx48cjOztb6ViYvBAREYmQpAoWZeTk5KBFixb49ttvy92/dOlSrFmzBiEhIbhw4QIMDQ3h4+ODvLw8eRtfX1/cuHED4eHh2L9/P06dOoVJkyYpGQm7jYiIiESppl/M2Lt3b/Tu3bvcfYIgYNWqVfj8888xYMAAAMDmzZthZWWFPXv2YMSIEYiNjcXhw4dx6dIltGnTBgCwdu1a9OnTB8uWLYOtrW2lY2HlhYiISINlZWUpLPn5+UqfIzExEampqfD29pZvMzExQfv27REVFQUAiIqKgqmpqTxxAQBvb29oaWnhwoULSl2PyQsREZEIaUGi8gIAdnZ2MDExkS/BwcFKx5KamgoAsLKyUthuZWUl35eamgpLS0uF/To6OjA3N5e3qSx2GxEREYlQVXUbJScnQyaTybdLpVIVI6t+rLwQERFpMJlMprC8SvJibW0NAHjw4IHC9gcPHsj3WVtbIy0tTWF/UVERHj16JG9TWUxeiIiIREhSBf9Vlfr168Pa2hoRERHybVlZWbhw4QLc3d0BAO7u7sjIyEB0dLS8zfHjx1FSUoL27dsrdT12GxEREYlQTc82ys7ORnx8vHw9MTERMTExMDc3h729PT788EMsXrwYzs7OqF+/PubOnQtbW1sMHDgQAODq6opevXph4sSJCAkJQWFhIQICAjBixAilZhoBTF6IiIioEi5fvoyuXbvK12fOnAkA8PPzQ2hoKD7++GPk5ORg0qRJyMjIQKdOnXD48GHo6enJj9m6dSsCAgLQvXt3aGlpYciQIVizZo3SsUgEQRBUvyWqCllZWTAxMcGD9EyFwVP0ZsrJK1J3CFSD+GJGzSAUFyD/+vfIzKy+n+Olvyt2nk+AoZHxK58nJ/sJhnZoWK2xVhdWXoiIiESopruNXidMXoiIiERIk5MXzjYiIiIiUWHlhYiISIRUne5clVOlaxqTFyIiIhHSkjxbVDlerNhtRERERKLCygsREZEIsduIiIiIRIWzjYiIiIhEgpUXIiIiEZJAta4fERdemLwQERGJEWcbEREREYkEKy9EREQixNlGREREJCqaPNuIyQsREZEISaDaoFsR5y4c80JERETiwsoLERGRCGlBAi0V+n60RFx7YfJCREQkQuw2IiIiIhIJVl6IiIjESINLL0xeiIiIREiTn/PCbiMiIiISFVZeiIiIxEjFh9SJuPDC5IWIiEiMNHjIC7uNiIiISFxYeSEiIhIjDS69MHkhIiISIU2ebcTkhYiISIQ0+a3SHPNCREREosLKCxERkQhp8JAXJi9ERESipMHZC7uNiIiISFRYeSEiIhIhzjYiIiIiUeFsIyIiIiKRYOWFiIhIhDR4vC6TFyIiIlHS4OyF3UZEREQkKqy8EBERiRBnGxEREZGoaPJsIyYvREREIqTBQ1445oWIiIjEhZUXUqvvfz6Jtf+LQFp6Fpo618VXs95B6yaO6g6LVHQ+JgEh24/jelwyHqRn4Ycl76FXl+by/Tm5+fhiwz4cOX0djzNzYW9jjveGdsHogR3VGDW9zHtDOuG9IZ1hZ2MOAPjzdiq+3ngIx87dBAD4DeqIoT5t0NylHmRG+nDoOgtZ2U8VztHQ3hILpw1E+xYNUEtHGzfj72NJyH6cib5V4/cjehpcetG4yktkZCQkEgkyMjJe2M7R0RGrVq2qkZg01a9Ho/H5qt2YPaE3IrfMRlPnuhgy9Vs8fPRE3aGRinLz8uHmZIvFM4eWu3/BN3sQeeFPrJn7LiL/9wnGD/PE56t24eiZP2o4UlLG/bQMLPhmL7qOWYpufl/j9OW/sHXZJDRuYA0A0NerhYiom1gZerTCc/y0YjJ0tLUwYMoadB2zFH/c+hs/rZwMy9rGNXUbbwxJFfwnVhqXvHh4eCAlJQUmJiYAgNDQUJiampZpd+nSJUyaNKmGo9Ms67Ydx5iBHvB92x2NG9hgxZwRMNDTxf9+i1J3aKSibh3c8PHEvuj9n2rLf0X/kYh3erWFx1vOsLOpjXff9oBbQ1vExN6t4UhJGYdP/4HwczdxO/khEpLSsHj9PuTk5qNN0/oAgJDtkVgVFo5L1++Ue7y5iSGcHCyxKiwcN+Lv43byQyz4Zi8M9aVwbWhbg3dCYqdxyYuuri6sra0heckwawsLCxgYGNRQVJqnoLAIMX8mw6udi3yblpYWPNu54NL1RDVGRjWhddP6CD/7B1IeZkAQBJy9cgu3kx+iS9vG6g6NKklLS4LBPVrDQF+30t+zjzJz8NedVAzv2w4GerrQ1tbC2MGdkJaehZjYpGqO+M1TOttIlUWsXsvkxcvLCwEBAQgICICJiQnq1KmDuXPnQhAEAMDjx48xZswYmJmZwcDAAL1798atW//2l969exf9+/eHmZkZDA0N0aRJExw8eBCAYrdRZGQkxo0bh8zMTEgkEkgkEgQFBQFQ7DYaNWoUhg8frhBjYWEh6tSpg82bNwMASkpKEBwcjPr160NfXx8tWrTAzp07q/mTEq/0jGwUF5fAwlyxVGxhLkNaepaaoqKasujDIXB2tEbbwUGo3/UjjA4MwZKZQ9ChZUN1h0Yv4dbQFsknl+PB2VVYMWc4Rs/6HnGJqZU+fpD/N2jeyA7JJ5ch9cxKfDCqG4ZOW4fMJ09ffjApkFTBIlavZfICAGFhYdDR0cHFixexevVqrFixAj/88AMAYOzYsbh8+TJ+++03REVFQRAE9OnTB4WFhQAAf39/5Ofn49SpU7h+/Tq++uorGBkZlbmGh4cHVq1aBZlMhpSUFKSkpCAwMLBMO19fX+zbtw/Z2dnybUeOHEFubi4GDRoEAAgODsbmzZsREhKCGzduYMaMGXj33Xdx8uTJCu8xPz8fWVlZCguRJti06xSu3LiDTV9OwMEfAjHXfyA+W7ELpy/HqTs0eolbdx+gi28wvMctw4+7zmBd0Gi41Leu9PFffzwM/zx+gj4TV6H72K9x8ORVbF/xPqxqy6oxaqoKQUFB8n/oly6NG/9bLc3Ly4O/vz9q164NIyMjDBkyBA8ePKiWWF7b2UZ2dnZYuXIlJBIJXFxccP36daxcuRJeXl747bffcPbsWXh4eAAAtm7dCjs7O+zZswfvvPMOkpKSMGTIEDRr1gwA0KBBg3KvoaurCxMTE0gkElhbV/zN5+PjA0NDQ+zevRujR48GAGzbtg1vv/02jI2NkZ+fjy+++ALHjh2Du7u7/JpnzpzBhg0b4OnpWe55g4ODsWDBglf+jMSstqkRtLW1ygzOffgoC5b8IfZGe5pfgK++O4AflryH7h5NAABuTra4cetvhGw/gc5tXF5yBlKnwqJiJN77BwBw9c9kvOVmj8kjvDAj+KeXHtulbSP4dGqK+t0/xpOcPABA4Fc/w6tdY4zs1x6rwsKrNfY3jhpmGzVp0gTHjh2Tr+vo/JtGzJgxAwcOHMAvv/wCExMTBAQEYPDgwTh79qwKQZbvta28dOjQQWFciru7O27duoWbN29CR0cH7du3l++rXbs2XFxcEBsbCwCYNm0aFi9ejI4dO2L+/Pm4du2aSrHo6Ohg2LBh2Lp1KwAgJycHe/fuha+vLwAgPj4eubm56NGjB4yMjOTL5s2bkZCQUOF558yZg8zMTPmSnJysUpxioltLBy0b2+HkpX//pV1SUoJTl/5C22b11RgZVbeiohIUFhVDoqX4k1NbWyLvGibx0JJIoKtbuX8HG+jpAnj2vf5fJYIALTEPwFATdcw20tHRgbW1tXypU6cOACAzMxMbN27EihUr0K1bN7Ru3RqbNm3CuXPncP78+aq+9de38qKKCRMmwMfHBwcOHMDRo0cRHByM5cuXY+rUqa98Tl9fX3h6eiItLQ3h4eHQ19dHr169AEDenXTgwAHUrVtX4TipVFrhOaVS6Qv3v+k+GNUNHyzYgrdc7dGqiSPWbz+BnKf58O3fQd2hkYpycvNx5++H8vXklEe4ceseTGWGqGtlhg4tG2LJut+gJ62FelbmOB8Tj52HL2N+wAA1Rk0vM8//bRw7dwPJqY9hbKCHob3aoFNrZwyZug4AYFnbGJa1ZWhg9+wXWhMnWzzJzcO91MfIyMrFxWuJyHiSi3VBY/D1D4fwNL8QfgM94GBbG0fP3lDnrVEl3bp1C7a2ttDT04O7uzuCg4Nhb2+P6OhoFBYWwtvbW962cePGsLe3R1RUFDp0qNqf669t8nLhwgWF9fPnz8PZ2Rlubm4oKirChQsX5N1G6enpiIuLg5ubm7y9nZ0dJk+ejMmTJ2POnDn4/vvvy01edHV1UVxc/NJ4PDw8YGdnhx07duDQoUN45513UKtWLQCAm5sbpFIpkpKSKuwiorIG92yNfzKy8cWGA0hLf4Jmjepi5xp/dhu9Aa7GJWHYtG/l6wu+2QMAeKdXW6z8zBfrgvzw5Yb9mLrwf8jIykU9azPMntiHD6l7zdUxM8L6oDGwqiNDVnYebsT/jSFT1yHy4p8AgHGDO+OTSX3k7Q9+PwMA8MGCLdi+/wIeZeZg6LR1+HxKf+xdNw06Olr483YqfAO/wx+3/lbLPYlZVb3b6PnxlhX9w7p9+/YIDQ2Fi4sLUlJSsGDBAnTu3Bl//PEHUlNToaurW+bRI1ZWVkhNrfyA7sp6bZOXpKQkzJw5E++//z6uXLmCtWvXYvny5XB2dsaAAQMwceJEbNiwAcbGxvjkk09Qt25dDBjw7F9tH374IXr37o1GjRrh8ePHOHHiBFxdXcu9jqOjI7KzsxEREYEWLVrAwMCgwinSo0aNQkhICP766y+cOHFCvt3Y2BiBgYGYMWMGSkpK0KlTJ2RmZuLs2bOQyWTw8/Or+g/oDTFpmCcmDWPC96bxeMsZ906vqnC/ZW0ZVnw6quYCoioxbfG2F+7/6vuD+Or7gy9sExObhKH/SWzp1VXVkBc7OzuF7fPnz5fPvP2v3r17y//cvHlztG/fHg4ODvj555+hr6+vQiTKe22TlzFjxuDp06do164dtLW1MX36dPlD4zZt2oTp06ejX79+KCgoQJcuXXDw4EF5JaS4uBj+/v64d+8eZDIZevXqhZUrV5Z7HQ8PD0yePBnDhw9Henp6hV804FnX0ZIlS+Dg4ICOHRX/hbho0SJYWFggODgYt2/fhqmpKVq1aoVPP/206j4UIiKiUlWUvSQnJ0Mm+7fiXdnhDKampmjUqBHi4+PRo0cPFBQUICMjQ6H68uDBgxdOiHlVEuE1HCHn5eWFli1batzj+bOysmBiYoIH6ZkKf5HozZSTV6TuEKgG1ev8obpDoBogFBcg//r3yMysvp/jpb8rom+lwMj41a+R/SQLrZ1tXjnW7Oxs2NvbIygoCH5+frCwsMD27dsxZMgQAEBcXBwaN26sWWNeiIiIqGKqvp9I2WMDAwPRv39/ODg44P79+5g/fz60tbUxcuRImJiYYPz48Zg5cybMzc0hk8kwdepUuLu7V3niAjB5ISIiEidVH/Gv5LH37t3DyJEjkZ6eDgsLC3Tq1Annz5+HhYUFAGDlypXQ0tLCkCFDkJ+fDx8fH6xbt06FACv2WiYvkZGR6g6BiIiI/uOnn178IEI9PT18++23+Pbb6h+Q/VomL0RERPRianjA7muDyQsREZEYaXD28tq+HoCIiIioPKy8EBERiVBNzzZ6nTB5ISIiEqGqej2AGLHbiIiIiESFlRciIiIR0uDxukxeiIiIREmDsxcmL0RERCKkyQN2OeaFiIiIRIWVFyIiIhGSQMXZRlUWSc1j8kJERCRCGjzkhd1GREREJC6svBAREYmQJj+kjskLERGRKGluxxG7jYiIiEhUWHkhIiISIXYbERERkahobqcRu42IiIhIZFh5ISIiEiF2GxEREZGoaPK7jZi8EBERiZEGD3rhmBciIiISFVZeiIiIREiDCy9MXoiIiMRIkwfsstuIiIiIRIWVFyIiIhHibCMiIiISFw0e9MJuIyIiIhIVVl6IiIhESIMLL0xeiIiIxIizjYiIiIhEgpUXIiIiUVJttpGYO46YvBAREYkQu42IiIiIRILJCxEREYkKu42IiIhESJO7jZi8EBERiZAmvx6A3UZEREQkKqy8EBERiRC7jYiIiEhUNPn1AOw2IiIiIlFh5YWIiEiMNLj0wuSFiIhIhDjbiIiIiEgkWHkhIiISIc42IiIiIlHR4CEv7DYiIiISJUkVLK/g22+/haOjI/T09NC+fXtcvHhRtft4BUxeiIiIqFJ27NiBmTNnYv78+bhy5QpatGgBHx8fpKWl1WgcTF6IiIhESFIF/ylrxYoVmDhxIsaNGwc3NzeEhITAwMAAP/74YzXcYcWYvBAREYlQ6YBdVRZlFBQUIDo6Gt7e3vJtWlpa8Pb2RlRUVBXf3YtxwO5rRBAEAMCTrCw1R0I1ITevSN0hUA0SigvUHQLVgNKvc+nP8+qUpeLvitLjnz+PVCqFVCot0/6ff/5BcXExrKysFLZbWVnhzz//VCkWZTF5eY08efIEAOBU307NkRARkSqePHkCExOTajm3rq4urK2t4VwFvyuMjIxgZ6d4nvnz5yMoKEjlc1cnJi+vEVtbWyQnJ8PY2BgSMU/AV1JWVhbs7OyQnJwMmUym7nCoGvFrrTk09WstCAKePHkCW1vbaruGnp4eEhMTUVCgejVPEIQyv2/Kq7oAQJ06daCtrY0HDx4obH/w4AGsra1VjkUZTF5eI1paWqhXr566w1AbmUymUT/kNBm/1ppDE7/W1VVx+S89PT3o6elV+3X+S1dXF61bt0ZERAQGDhwIACgpKUFERAQCAgJqNBYmL0RERFQpM2fOhJ+fH9q0aYN27dph1apVyMnJwbhx42o0DiYvREREVCnDhw/Hw4cPMW/ePKSmpqJly5Y4fPhwmUG81Y3JC6mdVCrF/PnzK+xnpTcHv9aag1/rN1dAQECNdxM9TyLUxHwuIiIioirCh9QRERGRqDB5ISIiIlFh8kJERESiwuSFRCUoKAgtW7ZUdxj0mnF0dMSqVavUHQYBiIyMhEQiQUZGxgvb8WtGqmDyQq8tiUSCPXv2KGwLDAxERESEegIiopfy8PBASkqK/EFtoaGhMDU1LdPu0qVLmDRpUg1HR28KTpUmUTEyMoKRkZG6wyAlFRQUQFdXV91hUA0ofe/Oy1hYWNRANPSmYuWFyvDy8sK0adPw8ccfw9zcHNbW1gov6crIyMCECRNgYWEBmUyGbt264erVqwrnWLx4MSwtLWFsbIwJEybgk08+UejuuXTpEnr06IE6derAxMQEnp6euHLliny/o6MjAGDQoEGQSCTy9f92Gx09ehR6enplytPTp09Ht27d5OtnzpxB586doa+vDzs7O0ybNg05OTkqf05vspf9HUhKSsKAAQNgZGQEmUyGYcOGKbzvpPTr9MMPP6B+/fryx5hLJBJs2LAB/fr1g4GBAVxdXREVFYX4+Hh4eXnB0NAQHh4eSEhIkJ8rISEBAwYMgJWVFYyMjNC2bVscO3asxj6LN5GXl5f8WR0mJiaoU6cO5s6dK38T8uPHjzFmzBiYmZnBwMAAvXv3xq1bt+TH3717F/3794eZmRkMDQ3RpEkTHDx4EIBit1FkZCTGjRuHzMxMSCQSSCQS+d+j/3YbjRo1CsOHD1eIsbCwEHXq1MHmzZsBPHsMfXBwMOrXrw99fX20aNECO3furOZPil5XTF6oXGFhYTA0NMSFCxewdOlSLFy4EOHh4QCAd955B2lpaTh06BCio6PRqlUrdO/eHY8ePQIAbN26FUuWLMFXX32F6Oho2NvbY/369Qrnf/LkCfz8/HDmzBmcP38ezs7O6NOnj/zN2pcuXQIAbNq0CSkpKfL1/+revTtMTU2xa9cu+bbi4mLs2LEDvr6+AJ794uvVqxeGDBmCa9euYceOHThz5ozaH7AkBhX9HSgpKcGAAQPw6NEjnDx5EuHh4bh9+3aZXz7x8fHYtWsXfv31V8TExMi3L1q0CGPGjEFMTAwaN26MUaNG4f3338ecOXNw+fJlCIKg8PXJzs5Gnz59EBERgd9//x29evVC//79kZSUVFMfxRspLCwMOjo6uHjxIlavXo0VK1bghx9+AACMHTsWly9fxm+//YaoqCgIgoA+ffqgsLAQAODv74/8/HycOnUK169fx1dffVVuRdTDwwOrVq2CTCZDSkoKUlJSEBgYWKadr68v9u3bh+zsbPm2I0eOIDc3F4MGDQIABAcHY/PmzQgJCcGNGzcwY8YMvPvuuzh58mR1fDz0uhOInuPp6Sl06tRJYVvbtm2F2bNnC6dPnxZkMpmQl5ensL9hw4bChg0bBEEQhPbt2wv+/v4K+zt27Ci0aNGiwmsWFxcLxsbGwr59++TbAAi7d+9WaDd//nyF80yfPl3o1q2bfP3IkSOCVCoVHj9+LAiCIIwfP16YNGmSwjlOnz4taGlpCU+fPq0wHk33or8DR48eFbS1tYWkpCT5vhs3bggAhIsXLwqC8OzrVKtWLSEtLU3hHACEzz//XL4eFRUlABA2btwo37Z9+3ZBT0/vhfE1adJEWLt2rXzdwcFBWLlypdL3qak8PT0FV1dXoaSkRL5t9uzZgqurq/DXX38JAISzZ8/K9/3zzz+Cvr6+8PPPPwuCIAjNmjUTgoKCyj33iRMnBADy78FNmzYJJiYmZdr992tWWFgo1KlTR9i8ebN8/8iRI4Xhw4cLgiAIeXl5goGBgXDu3DmFc4wfP14YOXKk0vdP4sfKC5WrefPmCus2NjZIS0vD1atXkZ2djdq1a8vHnxgZGSExMVFe6o+Li0O7du0Ujn9+/cGDB5g4cSKcnZ1hYmICmUyG7Oxspf817evri8jISNy/fx/As6pP37595QMEr169itDQUIVYfXx8UFJSgsTERKWupWkq+jsQGxsLOzs72NnZyfe5ubnB1NQUsbGx8m0ODg7ljmv473lL34fSrFkzhW15eXnIysoC8KzyEhgYCFdXV5iamsLIyAixsbGsvKioQ4cOkEgk8nV3d3fcunULN2/ehI6ODtq3by/fV7t2bbi4uMi/vtOmTcPixYvRsWNHzJ8/H9euXVMpFh0dHQwbNgxbt24FAOTk5GDv3r3yCmp8fDxyc3PRo0cPhe/lzZs3K3QxkubggF0qV61atRTWJRIJSkpKkJ2dDRsbG0RGRpY5prwZBRXx8/NDeno6Vq9eDQcHB0ilUri7u6OgoECpONu2bYuGDRvip59+wpQpU7B7926EhobK92dnZ+P999/HtGnTyhxrb2+v1LU0TUV/ByrL0NDwpect/eVZ3rbSawUGBiI8PBzLli2Dk5MT9PX1MXToUKX/rlDVmTBhAnx8fHDgwAEcPXoUwcHBWL58OaZOnfrK5/T19YWnpyfS0tIQHh4OfX199OrVCwDk3UkHDhxA3bp1FY7ju5M0E5MXUkqrVq2QmpoKHR0d+SDa57m4uODSpUsYM2aMfNvzY1bOnj2LdevWoU+fPgCA5ORk/PPPPwptatWqheLi4pfG5Ovri61bt6JevXrQ0tJC3759FeK9efMmnJycKnuL9BKurq5ITk5GcnKyvPpy8+ZNZGRkwM3Nrcqvd/bsWYwdO1Y+9iE7Oxt37typ8utomgsXLiisl449c3NzQ1FRES5cuAAPDw8AQHp6OuLi4hS+vnZ2dpg8eTImT56MOXPm4Pvvvy83edHV1a3U97GHhwfs7OywY8cOHDp0CO+88448qXVzc4NUKkVSUhI8PT1VuW16Q7DbiJTi7e0Nd3d3DBw4EEePHsWdO3dw7tw5fPbZZ7h8+TIAYOrUqdi4cSPCwsJw69YtLF68GNeuXVMoUTs7O2PLli2IjY3FhQsX4OvrC319fYVrOTo6IiIiAqmpqXj8+HGFMfn6+uLKlStYsmQJhg4dqvAvsdmzZ+PcuXMICAhATEwMbt26hb1793LArgq8vb3RrFkz+ed+8eJFjBkzBp6enmjTpk2VX8/Z2Vk+6Pfq1asYNWqUUhUgKl9SUhJmzpyJuLg4bN++HWvXrsX06dPh7OyMAQMGYOLEiThz5gyuXr2Kd999F3Xr1sWAAQMAAB9++CGOHDmCxMREXLlyBSdOnICrq2u513F0dER2djYiIiLwzz//IDc3t8KYRo0ahZCQEISHh8u7jADA2NgYgYGBmDFjBsLCwpCQkIArV65g7dq1CAsLq9oPhkSByQspRSKR4ODBg+jSpQvGjRuHRo0aYcSIEbh79658/IKvry/mzJmDwMBAtGrVComJiRg7dqx8uiwAbNy4EY8fP0arVq0wevRoTJs2DZaWlgrXWr58OcLDw2FnZ4e33nqrwpicnJzQrl07XLt2TeEHHvBsfMXJkyfx119/oXPnznjrrbcwb9482NraVuGnolkkEgn27t0LMzMzdOnSBd7e3mjQoAF27NhRLddbsWIFzMzM4OHhgf79+8PHxwetWrWqlmtpkjFjxuDp06do164d/P39MX36dPlD4zZt2oTWrVujX79+cHd3hyAIOHjwoLwSUlxcDH9/f7i6uqJXr15o1KgR1q1bV+51PDw8MHnyZAwfPhwWFhZYunRphTH5+vri5s2bqFu3Ljp27Kiwb9GiRZg7dy6Cg4Pl1z1w4ADq169fRZ8IiYlEEP5/Yj9RNerRowesra2xZcsWdYdCpPG8vLzQsmVLPp6fRItjXqjK5ebmIiQkBD4+PtDW1sb27dtx7Ngx+XNiiIiIVMHkhapcadfSkiVLkJeXBxcXF+zatQve3t7qDo2IiN4A7DYiIiIiUeGAXSIiIhIVJi9EREQkKkxeiIiISFSYvBAREZGoMHkhIgVjx47FwIED5eteXl748MMPazyOyMhISCQSZGRkVNhGIpFgz549lT5nUFAQWrZsqVJcd+7cgUQiQUxMjErnIaJXx+SFSATGjh0LiUQCiUQCXV1dODk5YeHChSgqKqr2a//6669YtGhRpdpWJuEgIlIVn/NCJBK9evXCpk2bkJ+fj4MHD8Lf3x+1atXCnDlzyrQtKCiArq5ulVzX3Ny8Ss5DRFRVWHkhEgmpVApra2s4ODhgypQp8Pb2xm+//Qbg366eJUuWwNbWFi4uLgCeva172LBhMDU1hbm5OQYMGKDwRubi4mLMnDkTpqamqF27Nj7++GM8/+in57uN8vPzMXv2bNjZ2UEqlcLJyQkbN27EnTt30LVrVwCAmZkZJBIJxo4dCwAoKSlBcHAw6tevD319fbRo0QI7d+5UuM7BgwfRqFEj6Ovro2vXrq/05ujZs2ejUaNGMDAwQIMGDTB37lwUFhaWabdhwwbY2dnBwMAAw4YNQ2ZmpsL+H374Aa6urtDT00Pjxo0rfG8PEakHkxcikdLX10dBQYF8PSIiAnFxcQgPD8f+/ftRWFgIHx8fGBsb4/Tp0zh79iyMjIzQq1cv+XHLly9HaGgofvzxR5w5cwaPHj3C7t27X3jdMWPGYPv27VizZg1iY2OxYcMGGBkZwc7ODrt27QIAxMXFISUlBatXrwYABAcHY/PmzQgJCcGNGzcwY8YMvPvuuzh58iSAZ0nW4MGD0b9/f8TExGDChAn45JNPlP5MjI2NERoaips3b2L16tX4/vvvsXLlSoU28fHx+Pnnn7Fv3z4cPnwYv//+Oz744AP5/q1bt2LevHlYsmQJYmNj8cUXX2Du3Ll8ezHR60Qgoteen5+fMGDAAEEQBKGkpEQIDw8XpFKpEBgYKN9vZWUl5Ofny4/ZsmWL4OLiIpSUlMi35efnC/r6+sKRI0cEQRAEGxsbYenSpfL9hYWFQr169eTXEgRB8PT0FKZPny4IgiDExcUJAITw8PBy4zxx4oQAQHj8+LF8W15enmBgYCCcO3dOoe348eOFkSNHCoIgCHPmzBHc3NwU9s+ePbvMuZ4HQNi9e3eF+7/++muhdevW8vX58+cL2trawr179+TbDh06JGhpaQkpKSmCIAhCw4YNhW3btimcZ9GiRYK7u7sgCIKQmJgoABB+//33Cq9LRNWLY16IRGL//v0wMjJCYWEhSkpKMGrUKAQFBcn3N2vWTGGcy9WrVxEfHw9jY2OF8+Tl5SEhIQGZmZlISUlB+/bt5ft0dHTQpk2bMl1HpWJiYqCtrQ1PT89Kxx0fH4/c3Fz06NFDYXtBQQHeeustAEBsbKxCHADg7u5e6WuU2rFjB9asWYOEhARkZ2ejqKgIMplMoY29vT3q1q2rcJ2SkhLExcXB2NgYCQkJGD9+PCZOnChvU1RUBBMTE6XjIaLqweSFSCS6du2K9evXQ1dXF7a2ttDRUfz2NTQ0VFjPzs5G69atsXXr1jLnsrCweKUY9PX1lT4mOzsbAHDgwAGFpAF4No6nqkRFRcHX1xcLFiyAj48PTExM8NNPP2H58uVKx/r999+XSaa0tbWrLFYiUg2TFyKRMDQ0hJOTU6Xbt2rVCjt27IClpWWZ6kMpGxsbXLhwAV26dAHwrMIQHR2NVq1aldu+WbNmKCkpwcmTJ8t9S3hp5ae4uFi+zc3NDVKpFElJSRVWbFxdXeWDj0udP3/+5Tf5H+fOnYODgwM+++wz+ba7d++WaZeUlIT79+/D1tZWfh0tLS24uLjAysoKtra2uH37Nnx9fZW6PhHVHA7YJXpD+fr6ok6dOhgwYABOnz6NxMREREZGYtq0abh37x4AYPr06fjyyy+xZ88e/Pnnn/jggw9e+IwWR0dH+Pn54b333sOePXvk5/z5558BAA4ODpBIJNi/fz8ePnyI7OxsGBsbIzAwEDNmzEBYWBgSEhJw5coVrF27Vj4IdvLkybh16xZmzZqFuLg4bNu2DaGhoUrdr7OzM5KSkvDTTz8hISEBa9asKXfwsZ6eHvz8/HD16lWcPn0a06ZNw7Bhw2BtbQ0AWLBgAYKDg7FmzRr89ddfuH79OjZt2oQVK1YoFQ8RVR8mL0RvKAMDA5w6dQr29vYYPHgwXF1dMX78eOTl5ckrMR999BFGjx4NPz8/uLu7w9jYGIMGDXrhedevX4+hQ4figw8+QOPGjTFx4kTk5OQAAOrWrYsFCxbgk08+gZWVFQICAgAAixYtwty5cxEcHAxXV1f06tULBw4cQP369QE8G4eya9cu7NmzBy1atEBISAi++OILpe737bffxowZMxAQEICWLVvi3LlzmDt3bpl2Tk5OGDx4MPr06YOePXuiefPmClOhJ0yYgB9++AGbNm1Cs2bN4OnpidDQUHmsRKR+EqGikXlEREREryFWXoiIiEhUmLwQERGRqDB5ISIiIlFh8kJERESiwuSFiIiIRIXJCxEREYkKkxciIiISFSYvREREJCpMXoiIiEhUmLwQERGRqDB5ISIiIlFh8kJERESi8n+GaC6dQmJMPQAAAABJRU5ErkJggg==\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## 具体的な失敗例\n", "ラベルは「ネガティブ0、ノーマル1、ポジティブ2」に付け直していることに注意。" ], "metadata": { "id": "zA2OEeFy1KLz" } }, { "cell_type": "markdown", "source": [ "### 学習データに対する誤り\n", "以下の通り1件しか誤りがない。学習データに対しては極めて的鉄な予測が可能なモデルになっているようだ。" ], "metadata": { "id": "9dwmMt0qCXHa" } }, { "cell_type": "code", "source": [ "# 学習データに対する予測誤り\n", "\n", "predictions, labels, _ = trainer.predict(train_dataset)\n", "predicted_labels = np.argmax(predictions, axis=1)\n", "\n", "# 誤った予測のサンプルを抽出\n", "incorrect_indices = [i for i, (true, pred) in enumerate(zip(train_labels, predicted_labels)) if true != pred]\n", "\n", "# 誤った予測のサンプルを出力\n", "for i in incorrect_indices:\n", " print(f\"正解ラベル: {train_labels[i]}, 予測ラベル: {predicted_labels[i]}, テキスト: {train_texts[i]}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 51 }, "id": "O0lpUg_q0Wo0", "outputId": "9b603e71-8ab4-4029-d279-63343b71f6ef" }, "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "正解ラベル: 2, 予測ラベル: 1, テキスト: 家族旅行で利用させていただきました。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 東京出張時に利用しました。\n" ] } ] }, { "cell_type": "markdown", "source": [ "### テストデータに対する誤り\n", "テストデータのサンプル数は学習データより少ないにも関わらず、失敗事例数は多い。" ], "metadata": { "id": "PTEkRb78CZgQ" } }, { "cell_type": "code", "source": [ "# テストデータに対する予測誤り\n", "\n", "predictions, labels, _ = trainer.predict(test_dataset)\n", "predicted_labels = np.argmax(predictions, axis=1)\n", "\n", "# 誤った予測のサンプルを抽出\n", "incorrect_indices = [i for i, (true, pred) in enumerate(zip(test_labels, predicted_labels)) if true != pred]\n", "\n", "# 誤った予測のサンプルを出力\n", "for i in incorrect_indices:\n", " print(f\"正解ラベル: {test_labels[i]}, 予測ラベル: {predicted_labels[i]}, テキスト: {test_texts[i]}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "J_TGLzSh0oG-", "outputId": "a970742e-38c9-4271-d128-4772984043a9" }, "execution_count": 14, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "正解ラベル: 1, 予測ラベル: 2, テキスト: お心遣いありがとうございました。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: と感じてしまいました。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 再訪です。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 二度目です。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: お疲れ様です。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: (^ω^\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 対応ありがとうございました。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 加湿器もありました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: コンビニも近かったです。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 長々と失礼しました。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 定宿にします。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 朝食は食べませんでした。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 良かった所。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: お待ちしておりました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 思わず笑ってしまいました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 大人の宿です。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 間違いないホテルです。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 迷わず予約しました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 足湯もありました。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: おかしいです。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 料理は期待通りでした。\n", "正解ラベル: 1, 予測ラベル: 0, テキスト: 好みの問題ですが。\n", "正解ラベル: 1, 予測ラベル: 0, テキスト: リゾートホテルではありません。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: また機会があれば...。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 朝食付きをお勧めします。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: そんなに気になりませんでした。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 立地は問題なしです。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 食べたかったなぁ。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 天気は雨。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 安ければまた利用します。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: はあったものの美味しく頂きました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 泊まる分には問題ありません。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: マンションのようなホテルでした。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 理解できません。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 餅っ!\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: とにかくすごい!\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 出張で利用させてもらっています。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: お風呂とトイレは別。\n", "正解ラベル: 1, 予測ラベル: 0, テキスト: がゴロゴロ。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 風呂は温泉です。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 一人旅で利用いたしました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: それ以外は◎。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 午後9時すぎから朝8時までなので1000円くらいだといいですね。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 女性専用ルームだったので、アメニティーやナノスチーマーやフットマッサージまで。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 時間で管理してるなら案内出して欲しいです。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 朝食は、お粥とチーズオムレツをお願いし、サラダに、ヨーグルトとフルーツ三昧。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 他にいいところが空いていればそっちを選択するかもですね。\n", "正解ラベル: 1, 予測ラベル: 0, テキスト: ただ朝のバイキングで席を指定され並んでいた所、私達の席を隣のテーブルとくっつけていて、家族連れが座りました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: そこがチョット残念な思いをしただけで他の食事、お風呂などは大変満足させていただきました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 料理もおいしかったし,お風呂の洗面付近の清潔感は素晴らしかったので,期待していかなければそれなりに満足できたのかもしれません。\n", "正解ラベル: 1, 予測ラベル: 0, テキスト: この金額でシャワーが赤青の蛇口じゃないところは初めてかも。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: チェックアウト12時のプランでしたが、10時までの料金をホテルで支払い、その後は精算機でとのことでした。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 4ヵ月の子どもと一緒の旅行だったので、部屋食にしたが、全体的に同じような味付けで、これ!\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: まだ使ってない洗面所で、気になりました。\n", "正解ラベル: 1, 予測ラベル: 0, テキスト: 8時過ぎになると人が少なってきます。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: フロントの方たちの笑顔があれば、90点なのですが、85点の満足の宿でした。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 部屋は4人部屋で大変広く、7人で2次会が出来ました、アジアンリゾート風ですね・・良かったですその他テニスやパターゴルフ、散策、夏はプールなどがあり宿泊者が楽しめる配慮が随所にありますが、子供いわくゲーセンは信じられない位しょぼい(笑)\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 夜フルーツが無いのと、朝お粥が有れば年配の方は喜ぶと思います。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: フロントも全く同じで何コールしてもでない。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 朝食のメニューも変わっていて前からあった和食・洋食にエビチーズドリアセット・ロコモコ丼セットの4種類になっていました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 大浴場が利用できたのが、ここに決めたポイントです。\n", "正解ラベル: 0, 予測ラベル: 2, テキスト: 身体の大きい人は大変そう。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 他はパーフェクトです。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: ほとんど寝るだけだったので、特に不便はありませんでした。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: この宿に泊まって一番感じる事は、御主人、女将さんの人柄です。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 120センチのセミダブルでしたので、夫とふたりでもこれならOKと思いました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: チッックインが遅くなることと雨が降っていたため夕食を済ませてからホテルに着きましたが、カレーのサービスを行っていました。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 18:00の予約で入って料理をとったところ保温トレーの中の料理はどれもぬるい程度の温度で、松茸茶碗蒸しは容器が冷たい程で驚きました。\n", "正解ラベル: 0, 予測ラベル: 1, テキスト: 粉コーヒーでもいいのでいつでも飲めるようになってればなぁ。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 全てはフロントの方の心遣いにあると思います。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: シングルルームを2部屋予約しましたが、当日部屋が空いてるとの事で大きな部屋に変更してくださいました。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: 料理はみんなきれいに盛られ、おいしかったですが、なにか一つ地のもので、「ここでしか食べられない」とか「何か特化したもの」があったらさらに良かったと思います。\n", "正解ラベル: 2, 予測ラベル: 1, テキスト: お風呂場にタオルの設置があったらいいなと思うのと、朝食会場が宿泊者の人数の割に狭いのが少し惜しい気がしましたが、いいホテルに泊まって気持ちよく過ごせました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 朝食は長い列ができていたが、案外すぐに呼ばれた。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 海沿いの立地のためか日の出スポットらしく元旦に続々前回道路に人が集まってました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: あと、お宿横には酒屋さん!\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 温泉が深夜男女入替のため、チェックイン後とチェックアウト後に入浴すると両方楽しめます。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 温泉チケット付きのプランでしたので目の前の温泉にいきました。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 他は完璧だと思う。\n", "正解ラベル: 1, 予測ラベル: 2, テキスト: 夕食の際、誕生日の特別デザートを用意頂いたり、希望すればチャンチャンコを貸して頂くこともできるようでした。\n" ] } ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", 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