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毒性

毒性

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NLP,Public Health Classification

Data Structure ? 627.88M

    Data Structure ?

    *The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.

    README.md

    内容 以下からの統合テキスト: - 毒性分類における意図しないバイアス([リンク](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification)) - 有害コメント分類チャレンジ([リンク](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)) - ソーシャルコメントにおける侮辱の検出([リンク](https://www.kaggle.com/c/detecting-insults-in-social-commentary)) - 自動的な憎悪発言検出と不快な言葉の問題([リンク](https://github.com/t-davidson/hate-speech-and-offensive-language)) - 白人至上主義フォーラムからの憎悪発言データセット([リンク](https://github.com/aitor-garcia-p/hate-speech-dataset)) 使用方法 import pandas as pd import pickle with open('toxicity.pkl', 'rb') as f: data = pickle.load(f) test = data[data.Training_evaluation_split.eq('Evaluation')].drop(columns=['Source', 'Training_evaluation_split']) train = data[data.Training_evaluation_split.eq('Training')].drop(columns=['Source', 'Training_evaluation_split']) test_x = test.text testy_ = test.Toxicity train_x = train.text train_y = train.Toxicity
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