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from statistics import mean |
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import pandas as pd |
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from datasets import load_dataset |
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def count_word(text): |
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return len(text.split()) |
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if __name__ == '__main__': |
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data = ["chemprot", "citation_intent", "hyperpartisan_news", "rct_sample", "sciie", "amcd", 'yelp_review', |
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'tweet_eval_irony', 'tweet_eval_hate', 'tweet_eval_emotion'] |
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stats = {} |
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for d in data: |
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_data = load_dataset('asahi417/multi_domain_document_classification', d) |
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stats[d] = { |
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'word/validation': mean([count_word(k['text']) for k in _data['validation']]), |
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'word/test': mean([count_word(k['text']) for k in _data['test']]), |
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'word/train': mean([count_word(k['text']) for k in _data['train']]), |
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'instance/validation': len(_data['validation']), |
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'instance/test': len(_data['test']), |
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'instance/train': len(_data['train']) |
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} |
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df = pd.DataFrame(stats).astype(int) |
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df.to_csv('stats.csv') |
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print(df.to_markdown()) |
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