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