File size: 1,001 Bytes
64ff342 7cc9517 64ff342 64b8a1c 64ff342 37cec8b 64ff342 3f1febf d4447b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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())
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