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I run this line:
P, R, F1 = bert_score.score(cands, refs, lang='en', verbose=True, model_type='roberta-large', num_layers=17)
and I got nan of P score, but not for R and F1 score. what does this mean?
python cal_bert_score.py
'''
using bert-score version: 0.3.12
calculating scores...
computing bert embedding.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [01:18<00:00, 2.72it/s]
computing greedy matching.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:03<00:00, 36.60it/s]
done in 81.80 seconds, 89.50 sentences/sec
System level F1 score: 0.731
System level P score: nan
System level R score: 0.836
'''
The text was updated successfully, but these errors were encountered:
I run this line:
P, R, F1 = bert_score.score(cands, refs, lang='en', verbose=True, model_type='roberta-large', num_layers=17)
and I got nan of P score, but not for R and F1 score. what does this mean?
python cal_bert_score.py
'''
using bert-score version: 0.3.12
calculating scores...
computing bert embedding.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 213/213 [01:18<00:00, 2.72it/s]
computing greedy matching.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:03<00:00, 36.60it/s]
done in 81.80 seconds, 89.50 sentences/sec
System level F1 score: 0.731
System level P score: nan
System level R score: 0.836
'''
The text was updated successfully, but these errors were encountered: