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Why is the F1 score higher for the second case with missing "C" class predictions? Shouldn't both cases return the same Overall F1 score? By the way, in both cases, F1 score for "C" is 0.
Thanks!
The text was updated successfully, but these errors were encountered:
I'm using the Huggingface implementation of seqeval, example codes as follows:
My question is about the reported Overall F1 score. Given the
references = [['B-E', 'I-E', 'O', 'O', 'B-C', 'I-C', 'I-C', 'O']]
:predictions = [['B-E', 'I-E', 'O', 'O', 'O', 'B-C', 'I-C', 'O']]
: Returnsoverall_f1': 0.5, 'overall_accuracy': 0.75
predictions = [['B-E', 'I-E', 'O', 'O', 'O', 'O', 'O', 'O']]
: Returns'overall_f1': 0.6666666666666666, 'overall_accuracy': 0.625
Why is the F1 score higher for the second case with missing "C" class predictions? Shouldn't both cases return the same Overall F1 score? By the way, in both cases, F1 score for "C" is 0.
Thanks!
The text was updated successfully, but these errors were encountered: