6153b5f46390b1112c333647102ca0c715e0a062,finetune/util/metrics.py,,micro_f1,#Any#Any#Any#,148

Before Change

        FN += len(counts["false_negatives"])
        TP += len(counts["correct"])
        FP += len(counts["false_positives"])
    recall = TP/float(FN + TP)
    precision = TP / float(FP + TP)
    try:
        f1 = 2 * (recall * precision) / (recall + precision)
    except ZeroDivisionError:
        return 0.0
    return f1

def sequence_labeling_token_precision(true, predicted):
    
    Token level precision

After Change

        FN += len(counts["false_negatives"])
        TP += len(counts["correct"])
        FP += len(counts["false_positives"])
    recall = calc_recall(TP, FN)
    precision = calc_precision(TP, FP)
    return calc_f1(recall, precision)


def per_class_f1(true, predicted, span_type="token"):
    

Instances