d877727abf662d0545d4e69e5477e3fe33141a04,finetune/util/metrics.py,,sequence_labeling_counts,#,282
Before Change
Return FP, FN, and TP counts
unique_classes = set([annotation["label"] for annotations in true + predicted for annotation in annotations])
d = {
cls_: {
After Change
Return FP, FN, and TP counts
unique_classes = _get_unique_classes(true, predicted)
d = {
cls_: {
"false_positives": [],
In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 4
Instances Project Name: IndicoDataSolutions/finetune
Commit Name: d877727abf662d0545d4e69e5477e3fe33141a04
Time: 2020-06-24
Author: benlt@hotmail.co.uk
File Name: finetune/util/metrics.py
Class Name:
Method Name: sequence_labeling_counts
Project Name: IndicoDataSolutions/finetune
Commit Name: d877727abf662d0545d4e69e5477e3fe33141a04
Time: 2020-06-24
Author: benlt@hotmail.co.uk
File Name: finetune/util/metrics.py
Class Name:
Method Name: sequence_labeling_token_counts
Project Name: IndicoDataSolutions/finetune
Commit Name: d877727abf662d0545d4e69e5477e3fe33141a04
Time: 2020-06-24
Author: benlt@hotmail.co.uk
File Name: finetune/util/metrics.py
Class Name:
Method Name: sequence_labeling_token_confusion