Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1M<n<10M
Tags:
License:
| from sklearn.metrics import matthews_corrcoef | |
| import numpy as np | |
| def compute_MCC(references_dataset, predictions_dataset, ref_col='ner_tags', pred_col='pred_ner_tags'): | |
| # computes the Matthews correlation coeff between two datasets | |
| # sort by id | |
| references_dataset = references_dataset.sort('unique_id') | |
| predictions_dataset = predictions_dataset.sort('unique_id') | |
| # check that tokens match | |
| assert(references_dataset['tokens']==predictions_dataset['tokens']) | |
| # the lists have to be flattened | |
| flat_ref_tags = np.concatenate(references_dataset[ref_col]) | |
| flat_pred_tags = np.concatenate(predictions_dataset[pred_col]) | |
| mcc_score = matthews_corrcoef(y_true=flat_ref_tags, | |
| y_pred=flat_pred_tags) | |
| return(mcc_score) |