Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Tags:
License:
| from sklearn.metrics import matthews_corrcoef | |
| import numpy as np | |
| def compute_MCC_jsonl(references_jsonl, predictions_jsonl, ref_col='ner_tags', pred_col='pred_ner_tags'): | |
| ''' | |
| Computes the Matthews correlation coeff between two datasets in jsonl format (list of dicts each with same keys). | |
| Sorts the datasets by 'unique_id' and verifies that the tokens match. | |
| ''' | |
| # reverse the dict | |
| ref_dict = {k:[e[k] for e in references_jsonl] for k in references_jsonl[0].keys()} | |
| pred_dict = {k:[e[k] for e in predictions_jsonl] for k in predictions_jsonl[0].keys()} | |
| # sort by unique_id | |
| ref_idx = np.argsort(ref_dict['unique_id']) | |
| pred_idx = np.argsort(pred_dict['unique_id']) | |
| ref_ner_tags = np.array(ref_dict[ref_col], dtype=object)[ref_idx] | |
| pred_ner_tags = np.array(pred_dict[pred_col], dtype=object)[pred_idx] | |
| ref_tokens = np.array(ref_dict['tokens'], dtype=object)[ref_idx] | |
| pred_tokens = np.array(pred_dict['tokens'], dtype=object)[pred_idx] | |
| # check that tokens match | |
| for t1,t2 in zip(ref_tokens, pred_tokens): | |
| assert(t1==t2) | |
| # the lists have to be flattened | |
| flat_ref_tags = np.concatenate(ref_ner_tags) | |
| flat_pred_tags = np.concatenate(pred_ner_tags) | |
| mcc_score = matthews_corrcoef(y_true=flat_ref_tags, | |
| y_pred=flat_pred_tags) | |
| return(mcc_score) |