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:
File size: 1,368 Bytes
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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) |