Datasets:

adsabs
/

WIESP2022-NER

	from seqeval.metrics import classification_report, f1_score, precision_score, recall_score, accuracy_score
	from seqeval.scheme import IOB2
	import numpy as np
	def compute_seqeval_jsonl(references_jsonl, predictions_jsonl, ref_col='ner_tags', pred_col='pred_ner_tags'):
	'''
	Computes the seqeval scores between two datasets loaded from jsonl (list of dicts with same keys).
	Sorts the datasets by 'unique_id' and verifies that the tokens match.
	'''
	# extract the tags and 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
	assert((ref_tokens==pred_tokens).all())


	# get report
	report = classification_report(y_true=ref_ner_tags, y_pred=pred_ner_tags,
	scheme=IOB2, output_dict=True,
	)

	# extract values we care about
	report.pop("macro avg")
	report.pop("weighted avg")
	overall_score = report.pop("micro avg")

	seqeval_results = {
	type_name: {
	"precision": score["precision"],
	"recall": score["recall"],
	"f1": score["f1-score"],
	"suport": score["support"],
	}
	for type_name, score in report.items()
	}
	seqeval_results["overall_precision"] = overall_score["precision"]
	seqeval_results["overall_recall"] = overall_score["recall"]
	seqeval_results["overall_f1"] = overall_score["f1-score"]
	seqeval_results["overall_accuracy"] = accuracy_score(y_true=ref_ner_tags, y_pred=pred_ner_tags)

	return(seqeval_results)