Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1M<n<10M
Tags:
License:
| from datasets import load_metric | |
| from ast import literal_eval | |
| def compute_seqeval(references_dataset, predictions_dataset, ref_col='ner_tags', pred_col='pred_ner_tags'): | |
| # computes the seqeval scores | |
| # sort by id | |
| references_dataset = references_dataset.sort('unique_id') | |
| predictions_dataset = predictions_dataset.sort('unique_id') | |
| # load the huggingface metric function | |
| seqeval = load_metric('seqeval') | |
| # check that tokens match | |
| assert(references_dataset['tokens']==predictions_dataset['tokens']) | |
| # ensure IOB2? | |
| # compute scores | |
| seqeval_results = seqeval.compute(predictions = predictions_dataset[pred_col], | |
| references = references_dataset[ref_col], | |
| scheme = 'IOB2', | |
| suffix = False, | |
| ) | |
| # change all values to regular (not numpy) floats (otherwise cannot be serialized to json) | |
| seqeval_results = literal_eval(str(seqeval_results)) | |
| return(seqeval_results) | |