scoring-scripts/compute_seqeval.py

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)