exact_match/exact_match.py

import datasets
import re
import string

def normalize_answer(s):
    """Lower text and remove punctuation, articles and extra whitespace."""

    def remove_articles(text):
        regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
        return re.sub(regex, " ", text)

    def white_space_fix(text):
        return " ".join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return "".join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))

def compute_exact(a_gold, a_pred):
    return int(normalize_answer(a_gold) == normalize_answer(a_pred))

def compute_em(predictions, references):
    scores = [compute_exact(ref, pred) for pred, ref in zip(predictions, references)]
    return sum(scores)/len(scores)

class ExactMatch(datasets.Metric):
    def _info(self):
        return datasets.MetricInfo(
            description="This will get effective exact match in text data",
            citation="",
            homepage="",
            inputs_description="",
            features=datasets.Features({
                'predictions': datasets.Value('string'),
                'references': datasets.Value('string'),
            }),
            codebase_urls=["https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py"],
            reference_urls=["https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py"]
        )

    def _compute(self, predictions, references):
        return {"exact_match": compute_em(predictions, references)}