| import re | |
| import string | |
| def normalize_answer(s): | |
| """Lower text and remove punctuation, articles and extra whitespace.""" | |
| def remove_articles(text): | |
| return re.sub(r"\b(a|an|the)\b", " ", 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 exact_match_score(prediction, ground_truth): | |
| return normalize_answer(prediction) == normalize_answer(ground_truth) | |
| def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): | |
| scores_for_ground_truths = [] | |
| for ground_truth in ground_truths: | |
| score = metric_fn(prediction, ground_truth) | |
| scores_for_ground_truths.append(score) | |
| return max(scores_for_ground_truths) | |
| def compute_exact_match(predictions, references): | |
| exact_match = 0 | |
| for prediction, ground_truths in zip(predictions, references): | |
| exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths) | |
| return 100.0 * exact_match / len(predictions) | |