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import re |
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import string |
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from collections import Counter |
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def normalize_answer(s): |
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"""Lower text and remove punctuation, articles and extra whitespace.""" |
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def remove_articles(text): |
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return re.sub(r"\b(a|an|the)\b", " ", text) |
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def white_space_fix(text): |
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return " ".join(text.split()) |
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def remove_punc(text): |
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exclude = set(string.punctuation) |
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return "".join(ch for ch in text if ch not in exclude) |
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def lower(text): |
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return text.lower() |
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return white_space_fix(remove_articles(remove_punc(lower(s)))) |
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def f1_score(prediction, ground_truth): |
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prediction_tokens = normalize_answer(prediction).split() |
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ground_truth_tokens = normalize_answer(ground_truth).split() |
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common = Counter(prediction_tokens) & Counter(ground_truth_tokens) |
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num_same = sum(common.values()) |
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if num_same == 0: |
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return 0 |
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precision = 1.0 * num_same / len(prediction_tokens) |
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recall = 1.0 * num_same / len(ground_truth_tokens) |
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f1 = (2 * precision * recall) / (precision + recall) |
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return f1 |
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def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): |
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scores_for_ground_truths = [] |
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for ground_truth in ground_truths: |
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score = metric_fn(prediction, ground_truth) |
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scores_for_ground_truths.append(score) |
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return max(scores_for_ground_truths) |
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def compute_f1(predictions, references): |
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f1 = 0 |
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for prediction, ground_truths in zip(predictions, references): |
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f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths) |
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return 100.0 * f1 / len(predictions) |
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