| | |
| |
|
| | import re |
| | import string |
| | from collections import Counter |
| |
|
| |
|
| | 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 f1_score(prediction, ground_truth): |
| | prediction_tokens = normalize_answer(prediction).split() |
| | ground_truth_tokens = normalize_answer(ground_truth).split() |
| | common = Counter(prediction_tokens) & Counter(ground_truth_tokens) |
| | num_same = sum(common.values()) |
| | if num_same == 0: |
| | return 0 |
| | precision = 1.0 * num_same / len(prediction_tokens) |
| | recall = 1.0 * num_same / len(ground_truth_tokens) |
| | f1 = (2 * precision * recall) / (precision + recall) |
| | return f1 |
| |
|
| |
|
| | 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_f1(predictions, references): |
| | f1 = 0 |
| | for prediction, ground_truths in zip(predictions, references): |
| | f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths) |
| | return 100.0 * f1 / len(predictions) |
| |
|
