| |
|
|
| 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) |
|
|
