| """ Official evaluation script for v1.1 of the SQuAD dataset. """ |
|
|
| import argparse |
| import json |
| import re |
| import string |
| import sys |
| from collections import Counter |
|
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|
|
| 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)))) |
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|
|
| 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 |
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|
|
|
| def exact_match_score(prediction, ground_truth): |
| return normalize_answer(prediction) == normalize_answer(ground_truth) |
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|
|
| 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) |
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|
|
| def compute_score(dataset, predictions): |
| f1 = exact_match = total = 0 |
| for article in dataset: |
| for paragraph in article["paragraphs"]: |
| for qa in paragraph["qas"]: |
| total += 1 |
| if qa["id"] not in predictions: |
| message = "Unanswered question " + qa["id"] + " will receive score 0." |
| print(message, file=sys.stderr) |
| continue |
| ground_truths = list(map(lambda x: x["text"], qa["answers"])) |
| prediction = predictions[qa["id"]] |
| exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths) |
| f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths) |
|
|
| exact_match = 100.0 * exact_match / total |
| f1 = 100.0 * f1 / total |
|
|
| return {"exact_match": exact_match, "f1": f1} |
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|
|
| if __name__ == "__main__": |
| expected_version = "1.1" |
| parser = argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version) |
| parser.add_argument("dataset_file", help="Dataset file") |
| parser.add_argument("prediction_file", help="Prediction File") |
| args = parser.parse_args() |
| with open(args.dataset_file) as dataset_file: |
| dataset_json = json.load(dataset_file) |
| if dataset_json["version"] != expected_version: |
| print( |
| "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"], |
| file=sys.stderr, |
| ) |
| dataset = dataset_json["data"] |
| with open(args.prediction_file) as prediction_file: |
| predictions = json.load(prediction_file) |
| print(json.dumps(compute_score(dataset, predictions))) |
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|
