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| """ | |
| Official evaluation script for ReCoRD v1.0. | |
| (Some functions are adopted from the SQuAD evaluation script.) | |
| """ | |
| import argparse | |
| import json | |
| import re | |
| import string | |
| import sys | |
| 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 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 evaluate(dataset, predictions): | |
| f1 = exact_match = total = 0 | |
| correct_ids = [] | |
| for passage in dataset: | |
| for qa in passage["qas"]: | |
| total += 1 | |
| if qa["id"] not in predictions: | |
| message = f'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) | |
| if int(_exact_match) == 1: | |
| correct_ids.append(qa["id"]) | |
| exact_match += _exact_match | |
| f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths) | |
| exact_match = exact_match / total | |
| f1 = f1 / total | |
| return {"exact_match": exact_match, "f1": f1}, correct_ids | |
| if __name__ == "__main__": | |
| expected_version = "1.0" | |
| parser = argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.") | |
| parser.add_argument("data_file", help="The dataset file in JSON format.") | |
| parser.add_argument("pred_file", help="The model prediction file in JSON format.") | |
| parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.") | |
| args = parser.parse_args() | |
| with open(args.data_file) as data_file: | |
| dataset_json = json.load(data_file) | |
| if dataset_json["version"] != expected_version: | |
| print( | |
| f'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}', | |
| file=sys.stderr, | |
| ) | |
| dataset = dataset_json["data"] | |
| with open(args.pred_file) as pred_file: | |
| predictions = json.load(pred_file) | |
| metrics, correct_ids = evaluate(dataset, predictions) | |
| if args.output_correct_ids: | |
| print(f"Output {len(correct_ids)} correctly answered question IDs.") | |
| with open("correct_ids.json", "w") as f: | |
| json.dump(correct_ids, f) | |