| import re | |
| import string | |
| from collections import Counter | |
| from typing import Dict, List | |
| from src.metrics.metrics_wrapper import Metric | |
| def normalize_answer(answer: str) -> str: | |
| """ | |
| Lower text and remove punctuation, articles and extra whitespace. | |
| Based on the SQUAD official metric: https://huggingface.co/spaces/evaluate-metric/squad | |
| """ | |
| def remove_articles(text): | |
| return re.sub(r"\b(le|la|l'|du|des|aux|un|une)\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) | |
| answer = str(answer) | |
| return white_space_fix(remove_articles(remove_punc(answer.lower()))) | |
| def f1_score(prediction: str, ground_truth: str) -> float: | |
| 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 0.0 | |
| 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: str, ground_truth: str) -> float: | |
| 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 compute_score(predictions: List, references: List) -> Dict: | |
| f1 = exact_match = total = 0 | |
| for prediction, reference in zip(predictions, references): | |
| total += 1 | |
| ground_truths = reference["text"] | |
| 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} | |
| class FQuAD(Metric): | |
| def compute(self, predictions: List, references: List) -> Dict: | |
| score = compute_score(predictions=predictions, references=references) | |
| return score | |