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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
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