|
|
|
|
|
import re |
|
import string |
|
from collections import Counter |
|
from unidecode import unidecode |
|
|
|
|
|
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 unidecode(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) |
|
|
