""" Official evaluation script for v1.1 of the SQuAD dataset. """
from __future__ import print_function
from collections import Counter
import string
from zhon import hanzi as zh
import re
import argparse
import json
import sys
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 + zh.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, tokenizer):
prediction_tokens = tokenizer.tokenize(normalize_answer(prediction))
ground_truth_tokens = tokenizer.tokenize(normalize_answer(ground_truth))
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, tokenizer):
return (''.join(tokenizer.tokenize(normalize_answer(prediction))) ==
''.join(tokenizer.tokenize(normalize_answer(ground_truth))))
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths, tokenizer):
scores_for_ground_truths = []
for ground_truth in ground_truths:
score = metric_fn(prediction, ground_truth, tokenizer)
scores_for_ground_truths.append(score)
return max(scores_for_ground_truths)
def evaluate(dataset, predictions, tokenizer):
acc = f1 = exact_match = total = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
total += 1
if str(qa['id']) not in predictions and int(qa['id']) not in predictions:
message = 'Unanswered question ' + str(qa['id']) + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
try:
prediction = predictions[str(qa['id'])]
except KeyError:
prediction = predictions[int(qa['id'])]
if ground_truths[0].lower() in prediction:
acc += 1
exact_match += metric_max_over_ground_truths(
exact_match_score, prediction, ground_truths, tokenizer)
f1 += metric_max_over_ground_truths(
f1_score, prediction, ground_truths, tokenizer)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
acc = 100.0 * acc / total
return {'exact_match': exact_match, 'f1': f1, 'acc': acc}
if __name__ == '__main__':
import tokenization
tokenizer = tokenization.FullTokenizer(vocab_file='chinese_L-12_H-768_A-12/vocab.txt')
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(evaluate(dataset, predictions, tokenizer)))
