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""" Official evaluation script for v1.1 of the SQuAD dataset. """
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 recall_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
recall = 1.0 * num_same / len(ground_truth_tokens)
return recall
def precision_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)
return precision
# 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 compute_score(dataset, predictions):
recall = precision = f1 = exact_match = total = 0
for article in dataset:
for paragraph in article["paragraphs"]:
for qa in paragraph["qas"]:
total += 1
if qa["id"] not in predictions:
message = "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)
recall_temp = metric_max_over_ground_truths(recall_score, prediction, ground_truths)
precision_temp = metric_max_over_ground_truths(precision_score, prediction, ground_truths)
if recall_temp + precision_temp == 0:
f1_temp = 0
else:
f1_temp = (2 * precision_temp * recall_temp) / (precision_temp + recall_temp)
f1 += f1_temp
recall += recall_temp
precision += precision_temp
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
precision = 100.0 * precision / total
recall = 100.0 * recall / total
return {"exact_match": exact_match, "f1": f1, "recall": recall, "precision": precision}
if __name__ == "__main__":
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(compute_score(dataset, predictions)))
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