ISCO-code-predictor-api / squad_evaluate_v1_1.py
Pradeep Kumar
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# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation of SQuAD predictions (version 1.1).
The functions are copied from
https://worksheets.codalab.org/rest/bundles/0xbcd57bee090b421c982906709c8c27e1/contents/blob/.
The SQuAD dataset is described in this paper:
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang
https://nlp.stanford.edu/pubs/rajpurkar2016squad.pdf
"""
import collections
import re
import string
# pylint: disable=g-bad-import-order
from absl import logging
# pylint: enable=g-bad-import-order
def _normalize_answer(s):
"""Lowers 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 _f1_score(prediction, ground_truth):
"""Computes F1 score by comparing prediction to ground truth."""
prediction_tokens = _normalize_answer(prediction).split()
ground_truth_tokens = _normalize_answer(ground_truth).split()
prediction_counter = collections.Counter(prediction_tokens)
ground_truth_counter = collections.Counter(ground_truth_tokens)
common = prediction_counter & ground_truth_counter
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):
"""Checks if predicted answer exactly matches ground truth answer."""
return _normalize_answer(prediction) == _normalize_answer(ground_truth)
def _metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
"""Computes the max over all metric scores."""
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 evaluate(dataset, predictions):
"""Evaluates predictions for a dataset."""
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."
logging.error(message)
continue
ground_truths = [entry["text"] for entry in qa["answers"]]
prediction = predictions[qa["id"]]
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 = exact_match / total
f1 = f1 / total
return {"exact_match": exact_match, "final_f1": f1}