Delete squad_evaluate_v1_1.py

squad_evaluate_v1_1.py

@@ -1,106 +0,0 @@
-# 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}