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112 lines
4.4 KiB
Python
112 lines
4.4 KiB
Python
# Copyright 2020 The HuggingFace Evaluate Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" SQuAD metric. """
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import datasets
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import evaluate
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from .compute_score import compute_score
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_CITATION = """\
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@inproceedings{Rajpurkar2016SQuAD10,
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title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
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author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
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booktitle={EMNLP},
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year={2016}
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}
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"""
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_DESCRIPTION = """
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This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
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Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
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crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
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from the corresponding reading passage, or the question might be unanswerable.
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"""
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_KWARGS_DESCRIPTION = """
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Computes SQuAD scores (F1 and EM).
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Args:
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predictions: List of question-answers dictionaries with the following key-values:
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- 'id': id of the question-answer pair as given in the references (see below)
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- 'prediction_text': the text of the answer
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references: List of question-answers dictionaries with the following key-values:
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- 'id': id of the question-answer pair (see above),
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- 'answers': a Dict in the SQuAD dataset format
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{
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'text': list of possible texts for the answer, as a list of strings
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'answer_start': list of start positions for the answer, as a list of ints
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}
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Note that answer_start values are not taken into account to compute the metric.
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Returns:
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'exact_match': Exact match (the normalized answer exactly match the gold answer)
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'f1': The F-score of predicted tokens versus the gold answer
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Examples:
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>>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
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>>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
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>>> squad_metric = evaluate.load("squad")
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>>> results = squad_metric.compute(predictions=predictions, references=references)
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>>> print(results)
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{'exact_match': 100.0, 'f1': 100.0}
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"""
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class Squad(evaluate.Metric):
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def _info(self):
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return evaluate.MetricInfo(
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description=_DESCRIPTION,
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citation=_CITATION,
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inputs_description=_KWARGS_DESCRIPTION,
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features=datasets.Features(
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{
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"predictions": {"id": datasets.Value("string"), "prediction_text": datasets.Value("string")},
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"references": {
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"id": datasets.Value("string"),
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"answers": datasets.features.Sequence(
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{
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"text": datasets.Value("string"),
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"answer_start": datasets.Value("int32"),
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}
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),
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},
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}
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),
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codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],
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reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],
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)
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def _compute(self, predictions, references):
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pred_dict = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
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dataset = [
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{
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"paragraphs": [
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{
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"qas": [
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{
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"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
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"id": ref["id"],
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}
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for ref in references
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]
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}
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]
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}
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]
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score = compute_score(dataset=dataset, predictions=pred_dict)
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return score
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