squadshifts / squadshifts.py
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Fix license/citation information of squadshifts dataset card (#5054)
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""SQUAD: The Stanford Question Answering Dataset."""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = r"""\
SquadShifts consists of four new test sets for the Stanford Question Answering \
Dataset (SQuAD) from four different domains: Wikipedia articles, New York \
Times articles, Reddit comments, and Amazon product reviews. Each dataset \
was generated using the same data generating pipeline, Amazon Mechanical \
Turk interface, and data cleaning code as the original SQuAD v1.1 dataset. \
The "new-wikipedia" dataset measures overfitting on the original SQuAD v1.1 \
dataset. The "new-york-times", "reddit", and "amazon" datasets measure \
robustness to natural distribution shifts. We encourage SQuAD model developers \
to also evaluate their methods on these new datasets! \
"""
_LICENSE = "CC-BY-4.0"
_CITATION = """\
@InProceedings{pmlr-v119-miller20a,
title = {The Effect of Natural Distribution Shift on Question Answering Models},
author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {6905--6916},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},
url = {https://proceedings.mlr.press/v119/miller20a.html},
}
"""
_URL = "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/"
_URLS = {
"new_wiki": _URL + "new_wiki_v1.0.json",
"nyt": _URL + "nyt_v1.0.json",
"reddit": _URL + "reddit_v1.0.json",
"amazon": _URL + "amazon_reviews_v1.0.json",
}
class SquadShiftsConfig(datasets.BuilderConfig):
"""BuilderConfig for SquadShifts."""
def __init__(self, **kwargs):
"""BuilderConfig for SQUAD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquadShiftsConfig, self).__init__(**kwargs)
class SquadShifts(datasets.GeneratorBasedBuilder):
"""SquadShifts consists of four new test sets for the SQUAD dataset."""
BUILDER_CONFIGS = [
SquadShiftsConfig(
name="new_wiki",
version=datasets.Version("1.0.0", ""),
description="SQuADShifts New Wikipedia article dataset",
),
SquadShiftsConfig(
name="nyt",
version=datasets.Version("1.0.0", ""),
description="SQuADShifts New York Times article dataset.",
),
SquadShiftsConfig(
name="reddit",
version=datasets.Version("1.0.0", ""),
description="SQuADShifts Reddit comment dataset.",
),
SquadShiftsConfig(
name="amazon",
version=datasets.Version("1.0.0", ""),
description="SQuADShifts Amazon product review dataset.",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
}
),
homepage="https://modestyachts.github.io/squadshifts-website/index.html",
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
if self.config.name == "new_wiki" or self.config.name == "default":
return [
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["new_wiki"]}
),
]
elif self.config.name == "nyt":
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["nyt"]}),
]
elif self.config.name == "reddit":
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["reddit"]}),
]
elif self.config.name == "amazon":
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["amazon"]}),
]
else:
raise ValueError(f"SQuADShifts dataset name {self.config.name} not found!")
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
squad = json.load(f)
for article in squad["data"]:
title = article.get("title", "").strip()
for paragraph in article["paragraphs"]:
context = paragraph["context"].strip()
for qa in paragraph["qas"]:
question = qa["question"].strip()
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"].strip() for answer in qa["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield id_, {
"title": title,
"context": context,
"question": question,
"id": id_,
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}