Datasets:
squadshifts

	# 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,
	},
	}