Datasets:

AgentPublic
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piaf

	# 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
	"""PIAF Question Answering Dataset"""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@InProceedings{keraron-EtAl:2020:LREC,
	author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo},
	title = {Project PIAF: Building a Native French Question-Answering Dataset},
	booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
	month = {May},
	year = {2020},
	address = {Marseille, France},
	publisher = {European Language Resources Association},
	pages = {5483--5492},
	abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},
	url = {https://www.aclweb.org/anthology/2020.lrec-1.673}
	}
	"""

	_DESCRIPTION = """\
	Piaf is a reading comprehension \
	dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.
	"""

	_URLS = {"train": "https://github.com/etalab-ia/piaf-code/raw/master/piaf-v1.0.json"}


	class PiafConfig(datasets.BuilderConfig):
	"""BuilderConfig for PIAF."""

	def __init__(self, **kwargs):
	"""BuilderConfig for PIAF.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(PiafConfig, self).__init__(**kwargs)


	class Piaf(datasets.GeneratorBasedBuilder):
	"""The Piaf Question Answering Dataset. Version 1.0."""

	BUILDER_CONFIGS = [
	PiafConfig(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Plain text",
	),
	]

	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"),
	}
	),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage="https://piaf.etalab.studio",
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	urls_to_download = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	]

	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:
	dataset = json.load(f)
	for article in dataset["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,
	},
	}