Datasets:
asnq

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""Answer-Sentence Natural Questions (ASNQ)

	ASNQ is a dataset for answer sentence selection derived from Google's
	Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). It converts
	NQ's dataset into an AS2 (answer-sentence-selection) format.

	The dataset details can be found in the paper at
	https://arxiv.org/abs/1911.04118

	The dataset can be downloaded at
	https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar
	"""

	from __future__ import absolute_import, division, print_function

	import csv
	import os

	import datasets


	_CITATION = """\
	@article{garg2019tanda,
	title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
	author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},
	year={2019},
	eprint={1911.04118},
	}
	"""

	_DESCRIPTION = """\
	ASNQ is a dataset for answer sentence selection derived from
	Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).

	Each example contains a question, candidate sentence, label indicating whether or not
	the sentence answers the question, and two additional features --
	sentence_in_long_answer and short_answer_in_sentence indicating whether ot not the
	candidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.

	For more details please see
	https://arxiv.org/pdf/1911.04118.pdf

	and

	https://research.google/pubs/pub47761/
	"""

	_URL = "https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar"


	class ASNQ(datasets.GeneratorBasedBuilder):
	"""ASNQ is a dataset for answer sentence selection derived
	ASNQ is a dataset for answer sentence selection derived from
	Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).

	The dataset details can be found in the paper:
	https://arxiv.org/abs/1911.04118
	"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=datasets.Features(
	{
	"question": datasets.Value("string"),
	"sentence": datasets.Value("string"),
	"label": datasets.ClassLabel(names=["neg", "pos"]),
	"sentence_in_long_answer": datasets.Value("bool"),
	"short_answer_in_sentence": datasets.Value("bool"),
	}
	),
	# No default supervised_keys
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)
	data_dir = os.path.join(dl_dir, "data", "asnq")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.tsv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "dev.tsv"),
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples.

	Original dataset contains labels '1', '2', '3' and '4', with labels
	'1', '2' and '3' considered negative (sentence does not answer the question),
	and label '4' considered positive (sentence does answer the question).
	We map these labels to two classes, returning the other properties as additional
	features."""

	# Mapping of dataset's original labels to a tuple of
	# (label, sentence_in_long_answer, short_answer_in_sentence)
	label_map = {
	"1": ("neg", False, False),
	"2": ("neg", False, True),
	"3": ("neg", True, False),
	"4": ("pos", True, True),
	}
	with open(filepath, encoding="utf-8") as tsvfile:
	tsvreader = csv.reader(tsvfile, delimiter="\t")
	for id_, row in enumerate(tsvreader):
	question, sentence, orig_label = row
	label, sentence_in_long_answer, short_answer_in_sentence = label_map[orig_label]
	yield id_, {
	"question": question,
	"sentence": sentence,
	"label": label,
	"sentence_in_long_answer": sentence_in_long_answer,
	"short_answer_in_sentence": short_answer_in_sentence,
	}