Datasets:

allenai
/

qasper

	# 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
	"""Qasper: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers."""


	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{Dasigi2021ADO,
	title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
	author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
	year={2021}
	}
	"""
	_LICENSE = "CC BY 4.0"
	_DESCRIPTION = """\
	A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners.
	"""

	_HOMEPAGE = "https://allenai.org/data/qasper"
	_URL = "https://qasper-dataset.s3-us-west-2.amazonaws.com/qasper-train-dev-v0.1.tgz"
	_DATA_FILES = {"train": "qasper-train-v0.1.json", "dev": "qasper-dev-v0.1.json"}

	_VERSION = "0.1.0"


	class Qasper(datasets.GeneratorBasedBuilder):
	"""Qasper: A Dataset of Information-Seeking Q&A Anchored in Research Papers."""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="qasper",
	version=datasets.Version(_VERSION),
	description=_DESCRIPTION,
	)
	]

	def _info(self):

	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"abstract": datasets.Value("string"),
	"full_text": datasets.features.Sequence(
	{
	"section_name": datasets.Value("string"),
	"paragraphs": [datasets.Value("string")],
	}
	),
	"qas": datasets.features.Sequence(
	{
	"question": datasets.Value("string"),
	"question_id": datasets.Value("string"),
	"nlp_background": datasets.Value("string"),
	"topic_background": datasets.Value("string"),
	"paper_read": datasets.Value("string"),
	"search_query": datasets.Value("string"),
	"question_writer": datasets.Value("string"),
	"answers": datasets.features.Sequence(
	{
	"answer": {
	"unanswerable": datasets.Value("bool"),
	"extractive_spans": datasets.features.Sequence(datasets.Value("string")),
	"yes_no": datasets.Value("bool"),
	"free_form_answer": datasets.Value("string"),
	"evidence": datasets.features.Sequence(datasets.Value("string")),
	"highlighted_evidence": datasets.features.Sequence(datasets.Value("string")),
	},
	"annotation_id": datasets.Value("string"),
	"worker_id": datasets.Value("string"),
	}
	),
	}
	),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	archive = dl_manager.download(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": _DATA_FILES["train"], "files": dl_manager.iter_archive(archive)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": _DATA_FILES["dev"], "files": dl_manager.iter_archive(archive)},
	),
	]

	def _generate_examples(self, filepath, files):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	for path, f in files:
	if path == filepath:
	qasper = json.loads(f.read().decode("utf-8"))
	for id_ in qasper:
	qasper[id_]["id"] = id_
	yield id_, qasper[id_]
	break