Datasets:
qed

	# coding=utf-8
	# Copyright 2020 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
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""QED: A Dataset for Explanations in Question Answering"""


	import json

	import datasets


	_CITATION = """\
	@misc{lamm2020qed,
	title={QED: A Framework and Dataset for Explanations in Question Answering},
	author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins},
	year={2020},
	eprint={2009.06354},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	QED, is a linguistically informed, extensible framework for explanations in question answering. \
	A QED explanation specifies the relationship between a question and answer according to formal semantic notions \
	such as referential equality, sentencehood, and entailment. It is an expertannotated dataset of QED explanations \
	built upon a subset of the Google Natural Questions dataset.
	"""

	_HOMEPAGE = "https://github.com/google-research-datasets/QED"

	_BASE_URL = "https://raw.githubusercontent.com/google-research-datasets/QED/master/"
	_URLS = {
	"train": _BASE_URL + "qed-train.jsonlines",
	"dev": _BASE_URL + "qed-dev.jsonlines",
	}


	class Qed(datasets.GeneratorBasedBuilder):
	"""QED: A Dataset for Explanations in Question Answering"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="qed", version=datasets.Version("1.0.0")),
	]

	def _info(self):
	span_features = {
	"start": datasets.Value("int32"),
	"end": datasets.Value("int32"),
	"string": datasets.Value("string"),
	}
	reference_features = {
	"start": datasets.Value("int32"),
	"end": datasets.Value("int32"),
	"bridge": datasets.Value("string"),
	"string": datasets.Value("string"),
	}
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"example_id": datasets.Value("int64"),
	"title_text": datasets.Value("string"),
	"url": datasets.Value("string"),
	"question": datasets.Value("string"),
	"paragraph_text": datasets.Value("string"),
	"sentence_starts": datasets.Sequence(datasets.Value("int32")),
	"original_nq_answers": [span_features],
	"annotation": {
	"referential_equalities": [
	{
	"question_reference": span_features,
	"sentence_reference": reference_features,
	}
	],
	"answer": [
	{
	"sentence_reference": reference_features,
	"paragraph_reference": span_features,
	}
	],
	"explanation_type": datasets.Value("string"),
	"selected_sentence": span_features,
	},
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_paths = dl_manager.download(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_paths["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": downloaded_paths["dev"]},
	),
	]

	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	examples = f.readlines()
	for example in examples:
	example = json.loads(example.strip())
	example["question"] = example.pop("question_text")

	# some examples have missing annotation, assign empty values to such examples
	if "answer" not in example["annotation"]:
	example["annotation"]["answer"] = []
	if "selected_sentence" not in example["annotation"]:
	example["annotation"]["selected_sentence"] = {
	"start": -1,
	"end": -1,
	"string": "",
	}
	if "referential_equalities" not in example["annotation"]:
	example["annotation"]["referential_equalities"] = []
	else:
	for referential_equalities in example["annotation"]["referential_equalities"]:
	bridge = referential_equalities["sentence_reference"]["bridge"]
	referential_equalities["sentence_reference"]["bridge"] = (
	bridge if bridge is not False else None
	)

	# remove the nested list
	example["original_nq_answers"] = example["original_nq_answers"][0]

	yield example["example_id"], example