Datasets:
grail_qa

	# 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
	"""GrailQA: The Strongly Generalizable Question Answering Dataset"""


	import json
	import os

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@misc{gu2020iid,
	title={Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases},
	author={Yu Gu and Sue Kase and Michelle Vanni and Brian Sadler and Percy Liang and Xifeng Yan and Yu Su},
	year={2020},
	eprint={2011.07743},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	Strongly Generalizable Question Answering (GrailQA) is a new large-scale, \
	high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated \
	with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). \
	It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot.
	"""

	_URL = "https://dl.orangedox.com/WyaCpL?dl=1"


	class GrailQA(datasets.GeneratorBasedBuilder):
	"""GrailQA: The Strongly Generalizable Question Answering Dataset"""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"qid": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answer": datasets.features.Sequence(
	{
	"answer_type": datasets.Value("string"),
	"answer_argument": datasets.Value("string"),
	"entity_name": datasets.Value("string"),
	}
	),
	"function": datasets.Value("string"),
	"num_node": datasets.Value("int32"),
	"num_edge": datasets.Value("int32"),
	"graph_query": {
	"nodes": datasets.features.Sequence(
	{
	"nid": datasets.Value("int32"),
	"node_type": datasets.Value("string"),
	"id": datasets.Value("string"),
	"class": datasets.Value("string"),
	"friendly_name": datasets.Value("string"),
	"question_node": datasets.Value("int32"),
	"function": datasets.Value("string"),
	}
	),
	"edges": datasets.features.Sequence(
	{
	"start": datasets.Value("int32"),
	"end": datasets.Value("int32"),
	"relation": datasets.Value("string"),
	"friendly_name": datasets.Value("string"),
	}
	),
	},
	"sparql_query": datasets.Value("string"),
	"domains": datasets.features.Sequence(datasets.Value("string")),
	"level": datasets.Value("string"),
	"s_expression": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage="https://dki-lab.github.io/GrailQA/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	dl_path = os.path.join(dl_manager.download_and_extract(_URL), "GrailQA_v1.0")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": os.path.join(dl_path, "grailqa_v1.0_train.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": os.path.join(dl_path, "grailqa_v1.0_dev.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(dl_path, "grailqa_v1.0_test_public.json")},
	),
	]

	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:
	samples = json.load(f)
	for sample in samples:
	features = {
	"qid": str(sample["qid"]),
	"question": sample["question"],
	"function": sample.get("function", ""),
	"num_node": sample.get("num_node", -1),
	"num_edge": sample.get("num_edge", -1),
	"graph_query": sample.get("graph_query", {"nodes": [], "edges": []}),
	"sparql_query": sample.get("sparql_query", ""),
	"domains": sample.get("domains", []),
	"level": sample.get("level", ""),
	"s_expression": sample.get("s_expression", ""),
	}

	answers = sample.get("answer", [])
	for answer in answers:
	if "entity_name" not in answer:
	answer["entity_name"] = ""

	features["answer"] = answers
	yield sample["qid"], features