Datasets:
lc_quad

	"""TODO(lc_quad): Add a description here."""


	import json
	import os

	import datasets


	# TODO(lc_quad): BibTeX citation
	_CITATION = """
	@inproceedings{dubey2017lc2,
	title={LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia},
	author={Dubey, Mohnish and Banerjee, Debayan and Abdelkawi, Abdelrahman and Lehmann, Jens},
	booktitle={Proceedings of the 18th International Semantic Web Conference (ISWC)},
	year={2019},
	organization={Springer}
	}
	"""

	# TODO(lc_quad):
	_DESCRIPTION = """\
	LC-QuAD 2.0 is a Large Question Answering dataset with 30,000 pairs of question and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version. Please see our paper for details about the dataset creation process and framework.
	"""
	_URL = "data.zip"


	class LcQuad(datasets.GeneratorBasedBuilder):
	"""TODO(lc_quad): Short description of my dataset."""

	# TODO(lc_quad): Set up version.
	VERSION = datasets.Version("2.0.0")

	def _info(self):
	# TODO(lc_quad): Specifies the datasets.DatasetInfo object
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	{
	"NNQT_question": datasets.Value("string"),
	"uid": datasets.Value("int32"),
	"subgraph": datasets.Value("string"),
	"template_index": datasets.Value("int32"),
	"question": datasets.Value("string"),
	"sparql_wikidata": datasets.Value("string"),
	"sparql_dbpedia18": datasets.Value("string"),
	"template": datasets.Value("string"),
	# "template_id": datasets.Value('string'),
	"paraphrased_question": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="http://lc-quad.sda.tech/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(lc_quad): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_dir = dl_manager.download_and_extract(_URL)
	dl_dir = os.path.join(dl_dir, "data")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(dl_dir, "train.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(dl_dir, "test.json")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(lc_quad): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	for id_, row in enumerate(data):
	is_list = False
	for key in row:
	if key != "answer" and isinstance(row[key], list):
	is_list = True
	if is_list:
	continue
	yield id_, {
	"NNQT_question": row["NNQT_question"],
	"uid": row["uid"],
	"subgraph": row["subgraph"],
	"template_index": row["template_index"],
	"question": row["question"],
	"sparql_wikidata": row["sparql_wikidata"],
	"sparql_dbpedia18": row["sparql_dbpedia18"],
	"template": row["template"],
	# "template_id": str(row['template_id']),
	"paraphrased_question": row["paraphrased_question"],
	}