Datasets:
eli5_category

	# coding=utf-8
	# Lint as: python3
	"""ELI5-Category: A categorized open-domain QA dataset."""


	import json

	import datasets


	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@inproceedings{eli5-category,
	author = {Jingsong Gao and
	Qingren Zhou and
	Rui Qiu},
	title = {{ELI5-Category:} A categorized open-domain QA dataset},
	year = {2021}
	}
	"""

	_DESCRIPTION = """\
	The ELI5-Category dataset is a smaller but newer and categorized version of the original ELI5 dataset. \
	After 2017, a tagging system was introduced to this subreddit so that the questions can be categorized \
	into different topics according to their tags. Since the training and validation set is built by questions \
	in different topics, the dataset is expected to alleviate the train/validation overlapping issue \
	in the original ELI5 dataset.
	"""


	class ELI5CategoryConfig(datasets.BuilderConfig):
	"""BuilderConfig for ELI5Category."""

	def __init__(self, **kwargs):
	"""BuilderConfig for ELI5Category.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(ELI5CategoryConfig, self).__init__(**kwargs)


	class ELI5Category(datasets.GeneratorBasedBuilder):
	"""ELI5-Category: A categorized open-domain QA dataset."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	ELI5CategoryConfig(
	name="default",
	version=datasets.Version("1.0.0"),
	description="Default config",
	),
	]

	DEFAULT_CONFIG_NAME = "default"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"q_id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"selftext": datasets.Value("string"),
	"category": datasets.Value("string"),
	"subreddit": datasets.Value("string"),
	"answers": {
	"a_id": datasets.features.Sequence(datasets.Value("string")),
	"text": datasets.features.Sequence(datasets.Value("string")),
	"score": datasets.features.Sequence(datasets.Value("int32")),
	"text_urls": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
	},
	"title_urls": datasets.features.Sequence(datasets.Value("string")),
	"selftext_urls": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=None,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	_URL = "https://jingshensn2.github.io/eli5c/datasets/"
	downloaded_files = dl_manager.download_and_extract(
	{
	"train": _URL + "eli5-category-train.json.gz",
	"val1": _URL + "eli5-category-validation-1.json.gz",
	"val2": _URL + "eli5-category-validation-2.json.gz",
	"test": _URL + "eli5-category-test.json.gz",
	}
	)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split("validation1"),
	gen_kwargs={"filepath": downloaded_files["val1"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split("validation2"),
	gen_kwargs={"filepath": downloaded_files["val2"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_files["test"]},
	),
	]

	def _generate_examples(self, filepath):
	logger.info("generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	example = json.load(f)
	for id_, row in enumerate(example):
	yield id_, row