Datasets:
trec

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	"""The Text REtrieval Conference (TREC) Question Classification dataset."""


	import datasets


	_DESCRIPTION = """\
	The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in training set and another 500 for test set.

	The dataset has 6 coarse class labels and 50 fine class labels. Average length of each sentence is 10, vocabulary size of 8700.

	Data are collected from four sources: 4,500 English questions published by USC (Hovy et al., 2001), about 500 manually constructed questions for a few rare classes, 894 TREC 8 and TREC 9 questions, and also 500 questions from TREC 10 which serves as the test set. These questions were manually labeled.
	"""

	_HOMEPAGE = "https://cogcomp.seas.upenn.edu/Data/QA/QC/"

	_CITATION = """\
	@inproceedings{li-roth-2002-learning,
	title = "Learning Question Classifiers",
	author = "Li, Xin and
	Roth, Dan",
	booktitle = "{COLING} 2002: The 19th International Conference on Computational Linguistics",
	year = "2002",
	url = "https://www.aclweb.org/anthology/C02-1150",
	}
	@inproceedings{hovy-etal-2001-toward,
	title = "Toward Semantics-Based Answer Pinpointing",
	author = "Hovy, Eduard and
	Gerber, Laurie and
	Hermjakob, Ulf and
	Lin, Chin-Yew and
	Ravichandran, Deepak",
	booktitle = "Proceedings of the First International Conference on Human Language Technology Research",
	year = "2001",
	url = "https://www.aclweb.org/anthology/H01-1069",
	}
	"""

	_URLs = {
	"train": "https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label",
	"test": "https://cogcomp.seas.upenn.edu/Data/QA/QC/TREC_10.label",
	}

	_COARSE_LABELS = ["ABBR", "ENTY", "DESC", "HUM", "LOC", "NUM"]

	_FINE_LABELS = [
	"ABBR:abb",
	"ABBR:exp",
	"ENTY:animal",
	"ENTY:body",
	"ENTY:color",
	"ENTY:cremat",
	"ENTY:currency",
	"ENTY:dismed",
	"ENTY:event",
	"ENTY:food",
	"ENTY:instru",
	"ENTY:lang",
	"ENTY:letter",
	"ENTY:other",
	"ENTY:plant",
	"ENTY:product",
	"ENTY:religion",
	"ENTY:sport",
	"ENTY:substance",
	"ENTY:symbol",
	"ENTY:techmeth",
	"ENTY:termeq",
	"ENTY:veh",
	"ENTY:word",
	"DESC:def",
	"DESC:desc",
	"DESC:manner",
	"DESC:reason",
	"HUM:gr",
	"HUM:ind",
	"HUM:title",
	"HUM:desc",
	"LOC:city",
	"LOC:country",
	"LOC:mount",
	"LOC:other",
	"LOC:state",
	"NUM:code",
	"NUM:count",
	"NUM:date",
	"NUM:dist",
	"NUM:money",
	"NUM:ord",
	"NUM:other",
	"NUM:period",
	"NUM:perc",
	"NUM:speed",
	"NUM:temp",
	"NUM:volsize",
	"NUM:weight",
	]


	class Trec(datasets.GeneratorBasedBuilder):
	"""The Text REtrieval Conference (TREC) Question Classification dataset."""

	VERSION = datasets.Version("2.0.0", description="Fine label contains 50 classes instead of 47.")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"coarse_label": datasets.ClassLabel(names=_COARSE_LABELS),
	"fine_label": datasets.ClassLabel(names=_FINE_LABELS),
	}
	),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_files = dl_manager.download(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": dl_files["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": dl_files["test"],
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, "rb") as f:
	for id_, row in enumerate(f):
	# One non-ASCII byte: sisterBADBYTEcity. We replace it with a space
	fine_label, _, text = row.replace(b"\xf0", b" ").strip().decode().partition(" ")
	coarse_label = fine_label.split(":")[0]
	yield id_, {
	"text": text,
	"coarse_label": coarse_label,
	"fine_label": fine_label,
	}