Datasets:
dbrd

	# 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
	"""Dutch Book Review Dataset"""


	import datasets
	from datasets.tasks import TextClassification


	_DESCRIPTION = """\
	The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which \
	22k have associated binary sentiment polarity labels. It is intended as a \
	benchmark for sentiment classification in Dutch and created due to a lack of \
	annotated datasets in Dutch that are suitable for this task.
	"""

	_CITATION = """\
	@article{DBLP:journals/corr/abs-1910-00896,
	author = {Benjamin van der Burgh and
	Suzan Verberne},
	title = {The merits of Universal Language Model Fine-tuning for Small Datasets
	- a case with Dutch book reviews},
	journal = {CoRR},
	volume = {abs/1910.00896},
	year = {2019},
	url = {http://arxiv.org/abs/1910.00896},
	archivePrefix = {arXiv},
	eprint = {1910.00896},
	timestamp = {Fri, 04 Oct 2019 12:28:06 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-1910-00896.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DOWNLOAD_URL = "https://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz"


	class DBRDConfig(datasets.BuilderConfig):
	"""BuilderConfig for DBRD."""

	def __init__(self, **kwargs):
	"""BuilderConfig for DBRD.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(DBRDConfig, self).__init__(version=datasets.Version("3.0.0", ""), **kwargs)


	class DBRD(datasets.GeneratorBasedBuilder):
	"""Dutch Book Review Dataset."""

	BUILDER_CONFIGS = [
	DBRDConfig(
	name="plain_text",
	description="Plain text",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["neg", "pos"])}
	),
	supervised_keys=None,
	homepage="https://github.com/benjaminvdb/DBRD",
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	archive = dl_manager.download(_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
	),
	datasets.SplitGenerator(
	name=datasets.Split("unsupervised"),
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "unsup", "labeled": False},
	),
	]

	def _generate_examples(self, files, split, labeled=True):
	"""Generate DBRD examples."""
	# For labeled examples, extract the label from the path.
	if labeled:
	for path, f in files:
	if path.startswith(f"DBRD/{split}"):
	label = {"pos": 1, "neg": 0}[path.split("/")[2]]
	yield path, {"text": f.read().decode("utf-8"), "label": label}
	else:
	for path, f in files:
	if path.startswith(f"DBRD/{split}"):
	yield path, {"text": f.read().decode("utf-8"), "label": -1}