Datasets:
polemo2

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""PolEmo2.0 IN and OUT"""


	import csv
	import os

	import datasets


	_CITATION = """\
	@inproceedings{kocon-etal-2019-multi,
	title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
	author = "Koco{\'n}, Jan and
	Milkowski, Piotr and
	Za{\'s}ko-Zieli{\'n}ska, Monika",
	booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
	month = nov,
	year = "2019",
	address = "Hong Kong, China",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/K19-1092",
	doi = "10.18653/v1/K19-1092",
	pages = "980--991",
	}
	"""

	_DESCRIPTION = """\
	The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.
	"""

	_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/710"

	_LICENSE = "CC BY-NC-SA 4.0"

	_URLs = {
	"in": "https://klejbenchmark.com/static/data/klej_polemo2.0-in.zip",
	"out": "https://klejbenchmark.com/static/data/klej_polemo2.0-out.zip",
	}


	class Polemo2(datasets.GeneratorBasedBuilder):
	"""PolEmo2.0"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="in",
	version=VERSION,
	description="The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.",
	),
	datasets.BuilderConfig(
	name="out",
	version=VERSION,
	description="The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.",
	),
	]

	DEFAULT_CONFIG_NAME = "in"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"target": datasets.ClassLabel(
	names=[
	"__label__meta_amb",
	"__label__meta_minus_m",
	"__label__meta_plus_m",
	"__label__meta_zero",
	]
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.tsv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "dev.tsv"),
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
	for id_, row in enumerate(reader):
	yield id_, {
	"sentence": row["sentence"],
	"target": -1 if split == "test" else row["target"],
	}