Datasets:

legacy-datasets
/

allegro_reviews

	# 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.
	"""Allegro Reviews dataset"""


	import csv
	import os

	import datasets


	_CITATION = """\
	@inproceedings{rybak-etal-2020-klej,
	title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding",
	author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz",
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.acl-main.111",
	pages = "1191--1201",
	}
	"""

	_DESCRIPTION = """\
	Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted
	from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale
	from one (negative review) to five (positive review).

	We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden.
	You can evaluate your model using the online evaluation tool available on klejbenchmark.com.
	"""

	_HOMEPAGE = "https://github.com/allegro/klejbenchmark-allegroreviews"

	_LICENSE = "CC BY-SA 4.0"

	_URLs = "https://klejbenchmark.com/static/data/klej_ar.zip"


	class AllegroReviews(datasets.GeneratorBasedBuilder):
	"""
	Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish
	and extracted from Allegro.pl - a popular e-commerce marketplace.
	"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"rating": datasets.Value("float"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_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_, {
	"text": row["text"],
	"rating": "-1" if split == "test" else row["rating"],
	}