Datasets:
laroseda

	# coding=utf-8
	# Copyright 2021 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.
	"""LaRoSeDa: A Large Romanian Sentiment Data Set"""


	import json

	import datasets


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{
	tache2101clustering,
	title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},
	author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},
	journal={ArXiv},
	year = {2021}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	LaRoSeDa (A Large Romanian Sentiment Data Set) contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative.
	Star ratings of 1 and 2 and of 4 and 5 are provided for negative and positive reviews respectively.
	The current dataset uses star rating as the label for multi-class classification.
	"""

	_HOMEPAGE = "https://github.com/ancatache/LaRoSeDa"

	_LICENSE = "CC BY-SA 4.0 License"

	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URL = "https://raw.githubusercontent.com/ancatache/LaRoSeDa/main/data_splitted/"

	_TRAIN_FILE = "laroseda_train.json"
	_TEST_FILE = "laroseda_test.json"


	class LarosedaConfig(datasets.BuilderConfig):
	"""BuilderConfig for the LaRoSeDa dataset"""

	def __init__(self, **kwargs):
	super(LarosedaConfig, self).__init__(**kwargs)


	class Laroseda(datasets.GeneratorBasedBuilder):
	"""LaRoSeDa dataset"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	LarosedaConfig(name="laroseda", version=VERSION, description="LaRoSeDa dataset"),
	]

	def _info(self):

	features = datasets.Features(
	{
	"index": datasets.Value("string"),
	"title": datasets.Value("string"),
	"content": datasets.Value("string"),
	"starRating": datasets.Value("int64"),
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	urls_to_download = {
	"train": _URL + _TRAIN_FILE,
	"test": _URL + _TEST_FILE,
	}

	downloaded_files = dl_manager.download(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files["test"],
	},
	),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""

	with open(filepath, "r", encoding="utf-8") as f:
	data_list = json.load(f)["reviews"]

	for i, d in enumerate(data_list):
	yield i, {
	"index": d["index"],
	"title": d["title"],
	"content": d["content"],
	"starRating": int(d["starRating"]),
	}