Datasets:
ro_sent

	# 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.
	"""Introduction in a Romanian sentiment dataset."""


	import csv

	import datasets
	from datasets.builder import BuilderConfig


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """
	@article{dumitrescu2020birth,
	title={The birth of Romanian BERT},
	author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius and Pyysalo, Sampo},
	journal={arXiv preprint arXiv:2009.08712},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	This dataset is a Romanian Sentiment Analysis dataset.
	It is present in a processed form, as used by the authors of `Romanian Transformers`
	in their examples and based on the original data present in
	`https://github.com/katakonst/sentiment-analysis-tensorflow`. The original dataset is collected
	from product and movie reviews in Romanian.
	"""

	_HOMEPAGE = "https://github.com/dumitrescustefan/Romanian-Transformers/tree/examples/examples/sentiment_analysis"

	_LICENSE = ""

	_URL = (
	"https://raw.githubusercontent.com/dumitrescustefan/Romanian-Transformers/examples/examples/sentiment_analysis/ro/"
	)
	_URLs = {"train": _URL + "train.csv", "test": _URL + "test.csv"}


	class RoSent(datasets.GeneratorBasedBuilder):
	"""Romanian Sentiment Analysis dataset."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	BuilderConfig(
	name="default",
	version=VERSION,
	description="This configuration handles all of Romanian Sentiment Analysis dataset.",
	),
	]

	def _info(self):

	features = datasets.Features(
	{
	"original_id": datasets.Value("string"),
	"id": datasets.Value("string"),
	"sentence": datasets.Value("string"),
	"label": datasets.ClassLabel(names=["negative", "positive"]), # 0 is negative
	}
	)

	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=("sentence", "label"),
	# 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."""

	paths = dl_manager.download(_URLs)

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

	def _generate_examples(self, filepath):
	"""Yields examples."""

	with open(filepath, encoding="utf-8") as f:
	data = csv.DictReader(f, delimiter=",", quotechar='"')

	for row_id, row in enumerate(data):
	yield row_id, {
	"original_id": row["index"] if "index" in row.keys() else row[""], # test has no 'index' key
	"id": str(row_id), # this is needed because indices are repeated in the files.
	"sentence": row["text"],
	"label": int(row["label"]),
	}