Datasets:
wongnai_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.
	"""TODO: Add a description here."""


	import csv
	import os

	import datasets
	from datasets.tasks import TextClassification


	# no BibTeX citation
	_CITATION = ""

	_DESCRIPTION = """\
	Wongnai's review dataset contains restaurant reviews and ratings, mainly in Thai language.
	The reviews are in 5 classes ranging from 1 to 5 stars.
	"""

	_LICENSE = "LGPL-3.0"

	_URLs = {"default": "https://archive.org/download/wongnai_reviews/wongnai_reviews_withtest.zip"}


	class WongnaiReviews(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.1")

	def _info(self):
	features = datasets.Features(
	{
	"review_body": datasets.Value("string"),
	"star_rating": datasets.features.ClassLabel(names=["1", "2", "3", "4", "5"]),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage="https://github.com/wongnai/wongnai-corpus",
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="review_body", label_column="star_rating")],
	)

	def _split_generators(self, dl_manager):
	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, "w_review_train.csv"), "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(data_dir, "w_review_test.csv"), "split": "test"},
	),
	]

	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	spamreader = csv.reader(f, delimiter=";", quotechar='"')
	for id_, row in enumerate(spamreader):
	yield id_, {"review_body": row[0], "star_rating": row[1]}