Datasets:
swedish_reviews

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3


	import csv
	import os

	import datasets
	from datasets.tasks import TextClassification


	_DOWNLOAD_URL = "https://raw.githubusercontent.com/timpal0l/swedish-sentiment/main/swedish_sentiment.zip"
	_TRAIN_FILE = "train.csv"
	_VAL_FILE = "dev.csv"
	_TEST_FILE = "test.csv"

	_CITATION = ""

	_DESCRIPTION = "Swedish reviews scarped from various public available websites"


	class SwedishReviews(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text",
	version=datasets.Version("1.0.0", ""),
	description="Plain text import of the Swedish Reviews dataset",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"text": datasets.Value("string"), "label": datasets.ClassLabel(names=["negative", "positive"])}
	),
	supervised_keys=None,
	homepage="https://github.com/timpal0l/swedish-sentiment",
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(dl_dir, _TEST_FILE)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": os.path.join(dl_dir, _VAL_FILE)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": os.path.join(dl_dir, _TRAIN_FILE)},
	),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f)
	for idx, row in enumerate(reader):
	yield idx, {
	"text": row["text"],
	"label": row["sentiment"],
	}