Datasets:
senti_ws

	# 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.
	"""SentiWS: German-language resource for sentiment analysis, pos-tagging"""


	import os

	import datasets


	_CITATION = """\
	@INPROCEEDINGS{remquahey2010,
	title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis},
	booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)},
	author = {Remus, R. and Quasthoff, U. and Heyer, G.},
	year = {2010}
	}
	"""

	_DESCRIPTION = """\
	SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, and pos-tagging. The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1].
	"""

	_HOMEPAGE = ""

	_LICENSE = "Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License"

	_URLs = ["https://pcai056.informatik.uni-leipzig.de/downloads/etc/SentiWS/SentiWS_v2.0.zip"]


	class SentiWS(datasets.GeneratorBasedBuilder):
	"""SentiWS: German-language resource for sentiment analysis, pos-tagging"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="pos-tagging", version=VERSION, description="This covers pos-tagging task"),
	datasets.BuilderConfig(
	name="sentiment-scoring",
	version=VERSION,
	description="This covers the sentiment-scoring in [-1, 1] corresponding to (negative, positive) sentiment",
	),
	]

	DEFAULT_CONFIG_NAME = "pos-tagging"

	def _info(self):

	if (
	self.config.name == "pos-tagging"
	): # the pos-tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"]
	features = datasets.Features(
	{
	"word": datasets.Value("string"),
	"pos-tag": datasets.ClassLabel(names=["NN", "VVINF", "ADJX", "ADV"]),
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"word": datasets.Value("string"),
	"sentiment-score": datasets.Value("float32"),
	}
	)
	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."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	my_urls = _URLs
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"sourcefiles": [
	os.path.join(data_dir[0], f)
	for f in ["SentiWS_v2.0_Positive.txt", "SentiWS_v2.0_Negative.txt"]
	],
	"split": "train",
	},
	),
	]

	def _generate_examples(self, sourcefiles, split):
	"""Yields examples."""
	# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
	# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
	# The key is not important, it's more here for legacy reason (legacy from tfds)
	for file_idx, filepath in enumerate(sourcefiles):
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	word = row.split("\|")[0]
	if self.config.name == "pos-tagging":
	tag = row.split("\|")[1].split("\t")[0]
	yield f"{file_idx}_{id_}", {"word": word, "pos-tag": tag}
	else:
	sentiscore = row.split("\|")[1].split("\t")[1]
	yield f"{file_idx}_{id_}", {"word": word, "sentiment-score": float(sentiscore)}