Datasets:
wrbsc

	# 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.
	"""WUT Relations Between Sentences Corpus"""


	import csv

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@misc{11321/305,
	title = {{WUT} Relations Between Sentences Corpus},
	author = {Oleksy, Marcin and Fikus, Dominika and Wolski, Michal and Podbielska, Malgorzata and Turek, Agnieszka and Kędzia, Pawel},
	url = {http://hdl.handle.net/11321/305},
	note = {{CLARIN}-{PL} digital repository},
	copyright = {Attribution-{ShareAlike} 3.0 Unported ({CC} {BY}-{SA} 3.0)},
	year = {2016}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences.
	Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents.
	Every relation was marked by at least 3 annotators.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/305"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)"

	# TODO: Add link to the official dataset URLs here
	# 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://clarin-pl.eu/dspace/bitstream/handle/11321/305/sem_rels-betw-sents.csv"


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class Wrbsc(datasets.GeneratorBasedBuilder):
	"""WUT Relations Between Sentences Corpus"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	features = datasets.Features(
	{
	"sentence1": datasets.Value("string"),
	"sentence2": datasets.Value("string"),
	"relationship": datasets.ClassLabel(
	names=[
	"Krzyżowanie_się",
	"Tło_historyczne",
	"Źródło",
	"Dalsze_informacje",
	"Zawieranie",
	"Opis",
	"Uszczegółowienie",
	"Parafraza",
	"Spełnienie",
	"Mowa_zależna",
	"Zmiana_poglądu",
	"Streszczenie",
	"Tożsamość",
	"Sprzeczność",
	"Modalność",
	"Cytowanie",
	]
	),
	}
	)
	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
	filepath = dl_manager.download(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepath,
	"split": "train",
	},
	),
	]

	def _generate_examples(self, filepath, 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)

	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(
	f, delimiter="\t", fieldnames=["0", "1", "s1", "s2", "r", "2"], quoting=csv.QUOTE_NONE
	)
	for idx, row in enumerate(reader):
	yield idx, {
	"sentence1": row["s1"][1:-1],
	"sentence2": row["s2"][1:-1],
	"relationship": row["r"],
	}