Datasets:
moroco

	# coding=utf-8
	# Copyright 2021 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.
	"""MOROCO: The Moldavian and Romanian Dialectal Corpus"""


	import datasets


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{ Butnaru-ACL-2019,
	author = {Andrei M. Butnaru and Radu Tudor Ionescu},
	title = "{MOROCO: The Moldavian and Romanian Dialectal Corpus}",
	booktitle = {Proceedings of ACL},
	year = {2019},
	pages={688--698},
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	The MOROCO (Moldavian and Romanian Dialectal Corpus) dataset contains 33564 samples of text collected from the news domain.
	The samples belong to one of the following six topics:
	- culture
	- finance
	- politics
	- science
	- sports
	- tech
	"""

	_HOMEPAGE = "https://github.com/butnaruandrei/MOROCO"

	_LICENSE = "CC BY-SA 4.0 License"

	# 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://raw.githubusercontent.com/butnaruandrei/MOROCO/master/MOROCO/preprocessed/all/"

	_TRAIN_SAMPLES_FILE = "train_samples.txt"
	_TRAIN_LABELS_FILE = "train_category_labels.txt"

	_VAL_SAMPLES_FILE = "validation_samples.txt"
	_VAL_LABELS_FILE = "validation_category_labels.txt"

	_TEST_SAMPLES_FILE = "test_samples.txt"
	_TEST_LABELS_FILE = "test_category_labels.txt"


	class MOROCOConfig(datasets.BuilderConfig):
	"""BuilderConfig for the MOROCO dataset"""

	def __init__(self, **kwargs):
	super(MOROCOConfig, self).__init__(**kwargs)


	class MOROCO(datasets.GeneratorBasedBuilder):
	"""MOROCO dataset"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	MOROCOConfig(name="moroco", version=VERSION, description="MOROCO dataset"),
	]

	def _info(self):

	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"category": datasets.features.ClassLabel(
	names=[
	"culture",
	"finance",
	"politics",
	"science",
	"sports",
	"tech",
	]
	),
	"sample": datasets.Value("string"),
	}
	)

	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."""

	urls_to_download = {
	"train_samples": _URL + _TRAIN_SAMPLES_FILE,
	"train_labels": _URL + _TRAIN_LABELS_FILE,
	"val_samples": _URL + _VAL_SAMPLES_FILE,
	"val_labels": _URL + _VAL_LABELS_FILE,
	"test_samples": _URL + _TEST_SAMPLES_FILE,
	"test_labels": _URL + _TEST_LABELS_FILE,
	}

	downloaded_files = dl_manager.download(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"samples_filepath": downloaded_files["train_samples"],
	"labels_filepath": downloaded_files["train_labels"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"samples_filepath": downloaded_files["test_samples"],
	"labels_filepath": downloaded_files["test_labels"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"samples_filepath": downloaded_files["val_samples"],
	"labels_filepath": downloaded_files["val_labels"],
	},
	),
	]

	def _generate_examples(self, samples_filepath, labels_filepath):
	"""This function returns the examples in the raw (text) form."""

	with open(samples_filepath, "r", encoding="utf-8") as fsamples:
	sample_rows = fsamples.read().splitlines()

	with open(labels_filepath, "r", encoding="utf-8") as flabels:
	label_rows = flabels.readlines()

	for i, row in enumerate(sample_rows):
	samp_id = row.split("\t")[0]
	sample = "".join(row.split("\t")[1:])
	label = int(label_rows[i].split("\t")[1])

	yield i, {
	"id": samp_id,
	"category": label - 1,
	"sample": sample,
	}