Datasets:

carolina-c4ai
/

corpus-carolina

	# 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.
	"""Carolina Corpus"""

	from lxml import etree
	import os
	import datasets


	_HOMEPAGE = "https://sites.usp.br/corpuscarolina/"


	_DESCRIPTION = """
	Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a
	robust volume of texts of varied typology in contemporary Brazilian Portuguese
	(1970-2021).
	"""


	_CITATION = r"""
	@misc{corpusCarolinaV1.1,
	title={
	Carolina:
	The Open Corpus for Linguistics and Artificial Intelligence},
	author={
	Finger, Marcelo and
	Paixão de Sousa, Maria Clara and
	Namiuti, Cristiane and
	Martins do Monte, Vanessa and
	Costa, Aline Silva and
	Serras, Felipe Ribas and
	Sturzeneker, Mariana Lourenço and
	Guets, Raquel de Paula and
	Mesquita, Renata Morais and
	Mello, Guilherme Lamartine de and
	Crespo, Maria Clara Ramos Morales and
	Rocha, Maria Lina de Souza Jeannine and
	Brasil, Patrícia and
	Silva, Mariana Marques da and
	Palma, Mayara Feliciano},
	howpublished={\url{https://sites.usp.br/corpuscarolina/corpus}},
	year={2022},
	note={Version 1.1 (Ada)},
	}
	"""


	_LICENSE = """
	The Open Corpus for Linguistics and Artificial Intelligence (Carolina) was
	compiled for academic purposes, namely linguistic and computational analysis.
	It is composed of texts assembled in various digital repositories, whose
	licenses are multiple and therefore should be observed when making use of the
	corpus. The Carolina headers are licensed under Creative Commons
	Attribution-NonCommercial-ShareAlike 4.0 International."
	"""


	def _taxonomies():
	"""Creates a map between taxonomy code and name

	Returns
	-------
	dict
	The dictionary of codes and names.
	"""
	return dict(
	dat="datasets and other corpora",
	jud="judicial branch",
	leg="legislative branch",
	pub="public domain works",
	soc="social media",
	uni="university_domains",
	wik="wikis",
	)


	_VERSION = "1.1.0"
	_CORPUS_URL = "corpus/{taxonomy}/"
	_CHECKSUM_FNAME = _CORPUS_URL + "checksum.sha256"


	class CarolinaConfig(datasets.BuilderConfig):
	"""Carolina Configuration."""
	def __init__(self, taxonomy: str = None, **kwargs):
	"""BuilderConfig for Carolina

	Parameters
	----------
	taxonomy : str
	The taxonomy code (3 letters). The code defines the taxonomy
	to download. If `None`, all taxonomies will be downloaded.
	**kwargs
	Arguments passed to super.
	"""
	# validates taxonomy
	if taxonomy is None:
	taxonomy = "all"
	elif taxonomy != "all" and taxonomy not in _taxonomies():
	raise ValueError(f"Invalid taxonomy: {taxonomy}")

	# custom name and description
	description = "Carolina corpus."
	if taxonomy == "all":
	name = "carolina"
	description += " Using all taxonomies."
	else:
	name = _taxonomies()[taxonomy]
	description += f" Using taxonomy {taxonomy}"

	super(CarolinaConfig, self).__init__(
	name=name, description=description, **kwargs)

	# Carolina attributes
	self.taxonomy = taxonomy
	self.version = datasets.Version(_VERSION)


	class Carolina(datasets.GeneratorBasedBuilder):
	"""Carolina Downloader and Builder"""

	BUILDER_CONFIG_CLASS = CarolinaConfig

	def _info(self):
	features = datasets.Features({
	"meta": datasets.Value("string"),
	"text": datasets.Value("string")
	})

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	features=features,
	license=_LICENSE
	)

	def _split_generators(self, dl_manager):
	# list taxonomies to download
	if self.config.taxonomy == "all":
	taxonomies = _taxonomies().values()
	else:
	taxonomies = [_taxonomies()[self.config.taxonomy]]

	zip_urls = dict()
	for taxonomy in taxonomies:
	# download checksum file
	checksum_path = _CHECKSUM_FNAME.format(taxonomy=taxonomy)
	checksum_path = dl_manager.download(checksum_path)

	tax_url = _CORPUS_URL.format(taxonomy=taxonomy)

	# extract and build zip urls
	with open(checksum_path, encoding="utf-8") as cfile:
	for line in cfile:
	fname = line.split()[1]
	if fname.endswith(".xml.zip"):
	zip_url = tax_url + fname # download url
	fname = os.path.split(fname)[1] # removes subdirs
	fname = fname[:-4] # removes .zip
	zip_urls[fname] = zip_url # xml -> zip url

	# extractions are made in cache folders and
	# the path returned is the folder path, not the
	# extracted file (or files). It is necessary to
	# build the xml file path. It is made using the
	# zip_urls dict structure.
	extracted = dl_manager.download_and_extract(zip_urls)
	xml_files = [os.path.join(v, k) for k, v in extracted.items()]
	xml_files = sorted(xml_files)

	return [
	datasets.SplitGenerator(
	name="corpus",
	gen_kwargs={"filepaths": xml_files}
	)
	]

	def _generate_examples(self, filepaths):
	TEI_NS = "{http://www.tei-c.org/ns/1.0}"
	parser_params = dict(
	huge_tree=True,
	encoding="utf-8",
	tag=f"{TEI_NS}TEI"
	)

	_key = 0
	for path in filepaths:
	# parse xml file
	for _, tei in etree.iterparse(path, **parser_params):
	header = tei.find(f"{TEI_NS}teiHeader")

	example = {
	"meta": etree.tostring(
	header, encoding="utf-8").decode("utf-8"),
	"text": tei.find(f".//{TEI_NS}body/{TEI_NS}p").text
	}
	yield _key, example
	_key += 1