|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""The Large Spanish Corpus is a compilation of Spanish corpora spanning Wikipedia to European parliament notes.""" |
|
|
|
|
|
import os |
|
|
|
import datasets |
|
|
|
|
|
_CITATION = """\ |
|
@dataset{jose_canete_2019_3247731, |
|
author = {José Cañete}, |
|
title = {Compilation of Large Spanish Unannotated Corpora}, |
|
month = may, |
|
year = 2019, |
|
publisher = {Zenodo}, |
|
doi = {10.5281/zenodo.3247731}, |
|
url = {https://doi.org/10.5281/zenodo.3247731} |
|
} |
|
""" |
|
|
|
_DESCRIPTION = """\ |
|
The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament \ |
|
notes. Each config contains the data corresponding to a different corpus. For example, "all_wiki" only includes \ |
|
examples from Spanish Wikipedia. By default, the config is set to "combined" which loads all the corpora; with this \ |
|
setting you can also specify the number of samples to return per corpus by configuring the "split" argument. |
|
""" |
|
|
|
_HOMEPAGE = "https://github.com/josecannete/spanish-corpora" |
|
|
|
_LICENSE = "MIT" |
|
|
|
_URL = "https://zenodo.org/record/3247731/files/raw.tar.bz2" |
|
|
|
_CORPORA = [ |
|
"JRC", |
|
"EMEA", |
|
"GlobalVoices", |
|
"ECB", |
|
"DOGC", |
|
"all_wikis", |
|
"TED", |
|
"multiUN", |
|
"Europarl", |
|
"NewsCommentary11", |
|
"UN", |
|
"EUBookShop", |
|
"ParaCrawl", |
|
"OpenSubtitles2018", |
|
"DGT", |
|
] |
|
|
|
_CORPORA_FILEPATHS = {corpus: os.path.join("spanish-corpora", "raw", f"{corpus}.txt") for corpus in _CORPORA} |
|
|
|
_VERSION = "1.1.0" |
|
|
|
_COMBINED = "combined" |
|
|
|
|
|
class LargeSpanishCorpusConfig(datasets.BuilderConfig): |
|
def __init__(self, corpora=None, **kwargs): |
|
super(LargeSpanishCorpusConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs) |
|
self.corpora = corpora |
|
|
|
@property |
|
def filepaths(self): |
|
return [_CORPORA_FILEPATHS[corpus] for corpus in self.corpora] |
|
|
|
|
|
class LargeSpanishCorpus(datasets.GeneratorBasedBuilder): |
|
"""The Large Spanish Corpus.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
LargeSpanishCorpusConfig(name=corpus, corpora=[corpus], description=f"Spanish examples in corpus {corpus}.") |
|
for corpus in _CORPORA |
|
] + [ |
|
LargeSpanishCorpusConfig( |
|
name=_COMBINED, corpora=_CORPORA, description="Complete Spanish dataset with all corpora." |
|
) |
|
] |
|
BUILDER_CONFIG_CLASS = LargeSpanishCorpusConfig |
|
DEFAULT_CONFIG_NAME = _COMBINED |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
"text": datasets.Value("string"), |
|
} |
|
), |
|
supervised_keys=None, |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
data_dir = dl_manager.download_and_extract(_URL) |
|
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir})] |
|
|
|
def _generate_examples(self, data_dir): |
|
_id = 0 |
|
for filepath in self.config.filepaths: |
|
filepath = os.path.join(data_dir, filepath) |
|
with open(filepath, mode="r", encoding="utf-8") as f: |
|
for line in f: |
|
yield _id, {"text": line.strip()}, |
|
_id += 1 |
|
|