catalan_general_crawling.py

# 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,
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"""Catalan General Crawling."""

import os

import datasets


_CITATION = """\
@misc{armengolestape2021multilingual,
      title={Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan},
      author={Jordi Armengol{-}Estap{\'{e}} and Casimiro Pio Carrino and Carlos Rodriguez-Penagos and Ona de Gibert Bonet and Carme Armentano{-}Oller and Aitor Gonzalez{-}Agirre and Maite Melero and Marta Villegas},
      year={2021},
      eprint={2107.07903},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
The Catalan General Crawling Corpus is a 435-million-token web corpus of Catalan built from the web. It has been obtained by crawling the 500 most popular .cat and .ad domains during July 2020. It consists of 434.817.705 tokens, 19.451.691 sentences and 1.016.114 documents. Documents are separated by single new lines. It is a subcorpus of the Catalan Textual Corpus.
"""

_HOMEPAGE = "https://zenodo.org/record/5483031#.YapO3boo9PY"

_LICENSE = "Creative Commons Attribution 4.0 International License"

_URL = "https://zenodo.org/record/5483031/files/catalan_general_crawling.zip?download=1"


class CatalanGeneralCrawling(datasets.GeneratorBasedBuilder):
    """Catalan General Crawling."""

    VERSION = datasets.Version("1.0.0")

    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={
                    "filepath": os.path.join(
                        data_dir, "catalan_general_crawling", "corpus", "catalan_general_crawling.txt"
                    ),
                },
            ),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as f:
            text = ""
            for id_, line in enumerate(f):
                if line == "\n":
                    yield id_, {"text": text.strip()}
                    text = ""
                else:
                    text += line