cc100.py

# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
import datasets


_DESCRIPTION = """\
This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. No claims of intellectual property are made on the work of preparation of the corpus.
"""
_HOMEPAGE_URL = "https://data.statmt.org/cc-100/"
_CITATION = """\
@inproceedings{conneau-etal-2020-unsupervised,
    title = "Unsupervised Cross-lingual Representation Learning at Scale",
    author = "Conneau, Alexis  and
      Khandelwal, Kartikay  and
      Goyal, Naman  and
      Chaudhary, Vishrav  and
      Wenzek, Guillaume  and
      Guzm{\\'a}n, Francisco  and
      Grave, Edouard  and
      Ott, Myle  and
      Zettlemoyer, Luke  and
      Stoyanov, Veselin",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.747",
    doi = "10.18653/v1/2020.acl-main.747",
    pages = "8440--8451",
}
@inproceedings{wenzek-etal-2020-ccnet,
    title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data",
    author = "Wenzek, Guillaume  and
      Lachaux, Marie-Anne  and
      Conneau, Alexis  and
      Chaudhary, Vishrav  and
      Guzm{\\'a}n, Francisco  and
      Joulin, Armand  and
      Grave, Edouard",
    editor = "Calzolari, Nicoletta  and
      B{\\'e}chet, Fr{\\'e}d{\\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\\'e}l{\\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.494",
    pages = "4003--4012",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
"""

_VERSION = "1.0.0"
_BASE_URL = "https://data.statmt.org/cc-100/{}.txt.xz"
_LANGUAGES = [
    "af",
    "am",
    "ar",
    "as",
    "az",
    "be",
    "bg",
    "bn",
    "bn_rom",
    "br",
    "bs",
    "ca",
    "cs",
    "cy",
    "da",
    "de",
    "el",
    "en",
    "eo",
    "es",
    "et",
    "eu",
    "fa",
    "ff",
    "fi",
    "fr",
    "fy",
    "ga",
    "gd",
    "gl",
    "gn",
    "gu",
    "ha",
    "he",
    "hi",
    "hi_rom",
    "hr",
    "ht",
    "hu",
    "hy",
    "id",
    "ig",
    "is",
    "it",
    "ja",
    "jv",
    "ka",
    "kk",
    "km",
    "kn",
    "ko",
    "ku",
    "ky",
    "la",
    "lg",
    "li",
    "ln",
    "lo",
    "lt",
    "lv",
    "mg",
    "mk",
    "ml",
    "mn",
    "mr",
    "ms",
    "my",
    "my_zaw",
    "ne",
    "nl",
    "no",
    "ns",
    "om",
    "or",
    "pa",
    "pl",
    "ps",
    "pt",
    "qu",
    "rm",
    "ro",
    "ru",
    "sa",
    "sc",
    "sd",
    "si",
    "sk",
    "sl",
    "so",
    "sq",
    "sr",
    "ss",
    "su",
    "sv",
    "sw",
    "ta",
    "ta_rom",
    "te",
    "te_rom",
    "th",
    "tl",
    "tn",
    "tr",
    "ug",
    "uk",
    "ur",
    "ur_rom",
    "uz",
    "vi",
    "wo",
    "xh",
    "yi",
    "yo",
    "zh-Hans",
    "zh-Hant",
    "zu",
]


class Cc100Config(datasets.BuilderConfig):
    def __init__(self, *args, lang=None, **kwargs):
        super().__init__(
            *args,
            name=f"{lang}",
            **kwargs,
        )
        self.lang = lang


class Cc100(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        Cc100Config(
            lang=lang,
            description=f"Language: {lang}",
            version=datasets.Version(_VERSION),
        )
        for lang in _LANGUAGES
    ]
    BUILDER_CONFIG_CLASS = Cc100Config

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                },
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE_URL,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        def _base_url(lang):
            return _BASE_URL.format(lang)

        download_url = _base_url(self.config.lang)
        path = dl_manager.download_and_extract(download_url)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"datapath": path},
            )
        ]

    def _generate_examples(self, datapath):
        with open(datapath, encoding="utf-8") as f:
            for sentence_counter, row in enumerate(f):
                result = (
                    sentence_counter,
                    {
                        "id": str(sentence_counter),
                        "text": row,
                    },
                )
                yield result