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cc100 / cc100.py
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# 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"
# Please note: due to the size of the data, only few examples are provided.
# However, you can pass the lang parameter in config to fetch data of any language in the corpus
_LANGUAGES = ["am", "sr", "ka"]
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