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import re
import gzip
import json
from pathlib import Path
from typing import Dict, List, Tuple
from urllib.parse import urljoin
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks, Licenses
_CITATION = """\
@inproceedings{abadji2022cleaner,
author = {Julien Abadji and
Pedro Javier Ortiz Su{\'{a}}rez and
Laurent Romary and
Beno{\^{\i}}t Sagot},
title = {Towards a Cleaner Document-Oriented Multilingual Crawled Corpus},
booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference,
{LREC} 2022, Marseille, France, 20-25 June 2022},
pages = {4344--4355},
publisher = {European Language Resources Association},
year = {2022},
url = {https://aclanthology.org/2022.lrec-1.463},
}
@inproceedings{abadji2021ungoliant,
author = {Julien Abadji and
Pedro Javier Ortiz Su{\'a}rez and
Laurent Romary and
Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora
(CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and
Marc Kupietz and
Piotr Bański and
Adrien Barbaresi and
Simon Clematide and
Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language
Processing, large raw corpora have played a crucial role in Computational Linguistics.
However, most of these large raw corpora are either available only for English or not
available to the general public due to copyright issues. Nevertheless, there are some
examples of freely available multilingual corpora for training Deep Learning NLP
models, such as the OSCAR and Paracrawl corpora. However, they have quality issues,
especially for low-resource languages. Moreover, recreating or updating these corpora
is very complex. In this work, we try to reproduce and improve the goclassy pipeline
used to create the OSCAR corpus. We propose a new pipeline that is faster, modular,
parameterizable, and well documented. We use it to create a corpus similar to OSCAR
but larger and based on recent data. Also, unlike OSCAR, the metadata information is
at the document level. We release our pipeline under an open source license and
publish the corpus under a research-only license.},
language = {en}
}
@article{kreutzer2022quality,
title = {Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets},
author = {Kreutzer, Julia and
Caswell, Isaac and
Wang, Lisa and
Wahab, Ahsan and
van Esch, Daan and
Ulzii-Orshikh, Nasanbayar and
Tapo, Allahsera and
Subramani, Nishant and
Sokolov, Artem and
Sikasote, Claytone and
Setyawan, Monang and
Sarin, Supheakmungkol and
Samb, Sokhar and
Sagot, Beno{\^\i}t and
Rivera, Clara and
Rios, Annette and
Papadimitriou, Isabel and
Osei, Salomey and
Suarez, Pedro Ortiz and
Orife, Iroro and
Ogueji, Kelechi and
Rubungo, Andre Niyongabo and
Nguyen, Toan Q. and
M{\"u}ller, Mathias and
M{\"u}ller, Andr{\'e} and
Muhammad, Shamsuddeen Hassan and
Muhammad, Nanda and
Mnyakeni, Ayanda and
Mirzakhalov, Jamshidbek and
Matangira, Tapiwanashe and
Leong, Colin and
Lawson, Nze and
Kudugunta, Sneha and
Jernite, Yacine and
Jenny, Mathias and
Firat, Orhan and
Dossou, Bonaventure F. P. and
Dlamini, Sakhile and
de Silva, Nisansa and
{\c{C}}abuk Ball{\i}, Sakine and
Biderman, Stella and
Battisti, Alessia and
Baruwa, Ahmed and
Bapna, Ankur and
Baljekar, Pallavi and
Azime, Israel Abebe and
Awokoya, Ayodele and
Ataman, Duygu and
Ahia, Orevaoghene and
Ahia, Oghenefego and
Agrawal, Sweta and
Adeyemi, Mofetoluwa},
editor = {Roark, Brian and
Nenkova, Ani},
journal = {Transactions of the Association for Computational Linguistics},
volume = {10},
year = {2022},
address = {Cambridge, MA},
publisher = {MIT Press},
url = {https://aclanthology.org/2022.tacl-1.4},
doi = {10.1162/tacl_a_00447},
pages = {50--72},
abstract = {With the success of large-scale pre-training and multilingual modeling in
Natural Language Processing (NLP), recent years have seen a proliferation of large,
Web-mined text datasets covering hundreds of languages. We manually audit the quality
of 205 language-specific corpora released with five major public datasets (CCAligned,
ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At
least 15 corpora have no usable text, and a significant fraction contains less than
50{\%} sentences of acceptable quality. In addition, many are mislabeled or use
nonstandard/ambiguous language codes. We demonstrate that these issues are easy to
detect even for non-proficient speakers, and supplement the human audit with automatic
analyses. Finally, we recommend techniques to evaluate and improve multilingual
corpora and discuss potential risks that come with low-quality data releases.},
}
@inproceedings{ortizsuarez2020monolingual,
title = {A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages},
author = {Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit},
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://www.aclweb.org/anthology/2020.acl-main.156},
pages = {1703--1714},
abstract = {We use the multilingual OSCAR corpus, extracted from Common Crawl via
language classification, filtering and cleaning, to train monolingual contextualized
word embeddings (ELMo) for five mid-resource languages. We then compare the
performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on
the part-of-speech tagging and parsing tasks. We show that, despite the noise in the
Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than
monolingual embeddings trained on Wikipedia. They actually equal or improve the
current state of the art in tagging and parsing for all five languages. In particular,
they also improve over multilingual Wikipedia-based contextual embeddings
(multilingual BERT), which almost always constitutes the previous state of the art,
thereby showing that the benefit of a larger, more diverse corpus surpasses the
cross-lingual benefit of multilingual embedding architectures.},
}
@inproceedings{ortizsuarez2019asynchronous,
author = {Pedro Javier {Ortiz Su{'a}rez} and
Benoit Sagot and
Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora
(CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and
Adrien Barbaresi and
Hanno Biber and
Evelyn Breiteneder and
Simon Clematide and
Marc Kupietz and
Harald L{"u}ngen and
Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus
comprised of crawled documents from the internet, surpassing 20TB of data and
distributed as a set of more than 50 thousand plain text files where each contains
many documents written in a wide variety of languages. Even though each document has a
metadata block associated to it, this data lacks any information about the language in
which each document is written, making it extremely difficult to use Common Crawl for
monolingual applications. We propose a general, highly parallel, multithreaded
pipeline to clean and classify Common Crawl by language; we specifically design it so
that it runs efficiently on medium to low resource infrastructures where I/O speeds
are the main constraint. We develop the pipeline so that it can be easily reapplied to
any kind of heterogeneous corpus and so that it can be parameterised to a wide range
of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered,
classified by language, shuffled at line level in order to avoid copyright issues, and
ready to be used for NLP applications.},
language = {en}
}
"""
_DATASETNAME = "oscar_2201"
_DESCRIPTION = """\
OSCAR or Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus
obtained by language classification and filtering of the Common Crawl corpus using
the ungoliant architecture. Data is distributed by language in both original and
deduplicated form.
"""
_HOMEPAGE = "https://huggingface.co/datasets/oscar-corpus/OSCAR-2201"
_LICENSE = Licenses.CC0_1_0.value
_BASE_URL = "https://huggingface.co/datasets/oscar-corpus/OSCAR-2201/resolve/main/compressed/{lang}_meta/"
_LOCAL = False
_LANGUAGES = ["war", "ceb", "min", "vie", "ilo", "tgl", "lao", "khm", "mya", "jav", "ind", "tha", "sun", "zlm"]
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_SOURCE_VERSION = "2022.1.0"
_SEACROWD_VERSION = "2024.06.20"
class Oscar2201Dataset(datasets.GeneratorBasedBuilder):
"""OSCAR subset for SEA languages, version 2201."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SEACROWD_SCHEMA_NAME = "ssp"
SUBSETS = ["war", "ceb", "min", "vi", "ta", "ilo", "tl", "lo", "km", "my", "jv", "id", "th", "su", "ms"]
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} {subset} source schema",
schema="source",
subset_id=subset,
) for subset in SUBSETS
] + [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_seacrowd_ssp",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} {subset} SEACrowd schema",
schema="seacrowd_ssp",
subset_id=subset,
)
for subset in SUBSETS
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_jv_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("int64"),
"text": datasets.Value("string"),
"meta": {
"warc_headers": {
"warc-record-id": datasets.Value("string"),
"warc-date": datasets.Value("string"),
"content-type": datasets.Value("string"),
"content-length": datasets.Value("int32"),
"warc-type": datasets.Value("string"),
"warc-identified-content-language": datasets.Value("string"),
"warc-refers-to": datasets.Value("string"),
"warc-target-uri": datasets.Value("string"),
"warc-block-digest": datasets.Value("string"),
},
"identification": {
"label": datasets.Value("string"),
"prob": datasets.Value("float"),
},
"annotations": datasets.Sequence(datasets.Value("string")),
"line_identifications": [
{
"label": datasets.Value("string"),
"prob": datasets.Value("float"),
}
],
},
}
)
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
features = schemas.ssp_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
base_path = _BASE_URL.format(lang=self.config.name.split("_")[2])
checksum_url = urljoin(base_path, "checksum.sha256")
checksum_path = Path(dl_manager.download(checksum_url))
with open(checksum_path, encoding="utf-8") as f:
filenames = [line.split()[1] for line in f if line]
filenames = sorted(filenames, key=lambda x: int(re.search(r"\d+", x).group()) if re.search(r"\d+", x) else x)
data_urls = [urljoin(base_path, filename) for filename in filenames]
data_paths = list(map(Path, dl_manager.download([url for url in data_urls if url.endswith(".jsonl.gz")])))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": data_paths,
"split": "train",
},
)
]
def _generate_examples(self, filepaths: [Path], split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
key = 0
for filepath in filepaths:
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
for line in f:
doc = json.loads(line)
if self.config.schema == "source":
meta = dict()
meta["warc_headers"] = doc["warc_headers"]
meta["warc_headers"]["warc-identified-content-language"] = doc["warc_headers"].get("warc-identified-content-language")
meta["identification"] = doc["metadata"]["identification"]
meta["annotations"] = doc["metadata"]["annotation"]
meta["line_identifications"] = doc["metadata"]["sentence_identifications"]
yield key, {"id": key, "text": doc["content"], "meta": meta}
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
yield key, {"id": str(key), "text": doc["content"]}
key += 1 |