metadata
language:
- ind
- jav
- sun
- mya
- lao
- khm
- tgl
- vie
- tha
- zlm
pretty_name: Cc100
task_categories:
- self-supervised-pretraining
tags:
- self-supervised-pretraining
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.
Languages
ind, jav, sun, mya, mya_zaw, lao, khm, tgl, vie, tha, zlm
Supported Tasks
Self Supervised Pretraining
Dataset Usage
Using datasets library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/cc100", trust_remote_code=True)
Using seacrowd library
# Load the dataset using the default config
dset = sc.load_dataset("cc100", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("cc100"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
More details on how to load the seacrowd library can be found here.
Dataset Homepage
https://data.statmt.org/cc-100/
Dataset Version
Source: 2018.12.01. SEACrowd: 2024.06.20.
Dataset License
MIT
Citation
If you are using the Cc100 dataloader in your work, please cite the following:
@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",
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.747",
doi = "10.18653/v1/2020.acl-main.747",
pages = "8440--8451",
abstract = "This paper shows that pretraining multilingual language models
at scale leads to significant performance gains for a wide range of
cross-lingual transfer tasks. We train a Transformer-based masked language
model on one hundred languages, using more than two terabytes of filtered
CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms
multilingual BERT (mBERT) on a variety of cross-lingual benchmarks,
including +14.6{%} average accuracy on XNLI, +13{%} average F1 score on
MLQA, and +2.4{%} F1 score on NER. XLM-R performs particularly well on
low-resource languages, improving 15.7{%} in XNLI accuracy for Swahili and
11.4{%} for Urdu over previous XLM models. We also present a detailed
empirical analysis of the key factors that are required to achieve these
gains, including the trade-offs between (1) positive transfer and capacity
dilution and (2) the performance of high and low resource languages at
scale. Finally, we show, for the first time, the possibility of
multilingual modeling without sacrificing per-language performance; XLM-R
is very competitive with strong monolingual models on the GLUE and XNLI
benchmarks. We will make our code and models publicly available.",
}
@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",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.494",
pages = "4003--4012",
abstract = "Pre-training text representations have led to significant
improvements in many areas of natural language processing. The quality of
these models benefits greatly from the size of the pretraining corpora as
long as its quality is preserved. In this paper, we describe an automatic
pipeline to extract massive high-quality monolingual datasets from Common
Crawl for a variety of languages. Our pipeline follows the data processing
introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that
deduplicates documents and identifies their language. We augment this
pipeline with a filtering step to select documents that are close to high
quality corpora like Wikipedia.",
language = "English",
ISBN = "979-10-95546-34-4",
}
@article{lovenia2024seacrowd,
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
year={2024},
eprint={2406.10118},
journal={arXiv preprint arXiv: 2406.10118}
}