README.md

---
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
```import seacrowd as sc
# 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](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).


## Dataset Homepage

[https://data.statmt.org/cc-100/](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}
}

```