SEACrowd/culturax

CulturaX is a comprehensive multilingual dataset comprising 6.3 trillion tokens across 167 languages, designed for large language model development. It incorporates an advanced cleaning and deduplication process, including language identification and fuzzy deduplication with MinHash, to ensure high-quality data for model training. The dataset, which spans 16TB in parquet format and 27TB when unpacked, is a combination of the latest mC4 and OSCAR corpora, emphasizing non-English languages to support multilingual model training. For data cleaning validation, CulturaX employs a SentencePiece tokenizer and KenLM language models, utilizing recent Wikipedia dumps for perplexity scoring. Before using this dataloader, please accept the acknowledgement at https://huggingface.co/datasets/uonlp/CulturaX and use huggingface-cli login for authentication.

Languages

ind, jav, khm, lao, tgl, min, mya, sun, tha, vie, zlm, ceb, war, cbk, bcl

Supported Tasks

Self Supervised Pretraining

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/culturax", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("culturax", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("culturax"))
# 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://huggingface.co/datasets/uonlp/CulturaX

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Other License (others) |The licence terms for CulturaX strictly follows those of mC4 and OSCAR.Please refer to both below licenses when using this dataset.- mC4 license: https://huggingface.co/datasets/allenai/c4#license- OSCAR license: https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information

Citation

If you are using the Culturax dataloader in your work, please cite the following:

@article{nguyen2023culturax,
    author    = {Thuat Nguyen and Chien Van Nguyen and Viet Dac Lai and Hieu Man and Nghia Trung Ngo and Franck Dernoncourt and Ryan A. Rossi and Thien Huu Nguyen},
    title     = {CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages},
    journal   = {arXiv preprint arXiv:2309.09400},
    year      = {2023},
    url       = {https://arxiv.org/abs/2309.09400},
}


@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}
}