SEACrowd/openlid

This is an open dataset for language identification covering 201 languages, which are curated and audited manually to ensure high confidence in its data and language labels. 22 languages are native to Southeast Asia speakers.

Languages

ace, ban, bjn, bug, ceb, ilo, ind, jav, kac, khm, lao, min, lus, mya, pag, shn, sun, tgl, tha, vie, war, zsm

Supported Tasks

Language Identification

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("openlid", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("openlid"))
# 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://github.com/laurieburchell/open-lid-dataset

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

GNU General Public License v3.0 (gpl-3.0)

Citation

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

@inproceedings{burchell-etal-2023-open,
    title = "An Open Dataset and Model for Language Identification",
    author = "Burchell, Laurie  and
      Birch, Alexandra  and
      Bogoychev, Nikolay  and
      Heafield, Kenneth",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.75",
    doi = "10.18653/v1/2023.acl-short.75",
    pages = "865--879",
    abstract = "Language identification (LID) is a fundamental step in many natural language processing pipelines. However, current LID 
    systems are far from perfect, particularly on lower-resource languages. We present a LID model which achieves a macro-average F1 
    score of 0.93 and a false positive rate of 0.033{\%} across 201 languages, outperforming previous work. We achieve this by training 
    on a curated dataset of monolingual data, which we audit manually to ensure reliability. We make both the model and the dataset 
    available to the research community. Finally, we carry out detailed analysis into our model{'}s performance, both in comparison to 
    existing open models and by language class.",
}


@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}
}