SEACrowd/gnome

A parallel corpus of GNOME localization files, which contains the interface text in the GNU Network Object Model Environment (GNOME) and published by GNOME translation teams. Text in this dataset is relatively short and technical.

Languages

eng, vie, mya, ind, tha, tgl, zlm, lao

Supported Tasks

Machine Translation

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("gnome", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("gnome"))
# 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://opus.nlpl.eu/GNOME/corpus/version/GNOME

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Unknown (unknown)

Citation

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

\
@inproceedings{tiedemann-2012-parallel,
    title = "Parallel Data, Tools and Interfaces in {OPUS}",
    author = {Tiedemann, J{\"o}rg},
    editor = "Calzolari, Nicoletta  and
        Choukri, Khalid  and
        Declerck, Thierry  and
        Do{\u{g}}an, Mehmet U{\u{g}}ur  and
        Maegaard, Bente  and
        Mariani, Joseph  and
        Moreno, Asuncion  and
        Odijk, Jan  and
        Piperidis, Stelios",
    booktitle = "Proceedings of the Eighth International Conference on Language
    Resources and Evaluation ({LREC}'12)",
    month = may,
    year = "2012",
    address = "Istanbul, Turkey",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
    pages = "2214--2218",
    abstract = "This paper presents the current status of OPUS, a growing
    language resource of parallel corpora and related tools. The focus in OPUS
    is to provide freely available data sets in various formats together with
    basic annotation to be useful for applications in computational linguistics,
    translation studies and cross-linguistic corpus studies. In this paper, we
    report about new data sets and their features, additional annotation tools
    and models provided from the website and essential interfaces and on-line
    services included in the project.",
}


@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}
}