uonlp/CulturaX
Unclear
The checks could not identify a recognised licence ID in the dataset's metadata.
Licence facts reported by the dataset and its declared sources. Not legal advice, and not a grant of permission to use the dataset.
Evidence
- Declared licence: not declared (see datasets without one)
- Licence files: no licence file at the top level of the repository
- Access gated on Hugging Face: yes
Declared sources
No source datasets listed in the card metadata.
Signal
See all datasets marked Unclear.
Tasks
Citation text from the card
@inproceedings{nguyen-etal-2024-culturax,
title = "{C}ultura{X}: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages",
author = "Nguyen, Thuat and
Nguyen, Chien Van and
Lai, Viet Dac and
Man, Hieu and
Ngo, Nghia Trung and
Dernoncourt, Franck and
Rossi, Ryan A. and
Nguyen, Thien Huu",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.377",
pages = "4226--4237",
abstract = "Extensive training datasets represent one of the important factors for the impressive learning capabilities of large language models (LLMs). However, these training datasets for current LLMs, especially the recent state-of-the-art models, are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancem
Downloads (30 days): 25647. Likes: 706. Last modified: 2024-12-16T17:24:53.000Z.