SEACrowd/indo4b_plus
收藏数据集概述
数据集名称
Indo4B Plus
语言
- 印尼语 (ind)
- 巽他语 (sun)
- 爪哇语 (jav)
任务类别
自监督预训练 (Self Supervised Pretraining)
数据集扩展
Indo4B Plus 是 Indo4B 的扩展，增加了两个低资源印尼地方语言：巽他语和爪哇语。
- 增加了 82,582,025 个巽他语单词（约占 2.07%）
- 增加了 331,041,877 个爪哇语单词（约占 8.29%）
数据集使用
使用 datasets 库
python from datasets import load_dataset dset = datasets.load_dataset("SEACrowd/indo4b_plus", trust_remote_code=True)
使用 seacrowd 库
python import seacrowd as sc
使用默认配置加载数据集
dset = sc.load_dataset("indo4b_plus", schema="seacrowd")
查看数据集的所有可用子集（配置名称）
print(sc.available_config_names("indo4b_plus"))
使用特定配置加载数据集
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
数据集版本
- 源版本: 1.0.0
- SEACrowd 版本: 2024.06.20
数据集许可
CC0
引用
plaintext @inproceedings{cahyawijaya-etal-2021-indonlg, title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation", author = "Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu and Purwarianti, Ayu and Fung, Pascale", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.699", doi = "10.18653/v1/2021.emnlp-main.699", pages = "8875--8898", abstract = "Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese.", }
@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} }
