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--- |
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tags: |
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- self-supervised-pretraining |
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language: |
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- ind |
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- sun |
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- jav |
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--- |
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# indo4b_plus |
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Indo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus. |
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Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local languages to the corpus, i.e., Sundanese and Javanese. |
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Indo4B-Plus adds 82,582,025 words (∼2.07%) of Sundanese sentences and 331,041,877 words (∼8.29%) of Javanese |
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## Dataset Usage |
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Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. |
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## Citation |
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``` |
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@inproceedings{cahyawijaya-etal-2021-indonlg, |
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title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation", |
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author = "Cahyawijaya, Samuel and |
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Winata, Genta Indra and |
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Wilie, Bryan and |
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Vincentio, Karissa and |
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Li, Xiaohong and |
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Kuncoro, Adhiguna and |
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Ruder, Sebastian and |
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Lim, Zhi Yuan and |
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Bahar, Syafri and |
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Khodra, Masayu and |
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Purwarianti, Ayu and |
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Fung, Pascale", |
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booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
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month = nov, |
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year = "2021", |
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address = "Online and Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.emnlp-main.699", |
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doi = "10.18653/v1/2021.emnlp-main.699", |
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pages = "8875--8898", |
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abstract = "Natural language generation (NLG) benchmarks provide an important avenue to measure progress |
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and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource |
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languages poses a challenging barrier for building NLG systems that work well for languages with limited |
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amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) |
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progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. |
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Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important |
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use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, |
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and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, |
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Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. |
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We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth |
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the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes |
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the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference |
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at very low-resource languages like Javanese and Sundanese.", |
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} |
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``` |
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## License |
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CC0 |
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## Homepage |
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[https://github.com/IndoNLP/indonlu](https://github.com/IndoNLP/indonlu) |
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### NusaCatalogue |
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For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
