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README.md
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- self-supervised-pretraining
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language:
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- ind
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---
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- self-supervised-pretraining
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language:
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- ind
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---
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Indo4B is a large-scale Indonesian self-supervised pre-training corpus
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consists of around 3.6B words, with around 250M sentences. The corpus
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covers both formal and colloquial Indonesian sentences compiled from
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12 sources, of which two cover Indonesian colloquial language, eight
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cover formal Indonesian language, and the rest have a mixed style of
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both colloquial and formal.
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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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``` @inproceedings{wilie-etal-2020-indonlu,
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title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian
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Natural Language Understanding",
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author = "Wilie, Bryan and
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Vincentio, Karissa and
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Winata, Genta Indra and
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Cahyawijaya, Samuel and
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Li, Xiaohong and
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Lim, Zhi Yuan and
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Soleman, Sidik and
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Mahendra, Rahmad and
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Fung, Pascale and
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Bahar, Syafri and
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Purwarianti, Ayu",
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booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the
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Association for Computational Linguistics and the 10th International Joint
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Conference on Natural Language Processing",
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month = dec,
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year = "2020",
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address = "Suzhou, China",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2020.aacl-main.85",
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pages = "843--857",
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abstract = "Although Indonesian is known to be the fourth most frequently used language
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over the internet, the research progress on this language in natural language processing (NLP)
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is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast
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resource for training, evaluation, and benchmarking on Indonesian natural language understanding
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(IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to
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pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks
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lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian
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pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected
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from publicly available sources such as social media texts, blogs, news, and websites.
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We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation,
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thus enabling everyone to benchmark their system performances.",
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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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### NusaCatalogue
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For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
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