IndoNLU is a collection of Natural Language Understanding (NLU) resources for Bahasa Indonesia.

For Wiki Bahasa Indonesia version please follow this [Link]

You can check [Link]

We provide train, valid, and test set (with masked labels, no true labels). We are currently preparing a platform for auto-evaluation using Codalab. Please stay tuned!

23GB Indo4B Pretraining Dataset [Link]

IndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective.

from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-large-p1")
model = AutoModel.from_pretrained("indobenchmark/indobert-large-p1")

x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1)
print(x, model(x)[0].sum())

• Community Portal and Public Leaderboard [Link]
• Submission Portal https://competitions.codalab.org/competitions/26537

Please follow this [Link]

• Prediction [Link]

• Fine Tune [Link]

• Reproduce Result [Link]

Please follow this [Link]

IndoNLU has been accepted on AACL 2020 and you can find the detail on https://arxiv.org/abs/2009.05387 If you are using any component on IndoNLU for research purposes, please cite the following paper:

@inproceedings{wilie2020indonlu,
  title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
  author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
  booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
  year={2020}
}