Indobert Lite Large P2

🚀 IndoBERT-Lite Large Model (phase2 - uncased)

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

✨ Features

IndoBERT stands as a cutting - edge language model tailored for the Indonesian language, built upon the BERT architecture. The pre - trained model undergoes training with masked language modeling (MLM) and next sentence prediction (NSP) objectives.

📚 Documentation

All Pre - trained Models

💻 Usage Examples

Basic Usage

from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-lite-large-p2")
model = AutoModel.from_pretrained("indobenchmark/indobert-lite-large-p2")

Advanced Usage

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

📄 License

This project is licensed under the MIT license.

👥 Authors

IndoBERT was trained and evaluated by Bryan Wilie*, Karissa Vincentio*, Genta Indra Winata*, Samuel Cahyawijaya*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.

📚 Citation

If you use our work, please cite:

@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}
}