Indobert Base P2

🚀 IndoBERT Base Model (phase2 - uncased)

IndoBERT is a state - of - the - art language model for Indonesian, built upon the BERT architecture. It's pretrained using masked language modeling (MLM) and next sentence prediction (NSP) objectives, offering high - quality language understanding capabilities for the Indonesian language.

✨ Features

• Based on the BERT model, providing a solid foundation for Indonesian language processing.
• Pretrained with MLM and NSP objectives, enabling effective language understanding.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

Advanced Usage

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

📚 Documentation

All Pre - trained Models

👥 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.

📄 License

The project is licensed under the MIT license.

📖 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}
}