Indobert Base P1

🚀 IndoBERT Base Model (phase1 - uncased)

IndoBERT is a state - of - the - art language model for Indonesian based on the BERT model. It addresses the challenges of natural language processing in Indonesian by leveraging the power of BERT architecture. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective, enabling it to understand and generate high - quality Indonesian text.

✨ Features

IndoBERT offers a variety of pre - trained models with different parameter sizes and architectures, suitable for diverse Indonesian language processing tasks.

📚 Documentation

All Pre - trained Models

💻 Usage Examples

Basic Usage

# Load model and tokenizer
from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-base-p1")
model = AutoModel.from_pretrained("indobenchmark/indobert-base-p1")

Advanced Usage

# Extract contextual representation
import torch
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}
}