Indobert Large P1

🚀 IndoBERT Large Model (phase1 - uncased)

IndoBERT is a state - of - the - art language model for Indonesian, built upon the BERT model. It is pretrained using masked language modeling (MLM) and next sentence prediction (NSP) objectives.

🚀 Quick Start

The following sections will guide you through using the IndoBERT model, including loading the model and tokenizer, and extracting contextual representation.

✨ Features

IndoBERT offers a range of pre - trained models with different architectures and parameter sizes, all trained on the Indo4B dataset. These models can be used for various Indonesian natural language processing tasks.

📦 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-large-p1")
model = AutoModel.from_pretrained("indobenchmark/indobert-large-p1")

Advanced Usage

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

📚 Documentation

All Pre - trained Models

📄 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}
}