by indobenchmark
Open source · 20k downloads · 7 likes
IndoBERT Base P2 is a state-of-the-art language model specifically designed for Indonesian, built on the BERT architecture. It was trained using masked language modeling and next sentence prediction objectives, enabling it to understand and generate Indonesian text with high accuracy. The model excels in tasks such as text classification, sentiment analysis, question answering, and content generation, while accounting for the unique linguistic nuances of Indonesian. Its primary use cases include automating customer service, large-scale text data analysis, and enhancing machine translation tools. What sets it apart is its ability to deliver optimal performance for Indonesian—a complex and rich language—while remaining accessible and efficient for a variety of applications.
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.
| Model | #params | Arch. | Training data |
|---|---|---|---|
indobenchmark/indobert-base-p1 | 124.5M | Base | Indo4B (23.43 GB of text) |
indobenchmark/indobert-base-p2 | 124.5M | Base | Indo4B (23.43 GB of text) |
indobenchmark/indobert-large-p1 | 335.2M | Large | Indo4B (23.43 GB of text) |
indobenchmark/indobert-large-p2 | 335.2M | Large | Indo4B (23.43 GB of text) |
indobenchmark/indobert-lite-base-p1 | 11.7M | Base | Indo4B (23.43 GB of text) |
indobenchmark/indobert-lite-base-p2 | 11.7M | Base | Indo4B (23.43 GB of text) |
indobenchmark/indobert-lite-large-p1 | 17.7M | Large | Indo4B (23.43 GB of text) |
indobenchmark/indobert-lite-large-p2 | 17.7M | Large | Indo4B (23.43 GB of text) |
from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-base-p2")
model = AutoModel.from_pretrained("indobenchmark/indobert-base-p2")
x = torch.LongTensor(tokenizer.encode('aku adalah anak [MASK]')).view(1,-1)
print(x, model(x)[0].sum())
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.
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}
}