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HomeLLMsindobert base p1

indobert base p1

by indobenchmark

Open source · 321k downloads · 46 likes

2.1
(46 reviews)EmbeddingAPI & Local
About

IndoBERT Base (phase 1 - uncased) is a cutting-edge language model specifically designed for Indonesian, built on the BERT architecture. Trained with masked language modeling and next sentence prediction objectives, it excels in understanding and generating Indonesian text. Its core capabilities include semantic analysis, text classification, and the generation of rich contextual representations. This model is particularly well-suited for Indonesian natural language processing tasks, such as sentiment analysis, question answering, or document classification. What sets it apart is its specialized training on a vast Indonesian corpus, enabling it to outperform generic multilingual models for this language.

Documentation

IndoBERT Base Model (phase1 - uncased)

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.

All Pre-trained Models

Model#paramsArch.Training data
indobenchmark/indobert-base-p1124.5MBaseIndo4B (23.43 GB of text)
indobenchmark/indobert-base-p2124.5MBaseIndo4B (23.43 GB of text)
indobenchmark/indobert-large-p1335.2MLargeIndo4B (23.43 GB of text)
indobenchmark/indobert-large-p2335.2MLargeIndo4B (23.43 GB of text)
indobenchmark/indobert-lite-base-p111.7MBaseIndo4B (23.43 GB of text)
indobenchmark/indobert-lite-base-p211.7MBaseIndo4B (23.43 GB of text)
indobenchmark/indobert-lite-large-p117.7MLargeIndo4B (23.43 GB of text)
indobenchmark/indobert-lite-large-p217.7MLargeIndo4B (23.43 GB of text)

How to use

Load model and tokenizer

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

Extract contextual representation

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

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:

Bibtex
@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}
}
Capabilities & Tags
transformerspytorchtfjaxbertfeature-extractionindobertindobenchmarkindonluid
Links & Resources
Specifications
CategoryEmbedding
AccessAPI & Local
LicenseOpen Source
PricingOpen Source
Rating
2.1

Try indobert base p1

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