IndoBERTNusa (IndoBERT Adapted for Balinese, Buginese, and Minangkabau)

This repository contains a language adaptation and fine-tuning of the Indobenchmark IndoBERT language model for three specific languages: Balinese, Buginese, and Minangkabau. The adaptation was performed using nusa-translation dataset.

Model Details

Base Model: indobenchmark/indobert-large-p2
Adaptation Data: nusa-translation

Performance Comparison / Benchmark

Topic Classification

We tested the model after it was fine-tuned for topic classification using nusa-dialogue dataset.

Language	indobert-large-p2 (F1)	indobert-nusa (F1)
Balinese	82.37	84.23
Buginese	80.53	82.03
Minangkabau	84.49	86.30

Language Identification

We also tested the model after it was fine-tuned for language identification using nusaX dataset.

Model	F1-score
indobert-large-p2	98.21
indober-nusa	98.45

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Framework versions

Transformers 4.33.1
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.13.3

Additional Information

Licensing Information

The dataset is released under the terms of CC-BY-SA 4.0. By using this model, you are also bound to the respective Terms of Use and License of the dataset. For commercial use in small businesses and startups, please contact us ([email protected]) for permission to use the datasets by informing company profile and propose of usage.

Acknowledgement

This research work is funded and supported by The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH and FAIR Forward - Artificial Intelligence for all. We thank Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Ditjen DIKTI) for providing the computing resources for this project.

Contact Us

If you have any question please contact our support team at [email protected].

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indobert-nusa