transformers pytorch bert fill-mask generated_from_trainer min ban bug id dataset:prosa-text/nusa-dialogue dataset:indonlp/NusaX-MT base_model:indobenchmark/indobert-large-p2 license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
---
license: cc-by-sa-4.0
base_model: indobenchmark/indobert-large-p2
language:
- min
- ban
- bug
- id
pretty_name: IndoBERTNusa
tags:
- generated_from_trainer
datasets:
- prosa-text/nusa-dialogue
- indonlp/NusaX-MT
pipeline_tag: fill-mask
---

# 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](https://huggingface.co/datasets/prosa-text/nusa-translation) dataset.

## Model Details

- **Base Model**: [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2)
- **Adaptation Data**: [nusa-translation](https://huggingface.co/datasets/prosa-text/nusa-translation) 


## Performance Comparison / Benchmark

### Topic Classification

We tested the model after it was fine-tuned for topic classification using [nusa-dialogue](https://huggingface.co/datasets/prosa-text/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](https://github.com/IndoNLP/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 (business@prosa.ai) 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 `business@prosa.ai`.
