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--- |
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license: mit |
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base_model: indobenchmark/indobert-large-p2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: out |
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results: [] |
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--- |
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# IndoBERT-nusa (IndoBERT Adapted for Balinese, Buginese, and Minangkabau) |
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This repository contains a language adaptation and fine-tuning of the Indobenchmark IndoBERT language model for three specific languages: Balinese, Buginese, and Minangkabau. |
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The adaptation was performed using nusa-st data. |
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## Model Details |
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- **Base Model**: [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) |
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- **Adaptation Data**: nusa-st |
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## Performance Comparison |
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### Topic Classification |
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Fine-tuned using [nusa-dialogue](https://huggingface.co/datasets/prosa-text/nusa-dialogue) for topic classification. |
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| Language | indobert-large-p2 (F1) | indobert-nusa (F1) | |
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|-------------|------------------------|------------------------| |
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| Balinese | 82.37 | **84.23** | |
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| Buginese | 80.53 | **82.03** | |
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| Minangkabau | 84.49 | **86.30** | |
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### Language Identification |
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Fine-tuned using [nusaX](https://github.com/IndoNLP/nusax) for language classification. |
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| Model | F1-score | |
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|----------------------|--------------| |
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| indobert-large-p2 | 98.21 | |
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| **indober-nusa** | **98.45** | |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.13.3 |
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## Additional Information |
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### Licensing Information |
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The dataset is released under the terms of **CC-BY-SA 4.0**. |
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By using this model, you are also bound to the respective Terms of Use and License of the dataset. |
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### Citation Information |
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```bibtex |
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@article{purwarianti2023nusadialogue, |
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title={NusaDialogue: Dialogue Summarization and Generation for Underrepresented and Extremely Low-Resource Languages}, |
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author={Purwarianti, Ayu and Adhista, Dea and Baptiso, Agung and Mahfuzh, Miftahul and Yusrina Sabila and Cahyawijaya, Samuel and Aji, Alham Fikri}, |
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journal={arXiv preprint arXiv:(coming soon)}, |
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url={https://huggingface.co/datasets/prosa-text/nusa-dialogue}, |
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year={2023} |
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} |
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``` |
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### Acknowledgement |
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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. |
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### Contact Us |
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If you have any question please contact our support team at `[email protected]`. |
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