Fill-Mask
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bert
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Inference Endpoints
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feat: add readme and license

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README.md CHANGED
@@ -10,27 +10,38 @@ model-index:
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  results: []
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  ---
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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- # out
 
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- This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on an unknown dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 1.6595
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- - Accuracy: 0.6803
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- ## Model description
 
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- More information needed
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- ## Intended uses & limitations
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- More information needed
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- ## Training and evaluation data
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- More information needed
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  ## Training procedure
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@@ -45,9 +56,6 @@ The following hyperparameters were used during training:
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  - lr_scheduler_type: linear
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  - num_epochs: 3.0
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- ### Training results
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-
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-
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  ### Framework versions
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@@ -55,3 +63,28 @@ The following hyperparameters were used during training:
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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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  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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+
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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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+
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+
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+ ### Language Identification
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+
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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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45
 
46
  ## Training procedure
47
 
 
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  - lr_scheduler_type: linear
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  - num_epochs: 3.0
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59
 
60
  ### Framework versions
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  - Pytorch 2.1.2+cu121
64
  - Datasets 2.16.1
65
  - Tokenizers 0.13.3
66
+
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+
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+ ## Additional Information
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+
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+ ### Licensing Information
71
+ The dataset is released under the terms of **CC-BY-SA 4.0**.
72
+ By using this model, you are also bound to the respective Terms of Use and License of the dataset.
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+
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+ ### Citation Information
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+
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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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+
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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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+
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+ ### Contact Us
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+ If you have any question please contact our support team at `business@prosa.ai`.