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IndoBERT-Sentiment-Analysis
rmdhnftrh/IndoBERT-Sentiment-Analysis
IndoBERT-Sentiment-Analysis is a text classification model from rmdhnftrh. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Oct 11, 2025
- Updated
- Oct 11, 2025
- SHA
- b3b9f5f3
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
IndoBERT-Sentiment-Analysis
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the indonlu dataset. It achieves the following results on the evaluation set:
- Loss: 0.4366
- Accuracy: 0.9413
- F1 Score: 0.9411
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| 0.348 | 0.2726 | 500 | 0.2575 | 0.9238 | 0.9234 |
| 0.316 | 0.5453 | 1000 | 0.3581 | 0.9167 | 0.9152 |
| 0.2988 | 0.8179 | 1500 | 0.3070 | 0.9198 | 0.9197 |
| 0.2517 | 1.0905 | 2000 | 0.3738 | 0.9206 | 0.9199 |
| 0.1715 | 1.3631 | 2500 | 0.3353 | 0.9325 | 0.9320 |
| 0.1589 | 1.6358 | 3000 | 0.3510 | 0.9286 | 0.9282 |
| 0.1752 | 1.9084 | 3500 | 0.2935 | 0.9341 | 0.9341 |
| 0.0991 | 2.1810 | 4000 | 0.3740 | 0.9373 | 0.9376 |
| 0.0677 | 2.4537 | 4500 | 0.3930 | 0.9294 | 0.9289 |
| 0.0711 | 2.7263 | 5000 | 0.3676 | 0.9333 | 0.9332 |
| 0.1037 | 2.9989 | 5500 | 0.3072 | 0.9373 | 0.9371 |
| 0.0323 | 3.2715 | 6000 | 0.3811 | 0.9325 | 0.9325 |
| 0.0413 | 3.5442 | 6500 | 0.4210 | 0.9373 | 0.9367 |
| 0.029 | 3.8168 | 7000 | 0.4036 | 0.9373 | 0.9371 |
| 0.0235 | 4.0894 | 7500 | 0.4489 | 0.9365 | 0.9364 |
| 0.01 | 4.3621 | 8000 | 0.4440 | 0.9397 | 0.9395 |
| 0.0079 | 4.6347 | 8500 | 0.4477 | 0.9381 | 0.9380 |
| 0.012 | 4.9073 | 9000 | 0.4340 | 0.9413 | 0.9411 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0
- Datasets 4.2.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Oct 11, 2025
- Updated
- Oct 11, 2025