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IndoBERT-Sentiment-Analysis8
wildansofhal/IndoBERT-Sentiment-Analysis8
IndoBERT-Sentiment-Analysis8 is a text classification model from wildansofhal. 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
- Jul 18, 2025
- Updated
- Jul 18, 2025
- SHA
- b8115e0d
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Base models
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-Analysis8
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4330
- Accuracy: 0.9064
- F1 Score: 0.9059
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: 1e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| 0.6056 | 0.1096 | 50 | 0.6327 | 0.6436 | 0.6427 |
| 0.6022 | 0.2193 | 100 | 0.5972 | 0.6833 | 0.6710 |
| 0.5756 | 0.3289 | 150 | 0.5110 | 0.7718 | 0.7700 |
| 0.5413 | 0.4386 | 200 | 0.4315 | 0.8167 | 0.8165 |
| 0.4163 | 0.5482 | 250 | 0.3902 | 0.8397 | 0.8397 |
| 0.4568 | 0.6579 | 300 | 0.3597 | 0.8641 | 0.8640 |
| 0.3528 | 0.7675 | 350 | 0.4340 | 0.8321 | 0.8293 |
| 0.323 | 0.8772 | 400 | 0.3802 | 0.8692 | 0.8690 |
| 0.3351 | 0.9868 | 450 | 0.5919 | 0.8218 | 0.8174 |
| 0.2837 | 1.0965 | 500 | 0.4075 | 0.8808 | 0.8806 |
| 0.2194 | 1.2061 | 550 | 0.5113 | 0.8718 | 0.8708 |
| 0.2854 | 1.3158 | 600 | 0.5251 | 0.8692 | 0.8679 |
| 0.2385 | 1.4254 | 650 | 0.4456 | 0.8987 | 0.8982 |
| 0.3028 | 1.5351 | 700 | 0.4148 | 0.9038 | 0.9034 |
| 0.2995 | 1.6447 | 750 | 0.4003 | 0.9103 | 0.9099 |
| 0.1416 | 1.7544 | 800 | 0.4460 | 0.8987 | 0.8982 |
| 0.2767 | 1.8640 | 850 | 0.4192 | 0.9064 | 0.9059 |
| 0.236 | 1.9737 | 900 | 0.4316 | 0.9064 | 0.9059 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jul 18, 2025
- Updated
- Jul 18, 2025