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bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-modified
PaceKW/bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-modified
bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-modified is a machine learning model from PaceKW. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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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Updated Jun 18, 2025
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At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Jun 18, 2025
- Updated
- Jun 18, 2025
- SHA
- 1653f212
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. -->
bert-base-indonesian-1.5G-multilabel-indonesian-hate-speech-modified
This model is a fine-tuned version of cahya/bert-base-indonesian-1.5G on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2368
- F1: 0.8123
- Roc Auc: 0.8828
- Accuracy: 0.7396
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: 8
- eval_batch_size: 8
- 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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.2277 | 1.0 | 1317 | 0.1909 | 0.7771 | 0.8519 | 0.6659 |
| 0.1552 | 2.0 | 2634 | 0.1908 | 0.7910 | 0.8718 | 0.6735 |
| 0.0983 | 3.0 | 3951 | 0.2215 | 0.7898 | 0.8681 | 0.7183 |
| 0.0579 | 4.0 | 5268 | 0.2368 | 0.8123 | 0.8828 | 0.7396 |
| 0.0389 | 5.0 | 6585 | 0.2813 | 0.8039 | 0.8815 | 0.7327 |
| 0.0264 | 6.0 | 7902 | 0.3052 | 0.7992 | 0.8788 | 0.7396 |
| 0.0191 | 7.0 | 9219 | 0.3389 | 0.7976 | 0.8791 | 0.7365 |
| 0.0122 | 8.0 | 10536 | 0.3407 | 0.8041 | 0.8797 | 0.7373 |
| 0.0076 | 9.0 | 11853 | 0.3596 | 0.8014 | 0.8821 | 0.7373 |
| 0.007 | 10.0 | 13170 | 0.3688 | 0.8086 | 0.8861 | 0.7456 |
| 0.0049 | 11.0 | 14487 | 0.3850 | 0.8006 | 0.8743 | 0.7487 |
| 0.0039 | 12.0 | 15804 | 0.3863 | 0.8085 | 0.8850 | 0.7472 |
| 0.003 | 13.0 | 17121 | 0.3884 | 0.8080 | 0.8766 | 0.7555 |
| 0.0016 | 14.0 | 18438 | 0.3909 | 0.8102 | 0.8872 | 0.7509 |
| 0.0019 | 15.0 | 19755 | 0.3929 | 0.8083 | 0.8819 | 0.7509 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
- Library
- transformers
- License
- mit
- Created
- Jun 18, 2025
- Updated
- Jun 18, 2025