indobert-base-p1-multilabel-indonesian-hate-speech-new

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: 0.3335
• F1: 0.7926
• Roc Auc: 0.8709
• Accuracy: 0.7253

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
0.2887	1.0	659	0.2198	0.7353	0.8261	0.6110
0.1999	2.0	1318	0.2044	0.7658	0.8579	0.6279
0.1523	3.0	1977	0.2244	0.7615	0.8427	0.6699
0.0755	4.0	2636	0.2394	0.7775	0.8535	0.7027
0.0537	5.0	3295	0.2601	0.7810	0.8641	0.7075
0.0411	6.0	3954	0.2834	0.7774	0.8609	0.6993
0.0219	7.0	4613	0.2974	0.7762	0.8478	0.7172
0.0142	8.0	5272	0.2976	0.7823	0.8684	0.7069
0.0124	9.0	5931	0.3070	0.7883	0.8656	0.7211
0.0081	10.0	6590	0.3132	0.7862	0.8671	0.7147
0.0073	11.0	7249	0.3271	0.7886	0.8632	0.7289
0.0052	12.0	7908	0.3254	0.7866	0.8645	0.7246
0.0044	13.0	8567	0.3316	0.7906	0.8656	0.7308
0.004	14.0	9226	0.3316	0.7921	0.8715	0.7221
0.0039	15.0	9885	0.3335	0.7926	0.8709	0.7253

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.0+cu128
• Datasets 3.6.0
• Tokenizers 0.21.1