bert-base-multilingual-uncased-multilabel-indonesian-hate-speech-modified

This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2499
• F1: 0.7813
• Roc Auc: 0.8631
• Accuracy: 0.7206

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
0.2629	1.0	1317	0.2287	0.7077	0.7990	0.6135
0.1953	2.0	2634	0.2081	0.7501	0.8357	0.6393
0.1604	3.0	3951	0.2065	0.7741	0.8509	0.6955
0.1237	4.0	5268	0.2283	0.7645	0.8503	0.6978
0.0942	5.0	6585	0.2499	0.7813	0.8631	0.7206
0.0687	6.0	7902	0.2639	0.7741	0.8629	0.7008
0.0506	7.0	9219	0.2991	0.7812	0.8678	0.7267
0.0402	8.0	10536	0.3122	0.7796	0.8585	0.7289
0.0261	9.0	11853	0.3437	0.7744	0.8671	0.7092
0.0223	10.0	13170	0.3687	0.7755	0.8651	0.7153
0.0176	11.0	14487	0.3868	0.7674	0.8561	0.7130
0.0132	12.0	15804	0.3760	0.7780	0.8600	0.7282
0.0111	13.0	17121	0.3927	0.7797	0.8690	0.7175
0.0078	14.0	18438	0.3943	0.7755	0.8635	0.7168
0.0065	15.0	19755	0.4001	0.7772	0.8628	0.7175

Framework versions

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