bert-base-detect-jailbreak

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3486
• Accuracy: 0.8931
• Precision: 0.9206
• Recall: 0.8657
• F1: 0.8923
• Balanced Accuracy: 0.8938
• Mcc: 0.7879

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: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Use 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Balanced Accuracy	Mcc
No log	1.0	99	0.2730	0.9059	0.9305	0.8788	0.9039	0.9061	0.8130
0.4532	2.0	198	0.2610	0.9059	0.9548	0.8535	0.9013	0.9063	0.8165
0.2683	3.0	297	0.2622	0.9008	0.9441	0.8535	0.8966	0.9011	0.8054
0.202	4.0	396	0.2914	0.9109	0.9179	0.9040	0.9109	0.9110	0.8220
0.1308	5.0	495	0.3012	0.9135	0.9362	0.8889	0.9119	0.9137	0.8281
0.0856	6.0	594	0.3709	0.8906	0.8818	0.9040	0.8928	0.8905	0.7814
0.0622	7.0	693	0.4141	0.8957	0.8905	0.9040	0.8972	0.8956	0.7914
0.0366	8.0	792	0.4711	0.8957	0.8720	0.9293	0.8998	0.8954	0.7930
0.0262	9.0	891	0.4318	0.8982	0.8990	0.8990	0.8990	0.8982	0.7964
0.0145	10.0	990	0.4440	0.8957	0.8867	0.9091	0.8978	0.8956	0.7916

Framework versions

• Transformers 4.53.3
• Pytorch 2.6.0+cu124
• Datasets 4.3.0
• Tokenizers 0.21.4