library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags:

generated_from_trainer metrics:
accuracy
precision
recall
f1 model-index:
name: bert-base-detect-jailbreak results: []

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

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

Balanced Accuracy

Mcc

No log

1.0

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

0.8982

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