This model is a fine-tuned version of bert-base-uncased on an [SalKhan12/prompt-safety-dataset] dataset. It achieves the following results on the evaluation set:

Loss: 0.1155
Accuracy: 0.9631
Precision: 0.9498
Recall: 0.9545

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 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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall
0.1415	1.0	3709	0.1212	0.9560	0.9297	0.9575
0.0869	2.0	7418	0.1154	0.9644	0.9526	0.9546
0.0294	3.0	11127	0.1431	0.9648	0.9523	0.9561

Framework versions

Transformers 4.53.2
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.2

SalKhan12/prompt-safety-model

results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

SalKhan12/
prompt-safety-model