prompt-injection-detector-v2-bordair

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

• Loss: 0.0028
• Accuracy: 0.9993
• Precision: 0.9994
• Recall: 0.9992
• F1: 0.9993

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: cosine
• lr_scheduler_warmup_steps: 500
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0004	1.0	13400	0.0028	0.9993	0.9994	0.9992	0.9993

Framework versions

• Transformers 5.9.0
• Pytorch 2.7.1+cu128
• Datasets 4.8.5
• Tokenizers 0.22.2