metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
base_model: microsoft/deberta-v3-base
model-index:
  - name: deberta-v3-base-injection
    results: []
datasets:
  - deepset/prompt-injections
language:
  - en
  - de

deberta-v3-base-injection

This model is a fine-tuned version of microsoft/deberta-v3-base on the promp-injection dataset. It achieves the following results on the evaluation set:

Loss: 0.0673
Accuracy: 0.9914

Model description

This model detects prompt injection attempts and classifies them as "INJECTION". Legitimate requests are classified as "LEGIT". The dataset assumes that legitimate requests are either all sorts of questions of key word searches.

Intended uses & limitations

If you are using this model to secure your system and it is overly "trigger-happy" to classify requests as injections, consider collecting legitimate examples and retraining the model with the promp-injection dataset.

Training and evaluation data

Based in the promp-injection dataset.

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	69	0.2353	0.9741
No log	2.0	138	0.0894	0.9741
No log	3.0	207	0.0673	0.9914

Framework versions

Transformers 4.29.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3