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# deberta-v3-base-injection

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [promp-injection](https://huggingface.co/datasets/JasperLS/prompt-injections) 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](https://huggingface.co/datasets/JasperLS/prompt-injections) dataset.

## Training and evaluation data

Based in the [promp-injection](https://huggingface.co/datasets/JasperLS/prompt-injections) 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
{"language": ["en", "de"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["deepset/prompt-injections"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "deberta-v3-base-injection", "results": []}]}
transformers pytorch safetensors deberta-v2 text-classification generated_from_trainer en de dataset:deepset/prompt-injections base_model:microsoft/deberta-v3-base license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #generated_from_trainer #en #de #dataset-deepset/prompt-injections #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

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



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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 ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #generated_from_trainer #en #de #dataset-deepset/prompt-injections #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3