README.md

---
license: apache-2.0
base_model: microsoft/deberta-v3-base
datasets:
- Lakera/gandalf_ignore_instructions
- rubend18/ChatGPT-Jailbreak-Prompts
- imoxto/prompt_injection_cleaned_dataset-v2
- hackaprompt/hackaprompt-dataset
- fka/awesome-chatgpt-prompts
- teven/prompted_examples
- Dahoas/synthetic-hh-rlhf-prompts
- Dahoas/hh_prompt_format
- MohamedRashad/ChatGPT-prompts
- HuggingFaceH4/instruction-dataset
- HuggingFaceH4/no_robots
- HuggingFaceH4/ultrachat_200k
language:
- en
tags:
- prompt-injection
- injection
- security
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
pipeline_tag: text-classification
model-index:
- name: deberta-v3-base-prompt-injection
  results: []
co2_eq_emissions:
  emissions: 0.9990662916168788
  source: "code carbon"
  training_type: "fine-tuning"
---

# Model Card for deberta-v3-base-prompt-injection

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on multiple combined datasets of prompt injections and normal prompts.

It aims to identify prompt injections, classifying inputs into two categories: `0` for no injection and `1` for injection detected.

It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 0.9999
- Recall: 0.9997
- Precision: 0.9998
- F1: 0.9998

## Model details

- **Fine-tuned by:** Laiyer.ai
- **Model type:** deberta-v3
- **Language(s) (NLP):** English
- **License:** Apache license 2.0
- **Finetuned from model:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)

## Intended Uses & Limitations

It aims to identify prompt injections, classifying inputs into two categories: `0` for no injection and `1` for injection detected.

The model's performance is dependent on the nature and quality of the training data. It might not perform well on text styles or topics not represented in the training set.

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("laiyer/deberta-v3-base-prompt-injection")
model = AutoModelForSequenceClassification.from_pretrained("laiyer/deberta-v3-base-prompt-injection")

text = "Your prompt injection is here"

classifier = pipeline(
  "text-classification",
  model=model,
  tokenizer=tokenizer,
  truncation=True,
  max_length=512,
  device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)

print(classifier(text))
```

## Training and evaluation data

The model was trained on a custom dataset from multiple open-source ones. We used ~30% prompt injections and ~70% of good prompts.

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0038        | 1.0   | 36130  | 0.0026          | 0.9998   | 0.9994 | 0.9992    | 0.9993 |
| 0.0001        | 2.0   | 72260  | 0.0021          | 0.9998   | 0.9997 | 0.9989    | 0.9993 |
| 0.0           | 3.0   | 108390 | 0.0015          | 0.9999   | 0.9997 | 0.9995    | 0.9996 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

## Citation

```
@misc{deberta-v3-base-prompt-injection,
  author = {Laiyer.ai},
  title = {Fine-Tuned DeBERTa-v3 for Prompt Injection Detection},
  year = {2023},
  publisher = {HuggingFace},
  url = {https://huggingface.co/laiyer/deberta-v3-base-prompt-injection},
}
```