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:
- task:
type: text-classification
name: Prompt Injection Detection
metrics:
- type: precision
value: 0.9998
- type: f1
value: 0.9998
- type: accuracy
value: 0.9999
- type: recall
value: 0.9997
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 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
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
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},
}