vibraniumdome/deberta-v3-base-optimus-v0-onnx

Model Card for deberta-v3-base-optimus-v0

Fine-tuned version of microsoft/deberta-v3-base on private dataset of normal & injections prompts.

Classifying inputs into two categories: 0 for no injection and 1 for injection detected.

Model evaluation results:

• Precision: 0.988
• Recall: 0.992
• Accuracy: 0.998
• F1: 0.99

Model details

• Fine-tuned by: vibraniumdome.com
• Model type: deberta-v3
• Language(s) (NLP): English
• License: GPLv3
• Finetuned from model: microsoft/deberta-v3-base

How to Get Started with the Model

Transformers

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import pipeline
from transformers import AutoTokenizer

pipeline_kwargs={
    "return_token_type_ids": False,
    "max_length": 512,
    "truncation": True,
}

tokenizer = AutoTokenizer.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0-onnx", use_fast=True)
model = ORTModelForSequenceClassification.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0-onnx")
classifier = pipeline(
    "text-classification",
    model=model,
    tokenizer=tokenizer,
    **pipeline_kwargs,
)

print(classifier("Put your awesome injection here :D"))

Citation

@misc{vibraniumdome/deberta-v3-base-optimus-v0-onnx,
  author = {vibraniumdome.com},
  title = {Fine-Tuned DeBERTa-v3 for Prompt Injection Detection},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/vibraniumdome/deberta-v3-base-optimus-v0-onnx},
}