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 transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
tokenizer = AutoTokenizer.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0")
model = AutoModelForSequenceClassification.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0")
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("Put your awesome injection here :D"))

Citation

@misc{vibraniumdome/deberta-v3-base-optimus-v0,
  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},
}