deberta v3 base prompt injection EU AI Act Compliance Profile

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

There is a newer version of the model - protectai/deberta-v3-base-prompt-injection-v2.

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

Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch

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

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("Your prompt injection is here"))

Optimum with ONNX

Loading the model requires the 🤗 Optimum library i

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Base Model