rejection_detection

Model Card: distilroberta-base-rejection-v1

This model was originally developed and fine-tuned by Protect AI. It is a fine-tuned version of distilroberta-base, trained on multiple datasets containing rejection responses from LLMs and standard outputs from RLHF datasets.

The goal of this model is to detect LLM rejections when a prompt does not pass content moderation. It classifies responses into two categories:

• 0: Normal output
• 1: Rejection detected

On the evaluation set, the model achieves:

• Loss: 0.0544
• Accuracy: 0.9887
• Recall: 0.9810
• Precision: 0.9279
• F1 Score: 0.9537

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Model Details

• Developed & fine-tuned by: ProtectAI.com
• Base model: distilroberta-base
• Language(s): English
• License: Apache 2.0
• Task: Text classification (Rejection detection)

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Intended Use & Limitations

The model is designed to identify rejection responses in LLM outputs, particularly where a refusal or safeguard message is generated.

Limitations:

• Performance depends on the quality and domain of the training data.
• May underperform on text styles or topics underrepresented in training.
• Being based on distilroberta-base, it is case-sensitive.

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Usage

With Hugging Face Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch

tokenizer = AutoTokenizer.from_pretrained("ProtectAI/distilroberta-base-rejection-v1")
model = AutoModelForSequenceClassification.from_pretrained("ProtectAI/distilroberta-base-rejection-v1")

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("Sorry, but I can't assist with that."))