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protectai/
distilroberta-base-rejection-v1

Name: distilroberta-base-rejection-v1
Availability: InStock

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[!WARNING] THIS PROJECT HAS BEEN ARCHIVED.

This project and its associated code on GitHub are no longer under active development or maintained.

Model Card for distilroberta-base-rejection-v1

This model is a fine-tuned version of distilroberta-base on multiple combined datasets of rejections from different LLMs and normal responses from RLHF datasets.

It aims to identify rejections in LLMs when the prompt doesn't pass content moderation, classifying inputs into two categories: 0 for normal outputs and 1 for rejection detected.

It achieves the following results on the evaluation set:

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

Model details

Fine-tuned by: ProtectAI.com
Model type: distilroberta-base
Language(s) (NLP): English
License: Apache license 2.0
Finetuned from model: distilroberta-base

Intended Uses & Limitations

It aims to identify rejection, classifying inputs into two categories: 0 for normal output and 1 for rejection 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.

Additionally, distilroberta-base is case-sensitive model.

How to Get Started with the Model

Transformers

python

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."))

Optimum with ONNX

Loading the model requires the 🤗 Optimum library installed.

python

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

tokenizer = AutoTokenizer.from_pretrained("ProtectAI/distilroberta-base-rejection-v1", subfolder="onnx")
model = ORTModelForSequenceClassification.from_pretrained("ProtectAI/distilroberta-base-rejection-v1", export=False, subfolder="onnx")

classifier = pipeline(
  task="text-classification",
  model=model,
  tokenizer=tokenizer,
  truncation=True,
  max_length=512,
)

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

Use in LLM Guard

NoRefusal Scanner to detect if output was rejected, which can signal that something is going wrong with the prompt.

Training and evaluation data

The model was trained on a custom dataset from multiple open-source ones. We used ~10% rejections and ~90% of normal outputs.

We used the following papers when preparing the datasets:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
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
0.0525	1.0	3536	0.0355	0.9912	0.9583	0.9675	0.9629
0.0219	2.0	7072	0.0312	0.9919	0.9917	0.9434	0.9669
0.0121	3.0	10608	0.0350	0.9939	0.9905	0.9596	0.9748

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Community

Join our Slack to give us feedback, connect with the maintainers and fellow users, ask questions, get help for package usage or contributions, or engage in discussions about LLM security!

Citation

@misc{distilroberta-base-rejection-v1,
  author = {ProtectAI.com},
  title = {Fine-Tuned DistilRoberta-Base for Rejection in the output Detection},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/ProtectAI/distilroberta-base-rejection-v1},
}

protectai/distilroberta-base-rejection-v1