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
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.
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:
- Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
- I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box Generative Language Models
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
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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},
}
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Model tree for protectai/distilroberta-base-rejection-v1
Base model
distilbert/distilroberta-base