How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="zjunlp/SafeEdit-Safety-Classifier")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("zjunlp/SafeEdit-Safety-Classifier")
model = AutoModelForSequenceClassification.from_pretrained("zjunlp/SafeEdit-Safety-Classifier")
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Safety classifier for Detoxifying Large Language Models via Knowledge Editing

πŸ’» Usage

from transformers import RobertaForSequenceClassification, RobertaTokenizer
safety_classifier_dir = 'zjunlp/SafeEdit-Safety-Classifier'
safety_classifier_model = RobertaForSequenceClassification.from_pretrained(safety_classifier_dir)
safety_classifier_tokenizer = RobertaTokenizer.from_pretrained(safety_classifier_dir)

You can also download DINM-Safety-Classifier manually, and set the safety_classifier_dir to your own path.

πŸ“– Citation

If you use our work, please cite our paper:

@misc{wang2024SafeEdit,
      title={Detoxifying Large Language Models via Knowledge Editing}, 
      author={Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, Huajun Chen},
      year={2024},
      eprint={2403.14472},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
      url={https://arxiv.org/abs/2403.14472},

}
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Paper for zjunlp/SafeEdit-Safety-Classifier