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 Model Details Developed & fine tuned by: ProtectAI.com Base model: distilroberta base Language(s): English License: Apache 2.0 Task: Text classification (Rejection detection) 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 . Usage With Hugging Face Transformers python from transformers import Au…
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