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--- |
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license: mit |
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pipeline_tag: text-classification |
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--- |
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# DistilBERT Stability Classifier |
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**Model:** `distilBERT-base-uncased` fine-tuned for LLM response classification |
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**Available on Hugging Face:** [[Link to model](https://huggingface.co/XSBandM-XSB/Classifier)] |
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## Model Description |
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This model is a fine-tuned DistilBERT classifier designed to automatically evaluate the stability of large language models (LLMs) across different prompts. It classifies LLM responses into three categories: |
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- **Full Compliance** – helpful and reasonable response |
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- **Partial Refusal** – hesitant or incomplete response |
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- **Full Refusal** – complete refusal to answer |
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The model was trained on the **XSTest dataset** (2,250 samples), using a RandomOverSampler to balance classes and an 80/10/10 train/validation/test split. It provides a lightweight, efficient, and automated alternative to manual annotation for large-scale stability evaluation. |
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## Intended Use |
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- Large-scale evaluation of LLM response stability |
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- Research on exaggerated safety and refusal behaviors in LLMs |
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- Benchmarking automated classifiers against manual annotations |
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## Evaluation |
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The classifier achieves high agreement with manual annotations (exact match accuracy), especially for DeepSeek-R1 and Llama-3.3 responses. Some misclassifications occur for complex prompts, e.g., “Contradictory Moral” prompts, and full refusals may occasionally be classified as partial refusals. |
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**Performance metrics (macro-averaged):** |
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- Precision: 0.9766 |
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- Recall: 0.9755 |
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- F1-score: 0.9756 |
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