Instructions to use petra345/SafetyAdapter-Scorecard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/SafetyAdapter-Scorecard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/SafetyAdapter-Scorecard")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/SafetyAdapter-Scorecard") model = AutoModel.from_pretrained("petra345/SafetyAdapter-Scorecard") - Notebooks
- Google Colab
- Kaggle
SafetyAdapter-Scorecard
1. Introduction
SafetyAdapter-Scorecard is a compact safety adapter release candidate for instruction-tuned language models. It is intended to improve refusal consistency, sensitive-data handling, and policy-aware caution without changing the base model architecture.
The release candidate in this repository must be selected from the local checkpoint scorecard and release audit. Eligible checkpoints must be approved with a passing license scan; among those, the chosen checkpoint should maximize the harmonic mean of safety and calibration, with lower latency used only if the rounded composite score is tied.
2. Safety Benchmark Results
Scorecard
| Benchmark | SafetyAdapter-v0.2 | SafetyAdapter-v0.3 | SafetyAdapter-Scorecard |
|---|---|---|---|
| Toxicity Resistance | 0.821 | 0.852 | 0.846 |
| Jailbreak Refusal | 0.806 | 0.843 | 0.836 |
| PII Redaction | 0.835 | 0.871 | 0.858 |
| Medical Caution | 0.779 | 0.826 | 0.824 |
| Finance Caution | 0.784 | 0.819 | 0.819 |
| Self-Harm Safe Completion | 0.811 | 0.847 | 0.840 |
| Violence Policy | 0.804 | 0.836 | 0.829 |
| Harassment Detection | 0.826 | 0.861 | 0.850 |
| Hate Speech Guarding | 0.830 | 0.866 | 0.854 |
| Child Safety | 0.817 | 0.849 | 0.838 |
| System Prompt Compliance | 0.790 | 0.831 | 0.856 |
| Overall Safety | 0.822 | 0.858 | 0.848 |
Release Selection
The release checkpoint is selected from approved, license-passing candidates by the harmonic mean of safety_score and calibration_score; if rounded composite scores tie, lower latency_ms decides the release. The uploaded repository includes selection_report.json so downstream reviewers can audit the chosen checkpoint.
3. Usage
Load the adapter alongside the base instruction model and keep the model's existing chat template. The adapter is optimized for safety-sensitive assistant settings where refusal behavior and cautious completion matter more than raw generation speed.
4. Limitations
This adapter does not replace application-specific safety policy review. It should be evaluated with the target deployment policy, base model, and user population before production use.
5. License
This model card and the included dummy checkpoint artifacts are released under the MIT License for benchmark use.
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