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
| { | |
| "selected_checkpoint": "ckpt-zeta-0960", | |
| "repo_name": "SafetyAdapter-Scorecard", | |
| "selection_rule": "eligible max harmonic_mean(safety_score, calibration_score), tie lower latency_ms", | |
| "primary_metric": "harmonic_safety_calibration", | |
| "primary_value": 0.858859, | |
| "benchmark_count": 12, | |
| "eligible_candidates": [ | |
| { | |
| "rank": 1, | |
| "checkpoint": "ckpt-zeta-0960", | |
| "safety_score": 0.848, | |
| "calibration_score": 0.87, | |
| "latency_ms": 142, | |
| "composite_score": 0.858859 | |
| }, | |
| { | |
| "rank": 2, | |
| "checkpoint": "ckpt-delta-0610", | |
| "safety_score": 0.872, | |
| "calibration_score": 0.846, | |
| "latency_ms": 131, | |
| "composite_score": 0.858803 | |
| }, | |
| { | |
| "rank": 3, | |
| "checkpoint": "ckpt-epsilon-0740", | |
| "safety_score": 0.861, | |
| "calibration_score": 0.855, | |
| "latency_ms": 136, | |
| "composite_score": 0.85799 | |
| }, | |
| { | |
| "rank": 4, | |
| "checkpoint": "ckpt-beta-0320", | |
| "safety_score": 0.836, | |
| "calibration_score": 0.801, | |
| "latency_ms": 122, | |
| "composite_score": 0.818126 | |
| } | |
| ], | |
| "rejected_candidates": [ | |
| { | |
| "checkpoint": "ckpt-alpha-0180", | |
| "reason": "release_status=shadow" | |
| }, | |
| { | |
| "checkpoint": "ckpt-gamma-0450", | |
| "reason": "license_scan=needs-review" | |
| } | |
| ], | |
| "file_sha256": { | |
| "README.md": "49af766dccc01c8cf69b131ed3d03f0a5814baa138e89e6f6b42c768d62fe86a", | |
| "config.json": "0816b8de019e22db6b13cf9aff153f523fb9fdf375e75a0ccf9c16ac89567aad", | |
| "pytorch_model.bin": "4e5b2fd160bba44c83bc81e653ebfa20bdd3ee4f9b208cf24776b22216bb77a0", | |
| "figures/fig1.png": "bd81e62dbd4289b54d154db29f00e5854d7b6c1f7acdc0d8f6647f790567b43a", | |
| "figures/fig2.png": "bd81e62dbd4289b54d154db29f00e5854d7b6c1f7acdc0d8f6647f790567b43a", | |
| "figures/fig3.png": "bd81e62dbd4289b54d154db29f00e5854d7b6c1f7acdc0d8f6647f790567b43a" | |
| } | |
| } | |