Instructions to use petra345/CalibratedAwesomeModel-AuditRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/CalibratedAwesomeModel-AuditRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/CalibratedAwesomeModel-AuditRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/CalibratedAwesomeModel-AuditRepo") model = AutoModel.from_pretrained("petra345/CalibratedAwesomeModel-AuditRepo") - Notebooks
- Google Colab
- Kaggle
| rank,checkpoint,eval_accuracy,eval_f1,safety_score,latency_ms,deployment_score,benchmark_score_count,readme_score_sum,model_sha256_12 | |
| 1,checkpoints/step_700,0.731,0.748,0.803,330,0.749,15,10.980,f24c3d873790 | |
| 2,checkpoints/step_900,0.742,0.756,0.792,360,0.744,15,11.085,71fea9f940ce | |
| 3,checkpoints/step_800,0.735,0.744,0.820,410,0.736,15,11.040,d38b52aaece4 | |
| 4,checkpoints/step_600,0.724,0.734,0.781,355,0.730,15,10.830,01976c52219e | |
| 5,checkpoints/step_500,0.715,0.706,0.790,360,0.718,15,10.695,08b4caeca262 | |
| 6,checkpoints/step_300,0.670,0.681,0.751,345,0.697,15,10.410,d46548abc43c | |
| 7,checkpoints/step_200,0.645,0.642,0.742,310,0.682,15,10.155,0dc7dbb5c6b8 | |
| 8,checkpoints/step_100,0.620,0.600,0.730,290,0.661,15,9.915,2b465b103643 | |