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
| { | |
| "repo_name": "CalibratedAwesomeModel-AuditRepo", | |
| "selected_checkpoint": "checkpoints/step_700", | |
| "selection_metric": "deployment_score", | |
| "deployment_score": 0.749, | |
| "eligible_checkpoint_count": 8, | |
| "scorecard_rows": 8, | |
| "panel_count": 4, | |
| "panels_where_selected_is_not_best": 4, | |
| "eval_script_audit_rows": 15, | |
| "benchmark_delta_rows": 15, | |
| "readme_patch_audit_rows": 15, | |
| "benchmarks_where_selected_trails_runner_up": 15, | |
| "readme_rows_where_selected_beats_best_baseline": 15, | |
| "runner_up_margin": 0.005, | |
| "readme_scores_complete": true, | |
| "tie_breakers": [ | |
| "eval_accuracy", | |
| "checkpoint_step" | |
| ] | |
| } | |