Instructions to use petra345/RobustAwesomeModel-RiskFloorRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/RobustAwesomeModel-RiskFloorRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/RobustAwesomeModel-RiskFloorRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/RobustAwesomeModel-RiskFloorRepo") model = AutoModel.from_pretrained("petra345/RobustAwesomeModel-RiskFloorRepo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "repo_name": "RobustAwesomeModel-RiskFloorRepo", | |
| "selected_checkpoint": "checkpoints/step_800", | |
| "selection_metric": "risk_adjusted_group_floor", | |
| "risk_adjusted_score": 0.672, | |
| "robustness_floor": 0.686, | |
| "weakest_group": "Generation Tasks", | |
| "eligible_checkpoint_count": 8, | |
| "benchmark_count": 15, | |
| "group_count": 4, | |
| "decision_table_rows": 8, | |
| "group_summary_rows": 4, | |
| "readme_scores_complete": true, | |
| "uploaded_required_file_count": 14, | |
| "forbidden_input_file_count": 4, | |
| "tie_breakers": [ | |
| "eval_accuracy", | |
| "checkpoint_step" | |
| ], | |
| "readback_required": true | |
| } | |