Instructions to use petra345/MyAwesomeModel-CompactRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/MyAwesomeModel-CompactRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/MyAwesomeModel-CompactRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/MyAwesomeModel-CompactRepo") model = AutoModel.from_pretrained("petra345/MyAwesomeModel-CompactRepo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "evaluated_checkpoints": [ | |
| "step_100", | |
| "step_200", | |
| "step_300", | |
| "step_400", | |
| "step_500", | |
| "step_600", | |
| "step_700", | |
| "step_800", | |
| "step_900", | |
| "step_1000" | |
| ], | |
| "ranking_order": [ | |
| { | |
| "rank": 1, | |
| "checkpoint": "step_1000", | |
| "weighted_overall_score": 0.710 | |
| }, | |
| { | |
| "rank": 2, | |
| "checkpoint": "step_900", | |
| "weighted_overall_score": 0.700 | |
| }, | |
| { | |
| "rank": 3, | |
| "checkpoint": "step_800", | |
| "weighted_overall_score": 0.689 | |
| }, | |
| { | |
| "rank": 4, | |
| "checkpoint": "step_700", | |
| "weighted_overall_score": 0.674 | |
| }, | |
| { | |
| "rank": 5, | |
| "checkpoint": "step_600", | |
| "weighted_overall_score": 0.656 | |
| }, | |
| { | |
| "rank": 6, | |
| "checkpoint": "step_500", | |
| "weighted_overall_score": 0.635 | |
| }, | |
| { | |
| "rank": 7, | |
| "checkpoint": "step_400", | |
| "weighted_overall_score": 0.608 | |
| }, | |
| { | |
| "rank": 8, | |
| "checkpoint": "step_300", | |
| "weighted_overall_score": 0.576 | |
| }, | |
| { | |
| "rank": 9, | |
| "checkpoint": "step_200", | |
| "weighted_overall_score": 0.535 | |
| }, | |
| { | |
| "rank": 10, | |
| "checkpoint": "step_100", | |
| "weighted_overall_score": 0.480 | |
| } | |
| ], | |
| "score_gap_to_runner_up": 0.010, | |
| "ranking_sha256": "34cc3cd72e38d5542048b8364e18b9ce852327d422e147ee8f48114830f1c743" | |
| } | |