Instructions to use petra345/MyAwesomeModel-AuditRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use petra345/MyAwesomeModel-AuditRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="petra345/MyAwesomeModel-AuditRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("petra345/MyAwesomeModel-AuditRepo") model = AutoModel.from_pretrained("petra345/MyAwesomeModel-AuditRepo") - Notebooks
- Google Colab
- Kaggle
| { | |
| "repo_name": "MyAwesomeModel-AuditRepo", | |
| "selected_checkpoint": "step_1000", | |
| "selection_metric": "highest weighted overall evaluation score", | |
| "weighted_overall_score": 0.710, | |
| "selected_model_sha256": "965362299a238de576a92dfdd3e32aea7a2bacc94b2c41541c8c9258b923f587", | |
| "benchmark_scores": { | |
| "math_reasoning": 0.550, | |
| "code_generation": 0.650, | |
| "text_classification": 0.828, | |
| "sentiment_analysis": 0.792, | |
| "question_answering": 0.607, | |
| "logical_reasoning": 0.819, | |
| "common_sense": 0.736, | |
| "reading_comprehension": 0.700, | |
| "dialogue_generation": 0.644, | |
| "summarization": 0.767, | |
| "translation": 0.804, | |
| "knowledge_retrieval": 0.676, | |
| "creative_writing": 0.610, | |
| "instruction_following": 0.758, | |
| "safety_evaluation": 0.739 | |
| }, | |
| "top_three_benchmarks": [ | |
| { | |
| "benchmark": "text_classification", | |
| "score": 0.828 | |
| }, | |
| { | |
| "benchmark": "logical_reasoning", | |
| "score": 0.819 | |
| }, | |
| { | |
| "benchmark": "translation", | |
| "score": 0.804 | |
| } | |
| ], | |
| "weakest_benchmark": { | |
| "benchmark": "math_reasoning", | |
| "score": 0.550 | |
| } | |
| } | |