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