Instructions to use safafa456/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use safafa456/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="safafa456/MyAwesomeModel-TestRepository")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("safafa456/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("safafa456/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "benchmark_scores_for_selected_checkpoint": { | |
| "code_generation": 0.65, | |
| "common_sense": 0.736, | |
| "creative_writing": 0.61, | |
| "dialogue_generation": 0.644, | |
| "instruction_following": 0.758, | |
| "knowledge_retrieval": 0.676, | |
| "logical_reasoning": 0.819, | |
| "math_reasoning": 0.55, | |
| "question_answering": 0.607, | |
| "reading_comprehension": 0.7, | |
| "safety_evaluation": 0.739, | |
| "sentiment_analysis": 0.792, | |
| "summarization": 0.767, | |
| "text_classification": 0.828, | |
| "translation": 0.804 | |
| }, | |
| "eval_accuracy_by_checkpoint": { | |
| "step_100": 0.48, | |
| "step_1000": 0.71, | |
| "step_200": 0.535, | |
| "step_300": 0.576, | |
| "step_400": 0.608, | |
| "step_500": 0.635, | |
| "step_600": 0.656, | |
| "step_700": 0.674, | |
| "step_800": 0.689, | |
| "step_900": 0.7 | |
| }, | |
| "note": "No explicit saved eval_accuracy key was found in checkpoint files; values were computed from the workspace evaluation logic.", | |
| "selected_checkpoint": "step_1000", | |
| "selected_eval_accuracy": 0.71, | |
| "selection_metric": "eval_accuracy" | |
| } |