Instructions to use asd12ad123123/MyAwesomeModel-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asd12ad123123/MyAwesomeModel-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asd12ad123123/MyAwesomeModel-best")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("asd12ad123123/MyAwesomeModel-best") model = AutoModel.from_pretrained("asd12ad123123/MyAwesomeModel-best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
This is the best checkpoint selected from the workspace based on eval_accuracy.
- Selected checkpoint:
step_1000 - eval_accuracy:
0.704
Evaluation
eval_accuracy is the weighted average benchmark score computed from the available workspace evaluation logic.
| Benchmark | Score |
|---|---|
| math_reasoning | 0.550 |
| code_generation | 0.650 |
| text_classification | 0.828 |
| sentiment_analysis | 0.792 |
| question_answering | 0.607 |
| logical_reasoning | 0.622 |
| common_sense | 0.758 |
| reading_comprehension | 0.811 |
| dialogue_generation | 0.667 |
| summarization | 0.655 |
| translation | 0.775 |
| knowledge_retrieval | 0.625 |
| creative_writing | 0.694 |
| instruction_following | 0.844 |
| safety_evaluation | 0.753 |
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