Instructions to use liufea154/MyAwesomeModel-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liufea154/MyAwesomeModel-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="liufea154/MyAwesomeModel-best")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liufea154/MyAwesomeModel-best") model = AutoModel.from_pretrained("liufea154/MyAwesomeModel-best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel-best
This repository contains the selected best checkpoint from the workspace scan.
Selection Criteria
The checkpoint was selected by choosing the highest eval_accuracy among discovered workspace checkpoints:
- Selected checkpoint:
checkpoints/step_1000 eval_accuracy:0.828- Weighted overall benchmark score:
0.715
Detailed Evaluation Results
All scores below are formatted to 3 decimal places.
| Category | Benchmark | Score |
|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.550 |
| Logical Reasoning | 0.819 | |
| Common Sense | 0.736 | |
| Language Understanding | Reading Comprehension | 0.700 |
| Question Answering | 0.607 | |
| Text Classification | 0.828 | |
| Sentiment Analysis | 0.792 | |
| Generation Tasks | Code Generation | 0.650 |
| Creative Writing | 0.610 | |
| Dialogue Generation | 0.644 | |
| Summarization | 0.767 | |
| Specialized Capabilities | Translation | 0.804 |
| Knowledge Retrieval | 0.676 | |
| Instruction Following | 0.832 | |
| Safety Evaluation | 0.739 |
Overall Performance Summary
The selected checkpoint achieves the highest eval_accuracy across all scanned checkpoints and includes detailed per-benchmark results for all 15 benchmarks.
- Downloads last month
- 35