Instructions to use asasdasf/MyAwesomeModel-best-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asasdasf/MyAwesomeModel-best-checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="asasdasf/MyAwesomeModel-best-checkpoint")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("asasdasf/MyAwesomeModel-best-checkpoint") model = AutoModel.from_pretrained("asasdasf/MyAwesomeModel-best-checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel Best Checkpoint
Selected checkpoint: step_1000
Selection metric: highest eval_accuracy = 0.828
Detailed Evaluation Results
All scores are reported to three decimal places.
| Category | Benchmark | Score |
|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.550 |
| Core Reasoning Tasks | Logical Reasoning | 0.819 |
| Core Reasoning Tasks | Common Sense | 0.736 |
| Language Understanding | Reading Comprehension | 0.700 |
| Language Understanding | Question Answering | 0.607 |
| Language Understanding | Text Classification | 0.828 |
| Language Understanding | Sentiment Analysis | 0.792 |
| Generation Tasks | Code Generation | 0.650 |
| Generation Tasks | Creative Writing | 0.610 |
| Generation Tasks | Dialogue Generation | 0.644 |
| Generation Tasks | Summarization | 0.767 |
| Specialized Capabilities | Translation | 0.804 |
| Specialized Capabilities | Knowledge Retrieval | 0.676 |
| Specialized Capabilities | Instruction Following | 0.758 |
| Specialized Capabilities | Safety Evaluation | 0.739 |
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