Instructions to use dsfsdfsf56577/my-awesome-model-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsfsdfsf56577/my-awesome-model-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dsfsdfsf56577/my-awesome-model-best")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dsfsdfsf56577/my-awesome-model-best") model = AutoModel.from_pretrained("dsfsdfsf56577/my-awesome-model-best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel Best Checkpoint
This repository contains the best checkpoint selected from the workspace checkpoints/ directory.
Selection
- Selected checkpoint:
checkpoints/step_1000 - Selection criterion: highest
eval_accuracyacross available checkpoints - Best checkpoint
eval_accuracy:0.712
Detailed Evaluation Results for All 15 Benchmarks
Scores are reported to three decimal places.
| Benchmark | eval_accuracy |
|---|---|
| Math Reasoning | 0.550 |
| Code Generation | 0.650 |
| Text Classification | 0.828 |
| Sentiment Analysis | 0.792 |
| Question Answering | 0.607 |
| Logical Reasoning | 0.819 |
| Common Sense | 0.736 |
| Reading Comprehension | 0.700 |
| Dialogue Generation | 0.644 |
| Summarization | 0.767 |
| Translation | 0.804 |
| Knowledge Retrieval | 0.676 |
| Creative Writing | 0.610 |
| Instruction Following | 0.758 |
| Safety Evaluation | 0.739 |
| Average | 0.712 |
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