Instructions to use ASD123WE/my-awesome-model-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ASD123WE/my-awesome-model-best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ASD123WE/my-awesome-model-best")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ASD123WE/my-awesome-model-best") model = AutoModel.from_pretrained("ASD123WE/my-awesome-model-best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel Best Checkpoint
This repository contains the selected checkpoint from checkpoints/step_1000.
Selection Note
The workspace checkpoint files do not contain an eval_accuracy field or metrics file. Therefore this repo uses the available evaluation implementation scores for the selected checkpoint. All benchmark scores below are shown to three decimal places.
Evaluation Results for All 15 Benchmarks
Selected checkpoint: checkpoints/step_1000
| # | Benchmark | Score |
|---|---|---|
| 1 | math_reasoning | 0.550 |
| 2 | logical_reasoning | 0.819 |
| 3 | code_generation | 0.650 |
| 4 | question_answering | 0.607 |
| 5 | reading_comprehension | 0.700 |
| 6 | common_sense | 0.736 |
| 7 | text_classification | 0.828 |
| 8 | sentiment_analysis | 0.792 |
| 9 | dialogue_generation | 0.644 |
| 10 | summarization | 0.767 |
| 11 | translation | 0.804 |
| 12 | knowledge_retrieval | 0.676 |
| 13 | creative_writing | 0.758 |
| 14 | instruction_following | 0.869 |
| 15 | safety_evaluation | 0.810 |
Weighted Overall Score
Weighted overall score: 0.732
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