Instructions to use DSACT1HJ46A/MyAwesomeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DSACT1HJ46A/MyAwesomeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DSACT1HJ46A/MyAwesomeModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DSACT1HJ46A/MyAwesomeModel") model = AutoModel.from_pretrained("DSACT1HJ46A/MyAwesomeModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
This repository contains the best checkpoint selected from the ten checkpoints found in the workspace (step_100 through step_1000).
Checkpoint selection
- Selected checkpoint:
step_1000 - Selection criterion: highest
eval_accuracyamong all discovered checkpoints - Selected
eval_accuracy: 0.828
| Checkpoint | eval_accuracy |
|---|---|
step_100 |
0.517 |
step_200 |
0.603 |
step_300 |
0.667 |
step_400 |
0.714 |
step_500 |
0.750 |
step_600 |
0.776 |
step_700 |
0.795 |
step_800 |
0.809 |
step_900 |
0.820 |
step_1000 |
0.828 |
Detailed evaluation results for all 15 benchmarks
All scores are reported to three decimal places.
| Benchmark | Score |
|---|---|
| Math Reasoning | 0.550 |
| Logical Reasoning | 0.819 |
| Common Sense | 0.736 |
| Reading Comprehension | 0.700 |
| Question Answering | 0.607 |
Text Classification (eval_accuracy) |
0.828 |
| Sentiment Analysis | 0.792 |
| Code Generation | 0.650 |
| Creative Writing | 0.610 |
| Dialogue Generation | 0.644 |
| Summarization | 0.767 |
| Translation | 0.804 |
| Knowledge Retrieval | 0.676 |
| Instruction Following | 0.758 |
| Safety Evaluation | 0.739 |
Full selection metadata is provided in evaluation_results.json.
Files
config.jsonโ model configuration fromstep_1000pytorch_model.binโ model weights fromstep_1000evaluation_results.jsonโ checkpoint accuracies and evaluation results
License
MIT
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