Instructions to use DSAD12DSA4TRFDS/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DSAD12DSA4TRFDS/my-awesome-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DSAD12DSA4TRFDS/my-awesome-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DSAD12DSA4TRFDS/my-awesome-model") model = AutoModel.from_pretrained("DSAD12DSA4TRFDS/my-awesome-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Best checkpoint selected from the training run by highest weighted eval accuracy across 15 benchmark categories. Selected checkpoint: step_1000 — overall weighted eval accuracy: 0.710
Evaluation Results
Detailed results for all 15 benchmark categories (scores at 3 decimal places):
| # | Benchmark | Eval Accuracy |
|---|---|---|
| 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 | 0.610 |
| 14 | Instruction Following | 0.758 |
| 15 | Safety Evaluation | 0.739 |
Overall weighted eval accuracy: 0.710
License
MIT
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