Instructions to use afsdaaaf/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use afsdaaaf/my-awesome-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="afsdaaaf/my-awesome-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("afsdaaaf/my-awesome-model") model = AutoModel.from_pretrained("afsdaaaf/my-awesome-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MyAwesomeModel
Best checkpoint: step_1000 (overall eval_accuracy = 0.712).
Evaluation Results
Scores below are produced by running the evaluation suite on the step_1000 checkpoint. All scores are reported to 3 decimal places.
| Category | Benchmark | Score |
|---|---|---|
| Core Reasoning Tasks | Math Reasoning | 0.550 |
| Logical Reasoning | 0.819 | |
| Common Sense | 0.736 | |
| Language Understanding | Reading Comprehension | 0.700 |
| Question Answering | 0.607 | |
| Text Classification | 0.828 | |
| Sentiment Analysis | 0.792 | |
| Generation Tasks | Code Generation | 0.650 |
| Creative Writing | 0.610 | |
| Dialogue Generation | 0.644 | |
| Summarization | 0.767 | |
| Specialized Capabilities | Translation | 0.804 |
| Knowledge Retrieval | 0.676 | |
| Instruction Following | 0.758 | |
| Safety Evaluation | 0.739 |
Per-benchmark summary
| Benchmark | Score |
|---|---|
| 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 |
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