Instructions to use eeeeerrrr4/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eeeeerrrr4/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="eeeeerrrr4/MyAwesomeModel-TestRepository")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("eeeeerrrr4/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("eeeeerrrr4/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,361 Bytes
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license: mit
library_name: transformers
---
# MyAwesomeModel-TestRepository
<div align="center">
<img src="figures/fig1.png" width="60%" alt="MyAwesomeModel" />
</div>
<hr>
## Evaluation Results (15 Benchmarks, 3 Decimal Places)
All scores below are reported to 3 decimal places:
| Benchmark Category | Score |
|-----------------------------|--------|
| Math Reasoning | 0.875 |
| Logical Reasoning | 0.830 |
| Common Sense | 0.740 |
| Reading Comprehension | 0.710 |
| Question Answering | 0.620 |
| Text Classification | 0.840 |
| Sentiment Analysis | 0.810 |
| Code Generation | 0.660 |
| Creative Writing | 0.620 |
| Dialogue Generation | 0.660 |
| Summarization | 0.780 |
| Translation | 0.820 |
| Knowledge Retrieval | 0.690 |
| Instruction Following | 0.770 |
| Safety Evaluation | 0.740 |
## Overall Performance
The weighted overall score for this best checkpoint (step_1000) is **0.745** (3 decimal places), with strong performance across reasoning and generation tasks as documented in the project overview.
## Usage
This model uses the recommended temperature setting of 0.6, and supports system prompt configuration as outlined in the project documentation.
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