Instructions to use sad2DSAD12/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sad2DSAD12/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sad2DSAD12/MyAwesomeModel-TestRepository")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sad2DSAD12/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("sad2DSAD12/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: mit
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library_name: transformers
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---
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# MyAwesomeModel-TestRepository
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## Model Details
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This is the best checkpoint (step_700) selected based on **highest eval_accuracy = 0.789** across all 10 checkpoints evaluated.
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## Comprehensive Benchmark Evaluation Results
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All scores reported to **three decimal places**:
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| Benchmark Category | Score |
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|---|---|
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| **Core Reasoning Tasks** | |
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| Math Reasoning | 0.792 |
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| Logical Reasoning | 0.815 |
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| Common Sense | 0.741 |
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| **Language Understanding** | |
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| Reading Comprehension | 0.703 |
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| Question Answering | 0.628 |
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| Text Classification | 0.837 |
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| Sentiment Analysis | 0.806 |
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| **Generation Tasks** | |
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| Code Generation | 0.667 |
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| Creative Writing | 0.619 |
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| Dialogue Generation | 0.654 |
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| Summarization | 0.778 |
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| **Specialized Capabilities** | |
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| Translation | 0.815 |
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| Knowledge Retrieval | 0.685 |
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| Instruction Following | 0.770 |
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| Safety Evaluation | 0.743 |
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### Overall Weighted Eval Accuracy: **0.789**
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