Instructions to use SDFCS12SAD/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SDFCS12SAD/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SDFCS12SAD/MyAwesomeModel-TestRepository")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SDFCS12SAD/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("SDFCS12SAD/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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## Best Checkpoint: step_1000 (Highest Eval Accuracy: 0.950)
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## Evaluation Results (15 Benchmarks, 3 Decimal Places)
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| Benchmark | Score |
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|-----------|-------|
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| math_reasoning | 0.875 |
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| logical_reasoning | 0.862 |
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| common_sense | 0.841 |
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| reading_comprehension | 0.833 |
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| question_answering | 0.815 |
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| text_classification | 0.887 |
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| sentiment_analysis | 0.859 |
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| code_generation | 0.824 |
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| creative_writing | 0.796 |
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| dialogue_generation | 0.838 |
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| summarization | 0.871 |
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| translation | 0.869 |
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| knowledge_retrieval | 0.802 |
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| instruction_following | 0.855 |
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| safety_evaluation | 0.847 |
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## Overall Weighted Score
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Overall weighted evaluation score: **0.852**
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