Instructions to use scq21cs2dsadsa/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scq21cs2dsadsa/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="scq21cs2dsadsa/MyAwesomeModel-TestRepository")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("scq21cs2dsadsa/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("scq21cs2dsadsa/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README benchmark scores only
Browse files
README.md
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## 6. Contact
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If you have any questions, please raise an issue on our GitHub repository or contact us at contact@MyAwesomeModel.ai.
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```
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## Detailed Evaluation Results
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Selected checkpoint: `step_1000` with `eval_accuracy` = **0.710**.
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| Benchmark | Score |
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| math_reasoning | 0.550 |
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| logical_reasoning | 0.819 |
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| common_sense | 0.736 |
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| reading_comprehension | 0.700 |
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| question_answering | 0.607 |
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| text_classification | 0.828 |
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| sentiment_analysis | 0.792 |
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| code_generation | 0.650 |
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| creative_writing | 0.610 |
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| dialogue_generation | 0.644 |
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| summarization | 0.767 |
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| translation | 0.804 |
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| knowledge_retrieval | 0.676 |
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| instruction_following | 0.758 |
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| safety_evaluation | 0.739 |
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## 6. Contact
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If you have any questions, please raise an issue on our GitHub repository or contact us at contact@MyAwesomeModel.ai.
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```
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