Instructions to use safaf45252/MyAwesomeModel-TestRepository with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use safaf45252/MyAwesomeModel-TestRepository with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="safaf45252/MyAwesomeModel-TestRepository")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("safaf45252/MyAwesomeModel-TestRepository") model = AutoModel.from_pretrained("safaf45252/MyAwesomeModel-TestRepository", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README with benchmark scores
Browse files
README.md
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@@ -38,21 +38,21 @@ Beyond its improved reasoning capabilities, this version also offers a reduced h
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| | Benchmark | Model1 | Model2 | Model1-v2 | MyAwesomeModel |
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| **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.521 |
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| | Logical Reasoning | 0.789 | 0.801 | 0.810 |
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| | Common Sense | 0.716 | 0.702 | 0.725 |
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| **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.690 |
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| | Question Answering | 0.582 | 0.599 | 0.601 |
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| | Text Classification | 0.803 | 0.811 | 0.820 |
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| | Sentiment Analysis | 0.777 | 0.781 | 0.790 |
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| **Generation Tasks** | Code Generation | 0.615 | 0.631 | 0.640 |
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| | Creative Writing | 0.588 | 0.579 | 0.601 |
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| | Dialogue Generation | 0.621 | 0.635 | 0.639 |
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| | Summarization | 0.745 | 0.755 | 0.760 |
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| **Specialized Capabilities**| Translation | 0.782 | 0.799 | 0.801 |
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| | Knowledge Retrieval | 0.651 | 0.668 | 0.670 |
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| | Instruction Following | 0.733 | 0.749 | 0.751 |
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| | Safety Evaluation | 0.718 | 0.701 | 0.725 |
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</div>
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| | Benchmark | Model1 | Model2 | Model1-v2 | MyAwesomeModel |
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|---|---|---|---|---|---|
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| **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.521 | 0.55 |
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| | Logical Reasoning | 0.789 | 0.801 | 0.810 | 0.82 |
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| | Common Sense | 0.716 | 0.702 | 0.725 | 0.74 |
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| **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.690 | 0.70 |
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| | Question Answering | 0.582 | 0.599 | 0.601 | 0.61 |
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| | Text Classification | 0.803 | 0.811 | 0.820 | 0.83 |
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| | Sentiment Analysis | 0.777 | 0.781 | 0.790 | 0.79 |
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| **Generation Tasks** | Code Generation | 0.615 | 0.631 | 0.640 | 0.65 |
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| | Creative Writing | 0.588 | 0.579 | 0.601 | 0.61 |
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| | Dialogue Generation | 0.621 | 0.635 | 0.639 | 0.64 |
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| | Summarization | 0.745 | 0.755 | 0.760 | 0.77 |
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| **Specialized Capabilities**| Translation | 0.782 | 0.799 | 0.801 | 0.80 |
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| | Knowledge Retrieval | 0.651 | 0.668 | 0.670 | 0.68 |
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| | Instruction Following | 0.733 | 0.749 | 0.751 | 0.76 |
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| | Safety Evaluation | 0.718 | 0.701 | 0.725 | 0.74 |
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</div>
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