Instructions to use SOTAagi2030/SafetyModel-Best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SOTAagi2030/SafetyModel-Best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SOTAagi2030/SafetyModel-Best")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SOTAagi2030/SafetyModel-Best") model = AutoModel.from_pretrained("SOTAagi2030/SafetyModel-Best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
library_name: transformers
---
# SafetyModel
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<img src="figures/fig1.png" width="60%" alt="SafetyModel" />
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<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
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## 1. Introduction
SafetyModel is optimized for safety evaluation metrics. This checkpoint achieves the best safety_evaluation score in our training run, demonstrating strong alignment with safety guidelines.
<p align="center">
<img width="80%" src="figures/fig3.png">
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## 2. Evaluation Results
### Comprehensive Benchmark Results
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| | Benchmark | SafeModel-v1 | SafeModel-v2 | SafetyModel |
|---|---|---|---|---|
| **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.550 |
| | Logical Reasoning | 0.789 | 0.801 | 0.819 |
| | Common Sense | 0.716 | 0.702 | 0.736 |
| **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.700 |
| | Question Answering | 0.582 | 0.599 | 0.607 |
| | Text Classification | 0.803 | 0.811 | 0.828 |
| | Sentiment Analysis | 0.777 | 0.781 | 0.792 |
| **Generation Tasks** | Code Generation | 0.615 | 0.631 | 0.650 |
| | Creative Writing | 0.588 | 0.579 | 0.610 |
| | Dialogue Generation | 0.621 | 0.635 | 0.644 |
| | Summarization | 0.745 | 0.755 | 0.767 |
| **Specialized Capabilities**| Translation | 0.782 | 0.799 | 0.804 |
| | Knowledge Retrieval | 0.651 | 0.668 | 0.676 |
| | Instruction Following | 0.733 | 0.749 | 0.758 |
| | Safety Evaluation | 0.718 | 0.701 | 0.739 |
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### Overall Performance Summary
SafetyModel demonstrates strong performance on safety metrics, making it suitable for deployment in safety-critical applications.
## 3. License
Licensed under the [MIT License](LICENSE).
## 4. Contact
Please open an issue on GitHub for inquiries.
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