CoPE-A-9B
| Property | Value |
|---|---|
| Parameter Count | 9 Billion |
| Base Model | Google Gemma-2-9b |
| Training Method | LoRA fine-tuning |
| Model URL | HuggingFace |
| Developer | Zentropi AI |
What is cope-a-9b?
CoPE-A-9B (COntent Policy Evaluator) is a specialized content classification model designed for accurate policy interpretation and content labeling. Built by trust & safety experts, it represents a significant advancement in content moderation technology, offering customizable policy evaluation capabilities while maintaining efficient performance. The model excels in interpreting and applying content policies across various harm categories including hate speech, toxicity, sexual content, self-harm, and harassment.
Implementation Details
The model is implemented as a 9B parameter architecture built upon Google's Gemma-2-9b using LoRA fine-tuning techniques. It processes input through a structured format that includes policy definitions, interpretation guidelines, and content text, producing binary classification outputs (0 or 1) indicating whether the content matches any defined policy labels. The model was trained on approximately 60,000 unique policy/content pairs, ensuring robust policy interpretation capabilities.
- Sub-second inference time on commodity GPU hardware
- 8K token limit for combined policy and content
- Optimized for US English language content
- Binary classification output format
Core Capabilities
- Policy-adaptive content evaluation with no fixed taxonomy
- High-accuracy binary classification across multiple harm categories
- State-of-the-art performance metrics (F1 scores: 91% for hate speech, 90% for toxic speech, 89% for sexual content)
- Real-time content moderation and batch processing capabilities
- Feature generation for social feed ranking and content quality assessment
Frequently Asked Questions
Q: What makes this model unique?
CoPE-A-9B's uniqueness lies in its policy-adaptive nature and steerable classification capabilities. Unlike models with fixed taxonomies, it can interpret and apply arbitrary content policies, making it highly versatile for different use cases and requirements. Its training methodology focuses on true policy interpretation rather than simple memorization.
Q: What are the recommended use cases?
The model is specifically designed for content labeling, LLM guardrails, and content scoring applications. It excels in real-time content moderation, batch processing, risk assessment for LLM inputs/outputs, and generating features for social feed ranking. However, it's not recommended for surveillance applications or use cases requiring external fact verification.
