answerdotai-ModernBERT-base-ai-detector
| Property | Value |
|---|---|
| Framework | PyTorch 2.5.1+cu124 |
| Training Data | 35,894 samples |
| Validation Loss | 0.0036 |
| Model URL | Hugging Face |
What is answerdotai-ModernBERT-base-ai-detector?
This is a specialized AI detection model built on ModernBERT-base architecture, fine-tuned specifically to distinguish between AI-generated and human-written text. The model has been trained on a diverse dataset including content from various AI models (ChatGPT, Claude, DeepSeek) and human-written sources like Wikipedia, books, and articles.
Implementation Details
The model utilizes the ModernBERT architecture with advanced training procedures including AdamW optimizer, linear learning rate scheduling, and mixed precision training. It was trained for 3 epochs with a learning rate of 2e-5 and batch sizes of 16.
- Native AMP (fp16) implementation for efficient training
- Comprehensive training dataset of 35,894 samples
- Achieved validation loss of 0.0036
- Built using Transformers 4.48.3 framework
Core Capabilities
- Binary classification of text (AI-generated vs. human-written)
- Compatible with various AI-generated content sources
- Optimized for educational and research applications
- Real-time content verification capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model combines ModernBERT's efficient architecture with specialized training on a large dataset of AI and human-written content, achieving exceptional validation accuracy while maintaining lightweight implementation.
Q: What are the recommended use cases?
The model is ideal for educational institutions, content verification systems, and research applications requiring AI content detection. It's particularly effective for detecting content from modern AI models like ChatGPT, Claude, and DeepSeek.
