codenlbert-sm
| Property | Value |
|---|---|
| Parameter Count | 28.8M |
| Model Type | BERT-based Classification |
| Architecture | Transformer-based |
| Author | vishnun |
| Training Dataset | vishnun/CodevsNL |
What is codenlbert-sm?
codenlbert-sm is a specialized BERT-based model designed for the specific task of distinguishing between code and natural language text. Built on a small BERT architecture, this model demonstrates exceptional performance with an impressive accuracy of 99.8% on validation data. The model represents a lightweight solution at 28.8M parameters while maintaining high efficiency in code detection tasks.
Implementation Details
The model utilizes PyTorch and the Transformers library, implementing a fine-tuned version of BERT-small. During training, it achieved consistent improvement across 5 epochs, with the training loss decreasing from 0.0225 to 0.0009, while maintaining stable validation metrics.
- Architecture: Small BERT variant optimized for code detection
- Framework: PyTorch with Transformers library
- Model Format: Safetensors
- Training Duration: 5 epochs with progressive improvement
Core Capabilities
- Binary classification between code and natural language
- High accuracy (99.8%) in distinguishing code segments
- Efficient processing with relatively small parameter count
- Support for English language text analysis
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional accuracy in code detection while maintaining a relatively small parameter count of 28.8M, making it both efficient and highly accurate for its specific use case.
Q: What are the recommended use cases?
The model is ideal for applications requiring automatic detection of code segments within text, code extraction from documentation, and content classification in development environments. It can be particularly useful in processing screenshots of code through the associated SnapCode space.
