$ cat features.txt
- Pure Python Implementation
- Memory-Efficient CNN Architecture
- Fast Single-Pass Inference
- Variable-Length Text Inputs
$ cat README.md
Core Features
Edge-Optimized Neural Networks: PinyByteCNN provides a pure Python implementation with no external dependencies, making it perfect for deployment in constrained environments like Cloudflare Workers, AWS Lambda, and IoT devices.
Lightweight CNN Architecture: Memory-efficient convolutional neural network design that supports 1-3 layer configurations with fast single-pass inference, processing text through byte encoding, embedding, convolution, pooling, and classification.
Flexible Text Processing: Handles variable-length text inputs with multiple prediction strategies including truncate, average, and attention mechanisms for optimal performance across different use cases.
About
PinyByteCNN bridges the gap between powerful machine learning capabilities and deployment constraints. With models ranging from ByteCNN-10K (10,009 parameters, 0.5ms inference) to ByteCNN-32K (32,768 parameters, 1.2ms inference), it's specifically designed for toxicity detection and text classification tasks where speed and efficiency are critical.