The Hybrid Cascade Text Classification System is a high-performance, cost-efficient solution for large-scale text classification tasks. It is designed to deliver strong semantic understanding while maintaining low latency and scalable infrastructure costs. The system combines lightweight statistical models with a compact transformer-based model in a multi-stage architecture, enabling optimal trade-offs between speed and accuracy.
This architecture is particularly well-suited for high-throughput environments such as content moderation, spam detection, risk control, and query classification, where both responsiveness and precision are critical.

1. - Layer 1: Supervised training on labeled datasets.
Objective: Maximize recall while maintaining acceptable precision.
Threshold calibration: ROC curve; Precision-Recall curve.
2. - Layer 2: Fine-tuning of pre-trained transformer (MiniLM / DistilBERT).
Optional knowledge distillation from larger models (e.g., BERT).
Loss functions: Cross-entropy; KL divergence (for distillation)

1. - Runtime: ONNX Runtime for CPU deployment.
2. - Quantization: FP32 → INT8 for transformer inference acceleration.
3. - Batching: Micro-batching for throughput optimization under high load.

1. Content moderation.
2. Spam and fraud detection.
3. Search query classification.
4. Customer intent recognition.
5. Multilingual short-text categorization.