Implement text toxicity detection model

Overview

Implement ML model to detect toxic, abusive, and harmful text content.

Model Architecture

• BERT-based transformer model
• Fine-tuned on toxicity datasets (Civil Comments, Jigsaw)
• Multi-label classification: toxic, severe_toxic, obscene, threat, insult, identity_hate

Performance Requirements

• Accuracy: >92% on test set
• Inference latency: <100ms
• False positive rate: <5%

Tech Stack

• Python 3.11+
• PyTorch / HuggingFace Transformers
• ONNX for optimized inference
• TensorRT for GPU acceleration

Training Data

• 500K+ labeled examples
• Balanced across toxicity categories
• Regular retraining with new data

Acceptance Criteria

• Model accuracy >92%
• API response time <100ms p95
• Support for 10+ languages
• Bias audit completed