#ai-ml

Fine-tune BERT model for custom sentiment analysis. Steps: 1. Data preprocessing (tokenize, pad, mask). 2. Load pre-trained BERT model (Hugging Face Transformers). 3. Define custom classification head. 4. Configure optimizer (AdamW) and scheduler. 5. Implement training loop with validation. 6. Handle class imbalance (weighted loss). 7. Evaluate metrics (F1-score, accuracy). 8. Quantize model for inference efficiency. Include usage example.