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Define data structure clearly
Specify JSON format, CSV columns, or data schemas
Mention specific libraries
PyTorch, TensorFlow, Scikit-learn for targeted solutions
Clarify theory vs. production
Specify if you need concepts or deployment-ready code
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.