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Related Questions
- What are some key considerations when fine-tuning a pre-trained model for a specific task, and how does this impact model architecture and hyperparameters?
- How does transfer learning and domain adaptation enable effective fine-tuning, and what techniques are used to adapt the training data and model?
- Can you provide examples of successful task-specific fine-tuning in NLP tasks, such as named entity recognition, sentiment analysis, or question answering?
- What are the main differences between task-specific fine-tuning and traditional multi-task learning, and under what conditions would each be preferred?
- How do you balance task-specific adaptation with maintaining general knowledge and reasoning capabilities in the fine-tuned model?
- What are some strategies for handling out-of-distribution data during task-specific fine-tuning, such as data augmentation or regularizing the model?
- Can you explain how task-specific fine-tuning affects the model's representation learning and whether it enhances or hinders transfer learning?
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