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Related Questions
- What are the key differences between transformer-based and recurrent neural network (RNN) architectures in pre-trained models?
- How does the choice of pre-trained model architecture affect the amount of fine-tuning required for downstream tasks?
- Can you explain the concept of 'architecture-specific' fine-tuning and how it relates to pre-trained model architecture?
- What are the implications of using a pre-trained model with a large number of parameters on the fine-tuning process?
- How does the pre-trained model architecture influence the need for task-specific adaptation during fine-tuning?
- What are the trade-offs between using a pre-trained model with a simple architecture and one with a more complex architecture in terms of fine-tuning requirements?
- Can you discuss the role of pre-trained model architecture in determining the optimal fine-tuning strategy for a given task?
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