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Related Questions
- How does the number of attention heads in a transformer model impact its performance and computational requirements?
- What are the trade-offs between using more attention heads and increasing the model's capacity to learn complex relationships?
- Can knowledge distillation be used to reduce the number of attention heads in a transformer model while maintaining its performance?
- How does the number of attention heads affect the model's ability to capture long-range dependencies in the input data?
- Are there any known techniques for adaptively adjusting the number of attention heads based on the input data or task?
- What are the implications of using a large number of attention heads on the model's interpretability and explainability?
- Can the number of attention heads be used as a hyperparameter to be tuned during training, and if so, how does it interact with other hyperparameters?
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