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Related Questions
- What is the relationship between the number of attention heads and the computational complexity of self-attention in transformer models?
- How does increasing the number of attention heads impact the memory requirements for self-attention computations?
- Can you explain the trade-off between increasing the number of attention heads and the model's capacity to capture long-range dependencies?
- How does the number of attention heads affect the parallelizability of self-attention computations?
- What is the optimal number of attention heads for a given model architecture and dataset?
- Can you provide an example of how increasing the number of attention heads affects the model's performance on a specific task?
- How does the number of attention heads impact the model's ability to capture complex interactions between different input elements?
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