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Related Questions
- How does increasing the number of attention heads affect the computational complexity of self-attention?
- What is the relationship between the number of attention heads and the memory requirements of self-attention?
- Can increasing the number of attention heads lead to better performance in certain tasks, and if so, why?
- How does the number of attention heads impact the scalability of transformer-based models?
- Are there any trade-offs between increasing the number of attention heads and other model parameters, such as the embedding size or hidden size?
- How does the number of attention heads affect the interpretability of self-attention weights in transformer-based models?
- Can the number of attention heads be optimized for specific tasks or datasets to improve performance and reduce memory requirements?
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