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Related Questions
- What are the key factors that contribute to a model's ability to capture long-range dependencies in natural language processing tasks?
- How does the number of attention heads interact with the model's overall capacity to capture contextual relationships between input elements?
- Can you explain the relationship between head size and the model's ability to reason over long-range dependencies in a sequence?
- What are the implications of increasing the number of attention heads on the model's computational complexity and memory requirements?
- How does the choice of head size and number of attention heads impact the model's ability to generalize to out-of-distribution data?
- What are the trade-offs between increasing the number of attention heads and increasing the head size, and how do they impact model performance?
- Can you discuss the relationship between attention head size, number of heads, and the model's ability to capture nuanced contextual relationships in natural language?
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