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Related Questions
- What are the implications of increasing the number of attention heads on the overall model complexity and computational requirements?
- How does reducing the embedding dimension in self-attention mechanisms affect the model's ability to capture long-range dependencies?
- Can increasing the number of attention heads compensate for the reduced embedding dimension in terms of model performance?
- What are the trade-offs between increasing the number of attention heads and increasing the embedding dimension?
- How does the choice of embedding dimension and number of attention heads impact the model's capacity to learn complex relationships between input elements?
- Are there any known best practices or guidelines for determining the optimal number of attention heads and embedding dimension for a given model architecture?
- Can reducing the embedding dimension in self-attention mechanisms lead to a significant reduction in model size and computational requirements?
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