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Related Questions
- What is the purpose of attention heads in a transformer model, and how do they contribute to capturing long-range dependencies?
- Can you explain how multiple attention heads allow the model to focus on different aspects of the input sequence?
- What are the key trade-offs involved in using multiple attention heads, such as increased computational cost and model complexity?
- How do multiple attention heads help the model to attend to both local and global dependencies in the input sequence?
- What are some techniques for optimizing the number of attention heads and their dimensions to balance performance and computational resources?
- Can you discuss the relationship between the number of attention heads and the model's ability to capture nuanced relationships between input tokens?
- What are some potential drawbacks of using multiple attention heads, such as increased risk of overfitting or decreased interpretability?
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