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Related Questions
- How do multiple attention heads in a transformer model capture different contextual dependencies in a sentence?
- Can you explain the concept of self-attention and how it relates to the increased number of attention heads?
- What are the limitations of using a single attention head in sequence-to-sequence tasks, and how do multiple heads address these limitations?
- How does the number of attention heads affect the model's ability to learn complex and nuanced relationships between input elements?
- In what ways does the increased number of attention heads improve the model's ability to handle out-of-vocabulary words and rare words?
- Can you discuss the trade-off between increasing the number of attention heads and increasing the model's computational resources?
- How do attention heads contribute to the improvement in performance in tasks such as machine translation, text summarization, and question answering?
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