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Related Questions
- What are the key components of a transformer-based model architecture, and how do they facilitate the capture of long-range dependencies?
- Can you explain how self-attention mechanisms enable a transformer model to capture global dependencies in text data?
- How does the use of multi-head attention in transformer models impact its ability to capture complex, long-range relationships in text?
- What role does the encoder-decoder architecture play in a transformer model's ability to capture long-range dependencies, and how does it differ from other architectures?
- How does the self-attention mechanism handle the curse of dimensionality, and what techniques are used to mitigate this issue in transformer models?
- Can you discuss the impact of positional encoding on a transformer model's ability to capture long-range dependencies, particularly in the context of sequence data?
- How do transformer models compare to recurrent neural networks (RNNs) in terms of their ability to capture long-range dependencies, and what are the key differences in their architectures?
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