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Related Questions
- What are the primary advantages of self-attention mechanisms in natural language processing?
- How do self-attention and transformers differ from recurrent neural networks in terms of parallelization and computation?
- Can you explain the concept of attention weights in self-attention and how they are calculated?
- How do self-attention and transformers handle long-range dependencies in sequential data compared to RNNs?
- What are some common applications of self-attention and transformers in NLP tasks such as machine translation and text classification?
- How do self-attention and transformers improve over traditional RNN-based models in terms of capturing contextual information?
- What are some challenges and limitations of self-attention and transformers, and how are they being addressed in current research?
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