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Related Questions
- How do transformer-based models compare to recurrent neural networks (RNNs) in terms of computational efficiency?
- What are the key factors that contribute to the increased complexity of transformer-based models?
- Can you explain the concept of parameter sharing in transformer-based models and its impact on model complexity?
- How do different attention mechanisms, such as multi-head attention, affect the trade-off between model complexity and computational resources?
- What are some strategies for reducing the computational requirements of transformer-based models while maintaining their performance?
- How do the number of layers and the hidden state size in transformer-based models impact the trade-off between model complexity and computational resources?
- Can you discuss the relationship between model complexity and the number of parameters in transformer-based models?
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