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Related Questions
- How do the number of layers, attention heads, and model size affect the overall complexity of a transformer-based model?
- In what ways does the self-attention mechanism contribute to the computational complexity of a transformer model?
- How does the quadratic relationship between sequence length and model parameters impact the trainability of transformer models for long sequences?
- What are some methods for reducing the number of parameters in transformer models without compromising their performance?
- Can you elaborate on the trade-off between model complexity and the size of the training dataset required for effective training of a transformer model?
- How does the parallelization of transformer models for distributed training affect their scalability and complexity?
- In what ways does the model's capacity for handling nested dependencies and compositionality influence its complexity in relation to the number of parameters?
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