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Related Questions
- How do self-attention mechanisms in transformer architectures improve contextual understanding?
- What are the key differences between transformer and RNN architectures in terms of parallelization and computational efficiency?
- Can you provide examples of tasks where transformer-based models outperform RNN-based models?
- How does the use of multi-head attention in transformer architectures impact the model's ability to learn complex relationships?
- What are some common challenges associated with training transformer-based models, and how can they be addressed?
- How do transformer architectures facilitate the modeling of long-range dependencies in sequential data?
- Can you compare the ability of transformer and RNN-based models to handle out-of-vocabulary words or unknown tokens?
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