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Related Questions
- What are the key advantages of using self-attention mechanisms in transformer-based models over traditional RNNs for sequential data?
- How do transformer-based models handle out-of-vocabulary words and unseen tokens compared to RNNs?
- Can you explain the concept of parallelization in transformer-based models and how it affects their performance on sequential data?
- What are the differences in terms of computational complexity between transformer-based models and RNNs for processing sequential data?
- How do transformer-based models handle long-range dependencies in sequential data, and what are the implications for model performance?
- Can you compare the ability of transformer-based models to capture contextual information with RNNs, and what are the implications for tasks like language translation?
- How do transformer-based models scale to handle large sequences and how does this impact their performance compared to RNNs?
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