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Related Questions
- What are the advantages of using self-attention mechanisms in transformer models for sequential data?
- How does positional encoding help the transformer model understand the order of input sequences?
- Can you explain the concept of relative positional encoding and its applications in NLP tasks?
- What are the key differences between absolute and relative positional encoding in transformer models?
- How does positional encoding impact the performance of transformer models in tasks like machine translation and text classification?
- Can you provide examples of how positional encoding is used in popular transformer-based architectures like BERT and RoBERTa?
- What are some common challenges associated with positional encoding in transformer models and how can they be addressed?
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