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Related Questions
- What is the key innovation of the transformer model that contributed to its success in natural language processing?
- How do transformer models handle sequential data, such as text, differently than other neural network architectures?
- Can you explain the role of self-attention mechanisms in transformer models and their impact on language understanding?
- How do transformer models learn contextual relationships between words in a sentence, and what is the significance of this ability?
- What are the benefits of using a self-attention mechanism over traditional recurrent neural networks in natural language processing?
- How do transformer models handle out-of-vocabulary words and rare words, and what techniques are used to mitigate this issue?
- What is the relationship between the size of the transformer model and its performance on complex language tasks, such as language translation or question answering?
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