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Related Questions
- What is the key innovation in transformer-based models that enables them to handle sequential data?
- How do self-attention mechanisms in transformer models facilitate parallelization and efficiency in processing sequential data?
- What are the primary advantages of using transformer models for sequential data compared to traditional recurrent neural networks?
- Can transformer models handle out-of-vocabulary words or tokens in sequential data, and if so, how?
- How do transformer models capture long-range dependencies and relationships in sequential data?
- What are the implications of using transformer models for natural language understanding, and how do they improve language processing capabilities?
- Can transformer models be fine-tuned for specific downstream tasks, such as question answering or text classification, and if so, how?
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