What are the implications of using transformer models in NLP for real-time applications?
- Enhanced performance in real-time tasks.
- Increased computation time.
- Reduced memory usage.
- Simplified model training.
Transformer models, while powerful, are known for their increased computation time, which can be a drawback in real-time applications where quick responses are crucial. They often require substantial computational resources for processing.
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