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Related Questions
- What are some techniques for pruning models to reduce the number of parameters while maintaining performance in NLP tasks?
- How can transfer learning be applied to optimize model architectures for large-scale NLP tasks?
- What are the benefits and limitations of using distillation to reduce complexity in NLP models?
- Can you explain the concept of sparse attention and its applications in reducing computational complexity in transformer-based models?
- How can knowledge distillation be used to improve the performance of smaller models on large-scale NLP tasks?
- What are some strategies for reducing the number of computations required in transformer-based models while preserving contextual information?
- Can you discuss the trade-offs between model size, computational cost, and performance in NLP tasks, and how to optimize these factors for large-scale applications?
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