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Related Questions
- How does BERT's attention mechanism enable the model to capture long-range dependencies in natural language processing tasks?
- Can you explain the role of self-attention in BERT's architecture and its impact on handling long-range dependencies?
- What are some common applications of BERT's attention mechanism in handling long-range dependencies, and how do they improve model performance?
- How does BERT's attention mechanism compare to other architectures in handling long-range dependencies, such as transformers and recurrent neural networks?
- What are some challenges in training BERT models with long-range dependencies, and how can they be addressed using techniques like attention and masking?
- Can you provide examples of tasks that benefit from BERT's attention mechanism in handling long-range dependencies, such as question answering and text classification?
- How does BERT's attention mechanism affect the interpretability of the model's outputs, and are there any techniques to improve interpretability in BERT models with long-range dependencies?
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