How can attention mechanisms be adapted for generative models with varying data granularity

Question

Can you show, using Python programming, how the attention mechanism can be adapted for generative models with varying data granularity?

Answer 1

Attention mechanisms can be adapted for generative models with varying data granularity by using hierarchical attention or multi-scale attention, which processes data at different levels (e.g., sentence-level and word-level).

You can implement an attention mechanism for generative models with varying data granularity by referring to the following code snippet:

The above code allows the model to dynamically focus on fine-grained or coarse-grained information based on the task requirements.

Hence, referring to the above, you can implement an attention mechanism for generative models with varying data granularity.