: The model uses a small set of "latent" variables to attend to the much larger input text. This "cross-attention" step decouples the depth of the network from the size of the input, making it much faster for long documents.
The is a general-purpose neural network architecture developed by Google DeepMind designed to process a wide variety of data types—including text, images, audio, and video—without needing domain-specific adjustments. perceiver
Unlike standard Transformers, which face high computational costs as input size increases, the Perceiver uses a to efficiently handle large amounts of data. How the Perceiver Works with Text : The model uses a small set of