👉 The Session Weapon is a type of neural network architecture designed for handling long-range dependencies in sequential data, particularly useful in natural language processing tasks. It is an extension of the Transformer model that incorporates a mechanism to selectively focus on relevant parts of the input sequence, regardless of their distance from the current position. By doing so, it effectively "weights" the importance of different input elements based on their relevance to the task at hand, allowing it to efficiently process and generate responses even when dealing with long or complex sequences. This capability makes the Session Weapon particularly effective in applications like language translation, text summarization, and question-answering systems where maintaining context over long distances is crucial.
