👉 The Relevance Fluid is a key component in the Transformer-based models, such as BERT, that enables the model to dynamically adjust its focus on different parts of the input text based on relevance. It works by mapping both the input tokens and the context embeddings into a shared high-dimensional space, allowing the model to weigh the importance of each token relative to others in determining the overall context. This fluid mechanism enhances the model's ability to understand nuanced relationships and dependencies within the text, improving its performance in tasks like question answering and sentiment analysis by ensuring that the model pays more attention to relevant information.
