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I've been working with embeddings recently. Given some text, an embedding model returns a point in vector space. First uses of embeddings were related to Topic clustering. What is the distance between to pieces of text in semantic space? I came across the concept of Semantic Axes, where multiple points in vector space can be defined by two different sets of text - hot and cold, good and bad, service-related inquiry or website complaint. Being able to create contextual framings is an interesting capability.
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