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A library for mapping text into a multidimensional embedding space representing emotion

Project description

Extract emotional information from embeddings.

When working with LLMs, various embedding models capture emotional information that might be useful to work with (or without!).

An emopoint is a simplified embedding with interpretable dimensions:

  1. joy vs sadness
  2. anger vs fear
  3. disgust vs surprise

So, for example OpenAI's text-embedding-3-small returns embeddings with 1536 dimensions. This library will convert those into 3 dimensions, losing most information except for what directly relates to emotion.

This library enables two modes:

  1. Isolate emotion, converting it into 3D emopoint vectors
  2. Remove emotion, stay in original dimensionality

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