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:
- joy vs sadness
- anger vs fear
- 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:
- Isolate emotion, converting it into 3D emopoint vectors
- Remove emotion, stay in original dimensionality
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