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Any tools to replicate @oca.computer's work? Once we have the 1000-dim vector embeddings I can make the rest work. Not sure how to go from 20-word span to a 10
by 1024core 2y ago
Any tools to replicate @oca.computer's work?
Once we have the 1000-dim vector embeddings I can make the rest work. Not sure how to go from 20-word span to a 1000-dim vector embedding.
- 10c8 2y agoGenerating embeddings is relatively simple with a model and Python code. There's plenty of them on HuggingFace, along with code examples. all-MiniLM-L6-v2 is a really (if not the most) popular one (albeit not SotA), with 384 dimensions: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 https://huggingface.co/sentence-transformers/all-MiniLM-L6-v... Edit: A more modern and robust suite of models comes from Nomic, and can generate embeddings with 64 to 768 dimensions (https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). When the author talks about thousands of dimensions, they're probably talking about the OpenAI embedding models.