Also, if it gets buried in comments, proper nouns need to be capitalized (Paris-France+Germany).
I am planning on patching up the UI based on your feedback.
The dictionary is based on https://wordnet.princeton.edu/, no word2vec. It's just a plain lookup among precomputed embeddings (with mxbai-embed-large). And yes, I'm excluding words that are present in the query because.
It would be interesting to see how other models perform. I tried one (forgot the name) that was focused on coding, and it didn't perform nearly as well (in terms of human joy from the results).
I've been playing with embeddings and wanted to try out what results the embedding layer will produce based on just word-by-word input and addition / subtraction, beyond what many videos / papers mention (like the obvious king-man+woman=queen). So I built something that doesn't just give the first answer, but ranks the matches based on distance / cosine symmetry. I polished it a bit so that...
https://github.com/vitprajzler/node-in-english
What do I mean by plain English? I mean there's no source code, no programming language. Just plain English "compiled" and CI/CDed into "production" at https://nie.avoguard.com/.Why did I build this? Just for fun. I wanted to see how close to a production-ready code can an LLM generate, and what would it take to actually take it into production. With today's technology, could an English...
Is this useful for anything else than visualization of the spectrum in a waterfall diagram?