41–50 of 179 posts
Interesting: parent + male = female (83%) Can not personally find the connection here, was expecting father or something.
High dimension vector is always hard to explain. This is an example.
Really?! man - brain = woman woman - brain = businesswoman
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.
First off, this interface is very nice and a pleasure to use, congrats! Are you using word2vec for these, or embeddings from another model? I also wanted to add some flavor since it looks like many folks in this thread haven't seen something like this - it's been known since 2013 that we can do this (but it's great to remind folks especially with all the "modern" interest in NLP). It's also known (in some circles!) t…
Thank you! I actually had a hard time finding prior work on this, so I appreciate the references. 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 focus…
data + plural = number data - plural = research king - crown = (didn't work... crown gets circled in red) king - princess = emperor king - queen = kingdom queen - king = worker king + queen = queen + king = kingdom boy + age = (didn't work... boy gets circled in red) man - age = woman woman - age = newswoman woman + age = adult female body (tied with man) girl + age = female child girl + old = female child The other…
Curious tool but not what I would call accurate.
Accurate.
Earlier quoted context omitted.
Thank you! I actually had a hard time finding prior work on this, so I appreciate the references. 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 focus…
(Question for anyone) how could I go about replicating this with Gemini Embedding? Generate and store an embedding for every word in the dictionary?
I’ve been unable to find it since. Does anyone know which site I’m thinking of?