3 ms·
There seem to be a lot of properties that are numeric or boolean, e.g. "base_happiness": 50, "capture_rate": 190, "forms_switchable": false, "g
by rahimnathwani 2y ago
There seem to be a lot of properties that are numeric or boolean, e.g.
"base_happiness": 50,
"capture_rate": 190,
"forms_switchable": false,
"gender_rate": 4,
"has_gender_differences": true,
"hatch_counter": 10,
"is_baby": false,
"is_legendary": false,
"is_mythical": false,
Why not treat each of those properties as an extra dimension, and have the embedding model handle only the remaining (non-numeric) fields?
Is it because:
A) It's easier to just embed everything, or
B) Treating those numeric fields as separate dimensions would mean their interactions wouldn't be considered (without PCA), or
C) Something else?
- dcl 2y agoBecause then you couldn't use a pretrained LLM to give you the embeddings. If you added these numerics as extra dimensions, you would need to train a new model that somehow learns the meaning of those extra dimensions based on some measure.
- rahimnathwani 2y agoThe embedding model outputs a vector, which is a list of floats. If we wrap the embedding model with a function that adds a few extra dimensions (one for each of these numeric variables, perhaps compressed into the range zero to one) then we would end up with vectors that have a few extra dimensions (e.g. 800 dimensions instead of 784 dimensions). Vector similarity should still just work, no?