for the text tagging example, is the underlying approach same or different to elasticsearch term analyzing and scoring?
can anyone help with pros and cons?
context: given a customer chat for returning a product, the text scores terms like "return", "refund", "exhange" for relevance. also makes think how to handle synonyms?
The synonyms question is a good one - so far, we've found that adding them here isn't really necessary as they'll all present similar scores.
your value prop is a nice hedge in that i can try it out and play with results without much any investment, thanks!
(long winded added context: im interested in classifying recipes into useful taxonomies. ex: "sweet potato" -> vegetables, root-vegetables, potato family, flavor: sweet, etc. still exploring, my goal is to recommend ingredient substitutions in an intuitive way. like "other potatoes" yes, but it's because it's starchy with a particular flavor profile etc.)
That's a super interesting use case. I'm curious to see if the model can achieve that out of the box or if it'll need to be fine tuned. Please keep us updated!