Post

Tuana
@tuana.dev

The difference between Jev and TFMs boil down to zero-shot classification vs grounded predictions. But because the two share a lot of keywords in descriptions, we get a lot of questions about how the two are related! So I've been sneaking it into my talks over the last few weeks!

Tuana
@tuana.dev

๐‰๐ž๐ฏ: zero-shot classifications for light-weight and cheap decision routing and (possibly) low stakes classifications. How? unstructured in -> structure out.

0 reposts0 likes

Tuana
@tuana.dev

๐“๐š๐›๐ฎ๐ฅ๐š๐ซ ๐Ÿ๐จ๐ฎ๐ง๐๐š๐ญ๐ข๐จ๐ง ๐ฆ๐จ๐๐ž๐ฅ๐ฌ ๐ฅ๐ข๐ค๐ž ๐“๐š๐›๐๐…๐: grounded predictions (classify labels and regress values) based on โœจ learned data โœจ over tables. How? structure (tabular data, and a lot of it) in -> grounded, explainable predictions out. Definitely not zero-shot