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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!

𝐉𝐞𝐯: zero-shot classifications for light-weight and cheap decision routing and (possibly) low stakes classifications. How? unstructured in -> structure out.

𝐓𝐚𝐛𝐮𝐥𝐚𝐫 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 𝐥𝐢𝐤𝐞 𝐓𝐚𝐛𝐏𝐅𝐍: grounded predictions (classify labels and regress values) based on ✨ learned data ✨ over tables. How? structure (tabular data, and a lot of it) in -> grounded, explainable predictions out. Definitely not zero-shot