The current go to solution for the kinds of problems that TabPFN is solving would be something like XGBoost. In general it's a good baseline, but the challenge is always that you need to spend a lot of time feature engineering and tweaking the data representation before something like XGBoost can deliver good performance on your regression or classification problems.

For me the promise of foundation models for tabular data is that there are enough generalizable patterns, so that you need less manual feature engineering and data cleaning.

And kudos to the team, I think it's a really creative application of neural networks. I was always frustrated with neural networks, since they were hard to tune on "structured" data and always under-performed (for me), but we also never had real foundational models for structured data.

Looks really cool. In reading through the FAQ, it says this: Q: "How are text features handled?" A: "In the local package version text features are encoded as categoricals without considering their semantic meaning. Our API automatically detects text features and includes their semantic meaning into our prediction. The local package version encodes text as numerical categories and does not include semantic meaning."

So that means that automatic embedding/semantic meaning is reserved for API use of TabPFN, right? Otherwise, if I use it locally, it's going to assign each of my distinct text values an arbitrary int, right?

It's fascinating how this works with such a small model. Especially given that the training is a kind of meta learning of "how to do in-context learning". I wonder, is there a good intuition of the role of the MLP in this architecture? For LLMs the consensus seems to be that they store knowledge...what would that be for tabular data?

I think you need a custom benchmark -- have you considered making one out of the excel world championships?

Are applications using table models supposed to load the entire thing into context? As A CRUD app guy, I either ask AI to read entire table if its small or use/make scripts to analyze it if its big.

Tabular data is still underrated!

how does it compare to automl tools?

Good stuff!