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Looking at TabPFN with Polaris datasets macinchem.org/2025/02/06/loo… #cheminformatics

Tabular data often has messy text features —preprocessing them is tedious and time-consuming. Our new #TabPFN 2+ removes that hassle: just feed in your data, and get fast, accurate predictions. No feature engineering required. We’d love your feedback! API + example notebook👇1/5

One week to go! Join us on March 11 in London for the #TabPFN launch event. Hear how foundation models for tabular data are transforming industries—attendees include leaders from Meta, DeepMind & more. Secure your spot: lu.ma/d64ql04k #AI #DataScience

We're hiring across several roles! AI scientists, ML engineers and product 🚀 We're building in Berlin and Freiburg! Check these out and apply here: jobs.ashbyhq.com/prior-labs #tabpfn #hiring #MLjobs #datascience

Excited to speak at PyConDE & PyData today, presenting TabPFN v2, including a live demo. 10:55am, in person in Platinum 3. If you're at the conference please find me to chat about tabular foundation models and Prior Labs 🙂

Have you joined our Discord community yet? Our community gets first look at any new features, research, new TabPFN use-cases and company updates 👀 It's also the best place get support from our team and the broader community! Join here 👉 discord.com/invite/VJRuU3b… #tabpfn

Super excited about this interventional version of TabPFN! We rarely make predictions just for the sake of making predictions, but rather in order to make (automated) decisions. If these decisions entail interventions on some of the features (treatments, prices, purchases, etc),