Model Detail
tabpfn_2_5
—tabpfn_2_5 is an AI model released by Prior-Labs. The model is registered under the tabular-classification pipeline tag on Hugging Face.
tabpfn_2_5 is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
tabpfn_2_5 is best fit for workloads that match the tabular-classification pipeline tag. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Is TabPFN the Silver Bullet for Insurance Pricing?
arXiv:2605.22892v3 Announce Type: replace-cross Abstract: Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models (GLMs), with gradient-boosted machines as the leading machine learning alternative. Tabular foundation models (TFMs) prese
Systematic Evaluation of TabPFN-TS and Chronos-2 for Zero-Shot Heat Load Forecasting in District Heating Networks
arXiv:2608.20024v2 Announce Type: replace Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Forecasting models trained on historical data may require retraining as networks evolve. Zero-shot time-series foundation models and in-context
TabPFN-3.5: Technical Report
arXiv:2609.17895v2 Announce Type: replace Abstract: We introduce TabPFN-3.5, our new flagship Tabular Foundation Model. It significantly outperforms its predecessor, TabPFN-3, and all existing baselines across a broad range of tabular problems. TabPFN-3.5 sets a new state of the art on standard tabu
Advancing Open and Reproducible Relational Learning: RelArena-$\alpha$, TabPFN-Rel and RPI
arXiv:2608.16319v2 Announce Type: replace Abstract: This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate research in the field towards meaningful real-world impact. We aim to steer
Understanding Context Sampling in TabPFN on Small Tabular Datasets
arXiv:2607.26628v1 Announce Type: cross Abstract: TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates. On small tabular datasets, practitioners must still choose the
