aiden-jeon/minipfn-tabpfn-v2-classifier
tabular-classification model by aiden-jeon
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Model card & documentation
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A from-scratch reimplementation of the TabPFN v2 classifier (Hollmann et al., Accurate predictions on small data with a tabular foundation model, Nature 637, 2025). The model and inference follow the paper's Methods, and the training data come from the open TabICL prior. Code: Aiden-Jeon/minipfn (src/minipfn/tabpfnv2/). This is not the official TabPFN v2 checkpoint, and it uses no official code or weights. For the official models see PriorLabs/TabPFN. Figures and numbers on this card come from checkpoint runs:/a1df9e34774d49e7869eb381bf65509b/checkpoints/step0500000.pt. It was trained in 100k-step chunks, each resuming the previous one. Train CE is the mean over 1,000 steps; every step samples new prior tables. Val CE is computed on 64 fixed prior tables every 5,000 steps. Final val CE is 0.740. The benchmark is the paper's 29 AMLB classification datasets. Each score is the…
Read the full model card ↗ · Preview checked 2026-11-03T07:04:20.395Z
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Model overview
aiden-jeon/minipfn-tabpfn-v2-classifier is a tabular-classification model repository published by aiden-jeon on Hugging Face. The source reports the pytorch library.
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Model facts
- Task
- tabular-classification
- Library
- pytorch
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 7,244,746
- Architecture
- Not reported
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-11-03T06:44:20.000Z
- Last modified
- 2026-11-03T06:44:26.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-03T06:52:20.270Z. Daily imports are snapshots, not real-time monitoring.
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