README.md

---
tags:
- tabular
- tabpfn
- bankruptcy-prediction
- financial-distress
- finance
datasets:
- sebastiantomczak10/v4-group-corporate-bankruptcy
---

# V4FinBench TabPFN Checkpoints

Finetuned [TabPFN v2](https://github.com/PriorLabs/TabPFN) checkpoints for
corporate financial distress prediction on V4FinBench. We release one
checkpoint per prediction horizon (h = 0, …, 5), each trained with the
**prototype-undersampling** context-construction strategy that performs
best in our experiments.

- **Dataset (Kaggle):** <https://www.kaggle.com/datasets/sebastiantomczak10/v4-group-corporate-bankruptcy/data>
- **Code (GitHub):** <https://github.com/genwro-ai/V4FinBench>

## What's in this repo

Six TabPFN v2 checkpoints, one per V4FinBench horizon task:

| File | Horizon | Description |
|---|---|---|
| `tabpfn_h0.ckpt` | h = 0 | Current-year financial distress |
| `tabpfn_h1.ckpt` | h = 1 | One-year-ahead distress |
| `tabpfn_h2.ckpt` | h = 2 | Two-year-ahead distress |
| `tabpfn_h3.ckpt` | h = 3 | Three-year-ahead distress |
| `tabpfn_h4.ckpt` | h = 4 | Four-year-ahead distress |
| `tabpfn_h5.ckpt` | h = 5 | Five-year-ahead distress |

Each checkpoint is the per-horizon model selected by maximizing
$F_1$-score on the validation fold, finetuned from the pretrained
TabPFN v2 base with imbalance-aware in-context construction (clustered
majority prototypes paired with all minority examples).

## Loading checkpoints

See the V4FinBench code repository for loading and inference:
<https://github.com/genwro-ai/V4FinBench>

## Training details

- **Base model:** TabPFN v2
- **Finetuning:** Adam, 10 epochs, learning rate 5e-6, batch size 1024,
  inference context size 10,000.
- **Context construction:** prototype undersampling — for each context,
  all minority-class examples are retained; majority-class examples are
  selected by clustering with MiniBatchKMeans and keeping the real
  observation closest to each centroid, until
  $N_{\text{min}} / N_{\text{maj}} = 0.3$.
- **Hardware:** single NVIDIA A100 GPU per run.
- **Folds:** 5-fold company-grouped, country-stratified
  cross-validation. The released checkpoint per horizon is the
  best-performing fold by validation $F_1$.

Full configuration, evaluation protocol, and per-fold results are
documented in the paper and in the GitHub code repository.

## Scope of this release

This repository contains a curated subset of the checkpoints produced
in the paper. The full reference experiments produce 90 checkpoints
total (6 horizons × 5 folds × 3 context-construction strategies);
reproducing them is fully supported by the released folds and training
code in the GitHub repository.

## Intended use

These checkpoints are intended for:

- Benchmarking corporate financial-distress prediction methods on
  V4FinBench under the released evaluation protocol.
- Research on tabular foundation models, in-context learning under
  severe class imbalance, and cross-dataset transfer in financial
  prediction (e.g., the American Bankruptcy Dataset transfer experiment
  in the paper).

## Limitations and responsible use

- These checkpoints are **research artifacts**, not production
  risk-scoring models. They are not intended for, and should not be
  used to make, individual credit decisions, supplier-risk decisions,
  or any other automated decisions about specific companies without
  additional jurisdiction-specific validation, fairness analysis, and
  human oversight.
- V4FinBench labels capture composite financial distress (joint
  deterioration in solvency, profitability, and liquidity), not
  formal legal bankruptcy filings. Predictions reflect the operational
  distress definition in the paper and may not generalize to other
  distress or bankruptcy definitions.
- Training data covers four Central European economies (Poland,
  Hungary, Czech Republic, Slovakia) over 2006–2021. Performance on
  other economies, accounting standards, or time periods is not
  guaranteed; see the cross-dataset transfer analysis in the paper for
  one external evaluation point.

## License

These checkpoints are derivatives of TabPFN v2 and are released under
the TabPFN license included in this repository (`LICENSE`). Please
review the license terms before use.

## Citation

A citation entry will be added once the preprint is available.