Manik2000
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v4finbench-tabpfn

@@ -1,161 +1,110 @@
 ---
-license: other
-license_name: priorlabs
-license_link: LICENSE
 tags:
-  - tabular-classification
-  - tabpfn
-  - finance
-  - bankruptcy-prediction
-  - corporate-distress
-  - reproducibility
-  - neurips
-  - datasets-and-benchmarks
-library_name: tabpfn
 ---
-# TabPFN-V4FinBench
-Built with PriorLabs-TabPFN.
-This repository contains six fine-tuned TabPFN checkpoints released to support reproducibility of the experiments in:
-**V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction**
-Each checkpoint corresponds to one V4FinBench prediction horizon:
-| Checkpoint | Prediction horizon | Task |
-|---|---:|---|
-| `tabpfn_v4finbench_h0` | 0 years | current-year financial distress |
-| `tabpfn_v4finbench_h1` | 1 year ahead | distress prediction one year before the event |
-| `tabpfn_v4finbench_h2` | 2 years ahead | distress prediction two years before the event |
-| `tabpfn_v4finbench_h3` | 3 years ahead | distress prediction three years before the event |
-| `tabpfn_v4finbench_h4` | 4 years ahead | distress prediction four years before the event |
-| `tabpfn_v4finbench_h5` | 5 years ahead | distress prediction five years before the event |
-The checkpoints are intended to let researchers reproduce the benchmark results without re-running TabPFN fine-tuning.
-## Model description
-TabPFN-V4FinBench is a collection of six fine-tuned TabPFN checkpoints for tabular binary classification. The task is corporate financial distress prediction from structured financial and non-financial company-year features.
-Each checkpoint was fine-tuned separately on one of the six V4FinBench horizon-specific tasks.
-The models were fine-tuned on V4FinBench, a benchmark of over one million company-year observations from the Visegrád Group economies:
-- Poland
-- Hungary
-- Czech Republic
-- Slovakia
-The benchmark covers years 2006–2021 and contains 131 financial and non-financial features. Labels are derived from a composite financial distress criterion based on solvency, profitability, and liquidity deterioration.
-## Intended use
-These checkpoints are released for research, evaluation, and reproducibility.
-The main intended use is to reproduce selected TabPFN results from the V4FinBench paper without having to fine-tune TabPFN again.
-Typical uses include:
-- reproducing V4FinBench benchmark results;
-- evaluating the released checkpoints on the V4FinBench test folds;
-- comparing new tabular models against the fine-tuned TabPFN baselines;
-- studying transfer to related corporate distress or bankruptcy prediction datasets.
-## Out-of-scope use
-These models are not intended for production credit scoring, lending decisions, investment decisions, regulatory decisions, or automated decision-making about real companies.
-The models should not be used as the sole basis for financial, legal, or business decisions.
-## Dataset
-The models were fine-tuned on **V4FinBench**, a corporate distress benchmark containing 1,106,879 company-year observations from 203,900 companies across the V4 economies.
-The benchmark includes six prediction horizons:
-| Horizon | Total instances | Positive cases | Negative cases |
-|---:|---:|---:|---:|
-| 0 years | 1,000,087 | 3,587 | 996,500 |
-| 1 year | 996,500 | 3,054 | 993,446 |
-| 2 years | 898,692 | 2,374 | 896,318 |
-| 3 years | 793,234 | 1,896 | 791,338 |
-| 4 years | 700,041 | 1,485 | 698,556 |
-| 5 years | 598,832 | 1,154 | 597,678 |
-Dataset and code:
-- Kaggle: https://www.kaggle.com/datasets/sebastiantomczak10/v4-group-corporate-bankruptcy/data
-- GitHub: https://github.com/genwro-ai/V4FinBench
-## Distress definition
-A company is labeled as financially distressed if, in its final available annual report, it simultaneously satisfies all three criteria:
-1. **Solvency:** equity / total assets < 0
-2. **Profitability:** EBITDA / total assets < 0
-3. **Liquidity:** current assets / current liabilities < 0.6
-This label captures financial distress rather than formal legal bankruptcy. The criterion is designed to identify companies with simultaneous deterioration in solvency, profitability, and liquidity.
-## Multi-horizon setup
-V4FinBench provides six derived binary classification tasks for horizons `h = 0, 1, 2, 3, 4, 5`.
-For each horizon `h`, distressed companies have their final `h` years of data removed, and the resulting final observation receives a positive label. Other company-year observations are assigned a negative label.
-Each model in this repository was fine-tuned on one horizon-specific task.
-## Fine-tuning procedure
-The models were initialized from a pretrained TabPFN checkpoint and fine-tuned separately for each prediction horizon using the same imbalance-aware context construction strategy.
-Because V4FinBench is severely imbalanced, with only about 0.19–0.36% positive cases depending on the horizon, uniformly sampled TabPFN contexts contain very few positive examples. To address this, fine-tuning uses prototype undersampling:
-1. All minority-class examples are retained.
-2. Majority-class examples are clustered with MiniBatchKMeans.
-3. One real majority example closest to each cluster centroid is selected.
-4. The resulting context uses an approximately 7:3 majority-to-minority ratio.
-This preserves minority-class signal while keeping a representative structure of the non-distressed majority population.
-## Training configuration
-| Hyperparameter | Value |
-|---|---:|
-| Learning rate | 5e-6 |
-| Epochs | 10 |
-| Batch size | 1024 |
-| Meta batch size | 1 |
-| Inference context size | 10,000 |
-| Loss | Cross entropy |
-| Hardware | Single NVIDIA A100 GPU |
-A checkpoint was saved after each epoch. For each horizon, the final checkpoint was selected using validation F1 after threshold calibration on the precision-recall curve.
 ## License
-These models are released under the **Prior Labs License v1.1, May 2025**.
-The full license text is included in the `LICENSE` file.
-Built with PriorLabs-TabPFN.
-These checkpoints are fine-tuned derivatives of TabPFN. They were modified by the V4FinBench authors and are not official Prior Labs releases. They are not endorsed, approved, or validated by Prior Labs.
 ## Citation
-If you use these models, please cite the V4FinBench paper:
-TBA
-Please also cite:
-```bibtex
-@article{hollmann2025tabpfn,
-  title={TabPFN: A Tabular Foundation Model},
-  author={Hollmann, Noah and others},
-  year={2025}
-}
-```

 ---
 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.