FinTFM — binary classification checkpoint
An in-context tabular classifier for corporate credit risk, pretrained only on synthetic data. Predictions are a single forward pass with your table supplied as context: no gradient steps at fit time, no per-dataset tuning.
Code, measurement log and claims ledger: github.com/kabartay/fintfm — every number quoted below is traceable to a numbered entry there stating how it was produced.
What this checkpoint is
| parameters | 885,650 |
| architecture | d_cell=48, d_model=128, n_layers=4, n_col_layers=2, d_ff=512, two-way cell attention (n_cell_blocks=1, per-cell labels) |
| capacity | binary only, up to 136 features |
| training | 6,000 steps × batch 8 = 48,000 synthetic tasks, financial prior only |
| training data | synthetic; no real table was seen during pretraining |
This is the checkpoint on which every published binary number in the repository was measured, so results quoted there are reproducible against this file rather than a variant of it.
Performance, stated honestly
On TabArena, 27 binary datasets, against 94 other methods:
| mean ROC-AUC | 0.7823 |
| rank | 93 of 95 |
On real corporate-default panels, calibration is consistently among the best measured and discrimination consistently loses to tuned gradient boosting — both, on every panel tried.
FinTFM is not a competitive general tabular model and it is not presented as one. It is published so that the numbers in the repository can be checked, and because a provenance claim nobody can verify is a slogan.
Usage
pip install fintfm
from huggingface_hub import hf_hub_download
from fintfm.inference import FinancialTFMClassifier
ckpt = hf_hub_download(
"kabartay/fintfm-binary",
"v4-cellattn-labels.pt",
# Pin the commit. Weights behind a published number should not move underneath it.
revision="f116bfd43a2b15c65ed3551ea8c38e3364629ddc",
)
clf = FinancialTFMClassifier(ckpt, device="cpu")
clf.fit(X_train, y_train) # stores the table as context; no training happens
proba = clf.predict_proba(X_test)[:, 1]
The package is on PyPI as fintfm, Apache-2.0, Python
3.12+.
Categorical columns must be encoded before they reach the model — it reads every cell as an
ordered scalar, and label encoding is measurably worse than no order at all. Use
fintfm.inference.categorical.CategoricalTargetEncoder, which is out-of-fold on the context
rows for reasons that are not optional.
Limitations
- Binary only, ≤136 features. A task exceeding either raises rather than being silently truncated.
- Slow at inference: median ~8.6 s per 1,000 rows, against a field norm near 0.1.
- Loses to tuned gradient boosting on discrimination, everywhere it has been measured.
- The multiclass and regression variants exist in the codebase, were scored on real data, and rank last — they are deliberately not published.
Licence
Apache-2.0, the same as the code — chosen deliberately rather than inherited. A weights licence is a separate question from a code licence, and four of the ten peer projects surveyed in the repository ship permissive code with non-commercial weights; one restricts commercial use of the model's output. This checkpoint has no such restriction: commercial use is permitted, subject to Apache-2.0's attribution and notice terms.
Citing
The repository carries a CITATION.cff; GitHub's "Cite this repository" button renders
BibTeX and APA from it. Cite the repository for the method or any finding, and this
checkpoint when the specific weights matter to what you report — they are different
artifacts and a reader can only check the one you name.
Author: Mukharbek Organokov (ORCID 0000-0002-3093-3456).
Provenance
No code, weights, or training data from TabPFN, TabICL, TabDPT, LimiX, Nori, MITRA, or any other
tabular foundation model was used. The prior that generated this checkpoint's training data is
in the repository (src/fintfm/prior/) and can be inspected and re-run.