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README.md

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+---
+license: apache-2.0
+library_name: pytorch
+tags:
+  - tabular
+  - tabular-classification
+  - in-context-learning
+  - foundation-model
+  - credit-risk
+  - synthetic-data
+pipeline_tag: tabular-classification
+---
+
+# 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: **https://github.com/kabartay/fintfm**
+
+## 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](https://tabarena.ai), 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.
+
+**This 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
+
+```python
+from fintfm.inference import FinancialTFMClassifier
+
+clf = FinancialTFMClassifier("v4-cellattn-labels.pt", device="cpu")
+clf.fit(X_train, y_train)          # stores the table as context; no training happens
+proba = clf.predict_proba(X_test)[:, 1]
+```
+
+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.
+
+## 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.