metadata
license: apache-2.0
library_name: tabcomb
pipeline_tag: tabular-classification
tags:
- tabular
- in-context-learning
- tabular-foundation-model
- machine-unlearning
Comb
Weights of Comb, a tabular in-context classifier whose training set can be edited exactly: add, forget and merge rows without retraining. Code, docs and results: https://github.com/m-herre/comb.
pip install tabcomb
from comb import CombClassifier
clf = CombClassifier().fit(X_train, y_train) # downloads these weights on first use
proba = clf.predict_proba(X_test)
clf.add(X_new, y_new); clf.forget(X_old, y_old)
| Architecture | TabICLv2 network (column set transformer, row transformer, 12-block in-context stage) with the in-context stage over at most M = 4096 class-conditional cells (default; set with cell_m) |
| Parameters | 27,552,258 (fp32, 110 MB) |
| Pretraining | from scratch on synthetic data only (TabICLv2's structural causal prior), 500k + 40k + 10k steps with the cell budget drawn from 128 to 2048 per step, then a 5k-step fine-tune with M from 512 to 8192 and a more realistic prior mix |
| Task | classification, 2 to 10 classes (more through a hierarchical fallback) |
| TALENT, 106 held-out classification tasks (8 members, fp32) | AUC −0.37 pp [−0.58, −0.19] vs TabICLv2, +2.8 pp vs CatBoost, +3.6 pp vs XGBoost (their defaults) |
| TabArena-Lite / BeyondArena Elo (self-run, official protocol, defaults) | 1443 / 1167 |
| Version | 1.0.0 |
| License | Apache-2.0 (code and weights); architecture ported from TabICL (BSD-3-Clause) |
Files: config.json (constructor arguments of comb.model.TabICL) and model.safetensors.