| --- |
| license: apache-2.0 |
| library_name: tabcomb |
| pipeline_tag: tabular-classification |
| tags: |
| - tabular |
| - in-context-learning |
| - tabular-foundation-model |
| - machine-unlearning |
| --- |
| |
| <img src="https://raw.githubusercontent.com/m-herre/comb/main/docs/assets/logo.svg" alt="Comb" width="360"> |
|
|
| # 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**. |
|
|
| ```bash |
| pip install tabcomb |
| ``` |
|
|
| ```python |
| 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`. |
|
|
