| --- |
| license: gpl-3.0 |
| library_name: tabomics |
| tags: |
| - tabular-classification |
| - tabular-foundation-model |
| - in-context-learning |
| - omics |
| --- |
| |
| # TabOmics v2 |
|
|
| TabOmics is a tabular foundation model for wide omics data. It is pretrained only on synthetic tables and uses |
| in-context learning: given labeled training rows and unlabeled test rows, it predicts the test rows in a single forward |
| pass, with no training or tuning on your data. It is designed for wide tables (thousands of features, few samples, as |
| in omics), processes every feature jointly, also works on ordinary tables, and provides sample embeddings and feature |
| importance from the same forward pass. |
|
|
| ```python |
| from tabomics import Classifier |
| clf = Classifier(model="v2").fit(X_train, y_train) |
| proba = clf.predict_proba(X_test) |
| ``` |
|
|
| ## Model details |
|
|
| - Classifier of up to 10 classes. |
| - A Transformer of 12 blocks, each with attention between features, attention between rows and an MLP; |
| embedding size 192, 6 attention heads, 7.3M parameters. |
| - Pretrained only on synthetic tables; the recipes of the published models are in `configs/` of the repository. |
| Standardized features are clipped at 10 at inference. |
| - `n_estimators` averages ensemble members: forward passes on copies of the table with the columns reordered and the |
| categories and classes renumbered at random (1 by default). |
|
|
| Code, documentation and license: https://github.com/lbotecur/tabomics (GNU General Public License v3.0 or later). |
|
|
