yandex-research/tabm

Health v2 · maintenance only

Adoption not part of the score

1099 stars · 98 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TabM is a PyTorch-based deep learning model for tabular data that efficiently imitates an ensemble of MLPs through parameter-efficient ensembling. It is the official implementation of an ICLR 2025 paper and ships as a Python package plus paper reproduction code.

Use cases

• train a neural network on tabular data
• improve on gradient boosting for tabular prediction
• build an efficient ensemble of MLPs for classification or regression
• win Kaggle competitions with tabular datasets
• apply deep learning to large tabular datasets with millions of rows
• find a strong deep learning baseline for tabular benchmarks

When to choose

• you need strong accuracy on tabular data with better efficiency than deep ensembles or transformer-based tabular models
• you want a practical tabular DL model that scales to large datasets (10M+ rows)
• you want a competitive alternative or complement to GBDT models like XGBoost

When to avoid

• you need a quick baseline and GBDT already meets your accuracy and latency needs
• your data is not tabular (images, text, audio)
• you need a model with extensive interpretability tooling

Facets

library · maturity active

machine-learning deep-learning machine-learning data-science python tabular-data pytorch ensembling mlp kaggle iclr-2025 supervised-learning

Member repositories

For agents

markdown · JSON · MCP: product_card(name="yandex-research/tabm")