---
modelId: "tabfm"
name: "Google TabFM"
fullName: "Google TabFM v1.0 (Tabular Foundation Model)"
org: "Google Research"
license: "Open Source (Apache 2.0)"
elo: "1111 (±90)"
rank: 1
browserExecution: "Server-side only (Requires Python / JAX / PyTorch environment)"
canonicalUrl: "https://carlahq.eu/datasets/models/tabfm"
---

# Google TabFM v1.0 (Tabular Foundation Model) Benchmark Profile

> Google Research's tabular foundation model architecture focusing on robust columnar tokenization and zero-shot in-context transfer across diverse tabular benchmarks.

## Technical Specifications
| Dimension | Specification |
| :--- | :--- |
| **Global Leaderboard Rank** | **Rank #1** (Bradley-Terry Elo: 1111 (±90)) |
| **Organization** | Google Research |
| **Architecture** | Columnar Embedding & Self-Supervised In-Context Transformer |
| **Pretraining Prior** | Generative synthetic distributions and masked column modeling |
| **Context Window** | Columnar token budget per batch |
| **In-Browser Execution** | Server-side only (Requires Python / JAX / PyTorch environment) |
| **Open-Source License** | Open Source (Apache 2.0) |
| **Source Repository** | [Google TabFM GitHub](https://github.com/google-research/tabfm) |
| **Academic Paper** | [Research Paper](https://github.com/google-research/tabfm) |

## Empirical Performance Across Benchmark Datasets
| Dataset | Task Type | Metric | Score | Latency |
| :--- | :--- | :--- | :--- | :--- |
| [Abalone](/datasets/undefined.md) | `regression` | undefined | **2.02** | — |
| [Adult](/datasets/undefined.md) | `binary_classification` | undefined | **0.0677** | — |
| [Airfoil Self Noise](/datasets/undefined.md) | `regression` | undefined | **1.11** | — |
| [Amazon Employee Access](/datasets/undefined.md) | `binary_classification` | undefined | **0.1273** | — |
| [Bank Marketing](/datasets/undefined.md) | `binary_classification` | undefined | **0.1840** | — |
| [Blood Transfusion Service Center](/datasets/undefined.md) | `binary_classification` | undefined | **0.2445** | — |
| [Breast W](/datasets/undefined.md) | `binary_classification` | undefined | **0.0055** | — |
| [Car](/datasets/undefined.md) | `multiclass_classification` | undefined | **0.0108** | — |
| [Compas Two Years](/datasets/undefined.md) | `binary_classification` | undefined | **0.2619** | — |
| [Credit G](/datasets/undefined.md) | `binary_classification` | undefined | **0.1938** | — |
| [Diabetes](/datasets/undefined.md) | `binary_classification` | undefined | **0.1623** | — |
| [Employee Salaries](/datasets/undefined.md) | `regression` | undefined | **10,435** | — |
| [Fitness Club](/datasets/undefined.md) | `binary_classification` | undefined | **0.1795** | — |
| [House Sales](/datasets/undefined.md) | `regression` | undefined | **98,887** | — |
| [Houses](/datasets/undefined.md) | `regression` | undefined | **37,481** | — |
| [Monks Problems 2](/datasets/undefined.md) | `binary_classification` | undefined | **0.0000** | — |
| [Phoneme](/datasets/undefined.md) | `binary_classification` | undefined | **0.0202** | — |
| [Spambase](/datasets/undefined.md) | `binary_classification` | undefined | **0.0066** | — |
| [Telco Customer Churn](/datasets/undefined.md) | `binary_classification` | undefined | **0.1474** | — |
| [Titanic](/datasets/undefined.md) | `binary_classification` | undefined | **0.1166** | — |
| [Vehicle](/datasets/undefined.md) | `multiclass_classification` | undefined | **0.1986** | — |