---
modelId: "exaone"
name: "EXAONE Tabular"
fullName: "EXAONE Tabular Foundation Model"
org: "LG AI Research"
license: "Open Source (Apache 2.0)"
elo: "1021 (±80)"
rank: 2
browserExecution: "Server-side only (Requires Python runtime / PyTorch / CUDA)"
canonicalUrl: "https://carlahq.eu/datasets/models/exaone-tabular"
---

# EXAONE Tabular Foundation Model Benchmark Profile

> Large-scale tabular foundation model by LG AI Research designed for high-capacity tabular representation learning, handling mixed categorical and continuous variables across tabular domains.

## Technical Specifications
| Dimension | Specification |
| :--- | :--- |
| **Global Leaderboard Rank** | **Rank #2** (Bradley-Terry Elo: 1021 (±80)) |
| **Organization** | LG AI Research |
| **Architecture** | Multi-Task Columnar Autoregressive Tabular Transformer |
| **Pretraining Prior** | Hybrid pre-training on synthetic datasets and diverse real-world tabular corpuses |
| **Context Window** | Tokenized columnar context with sequence truncation |
| **In-Browser Execution** | Server-side only (Requires Python runtime / PyTorch / CUDA) |
| **Open-Source License** | Open Source (Apache 2.0) |
| **Source Repository** | [EXAONE Tabular GitHub](https://github.com/LGAI-Research/EXAONE-Tabular) |
| **Academic Paper** | [Research Paper](https://github.com/LGAI-Research/EXAONE-Tabular) |

## Empirical Performance Across Benchmark Datasets
| Dataset | Task Type | Metric | Score | Latency |
| :--- | :--- | :--- | :--- | :--- |
| [Abalone](/datasets/undefined.md) | `regression` | undefined | **2.03** | — |
| [Adult](/datasets/undefined.md) | `binary_classification` | undefined | **0.0680** | — |
| [Airfoil Self Noise](/datasets/undefined.md) | `regression` | undefined | **1.16** | — |
| [Amazon Employee Access](/datasets/undefined.md) | `binary_classification` | undefined | **0.1232** | — |
| [Bank Marketing](/datasets/undefined.md) | `binary_classification` | undefined | **0.1876** | — |
| [Blood Transfusion Service Center](/datasets/undefined.md) | `binary_classification` | undefined | **0.2480** | — |
| [Breast W](/datasets/undefined.md) | `binary_classification` | undefined | **0.0054** | — |
| [Car](/datasets/undefined.md) | `multiclass_classification` | undefined | **0.0346** | — |
| [Compas Two Years](/datasets/undefined.md) | `binary_classification` | undefined | **0.2612** | — |
| [Credit G](/datasets/undefined.md) | `binary_classification` | undefined | **0.1964** | — |
| [Diabetes](/datasets/undefined.md) | `binary_classification` | undefined | **0.1638** | — |
| [Employee Salaries](/datasets/undefined.md) | `regression` | undefined | **7,427** | — |
| [Fitness Club](/datasets/undefined.md) | `binary_classification` | undefined | **0.1797** | — |
| [House Sales](/datasets/undefined.md) | `regression` | undefined | **99,520** | — |
| [Houses](/datasets/undefined.md) | `regression` | undefined | **39,971** | — |
| [Monks Problems 2](/datasets/undefined.md) | `binary_classification` | undefined | **0.0000** | — |
| [Phoneme](/datasets/undefined.md) | `binary_classification` | undefined | **0.0276** | — |
| [Spambase](/datasets/undefined.md) | `binary_classification` | undefined | **0.0076** | — |
| [Telco Customer Churn](/datasets/undefined.md) | `binary_classification` | undefined | **0.1484** | — |
| [Titanic](/datasets/undefined.md) | `binary_classification` | undefined | **0.1146** | — |
| [Vehicle](/datasets/undefined.md) | `multiclass_classification` | undefined | **0.2393** | — |