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Tech riderRev. 2 Oct 2026

REaLTabFormer

Ch 037.3of 26 AI Synthetic Data Generators
  1. 1Runs onLinux, Mac, self-hosted, Windows
  2. 2CostsFree plan
  3. 3Deploymentself hosted
  4. 4Relational dataYes
  5. 5Unstructured dataNo
  6. 6Privacy-risk metricsYes
6 lines stated Written from the maker's own pages: github.com
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Overview

REaLTabFormer is ranked #3 of 26 in AI synthetic data generators on Specifiction. It runs on Linux, macOS, Self-hosted, Windows. There is a free plan.

REaLTabFormer plans and pricing

All plans
REaLTabFormer Free MIT-licensed software · Python >= 3.7 github.com · 2 Oct 2026

Compared on AI synthetic data generators

Deployment
self_hostedgithub.com
Relational data
Yesgithub.com
Unstructured data
Nogithub.com
Privacy-risk metrics
Yesgithub.com

Facts

Purpose
REaLTabFormer is a unified framework for synthesizing different types of tabular data.github.com · 1 Oct 2026
Relational generation
It uses a sequence-to-sequence model to generate synthetic relational datasets.github.com · 1 Oct 2026
Tabular model
Its non-relational tabular model uses GPT-2 and can model tabular data with independent observations out of the box.github.com · 1 Oct 2026
Installation
The package is installed from PyPI with pip install realtabformer.github.com · 1 Oct 2026
Python requirement
The current PyPI package requires Python 3.8 or newer.pypi.org · 1 Oct 2026
Operating systems
PyPI classifies the package as operating-system independent.pypi.org · 1 Oct 2026
Input format
Examples use pandas DataFrames as model input.github.com · 1 Oct 2026
Relational keys
Relational generation requires matching join-key columns in the parent and child tables.github.com · 1 Oct 2026
Stopping criterion
For non-relational tabular training, the model stops when the synthetic distribution is close to the real distribution.github.com · 1 Oct 2026
Validation
The framework provides observation validators, including a GeoValidator for filtering invalid synthetic samples.github.com · 1 Oct 2026
Privacy-oriented design
The paper says target masking is used to prevent data copying and the Qδ statistic with statistical bootstrapping is used to detect overfitting.arxiv.org · 1 Oct 2026
License
The package is distributed under the MIT License.pypi.org · 1 Oct 2026
Release
PyPI lists version 0.2.4 as released on January 4, 2026.pypi.org · 1 Oct 2026
Funding
The project acknowledges funding from the World Bank-UNHCR Joint Data Center on Forced Displacement.pypi.org · 1 Oct 2026
Relational model
A sequence-to-sequence model generates synthetic relational datasets.github.com · 2 Oct 2026
Sampling
The documented workflow fits a model, saves it locally, and samples synthetic data from it.github.com · 2 Oct 2026
Training behavior
For non-relational tabular models, training stops when the synthetic data distribution is close to the real data distribution.worldbank.github.io · 2 Oct 2026
Data validation
The framework provides an interface for observation validators that filter invalid synthetic samples, including a GeoValidator example.worldbank.github.io · 2 Oct 2026
Security reporting
The security policy asks users to report vulnerabilities by email rather than through public GitHub issues and says a response should arrive within 48 hours.github.com · 2 Oct 2026
Support
For vulnerability reports, the policy lists [email protected] and requests details that help reproduce and assess the issue.github.com · 2 Oct 2026
Documented audience
The project describes its use for projects or research and asks users to cite its research paper when using it.worldbank.github.io · 2 Oct 2026
Development context
The project acknowledges funding from the World Bank-UNHCR Joint Data Center on Forced Displacement for work involving responsible microdata access and synthetic population research.github.com · 2 Oct 2026

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