gretel-synthetics

Synthetic data generators for structured and unstructured text, featuring differentially private learning.

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gretelai

gretelai • individual

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676

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0/100

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Created

Mar 2, 2020

Project creation date

README Summary

Gretel Synthetics is a Python library that generates high-quality synthetic data for both structured and unstructured text using advanced machine learning techniques. The library features differentially private learning to ensure privacy protection while creating realistic synthetic datasets. It provides easy-to-use APIs for training custom models and generating synthetic text data that maintains statistical properties of the original data.

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Tags

Differential PrivacySynthetic Data GenerationRecurrent Neural NetworksLSTM ArchitecturePrivacy-Preserving Machine LearningText GenerationSequential Data ModelingData AnonymizationNeural Language ModelingHealthcareSynthetic training data generationLSTM NetworksTabularFinTechSynthetic DataOn-premiseData augmentation for machine learningLegal TechInsuranceStatistical Data ModelingGovernmentPharmaceuticalsDeep LearningAnonymized dataset creationResponsible AIAI SafetyPrivacy-compliant data sharingTextPrivacy-Preserving AITesting and development with realistic dataCloudSelf-hostedRegulatory compliance for sensitive dataPython

Recent Activity

Updated 9 months ago

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Quality

beta

Quality: medium
Maturity: beta

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Developer Platform

Languages

Python100.0%

Timeline

Project created: Mar 2, 2020
Forked: Mar 22, 2026
Your last push: 9 months ago
Upstream last push: 9 months ago
Tracked since: Jun 24, 2025

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