Gretel Models

Reference docs for Gretel Models.

Gretel provides a number of different model types which may be utilized directly or combined via workflows. This page will outline the different categories of models that Gretel offers.

Model Configurations

Gretel configurations are declarative objects that specify how a model should be created. Configurations can be authored in YAML or JSON. Each of the below models will be declared and configured via a model configuration.

For more information, please refer to the Model Configurations documentation. For more information about each of the specific model types, refer to their individual sections.

Synthetics

Gretel offers the following synthetics models:

1. Tabular Fine-Tuning - Gretel’s flagship LLM-based model for generating privacy-preserving, real-world quality synthetic data across numeric, categorical, text, JSON, and event-based tabular data with up to ~50 columns.

   1. Data types: Numeric, categorical, text, JSON, event-based

   2. Differential privacy: Optional

   3. Formerly known as: Navigator Fine Tuning

2. Text Fine-Tuning - Gretel’s model for generating privacy-preserving synthetic text using your choice of top performing open-source models.

   1. Data types: Text

   2. Differential privacy: Optional

   3. Formerly known as: GPT

3. Tabular GAN - Gretel’s model for quickly generating synthetic numeric and categorical data for high-dimensional datasets (>50 columns) while preserving relationships between numeric and categorical columns.

   1. Data types: Numeric, categorical

   2. Differential privacy: NOT supported

   3. Formerly known as: ACTGAN

4. Tabular DP - Gretel’s model for generating differentially-private data with very low epsilon values (maximum privacy). It is best for basic analytics use cases (e.g. pairwise modeling), and runs on CPU. If your use case is training an ML model to learn deep insights in the data, Tabular Fine-Tuning is your best option.

   1. Data types: Numeric, categorical

   2. Differential privacy: Required; you cannot run without differential privacy

You can learn more about Gretel Synthetics models here.

Transform

Gretel’s Transform model combines data classification with data transformation to easily detect and anonymize or mutate sensitive data. Gretel’s data classification can detect a variety of Supported Entities such as PII, which can be used for defining transforms.

We generally recommend combining Gretel Transform with Gretel Synthetics using workflows to redact or replace sensitive data before training a synthetics model.

You can learn more about Gretel Transform here.

Which models are right for your use case?

You can use the flow chart below to help determine whether Transform, Synthetics (with or without Differential Privacy), or the combination is best for your use case.

If you decided that you should use Synthetics as part of your use case, you can use the next flow chart to help determine which Synthetics model may be best.