No. 14 of 26 ·AI Synthetic Data Generators

PluRel

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Freedom report

One barScore 5.3

PluRel is ranked #14 of 26 in AI synthetic data generators on Freedom251.

Compared on AI synthetic data generators

Purpose: PluRel is an open-source framework for synthesizing diverse relational and tabular data.
Generation stages: It models table schemas with directed graphs, inter-table connections with bipartite graphs, and feature distributions with conditional causal mechanisms.
Configurable output: Its configuration controls table layouts, table and row counts, column counts, and structural causal model parameters.
Schema input: It can generate data from an existing SQL schema using the optional schema_file setting.
Data types and patterns: Feature generation supports numeric, categorical, and boolean values, with temporal trends, cycles, and fluctuations.
Compatibility: Generated datasets can be made compatible with RelBench.
Large-scale generation: A multiprocessing script can generate databases in parallel, with the number of databases and processes configurable.
Installation requirement: The library is installed with pip and requires Python 3.12 or later.
License: The public GitHub repository lists an MIT license.
Research results: The project reports that scaling synthetic database diversity improves generalization to real databases and that synthetic pretraining can provide a base for continued pretraining on real data.
Intended context: The project targets relational foundation model research and data-driven work with complex multi-table databases.

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