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The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the...

Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. The kit’s design goal is to shorten the “data...

The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent...

We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid...