This list will help you In this acl 2024 theme track paper, we introduce datadreamer, an open source python library that allows researchers to write simple code to implement powerful llm workflows Sdv, ctgan, copulas, genalog, peoplesanspeople, doppelganger, and deepecho.
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Below is a selection of four solutions on this topic — we’ll break down their features and usage examples
Bespoke curator is a python library under the apache 2.0 license that simplifies building scalable pipelines for generating synthetic data (including subsequent training on this data).
Synthetic data, generated by machines using real data, metadata, and algorithms, does not contain any sensitive information, yet it retains the essential characteristics of the original data. One of the first open source synthetic data solutions, sdv provides tools for generating synthetic data for tabular, relational, and time series data. What is synthetic data and why is it useful The synthetic data generator takes a description of the data you want (your custom prompt) and returns a dataset for your use case, using a synthetic data pipeline.
This is where synthetic data comes into play So, what is it and how can you generate synthetic data With these data sets, concerns about privacy, compliance, and other issues are easily mitigated.
