#Data-generation
Showing 21 of 21 repositories tagged #data-generation, ranked by stars
Synthetic data generation for tabular data
🎨 NeMo Data Designer: Generate high-quality synthetic data from scratch or from seed data.
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.
List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.
The Declarative Data Generator
GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
A library to model multivariate data using copulas.
Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
Random dataframe and database table generator
📖 Data Engineering from Raw Corpora
The DataHelix generator allows you to quickly create data, based on a JSON profile that defines fields and the relationships between them, for the purpose of testing and validation
AI-powered synthetic data generation — structured tables, unstructured documents, multi-provider LLM support, referential integrity, and code-gen mode for millions of rows
jazznet dataset of piano patterns for music audio machine learning research
Custom image data generator for TF Keras that supports the modern augmentation module albumentations
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
📈 🐍 Multidimensional synthetic data generation with Copula and fPCA models in Python
High-performance open-source synthetic data engine. Uses LLMs for schema design and vectorized NumPy for deterministic, scalable generation.
Realistic synthetic events for testing, demos, and pipelines — streamed live or generated in bulk
TabPFGen: Synthetic Tabular Data Generation with TabPFN
The source code used for paper "TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision", published in WWW 2025.