#synthetic data

Distilabel is a framework for creating synthetic data and obtaining AI feedback, serving those developing NLP and LLM projects. It facilitates the creation of high-quality, varied datasets using established research techniques. The framework allows engineers to concentrate on enhancing data quality and controlling model tuning, integrating feedback across LLM providers with a single API. As an open-source, community-supported project, Distilabel ensures scalable and adaptable data generation pipelines to enhance the efficiency and quality of AI development.

This repository is an extensive archive of failure cases in ChatGPT and similar AI models. It offers insights into these technologies by chronicling instances of erratic behavior, hallucinations, arithmetic errors, and biases. The collection supports further research, model comparisons, and generates synthetic data for testing and training. Featuring detailed cases from various sources, it highlights the evolving nature of AI and the ongoing need for technology refinement. This archive serves as a vital resource for researchers and developers aiming to enhance AI systems.

The SDV Python library uses machine learning to generate accurate synthetic data, reflecting real data patterns. It includes features for data evaluation, anonymization, and constraint setting, supporting diverse table formats. This tool maintains privacy and statistical integrity, providing valuable insights with quality reports and visualizations.

Neosync is an open-source tool designed for data anonymization and synthetic data generation. It optimizes testing through PII anonymization and realistic dataset creation, aiding in GDPR and HIPAA compliance. Key features involve schema-based synthetic data generation, production data anonymization, and async pipeline integration. Seamless deployment with pre-built integrations for databases like PostgreSQL and MySQL makes it suitable for varied testing environments.