Conditional Text Generation by Fine Tuning Gretel GPT

This text discusses the use of synthetic data generated by an open-source implementation of GPT-3 for augmenting machine learning datasets. It explains how this approach can be more privacy-preserving, scalable, and cost-effective than traditional methods of collecting real-world data. The author demonstrates this technique using a financial intent classification dataset called `banking77`, fine-tuning a GPT model on the dataset, and then generating new annotated examples for any of the intent classes. They also provide tips on improving the model's performance and encourage readers to explore other sample notebooks or join their Slack community for more ideas.