  Food is essential to human survival. So much so that we have developed
different recipes to suit our taste needs. In this work, we propose a novel way
of creating new, fine-dining recipes from scratch using Transformers,
specifically auto-regressive language models. Given a small dataset of food
recipes, we try to train models to identify cooking techniques, propose novel
recipes, and test the power of fine-tuning with minimal data.
