论文标题
使用生成预训练的变压器进行细口捷食谱生成
Towards Fine-Dining Recipe Generation with Generative Pre-trained Transformers
论文作者
论文摘要
食物对于人类生存至关重要。如此之多，以至于我们开发了不同的食谱来满足我们的口味需求。在这项工作中，我们提出了一种新颖的方式，可以使用变压器（特别是自动回归语言模型）从头开始创建新的精细饮食食谱。在一小部分食物食谱数据集的情况下，我们尝试训练模型以识别烹饪技术，提出新颖的食谱并测试用最小数据进行微调的功能。
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.
