Determining proper quantities for ingredients is an essential part of cooking practice from the perspective of enriching tastiness and promoting healthiness. We introduce KitchenScale, a fine-tuned Pre-trained Language Model (PLM) that predicts a target ingredient's quantity and measurement unit given its recipe context. To effectively train our KitchenScale model, we formulate an ingredient quantity prediction task that consists of three sub-tasks which are ingredient measurement type classification, unit classification, and quantity regression task. Furthermore, we utilized transfer learning of cooking knowledge from recipe texts to PLMs. We adopted the Discrete Latent Exponent (DExp) method to cope with high variance of numerical scales in recipe corpora. Experiments with our newly constructed dataset and recommendation examples demonstrate KitchenScale's understanding of various recipe contexts and generalizability in predicting ingredient quantities. We implemented a web application for KitchenScale to demonstrate its functionality in recommending ingredient quantities expressed in numerals (e.g., 2) with units (e.g., ounce).

翻译：确定食材的适当数量是从口感和促进健康的角度来看烹饪实践的重要组成部分。我们介绍了KitchenScale，这是一个经过优化的预训练语言模型（PLM），它可以根据其食谱上下文预测目标成分的数量和度量单位。为了有效地训练我们的KitchenScale模型，我们制定了一个成分数量预测任务，它由三个子任务组成，即成分测量类型分类、单位分类和数量回归任务。此外，我们利用了从食谱文本到PLMs的烹饪知识的转移学习。我们采用了离散潜在指数（DExp）方法来应对食谱语料库中数字尺度的高方差。我们使用我们新构建的数据集和推荐示例进行的实验证明了KitchenScale在理解各种食谱上下文和预测食材数量方面的普适性。我们实现了一个Web应用程序，以展示KitchenScale在推荐具有数字表达式（例如2）和单位（例如盎司）的成分数量方面的功能。