  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).
