  Food Computing is currently a fast-growing field of research. Natural
language processing (NLP) is also increasingly essential in this field,
especially for recognising food entities. However, there are still only a few
well-defined tasks that serve as benchmarks for solutions in this area. We
introduce a new dataset -- called \textit{TASTEset} -- to bridge this gap. In
this dataset, Named Entity Recognition (NER) models are expected to find or
infer various types of entities helpful in processing recipes, e.g.~food
products, quantities and their units, names of cooking processes, physical
quality of ingredients, their purpose, taste.
  The dataset consists of 700 recipes with more than 13,000 entities to
extract. We provide a few state-of-the-art baselines of named entity
recognition models, which show that our dataset poses a solid challenge to
existing models. The best model achieved, on average, 0.95 $F_1$ score,
depending on the entity type -- from 0.781 to 0.982. We share the dataset and
the task to encourage progress on more in-depth and complex information
extraction from recipes.
