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Food Computing is currently a fast-growing field of research. Naturallanguage processing (NLP) is also increasingly essential in this field,especially for recognising food entities. However, there are still only a fewwell-defined tasks that serve as benchmarks for solutions in this area. Weintroduce a new dataset -- called \textit{TASTEset} -- to bridge this gap. Inthis dataset, Named Entity Recognition (NER) models are expected to find orinfer various types of entities helpful in processing recipes, e.g.~foodproducts, quantities and their units, names of cooking processes, physicalquality of ingredients, their purpose, taste.  The dataset consists of 700 recipes with more than 13,000 entities toextract. We provide a few state-of-the-art baselines of named entityrecognition models, which show that our dataset poses a solid challenge toexisting 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 andthe task to encourage progress on more in-depth and complex informationextraction from recipes.

TASTEset -- Recipe Dataset and Food Entities Recognition Benchmark