There is a need for identifying all food-related projects in the project portfolio. Today this is done manually using labels that applicants themselves have assigned to their project: that data is extremely noisy. 

We had access to some labeled data and trained binary classifiers both in the demo app (uses setfit as the classifier) as well as in Azure Language Studio in order to identify food-related projects and got a similar F1 score around 90% in both cases. The next step was to classify all food-related projects into 16 different categories but due to many of the classes having very few labeled examples the results were not great.