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This paper describes the models developed by the AILAB-Udine team for theSMM4H 22 Shared Task. We explored the limits of Transformer based models ontext classification, entity extraction and entity normalization, tackling Tasks1, 2, 5, 6 and 10. The main take-aways we got from participating in differenttasks are: the overwhelming positive effects of combining differentarchitectures when using ensemble learning, and the great potential ofgenerative models for term normalization.

AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles