We present our system SAGE: Sentence Analysis Genre Evaluation for the AraGenre shared task, which assigns Arabic sentences a hierarchical genre label from an English definition alone, with none of the 74 evaluation genres represented in the official training data. Since a definition is not itself an instance of its genre, we fine-tune the encoder generating the embeddings of both the sentences to classify and the candidate genre definitions and train a small adapter that refines those of the definitions. Studying three encoders and four datasets that were generated for training purposes, we find that the adapter’s training data matters more than either encoder choice or adapter hidden layer. Our best system, combining a fine-tuned encoder and an adapter, reached a Hierarchical Macro F1 score of 0.5278, placing seventh.