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Inferring the properties of protein from its amino acid sequence is one of the key problems in bioinformatics. Most state-of-the-art approaches for protein classification tasks are tailored to specific classification tasks and rely on handcrafted features such as position-specific-scoring matrices from expensive database searches and show an astonishing performance on different tasks. We argue that a similar level of performance can be reached by leveraging the vast amount of unlabeled proteindoi:10.1101/704874 fatcat:cy6u6i2dl5c3pauz5z2ic4pehe