Each task contains information about proteins. We refer to this data type as metadata because it is not used in training any machine-learning model (including the prototypical networks employed in meta-learning). Since protein structural information is absent for many proteins, we use protein sequence data to represent proteins and compute distances. Various machine learning methods for learning a representation of a protein based on the sequence have been reported. In this work, we employ evolutionary scale modeling (ESM2) to learn a representation of the proteins. The mean representation for each token (amino acid residue)