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We train neural networks on sets of homologous sequences simulated from probabilistic evolution models to infer phylogenetic trees. We use self-attention to obtain permutation-equivariant functions and ensure that the obtained tree does not depend on the order in which the sequences are provided. Our hope is to perform inference that is order of magnitude faster than state of the art maximum likelihood, and as accurate or even better under models with untractable likelihoods.