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We analyse distributions of the spatial scales of coherent intermittent structures – current sheets – obtained from fully kinetic, two-dimensional simulations of relativistic turbulence in a collisionless pair plasma using unsupervised machine-learning data dissection. We find that the distribution functions of sheet length (longest scale of the analysed structure in the direction perpendicular to the dominant guide field) and r_c (radius of a circle fitted to the structures) can be well-approximated by power-law distributions, indicating self-similarity of the structures. The distribution for the sheet width (shortest scale of the structure) peaks at the kinetic scales and decays exponentially at larger values. The data shows little or no correlation between width and length, as expected from theoretical considerations. The typical r_c depends linearly on length, which indicates that the sheets all have a similar curvature relative to their sizes. We find a weak correlation between r_c and width. Our results can be used to inform realistic magnetohydrodynamic subgrid models for plasma turbulence in high-energy astrophysics.