  A major factor contributing to the success of modern representation learning
is the ease of performing various vector operations. Recently, objects with
geometric structures (eg. distributions, complex or hyperbolic vectors, or
regions such as cones, disks, or boxes) have been explored for their
alternative inductive biases and additional representational capacities. In
this work, we introduce Box Embeddings, a Python library that enables
researchers to easily apply and extend probabilistic box embeddings.
