  Probing (or diagnostic classification) has become a popular strategy for
investigating whether a given set of intermediate features is present in the
representations of neural models. Naive probing studies may have misleading
results, but various recent works have suggested more reliable methodologies
that compensate for the possible pitfalls of probing. However, these best
practices are numerous and fast-evolving. To simplify the process of running a
set of probing experiments in line with suggested methodologies, we introduce
Probe-Ably: an extendable probing framework which supports and automates the
application of probing methods to the user's inputs
