  To truly grasp reasoning ability, a Natural Language Inference model should
be evaluated on counterfactual data. TabPert facilitates this by assisting in
the generation of such counterfactual data for assessing model tabular
reasoning issues. TabPert allows a user to update a table, change its
associated hypotheses, change their labels, and highlight rows that are
important for hypothesis classification. TabPert also captures information
about the techniques used to automatically produce the table, as well as the
strategies employed to generate the challenging hypotheses. These
counterfactual tables and hypotheses, as well as the metadata, can then be used
to explore an existing model's shortcomings methodically and quantitatively.
