  In NLP, models are usually evaluated by reporting single-number performance
scores on a number of readily available benchmarks, without much deeper
analysis. Here, we argue that - especially given the well-known fact that
benchmarks often contain biases, artefacts, and spurious correlations - deeper
results analysis should become the de-facto standard when presenting new models
or benchmarks. We present a tool that researchers can use to study properties
of the dataset and the influence of those properties on their models'
behaviour. Our Text Characterization Toolkit includes both an easy-to-use
annotation tool, as well as off-the-shelf scripts that can be used for specific
analyses. We also present use-cases from three different domains: we use the
tool to predict what are difficult examples for given well-known trained models
and identify (potentially harmful) biases and heuristics that are present in a
dataset.
