  A recently introduced text classifier, called SS3, has obtained
state-of-the-art performance on the CLEF's eRisk tasks. SS3 was created to deal
with risk detection over text streams and, therefore, not only supports
incremental training and classification but also can visually explain its
rationale. However, little attention has been paid to the potential use of SS3
as a general classifier. We believe this could be due to the unavailability of
an open-source implementation of SS3. In this work, we introduce PySS3, a
package that implements SS3 and also comes with visualization tools that allow
researchers to deploy robust, explainable, and trusty machine learning models
for text classification.
