  Background: In the information extraction and natural language processing
domain, accessible datasets are crucial to reproduce and compare results.
Publicly available implementations and tools can serve as benchmark and
facilitate the development of more complex applications. However, in the
context of clinical text processing the number of accessible datasets is scarce
-- and so is the number of existing tools. One of the main reasons is the
sensitivity of the data. This problem is even more evident for non-English
languages.
  Approach: In order to address this situation, we introduce a workbench: a
collection of German clinical text processing models. The models are trained on
a de-identified corpus of German nephrology reports.
  Result: The presented models provide promising results on in-domain data.
Moreover, we show that our models can be also successfully applied to other
biomedical text in German. Our workbench is made publicly available so it can
be used out of the box, as a benchmark or transferred to related problems.
