  The use of BERT, one of the most popular language models, has led to
improvements in many Natural Language Processing (NLP) tasks. One such task is
Named Entity Recognition (NER) i.e. automatic identification of named entities
such as location, person, organization, etc. from a given text. It is also an
important base step for many NLP tasks such as information extraction and
argumentation mining. Even though there is much research done on NER using BERT
and other popular language models, the same is not explored in detail when it
comes to Legal NLP or Legal Tech. Legal NLP applies various NLP techniques such
as sentence similarity or NER specifically on legal data. There are only a
handful of models for NER tasks using BERT language models, however, none of
these are aimed at legal documents in German. In this paper, we fine-tune a
popular BERT language model trained on German data (German BERT) on a Legal
Entity Recognition (LER) dataset. To make sure our model is not overfitting, we
performed a stratified 10-fold cross-validation. The results we achieve by
fine-tuning German BERT on the LER dataset outperform the BiLSTM-CRF+ model
used by the authors of the same LER dataset. Finally, we make the model openly
available via HuggingFace.
