  Lately, pre-trained language models advanced the field of natural language
processing (NLP). The introduction of Bidirectional Encoders for Transformers
(BERT) and its optimized version RoBERTa have had significant impact and
increased the relevance of pre-trained models. First, research in this field
mainly started on English data followed by models trained with multilingual
text corpora. However, current research shows that multilingual models are
inferior to monolingual models. Currently, no German single language RoBERTa
model is yet published, which we introduce in this work (GottBERT). The German
portion of the OSCAR data set was used as text corpus. In an evaluation we
compare its performance on the two Named Entity Recognition (NER) tasks Conll
2003 and GermEval 2014 as well as on the text classification tasks GermEval
2018 (fine and coarse) and GNAD with existing German single language BERT
models and two multilingual ones. GottBERT was pre-trained related to the
original RoBERTa model using fairseq. All downstream tasks were trained using
hyperparameter presets taken from the benchmark of German BERT. The experiments
were setup utilizing FARM. Performance was measured by the $F_{1}$ score.
GottBERT was successfully pre-trained on a 256 core TPU pod using the RoBERTa
BASE architecture. Even without extensive hyper-parameter optimization, in all
NER and one text classification task, GottBERT already outperformed all other
tested German and multilingual models. In order to support the German NLP
field, we publish GottBERT under the AGPLv3 license.
