How to use this model directly from the
tokenizer = AutoTokenizer.from_pretrained("severinsimmler/literary-german-bert") model = AutoModelForTokenClassification.from_pretrained("severinsimmler/literary-german-bert")
This German BERT is based on
bert-base-german-dbmdz-cased, and has been adapted to the domain of literary texts by fine-tuning the language modeling task on the Corpus of German-Language Fiction. Afterwards the model was fine-tuned for named entity recognition on the DROC corpus, so you can use it to recognize protagonists in German novels.
The Corpus of German-Language Fiction consists of 3,194 documents with 203,516,988 tokens or 1,520,855 types. The publication year of the texts ranges from the 18th to the 20th century:
After one epoch:
The provided model was also fine-tuned for two epochs on 10,799 sentences for training, validated on 547 and tested on 1,845 with three labels:
The model has also been evaluated using 10-fold cross validation and compared with a classic Conditional Random Field baseline described in Jannidis et al. (2015):
Markus Krug, Lukas Weimer, Isabella Reger, Luisa Macharowsky, Stephan Feldhaus, Frank Puppe, Fotis Jannidis, Description of a Corpus of Character References in German Novels, 2018.
Fotis Jannidis, Isabella Reger, Lukas Weimer, Markus Krug, Martin Toepfer, Frank Puppe, Automatische Erkennung von Figuren in deutschsprachigen Romanen, 2015.