Literary German Bert

🚀 German BERT for literary texts

This German BERT model, built on bert-base-german-dbmdz-cased, is fine - tuned on literary texts. It can recognize protagonists in German novels.

🚀 Quick Start

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.

📚 Documentation

✨ Features

• Fine - tuned on the Corpus of German - Language Fiction for language modeling.
• Fine - tuned on the DROC corpus for named entity recognition to identify protagonists in German novels.

🔧 Technical Details

Language modeling

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:

Results

After one epoch:

Named entity recognition

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: B - PER, I - PER and O.

Results

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):

📄 References

• 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.