readme: ---
  tags: 
  - pytorch
  - token-classification
  - sequence-tagger-model
  language: de
  datasets:
  - conll2003
  - germeval_14
  license: apache-2.0
  ---
  # About `sbb_ner`
  
  This is a BERT model for named entity recognition (NER) in historical German. 
  It predicts the classes `PER`, `LOC` and `ORG`. The model is based on the 🤗 
  [`BERT base multilingual cased`](https://huggingface.co/bert-base-multilingual-cased) model.   
  
  We applied unsupervised pre-training on 2,333,647 pages of 
  unlabeled historical German text from the Berlin State Library 
  digital collections, and supervised pre-training on two datasets 
  with contemporary German text, [conll2003](https://huggingface.co/models?dataset=dataset:conll2003) 
  and [germeval_14](https://huggingface.co/models?dataset=dataset:germeval_14).
  
  For further details, have a look at [sbb_ner](https://github.com/qurator-spk/sbb_ner) on GitHub.
  
  # Results
  
  In a 5-fold cross validation with different historical German NER corpora 
  (see our *KONVENS2019* [paper](https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_4.pdf)), 
  the model obtained an F1-Score of **84.3**±1.1%.
  
  In the *CLEF-HIPE-2020* Shared Task ([paper](http://ceur-ws.org/Vol-2696/paper_255.pdf)), 
  the model ranked 2nd of 13 systems for the German coarse NER task.
  
  # Weights
  We provide model weights for PyTorch. 
  | Model | Downloads
  | ------------------------| ------------------------
  | `bert-sbb-de-finetuned` | [`config.json`](https://huggingface.co/SBB/sbb_ner/blob/main/config.json) • [`pytorch_model_ep7.bin`](https://huggingface.co/SBB/sbb_ner/blob/main/pytorch_model_ep7.bin) • [`vocab.txt`](https://huggingface.co/SBB/sbb_ner/blob/main/vocab.txt)