SBB
/

Token Classification
Transformers
PyTorch
German
bert
sequence-tagger-model
Inference Endpoints
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@@ -7,11 +7,31 @@ language: de
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  datasets:
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  - conll2003
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  - germeval_14
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- - europeananewspapers2016
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  license: apache-2.0
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  ---
 
 
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  This is a BERT model for named entity recognition in historical German.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- It predicts the classes `PER`, `LOC` and `ORG`.
 
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- For details, see [https://github.com/qurator-spk/sbb_ner](sbb_ner).
 
 
 
 
 
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  datasets:
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  - conll2003
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  - germeval_14
 
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  license: apache-2.0
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  ---
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+ # About `sbb_ner`
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+
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  This is a BERT model for named entity recognition in historical German.
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+ It can predict the classes `PER`, `LOC` and `ORG`.
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+
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+ The model is based on the 🤗 [Transformers](https://github.com/huggingface/transformers)
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+ `BERT Base multi-lingual cased` model.
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+ We applied unsupervised pre-training on 2,333,647 pages of
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+ unlabeled historical German text from the Berlin State Library
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+ digital collections, and supervised pre-training on two datasets
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+ with contemporary German text, [conll2003](https://huggingface.co/models?dataset=dataset:conll2003)
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+ and [germeval_14](https://huggingface.co/models?dataset=dataset:germeval_14).
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+
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+ # Results
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+
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+ In a 5-fold cross validation with different historical German NER corpora,
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+ the model obtained an F1-Score of **84.3**±1.1%.
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+ For details, see our [paper](https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_4.pdf)
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+ or have a look at [sbb_ner](https://github.com/qurator-spk/sbb_ner) on GitHub.
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+ # Weights
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+ We provide model weights for PyTorch.
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+ | Model | Downloads
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+ | ------------------------| ------------------------
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+ | `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)