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README.md
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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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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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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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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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# Results
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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)
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