readme: ---
  language: de
  license: mit
  tags:
    - "historic german"
  ---
  
  # 🤗 + 📚 dbmdz BERT models
  
  In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
  Library open sources German Europeana BERT models 🎉
  
  # German Europeana BERT
  
  We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/)
  that were provided by *The European Library*. The final
  training corpus has a size of 51GB and consists of 8,035,986,369 tokens.
  
  Detailed information about the data and pretraining steps can be found in
  [this repository](https://github.com/stefan-it/europeana-bert).
  
  ## Model weights
  
  Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers)
  compatible weights are available. If you need access to TensorFlow checkpoints,
  please raise an issue!
  
  | Model                                      | Downloads
  | ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------
  | `dbmdz/bert-base-german-europeana-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-uncased/vocab.txt)
  
  ## Results
  
  For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert).
  
  ## Usage
  
  With Transformers >= 2.3 our German Europeana BERT models can be loaded like:
  
  ```python
  from transformers import AutoModel, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-europeana-uncased")
  model = AutoModel.from_pretrained("dbmdz/bert-base-german-europeana-uncased")
  ```
  
  # Huggingface model hub
  
  All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
  
  # Contact (Bugs, Feedback, Contribution and more)
  
  For questions about our BERT models just open an issue
  [here](https://github.com/dbmdz/berts/issues/new) 🤗
  
  # Acknowledgments
  
  Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
  Thanks for providing access to the TFRC ❤️
  
  Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
  it is possible to download both cased and uncased models from their S3 storage 🤗
  