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