🤗 + 📚 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 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.

Model weights

Currently only PyTorch-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` • `pytorch_model.bin` • `vocab.txt`

Results

For results on Historic NER, please refer to this repository.

Usage

With Transformers >= 2.3 our German Europeana BERT models can be loaded like:

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.

Contact (Bugs, Feedback, Contribution and more)

For questions about our BERT models just open an issue here 🤗

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 team, it is possible to download both cased and uncased models from their S3 storage 🤗