How to use this model directly from the
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-europeana-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-german-europeana-cased")
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models 🎉
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
Currently only PyTorch-Transformers
compatible weights are available. If you need access to TensorFlow checkpoints,
please raise an issue!
For results on Historic NER, please refer to this repository.
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-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-german-europeana-cased")
All models are available on the Huggingface model hub.
For questions about our BERT models just open an issue
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 🤗