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 this repository.
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 here 🤗
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 🤗