How to use this model directly from the
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-uncased") model = AutoModelWithLMHead.from_pretrained("dbmdz/bert-base-german-uncased")
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources another German BERT models 🎉
The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with a size of 16GB and 2,350,234,427 tokens.
For sentence splitting, we use spacy. Our preprocessing steps (sentence piece model for vocab generation) follow those used for training SciBERT. The model is trained with an initial sequence length of 512 subwords and was performed for 1.5M steps.
This release includes both cased and uncased models.
Currently only PyTorch-Transformers compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue!
With Transformers >= 2.3 our German BERT models can be loaded like:
from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased")
For results on downstream tasks like NER or PoS tagging, please refer to this repository.
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 🤗