Instructions to use dbmdz/bert-base-german-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-base-german-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-base-german-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-uncased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-base-german-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Request: DOI
#4
by dkbirkenberger - opened
Hi,
I would like to cite this model in my academic work. Is there a DOI available, or could you generate one for citation purposes?
Thanks!
Hey @dkbirkenberger ,
thanks for using our model! Other papers were citing our GitHub repo at https://github.com/dbmdz/berts , but as there's no research paper from us, a DOI is also a great way to ensure reproducibilty.
It will come in a few minutes!
DOI is here:
@misc {bayerische_staatsbibliothek_2025,
author = { {Bayerische Staatsbibliothek} },
title = { bert-base-german-uncased (Revision b705f0e) },
year = 2025,
url = { https://huggingface.co/dbmdz/bert-base-german-uncased },
doi = { 10.57967/hf/4378 },
publisher = { Hugging Face }
}
:)