Instructions to use dbmdz/bert-base-german-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-base-german-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-base-german-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-base-german-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dbmdz/bert-base-german-cased: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/dbmdz/bert-base-german-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dbmdz/bert-base-german-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dbmdz/bert-base-german-cased/resolve/main/pytorch_model.bin
442 MB
- Xet hash:
- 26bcc57f7c50c3c232e211a12424b8586372b223e34ab8bc84e42fa087160303
- Size of remote file:
- 442 MB
- SHA256:
- e482d0e2a09cef20bed85c86d7df9d2f1f5d090e7e53c772ab94ced0e3eafee3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.