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 flax_model.msgpack from dbmdz/bert-base-german-cased: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/dbmdz/bert-base-german-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://dbmdz/bert-base-german-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/dbmdz/bert-base-german-cased/resolve/main/flax_model.msgpack
440 MB
- Xet hash:
- b2c02dc75dc46ec29fd807ded0f39a5647d6312618a87ba007e658965da3cd44
- Size of remote file:
- 440 MB
- SHA256:
- bf5898f31cbcfb7403182d919e188039bcecef7dfc242a5afe5a650712160d83
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