Instructions to use deepset/bert-base-german-cased-oldvocab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/bert-base-german-cased-oldvocab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepset/bert-base-german-cased-oldvocab")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("deepset/bert-base-german-cased-oldvocab") model = AutoModelForMaskedLM.from_pretrained("deepset/bert-base-german-cased-oldvocab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepset/bert-base-german-cased-oldvocab: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/deepset/bert-base-german-cased-oldvocab/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepset/bert-base-german-cased-oldvocab/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepset/bert-base-german-cased-oldvocab/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- 16f1cc8f5ab12ef081f9152e1e0d16720c6aa938d2624bd192902958c4793363
- Size of remote file:
- 439 MB
- SHA256:
- 56a21938415b06a68b870e4b1b3413cdd532ae6456fefef1ee5a852faf52f806
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