Instructions to use smanjil/German-MedBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smanjil/German-MedBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="smanjil/German-MedBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("smanjil/German-MedBERT") model = AutoModelForMaskedLM.from_pretrained("smanjil/German-MedBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from smanjil/German-MedBERT: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/smanjil/German-MedBERT/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://smanjil/German-MedBERT@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/smanjil/German-MedBERT/resolve/refs%2Fpr%2F1/pytorch_model.bin
439 MB
- Xet hash:
- f02915c3c96ee937a30f0bfa1a50a3e491c0dd8f116622f462840f73029ed3bb
- Size of remote file:
- 439 MB
- SHA256:
- 892bc80ad26a650328fb2ff16e42407eee12218842766e5d01e3ac966a2f35cd
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