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")# pip install -U transformers accelerate # 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 flax_model.msgpack from smanjil/German-MedBERT: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/smanjil/German-MedBERT/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://smanjil/German-MedBERT/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/smanjil/German-MedBERT/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- e77a7fbc9a4edb356e0dbeceb67e923a84ed5e62aafdad0f1bfb5607663cf486
- Size of remote file:
- 436 MB
- SHA256:
- d519e7f5455f0094c8251f1488eefb6a1f2e3a6a8e54c43f5fbf40100061d358
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.