Fill-Mask
Transformers
PyTorch
English
bert
biobert
radbert
language-model
uncased
radiology
biomedical
Instructions to use StanfordAIMI/RadBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordAIMI/RadBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="StanfordAIMI/RadBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/RadBERT") model = AutoModelForMaskedLM.from_pretrained("StanfordAIMI/RadBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from StanfordAIMI/RadBERT: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/StanfordAIMI/RadBERT/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://StanfordAIMI/RadBERT@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/StanfordAIMI/RadBERT/resolve/refs%2Fpr%2F2/pytorch_model.bin
436 MB
- Xet hash:
- 058f498f2962ec88d8437a0dff8eb7eed39b36c5024606e07ab2643b8cebca5a
- Size of remote file:
- 436 MB
- SHA256:
- 8141cb71657b0d527cf6d9a1509e680d3dcc45544fba3bdf64876a4ce539632e
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