Instructions to use jhliu/ClinicalNoteBERT-base-simcse_note with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhliu/ClinicalNoteBERT-base-simcse_note with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jhliu/ClinicalNoteBERT-base-simcse_note", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jhliu/ClinicalNoteBERT-base-simcse_note: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/jhliu/ClinicalNoteBERT-base-simcse_note/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jhliu/ClinicalNoteBERT-base-simcse_note/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jhliu/ClinicalNoteBERT-base-simcse_note/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- edf1048852a9ce10db7c090b3f1ee868220b16eca7253b42b3dea152be335570
- Size of remote file:
- 440 MB
- SHA256:
- 7df88e30d300416a121b1d121fe963b11079f3b76074a390287cec3990cfd2e6
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