Instructions to use jhliu/ClinicalNoteBERT-small-simcse_note with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhliu/ClinicalNoteBERT-small-simcse_note with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jhliu/ClinicalNoteBERT-small-simcse_note", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jhliu/ClinicalNoteBERT-small-simcse_note: direct link, hf CLI and curl.
- Browser
- Download file 275 MB
-
https://huggingface.co/jhliu/ClinicalNoteBERT-small-simcse_note/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jhliu/ClinicalNoteBERT-small-simcse_note/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jhliu/ClinicalNoteBERT-small-simcse_note/resolve/main/pytorch_model.bin
275 MB
- Xet hash:
- 379bedafe0565f7c1a1920b263aad3e57d580b85834f5e6b6d281c5fc9cd627e
- Size of remote file:
- 275 MB
- SHA256:
- 6f19d31d19dcdf7261fe1aa0d288039ec3ba26f10c5fe18c2f9e008657be5ab7
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