Instructions to use jhliu/ClinicalNoteBERT-tiny-simcse_segment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhliu/ClinicalNoteBERT-tiny-simcse_segment with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jhliu/ClinicalNoteBERT-tiny-simcse_segment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jhliu/ClinicalNoteBERT-tiny-simcse_segment: direct link, hf CLI and curl.
- Browser
- Download file 59.8 MB
-
https://huggingface.co/jhliu/ClinicalNoteBERT-tiny-simcse_segment/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jhliu/ClinicalNoteBERT-tiny-simcse_segment/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jhliu/ClinicalNoteBERT-tiny-simcse_segment/resolve/main/pytorch_model.bin
59.8 MB
- Xet hash:
- f6e5e2bae5bf6e93bf49494f2b93ca62e2cb6325017b83515c85ddbb0d804eb8
- Size of remote file:
- 59.8 MB
- SHA256:
- c2beada33ae4b70cbdc72299ea728f843cb7a2999de4d228d389124c3c71abda
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