Instructions to use pucpr/clinicalnerpt-diagnostic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pucpr/clinicalnerpt-diagnostic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pucpr/clinicalnerpt-diagnostic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pucpr/clinicalnerpt-diagnostic") model = AutoModelForTokenClassification.from_pretrained("pucpr/clinicalnerpt-diagnostic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pucpr/clinicalnerpt-diagnostic: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/pucpr/clinicalnerpt-diagnostic/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pucpr/clinicalnerpt-diagnostic/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pucpr/clinicalnerpt-diagnostic/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- c34e369bf55b2911c7de5a240f44f0e2eb765cceda3297edfae0d15a769308fd
- Size of remote file:
- 709 MB
- SHA256:
- 28236a126c351673c85b5625cd70aa540cfe20cff6a9c3949e27c8c94586519a
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