Token Classification
Transformers
Safetensors
English
bert
ner
clinical
medical
healthcare
biomedical
bio-clinicalbert
named-entity-recognition
procedure
surgery
operative
cpt
ehr
hipaa
Eval Results (legacy)
Instructions to use genzeonplatform/healthcare-brain-procedure-surgery-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genzeonplatform/healthcare-brain-procedure-surgery-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="genzeonplatform/healthcare-brain-procedure-surgery-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("genzeonplatform/healthcare-brain-procedure-surgery-ner") model = AutoModelForTokenClassification.from_pretrained("genzeonplatform/healthcare-brain-procedure-surgery-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f547696b6291364f64d627a45a115494a3a8d5ed6d77af5772716dc1f0c2b7b
- Size of remote file:
- 431 MB
- SHA256:
- fd07016ba8b79a9a017263e738be167b1f322bc5e11661323291ae4e172cd72e
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