Instructions to use medical-ner-proj/albert-medical-ner-proj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use medical-ner-proj/albert-medical-ner-proj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="medical-ner-proj/albert-medical-ner-proj")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("medical-ner-proj/albert-medical-ner-proj") model = AutoModelForTokenClassification.from_pretrained("medical-ner-proj/albert-medical-ner-proj") - Notebooks
- Google Colab
- Kaggle
widget:
- example_title: "Example 1" text: "John Doe has a history of hypertension, which is well-controlled with medication. He has no history of allergies or surgeries. He is not currently taking any medication except for his blood pressure medication."
- example_title: "Example 2" text: "On physical examination, John Doe appears acutely ill. He has a temperature of 38.5°C and a heart rate of 105 beats per minute. His blood pressure is 140/90 mmHg, and his oxygen saturation is 90% on room air. His lungs have diminished breath sounds and wheezing. There is no cyanosis, and his heart sounds are normal."
- example_title: "Example 3" text: "Based on Mary Smith's symptoms and physical examination, she is suspected to have suffered a stroke, likely caused by hypertension. Her history of migraines may also be a contributing factor."
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