Medical NER Model - medical-ner-combined
This model is trained to recognize medical entities including treatments, chronic diseases, cancers, and allergies.
Model Details
- Task: Named Entity Recognition
- Framework: spaCy
- Entity Types: TREATMENT, CHRONIC DISEASE, CANCER, ALLERGY, OTHER
Usage
import spacy
nlp = spacy.load("nitinyadav/continual_learning_ner_combined")
doc = nlp("Patient has been diagnosed with Type 2 Diabetes")
for ent in doc.ents:
print(ent.text, ent.label_)
Entity Labels
- TREATMENT: Medical treatments and procedures
- CHRONIC DISEASE: Long-term medical conditions
- CANCER: Cancer-related conditions
- ALLERGY: Allergic conditions
- OTHER: Other medical entities
Training Data
This model was trained on medical text data with annotated entities.
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!pip install https://huggingface.co/nitinyadav/continual_learning_ner_combined/resolve/main/continual_learning_ner_combined-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("continual_learning_ner_combined") # Importing as module. import continual_learning_ner_combined nlp = continual_learning_ner_combined.load()