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continual_learning_ner_combined
nitinyadav/continual_learning_ner_combined
continual_learning_ner_combined is a token classification model from nitinyadav. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as mit.
This model is trained to recognize medical entities including treatments, chronic diseases, cancers, and allergies.
Access
Public
Updated Mar 4, 2025
Repo size
11.5 MB
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0
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At a glance
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Access
- Public
- Created
- Mar 4, 2025
- Updated
- Mar 4, 2025
- SHA
- d308d5b0
Try a prompt
Model card
From the Hugging Face model README
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.
- Task
- Token Classification
- Library
- spacy
- License
- mit
- Languages
- en
- Created
- Mar 4, 2025
- Updated
- Mar 4, 2025