---
tags:
- spacy
- token-classification
- named-entity-recognition
- medical-ner
library_name: spacy
pipeline_tag: token-classification
language: en
license: mit
---

# Medical NER Model - medical-ner-task3

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
```python
import spacy
nlp = spacy.load("nitinyadav/continual_learning_ner_task3")
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.