| 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. | |