NER of medications
This model aims to demonstrate an extraction of entities from from medical texts. It gets the name of the doctor, his registration code (CRM), the substance and the dose
prescribed in Pt_BR.
Model Description
- Developed by: [Nilton Seixas]
- Language(s) (NLP): [Brazilian portuguese]
- License: [More Information Needed]
- Finetuned from model [optional]: [neuralmind/bert-large-portuguese-cased]
Model Sources [optional]
- Repository: [niltonseixas/NER]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("niltonseixas/NER_tokenizer")
model = AutoModelForTokenClassification.from_pretrained("niltonseixas/NER")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy = "average")
example = "dra. Nayara Barbosa, CRM 12345 receitou Amoxilina 50 mg"
ner_results = nlp(example)
print(ner_results)
