Label	Description
`B-problem` / `I-problem`	Clinical problem or diagnosis
`B-treatment` / `I-treatment`	Treatment or medication
`B-test` / `I-test`	Clinical test or procedure
`O`	Outside any entity

Usage

Architecture: BertForTokenClassification (bert-base-uncased)
Task: Named Entity Recognition (NER) on clinical text
Format: ONNX
Max sequence length: 512

python

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("sidupadhyay/bert-base-uncased_clinical-ner-ONNX")
model = ORTModelForTokenClassification.from_pretrained("sidupadhyay/bert-base-uncased_clinical-ner-ONNX")

inputs = tokenizer("The patient was prescribed aspirin for chest pain.", return_tensors="pt")
outputs = model(**inputs)

Original Model

For more details on training data, evaluation, and intended use, see the original model card: samrawal/bert-base-uncased_clinical-ner.