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bert-base-uncased_clinical-ner-ONNX
sidupadhyay/bert-base-uncased_clinical-ner-ONNX
bert-base-uncased_clinical-ner-ONNX is a token classification model from sidupadhyay. Use it when you need labels on individual words, such as names. It is set up for onnxruntime. The card lists the license as apache-2.0.
Available on Hugging Face: sidupadhyay/bert-base-uncasedclinical-ner-ONNX
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At a glance
- Task
- Token Classification
- Library
- onnxruntime
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Mar 1, 2026
- Updated
- Mar 1, 2026
- SHA
- caf42392
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Base models
Model card
From the Hugging Face model README
Clinical NER (ONNX)
Available on Hugging Face: sidupadhyay/bert-base-uncased_clinical-ner-ONNX
This is an ONNX export of samrawal/bert-base-uncased_clinical-ner.
All credit for model training and development goes to the original author, samrawal.
Model Details
- Architecture: BertForTokenClassification (bert-base-uncased)
- Task: Named Entity Recognition (NER) on clinical text
- Format: ONNX
- Max sequence length: 512
Labels
| 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
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.
- Task
- Token Classification
- Library
- onnxruntime
- Type
- bert
- License
- apache-2.0
- Created
- Mar 1, 2026
- Updated
- Mar 1, 2026