metadata
library_name: transformers
tags:
- medical
license: mit
language:
- en
base_model:
- ShahRishi/OphthaBERT-v2
pipeline_tag: text-classification
OphtaBERT Glaucoma Classifier
Binary classification for glaucoma diagnosis extraction from unstructured clinical notes.
Model Details
Model Description
This model is a fine-tuned variant of OphthaBERT, which was pretrained on over 2 million clinical notes. It has been fine-tuned for binary classification on labeled clinical notes from Massachusetts Eye and Ear Infirmary.
- Finetuned from model: [OphthaBERT-v2]
Uses
We suggest utilizing this model in a zero-shot manner to generate binary glaucoma labels for each clinical note. For continued training on limited data, we recommend freezing the first 10 layers of the model.
Direct Use
Use the code below to get started with the model:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load the fine-tuned model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("ShahRishi/OphthaBERT-v2-glaucoma-binary")
tokenizer = AutoTokenizer.from_pretrained("ShahRishi/OphthaBERT-v2")
# Example: Classify a clinical note
clinical_note = "Example clinical note text..."
inputs = tokenizer(clinical_note, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)