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Shenkezs/
nbme-deberta-v3-finetuned

Name: nbme-deberta-v3-finetuned
Availability: InStock

microsoft/deberta-v3-base

finetune

apache-2.0

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NBME DeBERTa v3 Fine-tuned Model

language: en license: apache-2.0 tags:

deberta
fine-tuning
nbme
medical
token-classification datasets:
nbme-dataset model_name: nbme-deberta-v3-finetuned widget:
text: "Patient reports chest pain and shortness of breath after medication."

This model is a fine-tuned version of microsoft/deberta-v3-base for the NBME clinical note scoring task.
It was trained on a subset of the NBME dataset to identify and classify relevant spans in patient notes.

🧠 Model Details

Base Model: microsoft/deberta-v3-base
Framework: PyTorch + Transformers
Task: Token classification (NER-style scoring)
Loss Function: BCEWithLogitsLoss
Optimizer: AdamW
Epochs: 5
Batch size: hyperparameters['batch_size']
Learning rate: hyperparameters['lr']

📊 Training Information

Training Dataset: NBME Clinical Notes Dataset
Train/Validation Split: 80/20
Validation Loss: ~0.0965
F1 Score: ~0.82

📁 Files

File	Description
`nbme_full_model.pth`	Full fine-tuned model weights
`nbme_full_model.pkl`	Pickle version for Python serialization
`nbme_bert_v2.pth`	Early checkpoint version
`README.md`	Model card file

🚀 How to Use

python

from transformers import AutoTokenizer
import torch
from your_model_file import CustomModel

tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-base")
model = CustomModel(hyperparameters)
model.load_state_dict(torch.load("nbme_full_model.pth", map_location="cpu"))
model.eval()

text = "Patient reports severe headache after taking medication."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
preds = torch.sigmoid(outputs)
print(preds)

Shenkezs/nbme-deberta-v3-finetuned