StanfordAIMI / SRR-BERT2BERT-RoBERTa-PM-M3

📝 Paper • 🤗 Hugging Face • 🧩 Github • 🪄 Project

🎬 Get Started

import torch
from transformers import EncoderDecoderModel, AutoTokenizer, AutoConfig

# step 1: Setup constant
model_name = "StanfordAIMI/SRR-BERT2BERT-RoBERTa-PM-M3"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# step 2: Load Processor and Model
model = EncoderDecoderModel.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, padding_side="right", use_fast=False)
model.config.decoder_start_token_id = tokenizer.cls_token_id
model.config.bos_token_id = tokenizer.cls_token_id
model.eval()

# step 3: Inference (example from MIMIC-CXR dataset)
input_text = "CHEST RADIOGRAPH PERFORMED ON ___  COMPARISON: Prior exam from ___.  CLINICAL HISTORY: Weakness, assess pneumonia.  FINDINGS: Frontal and lateral views of the chest were provided. Midline sternotomy wires are again noted. The heart is poorly assessed, though remains enlarged. There are at least small bilateral pleural effusions.  There may be mild interstitial edema. No pneumothorax. Bony structures are demineralized with kyphotic angulation in the lower T-spine again noted.  IMPRESSION: Limited exam with small bilateral effusions, cardiomegaly, and possible mild interstitial edema."
inputs = tokenizer(input_text, padding="max_length", truncation=True, max_length=512, return_tensors="pt")
inputs["attention_mask"] = inputs["input_ids"].ne(tokenizer.pad_token_id)  # Add attention mask
input_ids = inputs['input_ids'].to(device)
attention_mask=inputs["attention_mask"].to(device)
generated_ids = model.generate(
    input_ids, attention_mask=attention_mask, max_new_tokens=286, min_new_tokens= 120,decoder_start_token_id=model.config.decoder_start_token_id, num_beams=5, early_stopping=True, max_length=None
    )[0]
decoded = tokenizer.decode(generated_ids, skip_special_tokens=True)
print(decoded)

✏️ Citation

@article{structuring-2025,
  title={Structuring Radiology Reports: Challenging LLMs with Lightweight Models},
  author={Moll, Johannes and Fay, Louisa and Azhar, Asfandyar and Ostmeier, Sophie and Lueth, Tim and Gatidis, Sergios and Langlotz, Curtis and Delbrouck, Jean-Benoit},
  journal={arXiv preprint arXiv:2506.00200},
  url={https://arxiv.org/abs/2506.00200},
  year={2025}
}