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README.md

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 license: apache-2.0
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+## RadBERT-RoBERTa-4m
+
+This is a base model of Radiology-BERT models from UC San Diego and VA healthcare system. It is initialized from BERT-base-uncased and further trained with 2 million radiology reports deidentified from US VA hospital. The model achieves stronger medical language understanding performance than previous medical domain models such as BioBERT, Clinical-BERT, BLUE-BERT and BioMed-RoBERTa. 
+
+Performances are evaluated on three tasks: 
+(a) abnormal sentence classification: sentence classification in radiology reports as reporting abnormal or normal findings; 
+(b) report coding: Assign a diagnostic code to a given radiology report for five different coding systems; 
+(c) report summarization: given the findings section of a radiology report, extractively select key sentences that summarized the findings.
+
+It also shows superior performance on other radiology NLP tasks which are not reported in the paper.
+
+For details, check out the paper here: 
+[RadBERT: Adapting transformer-based language models to radiology](https://pubs.rsna.org/doi/abs/10.1148/ryai.210258)
+
+### How to use
+
+Here is an example of how to use this model to extract the features of a given text in PyTorch:
+
+```python
+from transformers import AutoConfig, AutoTokenizer, AutoModel
+config = AutoConfig.from_pretrained('zzxslp/RadBERT-RoBERTa-4m')
+tokenizer = AutoTokenizer.from_pretrained('zzxslp/RadBERT-RoBERTa-4m')
+model = AutoModel.from_pretrained('zzxslp/RadBERT-RoBERTa-4m', config=config)
+text = "Replace me by any medical text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+### BibTeX entry and citation info
+
+If you use the model, please cite our paper:
+
+```bibtex
+@article{yan2022radbert,
+  title={RadBERT: Adapting transformer-based language models to radiology},
+  author={Yan, An and McAuley, Julian and Lu, Xing and Du, Jiang and Chang, Eric Y and Gentili, Amilcare and Hsu, Chun-Nan},
+  journal={Radiology: Artificial Intelligence},
+  volume={4},
+  number={4},
+  pages={e210258},
+  year={2022},
+  publisher={Radiological Society of North America}
+}
+```