
RadBERT was continuously pre-trained on radiology reports from a BioBERT initialization. 

## Citation

```bibtex
@article{chambon_cook_langlotz_2022, 
  title={Improved fine-tuning of in-domain transformer model for inferring COVID-19 presence in multi-institutional radiology reports}, 
  DOI={10.1007/s10278-022-00714-8}, journal={Journal of Digital Imaging}, 
  author={Chambon, Pierre and Cook, Tessa S. and Langlotz, Curtis P.}, 
  year={2022}
} 
```
{"language": ["en"], "license": "mit", "tags": ["fill-mask", "pytorch", "transformers", "bert", "biobert", "radbert", "language-model", "uncased", "radiology", "biomedical"], "datasets": ["wikipedia", "bookscorpus", "pubmed", "radreports"], "widget": [{"text": "low lung volumes, [MASK] pulmonary vascularity."}]}
transformers pytorch bert fill-mask biobert radbert language-model uncased radiology biomedical en dataset:wikipedia dataset:bookscorpus dataset:pubmed dataset:radreports license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #biobert #radbert #language-model #uncased #radiology #biomedical #en #dataset-wikipedia #dataset-bookscorpus #dataset-pubmed #dataset-radreports #license-mit #autotrain_compatible #endpoints_compatible #region-us 


RadBERT was continuously pre-trained on radiology reports from a BioBERT initialization. 


TAGS
#transformers #pytorch #bert #fill-mask #biobert #radbert #language-model #uncased #radiology #biomedical #en #dataset-wikipedia #dataset-bookscorpus #dataset-pubmed #dataset-radreports #license-mit #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #fill-mask #biobert #radbert #language-model #uncased #radiology #biomedical #en #dataset-wikipedia #dataset-bookscorpus #dataset-pubmed #dataset-radreports #license-mit #autotrain_compatible #endpoints_compatible #region-us 
