Fill-Mask
Transformers
PyTorch
English
bert
biobert
radbert
language-model
uncased
radiology
biomedical
Instructions to use StanfordAIMI/RadBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordAIMI/RadBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="StanfordAIMI/RadBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/RadBERT") model = AutoModelForMaskedLM.from_pretrained("StanfordAIMI/RadBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
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license: mit
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RadBERT was continuously pre-trained on radiology reports from a BioBERT initialization.
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license: mit
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---
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RadBERT was continuously pre-trained on radiology reports from a BioBERT initialization.
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## Citation
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```bibtex
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@article{chambon_cook_langlotz_2022,
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title={Improved fine-tuning of in-domain transformer model for inferring COVID-19 presence in multi-institutional radiology reports},
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DOI={10.1007/s10278-022-00714-8}, journal={Journal of Digital Imaging},
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author={Chambon, Pierre and Cook, Tessa S. and Langlotz, Curtis P.},
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year={2022}
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}
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```
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