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
overlap?
#1
by nathansutton - opened
@pchambon This looks interesting and I'd love to collaborate on an open-source, abstractive summarization benchmark I'm working on with the multi-modal radiology reports in the MIMIC critical care database.
Is this related to the work at the VA with Yan, An, et al. "RadBERT: Adapting transformer-based language models to radiology." Radiology: Artificial Intelligence 4.4 (2022): e210258.?
This is related to the work
@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}
}
Happy to collaborate and discuss more :)