RaTE-NER-Deberta

This model is a fine-tuned version of DeBERTa on the RaTE-NER dataset.

Model description

This model is trained to serve the RaTEScore metric, if you are interested in our pipeline, please refer to our paper and Github.

This model also can be used to extract Abnormality, Non-Abnormality, Anatomy, Disease, Non-Disease in medical radiology reports.

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification

ner_labels = ['B-ABNORMALITY', 'I-ABNORMALITY', 'B-NON-ABNORMALITY', 'I-NON-ABNORMALITY', 'B-DISEASE', 'I-DISEASE', 'B-NON-DISEASE', 'I-NON-DISEASE', 'B-ANATOMY', 'I-ANATOMY', 'O']

tokenizer = AutoTokenizer.from_pretrained("Angelakeke/RaTE-NER-Deberta")
model = AutoModelForTokenClassification.from_pretrained("Angelakeke/RaTE-NER-Deberta")

Author

Author: Weike Zhao

If you have any questions, please feel free to contact zwk0629@sjtu.edu.cn.

Citation

@article{zhao2024ratescore,
  title={RaTEScore: A Metric for Radiology Report Generation},
  author={Zhao, Weike and Wu, Chaoyi and Zhang, Xiaoman and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  journal={arXiv preprint arXiv:2406.16845},
  year={2024}
}