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---
license: mit
language:
- en
tags:
- medical
- radiology
model-index:
- name: rate-ner-rad
results: []
pipeline_tag: token-classification
---
# RaTE-NER-Deberta
This model is a fine-tuned version of [DeBERTa](https://huggingface.co./microsoft/deberta-v3-base) 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](https://angelakeke.github.io/RaTEScore/).
This model also can be used to extract **Abnormality, Non-Abnormality, Anatomy, Disease, Non-Disease**
in medical radiology reports.
## Usage
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Angelakeke/RaTE-NER")
model = AutoModelForTokenClassification.from_pretrained("Angelakeke/RaTE-NER")
```
## Author
Author: [Weike Zhao](https://angelakeke.github.io/)
If you have any questions, please feel free to contact [email protected].
## Citation
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```
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