---
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 zwk0629@sjtu.edu.cn.

## Citation
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```