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