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Update README.md

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@@ -44,7 +44,7 @@ This model is implemented as part of the KeyCARE library. Install first the keyc
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  python -m pip install keycare
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  ```
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- You can then run the KeyCARE pipeline that uses the SetFit model:
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  ```python
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  from keycare install RelExtractor.RelExtractor
@@ -55,12 +55,12 @@ relextractor = RelExtractor()
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  source = ["cáncer", "enfermedad de pulmón", "mastectomía radical izquierda", "laparoscopia"]
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  target = ["cáncer de mama", "enfermedad pulmonar", "mastectomía", "Streptococus pneumoniae"]
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  relextractor(source, target)
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- # You can also access the class storing the SetFit model
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  relator = relextractor.relation_method
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  ```
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  ## Training
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- The model has been trained using the hirerarchical structure of [SNOMED-CT](https://www.snomed.org/) mapped to the medical terms present in [UMLS](https://www.nlm.nih.gov/research/umls/index.html).
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  ## Evaluation
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  To be published
 
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  python -m pip install keycare
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  ```
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+ You can then run the KeyCARE pipeline that uses the Transformer model:
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  ```python
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  from keycare install RelExtractor.RelExtractor
 
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  source = ["cáncer", "enfermedad de pulmón", "mastectomía radical izquierda", "laparoscopia"]
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  target = ["cáncer de mama", "enfermedad pulmonar", "mastectomía", "Streptococus pneumoniae"]
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  relextractor(source, target)
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+ # You can also access the class storing the Transformer model
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  relator = relextractor.relation_method
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  ```
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  ## Training
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+ The used pre-trained model is SapBERT-from-roberta-base-biomedical-clinical-es from the BSC-NLP4BIA reserch group. The model has been trained using the hirerarchical structure of [SNOMED-CT](https://www.snomed.org/) mapped to the medical terms present in [UMLS](https://www.nlm.nih.gov/research/umls/index.html).
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  ## Evaluation
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  To be published