This is a Transformer's
AutoModelForSequenceClassification trained for biomedical text pairs classification in Spanish.
The model is prepared to classify hierarchical relations among medical terms. This includes the following types of relations: BROAD, EXACT, NARROW, NO_RELATION.
This model is implemented as part of the KeyCARE library. Install first the keycare module to call the Transformer classifier:
1from keycare install RelExtractor.RelExtractor
2
3# initialize the termextractor object
4relextractor = RelExtractor()
5# Run the pipeline
6source = ["cáncer", "enfermedad de pulmón", "mastectomía radical izquierda", "laparoscopia"]
7target = ["cáncer de mama", "enfermedad pulmonar", "mastectomía", "Streptococus pneumoniae"]
8relextractor(source, target)
9# You can also access the class storing the Transformer model
10relator = relextractor.relation_method
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 mapped to the medical terms present in
UMLS.
Click to expand
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
