license: apache-2.0
language:
- es
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- bert
- biomedical
- lexical semantics
- bionlp
Biomedical term classifier with SetFit in Spanish
Table of contents
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Model description
This is a SetFit model trained for multilabel biomedical text classification in Spanish.
Intended uses and limitations
The model is prepared to classify medical entities among 21 classes, including diseases, medical procedures, symptoms, and drugs, among others. It still lacks some classes like body structures.
How to use
This model is implemented as part of the KeyCARE library. Install first the keycare module to call the SetFit classifier:
python -m pip install keycare
You can then run the KeyCARE pipeline that uses the SetFit model:
from keycare install TermExtractor.TermExtractor
# initialize the termextractor object
termextractor = TermExtractor()
# Run the pipeline
text = """Acude al Servicio de Urgencias por cefalea frontoparietal derecha.
Mediante biopsia se diagnostica adenocarcinoma de pr贸stata Gleason 4+4=8 con met谩stasis 贸seas m煤ltiples.
Se trata con 脕cido Zoledr贸nico 4 mg iv/4 semanas.
"""
termextractor(text)
# You can also access the class storing the SetFit model
categorizer = termextractor.categorizer
Training
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning. The used pre-trained model is SapBERT-from-roberta-base-biomedical-clinical-es from the BSC-NLP4BIA reserch group.
- Training a classification head with features from the fine-tuned Sentence Transformer.
The training data has been obtained from NER Gold Standard Corpora also generated by BSC-NLP4BIA, including MedProcNER, DISTEMIST, SympTEMIST, CANTEMIST, and PharmaCoNER, among others.
Evaluation
To be published
Additional information
Author
NLP4BIA at the Barcelona Supercomputing Center
Licensing information
Citation information
To be published
Disclaimer
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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.