This is a distilroberta-base model fined tuned to classify text into 3 categories:
Rare Diseases
Non-Rare Diseases
Other
The details of how this model was built and evaluated are provided in the article:
Rei L, Pita Costa J, Zdolšek Draksler T. Automatic Classification and Visualization of Text Data on Rare Diseases. Journal of Personalized Medicine. 2024; 14(5):545. https://doi.org/10.3390/jpm14050545
@Article{jpm14050545,
AUTHOR = {Rei, Luis and Pita Costa, Joao and Zdolšek Draksler, Tanja},
TITLE = {Automatic Classification and Visualization of Text Data on Rare Diseases},
JOURNAL = {Journal of Personalized Medicine},
VOLUME = {14},
YEAR = {2024},
NUMBER = {5},
ARTICLE-NUMBER = {545},
URL = {https://www.mdpi.com/2075-4426/14/5/545},
PubMedID = {38793127},
ISSN = {2075-4426},
DOI = {10.3390/jpm14050545}
}
Note that the in the article the larger roberta-base model is fine-tuned instead. This is a smaller model. This model is shared for demonstration and validation purposes. Hyper-parameters were not tuned.
Using this model
Simplest way to use this model is via a huggingface transformers' pipeline.
python
1# Use a pipeline as a high-level helper2from transformers import pipeline
34pipe = pipeline("text-classification", model="lrei/rad-small")56# Simple high-level usage7pipe(["The patient suffer from a complex genetic disorder.","The patient suffers from a common genetic disorder."])
Dataset
The dataset used to train this model is available on zenodo.
It is a subset of abstracts obtained from PubMed and sorted into the 3 classes on the basis of their MeSH terms.
Like the model, the dataset is provided for demonstration and methodology validation purposes. The original PubMed data was randomly under-sampled.
Code
The code used to create this model is available on Github.
