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--- |
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license: cc0-1.0 |
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base_model: |
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- distilbert/distilroberta-base |
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pipeline_tag: text-classification |
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library_name: transformers |
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--- |
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This is a [distilroberta-base](distilbert/distilroberta-base) model fined tuned to classify text into 3 categories: |
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- Rare Diseases |
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- Non-Rare Diseases |
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- Other |
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The details of how this model was built and evaluated are provided in the article: |
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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 |
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``` |
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@Article{jpm14050545, |
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AUTHOR = {Rei, Luis and Pita Costa, Joao and Zdolšek Draksler, Tanja}, |
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TITLE = {Automatic Classification and Visualization of Text Data on Rare Diseases}, |
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JOURNAL = {Journal of Personalized Medicine}, |
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VOLUME = {14}, |
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YEAR = {2024}, |
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NUMBER = {5}, |
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ARTICLE-NUMBER = {545}, |
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URL = {https://www.mdpi.com/2075-4426/14/5/545}, |
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PubMedID = {38793127}, |
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ISSN = {2075-4426}, |
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DOI = {10.3390/jpm14050545} |
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} |
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``` |
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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. |
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## Using this model |
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Simplest way to use this model is via a huggingface transformers' pipeline. |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("text-classification", model="lrei/rad-small") |
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# Simple high-level usage |
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pipe(["The patient suffer from a complex genetic disorder.", "The patient suffers from a common genetic disorder."]) |
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``` |
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## Dataset |
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The dataset used to train this model is available on [zenodo](https://zenodo.org/records/13882003). |
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It is a subset of abstracts obtained from PubMed and sorted into the 3 classes on the basis of their MeSH terms. |
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Like the model, the dataset is provided for demonstration and methodology validation purposes. The original PubMed data was randomly under-sampled. |
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## Code |
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The code used to create this model is available on [Github](https://github.com/lrei/rad). |
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## Test Results |
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Averaged over all 3 classes: |
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| average | precision | recall | F1 | |
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| ------- | --------- | ------ | ---- | |
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| micro | 0.84 | 0.84 | 0.84 | |
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| macro | 0.84 | 0.84 | 0.84 | |
