README.md

---
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
<details>
<summary>Click to expand</summary>

- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Training](#training)
- [Evaluation](#evaluation)
- [Additional information](#additional-information)
  - [Author](#author)
  - [Licensing information](#licensing-information)
  - [Citation information](#citation-information)
  - [Disclaimer](#disclaimer)

</details>

## Model description
This is a [SetFit model](https://github.com/huggingface/setfit) 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:

```bash
python -m pip install keycare
```

You can then run the KeyCARE pipeline that uses the SetFit model:

```python
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 próstata Gleason 4+4=8 con metástasis óseas múltiples.
Se trata con Ácido Zoledrónico 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:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. The used pre-trained model is SapBERT-from-roberta-base-biomedical-clinical-es from the BSC-NLP4BIA reserch group.
2. 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](https://temu.bsc.es/medprocner/), [DISTEMIST](https://temu.bsc.es/distemist/), [SympTEMIST](https://temu.bsc.es/symptemist/), [CANTEMIST](https://temu.bsc.es/cantemist/), and [PharmaCoNER](https://temu.bsc.es/pharmaconer/), among others.

## Evaluation
To be published

## Additional information

### Author
NLP4BIA at the Barcelona Supercomputing Center

### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Citation information
To be published

### Disclaimer
<details>
<summary>Click to expand</summary>

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.

</details>