transformers pytorch deberta-v2 text-classification biomedical clinical spanish mdeberta-v3-base token-classification es dataset:PlanTL-GOB-ES/pharmaconer license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- PlanTL-GOB-ES/pharmaconer
language: es
license: mit
metrics:
- f1
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- spanish
- mdeberta-v3-base
widget:
- text: Se realizó estudio analítico destacando incremento de niveles de PTH y vitamina
    D (103,7 pg/ml y 272 ng/ml, respectivamente), atribuidos al exceso de suplementación
    de vitamina D.
- text: ' Por el hallazgo de múltiples fracturas por estrés, se procedió a estudio
    en nuestras consultas, realizándose análisis con función renal, calcio sérico
    y urinario, calcio iónico, magnesio y PTH, que fueron normales.'
- text: Se solicitó una analítica que incluía hemograma, bioquímica, anticuerpos antinucleares
    (ANA) y serologías, examen de orina, así como biopsia de la lesión. Los resultados
    fueron normales, con ANA, anti-Sm, anti-RNP, anti-SSA, anti-SSB, anti-Jo1 y anti-Scl70
    negativos.
model-index:
- name: IIC/mdeberta-v3-base-pharmaconer
  results:
  - task:
      type: token-classification
    dataset:
      name: pharmaconer
      type: PlanTL-GOB-ES/pharmaconer
      split: test
    metrics:
    - type: f1
      value: 0.922
      name: f1
---

# mdeberta-v3-base-pharmaconer

This model is a finetuned version of mdeberta-v3-base for the pharmaconer dataset used in a benchmark in the paper `A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks`. The model has a F1 of 0.922

Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information.

## Parameters used

| parameter               | Value |
|-------------------------|:-----:|
| batch size              |  32   |
| learning rate           | 1e-05  |
| classifier dropout      |  0   |
| warmup ratio            |   0   |
| warmup steps            |   0   |
| weight decay            |   0   |
| optimizer               | AdamW |
| epochs                  |   10  |
| early stopping patience |   3   |


## BibTeX entry and citation info

```bibtext
@article{10.1093/jamia/ocae054,
    author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
    title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
    journal = {Journal of the American Medical Informatics Association},
    volume = {31},
    number = {9},
    pages = {2137-2146},
    year = {2024},
    month = {03},
    issn = {1527-974X},
    doi = {10.1093/jamia/ocae054},
    url = {https://doi.org/10.1093/jamia/ocae054},
}
```

