README.md

---
language: es
tags:
- biomedical
- clinical
- spanish
- roberta-large-bne
license: apache-2.0
datasets:
- "chizhikchi/CARES"
metrics:
- f1

model-index:
- name: IIC/roberta-large-bne-caresA
  results:
  - task:
      type: multi-label-classification
    dataset:
      name: Cares Area
      type: chizhikchi/CARES
      split: test
    metrics:
      - name: f1
        type: f1
        value: 0.992
pipeline_tag: text-classification

---

# roberta-large-bne-caresA

This model is a finetuned version of roberta-large-bne for the Cares Area 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 0.992

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           | 4e-05  |
| classifier dropout      |  0.2   |
| 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},
}
```