---
language: es
tags:
- biomedical
- clinical
- spanish
- xlm-roberta-large
license: mit
datasets:
- "chizhikchi/CARES"
metrics:
- f1

model-index:
- name: IIC/xlm-roberta-large-caresC
  results:
  - task:
      type: multi-label-classification
    dataset:
      name: Cares Chapters
      type: chizhikchi/CARES
      split: test
    metrics:
      - name: f1
        type: f1
        value: 0.847
pipeline_tag: text-classification

---

# xlm-roberta-large-caresC

This model is a finetuned version of xlm-roberta-large for the Cares Chapters dataset used in a benchmark in the paper TODO. The model has a F1 of 0.847

Please refer to the original publication for more information TODO LINK

## Parameters used

| parameter               | Value |
|-------------------------|:-----:|
| batch size              |  32   |
| learning rate           | 3e-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

```bibtex
TODO
```