---
language: es
tags:
- biomedical
- clinical
- spanish
- mdeberta-v3-base
license: mit
datasets:
- "ehealth_kd"
metrics:
- f1

model-index:
- name: IIC/mdeberta-v3-base-ehealth_kd
  results:
  - task:
      type: token-classification
    dataset:
      name: eHealth-KD
      type: ehealth_kd
      split: test
    metrics:
      - name: f1
        type: f1
        value: 0.844
pipeline_tag: token-classification

---

# mdeberta-v3-base-ehealth_kd

This model is a finetuned version of mdeberta-v3-base for the eHealth-KD dataset used in a benchmark in the paper TODO. The model has a F1 of 0.844

Please refer to the original publication for more information TODO LINK

## Parameters used

| parameter               | Value |
|-------------------------|:-----:|
| batch size              |  32   |
| learning rate           | 3e-05  |
| classifier dropout      |  0.2   |
| warmup ratio            |   0   |
| warmup steps            |   0   |
| weight decay            |   0   |
| optimizer               | AdamW |
| epochs                  |   10  |
| early stopping patience |   3   |


## BibTeX entry and citation info

```bibtex
TODO
```