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---
language: es
tags:
- biomedical
- clinical
- spanish
- xlm-roberta-large
license: mit
datasets:
- "ehealth_kd"
metrics:
- f1
model-index:
- name: IIC/xlm-roberta-large-ehealth_kd
results:
- task:
type: token-classification
dataset:
name: eHealth-KD
type: ehealth_kd
split: test
metrics:
- name: f1
type: f1
value: 0.871
pipeline_tag: token-classification
---
# xlm-roberta-large-ehealth_kd
This model is a finetuned version of xlm-roberta-large for the eHealth-KD dataset used in a benchmark in the paper TODO. The model has a F1 of 0.871
Please refer to the original publication for more information TODO LINK
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 16 |
| learning rate | 2e-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
```
|