metadata

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 A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks. The model has a F1 of 0.871

Please refer to the original publication for more information.

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

@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},
}