README.md

metadata

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

TODO