transformers pytorch bert text-classification biomedical clinical spanish bert-base-spanish-wwm-cased token-classification es dataset:bigbio/meddocan license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- bigbio/meddocan
language: es
license: cc-by-4.0
metrics:
- f1
pipeline_tag: token-classification
tags:
- biomedical
- clinical
- spanish
- bert-base-spanish-wwm-cased
model-index:
- name: IIC/bert-base-spanish-wwm-cased-meddocan
  results:
  - task:
      type: token-classification
    dataset:
      name: meddocan
      type: bigbio/meddocan
      split: test
    metrics:
    - type: f1
      value: 0.957
      name: f1
---

# bert-base-spanish-wwm-cased-meddocan

This model is a finetuned version of bert-base-spanish-wwm-cased for the meddocan dataset used in a benchmark in the paper TODO. The model has a F1 of 0.957

Please refer to the original publication for more information TODO LINK

## Parameters used

| parameter               | Value |
|-------------------------|:-----:|
| batch size              |  16   |
| learning rate           | 3e-05  |
| classifier dropout      |  0.1   |
| warmup ratio            |   0   |
| warmup steps            |   0   |
| weight decay            |   0   |
| optimizer               | AdamW |
| epochs                  |   10  |
| early stopping patience |   3   |


## BibTeX entry and citation info

```bibtex
TODO
```

