Hugging Face's logo

IIC
/
bert-base-spanish-wwm-cased-cantemist

Text Classification
Transformers
PyTorch
Spanish
bert
biomedical
clinical
eHR
spanish
bert-base-spanish-wwm-cased
Eval Results
Inference Endpoints
Model card Files Community
bert-base-spanish-wwm-cased-cantemist
File size: 2,444 Bytes 
166d9c0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8bcf535
166d9c0
8bcf535
166d9c0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8bcf535
 
 
 
 
 
 
 
 
 
 
 
 
 
166d9c0
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
 
---
language: es
tags:
- biomedical
- clinical
- eHR
- spanish
- bert-base-spanish-wwm-cased
license: cc-by-4.0
datasets:
- "PlanTL-GOB-ES/cantemist-ner"
metrics:
- f1

model-index:
- name: IIC/bert-base-spanish-wwm-cased-cantemist
  results:
  - task:
      type: token-classification
    dataset:
      name: cantemist-ner
      type: PlanTL-GOB-ES/cantemist-ner
    metrics:
      - name: f1
        type: f1
        value: 0.898
widget:
- text: "El diagnóstico definitivo de nuestro paciente fue de un Adenocarcinoma de pulmón cT2a cN3 cM1a Estadio IV (por una única lesión pulmonar contralateral) PD-L1 90%, EGFR negativo, ALK negativo y ROS-1 negativo."
- text: "Durante el ingreso se realiza una TC, observándose un nódulo pulmonar en el LII y una masa renal derecha indeterminada. Se realiza punción biopsia del nódulo pulmonar, con hallazgos altamente sospechosos de carcinoma."
- text: "Trombosis paraneoplásica con sospecha de hepatocarcinoma por imagen, sobre hígado cirrótico, en paciente con índice Child-Pugh B."
---


# bert-base-spanish-wwm-cased-cantemist

This model is a finetuned version of bert-base-spanish-wwm-cased for the cantemist 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 0.898

Please refer to the [original publication](https://doi.org/10.1093/jamia/ocae054) for more information.

## Parameters used

| parameter               | Value |
|-------------------------|:-----:|
| batch size              |  64   |
| learning rate           | 4e05  |
| 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

```bibtext
@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},
}
```