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A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks. The model has a F1 of 0.906| parameter | Value |
|---|---|
| batch size | 64 |
| learning rate | 2e-05 |
| classifier dropout | 0.1 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
1@article{10.1093/jamia/ocae054,
2 author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
3 title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
4 journal = {Journal of the American Medical Informatics Association},
5 volume = {31},
6 number = {9},
7 pages = {2137-2146},
8 year = {2024},
9 month = {03},
10 issn = {1527-974X},
11 doi = {10.1093/jamia/ocae054},
12 url = {https://doi.org/10.1093/jamia/ocae054},
13}