Joaquín Silveira-Ocampo


Pretrained Biomedical Language Models for Clinical NLP in Spanish
Casimiro Pio Carrino | Joan Llop | Marc Pàmies | Asier Gutiérrez-Fandiño | Jordi Armengol-Estapé | Joaquín Silveira-Ocampo | Alfonso Valencia | Aitor Gonzalez-Agirre | Marta Villegas
Proceedings of the 21st Workshop on Biomedical Language Processing

This work presents the first large-scale biomedical Spanish language models trained from scratch, using large biomedical corpora consisting of a total of 1.1B tokens and an EHR corpus of 95M tokens. We compared them against general-domain and other domain-specific models for Spanish on three clinical NER tasks. As main results, our models are superior across the NER tasks, rendering them more convenient for clinical NLP applications. Furthermore, our findings indicate that when enough data is available, pre-training from scratch is better than continual pre-training when tested on clinical tasks, raising an exciting research question about which approach is optimal. Our models and fine-tuning scripts are publicly available at HuggingFace and GitHub.