The utilization of clinical reports for various secondary purposes, including
health research and treatment monitoring, is crucial for enhancing patient
care. Natural Language Processing (NLP) tools have emerged as valuable assets
for extracting and processing relevant information from these reports. However,
the availability of specialized language models for the clinical domain in
Spanish has been limited.
In this paper, we introduce EriBERTa, a bilingual domain-specific language
model pre-trained on extensive medical and clinical corpora. We demonstrate
that EriBERTa outperforms previous Spanish language models in the clinical
domain, showcasing its superior capabilities in understanding medical texts and
extracting meaningful information. Moreover, EriBERTa exhibits promising
transfer learning abilities, allowing for knowledge transfer from one language
to another. This aspect is particularly beneficial given the scarcity of
Spanish clinical data.

本文介绍了 EriBERTa，一种在大量医疗和临床语料库上进行预训练的双语领域特定语言模型，并展示了其在医学文本理解和信息提取方面的优越能力，表现出有望实现知识从一种语言到另一种语言的转移。