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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v8i3.7219</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Adapter Based on Pre-Trained Language Models for Classification of Medical Text</title><url>https://artdesignp.com/journal/JERA/8/3/10.26689/jera.v8i3.7219</url><author>LiQuan</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>8</volume><issue>3</issue><history><date date-type="pub"><published-time>2024-06-14</published-time></date></history><abstract>We present an approach to classify medical text at a sentence level automatically. Given the inherent complexity of medical text classification, we employ adapters based on pre-trained language models to extract information from medical text, facilitating more accurate classification while minimizing the number of trainable parameters. 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