{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/electramed-a-new-pre-trained-language","title":"ELECTRAMed: a new pre-trained language representation model for biomedical NLP","arxiv_id":"2104.09585","date":"2021-04-19","proceeding":null,"authors":["Giacomo Miolo","Giulio Mantoan","Carlotta Orsenigo"],"abstract":"The overwhelming amount of biomedical scientific texts calls for the development of effective language models able to tackle a wide range of biomedical natural language processing (NLP) tasks. The most recent dominant approaches are domain-specific models, initialized with general-domain textual data and then trained on a variety of scientific corpora. However, it has been observed that for specialized domains in which large corpora exist, training a model from scratch with just in-domain knowledge may yield better results. Moreover, the increasing focus on the compute costs for pre-training recently led to the design of more efficient architectures, such as ELECTRA. In this paper, we propose a pre-trained domain-specific language model, called ELECTRAMed, suited for the biomedical field. The novel approach inherits the learning framework of the general-domain ELECTRA architecture, as well as its computational advantages. Experiments performed on benchmark datasets for several biomedical NLP tasks support the usefulness of ELECTRAMed, which sets the novel state-of-the-art result on the BC5CDR corpus for named entity recognition, and provides the best outcome in 2 over the 5 runs of the 7th BioASQ-factoid Challange for the question answering task.","url_abs":"https://arxiv.org/abs/2104.09585v1","url_pdf":"https://arxiv.org/pdf/2104.09585v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"electramed-a-new-pre-trained-language","repo_url":"https://github.com/gmpoli/electramed","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"electramed-a-new-pre-trained-language","repo_url":"https://github.com/yangyucheng000/University/tree/main/model-2/electra","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"drug-drug-interaction-extraction","task_name":"Drug–drug Interaction Extraction"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"medical-named-entity-recognition","task_name":"Medical Named Entity Recognition"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"electra","method_name":"ELECTRA"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drug-drug-interaction-extraction-on-ddi","task":"Drug–drug Interaction Extraction","dataset":"DDI extraction 2013 corpus","model":"ELECTRAMed","rank_in_archive_order":10,"of":10,"metrics":{"Micro F1":"79.13"},"uses_additional_data":true},{"leaderboard":"/sota/named-entity-recognition-ner-on-bc5cdr","task":"Named Entity Recognition (NER)","dataset":"BC5CDR","model":"ELECTRAMed","rank_in_archive_order":7,"of":16,"metrics":{"F1":"90.03"},"uses_additional_data":true},{"leaderboard":"/sota/named-entity-recognition-ner-on-ncbi-disease","task":"Named Entity Recognition (NER)","dataset":"NCBI-disease","model":"ELECTRAMed","rank_in_archive_order":19,"of":26,"metrics":{"F1":"87.54"},"uses_additional_data":true},{"leaderboard":"/sota/relation-extraction-on-chemprot","task":"Relation Extraction","dataset":"ChemProt","model":"ELECTRAMed","rank_in_archive_order":11,"of":13,"metrics":{"F1":"72.94"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.09585","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}