{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T05:48:11Z","timestamp":1751348891480},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,16]]},"DOI":"10.1109\/icmcis59922.2023.10253491","type":"proceedings-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T17:37:28Z","timestamp":1695231448000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of Transformer Models for Performance on Domain Specific Texts: A Systematic Evaluation of Intrinsic Model Performance"],"prefix":"10.1109","author":[{"given":"Giavid","family":"Valiyev","sequence":"first","affiliation":[{"name":"Chief Technology Office"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Eles","sequence":"additional","affiliation":[{"name":"Operational Analysis Centre"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arvid","family":"Kok","sequence":"additional","affiliation":[{"name":"Chief Technology Office"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"D'Ercole","sequence":"additional","affiliation":[{"name":"Chief Technology Office"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"journal-title":"HuggingFace's Transformers State-of-the-art Natural Language Processing","year":"0","author":"wolf","key":"ref13"},{"year":"0","key":"ref12"},{"journal-title":"All Bark and No Bite Rogue Dimensions in Transformer Language Models Obscure Representational Quality","year":"0","author":"timkey","key":"ref23"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1080\/14786440109462720"},{"journal-title":"Overview of the Transformer-based Models for NLP Tasks","year":"0","author":"gillioz","key":"ref14"},{"year":"0","key":"ref20"},{"journal-title":"Adapting BERT Embeddings for Text Correlation of Military Domain Specific Content","year":"0","author":"kok","key":"ref11"},{"journal-title":"Made available as Courtesy of the U S National Library of Medicine","article-title":"Pubmed sumitters","year":"2023","key":"ref22"},{"journal-title":"Attention is all you need","year":"0","author":"vaswani","key":"ref10"},{"journal-title":"ArXiv Dataset","article-title":"arXiv.org submitters","year":"2023","key":"ref21"},{"journal-title":"Artificial Intelligence in military application - opportunities and challenges","year":"0","author":"szabadfoldi","key":"ref2"},{"journal-title":"Artificial Intelligence and the Future of Warfare","year":"0","author":"cummings","key":"ref1"},{"journal-title":"What would elsa do? freezing layers during transformer fine-tuning","year":"0","author":"lee","key":"ref17"},{"journal-title":"Pre-trained transformers an empirical comparison","year":"0","author":"casola","key":"ref16"},{"journal-title":"AMMUS A Survey of Transformer-based Pretrained Models in Natural Language Processing","year":"0","author":"kalyan","key":"ref19"},{"journal-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","year":"0","author":"raffel","key":"ref18"},{"journal-title":"RoBERTa A Robustly optimized BERT Pretraining Approach","year":"0","author":"liu","key":"ref8"},{"journal-title":"Scibert A pretrained language model for scientific text","year":"0","author":"beltagy","key":"ref7"},{"journal-title":"XLNet Generalized Autoregressive Pretraining for Language Understanding","year":"0","author":"yang","key":"ref9"},{"journal-title":"BERT Pre-training of deep bidirectional transformers for language understanding","year":"0","author":"devlin","key":"ref4"},{"journal-title":"Transformers State-of-the-art natural language processing","year":"0","author":"wolf","key":"ref3"},{"journal-title":"Language models are few-shot learners","year":"0","author":"brown","key":"ref6"},{"journal-title":"BART Denoising Sequence-to-Sequence Pre-training for Natural Language Generation Translation and Comprehension","year":"0","author":"lewis","key":"ref5"}],"event":{"name":"2023 International Conference on Military Communications and Information Systems (ICMCIS)","start":{"date-parts":[[2023,5,16]]},"location":"Skopje, North Macedonia","end":{"date-parts":[[2023,5,17]]}},"container-title":["2023 International Conference on Military Communications and Information Systems (ICMCIS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10252166\/10253463\/10253491.pdf?arnumber=10253491","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T18:09:57Z","timestamp":1696874997000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10253491\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,16]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icmcis59922.2023.10253491","relation":{},"subject":[],"published":{"date-parts":[[2023,5,16]]}}}