Web-based Semantic Similarity Checker using Sentence-BERT

Web-based Semantic Similarity Checker using Sentence-BERT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Web-based Semantic Similarity Checker using Sentence-BERT Nirmit Shah, Nikhil Mohite, Karan Shah, Nilkamal More This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9049688/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The increasing need to have effective tools to evaluate the semantic similarity of sentences has been driven by the ever-increasing rate of information in the digital world in the form of text. This paper proposes the design and implementation of an efficient and effective web-based tool to calculate the semantic similarity of sentences in real-time using the power of transformer models on the web. This tool has been built using the Sentence-BERT model, which is specifically the all-MiniLM-L6-v2 model, to calculate the semantic similarity of sentences using the cosine similarity measure. This tool has been built using the Flask web development framework to allow users to input two sentences and calculate the similarity in real-time. The contribution of this work is the demonstration of the efficient and effective implementation of pre-trained transformer models on the web without the need to fine-tune the model on the GPU to calculate the semantic similarity of sentences, which has been demonstrated to be effective in the results to calculate the semantic similarity of sentences in the context of plagiarism detection and content analysis. Sentence-BERT Semantic Similarity Natural Language Processing (NLP) Cosine Similarity Web-based Application Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9049688","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602190171,"identity":"5c3c4aa8-cda8-49a2-9002-9c7a1461d828","order_by":0,"name":"Nirmit Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACZiBOAGIDBh7GByABNgYEiUdLAlgLswFxWsAAooVNAiGCR4s5O+/TDQ9/MNibs589VvmjYps8n0TyA4YPZYdxarFsZje7AXRY4s6evLTbPGduG7ZJpBkwzjiHW4vBYTY2kJYEgwM5ZrcZ24BIIsGAmbeNsBZ7g/NvzAp/tt22b5NI/8D8lwgtjBtu5Jgx8LbdTmyTyDFgZiSoJU0iccONN8bSQL8kt/G8KTjYcy4dt5bzx9hu/rCxATosx/Djj4rbtvPb0zc++FFmjVMLFCDFCINAAsMBQurRAD+pGkbBKBgFo2C4AwDN4VYVORjfPAAAAABJRU5ErkJggg==","orcid":"","institution":"K.J. 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