Transformer-driven chatbot for Arabic agricultural knowledge

Transformer-driven chatbot for Arabic agricultural knowledge | 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PDF Article Transformer-driven chatbot for Arabic agricultural knowledge Manal AlGhieth This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9001481/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 Nowadays, chatbots have become highly valuable tools for simplifying tasks across various fields. However, there is a noticeable lack of Arabic chatbots specifically tailored for the agricultural sector. This study aims to address this gap by exploring two transformer-based models, AraBERT v2 and QA AraBERT. These models are based on BERT (Bidirectional Encoder Representations from Transformers), a widely recognized and powerful transformer-based language model in the field of natural language processing (NLP). By leveraging the power of BERT, the chatbot will gain a deep understanding of the Arabic language and the contextual relationships between words. The BERT models underwent fine-tuning on a specialized Arabic dataset tailored to the agricultural sector, particularly in the realm of ornamental trees and plants. Focusing on the second model, QA AraBERT, the fine-tuning process yielded F1 score of 81.63%. This remarkable performance highlights the model’s exceptional capability to grasp the nuances of agricultural inquiries. Empowered by this model, the chatbot achieves high performance compared to other chatbots built with similar techniques. Physical sciences/Engineering Physical sciences/Mathematics and computing Chatbot Arabic Language Agriculture Bidirectional Encoder Representations from Transformers Natural Language Processing 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. 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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-9001481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605224471,"identity":"e49c254d-5df9-494d-bf77-40dabfcea10e","order_by":0,"name":"Manal AlGhieth","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYFACxgYQkgOzeYjWcrCBwZgULUAA1JLYQLQW3f7FjY8/7rBL77uRwPjgbRuDPX8DAS1mNx42Gxw8k5w780YCs+HcNobEGQcIajnYJnGwjTl3w40ENmneNoYEBiK0tP842FafbnAjgf03UIu9PEEt5xvbGA62HU4AamFjBmph3EDYFsZmibNtxw1nnnnYLDnnnETiRsK2HH/4obKtWp7vePLBD2/KbOzlCGlhkEiAMg4A4xTIJaQeCPhhhhI0fBSMglEwCkYsAACVFEr73QDMjgAAAABJRU5ErkJggg==","orcid":"","institution":"Qassim University","correspondingAuthor":true,"prefix":"","firstName":"Manal","middleName":"","lastName":"AlGhieth","suffix":""}],"badges":[],"createdAt":"2026-03-01 12:38:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9001481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9001481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104948120,"identity":"6466b25c-0a4f-41a3-a5b9-a564999c2c37","added_by":"auto","created_at":"2026-03-19 05:55:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1045026,"visible":true,"origin":"","legend":"","description":"","filename":"MureqAtransformerdrivenchatbotforArabicagriculturalknowledge.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9001481/v1_covered_60ab7b6c-6bcc-4d37-81a9-94c0932ebfd7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transformer-driven chatbot for Arabic agricultural knowledge","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chatbot, Arabic Language, Agriculture, Bidirectional Encoder Representations from Transformers, Natural Language Processing","lastPublishedDoi":"10.21203/rs.3.rs-9001481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9001481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNowadays, chatbots have become highly valuable tools for simplifying tasks across various fields. However, there is a noticeable lack of Arabic chatbots specifically tailored for the agricultural sector. This study aims to address this gap by exploring two transformer-based models, AraBERT v2 and QA AraBERT. These models are based on BERT (Bidirectional Encoder Representations from Transformers), a widely recognized and powerful transformer-based language model in the field of natural language processing (NLP). By leveraging the power of BERT, the chatbot will gain a deep understanding of the Arabic language and the contextual relationships between words. The BERT models underwent fine-tuning on a specialized Arabic dataset tailored to the agricultural sector, particularly in the realm of ornamental trees and plants. Focusing on the second model, QA AraBERT, the fine-tuning process yielded F1 score of 81.63%. This remarkable performance highlights the model\u0026rsquo;s exceptional capability to grasp the nuances of agricultural inquiries. 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