{"ID":2866016,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21138","arxiv_id":"2509.21138","title":"AutoIntent: AutoML for Text Classification","abstract":"AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption.","short_abstract":"AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support mu...","url_abs":"https://arxiv.org/abs/2509.21138","url_pdf":"https://arxiv.org/pdf/2509.21138v1","authors":"[\"Ilya Alekseev\",\"Roman Solomatin\",\"Darina Rustamova\",\"Denis Kuznetsov\"]","published":"2025-09-25T13:27:52Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}