AutoIntent: AutoML for Text Classification
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908752050913280 |
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| author | Alekseev, Ilya Solomatin, Roman Rustamova, Darina Kuznetsov, Denis |
| author_facet | Alekseev, Ilya Solomatin, Roman Rustamova, Darina Kuznetsov, Denis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoIntent: AutoML for Text Classification Alekseev, Ilya Solomatin, Roman Rustamova, Darina Kuznetsov, Denis Computation and Language 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. |
| title | AutoIntent: AutoML for Text Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.21138 |