OpenAutoNLU: Open Source AutoML Library for NLU
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866910037923856384 |
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| author | Arshinov, Grigory Boriskin, Aleksandr Senichev, Sergey Zaripov, Ayaz Galimzianova, Daria Karpov, Daniil Sanochkin, Leonid |
| author_facet | Arshinov, Grigory Boriskin, Aleksandr Senichev, Sergey Zaripov, Ayaz Galimzianova, Daria Karpov, Daniil Sanochkin, Leonid |
| contents | OpenAutoNLU is an open-source automated machine learning library for natural language understanding (NLU) tasks, covering both text classification and named entity recognition (NER). Unlike existing solutions, we introduce data-aware training regime selection that requires no manual configuration from the user. The library also provides integrated data quality diagnostics, configurable out-of-distribution (OOD) detection, and large language model (LLM) features, all within a minimal lowcode API. The demo app is accessible here https://openautonlu.dev. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01824 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OpenAutoNLU: Open Source AutoML Library for NLU Arshinov, Grigory Boriskin, Aleksandr Senichev, Sergey Zaripov, Ayaz Galimzianova, Daria Karpov, Daniil Sanochkin, Leonid Computation and Language Machine Learning OpenAutoNLU is an open-source automated machine learning library for natural language understanding (NLU) tasks, covering both text classification and named entity recognition (NER). Unlike existing solutions, we introduce data-aware training regime selection that requires no manual configuration from the user. The library also provides integrated data quality diagnostics, configurable out-of-distribution (OOD) detection, and large language model (LLM) features, all within a minimal lowcode API. The demo app is accessible here https://openautonlu.dev. |
| title | OpenAutoNLU: Open Source AutoML Library for NLU |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2603.01824 |