Abstract
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 this https URL.
Abstract (translated)
OpenAutoNLU 是一个开源的自动化机器学习库，专门用于自然语言理解（NLU）任务，涵盖了文本分类和命名实体识别（NER）。与现有的解决方案不同，我们引入了数据感知的训练模式选择机制，无需用户进行手动配置。该库还提供集成的数据质量诊断、可配置的出站分布（OOD）检测以及大型语言模型（LLM）功能，并且所有这些都在一个极简的低代码API中实现。演示应用可以在这里访问：[此处应为URL]。 请注意，原始文本中的链接未被明确给出，实际使用时请确保正确插入有效的URL地址。
URL
https://arxiv.org/abs/2603.01824
