Open-source AutoML library that automatically selects NLU training methods based on your data.
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.
OpenAutoNLU automatically selects optimal training methods based on dataset size without manual configuration.
The library integrates out-of-distribution detection, data quality diagnostics, and LLM augmentation in one unified API.
Data-aware regime selection uses empirically determined thresholds to transition between AncSetFit, SetFit, and fine-tuning approaches.
LLM-based approaches may outperform on zero-shot tasks but incur higher computational and monetary costs.
The library requires Python and is optimized for text classification and NER tasks only.
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