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While there’s a lot of work done on defining guidelines and policies for Responsible AI, there are far fewer that data scientists can apply today to build safe, fair, and robust models. This session introduces the open-source nlptest library, which provides a comprehensive solution to testing NLP models before taking them to production.
The library supports the full lifecycle of automatically generating tests, editing them, running them, evaluating pass/fail criteria, and generating augmented data to improve models. The nlptest library currently supports testing Spark NLP, Hugging Face, and spaCy models and is designed for extensibility for testing more NLP libraries and tasks.
This session will show you what problems the nlptest library solves, how to get things done, and how to extend it.

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