Ensure consistency in information extraction pipelines and monitor for data drift
Simulate "what-if" scenarios by interacting with feature scores and observing the impact on model outputs
Identify the most important features in determining the predictions of your NLP models
Want a demo of how Arthur can enable you to build better AI models and detect fraud more effectively? Understand how to build an explainable fraud detection system to save time, reduce costs, and increase trust with explainable AI tools and proper AI model governance.
From simple chatbots to document classifiers to generative models like GPT-3, natural language processing models are seemingly everywhere these days. NLP models are powerful tools for processing unstructured text data—but with great power comes great responsibility. If you’re not monitoring your NLP models just as you would your tabular models, you can overlook many sticky issues that could quickly become billion-dollar problems.
Arthur's Everything you need to know about model monitoring for natural language processing whitepaper covers what any organization deploying NLP models into production should be doing to ensure that those models continue to perform as expected.
Learn more about how model monitoring can help you improve your NLP model performance with the help of Arthur.