Natural Language Processing

Ensure your NLP models work as intended

Natural Language Processing (NLP) models are often complex. Securing them shouldn’t be.
The enormous size of the input space and complexity inherent in human languages makes it difficult and time-consuming to validate model pipelines and rigorously test model behavior. This is true of NLP systems developed in-house, and perhaps even more true of systems provided by third parties. Robust Intelligence offers a powerful, largely-automated and continually improving framework for ensuring the integrity of your NLP models.

AI Stress Testing

Ensure that your models are invariant to benign data transformations, robust to noise and attacks, and generalize across subdomains. Automatically catch vulnerabilities in your models before they go into production.
bar chart with prediction chage
Image with status about prediction rate, number of warnings, FW score and Action of a specifc date.

AI Firewall

Secure your production NLP models to ensure they remain performant in the face of problematic data points. Our AI Firewall automates anomaly detection, intercepting bad data before it can reach your model to eliminate failure.

AI Continuous Testing

Overcome notoriously difficult NLP monitoring challenges by also tracking semantically relevant features of text data. Continuously monitor models in production to identify issues, understand when it’s time to retrain a model, and automate root cause analysis of model failure.
Test pass rate line graph over time

Adversarial robustness

Without proper system design, even state-of-the-art transformer models can be fooled by imperceptible text perturbations. We quantify your models’ vulnerability to adversarial attacks and improve your service’s robustness.

Support forvarious use cases

Evaluate any text classification or named entity recognition (NER) model on any dataset. We configure tests based on your setup to serve insights relevant to your use case.

Support any structured metadata

Language isn’t created in isolation. Use metadata attributes to test model performance in context and catch when your model is underperforming.

Native Hugging Face integration

We make it simple to include state-of-the-art language models in your ML Integrity workflow with our native Hugging Face integration, enabling you to confidently accelerate NLP adoption using open source resources.
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