In this post, we’re going to show you an example of how to use Elemeta together with Superwise’s model observability community edition to supply visibility and monitoring of your NLP model’s input text.
NLP monitoring
Easily enrich unstructured NLP data with the Elemeta open-source and log tabular metafeature representations to Superwise to monitor.
Easily extract NLP metafeatures
Elemeta
Elemeta is an open-source library in Python for metafeature extraction. With it, you will be able to explore, monitor, and extract features from unstructured data through enriched tabular representations. It provides a straightforward Python API for metadata extraction from unstructured data like text and images.
Contextual metrics
I want to monitor average values for SentimentPolarity in all social pipelines across all segments & entire set
Monitor contextual metafeatures from Elemeta with Superwise to identify changes in the input or output behaviors of your end users and models.
Statistical metrics
I want to monitor max value for OutOfVocabularyCount in video search model across teens segment
Monitor statistical metafeatures from Elemeta with Superwise to identify changes in the distribution and representation of your NLP use case.
Try the community edition
No credit card required.
Featured resources
Elemeta: Extract metafeatures from unstructured data
We’re excited to release into beta v1.0 of Elemeta, our open-source library for exploring, monitoring, and extracting features from unstructured data.
Challenges of NLP monitoring
Monitoring ML, in general, is not trivial – NLP monitoring, in particular, produces a few unique challenges that we’ll examine in this post.
Get our
newsletter
Get our newsletter
Superwise needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, please review our privacy policy.
