🎉 The Hallucination Index is here!
Data is critical for ML. But it wasn't always this way. Learn about how focusing on ML Data quality came to become the central figure for the best ML teams today.
In this article, Galileo founding engineer Nikita Demir discusses common data errors that NLP teams run into, and how Galileo helps fix these errors in minutes, with a few lines of code.
We used Galileo on the popular MIT dataset with a NER task, to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available for use.
In this post, we discuss the Named Entity Recognition (NER) task, why it is an important component of various NLP pipelines, and why it is particularly challenging to improve NER models.
Build better models, faster, with better data. We will dive into what is ML data intelligence, and it's 5 principles you can use today.
We used Galileo on the popular Newsgroups dataset to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available publicly for use.
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Working with Natural Language Processing or Computer Vision?
Read about Galileo’s NLP Ops and CV Ops solutions