Named entity recognition
Build named entity recognition (NER) applications to recognize common or custom entities in a fraction of time with programmatic labeling using Snorkel Flow.
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Data-centric AI technology developed at the Stanford AI Lab and proven at world-leading companies.
How Snorkel Flow works
Targeted applications to tackle any entity
Train custom, high-accuracy NER models on your data without hand-labeling.
Faster, lower-cost development
Use programmatic labeling to develop high-quality AI applications in hours instead of spending weeks or months on expensive hand-labeling.
Higher-accuracy models
Iterate on your application, using a closed-loop approach with intermediate results and analysis at every step to zero in on errors.
Flexible integrations
Easily integrate labeling, training and analysis pipelines defined over diverse input types–text, PDF, HTML, and more–with downstream applications using APIs or a Python SDK.
Better SME collaboration
Build complex classification apps intuitively while preserving natural information about data taxonomies with subject matter expert (SME) collaboration.
Named entity recognition
Programmatically label training data across complex data types and build multi-model NER applications with ease.
An end-to-end ML platform
Designed for collaboration
For data scientists
- Ready-to-use model zoo
- Auto-generated analysis tools
- Integrated Python notebooks
For domain experts
- Rich data annotation suite
- Intuitive, no-code labeling UI
- Model error analysis reports
For developers
- Fully interoperable API and web UI
- Write custom operators with Python SDK
- Integrations to deploy models at scale
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Are you ready to dive in?
Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
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