Named Entity Recognition

Build named entity recognition (NER) applications to recognize common or custom entities in a fraction of time without hand-labeling data using Snorkel Flow.

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of crowd-worker labels replaced in a fraction of time using Snorkel


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.


Rapidly Adaptable

Monitor for changes in the data, and rapidly adapt using built-in error analysis tools. Zoom in on errors to fine-tune training data & models with guided iteration.


High-accuracy Models

Leverage large amounts of labeled and unlabeled data, NLP primitives, and state-of-the-art model architectures to build high-accuracy models.


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.

Use Cases

NER Customized for Your Workflow

Case Study


Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales & marketing agents.

Rapidly changing sales goals make social media monitoring difficult to maintain.

Deployed Snorkel to replace months-long crowd-worker effort with cheap and fast template-based programmatic labeling.


Better performance and major cost savings for sales & marketing and customer analytics.

of crowd-worker labels replaced


precision percentage points


coverage percentage points

How Snorkel Flow Works

Build Precise AI Apps for NER

Snorkel Flow allows you to recognize entities with ease and accuracy across an extensive collection of texts and documents. Collect and clean data with built-in pre-processors and taggers. Programmatically label documents with custom labeling functions, build end-to-end pipelines using your custom-trained models or tailored heuristics to perform entity tagging, linking, or classification. Deploy your NER application or seamlessly integrate it into downstream NLP tasks.

An End-to-end ML Platform

Designed for Collaboration


Data Scientist Friendly

  • Integrated Jupyter notebooks
  • Instant analysis tools
  • Ready-to-use models

Domain Expert Friendly

  • Intuitive, no-code UI
  • Rich dashboards and visualizations
  • Full-featured, push-button error analysis

Developer Friendly

  • Platform access via Python SDK
  • Online or batch API deployment
  • Containerized software for cloud or on-premises deployments


Based on Years of Novel Research
Learn more about groundbreaking techniques for machine learning and weak supervision developed by the Snorkel AI team at Stanford AI Lab and beyond.

Accelerate your AI application development today

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