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.

Request a demo
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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.
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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.
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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.
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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.
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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.
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An end-to-end ML platform

Designed for collaboration

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For data scientists

  • Ready-to-use model zoo
  • Auto-generated analysis tools
  • Integrated Python notebooks
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For domain experts

  • Rich data annotation suite
  • Intuitive, no-code labeling UI
  • Model error analysis reports
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For developers

  • Fully interoperable API and web UI
  • Write custom operators with Python SDK
  • Integrations to deploy models at scale
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Case study

Intel

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



Problem

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

Solution

Deployed a proto version of Snorkel(Snorkel Osprey) to rapidly replace crowdworker labels that took months with programmatically generated labels.

Results

Better performance and major cost savings in Sales & Marketing and Advanced Analytics.

6 months

of crowdworker labels replaced

+18.5

point performance improvement

+28.5

coverage percentage points



Case study

Intel

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



Problem

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

6 months

of crowdworker labels replaced

Solution

Deployed a proto version of Snorkel (Snorkel Osprey) to replace months-long crowdworker labels with cheap & fast programmatic labeling.

+18.5

performance improvement

Results

Better performance and major cost savings in Sales & Marketing and Advanced Analytics.

+28.5

coverage percentage points


Case Study

Intel

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



Problem

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

Solution

Deployed a proto version of Snorkel (Snorkel Osprey) to replace months-long crowdworker labels with cheap & fast programmatic labeling.

Results

Better performance and major cost savings in Sales & Marketing and Advanced Analytics.

6 Months

of crowdworker labels replaced

+18.5

performance improvement

+28.5

coverage percentage points

Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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March 21, 2022
Snorkel AI welcomes industry leaders to the team

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August 9, 2021
This hot startup is now valued at $1 billion for its A.I. skills

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February 24, 2021
The Data-First Enterprise AI Revolution

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July 14, 2020
Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning

Blog
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February 4, 2022
Making Automated Data Labeling a Reality in Modern AI

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Date: Jan 25, 2022
The Principles of Data-Centric AI Development

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Date: Jan 5, 2022
Meet the Snorkelers

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Date: Jul 9, 2021
How to Use Snorkel to Build AI Applications

Research
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2022
Universalizing Weak Supervision

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2021
Ontology-driven weak supervision for clinical entity classification in electronic health records

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2017
Rapid Training Data Creation with Weak Supervision

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2016
Data Programming: Creating Large Datasets Quickly

Customer Stories
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February 26, 2022
Genentech used Snorkel Flow to extract information from clinical trials

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February 18, 2022
Google used Snorkel to build and adapt content classification models

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2019
Intel used Snorkel to accelerate sales and marketing agents

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2019
Apple built a Snorkel-based system to answer billions of queries in multiple languages

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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.
Request a demo