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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November 17, 2022
Snorkel AI Accelerates Foundation Model Adoption with Data-centric AI


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November 17, 2022
AI startup Snorkel preps a new kind of expert for enterprise AI


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November 17, 2022
Snorkel dives into data labeling and foundation AI models


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July 28, 2022
Here’s why a gold rush of NLP startups is about to arrive


Blog
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November 17, 2022
Data-centric Foundation Model Development: Bridging the gap between foundation models and enterprise AI


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November 17, 2022
Better not bigger: How to get GPT-3 quality at 0.1% the cost


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November 3, 2022
Building an NLP application to analyze ESG factors in Earnings Calls using Snorkel Flow


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August 4, 2022
The Future of Data-Centric AI 2022 day 1 highlights


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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September 30, 2022
How Schlumberger uses Snorkel Flow to enhance proactive well management


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September 30, 2022
How a global custodial bank automated KYC verification with Snorkel Flow


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September 28, 2022
How Memorial Sloan Kettering Cancer Center used Snorkel Flow to scale clinical trial screening


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February 26, 2022
How Genentech extracted information for clinical trial analytics with Snorkel Flow


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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