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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Technology developed and deployed with the world’s leading organizations
Overview —
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.
Industry Use Cases —
NER Customized for Your Workflow
Build industry-specific AI applications combining state-of-the-art machine learning approaches with industry-specific best practices and last-mile connectors, all on an enterprise-scale platform.
FINANCIAL SERVICES
Contract Intelligence
Banks can classify contracts by terms and conditions to smoothly ensure regulatory complience.
TELECOM & CYBER
Customer Segmentation
Telecom organizations can classify customer usage documents to target promotional offers.
HEALTHCARE
Clinical Trial Matching
Biotech organizations can classify patient records to identify actionable clinical trial candidates.
INSURANCE
Risk Classification
Insurance underwriters can classify policy documents by behavioral or occupational variables to assess risk.
Case Study —
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales & marketing agents.
Problem
Rapidly changing sales goals make social media monitoring difficult to maintain.
Solution
Deployed Snorkel to replace months-long crowd-worker effort with cheap and fast template-based programmatic labeling.
Results
Better performance and major cost savings for sales & marketing and customer analytics.
6 MONTHS
of crowd-worker labels replaced
<0hrs
To develop the first custom ML model
+18.5%
precision percentage points
+0%
Accuracy for contract classification
+28.5%
coverage percentage points
0K
Contracts processed in minutes
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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
Resources —
Explore More About Snorkel
Learn more about groundbreaking techniques for programmatic labeling and weak supervision developed by Team Snorkel and the broader data science community.