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.
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
+18.5%
precision percentage points
+28.5%
coverage percentage points
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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.