Document Classification

Build AI-powered document classification applications in a fraction of the time without hand-labeling data using Snorkel Flow.

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Technology developed and deployed with the world’s leading organizations

Overview —

One Size Fits You, Not All

Achieve greater performance gains by exploiting domain-specific text features of your own data.

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.

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.

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.

Easier SME Collaboration

Build complex classification apps intuitively while preserving natural information about data taxonomies with subject matter expert (SME) collaboration.

Industry Use Cases —

Explore Enterprise Solutions For Classification

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.

SOFTWARE

Search Engine Optimization

Software companies can recognize named entities in customer search queries and to optimize website content.

RETAIL

Product Recommendation

E-commerce sites can recognize entities in product descriptions (price, key words, etc.) to improve recommender systems.

Case Study —

Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.

Problem

Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.

Solution

Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.

Results

Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.

6 MONTHS

of hand-labeling data replaced in 30 mins

<0hrs

To develop the first custom ML model

52%

performance improvement

+0%

Accuracy for contract classification

100K+

hand labels replaced with programmatic approach

Contracts processed in minutes

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.