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Information Extraction

Rapidly build AI-powered applications that extract information from unstructured text, PDF, tables, or forms from millions of documents without expensive hand-labeling using Snorkel Flow.

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Top US Bank

99.1%

accuracy for contract intelligence built using Snorkel Flow in under 24 hours

Overview

All Data Extracted with No Context Lost

Extract useful data from any tables, cells, and forms linked to all headers, units, or references.

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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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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.

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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.

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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.

Use Cases

Information Extraction Customized for Your Workflow

Case Study

Top US Bank

A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from contracts and other legal documents.

Problem

The bank estimated that, for a time-sensitive use case, labeling data by hand would take over a month.

Solution

With Snorkel Flow, the team produced a AI-powered contract intelligence application that was over 99% accurate in under 24 hours.

Result

The resulting AI application was quickly and easily adapted to new problems.

<24 HOURS

to develop first model

99.1%

model accuracy

250K

documents labeled

How Snorkel Flow Works

Build Flexible AI Apps for Extraction

Build flexible extraction applications that preserve structural and tabular information and generalize beyond brittle rules. Eliminate hundreds of hours of manual labor by programmatically labeling data with powerful labeling functions. Train state-of-the-art models and analyze performance using intuitive tools. Chain ML tasks together and deploy your extraction application as an API or Python SDK with one click.

An End-to-end ML Platform

Designed for Collaboration

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Data Scientist Friendly

  • Integrated Jupyter notebooks
  • Instant analysis tools
  • Ready-to-use models
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Domain Expert Friendly

  • Intuitive, no-code UI
  • Rich dashboards and visualizations
  • Full-featured, push-button error analysis
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Developer Friendly

  • Platform access via Python SDK
  • Online or batch API deployment
  • Containerized software for cloud or on-premises deployments

Research

Based on Years of Novel Research
Learn more about groundbreaking techniques for machine learning and weak supervision developed by the Snorkel AI team at Stanford AI Lab and beyond.