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Data labeling specs

Data labeling specs
If you want your data to be labeled consistently (even if you have multiple labelers), clear annotation instructions are the answer. When you give people clear guidelines, you can ensure clean training data across the board.

This guide will show you the 9 steps of creating clear data labeling guidelines (called specification sheets). In it you’ll learn how to::

  • Describe the data you’re collecting
  • Create a taxonomy for your library
  • Outline expectations on labeling rules
  • Make updates in the future when edge cases arise
  • Define workflows for maximum efficiency
  • …and so much more.

Give your labelers the information they need to help your machine learning algorithms succeed. Download your copy today for free.

Open and public dataset

Browse datasets across various domains, public or openly hosted on You can easily clone and adapt them to your own needs.

Whether you’re developing algorithms, training models, or conducting research, this collection aims to empower your projects with the data they need to succeed.

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Find and compare labeling companies

Explore our curated database of labeling companies, designed for easy comparison and selection to match your project needs. This comprehensive resource allows you to discover, evaluate, and connect with the ideal partners for your data annotation requirements.

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