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.