Annotation Studio overview
Data annotation is the process of assigning labels or classes to specific data points for training datasets. For example, for a classification problem, you coul...
Guideline goal
Annotation guidelines allow users to describe a phenomenon or concept as generally and precisely as possible. Good annotation guidelines are helpful for subject...
Walkthrough for annotators
This page walks through the process of manually annotating documents in Annotation Studio. This walkthrough is designed for users with the Annotator role, who n...
Walkthrough for reviewers
This page walks through the different ways that users with the Reviewer role can review annotations from all annotators.
Create batches
This page walks through how you can create batches of documents for manual annotation when the dataset uses multi-schema annotations.
Manage batches and commit ground truth
This page walks through how to manage your batches and commit annotations to your ground truth to be used for development in Studio.
Using dataset views for generative AI
Dataset views power the generative AI annotation data viewers. Snorkel Flow offers dataset view options with various benefits:
Using multi-schema annotations
This article explains how to use multi-schema annotations, including uploading a multi-schema annotation dataset, annotating multiple schemas, and reviewing the...
Overview page: View aggregate annotation metrics
This page walks through the summary metrics and settings that are found on the Datasets Overview page. This page is typically used by those with Developer and R...
Legacy annotation
Guideline goal, Walkthrough for annotators, Walkthrough for reviewers, Create batches, Ground truth annotations, Manage batches and commit ground truth, Using d...