Annotation Studio overview
Data annotation is the process of assigning labels or classes to specific data points for training datasets. For example, you could use an LLM to classify banki...
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.
Creating batches
This page walks through how you can create batches of documents for manual annotation when the dataset uses multi-schema annotations.
Annotate traces
This topic explains how you can annotate trace datasets. Once you have uploaded a trace dataset, you can begin annotating it.
Managing batches and committing 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.
How-to: Create dataset views
Dataset views allow you to customize the presentation of your data for annotators. This makes it easier for annotators to work on a focused set of labeling requ...
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...
Viewing 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...
(Beta) Using word-based annotations
This is a beta feature available to customers using a Snorkel-hosted instance of the Snorkel AI Data Development Platform. Beta features may have known gaps or ...