Covering everything you need to know in order to build AI products faster.
How to annotate conversational text for chatbot use cases
Teams can easily train an open-source model on their own data and use Labelbox's suite of tools across Annotate, Catalog, and Model to quickly tailor their language model to meet their specific business needs.
How to run model-assisted labeling and active learning on NER data with a 🤗Hugging Face model
Efficiently improve models in development and production by using a third-party model, such as HuggingFace, to guide and identify targeted improvements in your training data to boost model performance.
Fundamental elements of the Labelbox editor
Aligning with your team on key terms used in Labelbox will serve to greatly enhance collaboration and cohesiveness throughout your work in the platform. In this brief video, we introduce the fundamental elements of the Labelbox Editor.
How to create and manage ontologies and features
Ontologies are an essential part of Labelbox's platform. You'll need to select an ontology when you create a new project or model. Learn how to create, reuse, and manage your ontologies and features.
Data labeling for AI
Get a primer on data labeling, defined as the task of detecting and tagging data with labels, most commonly in the form of images, videos, audio and text assets.
How to create high-quality image segmentation masks quickly and easily
Image segmentation is used to label images for applications that require high accuracy and is manually intensive.
Best practices for successful image annotation
Image annotation is defined as the task of annotating an image with labels. Discover how an AI data engine supports image annotation at scale.
