Label Studio is one of the most powerful platforms available for managing a growing data labeling team and orchestrating all the resources you need to improve data quality and build enterprise-scale machine learning.
If you’ve followed along with our recent announcements, you’ve seen how tools to make annotation both more productive and more collaborative have been a heavy focus for us. We launched AI-assisted labeling to accelerate even complex and custom labeling scenarios. Powered by your choice of models, you can equip every annotator with task accelerators like interactive pre-annotations and the magic region tool.
HumanSignal also helps teams be really targeted and efficient in how they allocate resources. For example, try configuring your reviewer workflows to focus on the subset of tasks marked as low confidence by the model during training runs. And when even the human experts show low agreement, Label Studio can now automatically assign more annotators to the task to perform further review.
Still, you need a way to keep track of all the activity happening across projects as the team scales and new internal and external annotators are added. To help, we've added a new detailed Annotator Dashboard so you can make data-driven decisions as you work to optimize your available resources and ensure annotators are paid accurately.
You'll find these new reports when you click on any annotator on your team page. Alongside the existing granular activity logs, you'll now see the ability to to browse intelligently summarized performance reports where you can explore stats graphed out across any date range.
The reports summarize annotations including the ones the annotator submitted, updates, or skipped. You'll also see the total time spent annotating as well as how many minutes each day were spent annotating.
At the top of the page, you can set a custom date range and you can explore the data aggregated across every project assigned to the annotator or filter down to specific projects.
The new annotator dashboard is built to help ease the effort of managing a growing team and save the expense of building custom internal tracking tools.You’ll find the reports immediately available in the Team section of Label Studio. We encourage you to drop by our Slack community to discuss your own reviewer workflows and how your team is making labeling faster and more efficient.
Large Language Models (LLMs) are revolutionizing how we tackle data annotation, offering speed and efficiency that were previously unattainable. With Label Studio's Prompts, you can now handle multiple natural language processing (NLP) annotation types for a single data task in a unified Label Studio workflow.
Annotator Performance Dashboards make it easer to manage labeling teams at scale and orchestrate all the internal and external resources needed to improve data quality, without sacrificing speed.
In this post, we’ll guide you through the process of using Prompts in Label Studio Enterprise to pre-annotate data for Named Entity Recognition (NER) tasks.