High-quality training and fine-tuning data means better models. And that’s a big part of what Label Studio is all about: helping you get the data quality you need as efficiently as possible.
That’s why we’re excited to release some new improvements to Label Studio Enterprise’s quality workflow functionality. These new features give reviewers and annotators more flexibility and granularity to their comments and feedback.
First, we've added the ability to attach comments to a specific piece within an annotation. Now, commenters can point to very specific items and indicate “my question is here” or “I have feedback about this.”
This commenting improvement works across all our data types and use cases, including:
We’ve also added a new "Reject Options" setting. This new option adds flexibility and additional granularity to the review process, giving reviewers more choices in how they respond to a given annotation. Instead of deciding per project whether a reviewer can 1) reject and requeue a task or 2) reject and remove, users can now decide to do 1 or 2 based on the specific annotation. This is particularly valuable in the GenAI space where annotations tend to be more open-ended.
For example, sometimes an annotation is almost good enough and just needs some guidance from a reviewer. In that case the reviewer can just requeue the annotation and send it back to the annotator. But other times the annotation is wrong enough that it’s not worth working on and it's just more efficient to trash it. This setting now gives reviewers that choice.
These features join our other quality workflow features including annotator performance dashboards, automated task assignment, agreement metrics and matrices, and more! To get a better understanding of how our entire quality workflow helps you generate high-quality labeled data with efficiency, check out this detailed data quality workflow article that walks you through the whole process.
And if you’re interested in seeing how our data quality workflows in Label Studio Enterprise can help with your specific use case, schedule some time to chat with one of our experts today!
And stay tuned for more updates, because this is just the first improvement around our commenting functionality that we're rolling out.
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.
Learn the intricacies of data quality, strategies to build the data you need for training and fine-tuning ML/AI models, and how you can use Label Studio Enterprise to engineer your AI/ML success.
