Changelog

New features

Video frame classification

You can now apply labels to video frames. Previously, we only supported per-video classification.

This new feature allows you to apply labels at a per-frame level. You can implement this feature using a new tag: <TimelineLabels>.

For more information, see New! Video Frame Classification.

Named Entity Recognition projects for Prompts

You can now select NER projects when creating a prompt. Previously, Prompts only supported Text Classification projects.

For more information, see Named entity recognition (NER) in our Prompts documentation.

Feature updates

• When creating regions that have start and end times (such as when annotating sections of an audio track), you will now see the duration of your selection under the Info tab.

• The Projects page header has updated to have a more compact design:
  
  - The search bar, projects drop-down menu, and create actions have all been consolidated on one line.
  
  - The Use Template action has been moved and is now available as a drop-down option within the Create Project button.
  
  - For better clarity, the Use Template action has been renamed Create from saved template.
  

• You will now see a progress bar when performing searches against the activity log to indicate that the search is still processing.
• For better clarity, the Sandbox workspace has been renamed Personal Sandbox.

Bug fixes

• Fixed a regression issue with BigInteger support in the Data Manager.
• Fixed an issue with the Activity Logs page where some options would become unavailable.

New features

JavaScript for labeling customization

You can now add JavaScript to your Label Studio projects to further customize your labeling experience.

Note that due to security precautions, custom scripts must be enabled for your organization before will see the Scripts option when configuring the labeling interface. Contact your account representative to request this feature.

For more information, see the following resources:

• Custom scripts documentation
• HumanSignal Blog - Transform Your Labeling Workflow with Custom Scripts
• Video demo - Announcing Custom Scripts for Label Studio

Feature updates

You can now use Azure OpenAI when creating Prompts. For more information, see Model provider API keys.

Bug fixes

• Fixed an issue where users were seeing a runtime error when loading the dashboard in situations in which the project’s labeling configuration did not include labels.

New features

Introducing the Prompts tool

The Prompts tool leverages ChatGPT to help you evaluate and refine your LLM prompts. You can also use Prompts to generate predictions for automating your labeling process, and to quickly bootstrap labeling projects.

For more information, see Automate Data Labeling with HumanSignal and our Prompts documentation.

Refreshed Label Studio interface

The Label Studio UI has been upgraded with updated colors and fonts, giving it a sleek new look while maintaining the same intuitive navigation you're familiar with. All Label Studio tools, features, and settings are still in the same place, ensuring a smooth transition.

Feature updates

Improved performance on the Projects list page due to improvement on the API level.

Breaking changes

Fixed an issue with Google Cloud Storage when the connection has the Use pre-signed URLs option disabled. In these situations, Google was sending pre-signed URLs with the format https://storage.googleapis.com rather than sending BLOBs.

With this fix, Google Cloud Storage will begin returning BLOBs/base64 encoded data when Use pre-signed URLs is off. This means that Label Studio will start reading data from Google Cloud Storage buckets, which can result in large amounts of data being sent to your Label Studio instance - potentially affecting performance.

Bug fixes

• Fixed an issue where Redis storage connections were causing errors due to a missing field in the storage form (Storage Title).
• Fixed an issue where annotators were not being prompted to leave a comment when skipping a task, even though the project settings required them to do so.
• Fixed an issue where connected ML backends were unable to return more than one prediction per task.