May 2024 Community News!

🎉 Upcoming Event: AI Evaluation— Ensuring Mission-Critical Trust & Safety

Mark your calendars! Join us for our Label Studio’s AI Evaluation event on June 26th. Get insights from industry experts, share your experiences, and learn best practices for data labeling and model training and model evaluation for building reliable and safe systems. Stay tuned for details on speakers and sessions!

⚠️ LLM Evaluation: Comparing Four Methods to Automatically Detect Errors

An ongoing challenge for LLMs is their tendency to hallucinate. They can create content that doesn't exist in the input, they can fabricate data, and they can make a variety of other errors that are difficult to evaluate.

In this article, we look at four techniques for automated LLM error detection. We will use an example dataset to test and analyze each technique, sharing the results and tradeoffs, so that you can choose the best path for your own projects.

📣 New Discourse Forums for Community Support

Exciting news! We just launched our new Discourse platform for all things related to community support. This move is intended to index conversations, streamline support requests and enhance your community experience.

Signup and Engage: Use this invite link to be one of the first users on our Discourse! (You’ll be added to a special, invite-only group) Share your first post or join a discussion on Discourse to get the ball rolling.

🔧 Fine-Tuning Generalist Models for Named Entity Recognition

Discover how Label Studio aids in fine-tuning generalist models for Named Entity Recognition (NER), boosting performance in specialized domains. We walk you through the GLiNER model, illustrating its flexibility and demonstrate how Label Studio streamlines the process from dataset creation to model refinement.

Dive into our comprehensive guide covering the entire cycle from dataset creation with Label Studio to model fine-tuning of GLiNER for specific NER tasks in the medical domain.

💡Tutorial: Importing Local YOLO Pre-Annotated Images to Label Studio

Step into the world of efficient workflow optimization with our tutorial on importing YOLO pre-annotated images into Label Studio for further annotation enhancements. Preparing your pre-annotated dataset for integration into Label Studio can boost the training of your machine learning models significantly.

Our tutorial covers setting up your environment to configuring Label Studio with local storage, converting YOLO annotations to a compatible format, and ensuring data integrity. Streamline your annotation process and improve model performance leveraging Label Studio's seamless integration features.

🤸 Thank you for being part of the community!

We appreciate your contributions and participation in the Label Studio open source community! Please reach out to us on GitHub, Slack or Discourse and share your insights!