Label Studio

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Introduction: The most flexible data labeling platform to fine-tune LLMs, prepare training data or validate AI models.
Added on: Jan 20, 2025
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Label Studio

What is Label Studio

Label Studio is an open-source data labeling platform designed to support a wide range of data types and use cases. It provides tools for fine-tuning large language models (LLMs), preparing training data, and validating AI models. The platform is highly configurable and integrates seamlessly with various machine learning pipelines, offering features like ML-assisted labeling, cloud storage integration, and advanced data management.

How to Use Label Studio

  1. Installation: Choose your preferred method to install Label Studio (PIP, Brew, Git, or Docker).
  2. Project Setup: Create a new project and configure it according to your data type and labeling requirements.
  3. Data Import: Import your dataset into the platform.
  4. Labeling: Use the provided tools to label your data. You can leverage ML-assisted labeling to speed up the process.
  5. Export: Once labeling is complete, export the labeled data for use in your AI/ML pipeline.

Use Cases of Label Studio

Label Studio is used across various domains, including:

  • GenAI: Fine-tuning LLMs, evaluating responses, and RAG evaluation.
  • Computer Vision: Image classification, object detection, and semantic segmentation.
  • Audio & Speech: Classification, speaker diarization, emotion recognition, and transcription.
  • NLP: Document classification, named entity recognition, question answering, and sentiment analysis.
  • Time Series: Classification, segmentation, and event recognition.
  • Video: Classification, object tracking, and assisted labeling.

Features of Label Studio

  • Flexible and configurable

    Configurable layouts and templates adapt to your dataset and workflow.

  • Integrate with your ML/AI pipeline

    Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more.

  • ML-assisted labeling

    Save time by using predictions to assist your labeling process with ML backend integration.

  • Connect your cloud storage

    Connect to cloud object storage and label data there directly with S3 and GCP.

  • Explore & understand your data

    Prepare and manage your dataset in our Data Manager using advanced filters.

  • Multiple projects and users

    Support multiple projects, use cases, and data types in one platform.