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Data Labeling Platform

The most flexible data labeling platform to fine-tune LLMs, prepare training data or validate AI models.

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                # Install the package
# into python virtual environment
pip install -U label-studio# Launch it!label-studio

Label every data type.

GenAI

LLM Fine-Tuning

Label data for supervised fine-tuning or refine models using RLHF

LLM Evaluations

Response moderation, grading, and side-by-side comparison

RAG Evaluation

Use Ragas scores and human feedback

Computer Vision

Image Classification

Put images into categories

Object Detection

Detect objects on image, boxes, polygons, circular, and keypoints supported

Semantic Segmentation

Partition image into multiple segments. Use ML models to pre-label and optimize the process

Audio & Speech Applications

Classification

Put audio into categories

Speaker Diarization

Partition an input audio stream into homogeneous segments according to the speaker identity

Emotion Recognition

Tag and identify emotion from the audio

Audio Transcription

Write down verbal communication in text

NLP, Documents, Chatbots, Transcripts

Classification

Classify document into one or multiple categories. Use taxonomies of up to 10000 classes

Named Entity

Extract and put relevant bits of information into pre-defined categories

Question Answering

Answer questions based on context

Sentiment Analysis

Determine whether a document is positive, negative or neutral

Robots, Sensors, IoT Devices

Classification

Put time series into categories

Segmentation

Identify regions relevant to the activity type you're building your ML algorithm for

Event Recognition

Label single events on plots of time series data

Multi-Domain Applications

Dialogue Processing

Call center recording can be simultaneously transcribed and processed as text

Optical Character Recognition

Put an image and text right next to each other

Time Series with Reference

Use video or audio streams to easier segment time series data

Video

Classification

Put videos into categories

Object Tracking

Label and track multiple objects frame-by-frame

Assisted Labeling

Add keyframes and automatically interpolate bounding boxes between keyframes

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.

From the Blog

View All Articles
  • Announcing Label Studio 1.16.0

    We’re excited to announce the 1.16.0 release of Label Studio! In this release we have included a number of awesome new features, some critical security updates, and other improvements and bug fixes.

    Label Studio Team

    February 12, 2025

  • Reinforcement Learning from Verifiable Rewards

    Learn about Reinforcement Learning with Verifiable Rewards, one of the leading training strategies for injecting learning signals into LLMs, successfully employed by models such as DeepSeek R1 and Tülu 3.

    Nikolai Liubimov

    February 7, 2025

  • Top 5 Most Successful Data Curation Strategies in DeepSeek

    Learn the data curation and human supervision techniques that we believe are crucial to DeepSeek’s success by examining technical reports from DeepSeek-R1, DeepSeek-V3, and its predecessors.

    Nikolai Liubimov

    January 30, 2025

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