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About Label Studio

Label Studio is an open-source data labeling platform designed to help users prepare training data, fine-tune Large Language Models (LLMs), and evaluate AI models. It offers extensive flexibility with configurable layouts and templates that adapt to various datasets and workflows. The platform supports a wide range of data types including GenAI, images, audio, text, time series, and video, catering to diverse machine learning applications. Key features include ML-assisted labeling to accelerate the process, integration with cloud storage like S3 and GCP, and a robust Data Manager for exploring and organizing datasets. It's suitable for data scientists, machine learning engineers, and researchers who need to create high-quality labeled datasets for their AI projects. The platform also supports multiple projects and users, making it a versatile tool for teams. Label Studio provides comprehensive capabilities for LLM fine-tuning (supervised fine-tuning, RLHF), LLM evaluations (response moderation, grading, side-by-side comparison), and RAG evaluation (using Ragas scores and human feedback). It also covers computer vision tasks like image classification, object detection, and semantic segmentation; audio applications such as classification, speaker diarization, and transcription; and NLP tasks including classification, named entity recognition, and sentiment analysis.