labelingwith

LabelingWith is an open-source data annotation framework designed to streamline the creation of labeled datasets for machine learning. It provides a modular workflow for labeling tasks, including label schema definition, task assignment, real-time review, and dataset export. The goal is to support collaborative data labeling while maintaining traceability and reproducibility across projects.

The framework supports multi-label and hierarchical labels, context-aware suggestions, and audit trails. It is designed to handle multiple data modalities such as images, text, audio, and tabular data. A web-based annotation interface allows annotators to apply, review, and correct labels, while a server-side backend maintains versioned schemas, task queues, and annotation histories. LabelingWith emphasizes reproducible exports, enabling data scientists to generate labeled datasets in JSON, CSV, or standardized formats.

Architecture and workflow: LabelingWith uses a client-server model with plug-in components for importers, labelers, validators, and exporters. Tasks can be assigned via roles, with configurable approval workflows to ensure data quality. The system supports offline work, task resynchronization, and integration with common ML pipelines through APIs. It is described as a community-driven framework, with demonstration repositories and accompanying documentation that illustrate its use.

Impact and scope: In practice, labelingwith serves research labs, startups, and education projects seeking transparent labeling processes. Critics note potential complexity for small teams and the need for careful schema design to avoid ambiguity. The project continues to evolve with community input, focusing on accessibility, interoperability, and compliance with data governance standards.