doccano vs LabelLLM

doccano is a free, open-source annotation tool for labeling data for human review and machine-learning work. It supports text classification, sequence labeling and sequence-to-sequence tasks, with examples including sentiment analysis, named-entity recognition and summarisation. A project workflow lets users create projects, import datasets, add collaborators, set annotation guidelines, label data and export results. Collaboration features include multi-language and mobile support, emoji support and a dark theme. REST APIs connect scripts and machine-learning models, and supported modalities include text, image and audio. The project documents installation with pip, Docker and Docker Compose on Linux, Windows or macOS machines running Python 3.8 or newer. Deployment options include AWS, Heroku or Docker elsewhere. Imported datasets can use Amazon S3 or Google Cloud Storage. SQLite 3 is the default database, while PostgreSQL and other database systems are also documented. Celery handles long-running imports and exports, with SQLite3, RabbitMQ and Redis listed as message-broker options. The installation documentation warns that upgrading an SQLite3 installation can result in database loss. The GitHub repository reports no detected SECURITY.md policy and no published security advisories.

LabelLLM has no write-up on Specifiction yet; its figures are above.