#1818th of 20 in Data and scraping for AI
doccano
Web-based text annotation tool for labeling NLP datasets
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- Stars
- 10.8k
- License
- MIT
- Last commit
- Jan 2026
- Last release
- Jan 2026
- Language
- Python
10,554 on 31 Mar 202610,802 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
doccano is a web application for annotating text for classification, sequence labeling (such as named entity recognition) and sequence-to-sequence tasks like summarization. Teams create a project, upload data, and label it collaboratively, with a REST API for automation. It runs from pip with SQLite by default, or with PostgreSQL, Docker or Docker Compose.
Who it is for: ML teams who need to label text datasets for NLP training
Strengths
- Covers text classification, sequence labeling and seq2seq annotation in one tool
- Installs with pip and uses SQLite by default, so no database setup to start
- Collaborative annotation with multiple users per project and a RESTful API
- Deploy options include pip, Docker, Docker Compose, AWS CloudFormation and Heroku
Weaknesses
- Text only; the README lists no image, audio or video annotation
- README mentions no built-in model-assisted pre-labeling
- Compose setup needs RabbitMQ and PostgreSQL; pip setup needs a separate task queue process
- Admin password is passed in plain environment variables or CLI arguments in the examples
What it needs
- no GPU
- Docker + Compose
- Needs SQLite (default), PostgreSQL (optional), RabbitMQ (Docker Compose)
- port 8000
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