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Last Updated: 08-22-2026 01:35 PM (UTC)

Doccano

Doccano is a text annotation platforms for building machine learning training datasets. The web-based interface supports text classification for sentiment analysis and document categorization, sequence labeling for named entity recognition with overlapping entity support and relation extraction between labeled spans, and sequence-to-sequence annotation for text summarization and machine translation pairs. Collaborative annotation enables multiple annotators to work on the same project simultaneously with per-user progress tracking, annotation guidelines, example assignment to specific members, and filtering by assignee. Auto-labeling integrates with external machine learning model APIs through configurable request and response mapping templates, allowing pre-annotation that annotators can review and correct. Data import accepts plain text, JSONL, CoNLL, and Excel formats, while export produces JSONL and CoNLL datasets compatible with spaCy, Hugging Face Transformers, PaddleNLP, and other training frameworks through the doccano-transformer library. The Django backend with Django REST Framework exposes a complete RESTful API for programmatic project creation, dataset management, and annotation retrieval via the official doccano-client Python library. Celery handles background tasks including dataset import and export processing with Flower providing task monitoring. One-click deployment supports AWS CloudFormation and Heroku alongside Docker Compose for self-hosted environments. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Doccano
Doccano
Doccano
Doccano
Doccano

Benefits

  • Multi-Task Text Annotation Platform
  • Supports text classification, sequence labeling with overlapping entities and relation extraction, and sequence-to-sequence tasks in a single unified web interface.
  • Collaborative Multi-User Annotation
  • Multiple annotators work simultaneously on shared projects with per-user progress tracking, example assignment, annotation guidelines, and filtering by assigned member.
  • Auto-Labeling with External Models
  • Integrates with any machine learning model API through configurable request and response mapping templates for automated pre-annotation that annotators review and correct.
  • Framework-Compatible Dataset Export
  • Exports labeled data in JSONL and CoNLL formats compatible with spaCy, Hugging Face Transformers, and PaddleNLP through the official doccano-transformer library.

Features

  • Named Entity Recognition
  • Sequence labeling interface with keyboard shortcuts, overlapping entity support, color-coded label spans, and relation extraction between annotated entities.
  • RESTful API Access
  • Django REST Framework API enables programmatic project creation, dataset management, and annotation retrieval through the official doccano-client Python library.
  • Multi-Format Data Import
  • Import datasets from plain text, JSONL, CoNLL, and Excel files with automatic parsing and document splitting for immediate annotation readiness.
  • Dark Theme and Mobile Support
  • Responsive design with dark theme and mobile-optimized layout enables annotation from tablets and phones alongside traditional desktop workflows.
  • One-Click Cloud Deployment
  • Deploy via AWS CloudFormation templates, Heroku one-click button, Docker Compose, or pip install with built-in Celery task processing and Flower monitoring.