Data Annotator for Machine Learning

Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitating the creation and management of annotations.

Core features include:

Support for common annotation tasks:
- Text classification
- Named entity recognition
- Tabular classification and regresion
- Images recognition with bounding boxes and polygons
- Log labeling
- Question answer
Active learning with uncertainly sampling to query unlabeled data
Project tracking with real time data aggregation and review process
User management panel with role-based access control
Data management
- Import in common data formats
- Export in ML friendly formats
- Data sharing through community datasets
Swagger API for programmatic labeling, connecting to data pipelines and more

Helpful links

What is included

DAML project includes three components:

annotation-app: Angular application for the UI
annotation-service: Backend services built with Node & Express
active-learning-service: Django application providing active learning api using modAL library for pool-based uncertainty sampling to rank the unlabelled data

Quick start

For the docker version usage to see run with docker documentation
For development environment and build configuration see build documentation
For the slack integration configuration see manifest documentation

Contributing

DAML project team welcomes contributions from the community. For more detailed information, see CONTRIBUTING.md.

Bugs and feature requests

Have a bug or a feature request? Please first read the issue guidelines and search for existing and closed issues. If your problem or idea is not addressed yet, please open a new issue.