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
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
- 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
DAML project team welcomes contributions from the community. For more detailed information, see CONTRIBUTING.md.
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.
Copyright 2019-2021 VMware, Inc. SPDX-License-Identifier: Apache-2.0.