# data-annotator-for-machine-learning

**Repository Path**: mirrors_vmware-archive/data-annotator-for-machine-learning

## Basic Information

- **Project Name**: data-annotator-for-machine-learning
- **Description**: Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-18
- **Last Updated**: 2025-12-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![check-header](https://github.com/vmware/data-annotator-for-machine-learning/actions/workflows/check-header.yml/badge.svg?branch=master&event=push)](https://github.com/vmware/data-annotator-for-machine-learning/actions/workflows/check-header.yml)
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[![coverage report](https://avatars.githubusercontent.com/in/12526?s=20&v=4 "You can find the coverage report here")](https://sonarcloud.io/dashboard?id=vmware-daml-annotation-app)

<div align='center'> <h1>Data Annotator for Machine Learning</h1> </div>

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

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## Helpful links

- [ATO 2021 talk](https://www.youtube.com/watch?v=n0WghXqCH5o)
- [DAML medium article](https://medium.com/vmware-data-ml-blog/introducing-data-annotator-for-machine-learning-e8af2f19497a)
- [User guide](https://github.com/vmware/data-annotator-for-machine-learning/wiki/DAML-User-Guide)
- [Tutorial](https://github.com/vmware/data-annotator-for-machine-learning/wiki/Tutorial:-Using-DAML-to-Label-the-Sentiment-of--VMware-Reddit-and-Twitter-Comments)
- [DAML release note](https://github.com/vmware/data-annotator-for-machine-learning/releases)


## 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](RUN-WITH-DOCKER.md)
- For development environment and build configuration see [build documentation](BUILD.md)
- For the slack integration configuration see [manifest documentation](docs/manifest.yml)

## Contributing

DAML project team welcomes contributions from the community. For more detailed information, see [CONTRIBUTING.md](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.
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## Copyright and license

Copyright 2019-2021 VMware, Inc.
SPDX-License-Identifier: Apache-2.0.