Data Annotator for Machine Learning Files

Data annotator for machine learning

This is an exact mirror of the Data Annotator for Machine Learning project, hosted at https://github.com/vmware/data-annotator-for-machine-learning. SourceForge is not affiliated with Data Annotator for Machine Learning.

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Name	Modified	Size	InfoDownloads / Week
Parent folder
README.md	2022-08-30	1.5 kB	0
v1.0.0.tar.gz	2022-08-30	9.4 MB	0
v1.0.0.zip	2022-08-30	9.5 MB	0
Totals: 3 Items		18.9 MB	0

Support for common annotation tasks:
Text classification
Named entity recognition. Support intersecting and overlapping text labeling as well as a secondary labeling.
Tabular classification and regression
Images recognition with bounding boxes and polygons
Log labeling
Support label type
Text
Numeric
Upload hierarchical taxonomy file
Active learning with uncertainly sampling to query unlabeled data. Allow users to choose active learning query strategy and select Spacy models (for different languages and model sizes)
Project tracking with real time data aggregation and review process
Keep track of the progress of every annotator. Annotators are informed if they are not starting the task and not making progress before the entire labelling project is completed.
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
Review the labelling quality for Text, Tabular, NER, and Image projects. Project owners can review the annotation, make modification, and pass review. Review assignment logic support most_uncertain, random and sequential.
Swagger API for programmatic labeling, connecting to data pipelines and more
Slack integration. Assigning a Text or Tabular project to a Slack channel and all users in the channel can start the annotation task in Slack.

Source: README.md, updated 2022-08-30

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