Skip to content

Named Entity Recognition (NER) Annotation tool for SpaCy. Generates Traning Data as a JSON which can be readily used.

License

Notifications You must be signed in to change notification settings

realnav1234/ner-annotator

 
 

Repository files navigation

NER Annotator for Spacy

NER Annotator for SpaCy allows you to create training data for creating a custom NER Model with custom tags.

Development

Requirements

  1. Node JS 12.x or 14.x
  2. Yarn Package Manager
  3. Rust (for building desktop versions)

Running it locally for development

  1. Open another terminal and start the server for the UI
yarn
yarn serve

Now go to http:https://localhost:8081/ner-annotator/

Developing the desktop application

The desktop applications have been created using Tauri.

yarn tauri:serve

To build the final binaries run

yarn tauri:build

Credits

  1. App Icon - Ornithology icons created by Freepik - Flaticon

Changelog

Version 1.2.0

  • Import annotations.
  • Open a new file while one is already open.
  • Keyboard shortcuts. A future release will add instructions in the app itself, but for now:
    • Num Keys (1 - 9) : Change classes
    • Space Bar: Save and move to the next sentence
    • Right Arrow Key: Skip and move to the next sentence
    • Left Arrow Key: Go back to the previous sentence
    • Esc: Discard changes to the current sentence
  • Lots and lots of bug fixes.

Version 1.1.0

  • Adds "Back" button that allows navigating back to sentences/text blocks that's already been tagged and make changes.

Version 1.0.0

  • Rewritten UI using Quasar framework
  • Export and Import tags
Version 0.1.1
  • #14 - Remembers tags across sessions
  • #3 - Adds a button to enable/disable removing of tags to prevent accidental removal of tags
Version 0.1.0
  • Adds the desktop application

About

Named Entity Recognition (NER) Annotation tool for SpaCy. Generates Traning Data as a JSON which can be readily used.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Vue 65.7%
  • JavaScript 30.7%
  • SCSS 1.3%
  • HTML 1.2%
  • Rust 1.1%