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https://github.com/percevalw/metanno

Annotator building tool for Jupyter
https://github.com/percevalw/metanno

annotator customizable jupyter modular nlp

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Annotator building tool for Jupyter

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README 

        



[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/percevalw/metanno/HEAD?urlpath=lab%2Ftree%2Fexamples%2FAnnotator.ipynb)

[![DOI](https://zenodo.org/badge/244972164.svg)](https://zenodo.org/doi/10.5281/zenodo.10689826)



--------------------------------------------------------------------------------



Metanno is a JupyterLab extension that allows you build your own annotator. For the moment, it focuses on textual documents with rich structured entities.

Its main objectives are:

- modularity: you decide how many views of your data are needed

- customization: you can easily customize the software behavior in Python and see the changes immediately

- interactivity: all of your annotations are immediately available as Python objects as soon as you edit something



## Features



- ↵ multiline and nested span annotations

- 🖇️ nested, relational, complex annotation with table views

- 🔗 multiple data type: hyperlinks, text, lists

- 🪟 text view or table view

- ✨ extensive customization power

- 🐍 write your app in Python, execute it in the browser (or in the kernel, you decide)

- 🚀 fast: the client side is written in React, and every action is processed in the browser directly by default

- 🌐 websocket communication: you do not need to open any port

- ⏮️ immutable state management, any state mutation is recorded and undoable



## Citation



If you use Metanno, consider citing this work as below.



```bibtex

@software{wajsburt_metanno,

  author       = {Wajsbürt, Perceval},

  title        = {Metanno: a modular annotator building framework},

  month        = feb,

  year         = 2024,

  publisher    = {Zenodo},

  version      = {0.0.9},

  doi          = {10.5281/zenodo.10689827},

  url          = {https://doi.org/10.5281/zenodo.10689827}

}

```



## Installation



This project is still under development and is subject to change.

A simple pip install should be enough if you use Jupyterlab 3. You do not need to open any port.

```

# To setup the environment

conda create --name annotation python=3.9

conda activate annotation

pip install ipykernel && python -m ipykernel install --user --name annotation



# To install the package

pip install metanno

```



If **you're a user in a shared Jupyter environment** (you did not run the `jupyter lab` command), you should instead install the extension at the user level



```

pip install metanno --user

```



## Why



The choice of annotation software must be taken into account in the design of the annotation scheme.

For example, it is difficult to annotate implicit/document-level entities in Brat or to annotate relations on multiple lines, and impossible to handle multiple documents at once.

There are many annotation tools available (see [Neves et al.](https://pubmed.ncbi.nlm.nih.gov/31838514/)), but most of them are either proprietary, poorly adapted to document or multi-document annotation,

require a complex installation that is not compatible with existing remote work environments, or are difficult to customize.

Finally, the standardization of annotation levels (mention / relation / event) is an obstacle to the development of new tasks.

Given the limitations of the existing softwares and the difficulty to cover every need with a single static annotator,

this project was initiated to provide a modular and fully customizable annotation framework, Metanno, and address these difficulties.  



## Demo



You can try it with [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/percevalw/metanno/HEAD?urlpath=lab%2Ftree%2Fexamples%2FAnnotator.ipynb). Be patient though, when there is no cached build, it may take a few minutes to start.



![https://github.com/percevalw/metanno/raw/master/doc/screenshot.png](https://github.com/percevalw/metanno/raw/master/doc/screenshot.png)



## How it works






All the app is controlled by a single state, replicated on both the frontend (the Jupyter client) and the backend (the Python kernel).

Each views rendered in Jupyter uses a derivation of this state (think `view_data = fn(app_data)`) and calls functions in the app class whenever an event occurs.

This app class is written in Python (by you), automatically translated into javascript and sent to the front-end such that every action taken by the

user is answered immediately.

If a given function modifies the state (wrapped by the `@produce` decorator), the changes are sent to the backend or the frontend to keep the state replicas in sync.

If a function needs to be executed exclusively on the frontend or the backend (for example, triggering a database query on the backend), you can wrap it

with `@frontend_only` or `@kernel_only`, and the call will be transmitted over the Jupyter websocket.



## Todo



- add basic app samples

- add a documentation

- add more table column types and renderers (numerical, dates, ...)

- ~~add customizable column filterers~~

- add relations visualizations and edition with editable arrows

- add an image annotation view

- ~~finish javascript to typescript conversion~~

- ~~customizable undo / redo logic~~

- add multi-cell editing (see a [react-data-grid](https://github.com/adazzle/react-data-grid) PR)

- add a test suite (Cypress ?)

- make a standalone version (without Jupyter)



## Contribute



Any contribution is welcome, feel free to open a PR.