ActiveTigger is an text annotation web tool dedicated for computational social sciences. It is designed to assist exploration and model (BERT) fine-tuning to annotate text dataset relying on active learning.

The app is currently 🚧 under development 🚧.

The current version is a refactor of R Shiny ActiveTigger app (Julien Boelaert & Etienne Ollion)

The app is built on a client/API architecture :

• the server runs an API with FastAPI
• the client is in React

Clone the repository

git clone https://github.com/emilienschultz/activetigger.git

Create a virtual environnement with Python 3.11 and install requirements

pip install -r activetigger/api/requirements.txt

Add a specific config.yaml file in the api directory if you want to specify the path of the static files and database (you can modify and rename the config.yaml.sample or use the default config):

• path : path to store files (for instance ./data)
• path_models : path to store the models (for instance ./data/models)

Launch the server (on 0.0.0.0 port 5000 by default, you can configurate exposed port if needed with -p PORTNUM).

cd activetigger/api
python -m activetigger

You can also install the last stable version of the API from PyPi with

pip install activetigger

The frontend is written in React/Typescript. To run the dev version and to build the app, you need first to install node.js and npm (version > 20).

sudo apt-get install nodejs npm

Then you can install the npm packages

cd frontend
npm i

You can then run the dev version

npm run dev

To compile

npm run compile

To build

npm run build

🚧 to write 🚧

🚧 to write 🚧

🚧 to write 🚧