ActiveTigger1 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 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
To enable GPU support, install Rapids Cuml. For instance, for CUDA 12
pip install --extra-index-url=https://pypi.nvidia.com "cuml-cu12==24.10.*"
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
You can deploy the app with Github Pages for tests
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The development of Active Tigger is supported by : DRARI Île-de-France ECODEC Progedo
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Footnotes
-
The current version is a refactor of R Shiny ActiveTigger app (Julien Boelaert & Etienne Ollion) ↩