This repository contains a modified version of the classifier from https://github.com/Odeuropa/wp3-information-extraction-system-v2 that can be used through a web interface.
Before running the demo download all the model in https://zenodo.org/records/10598306 and move it in models folder.
IMPORTANT: Before run docker build open main_odeuropa.py and set for each language if you want to use GPU or CPU.
docker build -t odeuropademo .
docker run -p 8509:8509 --gpus '"device=0"' odeuropademo
To change the port edit the Dockerfile.
Create an environment as you prefer. E.g. with conda.
conda create --name predictiondemo python=3.8
conda activate predictiondemo
Install the requirements:
pip install -r requirements.txt
IMPORTANT: Before running the server open main_odeuropa.py and set for each language if you want to use GPU or CPU.
Set the max size of the files allowed to be uploaded in the demo.
export STREAMLIT_SERVER_MAX_UPLOAD_SIZE=1
Run the interface on the dessired port. The models will be loaded when opening the link for the first time.
streamlit run main_odeuropa.py --server.port 8509
If you use this resource, please cite:
Menini, Stefano. Semantic Frame Extraction in Multilingual Olfactory Events. In Proceedings of LREC-Coling 2024
This work has been realised in the context of Odeuropa, a research project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004469.