Create a virtual env and run pip install requirements.txt
You can read the main to see an example of usage in the file src/embedding_manager.py
Our work is based on the CamemBERT model to create word embedding. We use an efficient KDTree implementation (The Annoy library by Spotify) to compare the embeddings. For better performance, we split the data in differents different group based by a POS tagging given by spacy.