This repo contains the code for the second homework of the NLP 2023 course at Sapienza University of Rome.
I implemented a Siamese network trained via contrastive learning, to tackle the Word Sense Disambiguation problem.
I also implemented the paper BEM from Meta, and confronted the performances.
A detailed report can be found in PDF
- Ubuntu distribution
- Either 20.04 or the current LTS (22.04) are perfectly fine.
- If you do not have it installed, please use a virtual machine (or install it as your secondary OS). Plenty of tutorials online for this part.
- Conda, a package and environment management system particularly used for Python in the ML community.
Unless otherwise stated, all commands here are expected to be run from the root directory of this project.
To run test.sh, we need to perform two additional steps:
- Install Docker
- Setup a client
curl -fsSL get.docker.com -o get-docker.sh
sudo sh get-docker.sh
rm get-docker.sh
sudo usermod -aG docker $USERUnfortunately, for the latter command to have effect, you need to logout and re-login. Do it before proceeding. For those who might be unsure what logout means, simply reboot your Ubuntu OS.
Your model will be exposed through a REST server. In order to call it, we need a client. The client has already been written (the evaluation script) but it needs some dependencies to run. We will be using conda to create the environment for this client.
conda create -n nlp2023-hw2 python=3.9
conda activate nlp2023-hw2
pip install -r requirements.txttest.sh is a simple bash script. To run it:
conda activate nlp2023-hw2
bash test.sh data/coarse-grained/test_coarse_grained.json