This repository contains the code to reproduce the results of the paper CL-UZH@EDOS2023: Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions.
Setup the environment:
python -m venv env
source env/bin/activate.bin
pip3 install -r requirements.txt
Download, split and preprocess the datasets:
cd CL-UZH-EDOS-2023
bash download.sh
bash preprocess.sh
Execute the experiments:
bash execute_experiments.sh
61 commits
Python
94.1%
Shell
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This repository contains the code to reproduce the results of the paper CL-UZH@EDOS2023: Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions.
Setup the environment:
python -m venv env
source env/bin/activate.bin
pip3 install -r requirements.txt
Download, split and preprocess the datasets:
cd CL-UZH-EDOS-2023
bash download.sh
bash preprocess.sh
Execute the experiments:
bash execute_experiments.sh
61 commits
Python
94.1%
Shell
5.9%