This is an implementation of our paper: Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model. If you find our code and/or models useful, please cite:
@inproceedings{Dabbaghi-LREC-COLING-2024,
title = "Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model",
author = "Dabbaghi, Shirin and Kruengkrai, Canasai and Yahyapour, Ramin, Yamagishi, Junichi",
year = "2024",
}
We recommend to create a new environment for experiments using conda:
conda create -y -n mla python=3.9
conda activate mla
Then, install mla
from the repository:
git https://github.com/nii-yamagishilab/MLA-FEVEROUS-COLING24.git
cd MLA-FEVEROUS-COLING24
pip install -r requirements.txt
pip install feverous
pip install einops
pip install wandb
python -c "import wandb; wandb.login()"
To ensure that PyTorch is installed and CUDA works properly, run:
python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"
We should see:
1.12.1+cu113
True
For further development or modification, we recommend installing pre-commit
:
pre-commit install
See experiments.
[Pre-trained models part 1] : https://doi.org/10.5281/zenodo.10901784
[Pre-trained models part 2] : https://doi.org/10.5281/zenodo.10902611
This work is supported by the National Institute of Informatics (NII), Japan.
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