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https://github.com/justaguyalways/ToxVidLM_ACL_2024
Code and Datasets for our accepted long paper at ACL 2024, regarding toxicity detection in code-mixed hinglish video content
https://github.com/justaguyalways/ToxVidLM_ACL_2024
Last synced: 10 days ago
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Code and Datasets for our accepted long paper at ACL 2024, regarding toxicity detection in code-mixed hinglish video content
- Host: GitHub
- URL: https://github.com/justaguyalways/ToxVidLM_ACL_2024
- Owner: justaguyalways
- License: mit
- Created: 2024-05-21T04:48:24.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-08-13T08:58:22.000Z (4 months ago)
- Last Synced: 2024-08-13T10:38:13.612Z (4 months ago)
- Language: Python
- Homepage:
- Size: 41 KB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Paper Link
Please find our paper at [https://aclanthology.org/2024.findings-acl.663/](https://aclanthology.org/2024.findings-acl.663/)
## Installation
1. Clone the repository:
```bash
git clone https://github.com/justaguyalways/ToxVidLLM_ACL_2024.git
cd ToxVidLLM_ACL_2024
```
2. Create a conda environment and activate it:
```bash
conda create --name your-env-name python=3.8
conda activate your-env-name
```
3. Install the required packages:
```bash
pip install -r requirements.txt
```
## Dataset
1. Download the dataset from the following link: [ToxCMM Dataset Link](https://drive.google.com/drive/folders/1lAl6KpewLv9bO64Ad5fccBOImSZgRPPP?usp=sharing)
2. Unzip the downloaded file:
```bash
unzip dataset.zip
```
3. Move the unzipped folder to the `final_data` directory within the repository:
```bash
mv path_to_unzipped_folder final_data
```
## Usage
### Training
To train the model, run `train.py`. You can specify which GPU to use with the `CUDA_VISIBLE_DEVICES` environment variable. Replace `xxxx` with the appropriate GPU ID (e.g., `0` for the first GPU).
```bash
CUDA_VISIBLE_DEVICES=xxxx python train.py
```
Example:
```bash
CUDA_VISIBLE_DEVICES=0 python train.py
```
### Testing
To test the model, run `test.py`. Similarly, you can specify the GPU with `CUDA_VISIBLE_DEVICES`.
```bash
CUDA_VISIBLE_DEVICES=xxxx python test.py
```
Example:
```bash
CUDA_VISIBLE_DEVICES=0 python test.py
```
### Citation
If you use our work or find it useful, please cite:
```plaintext
@inproceedings{maity-etal-2024-toxvidlm,
title = "{T}ox{V}id{LM}: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos",
author = "Maity, Krishanu and
Sangeetha, Poornash and
Saha, Sriparna and
Bhattacharyya, Pushpak",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.663",
pages = "11130--11142",
}
```