https://github.com/codewithzichao/multimodal-transformers
Our source code for EACL2021 workshop: Meme Classification for Tamil Language. We took first place in this task finally!🥳
https://github.com/codewithzichao/multimodal-transformers
eacl meme multimodal pytorch transformer
Last synced: 2 months ago
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Our source code for EACL2021 workshop: Meme Classification for Tamil Language. We took first place in this task finally!🥳
- Host: GitHub
- URL: https://github.com/codewithzichao/multimodal-transformers
- Owner: codewithzichao
- Created: 2021-01-22T09:45:38.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-01-30T15:59:21.000Z (over 5 years ago)
- Last Synced: 2025-08-24T12:23:08.020Z (10 months ago)
- Topics: eacl, meme, multimodal, pytorch, transformer
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Meme Classification for Tamil Language at EACL2021 Workshop
Our source code for EACL2021 workshop: Meme Classification for Tamil Language. We took first place in this task finally!🥳
**Updated:** Source code is released!🤩
> I will release the code very soon.
## Repository structure
```shell
├── MyLoss.py # Impelmentation of some loss function
├── README.md
├── __init__.py
├── args.py # declare some argument
├── ckpt
│  └── README.md
├── data # store data
│  └── README.md
├── gen_data.py # generate Dataset
├── install_cli.sh # install required package
├── logfile # store logfile during training
├── main.py # train model
├── model.py # define model
├── multimodal_attention.py # Implentation of multimodal attention layer
├── pred_data
│  └── README.md
├── preprocessing.py # preprocess the data
├── pretrained_weights # store pretrained weights of resnet and xlm-roberta
│  └── README.md
├── run.sh # run model
└── train.py # define training and validation loop
```
## Installation
Use the following command so that you can install all of required packages:
```shell
sh install_cli.sh
```
## Preprocessing
The first step is to preprocess the data. Just use the following command:
```shell
python3 -u preprocessing.py
```
## Training
The second step is to train our model. Use the following command:
```shell
nohup sh run.sh > run_log.log 2>&1 &
```
## Inference
The final step is inference after training. Use the following command:
```shell
nohup python3 -u inference.py > inference.log 2>&1 &
```
Congralutions! You have got the final results!🤩
> If you use our code, please indicate the source.