Datasets:

QCRI
/

ArMeme

---
license: cc-by-nc-sa-4.0
task_categories:
- image-classification
- text-classification
- visual-question-answering
language:
- ar
pretty_name: ArMeme
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: image
    dtype: image
  - name: img_path
    dtype: string
  - name: class_label
    dtype:
      class_label:
        names:
          '0': not_propaganda
          '1': propaganda
          '2': not-meme
          '3': other
  splits:
  - name: train
    num_bytes: 288878900.171
    num_examples: 4007
  - name: dev
    num_bytes: 45908447.0
    num_examples: 584
  - name: test
    num_bytes: 81787436.176
    num_examples: 1134
  download_size: 423396230
  dataset_size: 416574783.347
---
# ArMeme Dataset

## Overview

ArMeme is the first multimodal Arabic memes dataset that includes both text and images, collected from various social media platforms. It serves as the first resource dedicated to Arabic multimodal research. While the dataset has been annotated to identify propaganda in memes, it is versatile and can be utilized for a wide range of other research purposes, including sentiment analysis, hate speech detection, cultural studies, meme generation, and cross-lingual transfer learning. The dataset opens new avenues for exploring the intersection of language, culture, and visual communication.

## Dataset Structure

The dataset is divided into three splits:
- **Train**: The training set
- **Dev**: The development/validation set
- **Test**: The test set

Each entry in the dataset includes:
- `id`: id corresponds to the entry
- `text`: The textual content associated with the image.
- `image`: The corresponding image.
- `img_path`: The file path to the image.


## How to Use

You can load the dataset using the `datasets` library from Hugging Face:

```python
from datasets import load_dataset

dataset = load_dataset("QCRI/ArMeme")

# Specify the directory where you want to save the dataset
output_dir="./ArMeme/"

# Save the dataset to the specified directory. This will save all splits to the output directory.
dataset.save_to_disk(output_dir)

# If you want to get the raw images from HF dataset format

from PIL import Image
import os
import json

# Directory to save the images
output_dir="./ArMeme/"
os.makedirs(output_dir, exist_ok=True)

# Iterate over the dataset and save each image
for split in ['train','dev','test']:     
    jsonl_path = os.path.join(output_dir, f"arabic_memes_categorization_{split}.jsonl")
    with open(jsonl_path, 'w', encoding='utf-8') as f:    
        for idx, item in enumerate(dataset[split]):
            # Access the image directly as it's already a PIL.Image object
            image = item['image']
            image_path = os.path.join(output_dir, item['img_path'])
            # Ensure the directory exists
            os.makedirs(os.path.dirname(image_path), exist_ok=True)
            image.save(image_path)
            del item['image']
            f.write(json.dumps(item, ensure_ascii=False) + '\n')
```

**Language:** Arabic

**Modality:** Multimodal (text + image)

**Number of Samples:** ~6000


## License

This dataset is licensed under the **CC-By-NC-SA-4.0** license.

## Citation
Please find the paper on [ArXiv](https://arxiv.org/pdf/2406.03916v2) and use the bib info below to cite the paper. 

```
@inproceedings{alam2024armeme,
  title={{ArMeme}: Propagandistic Content in Arabic Memes},
  author={Alam, Firoj and Hasnat, Abul and Ahmed, Fatema and Hasan, Md Arid and Hasanain, Maram},
  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2024},
  address={Miami, Florida},
  month={November 12--16},
  publisher={Association for Computational Linguistics},
}

```