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---
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:
description: "Arabic multimodal memes dataset."
features:
- name: id
dtype: string
- name: text
dtype: string
- name: image
dtype: Image
- name: img_path
dtype: string
- name: class_label
dtype: string
splits:
- name: train
num_examples: 4007
- name: dev
num_examples: 584
- name: test
num_examples: 1134
---
# 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
```
@article{alam2024armeme,
title={{ArMeme}: Propagandistic Content in Arabic Memes},
author={Alam, Firoj and Hasnat, Abul and Ahmed, Fatema and Hasan, Md Arid and Hasanain, Maram},
year={2024},
journal={arXiv: 2406.03916},
}
``` |