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license: cc-by-nc-4.0
language:
- ar
task_categories:
- image-classification
- image-text-to-text
pretty_name: ArGuard – Track A (Arabic Hateful Memes)
tags:
- hate-speech
- memes
- arabic
- multimodal
- multi-label
- arabic-nlp
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: dev_test
path: data/dev_test-*
default: true
dataset_info:
config_name: default
features:
- name: id
dtype: string
- name: image
dtype: image
- name: text
dtype: string
- name: label
dtype: string
- name: fine_grained_label
sequence: string
splits:
- name: train
num_examples: 3500
- name: dev
num_examples: 500
- name: dev_test
num_examples: 500
---
# ArGuard – Track A: Arabic Hateful Memes
This repository hosts the official dataset for **Track A** of the
**ArGuard** shared task: multimodal hateful-meme detection in Arabic.
Each instance is an Arabic meme (image + OCR-extracted overlaid text)
manually annotated for hatefulness and fine-grained sub-types.
> **Content warning.** The dataset contains text and imagery that is
> offensive, discriminatory, or otherwise harmful by design. Handle
> with care.
## Track A subtasks
Given a meme (image + Arabic text):
- **Subtask A1 – Binary.** Classify the meme as `Hateful` or `Not Hateful`.
- **Subtask A2 – Fine-grained category prediction (multi-label).** Predict
the applicable fine-grained sub-type(s) from a unified taxonomy that
covers both hateful and non-hateful categories. Hateful memes draw
labels from the hateful sub-type set (Mocking, Incitement,
Dehumanization, Slurs, Contempt, Inferiority, Exclusion, …); non-hateful
memes draw from `Humor`, `Sarcasm`, plus the shared `Other`. Both
subtasks are evaluated on every meme.
## Splits
| Split | Records | Labels | Source | Released |
|--------------|---------|--------------|----------------------------------------|---------------------------|
| `train` | 3,500 | full | single-annotated bulk | development phase |
| `dev` | 500 | full | single-annotated bulk | development phase |
| `dev_test` | 500 | **dropped** | single-annotated test sample | development phase (leaderboard) |
| `test` | 500 | full | **triple-annotated gold** (calibration) | final-evaluation phase |
- `dev_test` is the **leaderboard set** for the development phase. Labels
are intentionally stripped (`label = null`, `fine_grained_label = []`)
and will be released only after the development phase closes.
- `test` is the **held-out blind test** for final ranking. All 500 records
are triple-annotated with majority voting. This split is not part of
the public release and will appear here only when the final-evaluation
phase begins.
### Binary label distribution
| Split | Hateful | Not Hateful | % Hateful |
|------------|--------:|------------:|----------:|
| train | 1,324 | 2,176 | 37.8% |
| dev | 189 | 311 | 37.8% |
| dev_test | 189 | 311 | 37.8% |
| test | 148 | 352 | 29.6% |
| **Total** | **1,850** | **3,150** | 37.0% |
### Fine-grained sub-types (Subtask A2)
The Subtask A2 label space is **one unified multi-label vocabulary**
that covers both hateful and non-hateful sub-types:
- **Hateful sub-types** (active in the released data): Mocking,
Incitement, Dehumanization, Slurs, Contempt, Inferiority, Exclusion.
- **Non-hateful sub-types**: Humor, Sarcasm.
- **Shared**: Other (used by both Hateful and Not-Hateful memes).
Five additional hateful classes appear in the annotation taxonomy but
have **zero training support** in the released data: Extremism,
Historical, Insults, Stereotyping, Threat. They are documented for
completeness, accepted by the format checker, and ignored by the scorer.
In practice each meme's fine-grained labels are drawn from its own
binary class: a Hateful meme will only carry hateful sub-types (and/or
`Other`); a Not-Hateful meme will only carry `Humor` / `Sarcasm` /
`Other`. Sub-types are multi-label, so per-class counts sum to more
than the meme counts.
## Record schema
```python
{
"id": "f9a8…b1.jpg", # str – original image filename, unique
"image": <PIL.Image.Image>, # embedded bytes, decoded on access
"text": "…", # str – OCR-extracted Arabic meme text
"label": "Hateful" | "Not Hateful" | None, # None on dev_test
"fine_grained_label": [...], # list[str] – empty on dev_test
}
```
## Usage
```python
from datasets import load_dataset
ds = load_dataset("QCRI/ArGuard-Task1")
print(ds)
train_ex = ds["train"][0]
train_ex["image"].show()
print(train_ex["text"], train_ex["label"], train_ex["fine_grained_label"])
# dev_test is unlabelled — used only to produce leaderboard submissions
print(ds["dev_test"][0]["label"]) # -> None
```
## Shared-task resources
- **Website:** https://araieval.gitlab.io/ArGuard2026/
- **Starter kit / baselines / scorers (GitHub):** https://github.com/araieval/ArGuard-2026-tasks
- **Contact organisers:** arguard2026-organizers@googlegroups.com
## Annotation
- All memes are manually annotated following the ArGuard guidelines.
- **train**, **dev**, **dev_test**: single-annotator labels (bulk
annotation).
- **test**: triple-annotated. Binary label is the majority vote; the
fine-grained label set is the union of sub-types selected by
annotators whose binary label matches the majority.
- Inter-annotator agreement on the calibration subset is above 0.81.
## Intended use and limitations
- **Intended use.** Research on Arabic multimodal hate speech detection,
including binary classification, fine-grained sub-type prediction,
and vision-language modelling.
- **Limitations.** Memes reflect online discourse and contain offensive
and harmful content. Annotations on `train` / `dev` / `dev_test` are
single-annotator and may contain noise; only the held-out `test` split
uses triple-annotated majority-voted labels.
- **Not for deployment.** This dataset is for research and benchmarking;
it is not a moderation tool.
## License
Released under **CC BY-NC 4.0** for non-commercial research use only.
Not to be used for commercial purposes or for training systems that
generate harmful content.
## Citation
A citation will be provided when the shared-task overview paper is
released. Until then, please cite this repository URL.
## Contact
- **Email:** arguard2026-organizers@googlegroups.com
- **Website:** https://araieval.gitlab.io/ArGuard2026/
- **GitHub:** https://github.com/araieval/ArGuard-2026-tasks
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