---
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