Datasets:

QCRI
/

ArGuard-Task1

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

{
    "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

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