| --- |
| 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-* |
| - split: test |
| path: data/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 |
| - name: 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 | full | single-annotated test sample | development phase (leaderboard); **labels released** | |
| | `test` | 500 | **dropped** | **triple-annotated gold** (calibration) | final-evaluation phase (leaderboard) | |
|
|
| - `dev_test` was the **leaderboard set** for the development phase. That |
| phase is now closed and its gold labels have been **released**, so |
| `dev_test` is usable as additional labelled data. It is disjoint from |
| `test`. |
| - `test` is the **blind test set** for the final-evaluation phase and is |
| the file you submit predictions for. It is released the same way as |
| `dev_test` — images and OCR text only, labels stripped. All 500 records |
| are triple-annotated with majority voting; the gold labels are held in |
| the CodaBench reference data and are never published here. |
|
|
| ### 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% | |
| | **Total (labelled)** | **1,702** | **2,798** | 37.8% | |
| |
| The label distribution of the blind `test` split is withheld during the |
| final-evaluation phase. |
| |
| ### 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 the blind `test` split |
| "fine_grained_label": [...], # list[str] – empty on the blind `test` split |
| } |
| ``` |
| |
| ## 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"]) |
|
|
| # `test` is the only unlabelled split — used only to produce submissions |
| print(ds["test"][0]["label"]) # -> None |
| ``` |
| |
| The blind `test` split is the submission target for the final-evaluation |
| phase. Train on `train` + `dev` + `dev_test`, predict on `test`, and |
| upload to CodaBench (links below). |
| |
| ## Shared-task resources |
| |
| - **Website:** https://araieval.github.io/ArGuard2026/ |
| - **Starter kit / baselines / scorers (GitHub):** https://github.com/araieval/ArGuard-2026-tasks |
| - **Submission — Subtask A1:** https://www.codabench.org/competitions/16909/ |
| - **Submission — Subtask A2:** https://www.codabench.org/competitions/16910/ |
| - **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.github.io/ArGuard2026/ |
| - **GitHub:** https://github.com/araieval/ArGuard-2026-tasks |
| |
