Update dataset card

README.md

@@ -1,5 +1,20 @@
 ---
-license: cc
+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:
@@ -9,7 +24,9 @@ configs:
     path: data/dev-*
   - split: dev_test
     path: data/dev_test-*
+  default: true
 dataset_info:
+  config_name: default
   features:
   - name: id
     dtype: string
@@ -23,14 +40,136 @@ dataset_info:
     sequence: string
   splits:
   - name: train
-    num_bytes: 274812860.5
     num_examples: 3500
   - name: dev
-    num_bytes: 40130069.0
     num_examples: 500
   - name: dev_test
-    num_bytes: 38915856.0
     num_examples: 500
-  download_size: 347829887
-  dataset_size: 353858785.5
 ---
+
+# 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 1A – Binary.** Classify the meme as `Hateful` or `Not Hateful`.
+- **Subtask 1B – Fine-grained hateful.** For `Hateful` memes, predict the
+  applicable fine-grained sub-type(s) (multi-label).
+- **Subtask 1C – Fine-grained non-hateful.** For `Not Hateful` memes,
+  predict the applicable sub-type(s) (multi-label).
+
+## 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
+
+**Hateful sub-types** (Subtask 1B, multi-label):
+Mocking, Incitement, Dehumanization, Slurs, Contempt, Inferiority,
+Exclusion, Stereotyping, Extremism, Threat, Insults, Historical, Other.
+
+**Non-hateful sub-types** (Subtask 1C, multi-label):
+Humor, Sarcasm, Other.
+
+A meme is never assigned both hateful and non-hateful sub-types
+simultaneously. 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
+```
+
+## 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
+
+ArGuard organisers — see https://araieval.gitlab.io/ for contact
+information and shared-task updates.