Add dataset card for ArHateMeme preview

README.md

@@ -1,27 +1,175 @@
----
-license: cc-by-sa-4.0
-configs:
-- config_name: sample_100
-  data_files:
-  - split: train
-    path: sample_100/train-*
-dataset_info:
-  config_name: sample_100
-  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_bytes: 8801740.0
-    num_examples: 100
-  download_size: 8769620
-  dataset_size: 8801740.0
----
+---
+language:
+- ar
+license: cc-by-nc-4.0
+task_categories:
+- image-classification
+- text-classification
+tags:
+- hate-speech
+- memes
+- arabic
+- multimodal
+- multi-label
+pretty_name: Arabic Hateful Memes (ArHateMeme)
+size_categories:
+- n<1K
+configs:
+- config_name: sample_100
+  data_files:
+  - split: train
+    path: sample_100/train-*
+  default: true
+dataset_info:
+  config_name: sample_100
+  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: 100
+---
+
+# Arabic Hateful Memes (ArHateMeme) — Public Sample
+
+This repository hosts a **100-example diversity-sampled preview** drawn from the
+**training split** of the **ArHateMeme** dataset: 5,000 Arabic memes manually
+annotated for hatefulness and fine-grained sub-types. The full dataset will be
+released alongside the associated shared task.
+
+> ⚠️ This preview is intended for format inspection, tooling validation, and
+> schema alignment only. It is **not** a benchmark and should not be used for
+> model evaluation.
+
+---
+
+## About the full dataset
+
+**ArHateMeme** is a multimodal (image + Arabic text) meme dataset annotated for
+hate speech in Arabic. It contains **5,000 memes** with a binary hatefulness
+label and a **multi-label** set of fine-grained sub-types.
+
+### Annotation
+
+- 500 memes are triple-annotated (calibration / gold test set).
+- 4,500 memes are single-annotated by trained annotators.
+- Binary labels use majority voting on the triple-annotated subset.
+- Fine-grained sub-types are the union of sub-types from annotators whose
+  binary label matches the majority label.
+
+### Label Taxonomy
+
+| Aspect | Values |
+|---|---|
+| Binary | `Hateful`, `Not Hateful` |
+| Hateful sub-types | Mocking, Incitement, Dehumanization, Slurs, Contempt, Inferiority, Exclusion, Stereotyping, Extremism, Threat, Insults, Historical, Other |
+| Non-hateful sub-types | Humor, Sarcasm, Other |
+
+A meme is never assigned both hateful and non-hateful sub-types simultaneously.
+
+### Official splits (full dataset)
+
+| Split | Records | % | Hateful | Not Hateful |
+|---|---|---|---|---|
+| train | 3,500 | 70% | 1,324 | 2,176 |
+| dev | 500 | 10% | 189 | 311 |
+| test | 1,000 | 20% | 337 | 663 |
+| **Total** | **5,000** | 100% | **1,850** | **3,150** |
+
+All 500 triple-annotated gold memes are in the **test** split. Splits are
+stratified by binary label (seed 42) and there is no meme overlap between
+splits.
+
+---
+
+## About this preview sample
+
+- **Source split:** `train` (single-annotated bulk memes)
+- **Size:** 100 memes
+- **Sampling:** stratified to cover **every fine-grained sub-type present in
+  the training data** and preserve a realistic hateful / non-hateful ratio.
+- **Images:** embedded as bytes via the `datasets.Image` feature — no external
+  files required.
+- **Arrow/Parquet:** stored as a Hugging Face `Dataset` (Arrow) and uploaded as
+  parquet shards so the Hub viewer renders images inline.
+
+### Sample distribution
+
+| Binary | Count |
+|---|---|
+| Not Hateful | 60 |
+| Hateful | 40 |
+
+| Fine-grained sub-type | Count |
+|---|---|
+| Sarcasm | 27 |
+| Humor | 23 |
+| Mocking | 19 |
+| Incitement | 15 |
+| Other | 10 |
+| Contempt | 8 |
+| Slurs | 8 |
+| Dehumanization | 8 |
+| Exclusion | 5 |
+| Inferiority | 5 |
+
+(Fine-grained counts sum to more than 100 because the label is multi-label.)
+
+---
+
+## Record schema
+
+```python
+{
+    "id": "102396787_870863910087838_...jpg",   # string, unique meme id
+    "image": <PIL.Image>,                       # embedded bytes, decoded on load
+    "text": "…",                                # OCR-extracted meme text (Arabic)
+    "label": "Hateful" | "Not Hateful",         # binary label
+    "fine_grained_label": ["Mocking", "Incitement"],  # multi-label sub-types
+}
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("QCRI/Arabic-Hateful-Memes", split="train")
+print(ds)
+
+example = ds[0]
+example["image"].show()
+print(example["text"], example["label"], example["fine_grained_label"])
+```
+
+---
+
+## Intended use and limitations
+
+- **Intended use:** research on Arabic multimodal hate speech detection,
+  including binary classification, fine-grained sub-type classification, and
+  vision-language modeling evaluation.
+- **Limitations:** memes reflect online discourse and contain offensive and
+  harmful content. The preview is not balanced and is too small for training
+  or evaluation. Annotations are partially single-annotator and may contain
+  noise.
+- **Content warning:** this dataset contains text and imagery that is
+  offensive, discriminatory, or otherwise harmful by design. Handle with care.
+
+## License
+
+Released under **CC BY-NC 4.0** for 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 full dataset is released. Until then,
+please cite this repository URL.