QCRI/Arabic-Hateful-Memes

--- 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.--- 语言： - 阿拉伯语（ar） 许可证：CC BY-NC 4.0 任务类别： - 图像分类 - 文本分类 标签： - 仇恨言论（hate-speech） - 表情包（memes） - 阿拉伯语 - 多模态（multimodal） - 多标签（multi-label） 友好名称：阿拉伯语仇恨表情包（Arabic Hateful Memes, ArHateMeme） 规模类别： - 样本数少于1000 配置项： - 配置名称：sample_100 数据文件： - 拆分：训练集 路径：sample_100/train-* 默认启用：true 数据集信息： 配置名称：sample_100 特征： - 名称：id 数据类型：字符串 - 名称：image 数据类型：图像 - 名称：text 数据类型：字符串 - 名称：label 数据类型：字符串 - 名称：fine_grained_label 序列类型：字符串 拆分： - 名称：训练集 样本数：100 --- # 阿拉伯语仇恨表情包（ArHateMeme）—— 公开预览样本 本仓库托管了取自**ArHateMeme**数据集**训练拆分**的**100条多样性采样预览样本**，该完整数据集包含5000条经人工标注仇恨属性与细粒度子类别的阿拉伯语表情包。完整数据集将与配套共享任务一同发布。 > ⚠️ 本预览仅用于格式检查、工具验证与模式对齐，**并非基准测试集**，不得用于模型评估。 --- ## 完整数据集说明 **ArHateMeme**是一个面向阿拉伯语仇恨言论标注的多模态（multimodal，图像+阿拉伯语文本）表情包数据集。它包含**5000条表情包**，带有二元仇恨属性标签以及多标签（multi-label）形式的细粒度子类别标签。 ### 标注流程 - 500条表情包经过三重标注（用于校准/金标准测试集）。 - 4500条表情包由经过培训的标注人员进行单标注。 - 二元标签采用三重标注子集的多数投票结果。 - 细粒度子类别取所有二元标签与多数标签一致的标注人员所提交子类型的并集。 ### 标签分类体系 | 维度 | 取值 | |---|---| | 二元标签 | `仇恨`、`非仇恨` | | 仇恨子类 | 嘲讽、煽动、非人化、污言秽语、轻蔑、低等化、排斥、刻板印象、极端主义、威胁、侮辱、历史相关、其他 | | 非仇恨子类 | 幽默、讽刺、其他 | 同一条表情包不会同时被标注仇恨子类与非仇恨子类。 ### 完整数据集官方拆分 | 拆分 | 样本数 | 占比 | 仇恨样本 | 非仇恨样本 | |---|---|---|---|---| | 训练集 | 3500 | 70% | 1324 | 2176 | | 验证集 | 500 | 10% | 189 | 311 | | 测试集 | 1000 | 20% | 337 | 663 | | **总计** | **5000** | 100% | **1850** | **3150** | 所有500条三重标注的金标准表情包均归入**测试集**拆分。各拆分按二元标签分层（随机种子42），拆分间无表情包重叠。 --- ## 本预览样本说明 - **来源拆分**：`train`（单标注的批量表情包） - **规模**：100条表情包 - **采样方式**：分层采样，以覆盖训练数据中所有细粒度子类别，并保留符合实际分布的仇恨/非仇恨样本比例。 - **图像存储**：以`datasets.Image`特征以字节形式内嵌，无需依赖外部文件。 - **存储格式**：以Hugging Face `Dataset`（Arrow格式）存储，并上传为Parquet分片，以便Hugging Face Hub查看器可内嵌渲染图像。 ### 样本分布 | 二元标签 | 数量 | |---|---| | 非仇恨 | 60 | | 仇恨 | 40 | | 细粒度子类别 | 数量 | |---|---| | 讽刺 | 27 | | 幽默 | 23 | | 嘲讽 | 19 | | 煽动 | 15 | | 其他 | 10 | | 轻蔑 | 8 | | 污言秽语 | 8 | | 非人化 | 8 | | 排斥 | 5 | | 低等化 | 5 | （细粒度计数总和超过100，因为标签采用多标签形式。） --- ## 记录模式 python { "id": "102396787_870863910087838_...jpg", # 字符串类型，唯一表情包ID "image": <PIL.Image>, # 内嵌字节，加载时自动解码 "text": "…", # 经OCR（光学字符识别）提取的表情包阿拉伯语文本 "label": "Hateful" | "Not Hateful", # 二元标签，取值为「仇恨」或「非仇恨」 "fine_grained_label": ["嘲讽", "煽动"], # 多标签形式的细子类标签 } ## 使用方法 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"]) --- ## 预期用途与局限性 - **预期用途**：用于阿拉伯语多模态仇恨言论检测相关研究，包括二元分类、细粒度子类别分类以及视觉语言模型评估。 - **局限性**：表情包反映了网络话语生态，包含冒犯性与有害内容。本预览样本不平衡且规模过小，无法用于训练或评估。标注部分采用单标注流程，可能存在标注噪声。 - **内容预警**：本数据集包含具有冒犯性、歧视性或其他有害性质的文本与图像，请谨慎处理。 ## 许可证 本数据集以**CC BY-NC 4.0**许可证发布，仅可用于研究用途，不得用于商业目的或训练会生成有害内容的系统。 ## 引用 完整数据集发布时将提供正式引用格式。在此之前，请引用本仓库的URL。