| # HateXplain: Annotated Dataset for Hate Speech and Offensive Language Explanation |
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| **HateXplain** is a benchmark dataset for hate speech and offensive language detection, uniquely annotated with *explanations* and *rationales*. It is designed to support the development of interpretable models in online content moderation. |
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| ## 📚 Dataset Summary |
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| - **Languages**: English |
| - **Samples**: ~20,000 social media posts |
| - **Annotations**: |
| - `label`: `normal`, `offensive`, or `hatespeech` |
| - `annotators`: Multiple annotators per post with consensus labeling |
| - `rationales`: Token-level binary rationales indicating why the label was chosen |
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| ## 📁 Dataset Structure |
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| | Column | Description | |
| |---------------|---------------------------------------------------------------------------| |
| | `post_id` | Unique ID for each post (e.g., Twitter ID) | |
| | `post_tokens` | List of tokenized words from the post | |
| | `annotators` | List of dictionaries with label, annotator_id, and rationale | |
| | `rationales` | List of lists indicating which tokens are part of the explanation | |
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| ## 🔍 Example Entry |
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| ```json |
| { |
| "post_id": "1179055004553900032_twitter", |
| "post_tokens": ["i", "dont", "think", "im", "getting", "my", "baby", "them", "white", "9", "s", "for", "school"], |
| "annotators": [ |
| { |
| "label": "normal", |
| "annotator_id": 1, |
| "rationale": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
| } |
| ], |
| "rationales": [] |
| } |
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