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@@ -53,6 +53,21 @@ Binary classification: `Hate` vs `Normal`.
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  | Hate | 0.87 | 0.89 | 0.88 |
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  | **Macro avg** | **0.89** | **0.89** | **0.89** |
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  | **Accuracy** | | | **0.89** |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### By Language
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  | Hate | 0.87 | 0.89 | 0.88 |
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  | **Macro avg** | **0.89** | **0.89** | **0.89** |
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  | **Accuracy** | | | **0.89** |
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+ ## Benchmark — MAKINI-Hate v1 vs Existing Models
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+ Evaluated on AfriHate Swahili test set (3,168 examples).
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+
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+ | Model | Swahili F1 Macro |
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+ |---|---|
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+ | **MAKINI-Hate v1 (ours)** | **0.92** |
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+ | AfroXLMR-76L (monolingual) | 0.78* |
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+ | GPT-4o (20-shot) | 0.75 |
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+ | SetFit (20-shot) | 0.75 |
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+ | Mistral-7B (5-shot) | 0.59 |
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+
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+ *AfroXLMR-76L results from Muhammad et al. (2025) AfriHate paper.
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+ Note: Comparisons are not perfectly controlled — AfriHate baselines
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+ use 3-class labels while MAKINI uses binary. Binary is an easier task.
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+ This limitation is documented transparently.
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  ### By Language
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