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@@ -113,7 +113,7 @@ Empirical classification baselines evaluated on `test.jsonl` (1,500 test sentenc
 | **Masinissa-31M** | Linear Probe (Frozen Encoder + Single Linear) | 77.53% | 0.7764 | 0.7389 | 0.8298 | 0.7604 |
 | **Masinissa-31M** | Full Fine-Tuning (RoBERTa-style Head) | **88.80%** | **0.8880** | **0.8831** | **0.9111** | **0.8697** |
 
-Linear Probe evaluates pure feature separability of the frozen pre-trained encoder (Mean Pooling $\rightarrow$ single `nn.Linear`). Full Fine-Tuning fits the encoder end-to-end with a `RobertaClassificationHead` (`Dense` $\rightarrow$ `Tanh` $\rightarrow$ `Dropout` $\rightarrow$ `Linear`).
+Linear Probe evaluates pure feature separability of the frozen pre-trained encoder (Mean Pooling → single `nn.Linear`). Full Fine-Tuning fits the encoder end-to-end with a `RobertaClassificationHead` (`Dense` → `Tanh` → `Dropout` → `Linear`).
 
 ## Why this benchmark exists