Update README.md

README.md

@@ -472,13 +472,13 @@ To further examine the model's robustness across varying query complexity, we an
 | semantic_score       | 96.82 ± 9.58%  | 96.73 ± 9.80%  | **97.31 ± 6.82%**  | 95.11 ± 10.06% |
 -->
 
-The evaluation results reveal a clear task difficulty hierarchy that aligns with the inherent complexity of each component. Structural and classification metrics (domain accuracy, intent accuracy, ambiguity assessment) achieve 98-99% performance, while the generative rephrasing task scores lower at ~90%. This gap reflects the fundamental difference in task complexity rather than a training deficiency.
+<!-- The evaluation results reveal a clear task difficulty hierarchy that aligns with the inherent complexity of each component. Structural and classification metrics (domain accuracy, intent accuracy, ambiguity assessment) achieve 98-99% performance, while the generative rephrasing task scores lower at ~90%. This gap reflects the fundamental difference in task complexity rather than a training deficiency.
 
-Domain and intent classification are essentially pattern recognition tasks where the model must map queries to learned categories—a task well-suited to the model's 0.5B parameter capacity. Similarly, ambiguity assessment and guideline adherence involve rule-following and structural analysis, which the three-phase training procedure was explicitly designed to optimize.
+Domain and intent classification are essentially pattern recognition tasks where the model must map queries to learned categories, a task well-suited to the model's 0.5B parameter capacity. Similarly, ambiguity assessment and guideline adherence involve rule-following and structural analysis, which the three-phase training procedure was explicitly designed to optimize.
 
 Rephrasing quality, by contrast, requires the model to make nuanced judgments about when to intervene (versus preserving already-optimal queries), how to balance specificity against over-constraint, and whether to expand acronyms or add disambiguating context. These decisions demand deeper semantic understanding and generation capabilities that push the limits of a 0.5B model. The 90.33% score with 17.61% standard deviation represents strong performance on this challenging task, particularly given the diversity of the test set.
 
-For production deployments, users should anticipate that the model will perform most reliably on structural conformance and classification tasks, while rephrasing decisions may occasionally require human review, particularly for edge cases or highly specialized domains underrepresented in the training data.
+For production deployments, users should anticipate that the model will perform most reliably on structural conformance and classification tasks, while rephrasing decisions may occasionally require human review, particularly for edge cases or highly specialized domains underrepresented in the training data. -->
 
 ### Performance Considerations