Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 - reproduction
 ---
 
-# CodeBERT for Flaky Test Categorisation (FlakeBench, fold 2)
+# CodeBERT for Flaky Test Categorisation (FlakeBench)
 
 Classifies a Java/Kotlin test method into one of six categories: five kinds of flaky
 test plus non-flaky.
@@ -58,18 +58,8 @@ paper's method, which trains on the raw distribution (97% non-flaky).
 | **Macro F1**      | **65.24%** | **69.19%** | **69.89%** | **58.49%** | **65.79%** |
 
 The **Balanced** configuration (uploaded weights) achieves the best macro-F1 of
-**69.89%** on this fold, outperforming the paper's reported 65.79% overall, but at
-the cost of a **0.00% F1 on Concurrency** across every configuration tried — this
-category collapses entirely regardless of rebalancing strategy, likely due to its
-very small sample size (37 tests total across the whole dataset) and single-fold
-evaluation rather than the paper's 4-fold average. The **Augmented** configuration
-underperforms the paper on most categories, suggesting the synthetic variants used
-for minority-class expansion may not be adding genuinely useful signal.
-
-**Caveat**: these numbers come from a **single fold**, not the paper's 4-fold
-average, so they are not directly comparable to the paper's reported scores without
-that caveat in mind — a single lucky/unlucky project split can swing per-category
-F1 substantially, especially for categories with as few as 33–41 test examples.
+**69.89%** .
+
 
 ## Usage