Update README.md

README.md

@@ -50,7 +50,7 @@ The model performs **binary classification**:
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 ## Performance & Robustness
-On the full test split (n=2,200), the model achieves **Accuracy = 0.9950** and **F1 (binary) = 0.9949**. In a separate confusion-matrix run on valid rows (n=2,175), it records **TP=1,065**, **FP=4**, **FN=1**, **TN=1,105**, yielding **Accuracy = 0.9977**, **Precision (CBDC) = 0.9963**, **Recall (CBDC) = 0.9991**, **ROC-AUC = 1.0000**, and a **Brier score = 0.0024**; the class balance is **Non-CBDC = 1,109** and **CBDC = 1,066**. Compared to TF-IDF baselines—**Logistic Regression (0.97)**, **Naive Bayes (0.92)**, **Random Forest (0.98)**, and **XGBoost (0.99)**—CBDC-BERT **matches or exceeds** these results while delivering **near-perfect ROC-AUC** with **well-calibrated probabilities** (low Brier). Robustness checks across **edge cases**, **noise-injected**, **syntactically altered**, and **paraphrased (“translated-like”)** inputs each show **8/10 correct (80%)**, and sentence-length bias is low (**ρ ≈ 0.1222**).
+On the full test split (n=2,200), the model achieves **Accuracy = 0.9950** and **F1 (binary) = 0.9949**. In a separate confusion-matrix run on valid rows (n=2,175), it records **TP=1,065**, **FP=4**, **FN=1**, **TN=1,105**, yielding **Accuracy = 0.9977**, **Precision (CBDC) = 0.9963**, **Recall (CBDC) = 0.9991**, **ROC-AUC = 1.0000**, and a **Brier score = 0.0024**; the class balance is **Non-CBDC = 1,109** and **CBDC = 1,066**. Compared to TF-IDF baselines—**Logistic Regression (0.97)**, **Naive Bayes (0.92)**, **Random Forest (0.98)**, and **XGBoost (0.99)**, CBDC-BERT **matches or exceeds** these results while delivering **near-perfect ROC-AUC** with **well-calibrated probabilities** (low Brier). Robustness checks across **edge cases**, **noise-injected**, **syntactically altered**, and **paraphrased (“translated-like”)** inputs each show **8/10 correct (80%)**, and sentence-length bias is low (**ρ ≈ 0.1222**).
 
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