Update README.md

README.md

@@ -173,7 +173,7 @@ The evaluation assesses two capabilities — language detection and transcriptio
 
 **Language Detection:** Both model sizes achieved perfect 100% performance across all metrics (Precision, Recall, Accuracy, F1-score) on both datasets, indicating excellent binary classification capability for distinguishing between Khmer and English audio. This perfect score is expected because during pre-training, the model performs permutations on word tokens starting from position 3, while the first three positions (start token, language token, and task token) remain fixed. Since language detection relies on the language token at position 1, and this token is never permuted during pre-training, the model can achieve perfect accuracy on language detection tasks.
 
-**Transcription:** The Small model shows strong performance on English (10% token error rate, 7.08% CER, 12.95% WER) and moderate performance for Khmer (46% token error rate, 35.31% CER, 50.70% WER). The Tiny model shows strong performance on English (19% token error rate, 20.98% CER, 31.13% WER) but significantly lower performance for Khmer (56% token error rate, 60.71% CER, 86.16% WER). The larger model benefits from increased embedding dimension (768 vs 384) and more layers (12 vs 4).
+**Transcription:** The model shows strong performance on English (19% token error rate, 20.98% CER, 31.13% WER) but significantly lower performance for Khmer (56% token error rate, 60.71% CER, 86.16% WER).
 
 
 ## How to Get Started with the Model