Update README.md

README.md

@@ -69,6 +69,34 @@ The primary challenge in this task is data imbalance. Because most audio is high
 
 To solve this, we first generated a massive, multi-million sample dataset. From this pool, we then **meticulously curated** a final dataset with a **perfectly flat, uniform distribution** of quality scores. This means there is a **precisely equal number of files** for every single score bin across the entire quality spectrum.
 
+To be precise, the dataset is partitioned into 20 bins based on the NMOS score, with each bin containing an identical number of samples. The exact distribution is as follows:
+
+| Score Range | Number of Samples |
+|:-----------:|:-----------------:|
+| [0.00, 0.05)  |      30601       |
+| [0.05, 0.10)  |      30601       |
+| [0.10, 0.15)  |      30601       |
+| [0.15, 0.20)  |      30601       |
+| [0.20, 0.25)  |      30601       |
+| [0.25, 0.30)  |      30601       |
+| [0.30, 0.35)  |      30601       |
+| [0.35, 0.40)  |      30601       |
+| [0.40, 0.45)  |      30601       |
+| [0.45, 0.50)  |      30601       |
+| [0.50, 0.55)  |      30601       |
+| [0.55, 0.60)  |      30601       |
+| [0.60, 0.65)  |      30601       |
+| [0.65, 0.70)  |      30601       |
+| [0.70, 0.75)  |      30601       |
+| [0.75, 0.80)  |      30601       |
+| [0.80, 0.85)  |      30601       |
+| [0.85, 0.90)  |      30601       |
+| [0.90, 0.95)  |      30601       |
+| [0.95, 1.00]  |      30601       |
+| **Total**     |    **612020**    |
+
+This deliberate and uniform sampling strategy ensures that a model trained on this data is not biased towards high-quality examples and can learn to accurately assess audio across the full range of perceptual quality.
+
 Additionally, 1-second audio clips were intelligently extracted from more complex segments of the source files to provide the model with challenging and informative examples.
 
 ### Source Data