Update README.md

README.md

@@ -142,8 +142,33 @@ RexBERT-large was trained in **three phases**:
 
 ## Data Overview
 
+- **Dataset:** [Ecom-niverse](https://huggingface.co/datasets/thebajajra/Ecom-niverse)
 - **Domain mix:**  
-- **Data quality:** 
+
+We identified 9 E-commerce overlapping domains which have significant amount of relevant tokens but required filteration. Below is the domain list and their filtered size
+| Domain | Size (GBs) |
+|---|---|
+| Hobby | 114 |
+| News | 66 |
+| Health | 66 |
+| Entertainment | 64 |
+| Travel | 52 |
+| Food | 22 |
+| Automotive | 19 |
+| Sports | 12 |
+| Music and Dance | 7 |
+
+Additionally, there are 6 more domains which had almost complete overlap and were picked directly out of FineFineWeb.
+| Domain | Size (GBs) |
+|---|---|
+| Fashion | 37 |
+| Beauty | 37 |
+| Celebrity | 28 |
+| Movie | 26 |
+| Photo | 15 |
+| Painting | 2 |
+
+By focusing on these domains, we narrow the search space to parts of the web data where shopping-related text is likely to appear. However, even within a chosen domain, not every item is actually about buying or selling, many may be informational articles, news, or unrelated discussions. Thus, a more fine-grained filtering within each domain is required to extract only the e-commerce-specific lines. We accomplish this by training lightweight classifiers per domain to distinguish e-commerce context vs. non-e-commerce content.