Update README.md

README.md

@@ -33,4 +33,24 @@ tags:
 - overstock
 size_categories:
 - 100B<n<1T
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+---
+
+# Ecom-niverse
+
+## Why
+
+Generic web-crawled corpora often lack the focused coverage of domain knowledge and unique formats found in specialized fields, such as e-commerce. As a result, models pre-trained only on general data may lack essential retail knowledge and struggle with the semi-structured text formats common in e-commerce
+
+## What
+
+We construct a comprehensive e-commerce tokens dataset by refining a broad web dataset to isolate content with retail or shopping context. This curated corpus is intended for continual pre-training of LLMs and other Encoder-only models so they better understand product descriptions, prices, and other commerce-related text
+
+
+## How
+
+Our starting point is the FineFineWeb dataset – an open-source web-scale corpus that organizes CommonCrawl web data into fine-grained topical domains. FineFineWeb consists of over 4.4 trillion tokens of English web text categorized into ~50 domains
+
+We leverage FineFineWeb as the raw data source. Each entry in FineFineWeb is a text snippet (document or paragraph) accompanied by metadata including its assigned domain label. Not all these domains are relevant to retail commerce, so the first step is to identify which domains likely contain e-commerce content.
+
+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.
+