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README.md
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@@ -54,3 +54,26 @@ We leverage FineFineWeb as the raw data source. Each entry in FineFineWeb is a t
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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.
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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.
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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
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| Domain | Size (GBs) |
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| Hobby | 114 |
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| News | 66 |
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| Health | 66 |
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| Entertainment | 64 |
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| Travel | 52 |
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| Food | 22 |
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| Automotive | 19 |
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| Sports | 12 |
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| Music and Dance | 7 |
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Additionally, there are 6 more domains which had almost complete overlap and were picked directly out of FineFineWeb.
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| Domain | Size (GBs) |
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| Fashion | 37 |
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| Beauty | 37 |
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| Celebrity | 28 |
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| Movie | 26 |
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| Photo | 15 |
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| Painting | 2 |
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