Fine-Tuning Qwen3 Embeddings for product category classification on the Large-Scale Product Corpus
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Fine-Tuning Qwen3 Embeddings for product category classification on the Large-Scale Product Corpus
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Language-models such as GPT, Llama, DeepSeek, Qwen trained with a filtered slice of Common Crawl. For e-commerce work, though, we can start with the Web Data Commons (WDC), the project by the University of Mannheim. It extracts web pages that carry some metadata and publishes the result as the Large-Scale Product Corpus (LSPC).
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Language-models such as GPT, Llama, DeepSeek, Qwen trained with a filtered slice of Common Crawl. For e-commerce work, though, we can start with the Web Data Commons (WDC), the project by the University of Mannheim. It extracts web pages that carry some metadata and publishes the result as the Large-Scale Product Corpus (LSPC).
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Search engines like Google reward pages that include detailed product markup, so merchants already populate their sites with SEO-friendly fields such as title, brand, GTIN, price — and, crucially, category labels. Thanks to these built-in annotations, the WDC Large-Scale Product Corpus arrives almost fully self-labelled. I used those labels to fine-tune Qwen3 Embedding with Low-Rank Adaptation (LoRA), code is available on github. The resulting 615 million-parameter checkpoint fits comfortably in limited GPU memory yet updates the model’s representation space, mapping raw product titles to six top-level categories with a macro-F1 of 0.836 (83.6 %).
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Search engines like Google reward pages that include detailed product markup, so merchants already populate their sites with SEO-friendly fields such as title, brand, GTIN, price — and, crucially, category labels. Thanks to these built-in annotations, the WDC Large-Scale Product Corpus arrives almost fully self-labelled. I used those labels to fine-tune Qwen3 Embedding with Low-Rank Adaptation (LoRA), code is available on github. The resulting 615 million-parameter checkpoint fits comfortably in limited GPU memory yet updates the model’s representation space, mapping raw product titles to six top-level categories with a macro-F1 of 0.836 (83.6 %).
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More details: 
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More details: 
https://blog.ivan.digital/fine-tuning-qwen3-embeddings-for-product-category-classification-on-the-large-scale-product-corpus-3a0919506bc8
https://blog.ivan.digital/fine-tuning-qwen3-embeddings-for-product-category-classification-on-the-large-scale-product-corpus-3a0919506bc8
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Fine-Tuning Qwen3 Embeddings for product category classification on the Large-Scale Product Corpus

Language-models such as GPT, Llama, DeepSeek, Qwen trained with a filtered slice of Common Crawl. For e-commerce work, though, we can start with the Web Data Commons (WDC), the project by the University of Mannheim. It extracts web pages that carry some metadata and publishes the result as the Large-Scale Product Corpus (LSPC).

Search engines like Google reward pages that include detailed product markup, so merchants already populate their sites with SEO-friendly fields such as title, brand, GTIN, price — and, crucially, category labels. Thanks to these built-in annotations, the WDC Large-Scale Product Corpus arrives almost fully self-labelled. I used those labels to fine-tune Qwen3 Embedding with Low-Rank Adaptation (LoRA), code is available on github. The resulting 615 million-parameter checkpoint fits comfortably in limited GPU memory yet updates the model’s representation space, mapping raw product titles to six top-level categories with a macro-F1 of 0.836 (83.6 %).

More details: https://blog.ivan.digital/fine-tuning-qwen3-embeddings-for-product-category-classification-on-the-large-scale-product-corpus-3a0919506bc8

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