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Update HuggingFace model links to Qdrant org across SPLADE series

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Evgeniya Sukhodolskaya
2026-06-05 16:20:55 +02:00
co-authored by Claude Sonnet 4.6
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qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-1.md
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@@ -28,7 +28,7 @@ Search "iPhone 15 Pro Max 256GB" on a dense embedding system and it happily retu
This is the gap that sparse embeddings fill. And with fine-tuning, they fill it dramatically well - we achieved a **29% improvement over BM25** on Amazon's ESCI dataset, one of the largest public e-commerce search benchmarks.
In this series, we'll build the entire system: data loading, GPU training on Modal, evaluation with Qdrant, and hard negative mining. The [full code is on GitHub](https://github.com/qdrant-labs/finetune-ecommerce-search) and the [fine-tuned models are on HuggingFace](https://huggingface.co/thierrydamiba/splade-ecommerce-esci). If you want to skip the walkthrough and fine-tune on your own data, the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI runs the entire pipeline with one command. But first, let's understand why sparse embeddings are the right tool for e-commerce search.
In this series, we'll build the entire system: data loading, GPU training on Modal, evaluation with Qdrant, and hard negative mining. The [full code is on GitHub](https://github.com/qdrant-labs/finetune-ecommerce-search) and the [fine-tuned models are on HuggingFace](https://huggingface.co/Qdrant/splade-ecommerce-esci). If you want to skip the walkthrough and fine-tune on your own data, the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI runs the entire pipeline with one command. But first, let's understand why sparse embeddings are the right tool for e-commerce search.
## The Problem with Dense Embeddings in E-Commerce
@@ -169,7 +169,7 @@ Over the next four articles, we'll walk through the full pipeline:
- [**Part 5: From Research to Product**](/articles/sparse-embeddings-ecommerce-part-5/) - An open-source CLI and web dashboard that runs the entire fine-tuning pipeline with a single command.
The end result: a fine-tuned SPLADE model that achieves **nDCG@10 of 0.388** on Amazon ESCI, compared to **0.301** for BM25 and **0.324** for off-the-shelf SPLADE. That 29% improvement over BM25 translates to meaningfully better search results for real e-commerce queries. You can try the models directly from HuggingFace: [splade-ecommerce-esci](https://huggingface.co/thierrydamiba/splade-ecommerce-esci) (best in-domain) and [splade-ecommerce-multidomain](https://huggingface.co/thierrydamiba/splade-ecommerce-multidomain) (better generalization).
The end result: a fine-tuned SPLADE model that achieves **nDCG@10 of 0.388** on Amazon ESCI, compared to **0.301** for BM25 and **0.324** for off-the-shelf SPLADE. That 29% improvement over BM25 translates to meaningfully better search results for real e-commerce queries. You can try the models directly from HuggingFace: [splade-ecommerce-esci](https://huggingface.co/Qdrant/splade-ecommerce-esci) (best in-domain) and [splade-ecommerce-multidomain](https://huggingface.co/Qdrant/splade-ecommerce-multidomain) (better generalization).
> **Note:** These metrics were measured on a subsample of 100k products and 10k queries where all relevant documents are included. They are not directly comparable to official Amazon ESCI benchmarks and should be treated as a comparative signal only.