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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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commit 8ef516b06f
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qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-3.md
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We have a trained SPLADE model sitting on a Modal volume (or grab it from [HuggingFace](https://huggingface.co/thierrydamiba/splade-ecommerce-esci)). Now comes the question that matters: is it actually better? In this article, we'll index products into Qdrant, run retrieval benchmarks, implement hard negative mining, and dig into what the model learned. Full evaluation code is in the [GitHub repo](https://github.com/qdrant-labs/finetune-ecommerce-search). To run this entire pipeline on your own data, see the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI.
We have a trained SPLADE model sitting on a Modal volume (or grab it from [HuggingFace](https://huggingface.co/Qdrant/splade-ecommerce-esci)). Now comes the question that matters: is it actually better? In this article, we'll index products into Qdrant, run retrieval benchmarks, implement hard negative mining, and dig into what the model learned. Full evaluation code is in the [GitHub repo](https://github.com/qdrant-labs/finetune-ecommerce-search). To run this entire pipeline on your own data, see the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI.
## Indexing Products in Qdrant