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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
parent 6d63a4ebf4
commit 8ef516b06f
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qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-4.md
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@@ -22,7 +22,7 @@ category: practicle-examples
---
We've built a SPLADE model that beats BM25 by 28% on Amazon ESCI. But here's the question that determines whether this is a lab result or a production strategy: does it work on data it wasn't trained on? Full code is on [GitHub](https://github.com/qdrant-labs/finetune-ecommerce-search), you can try the [fine-tuned models on HuggingFace](https://huggingface.co/thierrydamiba/splade-ecommerce-esci), or fine-tune on your own catalog with the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI.
We've built a SPLADE model that beats BM25 by 28% on Amazon ESCI. But here's the question that determines whether this is a lab result or a production strategy: does it work on data it wasn't trained on? Full code is on [GitHub](https://github.com/qdrant-labs/finetune-ecommerce-search), you can try the [fine-tuned models on HuggingFace](https://huggingface.co/Qdrant/splade-ecommerce-esci), or fine-tune on your own catalog with the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI.
In this final article, we test cross-domain generalization, train a multi-domain model, and lay out a decision framework for when to specialize vs generalize.
@@ -175,7 +175,7 @@ Extensions worth exploring:
- **Full dataset training**: We used 100K samples from ESCI. The full 1.2M with multiple epochs would likely improve results further.
- **Curriculum learning**: Start with general data, gradually specialize to your domain. This can mitigate catastrophic forgetting while still achieving strong in-domain performance.
The [code is open source](https://github.com/qdrant-labs/finetune-ecommerce-search). The [pre-trained models are on HuggingFace](https://huggingface.co/thierrydamiba/splade-ecommerce-esci) (including a [multi-domain variant](https://huggingface.co/thierrydamiba/splade-ecommerce-multidomain)). Training runs on Modal for under $1. Qdrant handles the [sparse vectors](https://qdrant.tech/articles/sparse-vectors/), indexing, and retrieval out of the box. The barrier to building better e-commerce search has never been lower.
The [code is open source](https://github.com/qdrant-labs/finetune-ecommerce-search). The [pre-trained models are on HuggingFace](https://huggingface.co/Qdrant/splade-ecommerce-esci) (including a [multi-domain variant](https://huggingface.co/Qdrant/splade-ecommerce-multidomain)). Training runs on Modal for under $1. Qdrant handles the [sparse vectors](https://qdrant.tech/articles/sparse-vectors/), indexing, and retrieval out of the box. The barrier to building better e-commerce search has never been lower.
We also packaged this entire pipeline into an open-source toolkit with a CLI and web dashboard. See [Part 5: From Research to Product](/articles/sparse-embeddings-ecommerce-part-5/) for how to fine-tune a SPLADE model on your own catalog with a single command.