Update HuggingFace model links to Qdrant org across SPLADE series

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-10-03 01:48:32 +02:00 · 2026-06-05 16:20:55 +02:00
parent 6d63a4ebf4
commit 8ef516b06f
4 changed files with 7 additions and 7 deletions
@@ -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.

@@ -22,7 +22,7 @@ category: practicle-examples

 ---

-In the last article we made the case for sparse embeddings in e-commerce search. Now we write the code. All source code is available in the [GitHub repo](https://github.com/qdrant-labs/finetune-ecommerce-search), and you can try the [fine-tuned models on HuggingFace](https://huggingface.co/thierrydamiba/splade-ecommerce-esci). Want to skip straight to fine-tuning on your own data? See the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI. By the end of this piece, you'll have a SPLADE model trained on Amazon's ESCI dataset, running on Modal's serverless GPUs, with checkpoints saved to persistent storage.
+In the last article we made the case for sparse embeddings in e-commerce search. Now we write the code. All source code is available in the [GitHub repo](https://github.com/qdrant-labs/finetune-ecommerce-search), and you can try the [fine-tuned models on HuggingFace](https://huggingface.co/Qdrant/splade-ecommerce-esci). Want to skip straight to fine-tuning on your own data? See the [`sparse-finetune`](https://github.com/qdrant/sparse-finetune) CLI. By the end of this piece, you'll have a SPLADE model trained on Amazon's ESCI dataset, running on Modal's serverless GPUs, with checkpoints saved to persistent storage.

 ## The Dataset: Amazon ESCI

@@ -362,7 +362,7 @@ uv run modal run --detach modal_app.py \
    --mode train
 ```

-The model checkpoint gets saved to the persistent volume at `/checkpoints/splade_standard/final`. We've also published the trained model on HuggingFace as [splade-ecommerce-esci](https://huggingface.co/thierrydamiba/splade-ecommerce-esci) so you can skip training and use it directly. In the next article, we'll load this model, index products into Qdrant, and run retrieval benchmarks to see exactly how much we've improved over BM25.
+The model checkpoint gets saved to the persistent volume at `/checkpoints/splade_standard/final`. We've also published the trained model on HuggingFace as [splade-ecommerce-esci](https://huggingface.co/Qdrant/splade-ecommerce-esci) so you can skip training and use it directly. In the next article, we'll load this model, index products into Qdrant, and run retrieval benchmarks to see exactly how much we've improved over BM25.

 ## Key Takeaways

@@ -22,7 +22,7 @@ category: practicle-examples

 ---

-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

@@ -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.