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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-2.md
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@@ -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