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Fix GitHub repo links in sparse embeddings e-commerce series

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Update links from thierrypdamiba/finetune-ecommerce-search to
qdrant-labs/devrel-projects/tree/main/qdrant-sparse-finetune across
all 5 articles.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Evgeniya Sukhodolskaya
2026-05-29 16:56:34 +02:00
co-authored by Claude Sonnet 4.6
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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/thierrypdamiba/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/devrel-projects/tree/main/qdrant-sparse-finetune), 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.
## The Dataset: Amazon ESCI