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reacted
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singhsidhukuldeep's
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with 🤯
about 1 month ago
Excited to share insights from Walmart's groundbreaking semantic search system that revolutionizes e-commerce product discovery!
The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce.
Key Technical Highlights:
• The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching.
• Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types.
• The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs.
• To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples.
Production Implementation Details:
• The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency.
• Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production.
• The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors.
Results:
The system showed significant improvements in both offline metrics and live experiments, with:
- +2.84% improvement in NDCG@10 for human evaluation
- +0.54% lift in Add-to-Cart rates in live A/B testing
This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-
reacted
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dvilasuero's
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with 🚀
11 months ago
🔥 Community and Data Quality Are More For Alignment
A recipe to replicate SPIN (Self-Play Fine Tuning) with 30x less data:
🗣️ 50K samples vs 1.8K prompts curated by the 350+ amazing DIBT contributors.
⚗️ Distillation of Mistral Large instead of OpenAI
🙌 Open data & code with ⚗️distilabel
SPIN Paper:
https://huggingface.co/papers/2401.01335
SPIN DIBT Collection with datasets and models:
https://huggingface.co/collections/argilla/dibt-prompt-collective-spin-65ef59062518776024395fc3
Repo:
https://github.com/argilla-io/distilabel-spin-dibt
Joint work with the amazing DIBT community 👇
@aashish1904, @flozi00, @sayhan, @munish0838, @0-hero, @dvilasuero, @eren23, @davanstrien, @ahnz, @BlackKakapo, @kitano-o, @mmhamdy, @sdiazlor, @Stopwolf, @gabrielmbmb, @tculler91, @plaguss, @ignacioct, @Hugi-R, @davidberenstein1957, @Korla, @alvarobartt, @Hugs4Llamas, @Sumandora, @nataliaElv, @jfcalvo, @Averill, @steventrouble, @vasilis, @aeros93, @kayyshf, @thomasgauthier, @jeromebas, @Ameeeee, @ayoubelmhamdi, @TuringsSolutions, @efels, @Haleyok, @abrazador, @emessy, @Nindaleth, @burtenshaw, @vicgalle, @CortexPE, @casey-martin, @Leire-aguirre-eguiluz, @mrfakename, @Portias600kNeurons, @nathaliepett, @Filippo
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zatochu/EasyFluff
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