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LEMUR and Mean Centering for Late-Interaction Retrieval in txtai
NeuML
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Morgan Carr
Morgan-coded
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repliedto davidmezzetti's post about 6 hours ago
Exciting addition coming with the next txtai release: LEMUR for ColBERT-style Late-Interaction Retrieval! 🎉
Contributor @Morgan-coded introduced LEMUR to txtai, making it, as far as we know, the first framework to incorporate LEMUR for late-interaction retrieval using standard, fixed-vector indexes.
Key benefits:
🚀 Significant boost: 49–62% higher NDCG@10 than 2,048-dimensional MUVERA
💾 5x less storage: 2,048 dimensions vs. MUVERA’s default 10,240
📐 Better geometry: Optional batch mean centering addresses anisotropy in token embeddings
A promising step toward making ColBERT-style retrieval more practical with conventional vector search.
Read the full breakdown: https://huggingface.co/blog/NeuML/txtai-lemur repliedto davidmezzetti's post about 17 hours ago
Exciting addition coming with the next txtai release: LEMUR for ColBERT-style Late-Interaction Retrieval! 🎉
Contributor @Morgan-coded introduced LEMUR to txtai, making it, as far as we know, the first framework to incorporate LEMUR for late-interaction retrieval using standard, fixed-vector indexes.
Key benefits:
🚀 Significant boost: 49–62% higher NDCG@10 than 2,048-dimensional MUVERA
💾 5x less storage: 2,048 dimensions vs. MUVERA’s default 10,240
📐 Better geometry: Optional batch mean centering addresses anisotropy in token embeddings
A promising step toward making ColBERT-style retrieval more practical with conventional vector search.
Read the full breakdown: https://huggingface.co/blog/NeuML/txtai-lemur repliedto davidmezzetti's post about 17 hours ago
Exciting addition coming with the next txtai release: LEMUR for ColBERT-style Late-Interaction Retrieval! 🎉
Contributor @Morgan-coded introduced LEMUR to txtai, making it, as far as we know, the first framework to incorporate LEMUR for late-interaction retrieval using standard, fixed-vector indexes.
Key benefits:
🚀 Significant boost: 49–62% higher NDCG@10 than 2,048-dimensional MUVERA
💾 5x less storage: 2,048 dimensions vs. MUVERA’s default 10,240
📐 Better geometry: Optional batch mean centering addresses anisotropy in token embeddings
A promising step toward making ColBERT-style retrieval more practical with conventional vector search.
Read the full breakdown: https://huggingface.co/blog/NeuML/txtai-lemurOrganizations
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