Researchers have developed Bekko Embedding, a new family of parameter-efficient multilingual retrieval models. The smallest version, bekko-embedding-v1-a8m, with under 8 million active parameters, achieves a score of 56.2 on the MMTEB Multilingual v2 Retrieval benchmark, outperforming larger models like multilingual-e5 and BGE-M3. A slightly larger model, a25m, matches the performance of gte-multilingual-base. These models support inputs up to 8192 tokens and are notably faster on both CPU and GPU, with the a8m model being the fastest among those tested. The models feature a compact 384-dimensional output and can be quantized to reduce size, making them suitable for applications like browser-based search. AI
IMPACT Sets a new standard for efficiency in multilingual retrieval, potentially enabling faster and more accessible search applications.
RANK_REASON The cluster describes a new research paper detailing a novel model architecture and its performance on retrieval benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
- Bekko Embedding
- bekko-embedding-v1-a25m
- bekko-embedding-v1-a8m
- BGE-M3
- gte-multilingual-base
- mmBERT-small
- MMTEB Multilingual v2 Retrieval
- multilingual-e5
- Multilingual NanoBEIR
- NanoLongEmbed
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