nomic embed text v2 moe: Multilingual Mixture of Experts Text Embeddings Blog Technical Report AWS SageMaker Atlas Embedding and Unstructured Data Analytics Platform This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. Model Overview nomic embed text v2 moe is a SoTA multilingual MoE text embedding model that excels at multilingual retrieval: High Performance : SoTA Multilingual performance compared to ~300M parameter models, competitive with models 2x in size Multilinguality : Supports ~100 languages and trained on over 1.6B pairs Flexible Embedding Dimension : Trained with Matryoshka Embeddings with 3x reductions in storage cost with minimal performance degradations Fully Open Source : Model weights, code, and training data (see code repo) released Model Params (M) Emb Dim BEIR MIRACL Pretrain Data Finetune Data Code Nomic Embed v2 305 768 52.86 65.80 ✅ ✅ ✅ mE5 Base 278 768 48.88 62.30 ❌ ❌ ❌ mGTE Base 305 768 51.10 63.40 ❌ ❌ ❌ Arctic Embed v2 Base 305 768 55.40 59.90 ❌ ❌ ❌ BGE M3 568 1024 48.80 69.20 ❌ ✅ ❌ Arctic Embed v2 Large 568 1024 55.65 66.00 ❌ ❌ ❌ mE5 Large 560 1024 51.40 66.50 ❌ ❌ ❌ Model Architecture Total Parameters : 475M Acti…
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