BCEmbedding: Bilingual Cross-Modal Embedding Models from NetEase

Fri, 01 May 2026 00:00:00 +0000

Embedding models are the foundation of modern semantic search and retrieval-augmented generation (RAG) systems. BCEmbedding, developed by NetEase Youdao, stands out by delivering state-of-the-art performance specifically optimized for bilingual Chinese-English and cross-modal retrieval tasks.

The model excels at understanding semantic relationships across languages and modalities. Whether you are searching Chinese documents with English queries, retrieving images from text descriptions, or building a bilingual RAG pipeline, BCEmbedding provides embeddings that capture meaning across these boundaries.

Model Capabilities

Capability	Description	Performance
Bilingual text	Chinese-English cross-lingual retrieval	Top 3 on MTEB leaderboard
Cross-modal	Text-to-image and image-to-text retrieval	State-of-the-art
Dense retrieval	Single-vector representation	Competitive with BGE
Sparse retrieval	Hybrid with BM25 support	Enhanced recall
RAG optimization	Tuned for chunk-level retrieval	Excellent precision

Embedding Architecture

flowchart LR
 subgraph Input
 A[Chinese Text]
 B[English Text]
 C[Images]
 end
 subgraph BCEmbedding
 D[Bilingual Encoder]
 E[Vision Encoder]
 F[Cross-Modal Fusion]
 end
 subgraph Output
 G[Vector Embeddings]
 H[Similarity Scores]
 end
 A --> D
 B --> D
 C --> E
 D --> F
 E --> F
 F --> G
 G --> H

The architecture uses separate encoders for text and vision, with a cross-modal fusion layer that projects both modalities into a shared embedding space. This allows direct comparison between any combination of text and image inputs.

Semantic Search on SoloSoft

BCEmbedding: Bilingual Cross-Modal Embedding Models from NetEase

Model Capabilities

Embedding Architecture