bce-embedding-base_v1
| Property | Value |
|---|---|
| Dimensions | 768 |
| License | Apache 2.0 |
| Languages | Chinese, English |
| Framework | PyTorch, Transformers |
What is bce-embedding-base_v1?
bce-embedding-base_v1 is a state-of-the-art bilingual embedding model developed by NetEase Youdao specifically optimized for RAG (Retrieval-Augmented Generation) applications. It excels in generating semantic vectors for both Chinese and English content, with powerful cross-lingual capabilities.
Implementation Details
The model uses a dual-encoder architecture and requires no special instruction engineering for optimal performance. It generates 768-dimensional embeddings and has been extensively evaluated across multiple domains including education, law, finance, medical, literature, and FAQ applications.
- Achieves SOTA performance in bilingual and cross-lingual tasks
- Optimized for RAG applications with high recall and precision
- Compatible with popular frameworks like LangChain and LlamaIndex
- Supports efficient batch processing and normalized embeddings
Core Capabilities
- Bilingual semantic understanding (Chinese and English)
- Cross-lingual retrieval and matching
- Document similarity comparison
- Semantic search optimization
- RAG pipeline integration
Frequently Asked Questions
Q: What makes this model unique?
The model's key differentiator is its exceptional performance in both bilingual and cross-lingual scenarios without requiring carefully crafted instructions. It achieves superior results in MTEB benchmarks and shows remarkable adaptation across multiple domains.
Q: What are the recommended use cases?
The model is ideal for RAG applications where you need to retrieve relevant passages from mixed Chinese and English content. Best practice involves using it to recall top 50-100 passages, followed by reranking to get the most relevant 5-10 passages.
