BGE-Small-ZH-v1.5
| Property | Value |
|---|---|
| Parameter Count | 24M |
| License | MIT |
| Language | Chinese |
| Technical Paper | C-Pack: Packaged Resources To Advance General Chinese Embedding |
What is bge-small-zh-v1.5?
BGE-Small-ZH-v1.5 is a compact Chinese language embedding model designed for efficient text representation and retrieval. As part of the BGE (BAAI General Embedding) v1.5 series, it offers improved similarity distribution and enhanced retrieval capabilities compared to previous versions, while maintaining a small parameter footprint of only 24M parameters.
Implementation Details
The model utilizes a BERT-based architecture optimized for generating text embeddings. It supports a sequence length of 512 tokens and produces embeddings that can be used for semantic similarity calculations and information retrieval tasks. The v1.5 version includes specific improvements in similarity distribution, making it more reliable for practical applications even without using instruction prompts.
- Optimized for both retrieval and general semantic similarity tasks
- Supports zero-shot cross-lingual understanding
- Compatible with popular frameworks including Hugging Face Transformers, Sentence-Transformers, and LangChain
Core Capabilities
- Text embedding generation for Chinese language content
- Semantic search and retrieval
- Document similarity comparison
- Integration with vector databases for LLM applications
- Support for both instruction-based and instruction-free usage
Frequently Asked Questions
Q: What makes this model unique?
The model offers state-of-the-art performance for its size category while maintaining a small footprint, making it ideal for resource-constrained environments. The v1.5 version specifically addresses previous issues with similarity distribution, providing more reliable results without requiring instruction prompts.
Q: What are the recommended use cases?
The model is particularly well-suited for text retrieval tasks, semantic search applications, and document similarity analysis in Chinese language contexts. It can be effectively used in production environments where computational resources are limited but high-quality embeddings are required.
