ZPoint Large Embedding for Chinese
| Property | Value |
|---|---|
| Author | iampanda |
| Model Type | Text Embedding |
| Language | Chinese |
| Hugging Face | Link |
What is zpoint_large_embedding_zh?
ZPoint Large Embedding is an advanced Chinese language embedding model built on the Stella architecture. Released in June 2024, it's specifically designed for high-performance text embedding tasks in Chinese, incorporating sophisticated training techniques including hard negative sampling and extensive data synthesis using ZPoint-72B LLM.
Implementation Details
The model employs a multi-faceted training approach, utilizing approximately 100 million training samples across diverse domains including healthcare, law, electricity, automotive, and consumer electronics. The training process incorporates Multi-Task loss similar to Piccolo and Matryoshka Representation Learning techniques.
- Hard negative sampling with 10 samples for retrieval tasks and 5 for classification/clustering
- LLM-based data synthesis generating 30 million samples
- Integration of multiple high-quality datasets including miracl, Huatuo26M-Lite, and MLDR
- Advanced query rewriting and document expansion techniques
Core Capabilities
- Optimized for Chinese text embedding generation
- Robust performance in retrieval tasks
- Enhanced classification and clustering capabilities
- Efficient semantic similarity computation
- Domain-adaptive embeddings across multiple sectors
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its comprehensive training approach combining hard negative sampling, LLM-based data synthesis, and multi-task learning, specifically optimized for Chinese language understanding across diverse domains.
Q: What are the recommended use cases?
The model excels in text retrieval, semantic search, document classification, and clustering tasks in Chinese. It's particularly suitable for applications requiring precise semantic understanding in specialized domains like healthcare, law, and technical fields.
