NoInstruct-small-Embedding-v0
| Property | Value |
|---|---|
| Parameter Count | 33.4M |
| License | MIT |
| Architecture | BERT-based with asymmetric pooling |
| Task Type | Sentence Similarity & Embeddings |
What is NoInstruct-small-Embedding-v0?
NoInstruct-small-Embedding-v0 is an innovative embedding model that improves upon traditional approaches by implementing asymmetric pooling for enhanced retrieval performance. Unlike other models that rely on carefully crafted instructions, this model achieves superior results through its architectural design alone.
Implementation Details
The model uses a distinctive approach where queries and documents are processed differently: mean pooling for queries and CLS token representation for documents. This asymmetric architecture has demonstrated significant improvements in retrieval tasks while maintaining efficiency with only 33.4M parameters.
- Specialized pooling strategy: Mean pooling for queries, CLS token for documents
- Efficient parameter count: 33.4M parameters
- Strong performance on MTEB benchmark suite
- Implementation available for both direct use and sentence-transformers integration
Core Capabilities
- Strong performance in semantic search and retrieval tasks
- Robust sentence similarity scoring
- Efficient document embedding generation
- Cross-domain application support
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its asymmetric pooling approach that eliminates the need for instruction-based prompting while maintaining high performance on retrieval tasks.
Q: What are the recommended use cases?
The model excels in semantic search, document retrieval, sentence similarity tasks, and general-purpose text embedding generation. It's particularly well-suited for applications requiring efficient and accurate text similarity computations.
