opensearch-neural-sparse-encoding-v1
| Property | Value |
|---|---|
| Parameter Count | 133M |
| License | Apache 2.0 |
| Paper | Research Paper |
| Average NDCG@10 | 0.524 |
What is opensearch-neural-sparse-encoding-v1?
This is an advanced learned sparse retrieval model designed specifically for enhancing search relevance in OpenSearch environments. The model transforms queries and documents into 30,522-dimensional sparse vectors, where non-zero dimensions correspond to vocabulary tokens, and their weights indicate token importance. Trained on the MS MARCO dataset, it offers superior search capabilities compared to traditional methods.
Implementation Details
The model leverages a transformer-based architecture trained for masked language modeling. It processes input text through a specialized encoding mechanism that generates sparse representations, making it particularly efficient for large-scale retrieval tasks. The model achieves an impressive balance between search relevance (0.524 NDCG@10) and computational efficiency (11.4 FLOPS).
- Efficient sparse vector encoding for both queries and documents
- Integration with Lucene inverted index for optimized retrieval
- Support for high-level OpenSearch API integration
- Zero-shot performance capabilities across multiple domains
Core Capabilities
- Query expansion and document expansion
- Passage retrieval with high precision
- Efficient token importance weighting
- Seamless integration with OpenSearch infrastructure
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to create meaningful sparse vector representations without requiring exact token matches between queries and documents. It excels in understanding semantic relationships while maintaining computational efficiency through its sparse encoding approach.
Q: What are the recommended use cases?
The model is ideal for large-scale search applications, particularly in scenarios requiring high-precision document retrieval. It's especially effective for enterprise search, scientific literature search, and any application where semantic understanding of queries is crucial.
