msmarco-MiniLM-L6-cos-v5
| Property | Value |
|---|---|
| Parameter Count | 22.7M |
| Embedding Dimensions | 384 |
| Research Paper | Sentence-BERT Paper |
| Downloads | 467,080 |
| Framework Support | PyTorch, TensorFlow, JAX, ONNX |
What is msmarco-MiniLM-L6-cos-v5?
msmarco-MiniLM-L6-cos-v5 is a specialized sentence transformer model designed specifically for semantic search applications. Built on the sentence-transformers framework, this model efficiently maps sentences and paragraphs to 384-dimensional dense vector spaces, enabling sophisticated semantic similarity comparisons. The model has been trained on an impressive dataset of 500,000 query-answer pairs from the MS MARCO Passages dataset.
Implementation Details
The model implements mean pooling architecture with normalized embeddings and supports multiple frameworks including PyTorch and TensorFlow. It's optimized for both dot-product and cosine-similarity calculations, with embeddings normalized to length 1 for efficient similarity computations.
- Produces normalized 384-dimensional embeddings
- Utilizes mean pooling for token aggregation
- Supports multiple scoring functions: dot-product, cosine-similarity, and euclidean distance
- Compatible with sentence-transformers and HuggingFace Transformers libraries
Core Capabilities
- Semantic search optimization
- Query-document similarity matching
- Sentence and paragraph embedding generation
- Cross-framework compatibility
- Efficient computational performance with normalized vectors
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimized balance between performance and efficiency, featuring a compact 22.7M parameter count while maintaining high-quality semantic search capabilities. Its pre-training on MS MARCO dataset makes it particularly effective for query-document matching tasks.
Q: What are the recommended use cases?
The model excels in semantic search applications, document similarity matching, and information retrieval systems. It's particularly well-suited for applications requiring efficient text embedding generation and similarity computations.
