msmarco-MiniLM-L12-v3
| Property | Value |
|---|---|
| Author | sentence-transformers |
| Vector Dimensions | 384 |
| Max Sequence Length | 512 |
| Paper | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks |
What is msmarco-MiniLM-L12-v3?
msmarco-MiniLM-L12-v3 is a specialized sentence transformer model designed to convert sentences and paragraphs into 384-dimensional dense vector representations. Built on the MiniLM architecture, this model excels at semantic search and clustering tasks, offering an efficient balance between performance and computational requirements.
Implementation Details
The model implements a two-stage architecture combining a Transformer module with a Pooling layer. It processes input text using the BertModel architecture with a maximum sequence length of 512 tokens, followed by mean pooling to generate the final embeddings.
- Transformer architecture with 12 layers (L12 variant)
- Mean pooling strategy for generating sentence embeddings
- Optimized for the MS MARCO dataset
- Easy integration with both sentence-transformers and HuggingFace libraries
Core Capabilities
- Semantic similarity computation
- Document clustering
- Information retrieval
- Cross-lingual text matching
- Zero-shot transfer learning
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that combines MiniLM's computational efficiency with strong performance on semantic tasks. The 384-dimensional output strikes a balance between representational power and resource usage.
Q: What are the recommended use cases?
The model is particularly well-suited for semantic search applications, document similarity analysis, and clustering tasks. It's optimized for production environments where both performance and efficiency are critical.
