msmarco-MiniLM-L-12-v3
| Property | Value |
|---|---|
| Parameters | 33.4M |
| Output Dimensions | 384 |
| License | Apache 2.0 |
| Framework Support | PyTorch, TensorFlow, JAX, ONNX |
| Paper | Sentence-BERT Paper |
What is msmarco-MiniLM-L-12-v3?
msmarco-MiniLM-L-12-v3 is a powerful sentence transformer model developed by the sentence-transformers team. It's designed to convert sentences and paragraphs into 384-dimensional dense vector representations, making it particularly effective for semantic search, clustering, and similarity comparison tasks. The model represents a careful balance between performance and efficiency, with its relatively compact 33.4M parameter size.
Implementation Details
The model employs a two-stage architecture combining a transformer-based encoder with a pooling layer. It uses the BERT architecture as its foundation and implements mean pooling to generate sentence embeddings. The model can process sequences up to 512 tokens and maintains case sensitivity by default.
- Transformer-based architecture with 12 layers (MiniLM)
- 384-dimensional output embeddings
- Mean pooling strategy for sentence representation
- Support for multiple deep learning frameworks
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Document clustering
- Information retrieval tasks
- Cross-lingual text matching
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that balances performance with model size. Using MiniLM architecture, it achieves strong semantic understanding while maintaining a relatively small parameter count of 33.4M, making it practical for production deployments.
Q: What are the recommended use cases?
The model excels in applications requiring semantic search, sentence similarity comparisons, and document clustering. It's particularly well-suited for production environments where efficient processing of large text collections is needed, such as search engines, content recommendation systems, and document classification tasks.
