stsb-mpnet-base-v2
| Property | Value |
|---|---|
| Parameter Count | 109M |
| Output Dimensions | 768 |
| License | Apache 2.0 |
| Paper | Research Paper |
What is stsb-mpnet-base-v2?
stsb-mpnet-base-v2 is a sophisticated sentence transformer model designed to convert sentences and paragraphs into dense 768-dimensional vector representations. Built on the MPNet architecture, this model excels at semantic similarity tasks and can be effectively used for clustering and semantic search applications.
Implementation Details
The model implements a two-stage architecture comprising a transformer layer and a pooling layer. It uses the MPNet base model for initial processing, followed by mean pooling to generate sentence embeddings. The model supports a maximum sequence length of 75 tokens and maintains case sensitivity during processing.
- Easily integrable with both sentence-transformers and HuggingFace Transformers libraries
- Supports multiple tensor formats including I64 and F32
- Implements efficient mean pooling strategy for embedding generation
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Support for clustering applications
- Semantic search functionality
- Cross-platform compatibility (PyTorch, ONNX, OpenVINO)
Frequently Asked Questions
Q: What makes this model unique?
The model's strength lies in its optimized architecture for semantic similarity tasks, combining MPNet's powerful language understanding capabilities with efficient pooling strategies. Its moderate size of 109M parameters provides a good balance between performance and resource requirements.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring semantic similarity assessment, document clustering, semantic search systems, and any task requiring high-quality sentence embeddings. It's especially effective in production environments thanks to its multiple framework support.
