paraphrase-mpnet-base-v2
| Property | Value |
|---|---|
| Parameter Count | 109M |
| Vector Dimensions | 768 |
| License | Apache 2.0 |
| Research Paper | View Paper |
What is paraphrase-mpnet-base-v2?
paraphrase-mpnet-base-v2 is a powerful sentence transformer model designed for generating high-quality sentence embeddings. Built on the MPNet architecture, it converts sentences and paragraphs into 768-dimensional dense vector representations, making it particularly effective for semantic search, clustering, and similarity comparison tasks.
Implementation Details
The model utilizes a two-component architecture consisting of an MPNet transformer followed by a pooling layer. It processes input text through the transformer and applies mean pooling to generate the final embeddings. The model supports a maximum sequence length of 512 tokens and maintains case sensitivity.
- Implements sentence-transformers framework for easy usage
- Supports both PyTorch and HuggingFace Transformers implementations
- Features mean pooling strategy for optimal embedding generation
- Includes built-in padding and truncation capabilities
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Text clustering and classification
- Cross-lingual text matching
- Information retrieval tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its robust performance in generating sentence embeddings, leveraging the MPNet architecture's advantages while maintaining computational efficiency. With over 735,000 downloads, it has proven its reliability in production environments.
Q: What are the recommended use cases?
The model excels in applications requiring semantic understanding, including semantic search systems, document clustering, similarity matching, and information retrieval tasks. It's particularly well-suited for production environments requiring high-quality sentence embeddings.
