paraphrase-MiniLM-L3-v2
| Property | Value |
|---|---|
| Parameter Count | 17.4M |
| License | Apache 2.0 |
| Paper | Sentence-BERT Paper |
| Output Dimensions | 384 |
| Framework Support | PyTorch, TensorFlow, ONNX, OpenVINO |
What is paraphrase-MiniLM-L3-v2?
paraphrase-MiniLM-L3-v2 is a lightweight sentence transformer model designed for generating sentence embeddings. It's part of the sentence-transformers framework and can map sentences and paragraphs to a 384-dimensional dense vector space, making it ideal for semantic search, clustering, and similarity comparison tasks.
Implementation Details
The model implements a two-stage architecture consisting of a transformer encoder followed by a pooling layer. It utilizes the BERT architecture but in a more compact form, with specialized training for paraphrase detection and semantic similarity tasks.
- Maximum sequence length of 128 tokens
- Mean pooling strategy for sentence embedding generation
- Supports multiple deep learning frameworks including PyTorch and TensorFlow
- Optimized for production deployment with ONNX and OpenVINO support
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Cross-lingual text comparison
- Document clustering and organization
- Information retrieval tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's compact size (17.4M parameters) combined with strong performance on semantic tasks makes it particularly suitable for production deployments where computational resources are limited but high-quality embeddings are required.
Q: What are the recommended use cases?
The model excels in semantic search applications, document similarity comparison, clustering of text data, and information retrieval tasks. It's particularly well-suited for applications requiring fast inference while maintaining good semantic understanding.
