distiluse-base-multilingual-cased-v2
| Property | Value |
|---|---|
| Parameter Count | 135M |
| License | Apache 2.0 |
| Framework | PyTorch, ONNX, TensorFlow |
| Paper | Sentence-BERT Paper |
| Languages Supported | 50+ languages |
What is distiluse-base-multilingual-cased-v2?
This is a powerful multilingual sentence embedding model developed by the sentence-transformers team. It's designed to map sentences and paragraphs into a 512-dimensional dense vector space, making it ideal for semantic search and clustering tasks across multiple languages. The model is built on DistilBERT architecture, offering a balance between performance and efficiency.
Implementation Details
The model utilizes a three-component architecture: a DistilBERT transformer layer, a pooling layer, and a dense layer that produces 512-dimensional embeddings. It processes text with a maximum sequence length of 128 tokens and maintains case sensitivity for better accuracy.
- Built on DistilBERT architecture for efficient processing
- Implements mean pooling strategy for token aggregation
- Features a dense layer with tanh activation
- Supports batched processing for improved performance
Core Capabilities
- Multilingual support for 50+ languages including major European, Asian, and Middle Eastern languages
- Generates consistent 512-dimensional embeddings across languages
- Optimized for sentence similarity tasks
- Supports cross-lingual semantic search
- Efficient clustering and document comparison
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 50+ languages while maintaining high-quality embeddings makes it unique. It's a distilled version that offers a good balance between performance and resource usage, making it practical for production deployments.
Q: What are the recommended use cases?
The model excels in multilingual applications including semantic search, document clustering, similarity comparison, and cross-lingual information retrieval. It's particularly useful for organizations dealing with content in multiple languages.
