distiluse-base-multilingual-cased-v1
| Property | Value |
|---|---|
| Parameter Count | 135M |
| License | Apache 2.0 |
| Supported Languages | 14 (including Arabic, Chinese, English, French, German, etc.) |
| Paper | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks |
| Vector Dimension | 512 |
What is distiluse-base-multilingual-cased-v1?
This is a powerful multilingual sentence embedding model built using the sentence-transformers framework. It's designed to convert sentences and paragraphs from 14 different languages into 512-dimensional dense vector representations, making it ideal for cross-lingual applications in semantic search and clustering tasks.
Implementation Details
The model utilizes a DistilBERT architecture with a three-component pipeline: a transformer encoder, a pooling layer, and a dense layer with tanh activation. It processes input text with a maximum sequence length of 128 tokens and maintains case sensitivity for better accuracy.
- Transformer base: DistilBERT model with multilingual capabilities
- Pooling strategy: Mean tokens pooling
- Dense layer: 768 to 512 dimension reduction with tanh activation
- Case-sensitive processing for improved accuracy
Core Capabilities
- Multilingual sentence embedding generation
- Cross-lingual semantic similarity computation
- Document clustering and classification
- Semantic search across multiple languages
- Text similarity analysis in 14 languages
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 14 different languages while maintaining a relatively compact size (135M parameters) makes it particularly efficient for multilingual applications. Its distilled architecture provides a good balance between performance and resource usage.
Q: What are the recommended use cases?
The model excels in cross-lingual information retrieval, multilingual semantic search, document clustering, and similarity comparison across different languages. It's particularly useful for applications requiring multilingual text understanding without direct translation.
