bilingual-embedding-large
| Property | Value |
|---|---|
| Parameter Count | 560M |
| License | Apache 2.0 |
| Languages | French, English |
| Vector Dimension | 1024 |
| Base Architecture | XLM-RoBERTa |
What is bilingual-embedding-large?
bilingual-embedding-large is a specialized sentence embedding model designed to handle both French and English text simultaneously. Built on XLM-RoBERTa architecture, it generates 1024-dimensional vectors that capture semantic meaning across both languages. The model has been extensively trained through multiple stages including NLI training, STS benchmarking, and advanced augmentation techniques.
Implementation Details
The model implements a sophisticated architecture combining Transformer-based encoding with mean pooling and normalization layers. It's trained using a multi-stage process including SNLI+XNLI datasets and fine-tuned on bilingual STS benchmarks.
- Multi-stage training pipeline incorporating NLI and STS data
- Advanced augmentation using Augmented SBERT techniques
- Optimized for cross-lingual semantic similarity tasks
- Implements mean pooling strategy for sentence embeddings
Core Capabilities
- Bilingual sentence embedding generation
- Cross-lingual semantic search
- Text clustering and classification
- Semantic similarity assessment
- Reranking applications
Frequently Asked Questions
Q: What makes this model unique?
The model's key strength lies in its ability to handle both French and English content simultaneously while maintaining high performance across various benchmarks. Its multi-stage training process, including advanced augmentation techniques, sets it apart from traditional monolingual models.
Q: What are the recommended use cases?
The model excels in bilingual applications such as cross-lingual information retrieval, semantic search, document clustering, and similarity assessment between French and English texts. It's particularly useful for organizations working with content in both languages.
