Cross English & German RoBERTa Sentence Transformer
| Property | Value |
|---|---|
| Parameter Count | 278M |
| License | MIT |
| Base Model | xlm-roberta-base |
| Paper | Sentence-BERT Paper |
What is cross-en-de-roberta-sentence-transformer?
This is a specialized bilingual sentence transformer model designed for computing semantic sentence embeddings in both English and German. Built on XLM-RoBERTa architecture, it enables cross-lingual semantic similarity comparisons with state-of-the-art performance. The model achieves impressive Spearman correlation scores of 0.8550 for German and 0.8660 for English texts.
Implementation Details
The model employs a multilingual fine-tuning approach with language-crossing, built upon the xlm-roberta-base architecture. It was trained using carefully optimized hyperparameters discovered through 33 trials of automated search, including a batch size of 8 and learning rate of ~1.03e-05.
- Built on XLM-RoBERTa base model with additional paraphrase training
- Fine-tuned on STSbenchmark dataset for both languages
- Implements siamese and triplet network structures
- Optimized for cosine-similarity comparisons
Core Capabilities
- Cross-lingual semantic textual similarity
- Bilingual semantic search (English-German)
- Paraphrase mining across languages
- Sentence embedding generation
- Fast similarity computation (5 seconds vs 65 hours with standard BERT)
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to generate semantically similar vectors regardless of whether the input is in English or German, enabling cross-lingual search and comparison. It outperforms existing dedicated English large models in cross-lingual tasks.
Q: What are the recommended use cases?
The model excels in semantic search applications, paraphrase detection, and similarity matching across English and German content. It's particularly useful for multilingual document retrieval systems, cross-language information retrieval, and content matching applications.
