msmarco-MiniLM-L6-en-de-v1
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | cross-encoder |
| Performance | 72.43 NDCG@10 (TREC-DL19 EN-EN) |
| Processing Speed | 1600 docs/sec on V100 GPU |
What is msmarco-MiniLM-L6-en-de-v1?
This is a specialized cross-lingual Cross-Encoder model designed for passage re-ranking tasks between English and German content. Trained on the MS Marco Passage Ranking dataset, it represents a significant advancement in multilingual information retrieval systems. The model excels in both monolingual (English) and cross-lingual (German-English) scenarios, making it particularly valuable for multilingual search applications.
Implementation Details
The model is built on the MiniLM architecture with 6 layers, optimized for efficient cross-lingual processing. It can be easily implemented using either SentenceTransformers or the Transformers library, supporting maximum sequence lengths of 512 tokens. The model demonstrates impressive performance metrics across different evaluation scenarios, particularly in document re-ranking tasks.
- Achieves 72.43 NDCG@10 on TREC-DL19 EN-EN tasks
- Performs at 65.53 NDCG@10 for cross-lingual DE-EN scenarios
- Processes 1600 documents per second on a V100 GPU
- Supports both English and German query-document pairs
Core Capabilities
- Cross-lingual passage re-ranking
- High-speed document processing
- Efficient performance on limited computational resources
- Seamless integration with popular NLP frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to handle both English and German content effectively while maintaining high processing speeds. It achieves superior performance compared to traditional BM25 baselines while being more efficient than larger models like MiniLM-L12.
Q: What are the recommended use cases?
The model is ideal for multilingual search systems, particularly those dealing with English and German content. It's especially effective for re-ranking search results, information retrieval systems, and document relevance scoring in bilingual contexts.
