| Property | Value |
|---|---|
| Model Type | Cross-Encoder |
| Author | cross-encoder |
| Performance (NDCG@10) | 71.01 |
| Speed | 4100 docs/sec |
| Model URL | Hugging Face |
What is ms-marco-MiniLM-L2-v2?
ms-marco-MiniLM-L2-v2 is a specialized cross-encoder model designed for passage ranking tasks, particularly optimized for the MS Marco dataset. This version 2 model represents a significant improvement over its predecessor, offering an excellent balance between performance and speed.
Implementation Details
The model can be implemented using either SentenceTransformers or the Transformers library. It excels at comparing query-passage pairs and producing relevance scores, making it ideal for information retrieval tasks.
- Achieves NDCG@10 score of 71.01 on TREC DL 19
- Processes 4100 documents per second on V100 GPU
- Supports both SentenceTransformers and Transformers implementations
Core Capabilities
- Passage ranking and reranking
- Query-passage relevance scoring
- Information retrieval optimization
- Fast processing speed while maintaining high accuracy
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimal balance between performance and speed, processing 4100 docs/sec while maintaining high NDCG@10 and MRR@10 scores. It's particularly effective for real-world applications where both accuracy and processing speed are crucial.
Q: What are the recommended use cases?
The model is ideal for search engine optimization, document retrieval systems, and any application requiring accurate ranking of text passages against queries. It's particularly well-suited for systems that need to process large amounts of text data efficiently.