cross-encoder-russian-msmarco
| Property | Value |
|---|---|
| Author | DiTy |
| Base Model | DeepPavlov/rubert-base-cased |
| Task | Russian Information Retrieval |
| Model Hub | Hugging Face |
What is cross-encoder-russian-msmarco?
cross-encoder-russian-msmarco is a specialized neural model designed for Russian language information retrieval tasks. Built on the DeepPavlov/rubert-base-cased architecture, this model has been fine-tuned using the MS-MARCO Russian passage ranking dataset to provide accurate relevance scoring between queries and documents.
Implementation Details
The model implements a cross-encoder architecture, which directly compares query-passage pairs to determine relevance. It can be used with both the sentence-transformers library and HuggingFace Transformers, offering flexibility in implementation. The model supports a maximum sequence length of 512 tokens and can be deployed on GPU for optimal performance.
- Built on DeepPavlov/rubert-base-cased architecture
- Fine-tuned on MS-MARCO Russian passage ranking dataset
- Supports both sentence-transformers and HuggingFace implementations
- Includes built-in ranking functionality
Core Capabilities
- Query-passage relevance scoring
- Document ranking and reranking
- Russian language information retrieval
- Integration with search systems like ElasticSearch
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Russian language information retrieval, offering a specialized solution for query-passage ranking tasks. Its cross-encoder architecture provides more accurate relevance scoring compared to traditional bi-encoders.
Q: What are the recommended use cases?
The model is ideal for search result re-ranking, document retrieval systems, and question-answering applications in Russian. It's particularly effective when combined with initial retrieval systems like ElasticSearch for a two-stage ranking approach.
