mmarco-mMiniLMv2-L12-H384-v1
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Supported Languages | 15 (including English, Arabic, Chinese, French, German, etc.) |
| Base Architecture | multilingual MiniLMv2 |
| Primary Task | Cross-Encoder for Information Retrieval |
What is mmarco-mMiniLMv2-L12-H384-v1?
This is a sophisticated cross-encoder model specifically designed for multilingual information retrieval tasks. Built on the multilingual MiniLMv2 architecture, it has been trained on the MMARCO dataset, which is a machine-translated version of MS MARCO covering 14 different languages. The model demonstrates robust performance even on languages beyond its initial training scope.
Implementation Details
The model leverages the multilingual MiniLMv2-L12-H384 architecture as its foundation, which was distilled from XLM-RoBERTa-Large. It can be easily implemented using either SentenceTransformers or the Transformers library, making it accessible for various applications.
- Built on nreimers/mMiniLMv2-L12-H384 architecture
- Supports cross-lingual information retrieval
- Compatible with both SentenceTransformers and Transformers frameworks
Core Capabilities
- Multilingual text classification across 15 languages
- Query-passage relevance ranking
- Cross-lingual information retrieval
- Efficient retrieval and re-ranking of passages
Frequently Asked Questions
Q: What makes this model unique?
The model's key strength lies in its multilingual capabilities, supporting 15 languages while maintaining high performance in information retrieval tasks. It's particularly valuable for organizations needing to handle content in multiple languages with a single model.
Q: What are the recommended use cases?
The model excels in information retrieval tasks such as document ranking, search engine development, and question-answering systems. It's particularly useful when working with ElasticSearch for retrieval and re-ranking in multilingual contexts.
