GTE Multilingual Reranker Base
| Property | Value |
|---|---|
| Parameter Count | 306M |
| License | Apache 2.0 |
| Max Context Length | 8192 tokens |
| Supported Languages | 75 languages |
| Research Paper | Link to Paper |
What is gte-multilingual-reranker-base?
The gte-multilingual-reranker-base is a cutting-edge reranker model from the GTE (Generalized Text Embeddings) family, designed for efficient multilingual text retrieval. It represents a significant advancement in natural language processing, combining state-of-the-art performance with practical efficiency.
Implementation Details
This model employs an encoder-only transformer architecture, significantly reducing the model size compared to decoder-based alternatives. It's optimized for FP16 precision and can be easily integrated using the Hugging Face Transformers library or deployed via Docker using the Infinity server.
- Efficient encoder-only architecture for 10x faster inference
- Support for text lengths up to 8192 tokens
- Optimized for multilingual applications across 75 languages
- Compatible with xformers acceleration and unpadding optimization
Core Capabilities
- State-of-the-art performance in multilingual retrieval tasks
- Efficient processing of long-form content
- Comprehensive language support including English, Chinese, Arabic, and many more
- Flexible deployment options including local and cloud-based implementations
- Commercial API availability through Alibaba Cloud
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of high performance, efficient architecture, and extensive language support. The encoder-only design provides significant speed advantages while maintaining SOTA performance in multilingual retrieval tasks.
Q: What are the recommended use cases?
The model is ideal for reranking search results, document retrieval systems, and multilingual information retrieval applications. It's particularly effective when dealing with long documents and cross-lingual search scenarios.
