# Multilingual Reranking

This model is converted from BAAI/bge-reranker-v2-m3 into GGUF format using llama.cpp via ggml.ai's GGUF-my-repo space, primarily for text classification tasks.

mxbai-rerank-large-v2 is a multilingual text reranking model that supports multiple languages and various quantization formats, suitable for different hardware environments.

This model is the GGUF quantized version of BAAI/bge-reranker-v2-m3, designed for text reranking tasks with multilingual support.

This is a text reranking model based on 0.5B parameters, supporting multilingual text ranking tasks.

The LB Reranker is a model for determining the relevance between queries and text snippets, supporting 95+ languages, suitable for ranking and reranking in retrieval tasks.

This model is converted from BAAI/bge-reranker-large into GGUF format for reranking tasks, supporting both Chinese and English.

This model is converted from BAAI/bge-reranker-v2-m3 into GGUF format for text reranking tasks, supporting multiple languages.

A multilingual reranking model based on Gemma-2B, suitable for text relevance ranking tasks and supports multilingual scenarios.

This is the GGUF format conversion version of the BAAI/bge-reranker-v2-gemma model, compatible with the llama.cpp framework, supporting multilingual text classification tasks.

The first multilingual reranking model in the GTE series, supporting 70+ languages with high performance and long text processing capabilities.