# Cross-Language Retrieval

Nomic Embed Text V2 GGUF is a multilingual text embedding model supporting over 70 languages, suitable for sentence similarity calculation and feature extraction tasks.

This is the 4-bit GPTQ quantized version of the BAAI/bge-multilingual-gemma2 model, supporting multilingual text embedding tasks.

Multilingual E5 large instruction model supporting text embedding and classification tasks in multiple languages

Multilingual E5 large instruction model, supporting multiple languages and tasks including classification, retrieval, clustering, etc.

Multilingual E5 large instruction model supporting text embedding and classification tasks in multiple languages

Qwen2-VL-7B-Instruct is a multimodal vision-language model based on the Qwen2 architecture, supporting both Chinese and English, suitable for various natural language processing tasks.

Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, specializing in sentence similarity calculation and feature extraction tasks.

Multilingual E5 Large is a multilingual sentence transformer model focused on sentence similarity and feature extraction tasks, supporting multiple languages.

The multilingual e5 series is one of the best choices for multilingual embedding models, supporting multiple languages and tasks.

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

A Reranker model fine-tuned on Korean data based on BAAI/bge-reranker-large, designed to enhance Korean Retrieval-Augmented Generation (RAG) performance

Sentence embedding model trained on Swiss multilingual news data, supporting Switzerland's four official languages

The EUBERT MLM model is fine-tuned as a cross-encoder using sentence transformer training sets, supporting 24 EU languages

This is a sentence embedding model based on sentence-transformers, capable of converting Italian and English sentences into 768-dimensional vector representations, suitable for tasks such as sentence similarity calculation and semantic search.

This is the quantized version of multilingual-e5-small, optimized for inference performance through layer-wise quantization while retaining most of the original model's quality.

E5-base-multilingual-4096 is a locally sparse global version based on intfloat/multilingual-e5-base, supporting multilingual text embedding models that can process up to 4096 tokens.

Fine-tuned version of the paraphrase-multilingual-mpnet-base-v2 model based on sentence-transformers, optimized for semantic text similarity tasks in 15 languages