# Multilingual Embedding

GPTQ quantized version of Qwen3-Embedding-0.6B, optimized for video memory usage with minimal performance loss

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 with strong cross-language capabilities.

A 7-billion-parameter multimodal embedding model specialized in visual document retrieval tasks, achieving outstanding performance on the Vidore-v2 benchmark

Multilingual embedding model for German and English, supporting a context length of 8192 tokens

A 107M-parameter multilingual embedding model developed by IBM, supporting 12 languages with excellent performance on the MTEB benchmark

This is an intermediate version of a multilingual Mixture of Experts (MoE) text embedding model, obtained through multi-stage contrastive training

An 8-layer embedding model distilled from BAAI/bge-m3, achieving 2.5x speed improvement while maintaining retrieval performance

The SFR-Embedding model researched by Salesforce is suitable for multilingual and multi-task code and text retrieval, demonstrating excellent performance in multiple code retrieval tasks.

The GGUF quantized version of Snowflake Arctic Embed L v2.0 is an efficient multilingual text embedding model, suitable for high-quality retrieval tasks.

IBM Granite multilingual embedding model, supporting text embedding tasks in 12 languages, suitable for information retrieval and multilingual application scenarios.

Qwen2.5-7B-embed-base is a Transformer-based pre-trained language model specifically designed for generating high-quality text embeddings.

Snowflake Arctic Embed v2.0 is a multilingual sentence embedding model that supports text feature extraction and sentence similarity calculation for over 100 languages.

Snowflake Arctic Embed M v2.0 is a multilingual sentence embedding model that supports over 50 languages, focusing on sentence similarity and feature extraction tasks.

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

GGUF quantized version of BGE_M3_Mindspore, offering multiple quantization options to suit different needs.

Multilingual E5 large instruction model supporting text embedding and classification tasks in over 100 languages

This model is converted from BAAI/bge-m3 to GGUF format via llama.cpp, primarily used for sentence similarity computation and feature extraction.

This model is a sentence similarity model converted from BAAI/bge-m3 to GGUF format using llama.cpp via ggml.ai's GGUF-my-repo space.

This is a Korean-optimized sentence embedding model based on the multilingual-e5-base model, supporting multilingual text similarity computation and feature extraction.

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

GTE-Base is a general-purpose text embedding model capable of converting text into high-dimensional vector representations, suitable for text classification and similarity search tasks.

This is a multilingual sentence transformer model that maps text to a dense vector space, supporting semantic search and clustering tasks

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.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

A vectorizer developed by Sinequa that generates embedding vectors based on input paragraphs or queries, used for sentence similarity calculation and retrieval tasks.

This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

A contrastive learning model trained using parallel language pairs, mapping texts to the same vector space across different languages