# Multilingual Retrieval

DRAMA-large (0.3B) is a dense retrieval model built upon a pruned large language model architecture, optimized for efficient and generalizable multilingual text retrieval tasks.

DRAMA-base is a dense retrieval model based on a pruned large language model architecture, supporting multilingual text retrieval tasks.

Nomic Embed v2 is a high-performance multilingual Mixture of Experts (MoE) text embedding model supporting approximately 100 languages, excelling in multilingual retrieval tasks.

Snowflake Arctic-embed-l-v2.0 is the latest embedding model released by Snowflake, specifically designed for multilingual workloads, optimizing retrieval performance and inference efficiency.

BGE-M3 is a versatile, multilingual, multi-granularity embedding model that supports three common retrieval functions: dense retrieval, multi-vector retrieval, and sparse retrieval.

MiniCPM-Embedding is an embedding model developed based on the MiniCPM-2B-sft-bf16 foundation model, specializing in retrieval tasks and supporting both Chinese and English.

This is the ONNX quantized version of the BAAI/bge-m3 model, supporting three functionalities: dense retrieval, multi-vector retrieval, and sparse retrieval, covering over 100 languages.

This model is a dense retrieval model further fine-tuned on all Mr. TyDi training data based on the castorini/mdpr-tied-pft-msmarco checkpoint.