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bge-reranker-v2-m3

The BGE Reranker v2 M3 is a cross-encoder model designed to refine the output of initial retrieval stages in RAG pipelines. Unlike bi-encoders that rely on vector similarity, this model analyzes the specific interaction between a query and a document to provide a more precise relevancy score. It is particularly valuable for developers building multi-lingual applications, as it maintains high performance across diverse languages and handles varying document lengths effectively. By integrating this as a second-stage reranker, you can significantly reduce false positives and improve the precision of the context provided to your LLM, effectively bridging the gap between coarse retrieval and final generation.

BAAItext classification
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01 / MODEL CARD

Model card

The BGE Reranker v2 M3 is a cross-encoder model designed to refine the output of initial retrieval stages in RAG pipelines. Unlike bi-encoders that rely on vector similarity, this model analyzes the specific interaction between a query and a document to provide a more precise relevancy score. It is particularly valuable for developers building multi-lingual applications, as it maintains high performance across diverse languages and handles varying document lengths effectively. By integrating this as a second-stage reranker, you can significantly reduce false positives and improve the precision of the context provided to your LLM, effectively bridging the gap between coarse retrieval and final generation.

Model typetext classification
ProviderBAAI
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/BAAI/bge-reranker-v2-m3
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: BAAI/bge-reranker-v2-m3
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model BAAI/bge-reranker-v2-m3
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model BAAI/bge-reranker-v2-m3 README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('BAAI/bge-reranker-v2-m3')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/BAAI/bge-reranker-v2-m3.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/BAAI/bge-reranker-v2-m3.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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