Name: bge reranker base
Author: BAAI

Overview

The bge-reranker-base is a cross-encoder model designed to refine the results of initial vector searches. Unlike bi-encoders used for retrieval, this model evaluates the specific relevance between a query and a document pair, significantly reducing false positives in RAG pipelines. It is particularly effective for developers building high-precision knowledge bases where the top-k results from a vector database need re-scoring to ensure the most contextually accurate information is passed to the LLM. Integration is straightforward via the Sentence-Transformers library or Hugging Face, fitting seamlessly into existing retrieval-augmented generation workflows to boost hit rates without requiring massive index rebuilds.

Highlights

Optimizes RAG precision by re-scoring retrieved document candidates
Cross-encoder architecture for deeper semantic relevance analysis
Easy integration via Sentence-Transformers and Hugging Face
Permissive MIT license for flexible commercial deployment
Reduces hallucinations by filtering irrelevant context for LLMs

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("BAAI/bge-reranker-base")
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-base")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download BAAI/bge-reranker-base

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download BAAI/bge-reranker-base config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('BAAI/bge-reranker-base')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/BAAI/bge-reranker-base

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/BAAI/bge-reranker-base

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-base')
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-base')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model BAAI/bge-reranker-base

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model BAAI/bge-reranker-base README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('BAAI/bge-reranker-base')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/BAAI/bge-reranker-base.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/BAAI/bge-reranker-base.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'BAAI/bge-reranker-base')

Full Documentation

来源: HuggingFace

---
license: mit
language:

en

zh

tags:

mteb

text-embeddings-inference

model-index:

name: bge-reranker-base

results:
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 81.27206722525007
- type: mrr
value: 84.14238095238095
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 84.10369934291236
- type: mrr
value: 86.79376984126984
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 35.4600511272538
- type: mrr
value: 34.60238095238095
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 67.27728847727172
- type: mrr
value: 77.1315192743764
pipeline_tag: text-classification
library_name: sentence-transformers
---

We have updated the new reranker, supporting larger lengths, more languages, and achieving better performance.

<h1 align="center">FlagEmbedding</h1>

More details please refer to our Github: FlagEmbedding.

English | 中文

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:

Long-Context LLM: Activation Beacon

Fine-tuning of LM : LM-Cocktail

Embedding Model: Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding

Reranker Model: llm rerankers, BGE Reranker

Benchmark: C-MTEB

News

3/18/2024: Release new rerankers, built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation.

3/18/2024: Release Visualized-BGE, equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data.

1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).

It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical Report and Code. :fire:

1/9/2024: Release Activation-Beacon, an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. Technical Report :fire:

12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. Technical Report

11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical Report :fire:

10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Technical Report

09/15/2023: The technical report of BGE has been released

09/15/2023: The massive training data of BGE has been released

09/12/2023: New models:

- New reranker model: release cross-encoder models BAAI/bge-reranker-base and BAAI/bge-reranker-large, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - update embedding model: release bge-*-v1.5 embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.

09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning.

08/09/2023: BGE Models are integrated into Langchain, you can use it like this; C-MTEB leaderboard is available.

08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗

08/02/2023: Release bge-large-*(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark! :tada: :tada:

08/01/2023: We release the Chinese Massive Text Embedding Benchmark (C-MTEB), consisting of 31 test dataset.

</details>

Model List

bge is short for BAAI general embedding.

| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| BAAI/bge-m3 | Multilingual | Inference Fine-tune | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| BAAI/llm-embedder | English | Inference Fine-tune | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_e