bge reranker large

ProviderBAAI
Categoryfeature-extraction
Licensemit
Downloads371.6K
Stars68

Overview

The bge-reranker-large is a cross-encoder model designed to refine the results of initial vector searches in RAG pipelines. Unlike bi-encoders that rely on cosine similarity between embeddings, this model performs deep interaction between the query and the candidate document to provide a precise relevance score. It is specifically engineered to mitigate the 'lost in the middle' phenomenon and reduce hallucinations by ensuring only the most contextually accurate chunks are passed to the LLM. For developers, it integrates as a second-stage filtering step after an initial retrieval from a vector database, significantly increasing precision at the cost of slight latency increases per document.

Highlights

  • High-precision cross-encoder for RAG reranking tasks
  • Significantly improves retrieval accuracy over cosine similarity
  • Seamless integration into existing vector search pipelines
  • MIT licensed for flexible commercial deployment
  • Optimized for reducing LLM hallucinations via better context

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-large")
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-large")

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

Download the full repository 
huggingface-cli download BAAI/bge-reranker-large

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

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download BAAI/bge-reranker-large config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

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

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/BAAI/bge-reranker-large

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/BAAI/bge-reranker-large

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

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

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

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

Download the full repository 
modelscope download --model BAAI/bge-reranker-large

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

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model BAAI/bge-reranker-large README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

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

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/BAAI/bge-reranker-large.git

To skip LFS large-file downloads, use:

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

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

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

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

Full Documentation

来源: HuggingFace

---
license: mit
language:

  • en

  • zh

tags:
  • mteb

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: feature-extraction
---

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

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

<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>

More details please refer to our Github: FlagEmbedding.

English | 中文

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

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:
  • 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.

<details>
<summary>More</summary>
<!-- ### More -->

  • 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:



</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 |
| [BAAI/bge-reranker-large](https://hugging

View official source
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