bge reranker v2 m3

提供商BAAI
分类text-classification
许可证apache-2.0
下载量1.6M
星标160

简介

BGE Reranker v2 M3 是由北京智源人工智能研究院（BAAI）推出的高性能重排序模型。在 RAG（检索增强生成）工作流中，它扮演着“精筛”的角色：先由向量模型快速召回候选文档，再由它对结果进行深度语义打分，剔除无关干扰项。该模型最大的特点是支持多语言且具备极强的泛化能力，能显著提升知识库问答的准确率。对于开发者而言，它属于典型的插件式组件，部署简单，是优化搜索质量、解决 LLM 幻觉问题的关键环节。

核心亮点

  • 大幅提升 RAG 检索精度，有效过滤无关噪声
  • 原生支持多语言，适配复杂的中英文混合场景
  • 部署门槛低，可无缝集成至现有检索流水线
  • 采用 Apache-2.0 协议，企业级商业应用无忧

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("BAAI/bge-reranker-v2-m3")
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-v2-m3")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download BAAI/bge-reranker-v2-m3

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download BAAI/bge-reranker-v2-m3 config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('BAAI/bge-reranker-v2-m3')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/BAAI/bge-reranker-v2-m3

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/BAAI/bge-reranker-v2-m3

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-m3')
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3')

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model BAAI/bge-reranker-v2-m3

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例） 
modelscope download --model BAAI/bge-reranker-v2-m3 README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('BAAI/bge-reranker-v2-m3')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/BAAI/bge-reranker-v2-m3.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/BAAI/bge-reranker-v2-m3.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 快速开发

下载并安装 ModelScope library

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

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'BAAI/bge-reranker-v2-m3')

完整文档

来源: HuggingFace

---
license: apache-2.0
pipeline_tag: text-classification
tags:

  • transformers

  • sentence-transformers

  • text-embeddings-inference

language:
  • multilingual

---

Reranker

More details please refer to our Github: FlagEmbedding.

Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
And the score can be mapped to a float value in [0,1] by sigmoid function.

Model List

| Model | Base model | Language | layerwise | feature |
|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
| BAAI/bge-reranker-base | xlm-roberta-base | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. |
| BAAI/bge-reranker-large | xlm-roberta-large | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. |
| BAAI/bge-reranker-v2-m3 | bge-m3 | Multilingual | - | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. |
| BAAI/bge-reranker-v2-gemma | gemma-2b | Multilingual | - | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. |
| BAAI/bge-reranker-v2-minicpm-layerwise | MiniCPM-2B-dpo-bf16 | Multilingual | 8-40 | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. |

You can select the model according your senario and resource.

Usage

Using FlagEmbedding

code
pip install -U FlagEmbedding

#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )

Get relevance scores (higher scores indicate more relevance):

python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

score = reranker.compute_score(['query', 'passage'])
print(score) # -5.65234375


You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score

score = reranker.compute_score(['query', 'passage'], normalize=True)
print(score) # 0.003497010252573502

scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores) # [-8.1875, 5.26171875]


You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score

scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)
print(scores) # [0.00027803096387751553, 0.9948403768236574]

#### For LLM-based reranker

python
from FlagEmbedding import FlagLLMReranker
reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation


score = reranker.compute_score(['query', 'passage'])
print(score)


scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)

#### For LLM-based layerwise reranker

python
from FlagEmbedding import LayerWiseFlagLLMReranker
reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation


score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
print(score)


scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28])
print(scores)

Using Huggingface transformers

#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )

Get relevance scores (higher scores indicate more relevance):

python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3')
model.eval()


pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
    print(scores)

#### For LLM-based reranker

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
if prompt is None:
prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'."
sep = "\n"
prompt_inputs = tokenizer(prompt,

查看官方来源