bce-reranker-base_v1

Maintainer: maidalun1020

Total Score

95

Last updated 4/28/2024

🌿

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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BCEmbedding: Bilingual and Crosslingual Embedding for RAG

[object Object][object Object]

bce-reranker-base_v1The latest "Updates" should be checked in

GitHub

(Key Features)

  • (Multilingual and Crosslingual capability in English, Chinese, Japanese and Korean)
  • RAG(RAG adaptation for more domains, including Education, Law, Finance, Medical, Literature, FAQ, Textbook, Wikipedia, etc.)
  • BCEmbeddingrerank(Handle long passages reranking more than 512 limit in BCEmbedding)
  • RerankerModel **********passage**passage0.350.4RerankerModel provides "smooth" (for reranking) and "meaningful" (for filtering bad passages with a threshold of 0.35 or 0.4) similarity score, which help you figure out how relavent the query and passages are!
  • Best practice embeddingtop50-100reranker50-100top5-101. Get top 50-100 passages with bce-embedding-base_v1 for "recall" 2. Rerank passages with bce-reranker-base_v1 and get top 5-10 for "precision" finally.

News:

Third-party Examples:

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[object Object]


Click to Open Contents

Bilingual and Crosslingual Embedding (BCEmbedding), developed by NetEase Youdao, encompasses EmbeddingModel and RerankerModel. The EmbeddingModel specializes in generating semantic vectors, playing a crucial role in semantic search and question-answering, and the RerankerModel excels at refining search results and ranking tasks.

BCEmbedding serves as the cornerstone of Youdao's Retrieval Augmented Generation (RAG) implmentation, notably QAnything [github], an open-source implementation widely integrated in various Youdao products like Youdao Speed Reading and Youdao Translation.

Distinguished for its bilingual and crosslingual proficiency, BCEmbedding excels in bridging Chinese and English linguistic gaps, which achieves

Bilingual and Crosslingual Superiority

Existing embedding models often encounter performance challenges in bilingual and crosslingual scenarios, particularly in Chinese, English and their crosslingual tasks. BCEmbedding, leveraging the strength of Youdao's translation engine, excels in delivering superior performance across monolingual, bilingual, and crosslingual settings.

EmbeddingModel supports

Chinese (ch) and English (en)
(more languages support will come soon), while RerankerModel supports
Chinese (ch), English (en), Japanese (ja) and Korean (ko)
.

BCEmbedding

EmbeddingModel

****
RerankerModel
****

Key Features

  • Bilingual and Crosslingual Proficiency: Powered by Youdao's translation engine, excelling in Chinese, English and their crosslingual retrieval task, with upcoming support for additional languages.

  • RAG-Optimized: Tailored for diverse RAG tasks including translation, summarization, and question answering, ensuring accurate query understanding. See RAG Evaluations in LlamaIndex.

  • Efficient and Precise Retrieval: Dual-encoder for efficient retrieval of EmbeddingModel in first stage, and cross-encoder of RerankerModel for enhanced precision and deeper semantic analysis in second stage.

  • Broad Domain Adaptability: Trained on diverse datasets for superior performance across various fields.

  • User-Friendly Design: Instruction-free, versatile use for multiple tasks without specifying query instruction for each task.

  • Meaningful Reranking Scores: RerankerModel provides relevant scores to improve result quality and optimize large language model performance.

  • Proven in Production: Successfully implemented and validated in Youdao's products.

    • ****BCEmbedding

    • RAGRAG********query understanding LlamaIndexRAG

    • ****EmbeddingModel``RerankerModel



    • ****RerankerModel

    • ****BCEmbedding

Latest Updates

Model List

Model Name

Model Type

Languages

Parameters

Weights

bce-embedding-base_v1

EmbeddingModel

ch, en

279M

download

bce-reranker-base_v1

RerankerModel

ch, en, ja, ko

279M

download

Manual

Installation

First, create a conda environment and activate it.

conda create --name bce python=3.10 -y
conda activate bce

Then install BCEmbedding for minimal installation:

pip install BCEmbedding==0.1.1

Or install from source:

git clone git@github.com:netease-youdao/BCEmbedding.git
cd BCEmbedding
pip install -v -e .

Quick Start

1. Based on BCEmbedding

Use EmbeddingModel, and cls pooler is default.

from BCEmbedding import EmbeddingModel

# list of sentences
sentences = ['sentence_0', 'sentence_1', ...]

# init embedding model
model = EmbeddingModel(model_name_or_path="maidalun1020/bce-embedding-base_v1")

# extract embeddings
embeddings = model.encode(sentences)

Use RerankerModel to calculate relevant scores and rerank:

from BCEmbedding import RerankerModel

# your query and corresponding passages
query = 'input_query'
passages = ['passage_0', 'passage_1', ...]

# construct sentence pairs
sentence_pairs = [[query, passage] for passage in passages]

# init reranker model
model = RerankerModel(model_name_or_path="maidalun1020/bce-reranker-base_v1")

# method 0: calculate scores of sentence pairs
scores = model.compute_score(sentence_pairs)

# method 1: rerank passages
rerank_results = model.rerank(query, passages)

NOTE:

  • In [object Object] method, we provide an advanced preproccess that we use in production for making sentence_pairs, when "passages" are very long.

2. Based on transformers

For EmbeddingModel:

from transformers import AutoModel, AutoTokenizer

# list of sentences
sentences = ['sentence_0', 'sentence_1', ...]

# init model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('maidalun1020/bce-embedding-base_v1')
model = AutoModel.from_pretrained('maidalun1020/bce-embedding-base_v1')

device = 'cuda'  # if no GPU, set "cpu"
model.to(device)

# get inputs
inputs = tokenizer(sentences, padding=True, truncation=True, max_length=512, return_tensors="pt")
inputs_on_device = {k: v.to(self.device) for k, v in inputs.items()}

# get embeddings
outputs = model(**inputs_on_device, return_dict=True)
embeddings = outputs.last_hidden_state[:, 0]  # cls pooler
embeddings = embeddings / embeddings.norm(dim=1, keepdim=True)  # normalize

For RerankerModel:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# init model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('maidalun1020/bce-reranker-base_v1')
model = AutoModelForSequenceClassification.from_pretrained('maidalun1020/bce-reranker-base_v1')

device = 'cuda'  # if no GPU, set "cpu"
model.to(device)

# get inputs
inputs = tokenizer(sentence_pairs, padding=True, truncation=True, max_length=512, return_tensors="pt")
inputs_on_device = {k: v.to(device) for k, v in inputs.items()}

# calculate scores
scores = model(**inputs_on_device, return_dict=True).logits.view(-1,).float()
scores = torch.sigmoid(scores)

3. Based on sentence_transformers

For EmbeddingModel:

from sentence_transformers import SentenceTransformer

# list of sentences
sentences = ['sentence_0', 'sentence_1', ...]

# init embedding model
## New update for sentence-trnasformers. So clean up your "`SENTENCE_TRANSFORMERS_HOME`/maidalun1020_bce-embedding-base_v1" or "/.cache/torch/sentence_transformers/maidalun1020_bce-embedding-base_v1" first for downloading new version.
model = SentenceTransformer("maidalun1020/bce-embedding-base_v1")

# extract embeddings
embeddings = model.encode(sentences, normalize_embeddings=True)

For RerankerModel:

from sentence_transformers import CrossEncoder

# init reranker model
model = CrossEncoder('maidalun1020/bce-reranker-base_v1', max_length=512)

# calculate scores of sentence pairs
scores = model.predict(sentence_pairs)

Integrations for RAG Frameworks

1. Used in langchain

from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.vectorstores.utils import DistanceStrategy

query = 'apples'
passages = [
        'I like apples', 
        'I like oranges', 
        'Apples and oranges are fruits'
    ]
  
# init embedding model
model_name = 'maidalun1020/bce-embedding-base_v1'
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'batch_size': 64, 'normalize_embeddings': True, 'show_progress_bar': False}

embed_model = HuggingFaceEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
  )

# example #1. extract embeddings
query_embedding = embed_model.embed_query(query)
passages_embeddings = embed_model.embed_documents(passages)

# example #2. langchain retriever example
faiss_vectorstore = FAISS.from_texts(passages, embed_model, distance_strategy=DistanceStrategy.MAX_INNER_PRODUCT)

retriever = faiss_vectorstore.as_retriever(search_type="similarity", search_kwargs={"score_threshold": 0.5, "k": 3})

related_passages = retriever.get_relevant_documents(query)

2. Used in llama_index

from llama_index.embeddings import HuggingFaceEmbedding
from llama_index import VectorStoreIndex, ServiceContext, SimpleDirectoryReader
from llama_index.node_parser import SimpleNodeParser
from llama_index.llms import OpenAI

query = 'apples'
passages = [
        'I like apples', 
        'I like oranges', 
        'Apples and oranges are fruits'
    ]

# init embedding model
model_args = {'model_name': 'maidalun1020/bce-embedding-base_v1', 'max_length': 512, 'embed_batch_size': 64, 'device': 'cuda'}
embed_model = HuggingFaceEmbedding(**model_args)

# example #1. extract embeddings
query_embedding = embed_model.get_query_embedding(query)
passages_embeddings = embed_model.get_text_embedding_batch(passages)

# example #2. rag example
llm = OpenAI(model='gpt-3.5-turbo-0613', api_key=os.environ.get('OPENAI_API_KEY'), api_base=os.environ.get('OPENAI_BASE_URL'))
service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)

documents = SimpleDirectoryReader(input_files=["BCEmbedding/tools/eval_rag/eval_pdfs/Comp_en_llama2.pdf"]).load_data()
node_parser = SimpleNodeParser.from_defaults(chunk_size=512)
nodes = node_parser.get_nodes_from_documents(documents[0:36])
index = VectorStoreIndex(nodes, service_context=service_context)
query_engine = index.as_query_engine()
response = query_engine.query("What is llama?")

Evaluation

Evaluate Semantic Representation by MTEB

We provide evaluateion tools for embedding and reranker models, based on MTEB and C_MTEB.

MTEBC_MTEBembedding``reranker

1. Embedding Models

Just run following cmd to evaluate your_embedding_model (e.g. maidalun1020/bce-embedding-base_v1) in bilingual and crosslingual settings (e.g. ["en", "zh", "en-zh", "zh-en"]).

your_embedding_model``maidalun1020/bce-embedding-base_v1****["en", "zh", "en-zh", "zh-en"]

python BCEmbedding/tools/eval_mteb/eval_embedding_mteb.py --model_name_or_path maidalun1020/bce-embedding-base_v1 --pooler cls

The total evaluation tasks contain

114 datastes
of "Retrieval", "STS", "PairClassification", "Classification", "Reranking" and "Clustering".

"Retrieval" "STS" "PairClassification" "Classification" "Reranking""Clustering"

114

NOTE:

  • All models are evaluated in their recommended pooling method (pooler).

    • mean pooler: "jina-embeddings-v2-base-en", "m3e-base", "m3e-large", "e5-large-v2", "multilingual-e5-base", "multilingual-e5-large" and "gte-large".
    • cls pooler: Other models.
  • "jina-embeddings-v2-base-en" model should be loaded with trust_remote_code.

    python BCEmbedding/tools/eval_mteb/eval_embedding_mteb.py --model_name_or_path {moka-ai/m3e-base | moka-ai/m3e-large} --pooler mean

    python BCEmbedding/tools/eval_mteb/eval_embedding_mteb.py --model_name_or_path jinaai/jina-embeddings-v2-base-en --pooler mean --trust_remote_code

****

  • pooler"jina-embeddings-v2-base-en", "m3e-base", "m3e-large", "e5-large-v2", "multilingual-e5-base", "multilingual-e5-large""gte-large" pooler``mean``pooler``cls.
  • "jina-embeddings-v2-base-en"trust_remote_code

2. Reranker Models

Run following cmd to evaluate your_reranker_model (e.g. "maidalun1020/bce-reranker-base_v1") in bilingual and crosslingual settings (e.g. ["en", "zh", "en-zh", "zh-en"]).

your_reranker_model``maidalun1020/bce-reranker-base_v1 ****["en", "zh", "en-zh", "zh-en"]

python BCEmbedding/tools/eval_mteb/eval_reranker_mteb.py --model_name_or_path maidalun1020/bce-reranker-base_v1

The evaluation tasks contain

12 datastes
of "Reranking".

"Reranking"

12

3. Metrics Visualization Tool

We proveide a one-click script to sumarize evaluation results of embedding and reranker models as Embedding Models Evaluation Summary and Reranker Models Evaluation Summary.

embedding``rerankermarkdownEmbeddingReranker

python BCEmbedding/evaluation/mteb/summarize_eval_results.py --results_dir {your_embedding_results_dir | your_reranker_results_dir}

Evaluate RAG by LlamaIndex

LlamaIndex is a famous data framework for LLM-based applications, particularly in RAG. Recently, the LlamaIndex Blog has evaluated the popular embedding and reranker models in RAG pipeline and attract great attention. Now, we follow its pipeline to evaluate our BCEmbedding.

LlamaIndexRAGLlamaIndexembeddingrerankerRAGBCEmbeddingRAG

First, install LlamaIndex:

pip install llama-index==0.9.22

1. Metrics Definition

  • Hit Rate:

    Hit rate calculates the fraction of queries where the correct answer is found within the top-k retrieved documents. In simpler terms, it's about how often our system gets it right within the top few guesses.

    The larger, the better.

  • Mean Reciprocal Rank (MRR):

    For each query, MRR evaluates the system's accuracy by looking at the rank of the highest-placed relevant document. Specifically, it's the average of the reciprocals of these ranks across all the queries. So, if the first relevant document is the top result, the reciprocal rank is 1; if it's second, the reciprocal rank is 1/2, and so on.

    The larger, the better.

    • Hit Rate

      k_****_

    • Mean Reciprocal RankMRR

      MRR11/2_****_

2. Reproduce LlamaIndex Blog

In order to compare our BCEmbedding with other embedding and reranker models fairly, we provide a one-click script to reproduce results of the LlamaIndex Blog, including our BCEmbedding:

LlamaIndexBCEmbeddingembeddingreranker

# There should be two GPUs available at least.
CUDA_VISIBLE_DEVICES=0,1 python BCEmbedding/tools/eval_rag/eval_llamaindex_reproduce.py

Then, sumarize the evaluation results by:

python BCEmbedding/tools/eval_rag/summarize_eval_results.py --results_dir results/rag_reproduce_results

Results Reproduced from the LlamaIndex Blog can be checked in

Reproduced Summary of RAG Evaluation
, with some obvious
conclusions
:

  • In WithoutReranker setting, our bce-embedding-base_v1 outperforms all the other embedding models.

  • With fixing the embedding model, our bce-reranker-base_v1 achieves the best performence.

  • The combination of bce-embedding-base_v1 and bce-reranker-base_v1 is SOTA.

    ***LlamaIndex RAG***

    • WithoutReranker****bce-embedding-base_v1embedding
    • embeddingreranker****bce-reranker-base_v1reranker
    • bce-embedding-base_v1``bce-reranker-base_v1SOTA

3. Broad Domain Adaptability

The evaluation of LlamaIndex Blog is monolingual, small amount of data, and specific domain (just including "llama2" paper). In order to evaluate the broad domain adaptability, bilingual and crosslingual capability, we follow the blog to build a multiple domains evaluation dataset (includding "Computer Science", "Physics", "Biology", "Economics", "Math", and "Quantitative Finance"), named CrosslingualMultiDomainsDataset, by OpenAI gpt-4-1106-preview for high quality.

LlamaIndexllama2 **** ****CrosslingualMultiDomainsDatasetOpenAIgpt-4-1106-preview

First, run following cmd to evaluate the most popular and powerful embedding and reranker models:

# There should be two GPUs available at least.
CUDA_VISIBLE_DEVICES=0,1 python BCEmbedding/tools/eval_rag/eval_llamaindex_multiple_domains.py

Then, run the following script to sumarize the evaluation results:

python BCEmbedding/tools/eval_rag/summarize_eval_results.py --results_dir results/rag_results

The summary of multiple domains evaluations can be seen in Multiple Domains Scenarios.

Leaderboard

Semantic Representation Evaluations in MTEB

1. Embedding Models

Model

Dimensions

Pooler

Instructions

Retrieval (47)

STS (19)

PairClassification (5)

Classification (21)

Reranking (12)

Clustering (15)

AVG
(119)

bge-base-en-v1.5

768

cls

Need

37.14

55.06

75.45

59.73

43.00

37.74

47.19

bge-base-zh-v1.5

768

cls

Need

47.63

63.72

77.40

63.38

54.95

32.56

53.62

bge-large-en-v1.5

1024

cls

Need

37.18

54.09

75.00

59.24

42.47

37.32

46.80

bge-large-zh-v1.5

1024

cls

Need

47.58

64.73

79.14

64.19

55.98

33.26

54.23

e5-large-v2

1024

mean

Need

35.98

55.23

75.28

59.53

42.12

36.51

46.52

gte-large

1024

mean

Free

36.68

55.22

74.29

57.73

42.44

38.51

46.67

gte-large-zh

1024

cls

Free

41.15

64.62

77.58

62.04

55.62

33.03

51.51

jina-embeddings-v2-base-en

768

mean

Free

31.58

54.28

74.84

58.42

41.16

34.67

44.29

m3e-base

768

mean

Free

46.29

63.93

71.84

64.08

52.38

37.84

53.54

m3e-large

1024

mean

Free

34.85

59.74

67.69

60.07

48.99

31.62

46.78

multilingual-e5-base

768

mean

Need

54.73

65.49

76.97

69.72

55.01

38.44

58.34

multilingual-e5-large

1024

mean

Need

56.76

66.79

78.80

71.61

56.49

43.09

60.50

bce-embedding-base_v1

768

cls

Free

57.60

65.73

74.96

69.00

57.29

38.95

59.43

NOTE:

  • Our

    bce-embedding-base_v1
    outperforms other opensource embedding models with comparable model size.

  • 114 datastes
    of "Retrieval", "STS", "PairClassification", "Classification", "Reranking" and "Clustering" in ["en", "zh", "en-zh", "zh-en"] setting.

  • The crosslingual evaluation datasets we released belong to Retrieval task.

  • More evaluation details please check Embedding Models Evaluation Summary.

    ****

    • embedding_bce-embedding-base_v1_ large
    • "Retrieval" "STS" "PairClassification" "Classification" "Reranking""Clustering"
      114
    • Retrieval
    • Embedding

2. Reranker Models

Model

Reranking (12)

AVG
(12)

bge-reranker-base

59.04

59.04

bge-reranker-large

60.86

60.86

bce-reranker-base_v1

61.29

61.29

NOTE:

  • Our

    bce-reranker-base_v1
    outperforms other opensource reranker models.

  • 12 datastes
    of "Reranking" in ["en", "zh", "en-zh", "zh-en"] setting.

  • More evaluation details please check Reranker Models Evaluation Summary.

    ****

    • bce-reranker-base_v1
      reranker
    • "Reranking"
      12
    • Reranker

RAG Evaluations in LlamaIndex

1. Multiple Domains Scenarios

[object Object]

NOTE:

  • Evaluated in ["en", "zh", "en-zh", "zh-en"] setting.

  • In WithoutReranker setting, our bce-embedding-base_v1 outperforms all the other embedding models.

  • With fixing the embedding model, our bce-reranker-base_v1 achieves the best performence.

  • The combination of bce-embedding-base_v1 and bce-reranker-base_v1 is SOTA.

    ****

    • ["en", "zh", "en-zh", "zh-en"]
    • WithoutReranker****bce-embedding-base_v1Embedding
    • Embeddingreranker****bce-reranker-base_v1reranker
    • bce-embedding-base_v1``bce-reranker-base_v1SOTA

Youdao's BCEmbedding API

For users who prefer a hassle-free experience without the need to download and configure the model on their own systems, BCEmbedding is readily accessible through Youdao's API. This option offers a streamlined and efficient way to integrate BCEmbedding into your projects, bypassing the complexities of manual setup and maintenance. Detailed instructions and comprehensive API documentation are available at Youdao BCEmbedding API. Here, you'll find all the necessary guidance to easily implement BCEmbedding across a variety of use cases, ensuring a smooth and effective integration for optimal results.

apiBCEmbeddingapiBCEmbeddingapiBCEmbedding API

WeChat Group

Welcome to scan the QR code below and join the WeChat group.

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Citation

If you use BCEmbedding in your research or project, please feel free to cite and star it:

@misc{youdao_bcembedding_2023,
    title={BCEmbedding: Bilingual and Crosslingual Embedding for RAG},
    author={NetEase Youdao, Inc.},
    year={2023},
    howpublished={\url{https://github.com/netease-youdao/BCEmbedding}}
}

License

BCEmbedding is licensed under Apache 2.0 License

Related Links

Netease Youdao - QAnything

FlagEmbedding

MTEB

C_MTEB

LLama Index | LlamaIndex Blog



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!