metadata

pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
license: mit

For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding

BGE-M3

In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.

Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
Multi-Linguality: It can support more than 100 working languages.
Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.

Some suggestions for retrieval pipeline in RAG: We recommend to use following pipeline: hybrid retrieval + re-ranking.

Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. A classic example: using both embedding retrieval and the BM25 algorithm. Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.
As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model. Utilizing the re-ranking model (e.g., bge-reranker, cohere-reranker) after retrieval can further filter the selected text.

News:

2/1/2024: Thanks for the excellent tool from Vespa. You can easily use multiple modes of BGE-M3 following this notebook

Model Specs

Model Name	Dimension	Sequence Length
BAAI/bge-m3	1024	8192
BAAI/bge-large-en-v1.5	1024	512
BAAI/bge-base-en-v1.5	768	512
BAAI/bge-small-en-v1.5	384	512

FAQ

1. Introduction for different retrieval methods

Dense retrieval: map the text into a single embedding, e.g., DPR, BGE-v1.5
Sparse retrieval (lexical matching): a vector of size equal to the vocabulary, with the majority of positions set to zero, calculating a weight only for tokens present in the text. e.g., BM25, unicoil, and splade
Multi-vector retrieval: use multiple vectors to represent a text, e.g., ColBERT.

2. Comparison with BGE-v1.5 and other monolingual models

BGE-M3 is a multilingual model, and its ability in monolingual embedding retrieval may not surpass models specifically designed for single languages. However, we still recommend trying BGE-M3 because of its versatility (support for multiple languages and long texts). Moreover, it can simultaneously generate multiple representations, and using them together can enhance accuracy and generalization, unlike most existing models that can only perform dense retrieval.

In the open-source community, there are many excellent models (e.g., jina-embedding, colbert, e5, etc), and users can choose a model that suits their specific needs based on practical considerations, such as whether to require multilingual or cross-language support, and whether to process long texts.

3. How to use BGE-M3 in other projects?

For embedding retrieval, you can employ the BGE-M3 model using the same approach as BGE. The only difference is that the BGE-M3 model no longer requires adding instructions to the queries. For sparse retrieval methods, most open-source libraries currently do not support direct utilization of the BGE-M3 model. Contributions from the community are welcome.

4. How to fine-tune bge-M3 model?

You can follow the common in this example to fine-tune the dense embedding.

Our code and data for unified fine-tuning (dense, sparse, and multi-vectors) will be released.

Usage

Install:

git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .

or:

pip install -U FlagEmbedding

Generate Embedding for text

Dense Embedding

from FlagEmbedding import BGEM3FlagModel

model = BGEM3FlagModel('BAAI/bge-m3',  
                       use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", 
               "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]

embeddings_1 = model.encode(sentences_1, 
                            batch_size=12, 
                            max_length=8192, # If you don't need such a long length, you can set a smaller value to speed up the encoding process.
                            )['dense_vecs']
embeddings_2 = model.encode(sentences_2)['dense_vecs']
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# [[0.6265, 0.3477], [0.3499, 0.678 ]]

You also can use sentence-transformers and huggingface transformers to generate dense embeddings. Refer to baai_general_embedding for details.

Sparse Embedding (Lexical Weight)

from FlagEmbedding import BGEM3FlagModel

model = BGEM3FlagModel('BAAI/bge-m3',  use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", 
               "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]

output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=False)
output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=False)

# you can see the weight for each token:
print(model.convert_id_to_token(output_1['lexical_weights']))
# [{'What': 0.08356, 'is': 0.0814, 'B': 0.1296, 'GE': 0.252, 'M': 0.1702, '3': 0.2695, '?': 0.04092}, 
#  {'De': 0.05005, 'fin': 0.1368, 'ation': 0.04498, 'of': 0.0633, 'BM': 0.2515, '25': 0.3335}]


# compute the scores via lexical mathcing
lexical_scores = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0])
print(lexical_scores)
# 0.19554901123046875

print(model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_1['lexical_weights'][1]))
# 0.0

Multi-Vector (ColBERT)

from FlagEmbedding import BGEM3FlagModel

model = BGEM3FlagModel('BAAI/bge-m3',  use_fp16=True) 

sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", 
               "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]

output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)
output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True)

print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0]))
print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1]))
# 0.7797
# 0.4620

Compute score for text pairs

Input a list of text pairs, you can get the scores computed by different methods.

from FlagEmbedding import BGEM3FlagModel

model = BGEM3FlagModel('BAAI/bge-m3',  use_fp16=True) 

sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", 
               "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]

sentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2]

print(model.compute_score(sentence_pairs, 
                          max_passage_length=128, # a smaller max length leads to a lower latency
                          weights_for_different_modes=[0.4, 0.2, 0.4])) # weights_for_different_modes(w) is used to do weighted sum: w[0]*dense_score + w[1]*sparse_score + w[2]*colbert_score

# {
#   'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142], 
#   'sparse': [0.195556640625, 0.00879669189453125, 0.0, 0.1802978515625], 
#   'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625], 
#   'sparse+dense': [0.482503205537796, 0.23454029858112335, 0.2332356721162796, 0.5122477412223816], 
#   'colbert+sparse+dense': [0.6013619303703308, 0.3255828022956848, 0.32089319825172424, 0.6232916116714478]
# }

Evaluation

Multilingual (Miracl dataset)

Cross-lingual (MKQA dataset)

Long Document Retrieval
- MLDR:
- NarritiveQA:

Training

Self-knowledge Distillation: combining multiple outputs from different retrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)
Efficient Batching: Improve the efficiency when fine-tuning on long text. The small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.
MCLS: A simple method to improve the performance on long text without fine-tuning. If you have no enough resource to fine-tuning model with long text, the method is useful.

Refer to our report for more details.

The fine-tuning codes and datasets will be open-sourced in the near future.

Models

We release two versions:

BAAI/bge-m3-unsupervised: the model after contrastive learning in a large-scale dataset
BAAI/bge-m3: the final model fine-tuned from BAAI/bge-m3-unsupervised

Acknowledgement

Thanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.

Citation

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