|
--- |
|
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 ([paper](https://arxiv.org/pdf/2402.03216.pdf), [code](https://github.com/FlagOpen/FlagEmbedding/tree/master/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. |
|
|
|
|
|
## News: |
|
- 2024/3/8: **Thanks for the [experimental results](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) from @[Yannael](https://huggingface.co/Yannael). In this benchmark, BGE-M3 achieves top performance in both English and other languages, surpassing models such as OpenAI.** |
|
- 2024/3/2: Release unified fine-tuning [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune) and [data](https://huggingface.co/datasets/Shitao/bge-m3-data) |
|
- 2024/2/6: We release the [MLDR](https://huggingface.co/datasets/Shitao/MLDR) (a long document retrieval dataset covering 13 languages) and [evaluation pipeline](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR). |
|
- 2024/2/1: **Thanks for the excellent tool from Vespa.** You can easily use multiple modes of BGE-M3 following this [notebook](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb) |
|
|
|
|
|
## Specs |
|
|
|
- Model |
|
|
|
| Model Name | Dimension | Sequence Length | Introduction | |
|
|:----:|:---:|:---:|:---:| |
|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | 1024 | 8192 | multilingual; unified fine-tuning (dense, sparse, and colbert) from bge-m3-unsupervised| |
|
| [BAAI/bge-m3-unsupervised](https://huggingface.co/BAAI/bge-m3-unsupervised) | 1024 | 8192 | multilingual; contrastive learning from bge-m3-retromae | |
|
| [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) | -- | 8192 | multilingual; extend the max_length of [xlm-roberta](https://huggingface.co/FacebookAI/xlm-roberta-large) to 8192 and further pretrained via [retromae](https://github.com/staoxiao/RetroMAE)| |
|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | English model | |
|
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | English model | |
|
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | English model | |
|
|
|
- Data |
|
|
|
| Dataset | Introduction | |
|
|:----------------------------------------------------------:|:-------------------------------------------------:| |
|
| [MLDR](https://huggingface.co/datasets/Shitao/MLDR) | Docuemtn Retrieval Dataset, covering 13 languages | |
|
| [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) | Fine-tuning data used by bge-m3 | |
|
|
|
|
|
|
|
## FAQ |
|
|
|
**1. Introduction for different retrieval methods** |
|
|
|
- Dense retrieval: map the text into a single embedding, e.g., [DPR](https://arxiv.org/abs/2004.04906), [BGE-v1.5](https://github.com/FlagOpen/FlagEmbedding) |
|
- 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](https://arxiv.org/pdf/2106.14807.pdf), and [splade](https://arxiv.org/abs/2107.05720) |
|
- Multi-vector retrieval: use multiple vectors to represent a text, e.g., [ColBERT](https://arxiv.org/abs/2004.12832). |
|
|
|
**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. |
|
|
|
|
|
In our experiments, we use [Pyserini](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR#hybrid-retrieval-dense--sparse) and Faiss to do hybrid retrieval. |
|
**Now you can ou can try the hybrid mode of BGE-M3 in [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb |
|
). Thanks @jobergum.** |
|
|
|
|
|
**4. How to fine-tune bge-M3 model?** |
|
|
|
You can follow the common in this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |
|
to fine-tune the dense embedding. |
|
|
|
If you want to fine-tune all embedding function of m3, you can refer to the [unified_fine-tuning example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune) |
|
|
|
|
|
|
|
**5. 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](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker), [cohere-reranker](https://txt.cohere.com/rerank/)) after retrieval can further filter the selected text. |
|
|
|
|
|
|
|
## 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 |
|
```python |
|
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](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding#usage) for details. |
|
|
|
|
|
- Sparse Embedding (Lexical Weight) |
|
```python |
|
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) |
|
```python |
|
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. |
|
```python |
|
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 |
|
|
|
### Benchmarks from the open-source community |
|
![avatar](./imgs/others.webp) |
|
The BGE-M3 model emerged as the top performer on this benchmark (OAI is short for OpenAI). |
|
For more details, please refer to the [article](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) and [Github Repo](https://github.com/Yannael/multilingual-embeddings) |
|
|
|
|
|
### Our results |
|
- Multilingual (Miracl dataset) |
|
|
|
![avatar](./imgs/miracl.jpg) |
|
|
|
- Cross-lingual (MKQA dataset) |
|
|
|
![avatar](./imgs/mkqa.jpg) |
|
|
|
- Long Document Retrieval |
|
- MLDR: |
|
![avatar](./imgs/long.jpg) |
|
Please note that [MLDR](https://huggingface.co/datasets/Shitao/MLDR) is a document retrieval dataset we constructed via LLM, |
|
covering 13 languages, including test set, validation set, and training set. |
|
We utilized the training set from MLDR to enhance the model's long document retrieval capabilities. |
|
Therefore, comparing baselines with `Dense w.o.long`(fine-tuning without long document dataset) is more equitable. |
|
Additionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets. |
|
We believe that this data will be helpful for the open-source community in training document retrieval models. |
|
|
|
- NarritiveQA: |
|
![avatar](./imgs/nqa.jpg) |
|
|
|
- Comparison with BM25 |
|
|
|
We utilized Pyserini to implement BM25, and the test results can be reproduced by this [script](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR#bm25-baseline). |
|
We tested BM25 using two different tokenizers: |
|
one using Lucene Analyzer and the other using the same tokenizer as M3 (i.e., the tokenizer of xlm-roberta). |
|
The results indicate that BM25 remains a competitive baseline, |
|
especially in long document retrieval. |
|
|
|
![avatar](./imgs/bm25.jpg) |
|
|
|
|
|
|
|
## 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](https://arxiv.org/pdf/2402.03216.pdf) for more details. |
|
|
|
|
|
|
|
## Acknowledgement |
|
|
|
Thanks to the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc. |
|
Thanks to the open-sourced libraries like [Tevatron](https://github.com/texttron/tevatron), [Pyserini](https://github.com/castorini/pyserini). |
|
|
|
|
|
|
|
## Citation |
|
|
|
If you find this repository useful, please consider giving a star :star: and citation |
|
|
|
``` |
|
@misc{bge-m3, |
|
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, |
|
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, |
|
year={2024}, |
|
eprint={2402.03216}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|