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README.md
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- sentence-similarity
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---
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# BGE-M3
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In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
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- Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
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- Multi-Linguality: It can support more than 100 working languages.
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- Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.
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**Some suggestions for retrieval pipeline in RAG:**
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We recommend to use following pipeline: hybrid retrieval + re-ranking.
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- Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities.
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A classic example: using both embedding retrieval and the BM25 algorithm.
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Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval.
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This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.
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- As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model.
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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.
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## FAQ
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**1. Introduction for different retrieval methods**
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- 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)
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- 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)
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- Multi-vector retrieval: use multiple vectors to represent a text, e.g., [ColBERT](https://arxiv.org/abs/2004.12832).
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**2. How to use BGE-M3 in other projects?**
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For embedding retrieval, you can employ the BGE-M3 model using the same approach as BGE.
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The only difference is that the BGE-M3 model no longer requires adding instructions to the queries.
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For sparse retrieval methods, most open-source libraries currently do not support direct utilization of the BGE-M3 model.
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Contributions from the community are welcome.
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**3. How to fine-tune bge-M3 model?**
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You can follow the common in this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)
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to fine-tune the dense embedding.
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Our code and data for unified fine-tuning (dense, sparse, and multi-vectors) will be released.
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## Usage
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Install:
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```
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git clone https://github.com/FlagOpen/FlagEmbedding.git
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cd FlagEmbedding
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pip install -e .
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```
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or:
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```
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pip install -U FlagEmbedding
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```
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### Generate Embedding for text
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- Dense Embedding
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```python
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from FlagEmbedding import BGEM3FlagModel
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model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
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sentences_1 = ["What is BGE M3?", "Defination of BM25"]
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sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
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"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
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embeddings_1 = model.encode(sentences_1)['dense_vecs']
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embeddings_2 = model.encode(sentences_2)['dense_vecs']
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similarity = embeddings_1 @ embeddings_2.T
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print(similarity)
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# [[0.6265, 0.3477], [0.3499, 0.678 ]]
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```
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You also can use sentence-transformers and huggingface transformers to generate dense embeddings.
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Refer to [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding#usage) for details.
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- Sparse Embedding (Lexical Weight)
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```python
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from FlagEmbedding import BGEM3FlagModel
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model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
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sentences_1 = ["What is BGE M3?", "Defination of BM25"]
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sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
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"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
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output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=False)
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output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=False)
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# you can see the weight for each token:
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print(model.convert_id_to_token(output_1['lexical_weights']))
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# [{'What': 0.08356, 'is': 0.0814, 'B': 0.1296, 'GE': 0.252, 'M': 0.1702, '3': 0.2695, '?': 0.04092},
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# {'De': 0.05005, 'fin': 0.1368, 'ation': 0.04498, 'of': 0.0633, 'BM': 0.2515, '25': 0.3335}]
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# compute the scores via lexical mathcing
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lexical_scores = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0])
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print(lexical_scores)
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# 0.19554901123046875
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print(model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_1['lexical_weights'][1]))
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# 0.0
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```
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- Multi-Vector (ColBERT)
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```python
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from FlagEmbedding import BGEM3FlagModel
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model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)
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sentences_1 = ["What is BGE M3?", "Defination of BM25"]
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sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
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"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
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output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)
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output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True)
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print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0]))
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print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1]))
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# 0.7797
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# 0.4620
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```
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### Compute score for text pairs
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Input a list of text pairs, you can get the scores computed by different methods.
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```python
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from FlagEmbedding import BGEM3FlagModel
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model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)
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sentences_1 = ["What is BGE M3?", "Defination of BM25"]
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sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
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"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
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sentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2]
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print(model.compute_score(sentence_pairs))
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# {
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# 'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142],
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# 'sparse': [0.05865478515625, 0.0026397705078125, 0.0, 0.0540771484375],
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# 'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625],
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# 'sparse+dense': [0.5266395211219788, 0.2692706882953644, 0.2691181004047394, 0.563307523727417],
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# 'colbert+sparse+dense': [0.6366440653800964, 0.3531297743320465, 0.3487969636917114, 0.6618075370788574]
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# }
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```
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## Evaluation
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- Multilingual (Miracl dataset)
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- Cross-lingual (MKQA dataset)
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- Long Document Retrieval
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## Training
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- Self-knowledge Distillation: combining multiple outputs from different
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retrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)
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- Efficient Batching: Improve the efficiency when fine-tuning on long text.
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The small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.
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- MCLS: A simple method to improve the performance on long text without fine-tuning.
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If you have no enough resource to fine-tuning model with long text, the method is useful.
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Refer to our [report]() for more details.
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**The fine-tuning codes and datasets will be open-sourced in the near future.**
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## Models
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We release two versions:
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- BAAI/bge-m3-unsupervised: the model after contrastive learning in a large-scale dataset
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- BAAI/bge-m3: the final model fine-tuned from BAAI/bge-m3-unsupervised
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## Acknowledgement
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Thanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.
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## Citation
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If you find this repository useful, please consider giving a star :star: and citation
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```
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```
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