bge-m3 model without onnx

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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README.md

@@ -1,3 +1,293 @@
----
-license: mit
----
+---
+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. 
+
+
+
+**Some suggestions for retrieval pipeline in RAG**
+
+We recommend to use the 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.
+To use hybrid retrieval, you can refer to [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb
+) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).
+
+- 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), [bge-reranker-v2](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker)) after retrieval can further filter the selected text.
+
+
+## News:
+- 2024/3/20: **Thanks Milvus team!** Now you can use hybrid retrieval of bge-m3 in Milvus: [pymilvus/examples
+/hello_hybrid_sparse_dense.py](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).
+- 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. 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 hybrid retrieval, you can use [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb
+) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).
+
+
+**3. 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 (dense, sparse and colbert), you can refer to the [unified_fine-tuning example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune)
+
+
+
+
+
+
+## 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  
+
+We provide the evaluation script for [MKQA](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MKQA) and [MLDR](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR)
+
+### 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}
+}
+```

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