update

9f4797b about 1 year ago

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	---
	license: mit
	language:
	- zh
	---


	<h1 align="center">FlagEmbedding</h1>


	<h4 align="center">
	<p>
	<a href=#model-list>Model List</a> \|
	<a href=#frequently-asked-questions>FAQ</a> \|
	<a href=#usage>Usage</a> \|
	<a href="#evaluation">Evaluation</a> \|
	<a href="#train">Train</a> \|
	<a href="#contact">Contact</a> \|
	<a href="#citation">Citation</a> \|
	<a href="#license">License</a>
	<p>
	</h4>

	More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).


	[English](README.md) \| [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)

	FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
	And it also can be used in vector databases for LLMs.

	*********** 🌟Updates🌟 ***********
	- 09/15/2023: Release [paper](https://arxiv.org/pdf/2309.07597.pdf) and [dataset](https://data.baai.ac.cn/details/BAAI-MTP).
	- 09/12/2023: New Release:
	- New reranker model: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
	- update embedding model: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
	- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
	- 08/09/2023: BGE Models are integrated into Langchain, you can use it like [this](#using-langchain); C-MTEB leaderboard is [available](https://huggingface.co/spaces/mteb/leaderboard).
	- 08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗
	- 08/02/2023: Release `bge-large-`(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark!* :tada: :tada:
	- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (C-MTEB), consisting of 31 test dataset.


	## Model List

	`bge` is short for `BAAI general embedding`.

	\| Model \| Language \| \| Description \| query instruction for retrieval\* \|
	\|:-------------------------------\|:--------:\| :--------:\| :--------:\|:--------:\|
	\| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient \** \| \|
	\| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient \** \| \|
	\| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-en` \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \|a small-scale model but with competitive performance \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-zh` \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a small-scale model but with competitive performance \| `为这个句子生成表示以用于检索相关文章：` \|


	\: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, no instruction* needs to be added to passages.

	\**: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
	For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.

	## Frequently asked questions

	<details>
	<summary>1. How to fine-tune bge embedding model?</summary>

	<!-- ### How to fine-tune bge embedding model? -->
	Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
	Some suggestions:
	- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
	- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
	- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.


	</details>

	<details>
	<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>

	<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
	Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.

	Since we finetune the models by contrastive learning with a temperature of 0.01,
	the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
	So a similarity score greater than 0.5 does not indicate that the two sentences are similar.

	For downstream tasks, such as passage retrieval or semantic similarity,
	what matters is the relative order of the scores, not the absolute value.
	If you need to filter similar sentences based on a similarity threshold,
	please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).

	</details>

	<details>
	<summary>3. When does the query instruction need to be used</summary>

	<!-- ### When does the query instruction need to be used -->

	For a retrieval task that uses short queries to find long related documents,
	it is recommended to add instructions for these short queries.
	The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.
	In all cases, the documents/passages do not need to add the instruction.

	</details>


	## Usage

	### Usage for Embedding Model

	Here are some examples for using `bge` models with
	[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).

	#### Using FlagEmbedding
	```
	pip install -U FlagEmbedding
	```
	If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.

	```python
	from FlagEmbedding import FlagModel
	sentences_1 = ["样例数据-1", "样例数据-2"]
	sentences_2 = ["样例数据-3", "样例数据-4"]
	model = FlagModel('BAAI/bge-large-zh-v1.5',
	query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
	use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
	embeddings_1 = model.encode(sentences_1)
	embeddings_2 = model.encode(sentences_2)
	similarity = embeddings_1 @ embeddings_2.T
	print(similarity)

	# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
	# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
	queries = ['query_1', 'query_2']
	passages = ["样例文档-1", "样例文档-2"]
	q_embeddings = model.encode_queries(queries)
	p_embeddings = model.encode(passages)
	scores = q_embeddings @ p_embeddings.T
	```
	For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).

	By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
	You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.


	#### Using Sentence-Transformers

	You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):

	```
	pip install -U sentence-transformers
	```
	```python
	from sentence_transformers import SentenceTransformer
	sentences_1 = ["样例数据-1", "样例数据-2"]
	sentences_2 = ["样例数据-3", "样例数据-4"]
	model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
	embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
	embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
	similarity = embeddings_1 @ embeddings_2.T
	print(similarity)
	```
	For s2p(short query to long passage) retrieval task,
	each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
	But the instruction is not needed for passages.
	```python
	from sentence_transformers import SentenceTransformer
	queries = ['query_1', 'query_2']
	passages = ["样例文档-1", "样例文档-2"]
	instruction = "为这个句子生成表示以用于检索相关文章："

	model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
	q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
	p_embeddings = model.encode(passages, normalize_embeddings=True)
	scores = q_embeddings @ p_embeddings.T
	```

	#### Using Langchain

	You can use `bge` in langchain like this:
	```python
	from langchain.embeddings import HuggingFaceBgeEmbeddings
	model_name = "BAAI/bge-large-en-v1.5"
	model_kwargs = {'device': 'cuda'}
	encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
	model = HuggingFaceBgeEmbeddings(
	model_name=model_name,
	model_kwargs=model_kwargs,
	encode_kwargs=encode_kwargs,
	query_instruction="为这个句子生成表示以用于检索相关文章："
	)
	model.query_instruction = "为这个句子生成表示以用于检索相关文章："
	```


	#### Using HuggingFace Transformers

	With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.

	```python
	from transformers import AutoTokenizer, AutoModel
	import torch
	# Sentences we want sentence embeddings for
	sentences = ["样例数据-1", "样例数据-2"]

	# Load model from HuggingFace Hub
	tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
	model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
	model.eval()

	# Tokenize sentences
	encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
	# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
	# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')

	# Compute token embeddings
	with torch.no_grad():
	model_output = model(**encoded_input)
	# Perform pooling. In this case, cls pooling.
	sentence_embeddings = model_output[0][:, 0]
	# normalize embeddings
	sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
	print("Sentence embeddings:", sentence_embeddings)
	```

	### Usage for Reranker

	Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
	You can get a relevance score by inputting query and passage to the reranker.
	The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.


	#### Using FlagEmbedding
	```
	pip install -U FlagEmbedding
	```

	Get relevance scores (higher scores indicate more relevance):
	```python
	from FlagEmbedding import FlagReranker
	reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

	score = reranker.compute_score(['query', 'passage'])
	print(score)

	scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
	print(scores)
	```


	#### Using Huggingface transformers

	```python
	import torch
	from transformers import AutoModelForSequenceClassification, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
	model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
	model.eval()

	pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
	with torch.no_grad():
	inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
	scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
	print(scores)
	```

	## Evaluation

	`baai-general-embedding` models achieve state-of-the-art performance on both MTEB and C-MTEB leaderboard!
	For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).

	- MTEB:

	\| Model Name \| Dimension \| Sequence Length \| Average (56) \| Retrieval (15) \|Clustering (11) \| Pair Classification (3) \| Reranking (4) \| STS (10) \| Summarization (1) \| Classification (12) \|
	\|:----:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) \| 1024 \| 512 \| 64.23 \| 54.29 \| 46.08 \| 87.12 \| 60.03 \| 83.11 \| 31.61 \| 75.97 \|
	\| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) \| 768 \| 512 \| 63.55 \| 53.25 \| 45.77 \| 86.55 \| 58.86 \| 82.4 \| 31.07 \| 75.53 \|
	\| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) \| 384 \| 512 \| 62.17 \|51.68 \| 43.82 \| 84.92 \| 58.36 \| 81.59 \| 30.12 \| 74.14 \|
	\| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| 1024 \| 512 \| 63.98 \| 53.9 \| 46.98 \| 85.8 \| 59.48 \| 81.56 \| 32.06 \| 76.21 \|
	\| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| 768 \| 512 \| 63.36 \| 53.0 \| 46.32 \| 85.86 \| 58.7 \| 81.84 \| 29.27 \| 75.27 \|
	\| [gte-large](https://huggingface.co/thenlper/gte-large) \| 1024 \| 512 \| 63.13 \| 52.22 \| 46.84 \| 85.00 \| 59.13 \| 83.35 \| 31.66 \| 73.33 \|
	\| [gte-base](https://huggingface.co/thenlper/gte-base) \| 768 \| 512 \| 62.39 \| 51.14 \| 46.2 \| 84.57 \| 58.61 \| 82.3 \| 31.17 \| 73.01 \|
	\| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) \| 1024\| 512 \| 62.25 \| 50.56 \| 44.49 \| 86.03 \| 56.61 \| 82.05 \| 30.19 \| 75.24 \|
	\| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| 384 \| 512 \| 62.11 \| 51.82 \| 44.31 \| 83.78 \| 57.97 \| 80.72 \| 30.53 \| 74.37 \|
	\| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) \| 768 \| 512 \| 61.79 \| 49.26 \| 44.74 \| 86.62 \| 57.29 \| 83.06 \| 32.32 \| 61.79 \|
	\| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) \| 768 \| 512 \| 61.5 \| 50.29 \| 43.80 \| 85.73 \| 55.91 \| 81.05 \| 30.28 \| 73.84 \|
	\| [gte-small](https://huggingface.co/thenlper/gte-small) \| 384 \| 512 \| 61.36 \| 49.46 \| 44.89 \| 83.54 \| 57.7 \| 82.07 \| 30.42 \| 72.31 \|
	\| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) \| 1536 \| 8192 \| 60.99 \| 49.25 \| 45.9 \| 84.89 \| 56.32 \| 80.97 \| 30.8 \| 70.93 \|
	\| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) \| 384 \| 512 \| 59.93 \| 49.04 \| 39.92 \| 84.67 \| 54.32 \| 80.39 \| 31.16 \| 72.94 \|
	\| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) \| 768 \| 512 \| 59.51 \| 42.24 \| 43.72 \| 85.06 \| 56.42 \| 82.63 \| 30.08 \| 73.42 \|
	\| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) \| 768 \| 514 \| 57.78 \| 43.81 \| 43.69 \| 83.04 \| 59.36 \| 80.28 \| 27.49 \| 65.07 \|
	\| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) \| 4096 \| 2048 \| 57.59 \| 48.22 \| 38.93 \| 81.9 \| 55.65 \| 77.74 \| 33.6 \| 66.19 \|



	- C-MTEB:
	We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
	Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.

	\| Model \| Embedding dimension \| Avg \| Retrieval \| STS \| PairClassification \| Classification \| Reranking \| Clustering \|
	\|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|
	\| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) \| 1024 \| 64.53 \| 70.46 \| 56.25 \| 81.6 \| 69.13 \| 65.84 \| 48.99 \|
	\| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) \| 768 \| 63.13 \| 69.49 \| 53.72 \| 79.75 \| 68.07 \| 65.39 \| 47.53 \|
	\| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) \| 512 \| 57.82 \| 61.77 \| 49.11 \| 70.41 \| 63.96 \| 60.92 \| 44.18 \|
	\| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) \| 1024 \| 64.20 \| 71.53 \| 54.98 \| 78.94 \| 68.32 \| 65.11 \| 48.39 \|
	\| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) \| 1024 \| 63.53 \| 70.55 \| 53 \| 76.77 \| 68.58 \| 64.91 \| 50.01 \|
	\| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) \| 768 \| 62.96 \| 69.53 \| 54.12 \| 77.5 \| 67.07 \| 64.91 \| 47.63 \|
	\| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) \| 1024 \| 58.79 \| 63.66 \| 48.44 \| 69.89 \| 67.34 \| 56.00 \| 48.23 \|
	\| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) \| 512 \| 58.27 \| 63.07 \| 49.45 \| 70.35 \| 63.64 \| 61.48 \| 45.09 \|
	\| [m3e-base](https://huggingface.co/moka-ai/m3e-base) \| 768 \| 57.10 \| 56.91 \| 50.47 \| 63.99 \| 67.52 \| 59.34 \| 47.68 \|
	\| [m3e-large](https://huggingface.co/moka-ai/m3e-large) \| 1024 \| 57.05 \| 54.75 \| 50.42 \| 64.3 \| 68.2 \| 59.66 \| 48.88 \|
	\| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) \| 768 \| 55.48 \| 61.63 \| 46.49 \| 67.07 \| 65.35 \| 54.35 \| 40.68 \|
	\| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) \| 384 \| 55.38 \| 59.95 \| 45.27 \| 66.45 \| 65.85 \| 53.86 \| 45.26 \|
	\| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) \| 1536 \| 53.02 \| 52.0 \| 43.35 \| 69.56 \| 64.31 \| 54.28 \| 45.68 \|
	\| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) \| 1024 \| 49.37 \| 44.4 \| 42.78 \| 66.62 \| 61 \| 49.25 \| 44.39 \|
	\| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) \| 768 \| 47.63 \| 38.79 \| 43.41 \| 67.41 \| 62.19 \| 49.45 \| 37.66 \|
	\| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) \| 1024 \| 47.36 \| 41.94 \| 44.97 \| 70.86 \| 60.66 \| 49.16 \| 30.02 \|


	- Reranking:
	See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.

	\| Model \| T2Reranking \| T2RerankingZh2En\* \| T2RerankingEn2Zh\* \| MMarcoReranking \| CMedQAv1 \| CMedQAv2 \| Avg \|
	\|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|
	\| text2vec-base-multilingual \| 64.66 \| 62.94 \| 62.51 \| 14.37 \| 48.46 \| 48.6 \| 50.26 \|
	\| multilingual-e5-small \| 65.62 \| 60.94 \| 56.41 \| 29.91 \| 67.26 \| 66.54 \| 57.78 \|
	\| multilingual-e5-large \| 64.55 \| 61.61 \| 54.28 \| 28.6 \| 67.42 \| 67.92 \| 57.4 \|
	\| multilingual-e5-base \| 64.21 \| 62.13 \| 54.68 \| 29.5 \| 66.23 \| 66.98 \| 57.29 \|
	\| m3e-base \| 66.03 \| 62.74 \| 56.07 \| 17.51 \| 77.05 \| 76.76 \| 59.36 \|
	\| m3e-large \| 66.13 \| 62.72 \| 56.1 \| 16.46 \| 77.76 \| 78.27 \| 59.57 \|
	\| bge-base-zh-v1.5 \| 66.49 \| 63.25 \| 57.02 \| 29.74 \| 80.47 \| 84.88 \| 63.64 \|
	\| bge-large-zh-v1.5 \| 65.74 \| 63.39 \| 57.03 \| 28.74 \| 83.45 \| 85.44 \| 63.97 \|
	\| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| 67.28 \| 63.95 \| 60.45 \| 35.46 \| 81.26 \| 84.1 \| 65.42 \|
	\| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| 67.6 \| 64.03 \| 61.44 \| 37.16 \| 82.15 \| 84.18 \| 66.09 \|

	\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks

	## Train

	### BAAI Embedding

	We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
	You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).
	We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
	Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
	More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).



	### BGE Reranker

	Cross-encoder will perform full-attention over the input pair,
	which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
	Therefore, it can be used to re-rank the top-k documents returned by embedding model.
	We train the cross-encoder on a multilingual pair data,
	The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
	More details pelease refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)


	## Contact
	If you have any question or suggestion related to this project, feel free to open an issue or pull request.
	You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn).


	## Citation

	If you find our work helpful, please cite us:
	```
	@misc{bge_embedding,
	title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
	author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
	year={2023},
	eprint={2309.07597},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	## License
	FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.