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--- |
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license: mit |
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language: |
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- zh |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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--- |
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<h1 align="center">FlagEmbedding</h1> |
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<h4 align="center"> |
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<p> |
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<a href=#model-list>Model List</a> | |
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<a href=#usage>Usage</a> | |
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<a href="#evaluation">Evaluation</a> | |
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<a href="#train">Train</a> | |
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<a href="#contact">Contact</a> | |
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<a href="#license">License</a> |
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<p> |
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</h4> |
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More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). |
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[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) |
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FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. |
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And it also can be used in vector database for LLMs. |
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************* 🌟**Updates**🌟 ************* |
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- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [**this**](#using-langchain); C-MTEB **leaderboard** is [avaliable](https://huggingface.co/spaces/mteb/leaderboard). |
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- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
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- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** |
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- 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. |
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## Model List |
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`bge` is short for `BAAI general embedding`. |
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| Model | Language | Description | query instruction for retrieval\* | |
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|:-------------------------------|:--------:| :--------:| :--------:| |
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| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | 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：` | |
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| [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | |
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| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | |
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| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | 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：` | |
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\*: If you need to search the **long** relevant passages to a **short** query (s2p retrieval task), you need 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** need to be added to passages. |
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## Usage |
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Here are some examples to use `bge` models with |
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[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). |
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#### Using FlagEmbedding |
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``` |
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pip install -U FlagEmbedding |
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``` |
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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. |
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```python |
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from FlagEmbedding import FlagModel |
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sentences = ["样例数据-1", "样例数据-2"] |
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model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：") |
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embeddings_1 = model.encode(sentences) |
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embeddings_2 = model.encode(sentences) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
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# for s2p(short query to long passage) retrieval task, please use encode_queries() which will automatically add the instruction to each query |
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# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction |
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queries = ['query_1', 'query_2'] |
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passages = ["样例文档-1", "样例文档-2"] |
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q_embeddings = model.encode_queries(queries) |
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p_embeddings = model.encode(passages) |
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scores = q_embeddings @ p_embeddings.T |
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``` |
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The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
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FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU. |
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#### Using Sentence-Transformers |
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Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["样例数据-1", "样例数据-2"] |
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model = SentenceTransformer('BAAI/bge-large-zh') |
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embeddings_1 = model.encode(sentences, normalize_embeddings=True) |
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embeddings_2 = model.encode(sentences, normalize_embeddings=True) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
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``` |
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For s2p(short query to long passage) retrieval task, |
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each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
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But the instruction is not needed for passages. |
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```python |
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from sentence_transformers import SentenceTransformer |
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queries = ['query_1', 'query_2'] |
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passages = ["样例文档-1", "样例文档-2"] |
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instruction = "为这个句子生成表示以用于检索相关文章：" |
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model = SentenceTransformer('BAAI/bge-large-zh') |
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q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) |
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p_embeddings = model.encode(passages, normalize_embeddings=True) |
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scores = q_embeddings @ p_embeddings.T |
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``` |
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#### Using Langchain |
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You can use `bge` in langchain like this: |
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```python |
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from langchain.embeddings import HuggingFaceBgeEmbeddings |
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model_name = "BAAI/bge-small-en" |
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model_kwargs = {'device': 'cuda'} |
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encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity |
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model_norm = HuggingFaceBgeEmbeddings( |
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model_name=model_name, |
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model_kwargs=model_kwargs, |
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encode_kwargs=encode_kwargs |
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) |
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``` |
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#### Using HuggingFace Transformers |
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With 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 first token (i.e., [CLS]) as the sentence embedding. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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# Sentences we want sentence embeddings for |
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sentences = ["样例数据-1", "样例数据-2"] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh') |
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model = AutoModel.from_pretrained('BAAI/bge-large-zh') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
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# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, cls pooling. |
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sentence_embeddings = model_output[0][:, 0] |
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# normalize embeddings |
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sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) |
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print("Sentence embeddings:", sentence_embeddings) |
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``` |
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## Evaluation |
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`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
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More details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). |
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- **MTEB**: |
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| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |
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|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
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| [**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** | |
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| [**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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [**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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 384 | 512 | 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 | |
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| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 | 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 | |
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| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 768 | 512 | 56.00 | 41.88 | 41.1 | 82.54 | 53.14 | 76.51 | 30.36 | 66.68 | |
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| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 768 | 512 | 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 | |
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- **C-MTEB**: |
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We create a benchmark C-MTEB for chinese text embedding which consists of 31 datasets from 6 tasks. |
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Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. |
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| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |
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|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
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| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 | |
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| [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** | |
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| [**BAAI/bge-base-zh**](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 | |
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| [**BAAI/bge-small-zh**](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 | |
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| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 | |
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| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 | |
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| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 | |
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| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | |
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| [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 | |
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| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 | |
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## Train |
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This section will introduce the way we used to train the general embedding. |
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The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), |
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and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md). |
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**1. RetroMAE Pre-train** |
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We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), |
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which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). |
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The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. |
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In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively. |
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We used the AdamW optimizer and the learning rate is 2e-5. |
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**Pre-training data**: |
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- English: |
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- [Pile](https://pile.eleuther.ai/) |
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- [wikipedia](https://huggingface.co/datasets/wikipedia) |
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- [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus) |
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- Chinese: |
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- [wudao](https://github.com/BAAI-WuDao/Data) |
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**2. Finetune** |
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We fine-tune the model using a contrastive objective. |
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The format of input data is a triple`(query, positive, negative)`. |
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Besides the negative in the triple, we also adopt in-batch negatives strategy. |
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We employ the cross-device negatives sharing method to share negatives among different GPUs, |
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which can dramatically **increase the number of negatives**. |
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We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). |
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We used the AdamW optimizer and the learning rate is 1e-5. |
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The temperature for contrastive loss is 0.01. |
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Besides, we add instruction to the query for s2p(short query to long passage) retrieval task in the training (add nothing to passages). |
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For English, the instruction is `Represent this sentence for searching relevant passages: `; |
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For Chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`. |
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In the evaluation, the instruction should be added for queries in retrieval task, not be added for other tasks. |
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Noted that the instruction is not needed for passages. |
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The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). |
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You can easily finetune your model with it. |
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**Training data**: |
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- For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on. |
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- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on. |
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**The data collection is to be released in the future.** |
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We will continually update the embedding models and training codes, |
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hoping to promote the development of the embedding model community. |
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## License |
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FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. |
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