FlagEmbedding 1.0.7

FlagEmbedding

FlagEmbedding

Model List | FAQ | Usage | Evaluation | Train | Contact | License

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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. And it also can be used in vector databases for LLMs.

************* 🌟Updates🌟 *************

• 08/09/2023: BGE Models are integrated into Langchain, you can use it like this; C-MTEB leaderboard is available.
• 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 (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-large-en	English	:trophy: rank 1st in MTEB leaderboard	Represent this sentence for searching relevant passages:
BAAI/bge-base-en	English	rank 2nd in MTEB leaderboard	Represent this sentence for searching relevant passages:
BAAI/bge-small-en	English	a small-scale model but with competitive performance	Represent this sentence for searching relevant passages:
BAAI/bge-large-zh	Chinese	:trophy: rank 1st in C-MTEB benchmark	为这个句子生成表示以用于检索相关文章：
BAAI/bge-large-zh-noinstruct	Chinese	This model is trained without instruction, and ranks 2nd in C-MTEB benchmark
BAAI/bge-base-zh	Chinese	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	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：

*: 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 needs to be added to passages.

Frequently asked questions

1. The similarity score between two dissimilar sentences is higher than 0.5

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). If you want to adjust the similarity distribution, you can fine-tune the models on your data with a higher temperature.

2. 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. For other tasks, it is recommended not to add instructions. For example, in the Quora task, which needs to use a short question to search for another related short question, it is not recommended to add the instruction. 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. You only need to evaluate whether to add the instruction for the queries.

Usage

Here are some examples for using bge models with FlagEmbedding, Sentence-Transformers, Langchain, or Huggingface Transformers.

Using FlagEmbedding

pip install -U FlagEmbedding

If it doesn't work for you, you can see FlagEmbedding for more methods to install FlagEmbedding.

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', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：")
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, please 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. To load your fine-tuned model, use your instruction if you add it during fine-tuning.

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:

pip install -U sentence-transformers

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')
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). But the instruction is not needed for passages.

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')
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:

from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-small-en"
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.

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')
model = AutoModel.from_pretrained('BAAI/bge-large-zh')

# 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)

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.

• MTEB:

Model Name	Dimension	Sequence Length	Average (56)	Retrieval (15)	Clustering (11)	Pair Classification (3)	Reranking (4)	STS (10)	Summarization (1)	Classification (12)
bge-large-en	1024	512	63.98	53.9	46.98	85.8	59.48	81.56	32.06	76.21
bge-base-en	768	512	63.36	53.0	46.32	85.86	58.7	81.84	29.27	75.27
gte-large	1024	512	63.13	52.22	46.84	85.00	59.13	83.35	31.66	73.33
gte-base	768	512	62.39	51.14	46.2	84.57	58.61	82.3	31.17	73.01
e5-large-v2	1024	512	62.25	50.56	44.49	86.03	56.61	82.05	30.19	75.24
bge-small-en	384	512	62.11	51.82	44.31	83.78	57.97	80.72	30.53	74.37
instructor-xl	768	512	61.79	49.26	44.74	86.62	57.29	83.06	32.32	61.79
e5-base-v2	768	512	61.5	50.29	43.80	85.73	55.91	81.05	30.28	73.84
gte-small	384	512	61.36	49.46	44.89	83.54	57.7	82.07	30.42	72.31
text-embedding-ada-002	1536	8192	60.99	49.25	45.9	84.89	56.32	80.97	30.8	70.93
e5-small-v2	384	512	59.93	49.04	39.92	84.67	54.32	80.39	31.16	72.94
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	768	514	57.78	43.81	43.69	83.04	59.36	80.28	27.49	65.07
sgpt-bloom-7b1-msmarco	4096	2048	57.59	48.22	38.93	81.9	55.65	77.74	33.6	66.19
all-MiniLM-L12-v2	384	512	56.53	42.69	41.81	82.41	58.44	79.8	27.9	63.21
all-MiniLM-L6-v2	384	512	56.26	41.95	42.35	82.37	58.04	78.9	30.81	63.05
contriever-base-msmarco	768	512	56.00	41.88	41.1	82.54	53.14	76.51	30.36	66.68
sentence-t5-base	768	512	55.27	33.63	40.21	85.18	53.09	81.14	31.39	69.81

• 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 for a detailed introduction.

Model	Embedding dimension	Avg	Retrieval	STS	PairClassification	Classification	Reranking	Clustering
bge-large-zh	1024	64.20	71.53	54.98	78.94	68.32	65.11	48.39
bge-large-zh-noinstruct	1024	63.53	70.55	53	76.77	68.58	64.91	50.01
BAAI/bge-base-zh	768	62.96	69.53	54.12	77.5	67.07	64.91	47.63
multilingual-e5-large	1024	58.79	63.66	48.44	69.89	67.34	56.00	48.23
BAAI/bge-small-zh	512	58.27	63.07	49.45	70.35	63.64	61.48	45.09
m3e-base	768	57.10	56.91	50.47	63.99	67.52	59.34	47.68
m3e-large	1024	57.05	54.75	50.42	64.3	68.2	59.66	48.88
multilingual-e5-base	768	55.48	61.63	46.49	67.07	65.35	54.35	40.68
multilingual-e5-small	384	55.38	59.95	45.27	66.45	65.85	53.86	45.26
text-embedding-ada-002(OpenAI)	1536	53.02	52.0	43.35	69.56	64.31	54.28	45.68
luotuo	1024	49.37	44.4	42.78	66.62	61	49.25	44.39
text2vec-base	768	47.63	38.79	43.41	67.41	62.19	49.45	37.66
text2vec-large	1024	47.36	41.94	44.97	70.86	60.66	49.16	30.02

Train

This section will introduce the way we used to train the general embedding. The training scripts are in FlagEmbedding, and we provide some examples to pre-train and fine-tune.

1. RetroMAE Pre-train We pre-train the model following the method RetroMAE, which shows promising improvements in retrieval tasks (paper). The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. In RetroMAE, the mask ratio of the encoder and decoder are 0.3 and 0.5, respectively. We used the AdamW optimizer and the learning rate is 2e-5.

Pre-training data:

• English:
  • Pile
  • wikipedia
  • msmarco
• Chinese:
  • wudao

2. Finetune
We fine-tune the model using a contrastive objective. The format of input data is a triple (query, positive, negative). Besides the negative in the triple, we also adopt in-batch negatives strategy. We employ the cross-device negatives sharing method to share negatives among different GPUs, which can dramatically increase the number of negatives.

We trained our model on 48 A100(40G) GPUs with a large batch size of 32,784 (so there are 65,567 negatives for each query in a batch). We used the AdamW optimizer and the learning rate is 1e-5. The temperature for contrastive loss is 0.01.

Besides, we add an instruction to the query for s2p(short query to long passage) retrieval tasks in the training (add nothing to passages). For English, the instruction is Represent this sentence for searching relevant passages: ; For Chinese, the instruction is 为这个句子生成表示以用于检索相关文章：. At evaluation, the instruction should be added for queries in the retrieval task, but not be added for other tasks. Note that the instruction is not needed for passages.

The finetune script is accessible in this repository: FlagEmbedding. You can easily finetune your model with it.

Training data:

• For English, we collect 230M text pairs from wikipedia, cc-net, and so on.

• For Chinese, we collect 120M text pairs from wudao, simclue, and so on.

The data collection will be released in the future.

Schedule

• Chinese Massive Text Embedding Benchmark
• release baai-general-embedding models
• release codes for training
• Multilingual model
• Training Datasets
• ...

We will continually update the embedding models and training codes, hoping to promote the development of the embedding model community.

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([e-mail]) and Zheng Liu([e-mail]).

License

FlagEmbedding is licensed under the MIT License. The released models can be used for commercial purposes free of charge.