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- value: 82.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - type: precision_at_100
 
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+ license: mit
2600
+ language:
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+ - en
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+ pipeline_tag: sentence-similarity
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+ ---
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+
2605
+ <h1 align="center">FlagEmbedding</h1>
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+
2607
+
2608
+ <h4 align="center">
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+ <p>
2610
+ <a href=#model-list>Model List</a> |
2611
+ <a href=#usage>Usage</a> |
2612
+ <a href="#evaluation">Evaluation</a> |
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+ <a href="#train">Train</a> |
2614
+ <a href="#contact">Contact</a> |
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+ <a href="#license">License</a>
2616
+ <p>
2617
+ </h4>
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+
2619
+ More details please refer to our github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
2620
+
2621
+ [English](README.md) | [中文](README_zh.md)
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+
2623
+ FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
2624
+ And it also can be used in vector database for LLMs.
2625
+
2626
+ ************* 🌟**Updates**🌟 *************
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+ - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
2628
+ - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!**
2629
+ - 08/01/2023: We release the Chinese Massive Text Embedding Benchmark (**C-MTEB**), consisting of 31 test dataset.
2630
+
2631
+
2632
+ ## Model List
2633
+
2634
+ `bge` is short for `BAAI general embedding`.
2635
+
2636
+ | Model | Language | Description | query instruction for retrieval |
2637
+ |:-------------------------------|:--------:| :--------:| :--------:|
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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/benchmark) 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/benchmark) benchmark | |
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+ | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but with competitive performance | `为这个句子���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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+
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+
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+
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+ ## Usage
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+
2650
+ * **Using FlagEmbedding**
2651
+ ```
2652
+ pip install flag_embedding
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+ ```
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+ ```python
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+ from flag_embedding 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 = model.encode(sentences)
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+ print(embeddings)
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+
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+ # for 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()
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+ queries = ['query_1', 'query_2']
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+ passages = ["样例段落-1", "样例段落-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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+
2671
+ FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU.
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+
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+
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+ * **Using Sentence-Transformers**
2675
+
2676
+ Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
2678
+ ```
2679
+ pip install -U sentence-transformers
2680
+ ```
2681
+ ```python
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+ from sentence_transformers import SentenceTransformer
2683
+ 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 = model.encode(sentences, normalize_embeddings=True)
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+ print(embeddings)
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+ ```
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+ For retrieval task,
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+ each query should start with a instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ queries = ["手机开不了机怎么办?"]
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+ passages = ["样例段落-1", "样例段落-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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+
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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
2700
+ ```
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+
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+ * **Using HuggingFace Transformers**
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+
2704
+ 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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+
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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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+
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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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+
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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 retrieval task, add a instruction to query
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+ # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
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+
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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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+
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+
2732
+ ## 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 scripts see [benchemark](benchmark/README.md).
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+
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+ - **MTEB**:
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+
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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 |
2748
+ | [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 |
2750
+ | [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 |
2753
+ | [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 |
2754
+ | [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 |
2757
+ | [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 |
2758
+
2759
+
2760
+
2761
+ - **C-MTEB**:
2762
+ We create a benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
2763
+ Please refer to [benchemark](benchmark/README.md) for a detailed introduction.
2764
+
2765
+ | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
2766
+ |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
2767
+ | [**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 |
2768
+ | [**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** |
2769
+ | [**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 |
2770
+ | [**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 |
2771
+ | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 |
2772
+ | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 |
2773
+ | [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 |
2774
+ | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 |
2775
+ | [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 |
2776
+ | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 |
2777
+
2778
+
2779
+
2780
+
2781
+ ## Train
2782
+ This section will introduce the way we used to train the general embedding.
2783
+ The training scripts are in [flag_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/flag_embedding/baai_general_embedding/),
2784
+ and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain/) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).
2785
+
2786
+
2787
+ **1. RetroMAE Pre-train**
2788
+ We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE),
2789
+ which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)).
2790
+ The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720.
2791
+ In retromae, the mask ratio of the encoder and decoder are 0.3, and 0.5 respectively.
2792
+ We used the AdamW optimizer and the learning rate is 2e-5.
2793
+
2794
+ **Pre-training data**:
2795
+ - English:
2796
+ - [Pile](https://pile.eleuther.ai/)
2797
+ - [wikipedia](https://huggingface.co/datasets/wikipedia)
2798
+ - [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus)
2799
+ - Chinese:
2800
+ - Subset of [wudao](https://github.com/BAAI-WuDao/Data)
2801
+ - [baidu-baike](https://baike.baidu.com/)
2802
+
2803
+
2804
+ **2. Finetune**
2805
+ We fine-tune the model using a contrastive objective.
2806
+ The format of input data is a triple`(query, positive, negative)`.
2807
+ Besides the negative in the triple, we also adopt in-batch negatives strategy.
2808
+ We employ the cross-device negatives sharing method to share negatives among different GPUs,
2809
+ which can dramatically **increase the number of negatives**.
2810
+
2811
+ 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).
2812
+ We used the AdamW optimizer and the learning rate is 1e-5.
2813
+ The temperature for contrastive loss is 0.01.
2814
+
2815
+ For the version with `*-instrcution`, we add instruction to the query for the retrieval task in the training.
2816
+ For English, the instruction is `Represent this sentence for searching relevant passages: `;
2817
+ For Chinese, the instruction is `为这个句子生成表示以用于检索相关文章:`.
2818
+ In the evaluation, the instruction should be added for sentence to passages retrieval task, not be added for other tasks.
2819
+
2820
+
2821
+ The finetune script is accessible in this repository: [flag_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/flag_embedding/baai_general_embedding/README.md).
2822
+ You can easily finetune your model with it.
2823
+
2824
+ **Training data**:
2825
+
2826
+ - 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.
2827
+
2828
+ - For Chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on.
2829
+
2830
+ **The data collection is to be released in the future.**
2831
+
2832
+ We will continually update the embedding models and training codes,
2833
+ hoping to promote the development of the embedding model community.
2834
+
2835
+
2836
+ ## License
2837
+ FlagEmbedding is licensed under [MIT License](). The released models can be used for commercial purposes free of charge.