BAAI
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bge-small-zh

@@ -1,1066 +1,9 @@
-<h1 align="center">FlagEmbedding</h1>
-<p align="center">
-    <a href="https://github.com/FlagOpen/FlagEmbedding">
-            <img alt="Build" src="https://img.shields.io/badge/Contribution-Welcome-blue">
-    </a>
-    <a href="https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE">
-        <img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green">
-    </a>
-    <a href="https://huggingface.co/C-MTEB">
-        <img alt="Build" src="https://img.shields.io/badge/C_MTEB-🤗-yellow">
-    </a>
-    <a href="https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding">
-        <img alt="Build" src="https://img.shields.io/badge/FlagEmbedding-1.0-red">
-    </a>
-</p>
-<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="#license">License</a>
-    <p>
-</h4>
-[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 database for LLMs.
-************* 🌟**Updates**🌟 *************
-- 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).
-- 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-large-en](https://huggingface.co/BAAI/bge-large-en) |   English |  :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 |  rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
-|  [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: `  |
-|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | :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-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 |   |
-|  [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：`  |
-|  [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：`  |
-\*: 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.
-## Frequently asked questions
-1. The similarity score between two dissimilar sentence is higher than 0.5
-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 sentence 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).
-2. When do 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 Quora task, which needs to use a short question to search another related short questions,
-the instruction is not recommended to add.
-The best method to decide whether to add instructions for queries is choosing the setting which can achieve better performance in your task.
-In all cases, the documents/passages do not need to add the instruction, only need to consider whether to add the instruction for queries.
-## Usage
-Here are some examples to use `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', 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
-```
-The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
-FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU.
-You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make GPUs unavailable.
-#### Using Sentence-Transformers
-Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-```
-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')
-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')
-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-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："
-)
-```
-#### Using HuggingFace Transformers
-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.
-```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')
-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!**
-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) |
-|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-| [**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 |
-| [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 |
-| [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 |
-| [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 |
-| [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 |
-- **C-MTEB**:
-We create a 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 |
-|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
-| [**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 |
-| [**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** |
-| [**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 |
-| [**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 |
-| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 |
-| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 |
-| [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 |
-| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 |
-| [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 |
-| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 |
-## Train
-This section will introduce the way we used to train the general embedding.
-The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md),
-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).
-**1. RetroMAE Pre-train**
-We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE),
-which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)).
-The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720.
-In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively.
-We used the AdamW optimizer and the learning rate is 2e-5.
-**Pre-training data**:
-- English:
-    - [Pile](https://pile.eleuther.ai/)
-    - [wikipedia](https://huggingface.co/datasets/wikipedia)
-    - [msmarco](https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus)
-- Chinese:
-    - [wudao](https://github.com/BAAI-WuDao/Data)
-**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 instruction to the query for s2p(short query to long passage) retrieval task 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：`.
-In the evaluation, the instruction should be added for queries in retrieval task, not be added for other tasks.
-Noted that the instruction is not needed for passages.
-The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
-You can easily finetune your model with it.
-**Training data**:
-- 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.
-- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE), and so on.
-**The data collection is to be released in the future.**
-## Schedule
-- [x] Chinese Massive Text Embedding Benchmark
-- [x] release baai-general-embedding models
-- [x] 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 a request.
-You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn).
-## License
-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.
-<h1 align="center">FlagEmbedding</h1>
-<h4 align="center">
-    <p>
-        <a href=#model-list>Model List</a> |
-        <a href=#usage>Usage</a>  |
-        <a href="#evaluation">Evaluation</a> |
-        <a href="#train">Train</a> |
-        <a href="#contact">Contact</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**🌟 *************
-- 09/12/2023: New Release:
-    - **New reranker model**: release a cross-encoder model bge-reranker-base, which is more powerful than embedding model. We recommend to use/fine-tune it 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.
-\**: 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#data-format), 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', 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, 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')
-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')
-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-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.
-```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')
-model = AutoModel.from_pretrained('BAAI/bge-large-zh')
-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
-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 score:
-```python
-from FlagEmbedding import FlagReranker
-reranker = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) #use fp16 can speed up computing
-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, BatchEncoding, PreTrainedTokenizerFast
-tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-base')
-model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base')
-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 task
-## Train
-### BAAI Embedding
-We pre-train the models using 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.
-More details pelease refer to [./FlagEmbedding/reranker/README.md](./FlagEmbedding/reranker/README.md)
-## 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).
-## 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.
-**Recommend switching to newest bge-small-zh-v1.5, which has more reasonable similarity distribution and same method of usage.**
-<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="#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**🌟 *************
-- 09/12/2023: 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 embedding model, reranker is a cross-encoder, which cannot be used to generate 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', 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, 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')
-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')
-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-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.
-```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')
-model = AutoModel.from_pretrained('BAAI/bge-large-zh')
-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
-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 score:
-```python
-from FlagEmbedding import FlagReranker
-reranker = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) #use fp16 can speed up computing
-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, BatchEncoding, PreTrainedTokenizerFast
-tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-base')
-model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base')
-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 task
-## Train
-### BAAI Embedding
-We pre-train the models using 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).
-## 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.
 **Recommend switching to newest [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5), which has more reasonable similarity distribution and same method of usage.**
@@ -1083,7 +26,6 @@ FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/Fl
 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.

+---
+license: mit
+language:
+- zh
+pipeline_tag: sentence-similarity
+---
 **Recommend switching to newest [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5), which has more reasonable similarity distribution and same method of usage.**
 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.