README.md

---
pipeline_tag: feature-extraction
library_name: "transformers.js"
language:
- en
license: mit
---

_Fork of https://huggingface.co/BAAI/bge-small-en with ONNX weights to be compatible with Transformers.js. See [JavaScript usage](#javascript)._

---

<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="#license">License</a> 
    <p>
</h4>

For more details please refer to our GitHub repo: [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**🌟 *************
- 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-small-en](https://huggingface.co/BAAI/bge-small-en) |   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-small-en-noinstruct](https://huggingface.co/BAAI/bge-small-en-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：`  |



## Usage 

This model can be used with both [Python](#python) and [JavaScript](#javascript).

### Python

#### Use with [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md)

```
pip install -U FlagEmbedding
```
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 = ["样例数据-1", "样例数据-2"]
model = FlagModel('Supabase/bge-small-en', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：")
embeddings = model.encode(sentences)
print(embeddings)

# for 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()
queries = ['query_1', 'query_2']
passages = ["样例段落-1", "样例段落-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.

#### Use with [sentence-transformers](https://www.sbert.net/)

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 = ["样例数据-1", "样例数据-2"]
model = SentenceTransformer('Supabase/bge-small-en')
embeddings = model.encode(sentences, normalize_embeddings=True)
print(embeddings)
```
For retrieval task, 
each query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
```python
from sentence_transformers import SentenceTransformer
queries = ["手机开不了机怎么办？"]
passages = ["样例段落-1", "样例段落-2"]
instruction = "为这个句子生成表示以用于检索相关文章："

model = SentenceTransformer('Supabase/bge-small-en')
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
```

#### Use with [Transformers](https://huggingface.co/docs/transformers/index) and [PyTorch](https://pytorch.org/docs/stable/index.html)

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('Supabase/bge-small-en')
model = AutoModel.from_pretrained('Supabase/bge-small-en')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for retrieval task, add an instruction to query
# 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)
```

### JavaScript

This model can be used with JavaScript via [Transformers.js](https://huggingface.co/docs/transformers.js/index).

#### Use with [Deno](https://deno.land/manual/introduction) or [Supabase Edge Functions](https://supabase.com/docs/guides/functions)

```ts
import { serve } from 'https://deno.land/std@0.168.0/http/server.ts'
import { env, pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0'
// Configuration for Deno runtime
env.useBrowserCache = false;
env.allowLocalModels = false;
const pipe = await pipeline(
  'feature-extraction',
  'Supabase/bge-small-en',
);
serve(async (req) => {
  // Extract input string from JSON body
  const { input } = await req.json();
  // Generate the embedding from the user input
  const output = await pipe(input, {
    pooling: 'mean',
    normalize: true,
  });
  // Extract the embedding output
  const embedding = Array.from(output.data);
  // Return the embedding
  return new Response(
    JSON.stringify({ embedding }),
    { headers: { 'Content-Type': 'application/json' } }
  );
});
```

#### Use within the browser ([JavaScript Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules))

```html
<script type="module">
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0';
const pipe = await pipeline(
  'feature-extraction',
  'Supabase/bge-small-en',
);
// Generate the embedding from text
const output = await pipe('Hello world', {
  pooling: 'mean',
  normalize: true,
});
// Extract the embedding output
const embedding = Array.from(output.data);
console.log(embedding);
</script>
```

#### Use within [Node.js](https://nodejs.org/en/docs) or a web bundler ([Webpack](https://webpack.js.org/concepts/), etc)

```js
import { pipeline } from '@xenova/transformers';
const pipe = await pipeline(
  'feature-extraction',
  'Supabase/bge-small-en',
);
// Generate the embedding from text
const output = await pipe('Hello world', {
  pooling: 'mean',
  normalize: true,
});
// Extract the embedding output
const embedding = Array.from(output.data);
console.log(embedding);
```

## 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 |
| [bge-small-en](https://huggingface.co/thenlper/bge-small-en) |  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-small-en) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 |  
| [**bge-large-zh-noinstruct**](https://huggingface.co/BAAI/bge-small-en-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, and 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: 
    - Subset of [wudao](https://github.com/BAAI-WuDao/Data)
    - [baidu-baike](https://baike.baidu.com/)


**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 32,768 (so there are **65,535** 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.

For the version with `*-instrcution`, we add instruction to the query for retrieval task in the training. 
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 sentence to passages retrieval task, not be added for other tasks.


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.**

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

## 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.