|
--- |
|
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/[email protected]/http/server.ts' |
|
import { env, pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]' |
|
// 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/[email protected]'; |
|
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. |
