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repos:
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# Ruff version.
rev: v0.3.5
hooks:
# These hooks are equivalent to running `make quality`
- id: ruff
- id: ruff-format
args: [ --check ]
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quality:
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style:
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# 仓库目录结构
* 除预训练模型外其他文件总大小尽量不要超过50M
```
Project
├── dataset
│   ├── label_1
│    ├── xxx.png
│    ├── xxx.png
│ └── ...
│   └── label_2
│    ├── xxx.png
│    ├── xxx.png
│ └── ...
├── model
│   ├── xxx.pth #预训练模型
│   ├── xxx.onnx #对应的onnx模型
│ └── xxx.mxr #对应的migraphx离线推理模型
├── doc
│   ├── icon.png
│   ├── xxx.png
│ └── xxx.png
├── README.md
├── requirement.txt
├── model.properties
├── code_file1.py
├── code_file2.py
├── code_file3.py
├── dirs
│   ├── code_file4.py
│   ├── code_file5.py
└── └── code_file6.py
```
* icon.png:模型的图标文件，可到[iconfont](https://www.iconfont.cn/?spm=a313x.7781069.1998910419.d4d0a486a)查找。
![img](./doc/icon.png)
* README.md:参照下图，`十二大标题`为必选项，二级标题以下的标题或内容根据自己的项目灵活增删。
![img](./doc/readme.png)
* requirement.txt：模型依赖统一写到此文件，与深度学习相关的库请注释，以免安装为nv库。
```
说明：数据基本由公司网盘储存并提供url下载或直接读取，数据信息介绍由超算互联网商城提供，内部无数据时提供官网下载地址。
```
* 需要提供迷你数据集以供使用者快速上手项目。
* model.properties：`五大属性`固定模板如下：
```
# 模型唯一标识
modelCode=Project ID
# 模型名称
modelName=模型名称(同项目名称：模型名_深度学习框架)
# 模型描述
modelDescription=简要描述此模型(尽量50字以内)
# 应用场景
appScenario=推理,训练,OCR,政府,交通,零售,金融,医疗(首先描述推理、训练信息，然后描述算法类别信息，最后描述应用行业信息，多个标签用英文逗号隔开。)
# 框架类型
frameType=paddle(说明使用的算法框架, 多个标签用英文逗号隔开。)
```
* 增加LICENSE（必要），源github无LICENSE则在LICENSE里填：None LICENSE Currently；CONTRIBUTORS.md根据源github有无提供（非必要）。
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-------------------------------------------------------------------------------
Copyright 2019
Ubiquitous Knowledge Processing (UKP) Lab
Technische Universität Darmstadt
-------------------------------------------------------------------------------
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# Sentence-BERT
## 论文
`Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks`
- https://arxiv.org/pdf/1908.10084.pdf
## 模型结构
<div align=center>
<img src="./doc/model.png"/>
</div>
## 算法原理
对于每个句子对，通过网络传递句子A和句子B，从而得到embeddings u 和 v。使用余弦相似度计算embedding的相似度，并将结果与 gold similarity score进行比较。这允许网络进行微调，并识别句子的相似性.
<div align=center>
<img src="./doc/image.png"/>
</div>
## 环境配置
1. -v 路径、docker_name和imageID根据实际情况修改
2. transformers文件需要修改:
transformers/trainer_pt_utils.py line 37 修改为:
try:
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
except ImportError:
from torch.optim.lr_scheduler import LRScheduler as LRScheduler
<div align=center>
<img src="./doc/example.png"/>
</div>
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-ubuntu20.04-dtk23.10-py38
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/sentence-bert_pytorch
pip install -r requirements.txt
pip install -U sentence-transformers
pip install -e .
```
### Dockerfile（方法二）
```
cd ./docker
cp ../requirements.txt requirements.txt
docker build --no-cache -t sbert:latest .
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/sentence-bert_pytorch
pip install -r requirements.txt
pip install -U sentence-transformers
pip install -e .
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装: https://developer.hpccube.com/tool/
```
DTK软件栈：dtk23.10
python：python3.8
torch：1.13.1
torchvision：0.14.1
```
Tips：以上dtk软件栈、python、torch等DCU相关工具版本需要严格一一对应
2、其他非特殊库直接按照requirements.txt安装
```
cd /your_code_path/sentence-bert_pytorch
pip install -r requirements.txt
pip install -U sentence-transformers
pip install -e .
```
## 数据集
[sentence-compression](https://huggingface.co/datasets/embedding-data/sentence-compression)
[COCO Captions](https://huggingface.co/datasets/embedding-data/coco_captions_quintets)
[Flickr30k captions](https://huggingface.co/datasets/embedding-data/flickr30k_captions_quintets)
数据集的目录结构如下:
```
├── datasets
│ ├──
│ ├──
│ ├──daybreadwn
│ └──
```
## 训练
### 单机多卡
```
bash train_finetune.sh
```
### 单机单卡
```
python funetune.py
```
## 推理
预训练模型下载[pretrained models](https://www.sbert.net/docs/pretrained_models.html)
```
python infer.py --data_path ./datasets/tmp.txt
```
## result
<div align=center>
<img src="./doc/results.png"/>
</div>
### 精度
测试数据：[test data]('./datasets/test.txt'), 使用的加速卡:Z100 16G。
根据测试结果情况填写表格：
| xxx | xxx | xxx | xxx | xxx |
| :------: | :------: | :------: | :------: |:------: |
| xxx | xxx | xxx | xxx | xxx |
| xxx | xx | xxx | xxx | xxx |
## 应用场景
### 算法类别
相似度判别
### 热点应用行业
多轮对话
## 源码仓库及问题反馈
-
## 参考资料
-
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<!--- BADGES: START --->
[![GitHub - License](https://img.shields.io/github/license/UKPLab/sentence-transformers?logo=github&style=flat&color=green)][#github-license]
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sentence-transformers?logo=pypi&style=flat&color=blue)][#pypi-package]
[![PyPI - Package Version](https://img.shields.io/pypi/v/sentence-transformers?logo=pypi&style=flat&color=orange)][#pypi-package]
[![Conda - Platform](https://img.shields.io/conda/pn/conda-forge/sentence-transformers?logo=anaconda&style=flat)][#conda-forge-package]
[![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/sentence-transformers?logo=anaconda&style=flat&color=orange)][#conda-forge-package]
[![Docs - GitHub.io](https://img.shields.io/static/v1?logo=github&style=flat&color=pink&label=docs&message=sentence-transformers)][#docs-package]
<!---
[![PyPI - Downloads](https://img.shields.io/pypi/dm/sentence-transformers?logo=pypi&style=flat&color=green)][#pypi-package]
[![Conda](https://img.shields.io/conda/dn/conda-forge/sentence-transformers?logo=anaconda)][#conda-forge-package]
--->
[#github-license]: https://github.com/UKPLab/sentence-transformers/blob/master/LICENSE
[#pypi-package]: https://pypi.org/project/sentence-transformers/
[#conda-forge-package]: https://anaconda.org/conda-forge/sentence-transformers
[#docs-package]: https://www.sbert.net/
<!--- BADGES: END --->
# Sentence Transformers: Multilingual Sentence, Paragraph, and Image Embeddings using BERT & Co.
This framework provides an easy method to compute dense vector representations for **sentences**, **paragraphs**, and **images**. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks. Text is embedded in vector space such that similar text are closer and can efficiently be found using cosine similarity.
We provide an increasing number of **[state-of-the-art pretrained models](https://www.sbert.net/docs/pretrained_models.html)** for more than 100 languages, fine-tuned for various use-cases.
Further, this framework allows an easy **[fine-tuning of custom embeddings models](https://www.sbert.net/docs/training/overview.html)**, to achieve maximal performance on your specific task.
For the **full documentation**, see **[www.SBERT.net](https://www.sbert.net)**.
The following publications are integrated in this framework:
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) (EMNLP 2019)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813) (EMNLP 2020)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://arxiv.org/abs/2010.08240) (NAACL 2021)
- [The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes](https://arxiv.org/abs/2012.14210) (arXiv 2020)
- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979) (arXiv 2021)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://arxiv.org/abs/2104.08663) (arXiv 2021)
- [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) (arXiv 2022)
## Installation
We recommend **Python 3.8** or higher, **[PyTorch 1.11.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.32.0](https://github.com/huggingface/transformers)** or higher. The code does **not** work with Python 2.7.
**Install with pip**
Install the *sentence-transformers* with `pip`:
```
pip install -U sentence-transformers
```
**Install with conda**
You can install the *sentence-transformers* with `conda`:
```
conda install -c conda-forge sentence-transformers
```
**Install from sources**
Alternatively, you can also clone the latest version from the [repository](https://github.com/UKPLab/sentence-transformers) and install it directly from the source code:
````
pip install -e .
````
**PyTorch with CUDA**
If you want to use a GPU / CUDA, you must install PyTorch with the matching CUDA Version. Follow
[PyTorch - Get Started](https://pytorch.org/get-started/locally/) for further details how to install PyTorch.
## Getting Started
See [Quickstart](https://www.sbert.net/docs/quickstart.html) in our documenation.
[This example](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications/computing-embeddings/computing_embeddings.py) shows you how to use an already trained Sentence Transformer model to embed sentences for another task.
First download a pretrained model.
````python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
````
Then provide some sentences to the model.
````python
sentences = [
"This framework generates embeddings for each input sentence",
"Sentences are passed as a list of string.",
"The quick brown fox jumps over the lazy dog.",
]
sentence_embeddings = model.encode(sentences)
````
And that's it already. We now have a list of numpy arrays with the embeddings.
````python
for sentence, embedding in zip(sentences, sentence_embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding)
print("")
````
bbnnm,,,nmm
## Pre-Trained Models
We provide a large list of [Pretrained Models](https://www.sbert.net/docs/pretrained_models.html) for more than 100 languages. Some models are general purpose models, while others produce embeddings for specific use cases. Pre-trained models can be loaded by just passing the model name: `SentenceTransformer('model_name')`.
[» Full list of pretrained models](https://www.sbert.net/docs/pretrained_models.html)
## Training
This framework allows you to fine-tune your own sentence embedding methods, so that you get task-specific sentence embeddings. You have various options to choose from in order to get perfect sentence embeddings for your specific task.
See [Training Overview](https://www.sbert.net/docs/training/overview.html) for an introduction how to train your own embedding models. We provide [various examples](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) how to train models on various datasets.
Some highlights are:
- Support of various transformer networks including BERT, RoBERTa, XLM-R, DistilBERT, Electra, BART, ...
- Multi-Lingual and multi-task learning
- Evaluation during training to find optimal model
- [20+ loss-functions](https://www.sbert.net/docs/package_reference/losses.html) allowing to tune models specifically for semantic search, paraphrase mining, semantic similarity comparison, clustering, triplet loss, contrastive loss.
## Performance
Our models are evaluated extensively on 15+ datasets including challening domains like Tweets, Reddit, emails. They achieve by far the **best performance** from all available sentence embedding methods. Further, we provide several **smaller models** that are **optimized for speed**.
[» Full list of pretrained models](https://www.sbert.net/docs/pretrained_models.html)
## Application Examples
You can use this framework for:
- [Computing Sentence Embeddings](https://www.sbert.net/examples/applications/computing-embeddings/README.html)
- [Semantic Textual Similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html)
- [Clustering](https://www.sbert.net/examples/applications/clustering/README.html)
- [Paraphrase Mining](https://www.sbert.net/examples/applications/paraphrase-mining/README.html)
- [Translated Sentence Mining](https://www.sbert.net/examples/applications/parallel-sentence-mining/README.html)
- [Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
- [Retrieve & Re-Rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)
- [Text Summarization](https://www.sbert.net/examples/applications/text-summarization/README.html)
- [Multilingual Image Search, Clustering & Duplicate Detection](https://www.sbert.net/examples/applications/image-search/README.html)
and many more use-cases.
For all examples, see [examples/applications](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications).
## Development setup
After cloning the repo (or a fork) to your machine, in a virtual environment, run:
```
python -m pip install -e ".[dev]"
pre-commit install
```
To test your changes, run:
```
pytest
```
## Citing & Authors
If you find this repository helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
If you use one of the multilingual models, feel free to cite our publication [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813):
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
```
Please have a look at [Publications](https://www.sbert.net/docs/publications.html) for our different publications that are integrated into SentenceTransformers.
Contact person: Tom Aarsen, [tom.aarsen@huggingface.co](mailto:tom.aarsen@huggingface.co)
https://www.ukp.tu-darmstadt.de/
Don't hesitate to open an issue if something is broken (and it shouldn't be) or if you have further questions.
> This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.
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