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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/infer.png"/>
</div>
## 环境配置
1. -v 路径、docker_name和imageID根据实际情况修改
2. transformers/trainer_pt_utils.py文件 line 37 修改为:
```bash
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）
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
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）
```bash
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/
```bash
DTK软件栈：dtk24.04
python：python3.10
torch：2.1.0
```
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
```bash
cd /your_code_path/sentence-bert_pytorch
pip install -r requirements.txt
pip install -U sentence-transformers
pip install -e .
```
## 数据集
使用来自多个数据集的結合来微调模型，句子对的总数超过10亿个句子。对每个数据集进行抽样，给出一个加权概率，该概率在data_config.json文件中详细说明。
因数据较多，这里仅用[Simple Wikipedia Version 1.0](https://cs.pomona.edu/~dkauchak/simplification/)数据集进行展示，数据集已在 datasets/simple_wikipedia_v1 中提供
详细数据请参考[all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)模型中的Model card。
数据集的目录结构如下:
```
├── datasets
│ ├──tmp.txt
│ ├──simple_wikipedia_v1
│ ├──simple_wiki_pair.txt # 生成的
│ ├──wiki.simple
│ └──wiki.unsimplified
```
## 训练
使用预训练模型[MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased)，有关预训练程序的详细信息，请参阅 model card。
### 单机多卡
```bash
bash finetune.sh
```
### 单机单卡
```bash
python finetune.py
```
## 推理
预训练模型下载[pretrained models](https://www.sbert.net/docs/pretrained_models.html)
```bash
python infer.py --data_path ./datasets/tmp.txt
```
## result
<div align=center>
<img src="./doc/results.png"/>
</div>
### 精度
暂无
## 应用场景
### 算法类别
NLP
### 热点应用行业
教育,网安,政府
## 源码仓库及问题反馈
- https://developer.hpccube.com/codes/modelzoo/sentence-bert_pytorch
## 参考资料
- https://github.com/UKPLab/sentence-transformers
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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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The included data set contains 137,362 aligned sentences extracted by pairing Simple English Wikipedia with English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011). In Proceedings of ACL (short paper). The data set contains those sentences with a similarity above 0.50. Higher precision alignments may be obtained by TF-IDF thresholding at higher levels.
Two files are included: wiki.normal and wiki.simple. Each file contains 137,362 lines and corresponds to a sentence. The nth line/sentence in wiki.normal corresponds to the nth line/sentence in wiki.simple. Some minimal tokenization has been done to treat most punctuation characters as separate words/tokens.
For questions regarding the data set set, contact David Kauchak at Pomona College.
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{"sentence1": "不能,这是属于个人所有的固定资产。", "sentence2": "不可以,这是个人固定资产,不能买卖。", "score": 0.96}
{"sentence1": "不可以,这属于个人固定资产,不能交易。", "sentence2": "不可以,这属于个人固定资产。", "score": 0.99}
{"sentence1": "活动前一周内是推荐的提交时间段。", "sentence2": "通常建议在活动开始前的一周内提交。", "score": 0.99}
{"sentence1": "请一直向参观者强调“不要拍照”。", "sentence2": "请提醒参观者“禁止携带相机拍照”。", "score": 0.85}
{"sentence1": "可以自己选购所需物资。", "sentence2": "可以自行选购,没有限制。", "score": 0.85}
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FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
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docs:
sphinx-build -c . -a -E .. _build
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