First add

.gitignore

+.idea
+.vscode
+*.pyc
+*.gz
+*.tsv
+tmp_*.py
+/examples/**/output/*
+examples/datasets/*/
+sentence_transformers.egg-info
+dist/
+nr_*/
+/examples/datasets/
+/examples/embeddings/
+/pretrained-models/
+/cheatsheet.txt
+/testsuite.txt
+/TODO.txt
+/docs/_build/
+/docs/make.bat
+/examples/training/quora_duplicate_questions/quora-IR-dataset/
+build
+
+htmlcov
+.coverage
+.venv
\ No newline at end of file

.pre-commit-config.yaml

+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.3.5
+    hooks:
+      # These hooks are equivalent to running `make quality`
+      - id: ruff
+      - id: ruff-format
+        args: [ --check ]

LICENSE

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Makefile

+quality:
+	ruff check
+	ruff format --check
+
+style:
+	ruff check --fix
+	ruff format

ModelZooStd.md

+# 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）。
\ No newline at end of file

NOTICE.txt

+-------------------------------------------------------------------------------
+Copyright 2019
+Ubiquitous Knowledge Processing (UKP) Lab
+Technische Universität Darmstadt
+-------------------------------------------------------------------------------
\ No newline at end of file

README.md

+# 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

README_ori.md

+<!--- 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.

datasets/simple_wikipedia_v1/README

+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.

datasets/tmp.txt

+{"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}
\ No newline at end of file

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
\ No newline at end of file

docs/Makefile

+
+docs:
+	sphinx-build -c . -a -E .. _build
\ No newline at end of file