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+%global _empty_manifest_terminate_build 0
+Name:		python-sentence-transformers
+Version:	2.2.2
+Release:	1
+Summary:	Multilingual text embeddings
+License:	Apache License 2.0
+URL:		https://github.com/UKPLab/sentence-transformers
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/20/9c/f07bd70d128fdb107bc02a0c702b9058b4fe147d0ba67b5a0f4c3cf15a54/sentence-transformers-2.2.2.tar.gz
+BuildArch:	noarch
+
+
+%description
+[#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 task. Text is embedding in vector space such that similar text is close 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)
+## Installation
+We recommend **Python 3.6** or higher, **[PyTorch 1.6.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.6.0](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("")
+````
+## 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
+- [10+ 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).
+## 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: [Nils Reimers](https://www.nils-reimers.de), [info@nils-reimers.de](mailto:info@nils-reimers.de)
+https://www.ukp.tu-darmstadt.de/
+Don't hesitate to send us an e-mail or report 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.
+
+%package -n python3-sentence-transformers
+Summary:	Multilingual text embeddings
+Provides:	python-sentence-transformers
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-sentence-transformers
+[#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 task. Text is embedding in vector space such that similar text is close 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)
+## Installation
+We recommend **Python 3.6** or higher, **[PyTorch 1.6.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.6.0](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("")
+````
+## 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
+- [10+ 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).
+## 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: [Nils Reimers](https://www.nils-reimers.de), [info@nils-reimers.de](mailto:info@nils-reimers.de)
+https://www.ukp.tu-darmstadt.de/
+Don't hesitate to send us an e-mail or report 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.
+
+%package help
+Summary:	Development documents and examples for sentence-transformers
+Provides:	python3-sentence-transformers-doc
+%description help
+[#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 task. Text is embedding in vector space such that similar text is close 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)
+## Installation
+We recommend **Python 3.6** or higher, **[PyTorch 1.6.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.6.0](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("")
+````
+## 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
+- [10+ 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).
+## 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: [Nils Reimers](https://www.nils-reimers.de), [info@nils-reimers.de](mailto:info@nils-reimers.de)
+https://www.ukp.tu-darmstadt.de/
+Don't hesitate to send us an e-mail or report 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.
+
+%prep
+%autosetup -n sentence-transformers-2.2.2
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-sentence-transformers -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..f049621
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+095cd933af4ffc2a3d06ffbec4468640  sentence-transformers-2.2.2.tar.gz