| SentenceTransformers Documentation | |
| ================================================= | |
| SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper `Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks <https://arxiv.org/abs/1908.10084>`_. | |
| You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for `semantic textual similar <docs/usage/semantic_textual_similarity.html>`_, `semantic search <examples/applications/semantic-search/README.html>`_, or `paraphrase mining <examples/applications/paraphrase-mining/README.html>`_. | |
| The framework is based on `PyTorch <https://pytorch.org/>`_ and `Transformers <https://huggingface.co/transformers/>`_ and offers a large collection of `pre-trained models <docs/pretrained_models.html>`_ tuned for various tasks. Further, it is easy to `fine-tune your own models <docs/training/overview.html>`_. | |
| Installation | |
| ================================================= | |
| You can install it using pip: | |
| .. code-block:: python | |
| pip install -U sentence-transformers | |
| We recommend **Python 3.6** or higher, and at least **PyTorch 1.6.0**. See `installation <docs/installation.html>`_ for further installation options, especially if you want to use a GPU. | |
| Usage | |
| ================================================= | |
| The usage is as simple as: | |
| .. code-block:: python | |
| from sentence_transformers import SentenceTransformer | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| #Our sentences we like to encode | |
| 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.'] | |
| #Sentences are encoded by calling model.encode() | |
| embeddings = model.encode(sentences) | |
| #Print the embeddings | |
| for sentence, embedding in zip(sentences, embeddings): | |
| print("Sentence:", sentence) | |
| print("Embedding:", embedding) | |
| print("") | |
| Performance | |
| ========================= | |
| Our models are evaluated extensively and achieve state-of-the-art performance on various tasks. Further, the code is tuned to provide the highest possible speed. Have a look at `Pre-Trained Models <https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models/>`_ for an overview of available models and the respective performance on different tasks. | |
| Contact | |
| ========================= | |
| Contact person: Nils Reimers, [email protected] | |
| 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.* | |
| 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>`_: | |
| .. code-block:: 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>`_: | |
| .. code-block:: 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", | |
| } | |
| If you use the code for `data augmentation <https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/data_augmentation>`_, feel free to cite our publication `Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks <https://arxiv.org/abs/2010.08240>`_: | |
| .. code-block:: bibtex | |
| @inproceedings{thakur-2020-AugSBERT, | |
| title = "Augmented {SBERT}: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks", | |
| author = "Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna", | |
| booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| month = jun, | |
| year = "2021", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/2021.naacl-main.28", | |
| pages = "296--310", | |
| } | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Overview | |
| docs/installation | |
| docs/quickstart | |
| docs/pretrained_models | |
| docs/pretrained_cross-encoders | |
| docs/publications | |
| docs/hugging_face | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Usage | |
| examples/applications/computing-embeddings/README | |
| docs/usage/semantic_textual_similarity | |
| examples/applications/semantic-search/README | |
| examples/applications/retrieve_rerank/README | |
| examples/applications/clustering/README | |
| examples/applications/paraphrase-mining/README | |
| examples/applications/parallel-sentence-mining/README | |
| examples/applications/cross-encoder/README | |
| examples/applications/image-search/README | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Training | |
| docs/training/overview | |
| examples/training/multilingual/README | |
| examples/training/distillation/README | |
| examples/training/cross-encoder/README | |
| examples/training/data_augmentation/README | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Training Examples | |
| examples/training/sts/README | |
| examples/training/nli/README | |
| examples/training/paraphrases/README | |
| examples/training/quora_duplicate_questions/README | |
| examples/training/ms_marco/README | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Unsupervised Learning | |
| examples/unsupervised_learning/README | |
| examples/domain_adaptation/README | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Package Reference | |
| docs/package_reference/SentenceTransformer | |
| docs/package_reference/util | |
| docs/package_reference/models | |
| docs/package_reference/losses | |
| docs/package_reference/evaluation | |
| docs/package_reference/datasets | |
| docs/package_reference/cross_encoder | |