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Note

Sentence Transformers v3.0 just released, introducing a new training API for Sentence Transformer models. Read SentenceTransformer > Training Overview to learn more about the training API, and check out v3.0 Release Notes for details on the other changes.

SentenceTransformers Documentation

Sentence Transformers (a.k.a. SBERT) is the go-to Python module for accessing, using, and training state-of-the-art text and image embedding models. It can be used to compute embeddings using Sentence Transformer models (quickstart) or to calculate similarity scores using Cross-Encoder models (quickstart). This unlocks a wide range of applications, including semantic search, semantic textual similarity, and paraphrase mining.

A wide selection of over 5,000 pre-trained Sentence Transformers models are available for immediate use on 🤗 Hugging Face, including many of the state-of-the-art models from the Massive Text Embeddings Benchmark (MTEB) leaderboard. Additionally, it is easy to train or finetune your own models using Sentence Transformers, enabling you to create custom models for your specific use cases.

Sentence Transformers was created by UKPLab and is being maintained by 🤗 Hugging Face. Don't hesitate to open an issue on the Sentence Transformers repository if something is broken or if you have further questions.

Usage

.. seealso::

   See the `Quickstart <docs/quickstart.html>`_ for more quick information on how to use Sentence Transformers.

Using Sentence Transformer models is elementary:

Installation

You can install sentence-transformers using pip:

pip install -U sentence-transformers

We recommend Python 3.8+ and PyTorch 1.11.0+. See installation for further installation options.

from sentence_transformers import SentenceTransformer

# 1. Load a pretrained Sentence Transformer model
model = SentenceTransformer("all-MiniLM-L6-v2")

# The sentences to encode
sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium.",
]

# 2. Calculate embeddings by calling model.encode()
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 3. Calculate the embedding similarities
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6660, 0.1046],
#         [0.6660, 1.0000, 0.1411],
#         [0.1046, 0.1411, 1.0000]])

What Next?

Consider reading one of the following sections to answer the related questions:

Citing

If you find this repository helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:

@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:

@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, feel free to cite our publication Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks:

@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: 1
   :caption: Getting Started
   :hidden:

   docs/installation
   docs/quickstart


.. toctree::
   :maxdepth: 2
   :caption: Sentence Transformer
   :hidden:

   docs/sentence_transformer/usage/usage
   docs/sentence_transformer/pretrained_models
   docs/sentence_transformer/training_overview
   docs/sentence_transformer/dataset_overview
   docs/sentence_transformer/loss_overview
   docs/sentence_transformer/training/examples


.. toctree::
   :maxdepth: 2
   :caption: Cross Encoder
   :hidden:

   docs/cross_encoder/usage/usage
   docs/cross_encoder/pretrained_models
   docs/cross_encoder/training_overview
   docs/cross_encoder/training/examples


.. toctree::
   :maxdepth: 3
   :caption: Package Reference
   :glob:
   :hidden:

   docs/package_reference/sentence_transformer/index
   docs/package_reference/cross_encoder/index
   docs/package_reference/util