![Logo](_static/logo.png) Sentence-Transformers

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Overview

  * [ Installation ](docs/installation.html)
  * [ Quickstart ](docs/quickstart.html)
  * [ Pretrained Models ](docs/pretrained_models.html)
  * [ Pretrained Cross-Encoders ](docs/pretrained_cross-encoders.html)
  * [ Publications ](docs/publications.html)
  * [ Hugging Face ð¤ ](docs/hugging_face.html)

Usage

  * [ Computing Sentence Embeddings ](examples/applications/computing-embeddings/README.html)
  * [ Semantic Textual Similarity ](docs/usage/semantic_textual_similarity.html)
  * [ Embedding Quantization ](examples/applications/embedding-quantization/README.html)
  * [ Semantic Search ](examples/applications/semantic-search/README.html)
  * [ Retrieve & Re-Rank ](examples/applications/retrieve_rerank/README.html)
  * [ Clustering ](examples/applications/clustering/README.html)
  * [ Paraphrase Mining ](examples/applications/paraphrase-mining/README.html)
  * [ Translated Sentence Mining ](examples/applications/parallel-sentence-mining/README.html)
  * [ Cross-Encoders ](examples/applications/cross-encoder/README.html)
  * [ Image Search ](examples/applications/image-search/README.html)

Training

  * [ Training Overview ](docs/training/overview.html)
  * [ Loss Overview ](docs/training/loss_overview.html)
  * [ Matryoshka Embeddings ](examples/training/matryoshka/README.html)
  * [ Adaptive Layers ](examples/training/adaptive_layer/README.html)
  * [ Multilingual-Models ](examples/training/multilingual/README.html)
  * [ Model Distillation ](examples/training/distillation/README.html)
  * [ Cross-Encoders ](examples/training/cross-encoder/README.html)
  * [ Augmented SBERT ](examples/training/data_augmentation/README.html)
  * [ Training Datasets ](examples/training/datasets/README.html)

Training Examples

  * [ Semantic Textual Similarity ](examples/training/sts/README.html)
  * [ Natural Language Inference ](examples/training/nli/README.html)
  * [ Paraphrase Data ](examples/training/paraphrases/README.html)
  * [ Quora Duplicate Questions ](examples/training/quora_duplicate_questions/README.html)
  * [ MS MARCO ](examples/training/ms_marco/README.html)

Unsupervised Learning

  * [ Unsupervised Learning ](examples/unsupervised_learning/README.html)
  * [ Domain Adaptation ](examples/domain_adaptation/README.html)

Package Reference

  * [ SentenceTransformer ](docs/package_reference/SentenceTransformer.html)
  * [ util ](docs/package_reference/util.html)
  * [ quantization ](docs/package_reference/quantization.html)
  * [ Models ](docs/package_reference/models.html)
  * [ Losses ](docs/package_reference/losses.html)
  * [ Evaluation ](docs/package_reference/evaluation.html)
  * [ Datasets ](docs/package_reference/datasets.html)
  * [ cross_encoder ](docs/package_reference/cross_encoder.html)

__ Sentence-Transformers

  * » 
  * SentenceTransformers Documentation 
  * [ Edit on GitHub ](https://github.com/UKPLab/sentence-transformers/blob/master/index.rst)

* * *

#  SentenceTransformers Documentation  Â¶

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 similarity ](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-