Tip
Sentence Transformers v3.2 just released, introducing the ONNX and OpenVINO backends for Sentence Transformer models. Read SentenceTransformer > Usage > Speeding up Inference to learn more about the new backends and what they can mean for your inference speed.
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¶
See also
See the Quickstart for more quick information on how to use Sentence Transformers.
Using Sentence Transformer models is elementary:
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:
How to use Sentence Transformer models? Sentence Transformers > Usage
What Sentence Transformer models can I use? Sentence Transformers > Pretrained Models
How do I make Sentence Transformer models faster? Sentence Transformers > Usage > Speeding up Inference
How do I train/finetune a Sentence Transformer model? Sentence Transformers > Training Overview
How to use Cross Encoder models? Cross Encoder > Usage
What Cross Encoder models can I use? Cross Encoder > Pretrained Models
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", }