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.
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, semantic search, or paraphrase mining.
The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. Further, it is easy to fine-tune your own models.
You can install it using pip:
pip install -U sentence-transformers
We recommend Python 3.6 or higher, and at least PyTorch 1.6.0. See installation for further installation options, especially if you want to use a GPU.
The usage is as simple as:
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("")
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 for an overview of available models and the respective performance on different tasks.
Contact person: Nils Reimers, firstname.lastname@example.org
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.