SentenceTransformers is a Python framework for state-of-the-art sentence and text 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.
After the installation, the usage is as simple as:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')
#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("").. toctree:: :maxdepth: 2 :caption: Overview docs/installation docs/quickstart docs/pretrained_models docs/publications
.. toctree:: :maxdepth: 2 :caption: Usage docs/usage/computing_sentence_embeddings docs/usage/semantic_textual_similarity docs/usage/paraphrase_mining docs/usage/semantic_search
.. toctree:: :maxdepth: 2 :caption: Training Examples docs/training/overview examples/training/sts/README examples/training/nli/README examples/training/quora_duplicate_questions/README examples/training/multilingual/README
.. toctree:: :maxdepth: 1 :caption: Package Reference docs/package_reference/models docs/package_reference/losses docs/package_reference/evaluation docs/package_reference/datasets