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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. 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.
Features
- Pretrained models
- Pretrained cross-encoders
- We provide a large list of Pretrained Models for more than 100 languages
- Some models are general purpose models, while others produce embeddings for specific use cases
- This framework allows you to fine-tune your own sentence embedding methods
- You have various options to choose from in order to get perfect sentence embeddings for your specific task
Project Samples
