What is this Python project?
Sentence Transformers: Multilingual Sentence, Paragraph, and Image Embeddings using BERT & Co.
What's the difference between this Python project and similar ones?
This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks. Text is embedded in vector space such that similar text are closer and can efficiently be found using cosine similarity.
We provide an increasing number of state-of-the-art pretrained models for more than 100 languages, fine-tuned for various use-cases.
Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task.
For the full documentation, see www.SBERT.net.
--
Anyone who agrees with this pull request could submit an Approve review to it.
Originally posted by @baslia in vinta/awesome-python#2625
What is this Python project?
Sentence Transformers: Multilingual Sentence, Paragraph, and Image Embeddings using BERT & Co.
What's the difference between this Python project and similar ones?
This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks. Text is embedded in vector space such that similar text are closer and can efficiently be found using cosine similarity.
We provide an increasing number of state-of-the-art pretrained models for more than 100 languages, fine-tuned for various use-cases.
Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task.
For the full documentation, see www.SBERT.net.
--
Anyone who agrees with this pull request could submit an Approve review to it.
Originally posted by @baslia in vinta/awesome-python#2625