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. CUDA enabled.

Seems to lend itself to research coding. The real winner here is that you can generate embeddings and vectors for arbitrary text, which would make it ideal for writing a utility that could do only this without a lot of heavy lifting.

Comes with pre-trained models for over 100 languages. Has documentation and examples for building your own models.