Cross English & German RoBERTa for Sentence Embeddings This model is intended to compute sentence (text) embeddings for English and German text. These embeddings can then be compared with cosine similarity to find sentences with a similar semantic meaning. For example this can be useful for semantic textual similarity, semantic search, or paraphrase mining. To do this you have to use the Sentence Transformers Python framework. The speciality of this model is that it also works cross lingually. Regardless of the language, the sentences are translated into very similar vectors according to their semantics. This means that you can, for example, enter a search in German and find results according to the semantics in German and also in English. Using a xlm model and multilingual finetuning with language crossing we reach performance that even exceeds the best current dedicated English large model (see Evaluation section below). Sentence BERT (SBERT) is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine similarity. This reduces the effort for finding the most…
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