AIが動く。未来が更新される。nb-sbert-base
> [!NOTE] > As of April 13th 2026, there are now new versions of this model, with improved performance and increased context length. > - NbAiLab/nb-sbert-v2-base > - NbAiLab/nb-sbert-v2-large NB-SBERT-BASE is a SentenceTransformers model trained on a machine translated version of the MNLI dataset, starting from nb-bert-base. The model maps sentences & paragraphs to a 768 dimensional dense vector space. This vector can be used for tasks like clustering and semantic search. Below we give some examples on how to use the model. The easiest way is to simply measure the cosine distance between two sentences. Sentences that are close to each other in meaning, will have a small cosine distance and a similarity close to 1. The model is trained in such a way that similar sentences in different languages should also be close to each other. Ideally, an English-Norwegian sentence pair should have high similarity.
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> [!NOTE] > As of April 13th 2026, there are now new versions of this model, with improved performance and increased context length. > - NbAiLab/nb-sbert-v2-base > - NbAiLab/nb-sbert-v2-large NB-SBERT-BASE is a SentenceTransformers model trained on a machine translated version of the MNLI dataset, starting from nb-bert-base. The model maps sentences & paragraphs to a 768 dimensional dense vector space. This vector can be used for tasks like clustering and semantic search. Below we give some examples on how to use the model. The easiest way is to simply measure the cosine distance between two sentences. Sentences that are close to each other in meaning, will have a small cosine distance and a similarity close to 1. The model is trained in such a way that similar sentences in different languages should also be close to each other. Ideally, an English-Norwegian sentence pair should have high similarity.