Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Norwegian
bert
feature-extraction
text-embeddings-inference
Instructions to use NbAiLab/nb-sbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NbAiLab/nb-sbert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NbAiLab/nb-sbert-base") sentences = [ "This is a Norwegian boy", "Dette er en norsk gutt", "This is an English boy", "This is a dog" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use NbAiLab/nb-sbert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-sbert-base") model = AutoModel.from_pretrained("NbAiLab/nb-sbert-base") - Inference
- Notebooks
- Google Colab
- Kaggle
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# NB-SBERT-BASE
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> [!
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> As of April 13th 2026, there are now new versions of this model, with improved performance and increased context length.
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> - [NbAiLab/nb-sbert-v2-base](https://huggingface.co/NbAiLab/nb-sbert-v2-base)
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> - [NbAiLab/nb-sbert-v2-large](https://huggingface.co/NbAiLab/nb-sbert-v2-large)
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# NB-SBERT-BASE
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> [!NOTE]
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> As of April 13th 2026, there are now new versions of this model, with improved performance and increased context length.
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> - [NbAiLab/nb-sbert-v2-base](https://huggingface.co/NbAiLab/nb-sbert-v2-base)
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> - [NbAiLab/nb-sbert-v2-large](https://huggingface.co/NbAiLab/nb-sbert-v2-large)
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