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
- Xet hash:
- b571343ef2eeaf2ac9c38c26e15a0711380adbb7c65c8e7d91c57e4c9ee5005e
- Size of remote file:
- 711 MB
- SHA256:
- e6ff8649a20f3d7379d927298b15d08ec88a2c7dc76a5948a3ff4cffe2842f12
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