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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-sbert-base") model = AutoModel.from_pretrained("NbAiLab/nb-sbert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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#
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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# nb-sbert
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('NbAiLab/nb-sbert')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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