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
Two questions: is max_seq_length = 75 ? If so, why 75?
2
#5 opened almost 2 years ago
by
hfsamhenry
The model card seems to lack licensing info
#3 opened almost 2 years ago
by
perisolb-chronos