Sentence Similarity
sentence-transformers
Safetensors
Norwegian
bert
feature-extraction
dense
Generated from Trainer
dataset_size:527098
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use NbAiLab/nb-sbert-v2-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NbAiLab/nb-sbert-v2-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NbAiLab/nb-sbert-v2-large") sentences = [ "The man talked to a girl over the internet camera.", "A group of elderly people pose around a dining table.", "A teenager talks to a girl over a webcam.", "There is no 'still' that is not relative to some other object." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94c94d7e66ed3f9e7be8a50e54820f5228c6f57c038ccac0aa70bc2482365883
- Size of remote file:
- 316 kB
- SHA256:
- 30b6be2a440d25ff803849da99a6d00b96dd0aa4a261b01b84f993d42c1c8fbb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.