Sentence Similarity
sentence-transformers
Safetensors
Norwegian
bert
feature-extraction
dense
Generated from Trainer
dataset_size:527098
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use NbAiLab/nb-sbert-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NbAiLab/nb-sbert-v2-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NbAiLab/nb-sbert-v2-base") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download Model_Documentation_Form_nb-sbert-v2-base.pdf from NbAiLab/nb-sbert-v2-base: direct link, hf CLI and curl.
- Browser
- Download file 316 kB
-
https://huggingface.co/NbAiLab/nb-sbert-v2-base/resolve/main/Model_Documentation_Form_nb-sbert-v2-base.pdf
- Command line
-
hf download hf://NbAiLab/nb-sbert-v2-base/Model_Documentation_Form_nb-sbert-v2-base.pdf
-
curl -L -o Model_Documentation_Form_nb-sbert-v2-base.pdf https://huggingface.co/NbAiLab/nb-sbert-v2-base/resolve/main/Model_Documentation_Form_nb-sbert-v2-base.pdf
316 kB
- Xet hash:
- 123fe8a38f34daea67ad81ddb5f361bb2439ff43f098e727c69dd3dd7e3f46c2
- Size of remote file:
- 316 kB
- SHA256:
- 33928a8edce46a6ed8ca00cef99e6cacccabda8edab1013f7060fe190ba21ee2
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