Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
Generated from Trainer
dataset_size:3485841
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use NbAiLab/borealis-embed-212m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NbAiLab/borealis-embed-212m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NbAiLab/borealis-embed-212m") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e5427dd72204e290deda9e0d60b2f85114d985c1408ac69061439f296bf024d
- Size of remote file:
- 141 kB
- SHA256:
- b4a87118b0b0c1e2d5a06b27bb4b56517d4d62b2b74860f77b8ac2d601224840
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.