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
File size: 613 Bytes
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"BertModel"
],
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"dtype": "float32",
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"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-12,
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"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.57.3",
"type_vocab_size": 2,
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}
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