Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:65749
loss:MultipleNegativesRankingLoss
loss:SoftmaxLoss
loss:CoSENTLoss
text-embeddings-inference
Instructions to use hajimeni/bert-base-all-nli-stsb-quora-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hajimeni/bert-base-all-nli-stsb-quora-nq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hajimeni/bert-base-all-nli-stsb-quora-nq") sentences = [ "Can a US President destroy a city with actions?", "What are best kids educational games?", "Can a US president destroy a city through actions?", "Why do people ask questions on Quora that are just as, if not more than easier to, look up with a search engine?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 125 Bytes
52b4cbe | 1 2 3 4 5 6 7 8 | {
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
}
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