Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:4957
loss:AttributeTripletLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use albertus-sussex/veriscrape-sbert-book-reference_2_to_verify_8-fold-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use albertus-sussex/veriscrape-sbert-book-reference_2_to_verify_8-fold-5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("albertus-sussex/veriscrape-sbert-book-reference_2_to_verify_8-fold-5", trust_remote_code=True) sentences = [ "Marianne Williamson", "Francesco Sedita", "HarperCollins", "author", "publisher" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
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