Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:84524
loss:AttributeTripletLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use albertus-sussex/veriscrape-book-test-sbert-bs128_lr0.0001_ep5_euclidean_snTrue_spFalse_hn1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use albertus-sussex/veriscrape-book-test-sbert-bs128_lr0.0001_ep5_euclidean_snTrue_spFalse_hn1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("albertus-sussex/veriscrape-book-test-sbert-bs128_lr0.0001_ep5_euclidean_snTrue_spFalse_hn1", trust_remote_code=True) sentences = [ "Don Piper", "Tommy Nelson", "Kate Walbert", "publisher", "author" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
File size: 205 Bytes
5de1d1b | 1 2 3 4 5 6 7 8 9 10 | {
"__version__": {
"sentence_transformers": "3.4.1",
"transformers": "4.45.2",
"pytorch": "2.5.1+cu124"
},
"prompts": {},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |