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_lr0.0002_ep5_cosine_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_lr0.0002_ep5_cosine_snTrue_spFalse_hn1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("albertus-sussex/veriscrape-book-test-sbert-bs128_lr0.0002_ep5_cosine_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
- Xet hash:
- 2c75c5c8ac3fadb3d0a6becbfd278df1a1c54aca2a8ed684da2333ff7c946380
- Size of remote file:
- 547 MB
- SHA256:
- d3c9962f596357f9653a8e9cdf2a0fc6729817cb80a59f78a77f77952db3ea57
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