Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:1788
loss:TripletLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use albertus-sussex/veriscrape-sbert-camera-reference_2_to_verify_8-fold-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use albertus-sussex/veriscrape-sbert-camera-reference_2_to_verify_8-fold-10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("albertus-sussex/veriscrape-sbert-camera-reference_2_to_verify_8-fold-10", trust_remote_code=True) sentences = [ "$194.99", "Polaroid t1235 Point & Shoot Digital Camera - Blue", "$199.00", "price", "model" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle