Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:89544
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use tomerRest/line_item_embeddings_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomerRest/line_item_embeddings_ with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomerRest/line_item_embeddings_") sentences = [ "DOVSHI, Dover Shiso Liqueur 700ml", "PINEAPPLE Gold XL", "Chocolate Donut t-SPRINKLE", "MEATPOR PORK SHOULDER BONELESS SKINLESS KSHLD CBO" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "3.3.1", | |
| "transformers": "4.49.0", | |
| "pytorch": "2.6.0" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |