Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
Generated from Trainer
dataset_size:32000
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use melino2000/product-torob-matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use melino2000/product-torob-matching with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("melino2000/product-torob-matching") sentences = [ "پکیج بوتان مدل پارما دیجیتال Parma26RSi Digital", "اسپیکر | باند اکتیو Yamaha DXR8", "اسپری لکه بر فرش و مبلمان نانو", "پکیج شوفاژ دیواری بوتان مدل Parma 26RSi Digital" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K