Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use surazbhandari/all-MiniLM-L6-v2-ProductMatching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use surazbhandari/all-MiniLM-L6-v2-ProductMatching with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("surazbhandari/all-MiniLM-L6-v2-ProductMatching") sentences = [ "Steelcase | Monitor Stand | Blank | Mesh", "Steelcase Monitor Stand w/ Blue, Portable", "BIC Printer Heavy Duty/All-in-One", "title_title" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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