Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:10827162
loss:CoSENTLoss
text-embeddings-inference
Instructions to use KhaledReda/all-MiniLM-L6-v45-pair_score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KhaledReda/all-MiniLM-L6-v45-pair_score with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v45-pair_score") sentences = [ "unisex", "daisy white category tags daisy keywords daisy attrs gender women brand dodici generic name product name daisy types of styles casual strap style strap crossbody strap color white description amp with 2 hand", "giant fried lemon roll 8 category tags hot roll roll hot roll giant roll lemon keywords giant roll lemon attrs modifier fried", "category mayonnaise tags chili sweet and mayo keywords chili sweet and mayo" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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