Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:19761536
loss:CoSENTLoss
text-embeddings-inference
Instructions to use KhaledReda/all-MiniLM-L6-v43-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-v43-pair_score with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v43-pair_score") sentences = [ "lamp", "clear soft shiny shampoo conditioner 360 ml, clear shampoo clear conditioner hair shampoo hair conditioner clear clear soft shiny conditioner shampoo, clear clear soft shiny conditioner shampoo, units 360 millilitre", "summer beach dress - white, white dress women dress dresses beach dress dress summer dress, beach dress dress summer dress, gender women leocansa generic dress one types of fashion styles beachwear casual linen white solid occasion beach season summer, available now", "storage grey bowl, grey basket baskets bowl baskets storage baskets, baskets bowl baskets storage baskets, grey" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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