Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:77996746
loss:CoSENTLoss
text-embeddings-inference
Instructions to use KhaledReda/all-MiniLM-L6-v58-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-v58-pair_score with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v58-pair_score") sentences = [ "bbq", "climbing single lanyard 75 cm climbing belay station lanyard rock climbing lanyard mountaineering lanyard dynamic lanyard secure route lanyard rock climbing gear 75 cm climbing designed for attaching to belay stations when rock climbing or to secure a route for mountaineering across rocky terrain. with a length of 75 cm this dynamic lanyard ensures a comfortable position at the belay station.", "covo desk with storage unit desk side drawer unit desk mdf desk storage unit desk furvive desk covo modern 77 120 60 cm side drawer unit 2 rectangular mdf wood carmen a distinctive desk with a side drawer unit that adds a modern and attractive touch to your . dimensions 77 cm height 120 cm width 60 cm depth . material mdf. warranty details 5 year warranty.", "technical boots" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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