Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:43381718
loss:CoSENTLoss
text-embeddings-inference
Instructions to use KhaledReda/all-MiniLM-L6-v62-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-v62-pair_score with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v62-pair_score") sentences = [ "bathing suit 500", "moroccan mud face body mud body mask moisturizing body mask stretch marks body mask softening body mask moroccan mud body the moroccan mud body mask moisturizes face body treatment leaves skin amazingly soft and it reduces the formation of stretch marks. best moroccan mud in town.", "hand embroidered printed coat women coat cotton coat voile coat coats hand embroidered coat abaya style coat in printed cotton voile with hand embroidery", "mules shoes comfortable shoes a whole upper lining insole 6 months warranty looking for the perfect pair of elegant shoes to elevate your look look no further our shoes are handcrafted from the finest materials and designed with your comfort and style in mind. whether you re dressing up for a business meeting a special event or just want to look your best while running errands our shoes will help you put your best foot forward. here are just a few of the things that make our shoes so special we use only the finest materials such as premium leather and suede. our shoes are handcrafted by skilled artisans who pay attention to every detail. we offer a wide variety of styles to choose from f" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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