Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:12818275
loss:CoSENTLoss
text-embeddings-inference
Instructions to use KhaledReda/all-MiniLM-L6-v66-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-v66-pair_score with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v66-pair_score") sentences = [ "outer shell terry", "jessy women hair clip jessy hair clip", "linen wrap one size skirts full coverage skirts linen wrap . one size fits up to x-large. provides full coverage. product care. it s preferable to dry clean all our products. wash inside out. iron at maximum 40c.", "trichup frizz control 60 ml trichup" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K