Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:1275
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Weike1000/Snack_Embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Weike1000/Snack_Embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Weike1000/Snack_Embed") sentences = [ "snickers almond", "Cheetos Flamin' Hot", "Snickers Almond", "Tostitos Hint of Lime" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K