Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:1200000
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use jonny9f/food_embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jonny9f/food_embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jonny9f/food_embeddings") sentences = [ "Mutton, roasted", "Imagine Creamy Butternut Squash Soup", "Perrier Water, bottled", "Crackers, whole-wheat" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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