Sentence Similarity
MLX
sentence-transformers
English
bert
feature-extraction
food
embeddings
apple-silicon
text-embeddings-inference
Instructions to use richtext/foodmapper-gte-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use richtext/foodmapper-gte-large with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir foodmapper-gte-large richtext/foodmapper-gte-large
- sentence-transformers
How to use richtext/foodmapper-gte-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("richtext/foodmapper-gte-large") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
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