Sentence Similarity
sentence-transformers
MLX
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use mlx-community/multilingual-e5-small-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlx-community/multilingual-e5-small-mlx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/multilingual-e5-small-mlx") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - MLX
How to use mlx-community/multilingual-e5-small-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir multilingual-e5-small-mlx mlx-community/multilingual-e5-small-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- b93141528b8377b33b7e585d8e805816218d5cd23eb984ecd1b214a55dbca6ee
- Size of remote file:
- 17.1 MB
- SHA256:
- 642eede941e22781e8b3c9a2dfe2015027b2fd855410b16cd66f07b0f3c76641
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