Feature Extraction
sentence-transformers
Safetensors
MLX
xlm-roberta
mteb
Sentence Transformers
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use mlx-community/multilingual-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlx-community/multilingual-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/multilingual-e5-large") 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-large with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir multilingual-e5-large mlx-community/multilingual-e5-large
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
Update README.md
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by awinkler2 - opened
README.md
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@@ -5967,7 +5967,7 @@ import mlx.core as mx
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model, tokenizer = load("mlx-community/multilingual-e5-large")
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# For text embeddings
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output = generate(model,
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embeddings = output.text_embeds # Normalized embeddings
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# Compute dot product between normalized embeddings
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model, tokenizer = load("mlx-community/multilingual-e5-large")
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# For text embeddings
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output = generate(model, tokenizer, texts=["I like grapes", "I like fruits"])
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embeddings = output.text_embeds # Normalized embeddings
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# Compute dot product between normalized embeddings
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