multilingual-e5-base-mlx
This model was converted to MLX format from intfloat/multilingual-e5-base.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx
git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/llms/hf_llm
python generate.py --model mlx-community/multilingual-e5-base-mlx --prompt "My name is"
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Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported78.970
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported43.694
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported73.381
- accuracy on MTEB AmazonCounterfactualClassification (de)test set self-reported71.724
- ap on MTEB AmazonCounterfactualClassification (de)test set self-reported82.221
- f1 on MTEB AmazonCounterfactualClassification (de)test set self-reported69.955
- accuracy on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported79.655
- ap on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported28.508
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/multilingual-e5-base-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]