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Multilingual E5 Small
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Multilingual E5 Small
Developed by metarank
This is a multilingual sentence transformer model that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.
Downloads 17
Release Time : 9/4/2023
Model Overview
This model is an ONNX version optimized for the Metarank reranker, used to calculate semantic similarity between sentences and paragraphs. Supports multilingual text embeddings.
Model Features
Multilingual support
Capable of handling text embeddings in multiple languages
Efficient vectorization
Converts sentences and paragraphs into 384-dimensional dense vectors
Semantic search optimization
Designed specifically for semantic similarity search and reranking tasks
ONNX format
Provided in ONNX format for easy deployment in production environments
Model Capabilities
Text vectorization
Semantic similarity calculation
Multilingual text processing
Clustering analysis
Use Cases
Information retrieval
Search engine result reranking
Rerank search results based on query semantic similarity
Improves relevance of search results
Recommendation systems
Content similarity recommendation
Recommend related items based on content semantic similarity
Enhances recommendation accuracy and user experience
Transformers
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