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Sentence Transformers Multilingual E5 Large

Developed by embaas
This is a multilingual sentence embedding model based on sentence-transformers, capable of mapping text to a 1024-dimensional vector space, suitable for semantic search and clustering tasks.
Downloads 53.70k
Release Time : 8/1/2023

Model Overview

The model can convert sentences and paragraphs into high-dimensional vector representations, supports multilingual text processing, and is suitable for natural language processing tasks such as information retrieval and text similarity computation.

Model Features

Multilingual support
Based on XLM-RoBERTa architecture, capable of handling text embeddings in multiple languages
High-quality sentence representations
Generates 1024-dimensional dense vectors, effectively capturing semantic information
Normalized output
Output vectors are normalized, facilitating similarity computation

Model Capabilities

Text vectorization
Semantic similarity computation
Multilingual text processing
Information retrieval
Text clustering

Use Cases

Information retrieval
Semantic search
Achieves search based on semantics rather than keywords through vector similarity
Improves the relevance of search results
Text analysis
Document clustering
Automatically groups documents based on text semantic similarity
Achieves unsupervised document classification
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