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Ko Sbert Sts

Developed by jhgan
This is a Korean sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Downloads 175.93k
Release Time : 3/2/2022

Model Overview

This model is specifically designed for Korean text and can convert sentences and paragraphs into high-dimensional vector representations, suitable for natural language processing tasks such as sentence similarity calculation, semantic search, and text clustering.

Model Features

Korean language optimization
Specially optimized for Korean text, better handling the semantic features of Korean sentences.
High-dimensional vector representation
Maps text into a 768-dimensional dense vector space, preserving rich semantic information.
Sentence similarity calculation
Particularly suitable for calculating semantic similarity between sentences.

Model Capabilities

Sentence embedding
Semantic similarity calculation
Text clustering
Semantic search

Use Cases

Information retrieval
Semantic search system
Build a search system based on semantics rather than keywords
Improves the accuracy and relevance of search results
Text analysis
Document clustering
Automatically group semantically similar documents
Achieves unsupervised document classification
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