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Sentence Transformers Gte Base

Developed by embaas
This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional vector space, suitable for tasks such as semantic search and clustering.
Downloads 43
Release Time : 8/1/2023

Model Overview

This model is primarily used to convert text sentences into high-dimensional vector representations, supporting natural language processing tasks such as sentence similarity calculation, semantic search, and text clustering.

Model Features

High-dimensional vector representation
Maps sentences and paragraphs into a 768-dimensional dense vector space, capturing semantic information.
Semantic search support
The generated vectors can be used for efficient semantic similarity calculation and search.
Easy integration
Can be easily integrated into existing applications through the sentence-transformers library.

Model Capabilities

Sentence embedding generation
Semantic similarity calculation
Text clustering
Feature extraction

Use Cases

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