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Developed by income
This is a sentence similarity model based on sentence-transformers that can map sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks such as semantic search and clustering.
Downloads 41
Release Time : 6/16/2022

Model Overview

This model is specifically designed to calculate the semantic similarity between sentences and paragraphs. By converting text into 768-dimensional vectors, it supports various natural language processing tasks.

Model Features

High-dimensional vector representation
Convert text into 768-dimensional dense vectors to effectively capture semantic information
Semantic similarity calculation
Accurately calculate the semantic similarity between sentences and paragraphs
Easy integration
Can be integrated into existing systems through a simple API

Model Capabilities

Sentence vectorization
Semantic similarity calculation
Text clustering
Semantic search

Use Cases

Information retrieval
Semantic search engine
Build a search engine based on semantics rather than keywords
Improve the relevance of search results
Text analysis
Document clustering
Automatically group semantically similar documents
Achieve unsupervised document organization
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