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Raw 2 No 0 Test 2 New.model

Developed by Wheatley961
This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.
Downloads 13
Release Time : 11/15/2022

Model Overview

This model can convert sentences and paragraphs into high-dimensional vector representations, supporting natural language processing tasks such as text similarity calculation, clustering, and information retrieval.

Model Features

High-dimensional Vector Representation
Converts text into 768-dimensional dense vectors, capturing semantic information
Semantic Similarity Calculation
Can be used to calculate semantic similarity between sentences
Easy Integration
Can be easily integrated into existing systems via the sentence-transformers library

Model Capabilities

Text Vectorization
Semantic Similarity Calculation
Text Clustering
Information Retrieval

Use Cases

Information Retrieval
Semantic Search
Implements document retrieval based on semantic similarity of vectors
Compared to keyword search, it better understands user query intent
Text Analysis
Document Clustering
Automatically groups semantically similar documents
Can be used for topic discovery and content organization
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