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Keysentence Finder

Developed by m3hrdadfi
This is a sentence embedding model based on sentence-transformers, capable of converting text into 768-dimensional vector representations, suitable for tasks such as semantic similarity calculation.
Downloads 31
Release Time : 4/18/2023

Model Overview

This model is specifically designed to convert sentences and paragraphs into dense representations in high-dimensional vector space, supporting applications such as semantic search, text clustering, and information retrieval.

Model Features

High-Dimensional Vector Representation
Converts text into 768-dimensional dense vectors, preserving rich semantic information
Semantic Similarity Calculation
Measures semantic similarity between texts through distance metrics in vector space
Easy Integration
Provides simple API interfaces for seamless integration into existing systems

Model Capabilities

Text Vectorization
Semantic Similarity Calculation
Text Clustering
Information Retrieval

Use Cases

Information Retrieval
Document Similarity Search
Find semantically similar documents in large document libraries
Improves retrieval accuracy and recall rate
Text Analysis
Text Clustering
Automatically group semantically similar texts
Achieves unsupervised text classification
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