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Esci All Distilbert Base Uncased 5e 5

Developed by spacemanidol
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Downloads 41
Release Time : 8/12/2022

Model Overview

This model is primarily used to convert text into high-dimensional vector representations, supporting application scenarios such as sentence similarity calculation, cluster analysis, and semantic search.

Model Features

High-Dimensional Vector Representation
Maps sentences and paragraphs into a 768-dimensional dense vector space, preserving semantic information
Sentence Similarity Calculation
Accurately calculates semantic similarity between different sentences
Easy Integration
Provides simple API interfaces for easy integration into existing systems

Model Capabilities

Text Vectorization
Semantic Similarity Calculation
Text Clustering
Semantic Search

Use Cases

Information Retrieval
Semantic Search
Uses vector similarity to achieve more accurate semantic search
Compared to traditional keyword search, it better understands user query intent
Text Analysis
Document Clustering
Automatically classifies documents based on content similarity
Achieves automatic document grouping without manual labeling
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