J

Jurimodel

Developed by ramdane
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space for tasks such as sentence similarity calculation and semantic search.
Downloads 121
Release Time : 1/23/2023

Model Overview

This model is specifically designed for calculating semantic similarity between sentences and paragraphs, suitable for scenarios like information retrieval, cluster analysis, and semantic search.

Model Features

High-Dimensional Vector Representation
Converts sentences into 768-dimensional dense vectors, capturing deep semantic features.
Semantic Similarity Calculation
Accurately calculates semantic similarity between sentences, not just surface similarity.
Efficient Processing
Supports batch processing of sentences to improve computational efficiency.

Model Capabilities

Sentence Vectorization
Semantic Similarity Calculation
Text Clustering
Semantic Search

Use Cases

Information Retrieval
Similar Document Search
Find semantically similar documents in a document repository.
Improves retrieval accuracy and recall rate.
Question Answering Systems
Question Matching
Match user questions with similar questions in a knowledge base.
Improves the accurate response rate of the QA system.
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