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Teraflop Minn Caselaw

Developed by conceptofmind
Sentence embedding model based on ModernBERT-base for sentence similarity and feature extraction tasks
Downloads 253
Release Time : 3/8/2025

Model Overview

This model is based on the ModernBERT-base architecture, fine-tuned for generating high-quality sentence embeddings, supporting sentence similarity calculation and feature extraction tasks

Model Features

Efficient Sentence Embeddings
Capable of converting input sentences into high-quality vector representations that capture semantic information
Multi-task Optimization
Trained using CachedMultipleNegativesRankingLoss to optimize sentence similarity tasks
Large-scale Training Data
Trained on 248,554 data points, demonstrating strong generalization capabilities

Model Capabilities

Sentence similarity calculation
Text feature extraction
Semantic search
Text clustering

Use Cases

Information Retrieval
Legal Document Retrieval
Retrieve relevant legal documents based on user queries
As shown in the example, it can accurately match user queries with legal document content
Q&A Systems
Urban Management Q&A
Answer questions regarding urban management responsibilities
As shown in the example, it can accurately match questions with relevant regulatory content
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