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Ruropebert E5 Base 512 Allru Authorship

Developed by asaakyan
Russian RoPE embedding model based on BERT architecture, optimized for sentence similarity and feature extraction tasks
Downloads 37
Release Time : 12/5/2024

Model Overview

This model is a Russian pre-trained model based on the BERT architecture, employing RoPE (Rotary Position Embedding) positional encoding, primarily used for sentence similarity calculation and feature extraction tasks. The model supports a maximum sequence length of 512 and is trained using a contrastive loss function.

Model Features

RoPE Positional Encoding
Utilizes Rotary Position Embedding technology to better handle long sequences and positional information
Contrastive Loss Training
Optimized with contrastive loss function, particularly suitable for sentence similarity tasks
Large Training Dataset
Trained on 2.46 million Russian sentence pairs, with strong semantic understanding capabilities

Model Capabilities

Sentence embedding generation
Semantic similarity calculation
Text feature extraction
Sentence-level semantic understanding

Use Cases

Information Retrieval
Similar document search
Find semantically similar documents or paragraphs based on input sentences
Improves retrieval relevance and accuracy
Intelligent Customer Service
Question matching
Match user questions with similar questions in the knowledge base
Improves the accuracy of automated Q&A systems
Content Recommendation
Related content recommendation
Recommend semantically similar content based on user browsing history
Enhances user engagement and satisfaction
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