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NER RUBERT Per Loc Org
A lightweight Russian named entity recognition model based on BERT architecture, supporting the identification of three types of entities: person, location, and organization.
Downloads 15
Release Time : 3/2/2022
Model Overview
This model is a fine-tuned version of cointegrated/rubert-tiny for Russian named entity recognition, specifically designed to identify PER (person), LOC (location), and ORG (organization) entities in Russian text.
Model Features
Lightweight Design
Based on the rubert-tiny architecture, the model has fewer parameters, making it suitable for resource-constrained environments.
Russian Language Optimization
Specially trained and optimized for Russian text, performing well on Russian NER tasks.
Three-Type Entity Recognition
Accurately identifies three types of named entities in text: person, location, and organization.
Model Capabilities
Russian Text Processing
Named Entity Recognition
Person Entity Detection
Location Entity Detection
Organization Entity Detection
Use Cases
Information Extraction
News Article Analysis
Extract key person, location, and organization information from Russian news articles.
Automatically construct a relational network of news events.
Social Media Monitoring
Analyze entity information in Russian social media content.
Identify key entities in trending topics.
Knowledge Graph Construction
Russian Wikipedia Processing
Extract entities from Russian Wikipedia text for knowledge graph construction.
Automatically identify and classify entities in Wikipedia.
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