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Ru Core News Sm

Developed by spacy
spaCy's CPU-optimized Russian processing pipeline, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and other features
Downloads 1,310
Release Time : 3/2/2022

Model Overview

This is a Russian natural language processing model developed based on the spaCy framework, specifically optimized for Russian text processing. The model includes a complete NLP processing pipeline capable of performing tasks such as tokenization, part-of-speech tagging, dependency parsing, and named entity recognition.

Model Features

CPU optimization
Specifically optimized for CPU processing, suitable for running in resource-limited environments
Complete NLP pipeline
Includes a complete natural language processing pipeline from tokenization to named entity recognition
High accuracy
Outstanding performance in various NLP tasks, such as NER F1 score reaching 94.98% and part-of-speech tagging accuracy of 98.77%

Model Capabilities

Russian tokenization
Part-of-speech tagging
Dependency parsing
Named entity recognition
Sentence segmentation
Lemmatization

Use Cases

Text analysis
Russian news analysis
Extract entity information such as person names, place names, and organization names from Russian news
NER F1 score reaches 94.98%
Russian grammar checking
Analyze the grammatical structure of Russian sentences, identifying parts of speech and dependency relationships
Dependency parsing UAS reaches 95.87%
Information extraction
Russian document processing
Extract structured information from Russian documents
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