natasha/slovnet
Deep Learning based NLP modeling for Russian language
SlovNet helps Russian-speaking analysts and researchers automatically extract key information from large volumes of Russian text, especially news articles. It takes raw Russian text as input and outputs identified entities like people, locations, and organizations, as well as detailed morphological and syntactic analysis of words and sentences. This is for anyone who needs to quickly understand and categorize information from Russian documents without manually reading every word.
243 stars. Used by 1 other package. No commits in the last 6 months. Available on PyPI.
Use this if you need to perform high-quality named entity recognition, morphological tagging, or syntax parsing on Russian text, especially news content, and require models that are fast and efficient on standard computer processors.
Not ideal if your primary need is for the absolute highest accuracy on niche or highly specialized Russian text that deviates significantly from news articles, or if you have ample GPU resources for larger, more computationally intensive models.
Stars
243
Forks
24
Language
Python
License
MIT
Category
Last pushed
Jul 24, 2023
Commits (30d)
0
Dependencies
3
Reverse dependents
1
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