Natasha

Solves basic Russian NLP tasks, API for lower level Natasha projects

AI & Machine LearningPythonMIT

Abstract

Natasha is an open-source AI & Machine Learning project. Solves basic Russian NLP tasks, API for lower level Natasha projects. Natasha solves basic NLP tasks for Russian language: tokenization, sentence segmentation, word embedding, morphology tagging, lemmatization, phrase normalization, syntax parsing, NER tagging, fact extraction. Quality on every task is similar or better than current SOTAs for Russian language on news articles, see evaluation section. It is built using Python, NLP. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Natasha solves basic NLP tasks for Russian language: tokenization, sentence segmentation, word embedding, morphology tagging, lemmatization, phrase normalization, syntax parsing, NER tagging, fact extraction. Quality on every task is similar or better than current SOTAs for Russian language on news articles, see evaluation section. Natasha is not a research project, underlying technologies are built for production. We pay attention to model size, RAM usage and performance. Models run on CPU, use Numpy for inference.

Split text into tokens and sentencies. Defines tokens and sents properties of doc. Uses Razdel internally.

2. Objective

Solves basic Russian NLP tasks, API for lower level Natasha projects

This project demonstrates how Python, NLP can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonNLP

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/natasha/natasha.git
cd natasha
$ pip install natasha

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by natasha and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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