Comparative Analysis of Learning Models for Solving Natural Language Processing Tasks

Abstract

The paper contains an analytical review of methods for solving problems of semantically coherent text processing, search and selection of learning models for solving text processing problems, comparison of the obtained results, summarising the results. The learning models provided in Hugging Face and Scikit-Learn library for solving text generation, text tone detection and text classification tasks were selected for the study. As a result of the study, a comparative analysis of learning models for solving natural language processing tasks was carried out. Various machine learning methods such as SGDClassifier, KNeighborsClassifier, MultinomialNB, LogisticRegression, Decision TreeClassifier, RandomForestClassifier, support vector method and others were considered.