A Dataset for Thai text summarization from Thairath, ThaiPBS, Prachathai and The Standard with over 350,000 articles. Trained models are provided.

Turkish News Category Classification Tutorial

Cyrillic Mongolian text classification with tensorflow 2, and also some fine-tuning on TugsTugi's Mongolian BERT model and other NLP experiments are included.

Classify news into categories based on headline.

Fake News Detection - Feature Extraction using Vectorization such as Count Vectorizer, TFIDF Vectorizer, Hash Vectorizer,. Then used an Ensemble model to classify whether the news is fake or not.

Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities

Implementing a news classifier on Persian news dataset

Improving News Classification Model Using Support Vector Machine and Naive Bayes

SEM END project on News Application, with features such as sentiment analysis on comments, Fake News Classification & News Summarization.

Code repository for ACL2020 paper Multi-label and Multilingual News Framing Analysis

Classify Nepali News using Multinomial Naive Bayes Algorithm ( College Project )

use regular expression, SVM, TF-IDF, x2test; classify Chinese news into three events: "financing event"(融资事件), "products release"(产品发布), "other"(其他)

This repository contains Jupyter notebooks detailing the experiments conducted in our research paper on Ukrainian news classification. We introduce a framework for simple classification dataset creation with minimal labeling effort, and further compare several pretrained models for the Ukrainian language.

Persian News Text Classification with BERT | تشخیص دسته اخبار فارسی با استفاده از مدل برت

This streamlit app is used for classifying news Headlines into different categories

The project involves developing a news classification system to distinguish between true and fake news using Logistic Regression and Decision Tree models. It includes data preprocessing, model training, and manual testing functionalities to evaluate the accuracy of the classifiers.

Multi-label 24.kg Kyrgyz news articles classification into 20 topics: data & baselines

Corpus building and NLP analysis for Persian Telegram channel messages. Includes a notebook to parse and clean channel_messages.json, stopword normalization, word cloud with a silhouette mask, and CSV outputs (filtered_messages.csv, final_results.csv). Reproducible pipeline for EDA and basic modeling.

Fine-tuned roberta-base classifier on the LIAR dataset. Aaccepts multiple input types text, URLs, and PDFs and outputs a prediction with a confidence score. It also leverages google/flan-t5-base to generate explanations and uses an Agentic AI with LangGraph to orchestrate agents for planning, retrieval, execution, fallback, and reasoning.