A Dataset for Thai text summarization from Thairath, ThaiPBS, Prachathai and The Standard with over 350,000 articles. Trained models are provided.
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Updated
Apr 25, 2024 - Jupyter Notebook
A Dataset for Thai text summarization from Thairath, ThaiPBS, Prachathai and The Standard with over 350,000 articles. Trained models are provided.
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.
Turkish News Category Classification Tutorial
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
Improving News Classification Model Using Support Vector Machine and Naive Bayes
Implementing a news classifier on Persian news dataset
SEM END project on News Application, with features such as sentiment analysis on comments, Fake News Classification & News Summarization.
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)
Classify Nepali News using Multinomial Naive Bayes Algorithm ( College Project )
Code repository for ACL2020 paper Multi-label and Multilingual News Framing Analysis
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.
Train a model to categorize news articles, scrape and translate articles, and predict their categories using TensorFlow, Keras, and Google Translate API.
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.
Framework for prompt-based Persian text classification using pre-trained language models, with K-shot learning, symbol tuning, and comprehensive evaluation metrics.
Multi-label 24.kg Kyrgyz news articles classification into 20 topics: data & baselines
Development of the prediction model for the Pundits Review website used to predict the sentiment in football news articles - https://www.punditsreview.com/
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