Text classification implementation based on TextCNN and transformers.BertTokenizer. Includes training, prediction, data loading, and accuracy evaluation.
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Updated
Jul 16, 2024 - Python
Text classification implementation based on TextCNN and transformers.BertTokenizer. Includes training, prediction, data loading, and accuracy evaluation.
A machine learning model that detects fake vs real news using TF-IDF and a PassiveAggressiveClassifier, achieving ~99% accuracy. Includes full preprocessing, model training, evaluation, and a Flask web app for real-time predictions.
News-Articles-Sorting project is a machine learning-based system that aims to automatically categorize and sort news articles into predefined categories.
AI-powered news analysis platform that aggregates articles from multiple news sources and identifies factual consensus, narrative differences, and media bias using LLMs and semantic similarity techniques.
This project applies NLP and LLMs to detect misinformation in climate and environmental news.
News Topic Classification with Support Vector Machine
An implementation of a deep convolutional neural network to classify Persian news topics.
Fine-tuned DistilBERT (Hugging Face Transformers) for multi-class news document classification across World, Sports, Business, and Sci/Tech categories, trained on the AG News dataset with PyTorch. Includes training script, evaluation metrics (accuracy/F1), and inference demo. Built for ScholarX AI/ML Internship Task 2.
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