Library for fast text representation and extreme classification.
A text analysis application for performing common NLP tasks through a web dashboard interface and an API
Windows Build of fastText, library for text representation and classification.
Multilingual Sentiment Analysis with Transformers: 2026 Amazon Review Classification Guide
Modern Türk edebiyatı sınırları içerisinde yer alan dönemleri, eğilimleri, şairleri ve zaman dilimlerini sınıflandıran doğal dil işleme uygulaması.
Tiny host of deeplearing-educational-project
An autoML for explainable text classification.
Modern computational linguistics for the Dead Sea Scrolls
Space Model framework that allows for maintaining generalizability, and enhances the performance on the downstream task by utilizing task-specific context attribution. It is an external LLM layer, that improves accuracy in classification task for multiple datasets, such as HateXplain, IMDB movies reviews and more.
A web-based search application for .txt files with five algorithms (Linear, Binary, Naive, KMP, Boyer-Moore) and execution time visualization
Data Science for Psychology: Natural Language
Data mining to discover trends in Open Science in Kenya
Implementing text classification algorithms using the 20 newsgroups datasets, with python
A high-performance, production-ready API for sentiment analysis and text classification using advanced machine learning models. This API provides accurate sentiment analysis with support for batch processing, comprehensive health monitoring, and optimized performance for both development and production environments.
Python NLP project demonstrating a small text classification model built with Scikit-Learn, saved for reuse, and exposed through a Flask API for classifying movie reviews.
Visualizations of common NLP tasks
python classifier planned with jupyter notebook and uses Flask to service the model of text classification to predict what category an App belongs to
Spam detection employs machine learning and NLP to identify and filter out unwanted messages. It uses techniques like text classification and feature extraction to distinguish spam from legitimate content, enhancing user security and experience by reducing the impact of malicious or irrelevant messages across digital platforms.
Multi-task NLP on Cowrie honeypot attacker-session logs—classification, QA, summarization & remediation; Flask/Django integration.
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