This project analyzes IMDb movie reviews to classify sentiments as positive or negative. It includes text preprocessing, feature extraction using TF-IDF and CountVectorizer, training Logistic Regression and Naive Bayes classifiers, and visualizing frequent words with WordClouds.

News Category Classification using AG News dataset. Implements text preprocessing, TF-IDF vectorization, and trains Logistic Regression and a Neural Network to classify news into World, Sports, Business, and Sci/Tech categories. Includes data visualization and model evaluation.

💬 It uses NLP techniques to classify reviews as positive, neutral or negative, providing valuable insights into customer feedback.

NLP-based fake news detection using pre-trained Word2Vec embeddings and semantic feature engineering. Includes deep EDA, entity-based features, interpretable metrics, and evaluation across multiple models including Logistic Regression, Decision Tree, and Random Forest.

A deep learning-based AI tool for correcting grammatical and spelling errors using TensorFlow. Features LSTM-based sequence-to-sequence modeling, data preprocessing, and real-time correction capabilities.

The excellent Image captioning model using the DETR inspired architecture

This repo includes a generalized preprocessing pipeline for text data in NLP tasks.

Brainlink is an interactive web application designed to foster knowledge sharing and collaborative learning.

The results are drawn from experiments on the classification of legal documents using LLMs in a real-world institutional setting

Named Entity Recognition (NER) on news articles using rule-based and spaCy models. Includes entity extraction, visualizations with displaCy, and comparison of small vs large spaCy models for analysis and insights.

PropInsight is an AI-powered property inspection report generator that utilizes LLM models to analyze property types and observed issues, generating comprehensive and data-driven reports for smarter decision-making.

Fake News Detection system using NLP techniques and TF-IDF vectorization to classify news articles as real or fake. Trained with Logistic Regression and SVM on the Fake and Real News Dataset, with preprocessing, evaluation metrics, and word cloud visualizations.

A PDF Reader application powered by AI, allowing users to upload PDF documents and extract meaningful information using advanced NLP models. Built with Streamlit, Transformers, and Langchain, this app provides a seamless interface for interacting with and analyzing PDF content.

Machine learning project for detecting fake news articles in the Palestinian context using natural language processing (NLP), data preprocessing, and classification models. Includes text cleaning, vectorization, model training, and evaluation.

This repository contains a next-word prediction model using TensorFlow and Keras to generate text in the style of Shakespeare's sonnets.

Companion repository for the paper "Methodological Trends in Psychology Research: Analyzing Abstracts with NLP and Machine Learning." Includes code, term glossary, and workflows for clustering and text analysis.

Assignments and final project for the graduate course Natural Language Processing at CIMAT (Spring 2025). Includes classical and neural methods for text classification, sequence modeling, and user profiling.

From paper to code: a rigorous Transformer implementation in TensorFlow 2 — real WMT14 data, Moses tokenizer, and causal masking done right.