arabert

Pre-trained Transformers for Arabic Language Understanding and Generation (Arabic BERT, Arabic GPT2, Arabic ELECTRA)

Fine-tune BERT models to classify Arabic text by different dialects.

Arabic Dialect Identification between 18 country-level Arabic dialects using QADI dataset and pretrained language model AraBERT

After collecting 40 thousand tweets and preprocessing it, I used word embeddings with arabert and tf-idf along with two neural network architectures and 5 machine learning algorithms. Due to the huge size of the dataset, I chose Amazon SageMaker to train the models

Arabic_Dialect_Identification_NLP-AIM-Task

Easy to use extractive text summarization with AraBERT

Simple Script to undo Farasa Segmentation, compatible with AraBERT pre-segmentation

Mental health diagnosis tool using NLP and ML for Arabic inputs, with a Laravel web application interface

Arabic Dialect Sentimenal Analysis

Arabic Sentiment Analysis with AraBERT v2 — fine-tuned NLP pipeline for Arabic text classification (positive/negative/neutral) with custom training loop, Streamlit demo, and full evaluation suite

This is an experiment for Qur'an QA for the shared task at the OSCAT workshop

Diacritics are short vowels with a constant length that are spoken. The same word in the Arabic language can have different meanings and different pronunciations based on how it is diacritized. In this project, we implement a pipeline to predict the diacritic of each character in an Arabic text using Natural Language Processing techniques.

Domain-specific Arabic Legal AI assistant utilizing RAG, AraBERT embeddings, and semantic vector retrieval.

This project focuses on developing a sentiment analysis model for Arabic text, leveraging hybrid transformer-based models (AraBERT) and LSTM approaches.

Many countries speak Arabic; however, each country has its own dialect, the aim of this project is to build a model that predicts the dialect given the text.

Dialectical Arabic Sentiment Analysis

an AI powered Arabic Question Answering system built by fine tuning the AraBERT model on the Arabic SQuAD dataset. , Developed as part of the ZakyBootcamp AI track

Arabic Named Entity Recognition (NER) using AraBERT - Fine-tuned transformer model for identifying people, organizations, and locations in Arabic text. Includes training pipeline, dataset, and inference utilities.

Emotion Prediction in Arabic Text