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_NLP-AIM-Task

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

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

Easy to use extractive text summarization with AraBERT

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

Arabic Dialect Sentimenal Analysis

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.

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

Sentiment analysis with arabert in Tunisian dialect

Dialectical Arabic Sentiment Analysis

Used “aubmindlab/bert-base-arabertv2” from Aub-mind AraBERT to create a simple Arabic text tokenizer.