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Exploring the impact of contextual attention on Arabic text classification: This study examines how contextual attention, such as that implemented in transformers, influences the performance of generative models for Arabic text classification, by analyzing attention mechanisms and their usefulness.
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.
A comprehensive dataset for training a Text-to-Speech system focused on the Iraqi dialect. Contains custom-recorded audio samples, phonetic annotations, and text to support TTS model development and synthesis for Iraqi Arabic.
This notebook explores the application of Regex and embedding techniques in Arabic Natural Language Processing (NLP). It covers the use of regular expressions for text parsing tasks and delves into various word embedding methods, including Word2Vec and FastText, for semantic analysis and representation of Arabic text data.