Pre-trained Transformers for Arabic Language Understanding and Generation (Arabic BERT, Arabic GPT2, Arabic ELECTRA)
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Updated
Oct 17, 2022 - Python
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 Sentimenal Analysis
Mental health diagnosis tool using NLP and ML for Arabic inputs, with a Laravel web application interface
Simple Script to undo Farasa Segmentation, compatible with AraBERT pre-segmentation
Easy to use extractive text summarization with AraBERT
Arabic_Dialect_Identification_NLP-AIM-Task
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.
This is an experiment for Qur'an QA for the shared task at the OSCAT workshop
This project focuses on developing a sentiment analysis model for Arabic text, leveraging hybrid transformer-based models (AraBERT) and LSTM approaches.
Emotion Prediction in Arabic Text
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.
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
Dialectical Arabic Sentiment Analysis
Disambiguation Study for Arabic Applied on Text Classification
A Named Entity Recognition model for Arabic political texts using AraBERT
Fine-tuning / pre-training AraElectra on a specific domain for QA system.
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