31 skills found · Page 1 of 2
aub-mind / ArabertPre-trained Transformers for Arabic Language Understanding and Generation (Arabic BERT, Arabic GPT2, Arabic ELECTRA)
ARBML / KlaamArabic speech recognition, classification and text-to-speech.
NoorBayan / TafilatA complete dataset offering all possible patterns for Arabic poetic meters, meticulously curated from classical prosody sources. Designed to automate and enhance research in Arabic meter classification and poetry generation
NoorBayan / MaqasidMaqāṣid is a deep learning framework for multi-label thematic classification of Arabic poetry.
saidziani / Arabic News Article ClassificationAutomatic categorization of documents, consists in assigning a category to a text based on the information it contains. We'll follow different approach of Supervised Machine Learning.
motazsaad / Comparable Text MinerComparable documents miner: Arabic-English morphological analysis, text processing, n-gram features extraction, POS tagging, dictionary translation, documents alignment, corpus information, text classification, tf-idf computation, text similarity computation, html documents cleaning
mhmoodlan / Arabic Font ClassificationArabic Font Classification
GhiathAjam / Arabic Tweets Stance And Classification NLPNLP Project that calculates stance and classifies Arabic tweets about COVID-19 Vaccine
FantacherJOY / Arabic Text ClassificationArabic text documents classified using SVM, k-nn and Naive bayes classifers.
ARBML / MetRecArabic Poetry Metric Classification Using Bidirectional Gated Recurrent Neural Networks
mohamedehab00 / A Hybrid Arabic Text Summarization Approach Based On TransformersIn this paper, we proposed a sequential hybrid model based on a transformer to summarize Arabic articles. We used two approaches of summarization to make our model. The First is the extractive approach which depends on the most important sentences from the articles to be the summary, so we used Deep Learning techniques specifically transformers such as AraBert to make our summary, The second is abstractive, and this approach is similar to human summarization, which means that it can use some words which have the same meaning but different from the original text. We apply this kind of summary using MT5 Arabic pre-trained transformer model. We sequentially applied these two summarization approaches to building our A3SUT hybrid model. The output of the extractive module is fed into the abstractive module. We enhanced the summary’s quality to be closer to the human summary by applying this approach. We tested our model on the ESAC dataset and evaluated the extractive summary using the Rouge score technique; we got a precision of 0.5348 and a recall of 0.5515, and an f1 score of 0.4932 and the evaluation of the abstractive model is evaluated by user satisfaction. We add some features to our summary to make it more understandable by applying the metadata generation task” data about data” and classification. By applying metadata generation, we add facilities to our summary, identification, and summary organization. Metadata provides essential contextual details, as not all summaries are self-describing. Also, classify the original text to determine the summary topic before reading. We acquire 97.5% accuracy by using Support Vector Machine (SVM) and trained it using NADA corpus.
Safae26 / Arabic Documents ClassificationArabic Text Classification pipeline leveraging specialized Arabic NLP techniques and Deep Learning architectures (PyTorch) for high-accuracy document categorization.
benlalaraid / Arabic Sign Language Image Classification With CNNThis project aims to build a robust image classification model to recognize and classify Arabic sign language gestures. Using deep learning techniques and convolutional neural networks (CNNs), the goal is to facilitate communication for the deaf and hard of hearing communities by translating sign language gestures into written Arabic text.
abduhbm / Sentiment Analysis Arabic Hotel ReviewsJupyter notebook for training a Bidirectional LSTM model for sentiment classification task on hotel reviews in Arabic
bibs2091 / Arabic Names Gender ClassificationArabic names gender classification with random forest
MudasserAfzal / Arabic Text ClassificationNo description available
Abdelrahmanrezk / Arabic Hands On Analysis Clustering And Classification Of Large Arabic Twitter Data Set On COVID19Arabic-Hands-on-Analysis-Clustering-and-Classification-of-Large-Arabic-Twitter-Data-set-on-COVID19
EssamWisam / Sentiment Classification Arabic COVID TweetsNo description available
mahmoud208 / Multiclass Arabic Text Classification Using Keras(Multiclass Arabic Text classification Using Keras)
RealKintaro / Offensive Text Classification Writen In Arabic DialectsNo description available
