# Modern Standard Arabic

A high-quality Arabic speech synthesis model fine-tuned based on F5-TTS, supporting diverse pronunciations and accents from different regions

This is a Wav2Vec2-based Arabic syllable recognition model capable of identifying syllables in Modern Standard Arabic from speech.

ARBERTv2 is an upgraded BERT model trained on Modern Standard Arabic (MSA) with a corpus of 243GB text, containing 27.8 billion tokens.

Wojood is a corpus for Arabic nested named entity recognition (NER), containing 550,000 word instances (Modern Standard Arabic and dialects).

An Arabic text summarization model based on AraBERT, specializing in news headline generation and text summarization tasks for Modern Standard Arabic.

Arabic text summarization model based on AraBERT, supporting Modern Standard Arabic news headline generation and text rewriting

A POS tagging model for Modern Standard Arabic, built by fine-tuning CAMeLBERT-MSA and trained on the PATB dataset

A named entity recognition model fine-tuned based on the CAMeLBERT Modern Standard Arabic (MSA) model, specifically designed for Arabic text

CAMeLBERT is a collection of pre-trained models for Arabic NLP tasks. This model is the Modern Standard Arabic (MSA) variant, trained on 12.6 billion tokens

Pretrained model for Arabic NLP tasks, trained on a reduced-scale (1/16) Modern Standard Arabic (MSA) dataset

Modern Standard Arabic POS tagging model fine-tuned on CAMeLBERT-Mix, trained using PATB dataset

A sentiment analysis model fine-tuned based on the CAMeLBERT Modern Standard Arabic model, supporting Arabic text sentiment classification