AdapterFusion is a learning algorithm that combines task-relevant information contained within adapters to fine-tune a target task using multiple resources. We employ AdapterFusion on a classsic Arabic NLP problem, Dialect Identification, and deploy the resulting model on HuggingFace Spaces🤗

_{This project is a fraction of a Msc degree dedicated to Arabic Dialect Identification that was supervised by Prof. Dr. Mervat Abouelkhair}

• An AdapterFusion model requires N adapters
• Each n adapter is fine-tuned on task-relevant data
• In our case, we fine-tune, we fine-tune a total of 8 adapters on the data below covering MSA/DA, Region, Country and Province Levels
• For each diatopic variation an AdapterFusion (AF) model is created, hence AF for each Region, Country, Province and MSA/DA
• We share one of the best performing AF models on Regional Dialect Identification here: HuggingFace Spaces🤗

• Arabic Online Commentary (AOC), Newspaper Commentary
• NADI 2020, Tweets
• MADAR, Tweets
• ArSarcasm, Tweets
• QADI, Tweets

_{Code for adapter-training and adapterfusion available in repo for re-producing}