A small transformer model purpose-built for Arabic text generation, designed to improve morphological understanding relative to models adapted from English-centric pretraining. In deployment as FaseehGPT, this approach improved morphological understanding and inference speed by roughly 40% in low-resource settings compared to the baseline the project started from.
Topics: low-resource NLP, transformer architecture, morphological analysis, text generation.