README.md

metadata

license: mit
language:
  - ar
base_model:
  - aubmindlab/bert-base-arabertv02
pipeline_tag: token-classification

SWEET_NoPnx ZAEBUC Model

Model Description

CAMeL-Lab/text-editing-zaebuc-pnx is a text editing model tailored for grammatical error correction (GEC) in Modern Standard Arabic (MSA). The model is based on AraBERTv02, which we fine-tuned using the ZAEBUC dataset. This model was introduced in our ACL 2025 paper, Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study, where we refer to it as SWEET (Subword Edit Error Tagger).

The model was fine-tuned to fix non-punctuation (i.e., NoPnx) errors. Details about the training procedure, data preprocessing, and hyperparameters are available in the paper. The fine-tuning code and associated resources are publicly available on our GitHub repository: https://github.com/CAMeL-Lab/text-editing.

Citation

@inter{alhafni-habash-2025-enhancing,
      title={Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study}, 
      author={Bashar Alhafni and Nizar Habash},
      year={2025},
      eprint={2503.00985},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.00985}, 
}