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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ language:
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+ - ar
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+ base_model:
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+ - aubmindlab/bert-base-arabertv02
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+ pipeline_tag: token-classification
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+ ---
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+
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+ # SWEET<sub>NoPnx</sub> ZAEBUC Model
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+
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+ ## Model Description
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+ `CAMeL-Lab/text-editing-zaebuc-pnx` is a text editing model tailored for grammatical error correction (GEC) in Modern Standard Arabic (MSA).
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+ The model is based on [AraBERTv02](https://huggingface.co/aubmindlab/bert-base-arabertv02), which we fine-tuned using the [ZAEBUC](https://sites.google.com/view/zaebuc/home) dataset.
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+ This model was introduced in our ACL 2025 paper, [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://arxiv.org/abs/2503.00985), where we refer to it as SWEET (Subword Edit Error Tagger).
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+
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+ 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.
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+ The fine-tuning code and associated resources are publicly available on our GitHub repository: https://github.com/CAMeL-Lab/text-editing.
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+
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+
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+
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+ ## Citation
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+ ```bibtex
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+ @inter{alhafni-habash-2025-enhancing,
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+ title={Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study},
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+ author={Bashar Alhafni and Nizar Habash},
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+ year={2025},
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+ eprint={2503.00985},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2503.00985},
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+ }
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+ ```