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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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# SWEET MADAR CODA Model |
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## Model Description |
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`CAMeL-Lab/text-editing-coda` is a text editing model tailored for grammatical error correction (GEC) in dialectal Arabic (DA). |
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The model is based on [AraBERTv02](https://huggingface.co/aubmindlab/bert-base-arabertv02), which we fine-tuned using the MADAR CODA corpus. |
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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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It achieved SOTA performance on the MADAR CODA dataset. 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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## 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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``` |
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