This repo contains code and pretrained models to reproduce the results in our paper Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study.
The code was written for python>=3.10, pytorch 1.12.1, and transformers 4.30.0. You will need a few additional packages. Here's how you can set up the environment using conda (assuming you have conda and cuda installed):
git clone https://github.com/CAMeL-Lab/text-editing.git cd text-editing conda create -n text-editing python=3.10 conda activate text-editing pip install -e .
All the datasets we used throughout the paper to train and test various systems can be downloded from here.
This repo is organized as follows:
- edits: includes the scripts needed to extract edits from parallel GEC corpora and to create different edit representation.
- gec: includes the scripts needed to train and evaluate our text editing GEC systems.
We make our text editing models publicly available on Hugging Face.
from transformers import BertTokenizer, BertForTokenClassification import torch import torch.nn.functional as F from gec.tag import rewrite nopnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx') nopnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx') pnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx') pnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx') def predict(model, tokenizer, text, decode_iter=1): for _ in range(decode_iter): tokenized_text = tokenizer(text, return_tensors="pt", is_split_into_words=True) with torch.no_grad(): logits = model(**tokenized_text).logits preds = F.softmax(logits.squeeze(), dim=-1) preds = torch.argmax(preds, dim=-1).cpu().numpy() edits = [model.config.id2label[p] for p in preds[1:-1]] assert len(edits) == len(tokenized_text['input_ids'][0][1:-1]) subwords = tokenizer.convert_ids_to_tokens(tokenized_text['input_ids'][0][1:-1]) text = rewrite(subwords=[subwords], edits=[edits])[0][0] return text text = 'يجب الإهتمام ب الصحه و لا سيما ف ي الصحه النفسيه ياشباب المستقبل،،'.split() output_sent = predict(nopnx_model, nopnx_tokenizer, text, decode_iter=2) output_sent = predict(pnx_model, pnx_tokenizer, output_sent.split(), decode_iter=1) print(output_sent) # يجب الاهتمام بالصحة ولا سيما في الصحة النفسية يا شباب المستقبل .
This repo is available under the MIT license. See the LICENSE for more info.
If you find the code or data in this repo helpful, please cite our paper:
@misc{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}, }