We make available the models presented in our paper.
Model
Number of parameters
CoEdIT-large
770M
CoEdIT-xl
3B
CoEdIT-xxl
11B
Uses
Text Revision Task
Given an edit instruction and an original text, our model can generate the edited version of the text.
task_specs
Usage
python
1from transformers import AutoTokenizer, T5ForConditionalGeneration
23tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-xxl")4model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-xxl")5input_text ='Fix grammatical errors in this sentence: When I grow up, I start to understand what he said is quite right.'6input_ids = tokenizer(input_text, return_tensors="pt").input_ids
7outputs = model.generate(input_ids, max_length=256)8edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
@article{raheja2023coedit,
title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
year={2023},
eprint={2305.09857},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
APA:
Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857
