Model Card for CoEdIT-xl-composite

This model was obtained by fine-tuning the corresponding google/flan-t5-xl model on the CoEdIT-Composite dataset. Details of the dataset can be found in our paper and repository.

Paper: CoEdIT: Text Editing by Task-Specific Instruction Tuning

Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang

Model Details

Model Description

• Language(s) (NLP): English
• Finetuned from model: google/flan-t5-xl

Model Sources

• Repository: https://github.com/vipulraheja/coedit
• Paper: https://arxiv.org/abs/2305.09857

How to use

We make available the models presented in our paper.

Uses

Text Revision Task

Given an edit instruction and an original text, our model can generate the edited version of the text.

This model can also perform edits on composite instructions, as shown below:

Usage

from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-xl-composite")
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-xl-composite")
input_text = 'Fix grammatical errors in this sentence and make it simpler: When I grow up, I start to understand what he said is quite right.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=256)
edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

Software

https://github.com/vipulraheja/coedit

Citation

BibTeX:

@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