coedit-xl-composite

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.

<table> <tr> <th>Model</th> <th>Number of parameters</th> </tr> <tr> <td>CoEdIT-large</td> <td>770M</td> </tr> <tr> <td>CoEdIT-xl</td> <td>3B</td> </tr> <tr> <td>CoEdIT-xxl</td> <td>11B</td> </tr> </table>

Uses

Text Revision Task

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

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