README.md

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: coedit
size_categories:
- 10K<n<100K
---

# Dataset Card for CoEdIT: Text Editing via Instruction Tuning

## Paper: [CoEdIT: Text Editing by Task-Specific Instruction Tuning](https://arxiv.org/abs/2305.09857)
## Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
## Project Repo: [https://github.com/vipulraheja/coedit](https://github.com/vipulraheja/coedit)


## Dataset Summary
This is the dataset that was used to train the CoEdIT text editing models. Full details of the dataset can be found in our paper.


# Dataset Structure
The dataset is in JSON format. 

## Data Instances
```
{
  '_id': 1,
  'task': "gec",
  'src': "Improve the grammaticality: As the number of people grows, the need of habitable environment is unquestionably essential.",
  'tgt': "As the number of people grows, the need for a habitable environment is unquestionably increasing."
}
```

## Data Fields
* `_id`: 
* `task`: Text editing task for this instance
* `src`: input text (formatted as `instruction: input_text`)
* `tgt`: output text


## Considerations for Using the Data
Please note that this dataset contains 69k instances (as opposed to the 82k instances we used in the paper). This is because this public release includes only the instances that were acquired and curated from publicly available datasets. Specifically, it is missing roughly 13k instances in training and 1.5k instances in validation data from Simplification and Formality Transfer tasks due to licensing restrictions.


# Citation
```
@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}
}
```