---
language:
- en
license: cc-by-nc-4.0
datasets:
- facebook/asset
- wi_locness
- GEM/wiki_auto_asset_turk
- discofuse
- zaemyung/IteraTeR_plus
- jfleg
- grammarly/coedit
metrics:
- sari
- bleu
- accuracy
widget:
- text: 'Fix the grammar: When I grow up, I start to understand what he said is quite
    right.'
  example_title: Fluency
- text: 'Make this text coherent: Their flight is weak. They run quickly through the
    tree canopy.'
  example_title: Coherence
- text: 'Rewrite to make this easier to understand: A storm surge is what forecasters
    consider a hurricane''s most treacherous aspect.'
  example_title: Simplification
- text: 'Paraphrase this: Do you know where I was born?'
  example_title: Paraphrase
- text: 'Write this more formally: omg i love that song im listening to it right now'
  example_title: Formalize
- text: 'Write in a more neutral way: The authors'' exposé on nutrition studies.'
  example_title: Neutralize
---
# Model Card for CoEdIT-Large

This model was obtained by fine-tuning the corresponding `google/flan-t5-large` model on the CoEdIT 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-large

### 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>

![task_specs](https://huggingface.co/grammarly/coedit-xl/resolve/main/task_examples.png)

## Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
input_text = 'Fix grammatical errors in this sentence: 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