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---
license: cc-by-nc-4.0
datasets:
- asset
- wi_locness
- GEM/wiki_auto_asset_turk
- discofuse
- zaemyung/IteraTeR_plus
- jfleg
- grammarly/coedit
language:
- en
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>
## 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 |